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"Income distribution Computer simulation."
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Simulating distributional impacts of macro-dynamics : theory and practical applications
\"Simulating Distributional Impacts of Macro-dynamics: Theory and Practical Applications is a comprehensive guide for analyzing and understanding the effects of macroeconomic shocks on income and consumption distribution, as well as for using the ADePT Simulation Module. Since real-time micro data is rarely available, the Simulation Module (part of the ADePT economic analysis software) takes advantage of historical household surveys to estimate how current or proposed macro changes might impact household and individual welfare\"--Back cover.
Book
Simulating distributional impacts of macro-dynamics
The automated DEC poverty tables (ADePT) simulation module, one of several modules in the ADePT platform, offers a useful methodological framework for analysts interested in measuring how macroeconomic projections may affect households. The modules approach falls between simple extrapolation and the most sophisticated methods such as top-down or top-down-up models based on linking household data with computable general equilibrium (CGE) models. By using simple macroeconomic projections as the macro-linkages to a micro-behavioral model built from household data, the model captures the complexities that influence how macro impacts are transmitted to households. The ADePT simulation module is an improvement over existing approaches because with minimal data and computational requirements it can evaluate in advance the distributional impacts of macroeconomic projections. By focusing on adjustments in employment and earnings, non-labor income, and price changes, it accounts for multiple transmission mechanisms and captures micro-level impacts across the entire income distribution. Using existing macroeconomic data and household surveys, the ADePT simulation module helps in identifying and profiling those groups of individuals - defined by characteristics such as occupational sector, location, and education level who are most likely to suffer income losses as a consequence of the change. This manual is organized in two parts. Part one covers the motivation, overview, and illustrations of the method. Part two describes each step the user must follow to create or obtain proper macro- and microeconomic inputs required for the simulation. It also explains how to enter these inputs into the module and the different options available for tailoring simulations.
eBook
The economic and operational value of using drones to transport vaccines
•Vaccine supply chains in low and middle income countries face numerous challenges.•Unmanned aerial vehicles (UAVs) are being developed for vaccine distribution.•HERMES-generated simulation modeling assessed UAV impact under various conditions.•UAVs raised vaccine availability and saved costs over traditional land transport.•With sufficient UAV utilization, cost savings were robust to sensitivity analyses.
Immunization programs in low and middle income countries (LMICs) face numerous challenges in getting life-saving vaccines to the people who need them. As unmanned aerial vehicle (UAV) technology has progressed in recent years, potential use cases for UAVs have proliferated due to their ability to traverse difficult terrains, reduce labor, and replace fleets of vehicles that require costly maintenance.
Using a HERMES-generated simulation model, we performed sensitivity analyses to assess the impact of using an unmanned aerial system (UAS) for routine vaccine distribution under a range of circumstances reflecting variations in geography, population, road conditions, and vaccine schedules. We also identified the UAV payload and UAS costs necessary for a UAS to be favorable over a traditional multi-tiered land transport system (TMLTS).
Implementing the UAS in the baseline scenario improved vaccine availability (96% versus 94%) and produced logistics cost savings of $0.08 per dose administered as compared to the TMLTS. The UAS maintained cost savings in all sensitivity analyses, ranging from $0.05 to $0.21 per dose administered. The minimum UAV payloads necessary to achieve cost savings over the TMLTS, for the various vaccine schedules and UAS costs and lifetimes tested, were substantially smaller (up to 0.40L) than the currently assumed UAV payload of 1.5L. Similarly, the maximum UAS costs that could achieve savings over the TMLTS were greater than the currently assumed costs under realistic flight conditions.
Implementing a UAS could increase vaccine availability and decrease costs in a wide range of settings and circumstances if the drones are used frequently enough to overcome the capital costs of installing and maintaining the system. Our computational model showed that major drivers of costs savings from using UAS are road speed of traditional land vehicles, the number of people needing to be vaccinated, and the distance that needs to be traveled.
Journal Article
Has Income Segregation Really Increased? Bias and Bias Correction in Sample-Based Segregation Estimates
Several recent studies have concluded that residential segregation by income in the United States has increased in the decades since 1970, including a significant increase after 2000. Income segregation measures, however, are biased upward when based on sample data. This is a potential concern because the sampling rate of the American Community Survey (ACS)—from which post-2000 income segregation estimates are constructed—was lower than that of the earlier decennial censuses. Thus, the apparent increase in income segregation post-2000 may simply reflect larger upward bias in the estimates from the ACS, and the estimated trend may therefore be inaccurate. In this study, we first derive formulas describing the approximate sampling bias in two measures of segregation. Next, using Monte Carlo simulations, we show that the bias-corrected estimators eliminate virtually all of the bias in segregation estimates in most cases of practical interest, although the correction fails to eliminate bias in some cases when the population is unevenly distributed among geographic units and the average within-unit samples are very small. We then use the bias-corrected estimators to produce unbiased estimates of the trends in income segregation over the last four decades in large U.S. metropolitan areas. Using these corrected estimates, we replicate the central analyses in four prior studies on income segregation. We find that the primary conclusions from these studies remain unchanged, although the true increase in income segregation among families after 2000 was only half as large as that reported in earlier work. Despite this revision, our replications confirm that income segregation has increased sharply in recent decades among families with children and that income inequality is a strong and consistent predictor of income segregation.
Journal Article
The Impact of Inventory Management on Stock-Outs of Essential Drugs in Sub-Saharan Africa: Secondary Analysis of a Field Experiment in Zambia
To characterize the impact of widespread inventory management policies on stock-outs of essential drugs in Zambia's health clinics and develop related recommendations.
Daily clinic storeroom stock levels of artemether-lumefantrine (AL) products in 2009-2010 were captured in 145 facilities through photography and manual transcription of paper forms, then used to determine historical stock-out levels and estimate demand patterns. Delivery lead-times and estimates of monthly facility accessibility were obtained through worker surveys. A simulation model was constructed and validated for predictive accuracy against historical stock-outs, then used to evaluate various changes potentially affecting product availability.
While almost no stock-outs of AL products were observed during Q4 2009 consistent with primary analysis, up to 30% of surveyed facilities stocked out of some AL product during Q1 2010 despite ample inventory being simultaneously available at the national warehouse. Simulation experiments closely reproduced these results and linked them to the use of average past monthly issues and failure to capture lead-time variability in current inventory control policies. Several inventory policy enhancements currently recommended by USAID | DELIVER were found to have limited impact on product availability.
Inventory control policies widely recommended and used for distributing medicines in sub-Saharan Africa directly account for a substantial fraction of stock-outs observed in common situations involving demand seasonality and facility access interruptions. Developing central capabilities in peripheral demand forecasting and inventory control is critical. More rigorous independent peer-reviewed research on pharmaceutical supply chain management in low-income countries is needed.
Journal Article
Estimating Income Distributions From Grouped Data: A Minimum Quantile Distance Approach
This paper focuses on the estimation of income distribution from grouped data in the form of quantiles. We propose a novel application of the minimum quantile distance (MQD) approach and compare its performance with the maximum likelihood (ML) technique. The estimation methods are applied using three parametric distributions: the generalized beta distribution of the second kind (GB2), the Dagum distribution, and the Singh–Maddala distribution. We provide the density-quantile functions for these distributions, along with reproducible R code. A simulation study is conducted to evaluate the performance of the MQD and ML methods. The proposed methods are then applied to data from 30 European countries, utilizing the aforementioned parametric distributions. To validate the accuracy of the estimates, we compare them with estimates obtained from more detailed and informative microdata sets. The findings confirm the excellent performance of the considered parametric distributions in estimating income distribution. Additionally, the MQD approach is identified as a straightforward and reliable method for this purpose. Notably, the MQD method displays superior robustness in comparison to the ML technique when it comes to selecting suitable starting values for the underlying computation algorithm, specifically when dealing with the GB2 distribution.
Journal Article
Using best–worst scaling to identify barriers to walkability: a study of Porto Alegre, Brazil
This paper pursues three goals: (1) determining the relative importance of built environment barriers limiting walkability, (2) analyzing the existence of an asymmetry in the way people evaluate positive and negative built environment characteristics, and (3) identifying solutions to tackle the main barriers and quantify their impact in walkability. A best–worst scaling survey was developed to compare the importance of eight different attributes of the built environment regarding walkability. Model results show an asymmetry negative–positive in the judgment and choice of built environment characteristics that promote and impede walkability. The most important barriers, obtained from worst responses, are connectivity, topography, sidewalk surface and absence of policemen. Walkability scores were computed for different neighbourhoods and different policy scenarios were forecasted. Simulation results from the worst responses indicate that improvements in sidewalk quality, along with an increase in the number of police officers, lead to an 85% increase in the walkability score for the lower income neighbourhoods.
Journal Article
Distributionally Robust Selection of the Best
Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large. In this paper, we consider the problem of selecting the best from a finite set of simulated alternatives, in the presence of such input uncertainty. We model such uncertainty by an ambiguity set consisting of a finite number of plausible input distributions and aim to select the alternative with the best worst-case mean performance over the ambiguity set. We refer to this problem as robust selection of the best (RSB). To solve the RSB problem, we develop a two-stage selection procedure and a sequential selection procedure; we then prove that both procedures can achieve at least a user-specified probability of correct selection under mild conditions. Extensive numerical experiments are conducted to investigate the computational efficiency of the two procedures. Finally, we apply the RSB approach to study a queueing system’s staffing problem using synthetic data and an appointment-scheduling problem using real data from a large hospital in China. We find that the RSB approach can generate decisions significantly better than other widely used approaches.
This paper was accepted by Noah Gans, stochastic models and simulation.
Journal Article
A Hierarchical Framework for Correcting Under-Reporting in Count Data
Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention. We present a comprehensive investigation and application of a Bayesian hierarchical approach to modeling and correcting under-reporting in tuberculosis counts, a general problem arising in observational count data. The framework is applicable to fully under-reported data, relying only on an informative prior distribution for the mean reporting rate to supplement the partial information in the data. Covariates are used to inform both the true count-generating process and the under-reporting mechanism, while also allowing for complex spatio-temporal structures. We present several sensitivity analyses based on simulation experiments to aid the elicitation of the prior distribution for the mean reporting rate and decisions relating to the inclusion of covariates. Both prior and posterior predictive model checking are presented, as well as a critical evaluation of the approach.
Supplementary materials
for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Journal Article
Islamic and capitalist economies: Comparison using econophysics models of wealth exchange and redistribution
Islamic and capitalist economies have several differences, the most fundamental being that the Islamic economy is characterized by the prohibition of interest ( riba ) and speculation ( gharar ) and the enforcement of Shariah -compliant profit–loss sharing ( mudaraba , murabaha , salam , etc.) and wealth redistribution ( waqf , sadaqah , and zakat ). In this study, I apply new econophysics models of wealth exchange and redistribution to quantitatively compare these characteristics to those of capitalism and evaluate wealth distribution and disparity using a simulation. Specifically, regarding exchange, I propose a loan interest model representing finance capitalism and riba and a joint venture model representing shareholder capitalism and mudaraba of an Islamic profit–loss sharing system; regarding redistribution, I create a transfer model representing inheritance tax and waqf of an Islamic wealth redistribution system. As exchanges are repeated from an initial uniform distribution of wealth, wealth distribution approaches a power-law distribution more quickly for the loan interest than the joint venture model; and the Gini index, representing disparity, rapidly increases. The joint venture model’s Gini index increases more slowly, but eventually, the wealth distribution in both models becomes a delta distribution, and the Gini index gradually approaches 1. Next, when both models are combined with the transfer model to redistribute wealth in every given period, the loan interest model has a larger Gini index than the joint venture model, but both converge to a Gini index of less than 1. These results quantitatively reveal that in the Islamic economy, disparity is restrained by prohibiting riba and promoting reciprocal exchange in mudaraba and redistribution through waqf . Comparing Islamic and capitalist economies provides insights into the benefits of economically embracing the ethical practice of mutual aid and suggests guidelines for an alternative to capitalism.
Journal Article