Rural non farm employment in India : Access, income and poverty impact

Attention has been paid to the significance of the non-farm sector in the rural Indian economy since the early 1970s. The importance of earnings from secondary non-farm occupations is not well documented. In this paper an attempt is made to assess the contribution of the nonfarm sector across population quintiles defined in terms of average per capita income. The correlates of employment in the non-farm sector and the direct impact of a growing non-farm sector on agricultural wage rates in rural India have also been examined. The study is based on rural data from 32,000 households belonging to 1765 villages across all parts of India collected by the National Council of Applied Economic Research in 1993-94. Analysis shows that non-farm incomes account for a significant proportion of household income in rural India with considerable variation across quintiles and across major Indian states. Education, wealth, caste, village level agricultural conditions, population densities and other regional effects influence in determining the access to non-farm occupations. Direct contribution of the nonfarm sector to poverty reduction is possibly quite muted as the poor lack the assets. It has also been found that the growth of certain non-farm sub-sectors is strongly associated with higher agricultural wage rates. The analysis presented in this study suggests that the policy makers seeking to maximise the impact of an expanding non-farm sector on rural poverty, should concentrate on two fronts. First, efforts should be focused on removing the barriers to the entry of the poor into the non-farm sector. This involves improving the educational level in rural areas. Second, the policy makers should note the strong evidence of an impact on agricultural wages of the expansion in rural construction employment.EmploymentRural EmploymentPoverty Eradication

LEAD Program Evaluation: Recidivism Report

The LEAD program was established in 2011 as a means of diverting those suspected of low-level drug and prostitution criminal activity to case management and other supportive services instead of jail and prosecution. The primary aim of the LEAD program is to reduce criminal recidivism. Secondary aims include reductions in criminal justice service utilization and associated costs as well as improvements for psychosocial, housing and quality-of-life outcomes. Because LEAD is the first known pre-booking diversion program of its kind in the United States, an evaluation is critically needed to inform key stakeholders, policy makers, and other interested parties of its impact. The evaluation of the LEAD program described in this report represents a response to this need.Background: This report was written by the University of Washington LEAD Evaluation Team at the request of the LEAD Policy Coordinating Group and fulfills the first of three LEAD evaluation aims. Purpose: This report describes findings from a quantitative analysis comparing outcomes for LEAD participants versus "system-as-usual" control participants on shorter- and longer-term changes on recidivism outcomes, including arrests (i.e., being taken into custody by legal authority) and criminal charges (i.e., filing of a criminal case in court). Arrests and criminal charges were chosen as the recidivism outcomes because they likely reflect individual behavior more than convictions, which are more heavily impacted by criminal justice system variables external to the individual. Findings: Analyses indicated statistically significant recidivism improvement for the LEAD group compared to the control group on some shorter- and longer-term outcomes

Modeling dynamic community acceptance of mining using agent-based modeling

This research attempts to provide fundamental understanding into the relationship between perceived sustainability of mineral projects and community acceptance. The main objective is to apply agent-based modeling (ABM) and discrete choice modeling to understand changes in community acceptance over time due to changes in community demographics and perceptions. This objective focuses on: 1) formulating agent utility functions for ABM, based on discrete choice theory; 2) applying ABM to account for the effect of information diffusion on community acceptance; and 3) explaining the relationship between initial conditions, topology, and rate of interactions, on one hand, and community acceptance on the other hand. To achieve this objective, the research relies on discrete choice theory, agent-based modeling, innovation and diffusion theory, and stochastic processes. Discrete choice models of individual preferences of mining projects were used to formulate utility functions for this research. To account for the effect of information diffusion on community acceptance, an agent-based model was developed to describe changes in community acceptance over time, as a function of changing demographics and perceived sustainability impacts. The model was validated with discrete choice experimental data on acceptance of mining in Salt Lake City, Utah. The validated model was used in simulation experiments to explain the model\u27s sensitivity to initial conditions, topology, and rate of interactions. The research shows that the model, with the base case social network, is more sensitive to homophily and number of early adopters than average degree (number of friends). Also, the dynamics of information diffusion are sensitive to differences in clustering in the social networks. Though the research examined the effect of three networks that differ due to the type of homophily, it is their differences in clustering due to homophily that was correlated to information diffusion dynamics --Abstract, page iii

The R Commander: A Basic-Statistics Graphical User Interface to R

Unlike S-PLUS, R does not incorporate a statistical graphical user interface (GUI), but it does include tools for building GUIs. Based on the tcltk package (which furnishes an interface to the Tcl/Tk GUI toolkit), the Rcmdr package provides a basic-statistics graphical user interface to R called the "R Commander." The design objectives of the R Commander were as follows: to support, through an easy-to-use, extensible, cross-platform GUI, the statistical functionality required for a basic-statistics course (though its current functionality has grown to include support for linear and generalized-linear models, and other more advanced features); to make it relatively difficult to do unreasonable things; and to render visible the relationship between choices made in the GUI and the R commands that they generate. The R Commander uses a simple and familiar menu/dialog-box interface. Top-level menus include File, Edit, Data, Statistics, Graphs, Models, Distributions, Tools, and Help, with the complete menu tree given in the paper. Each dialog box includes a Help button, which leads to a relevant help page. Menu and dialog-box selections generate R commands, which are recorded in a script window and are echoed, along with output, to an output window. The script window also provides the ability to edit, enter, and re-execute commands. Error messages, warnings, and some other information appear in a separate messages window. Data sets in the R Commander are simply R data frames, and can be read from attached packages or imported from files. Although several data frames may reside in memory, only one is "active" at any given time. There may also be an active statistical model (e.g., an R lm or glm ob ject). The purpose of this paper is to introduce and describe the use of the R Commander GUI; to describe the design and development of the R Commander; and to explain how the R Commander GUI can be extended. The second part of the paper (following a brief introduction) can serve as an introductory guide for students who will use the R Commander.

Modelling drivers’ braking behaviour and comfort under normal driving

The increasing growth of population and a rising number of vehicles, connected to an individual, demand new solutions to reduce traffic delays and enhance road safety. Autonomous Vehicles (AVs) have been considered as an optimal solution to overcome those problems. Despite the remarkable research and development progress in the area of (semi) AVs over the last decades, there is still concern that occupants may not feel safe and comfortable due to the robot-like driving behaviour of the current technology. In order to facilitate their rapid uptake and market penetration, ride comfort in AVs must be ensured.Braking behaviour has been identified to be a crucial factor in ride comfort. There is a dearth of research on which factors affect the braking behaviour and the comfort level while braking and which braking profiles make the occupants feel safe and comfortable. Therefore, the primary aim of this thesis is to model the deceleration events of drivers under normal driving conditions to guide comfortable braking design. The aim was achieved by exploiting naturalistic driving data from three projects: (1) the Pan-European TeleFOT (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles) project, (2) the Field Operational Test (FOT) conducted by Loughborough University and Original Equipment Manufacturer (OEM), and (3) the UDRIVE Naturalistic Driving Study.A total of about 35 million observations were examined from 86 different drivers and 644 different trips resulting in almost 10,000 deceleration events for the braking features analysis and 21,600 deceleration events for the comfort level analysis. Since deceleration events are nested within trips and trips within drivers, multilevel mixed-effects linear models were employed to develop relationships between deceleration value and duration and the factors influencing them. The examined factors were kinematics, situational, driver and trip characteristics with the first two categories to affect the most the deceleration features. More specifically, the initial speed and the reason for braking play a significant role, whereas the driver’s characteristics, i.e. the age and gender do not affect the deceleration features, except for driver’s experience which significantly affects the deceleration duration.An algorithm was developed to calculate the braking profiles, indicating that the most used profile follows smooth braking at the beginning followed by a harder one. Moreover, comfort levels of drivers were analysed using the Mixed Multinomial Logit models to identify the effect of the explanatory factors on the comfort category of braking events. Kinematic factors and especially TTC and time headway (THW) were found to affect the most the comfort level. Particularly, when TTC or THW are increased by 1 second, the odds of the event to be “very comfortable” are respectively 1.03 and 4.5 times higher than being “very uncomfortable”. Moreover, the driver’s characteristic, i.e. age and gender affect significantly the comfort level of the deceleration event. Findings from this thesis can support vehicle manufacturers to ensure comfortable and safe braking operations of AVs.</div

SOCIAL NETWORK INFLUENCE ON RIDESHARING, DISASTER COMMUNICATIONS, AND COMMUNITY INTERACTIONS

The complex topology of real networks allows network agents to change their functional behavior. Conceptual and methodological developments in network analysis have furthered our understanding of the effects of interpersonal environment on normative social influence and social engagement. Social influence occurs when network agents change behavior being influenced by others in the social network and this takes place in a multitude of varying disciplines. The overarching goal of this thesis is to provide a holistic understanding and develop novel techniques to explore how individuals are socially influenced, both on-line and off-line, while making shared-trips, communicating risk during extreme weather, and interacting in respective communities. The notion of influence is captured by quantifying the network effects on such decision-making and characterizing how information is exchanged between network agents. The methodologies and findings presented in this thesis will benefit different stakeholders and practitioners to determine and implement targeted policies for various user groups in regular, special, and extreme events based on their social network characteristics, properties, activities, and interactions

Contract Flexibility and Dispute Resolution in African Manufacturing

This paper examines the contractual practices of African manufacturing firms using survey data collected in Burundi, Cameroon, Côte d’Ivoire, Kenya, Zambia, and Zimbabwe. Descriptive statistics and econometric results are presented. They show that contractual flexibility is pervasive and that relational contracting is the norm between manufacturers, their suppliers, and their clients. The existence of long-term relations between firms helps them deal with contract non-performance through negotiation. Confrontational methods such as lawyers and courts are used only by large firms and when negotiations fail. Whenever confrontation can be avoided, business is resumed. Of the six studied countries, incidence of breach and the use of lawyers and courts are highest in Zimbabwe which is also the country with legal institutions that best support business. Our favored interpretation is that good legal institutions incite firms to take more chances, thereby encouraging trade and leading to more cases of breach and more recourse to courts and lawyers. A high frequency of contract non-compliance should thus not be interpreted as a sign of imperfect legal institutions.

Transgenerational risk for low birth weight and preterm birth: The role of biology and neighborhood factors in racial disparities

The purpose of this dissertation research is to ascertain the impact of biological factors as well as social and economic environmental factors on the risk of low birth weight (LBW) and preterm birth (PTB) among infants of non-Hispanic (NH) white and NH black mothers, under the hypothesis that intergenerational factors could be explanatory variables in the perpetuated trend in racial/ethnic disparities in birth outcomes. Three separate research studies were performed. The first is a systematic review and meta-analysis of studies reporting the association between LBW/PTB and neighborhood disadvantage, where the results demonstrate that there is a statistically significant higher odds of LBW and PTB among mothers resident in the most disadvantaged neighborhoods relative to those in the least disadvantaged neighborhoods. This relationship was found only when race-stratified, rather than race-adjusted, models were performed. The second and third studies use a transgenerational dataset of births in Allegheny County, Pennsylvania with birth records of infants born in the years 2009-2011 to mothers who were also born in the County in the years 1979-1998. The second study focuses on the role of mothers’ birth weight (MBW) along with social and economic contextual factors on infant risk of LBW; while the third study focuses on the role of mothers’ gestational age (MGA) coupled with social and economic contextual factors on infant risk of PTB. This research makes significant unique contributions to this field of public health research by examining both biological and neighborhood context factors as predictors of PTB and LBW in multivariate and multilevel models. Even more important is the novel examination of the subcategories of birth weight and gestational age, which led to results suggesting differing roles of biology and neighborhood context among these subcategories. LBW and PTB are of public health significance because they increase an infant’s risk of death in the first year of life, developmental disabilities, and chronic diseases in adulthood. The healthcare costs related to treatment of a prematurely born infant costs the United States billions of dollars a year and can be associated with billions more decades later when chronic diseases develop in adulthood

Bayesian spatial modeling of malnutrition and mortality among under-five children in sub-Saharan Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.The aim of this thesis is to develop and extend Bayesian statistical models in the area of spatial modeling and apply them to child health outcomes, with particular focus on childhood malnutrition and mortality among under-five children. The easy availability of a geo-referenced database has stimulated a paradigm shift in methodological approaches to spatial analysis. This study reviewed the spatial methods and disease mapping models developed for areal (lattice) data analysis. Observational data collected from complex design surveys and geographical locations often violates the independent assumption of classical regression models. By relaxing the restrictive linearity and normality assumptions of classical regression models, this study first developed a flexible semi-parametric spatial model that accommodates the usual fixed effect, nonlinear and geographical component in a unified model. The approach was explored in the analysis of spatial patterns of child birth outcomes in Nigeria. The study also addressed the issue of disease clustering, which is of interest to epidemiologists and public health officials. The study then proposed a Bayesian hierarchical analysis approach for Poisson count data and formulated a Poisson version of generalized linear mixed models (GLMMs) for analyzing childhood mortality. The model simultaneously addressed the problem of overdispersion and spatial dependence by the inclusion of the risk factors and random effects in a single model. The proposed approach identified regions with elevated relative risk or clustering of high mortality and evaluated the small scale geographical disparities in sub-populations across the regions. The study identified another challenge in spatial data analysis, which are spatial autocorrelation and model misspecification. The study then fitted geoadditive mixed (GAM) models to analyze childhood anaemia data belonging to a family of exponential distributions (Gaussian, binary and multinomial). The GAM models are extension of generalized linear mixed models by allowing the inclusion of splines for continuous covariate (or time) trends with the parametric function. Lastly, the shared component model originally developed for multiple disease mapping was reviewed and modified to suit the binary data at hand. A multivariate conditional autoregressive (MCAR) model was developed and applied to jointly analyze three child malnutrition indicators. The approach facilitated the estimation of conditional correlation between the diseases; assess the spatial association with the regions and geographical variation of individual disease prevalence. The spatial analysis presented in this thesis is useful to inform health-care policy and resource allocation. This thesis contributes to methodological applications in life sciences, environmental sciences, public health and agriculture. The present study expands the existing methods and tools for health impact assessment in public health studies. KEYWORDS: Conditional Autoregressive (CAR) model, Disease Mapping Models, Multiple Disease mapping, Health Geography, Ecology Models, Spatial Epidemiology, Childhood Health outcomes