561 research outputs found
CHINA'S INCOME DISTRIBUTION OVER TIME: REASONS FOR RISING INEQUALITY
No full textWe estimate China's rural, urban and overall income distributions using grouped data from 1985-2001. We show how the distributions evolve as well as examine trends in welfare indices. We find the growing rural-urban income gap and increases in inequality within either sector have been equally responsible for overall inequality growth.Consumer/Household Economics,
China's Income Distribution and Inequality
No full textWe use a new method to estimate China’s income distributions based on publicly available interval summary statistics from China’s largest national household survey. We examine rural, urban, and overall income distributions for each year from 1985-2001. By estimating the entire distributions, we can show how the distributions change directly as well as examine trends in traditional welfare indices such as the Gini. We find that inequality has increased substantially in both rural and urban areas. Using an inter-temporal decomposition of aggregate inequality, we determine that increases in inequality within the rural and urban sectors and the growing gap in rural and urban incomes have been equally responsible for the growth in overall inequality over the last two decades. However, the rural-urban income gap has played an increasingly important role in recent years. In contrast, only the growth of inequality within rural and urban areas is responsible for the increase in inequality in the United States, where the overall inequality is close to that of China. As a robustness check, we show that consumption inequality (which may be a proxy for permanent income inequality) in urban areas also rose considerablyincome distribution, inequality, maximum entropy
Three Essays on Mixture Model and Gaussian Processes
No full textThis dissertation includes three essays. In the first essay I study the problem of density estimation using normal mixture models. Instead of selecting the ���right��� number of components in a normal mixture model, I propose an Averaged Normal Mixture (ANM) model to estimate the underlying densities based on model averaging methods, combining normal mixture models with different number of components. I use two methods to estimate the mixing weights of the proposed Averaged Normal Mixture model, one is based on likelihood cross validation and the other is based on Bayesian information criterion (BIC) weights. I also establish the theoretical properties of the proposed estimator and the simulation results demonstrate its good performance in estimating different types of underlying densities. The proposed method is also employed to a real world data set, empirical evidence demonstrates the efficiency of this estimator. The second essay studies short term electricity demand forecasting using Gaussian Processes and different forecast strategies. I propose a hybrid forecasting strategy that combines the strength of different forecasting schemes to predict 24 hourly electricity demand for the next day. This method is shown to provide superior point and overall probabilistic forecasts. I demonstrate the economic utility of the proposed method by illustrating how the Gaussian Process probabilistic forecasts can be used to reduce the expected cost of electricity supply relative to conventional regression methods, and in a decision-theoretic framework to derive an optimal bidding strategy under a stylized asymmetric loss function for electricity suppliers.
The third essay studies a non-stationary modeling approach based on the method of Gaussian process regression for crop yields modeling and crop insurance rate estimation. Our approach departs from the conventional two-step estimation procedure and allows the yield distributions to vary over time. I further develop a performance weighted model averaging method to construct densities as mixture of Gaussian processes. This method not only facilitates information pooling but greatly improves the flexibility of the resultant predictive density of crop yields. The simulation results on corp insurance premium rates show that the proposed method compares favorably to conventional two stage estimators, especially when the underlying distributions are non-stationary. I illustrate the efficacy of the proposed method with an application to crop insurance policy selection by insurance companies. I adopt a decision theoretic framework in this exploration and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions
Welfare Effects of Minimum Wage and Other Government Policies
No full textThe minimum wage, unlike most government transfer programs, lowered welfare in the 1980s and 1990s as measured by all commonly used welfare or inequality measures, including various Atkinson indexes, the Gini index, standard deviation of logarithms, and others. The effects of most government programs, macroeconomic variables, and aggregate demographic characteristics were qualitatively the same for all the inequality measures
Adverse Selection and Advantageous Selection in Insurance Markets
No full textThis dissertation consists of three essays about adverse selection and advantageous selection in life insurance and health insurance markets.
Firstly, I confirm the advantageous selection in voluntary private health insurance markets in Europe and detect the sources of such advantageous selection by using data from Survey of Health, Ageing and Retirement in Europe (SHARE). Specifically, I find, on the extensive margin, individuals with symptom are less likely to own VPHI than those without any symptom; on the intensive margin, the more the number of symptoms the individual has, the less likely she has VPHI. Same conclusion can be obtained when using a subjective measure of health. The sources of this advantageous selection include asset, education, longevity expectations, as well as cognitive ability. Conditional on these factors, individuals whose health is worse are more likely to purchase VPHI.
Secondly, I identify the adverse selection problem in life insurance markets in the presence of both adverse and advantageous private information. Conventional theory for private information of adverse selection predicts a positive correlation between insurance coverage and ex post risk. However, Cawley and Philipson (1999) reported a neutral or even negative correlation between mortality risk and insurance coverage in the life insurance market. A recent growing literature has shown that such puzzle could be attributed to the multiple dimensions of private information coexisting in the market. Specifically, I provide evidence of the existence of private information both on mortality risk and on life insurance preferences. I show that these two dimensions of private information have an offsetting effect on the relationship between subsequent mortality and life insurance purchases, which makes the identification of the private information on mortality risk difficult under the traditional setting. Instead, I apply the mixture density model and successfully detect a positive correlation between individual mortality and insurance coverage.
Moreover, I examine the mortality risk related to each of the two main types of life insurance contracts ��� term and whole life insurance. Our two-period model shows that, given an individual, the relative income, rather than the risk, dominates the choice between whole and term life insurance policies, indicating that a systematic risk difference between these two pools should not be observed. Moreover, when the income of these two periods are the same, whole life insurance policies, the one with more capability of avoiding reclassification risk, would be always favored if the individual is risk averse. Empirical results support the conclusions made in the theoretical model. This paper also, empirically confirms the partial lock-in of consumers embodied in the more front-loading contract as proposed by Hendel and Lizzeri (2003). Specifically, I find as a more front-loaded contract, whole life insurance policy is associated with a lower lapsation rate and thus retains a healthier pool after 65 years old
A Weighted Generalized Maximum Entropy Estimator with a Data-driven Weight
No full textThe method of Generalized Maximum Entropy (GME), proposed in Golan, Judge and Miller (1996), is an information-theoretic approach that is robust to multicolinearity problem. It uses an objective function that is the sum of the entropies for coefficient distributions and disturbance distributions. This method can be generalized to the weighted GME (W-GME), where different weights are assigned to the two entropies in the objective function. We propose a data-driven method to select the weights in the entropy objective function. We use the least squares cross validation to derive the optimal weights. MonteCarlo simulations demonstrate that the proposedW-GME estimator is comparable to and often outperforms the conventional GME estimator, which places equal weights on the entropies of coefficient and disturbance distributions
Essays on pricing under uncertainty
No full textThis dissertation analyzes pricing under uncertainty focusing on the U.S. airline
industry. It sets to test theories of price dispersion driven by uncertainty in the demand
by taking advantage of very detailed information about the dynamics of airline
prices and inventory levels as the flight date approaches. Such detailed information
about inventories at a ticket level to analyze airline pricing has been used previously
by the author to show the importance of capacity constraints in airline pricing.
This dissertation proposes and implements many new ideas to analyze airline pricing.
Among the most important are: (1) It uses information about inventories at a
ticket level. (2) It is the first to note that fare changes can be explained by adding
dummy variables representing ticket characteristics. Therefore, the load factor at a
ticket level will lose its explanatory power on fares if all ticket characteristics are
included in a pricing equation. (3) It is the first to propose and implement a measure
of Expected Load Factor as a tool to identify which flights are peak and which ones
are not. (4) It introduces a novel idea of comparing actual sales with average sales
at various points prior departure. Using these deviations of actual sales from sales
under average conditions, it presents is the first study to show empirical evidence of
peak load pricing in airlines. (5) It controls for potential endogeneity of sales using
dynamic panels.
The first essay tests the empirical importance of theories that explain price dispersion
under costly capacity and demand uncertainty. The essay calculates a measure of an Expected Load Factor, that is used to calibrate the distribution of demand
uncertainty and to identify which flights are peak and which ones are off-peak. It
shows that different prices can be explained by the different selling probabilities. The
second essay is the first study to provide formal evidence of stochastic peak-load pricing
in airlines. It shows that airlines learn about the demand and respond to early
sales setting higher prices when expected demand is high and more likely to exceed
capacity
Relocating operational and damaged bikes in free-floating systems: A data-driven modeling framework for level of service enhancement
No full textFree-floating bike sharing is an innovative and sustainable travel mode, where shared bikes can be picked up and returned at any proper place on the streets and not just at docking stations. Nevertheless, in these systems, two major problems arise. One is the imbalance of free-floating shared bikes (FFSB) between zones due to one-way trips, the other is the damaged bikes that must be brought for repair. In this study, a modeling framework for dynamic relocating operational and damaged bikes is proposed that starts with predicting the number and location of shared bikes using deep learning algorithms. The demand forecasting model adopts the Encoder-Decoder architecture embedded with the attention mechanism to further enhance the model's prediction ability and flexibility. Then, a data-driven optimization model for FFSB relocations is presented, where the multi-period optimization is applied to dynamically plan the relocation activities throughout the day. A new hybrid metaheuristic algorithm that incorporates variable neighborhood search (VNS) and enhanced simulated annealing (ESA) algorithm is developed for solving the relocating problem, in which satisfactory performance is observed from the numerical example. We test the proposed framework with the real-world FFSB data from Beijing, China. The results show that relocating both operational and damaged bikes timely decreases the probability of users finding damaged bikes in the system, but leads to higher relocation costs. For peak-hours, considering only the operational bikes for relocation is the most effective strategy given the limited relocation resources. It is urgent at those times of the day to focus on providing bikes to clients where they are undersupplied.Accepted author manuscriptTransport and Plannin
Quantile Forecasting of Commodity Futures' Returns: Are Implied Volatility Factors Informative?
No full textThis study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied volatilities of the corresponding prices for the period from 1/16/2008 to 7/29/2011. The statistical approach employs dynamic log-returns quantile regression models to forecast price densities using implied volatilities (IVs) and factors estimated through principal component analysis (PCA) from the IVs, pooled IVs and lagged returns. Extensive in-sample and out-of-sample analyses are conducted, including assessment of excess trading returns, and evaluations of several combinations of quantiles, model specifications, and NCFC's. The results suggest that the IV-PCA-factors, particularly pooled return-IV-PCA-factors, improve quantile forecasting power relative to models using only individual IV information. The ratio of the put-IV to the call-IV is also found to improve quantile forecasting performance of log returns. Improvements in quantile forecasting performance are found to be better in the tails of the distribution than in the center. Trading performance based on quantile forecasts from the models above generated significant excess returns. Finally, the fact that the single IV forecasts were outperformed by their quantile regression (QR) counterparts suggests that the conditional distribution of the log-returns is not normal
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