This dissertation studies household income and consumption. In the first chapter, I identify the causal effect of retirement on health service utilization in China. In the second chapter, I investigates the impact that retirement has on the family support network of “sandwich” generations in China. In the third chapter, I propose a new estimator for linear quantile regression models with generated regressors, and apply it to study Engel curves for various commodity consumption for families in the UK. In the first chapter, I apply a regression discontinuity design by exploiting the exogenous mandatory retirement age rules in China in order to identify the causal effect of retirement on health service utilization. In China, the social insurance Urban Employee Basic Medical Insurance (UEBMI) provision continues after individuals retire. Employees, however, stop paying the premium and enjoy reduced cost sharing after they retire. Individual medical expenses, insurance costs, and benefits are recorded in the China Household Finance Survey 2013 (CHFS). Significantly, males and females respond differently to this decrease in the relative price of health insurance at the time of retirement. Females are generally more willing to increase their out-of-pocket expenditures in order to take advantage of better health insurance benefits and utilize more medical care. Males, by contrast, do not respond to this change in relative price in the same manner. In the second chapter, I investigates the impact that retirement has on the family support networks of “sandwich” generations in China. These middle-aged households have an inter-generational support network that includes both upward transfers (their parents or parents-in-law), as well as downward transfers (their children). I use micro data from CHARLS (China Health and Retirement Longitudinal Study) concerning middle-aged and elderly households in order to evaluate the changes that retirement can have on this family support network, primarily by exploiting the exogenous mandatory retirement age rules in China. I make the identifying assumption that inter-generational transfers would evolve more smoothly if households would not retire and apply a regression discontinuity approach. I find that retirement induces “sandwich” generations to switch roles in the private network as well as in the public transfer channel; indeed, is 55 percentage point more likely that households will switch from resource providers to resource recipients in the channel of private transfers. In addition, these “sandwich” generations are about 47 percentage point more likely to receive money from their non-coresident children when they retire. In the third chapter, we studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of the two-step estimator, namely, consistency and asymptotic normality are established. We show that the asymptotic variance-covariance matrix needs to be adjusted to account for the first-step estimation error. We propose a general estimator for the asymptotic variance-covariance, establish its consistency, and develop testing procedures for linear hypotheses in these models. Monte Carlo simulations to evaluate the finite-sample performance of the estimation and inference procedures are provided. Finally, we apply the proposed methods to study Engel curves for various commodities using data from the UK Family Expenditure Survey. We document strong heterogeneity in the estimated Engel curves along the conditional distribution of the budget share of each commodity. The empirical application also emphasizes that correctly estimating confidence intervals for the estimated Engel curves by the proposed estimator is of importance for inference.

Governments around the world have regulated multinational enterprises to adopt arm¡¦s length transactions to facilitate identifications and comparisons between non-transfer pricing transactions with independent, non-related enterprises and transactions with related enterprises that are suspected of transfer pricing. Currently, most of the optimal transfer pricing methods for establishing arm¡¦s length principles for multinational enterprises have been addressed in Organization for Economic Cooperation and Development's "Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations". These guidelines emphasize the establishment of a range of arm¡¦s length transactions through the comparability analysis and the economic analysis of transfer pricing transactions; a taxpayer's returns from transactions with related companies are then compared to the range of arm¡¦s length transactions. Currently the academic world is taking the initiative in the development of relevant models to describe corporate transfer pricing decisions or to measure the net income of corporate transfer pricing transactions. This research stems from these purposes and attempts to describe transfer pricing decisions in real practice through stringent modelling; this model is then used to measure the net income of transfer pricing transactions that took place among electronic industry participants who are publicly listed in the TSE or OTC in Taiwan. We further investigated the main factors that affect the levels of net income transferred by enterprises. Based on the empirical results of this research, we discovered that the impact of raw material costs is highly significant to the corporate transfer pricing decisions, and the magnitude of impacts vary depending on the allocation of net income from transfer pricing. We recommend that the tax administration detect corporate transfer pricing decisions by monitoring the weight of raw material costs in a company.

yes / This note highlights the value of using UQR for addressing the limitations inherent within previous methods involving conditional parameter distributions for spending analysis (QR and OLS). Using unique data and robust analysis using improved methods, our paper clearly demonstrates the over-importance attached to length of stay and the inadequate attention given to business travelers in previous research. There are clear benefits from UQR’s methodological robustness for assessing the multitude of variables related to tourist expenditures, particularly given UQR’s ability to inform across the spending distribution. Given tourism’s importance for the UK it is critical for expensive promotional activities to be targeted efficiently for ensuring effective policy making.

Yes / We employ unconditional quantile regression with region of origin fixed effects, whereby we find that attending live football matches significantly increases expenditures by inbound tourist in the UK, and surprisingly we find that such effects are strongest for those who overall spend the least. Higher spending individuals spend significantly more than those who do not attend football matches, even when such individuals are otherwise similar. We analyse the impact of football attendance across the tourism expenditure distribution which is a relatively neglected aspect within previous research.

Yes / The inbound tourist expenditure generating role of football (soccer), particularly the English Premier League 15 (EPL) is evaluated. An enhanced economic and management understanding of the role of regular sporting fixtures emerges, as well as quantification of their impact. Expenditure on football tickets is isolated to identify local economic spillovers outside the stadium walls. Using the UK International Passenger Survey, unconditional quantile regressions (UQR) is used to evaluate the distributional impact of football attendance on tourist expenditures. Both total expenditure and a new measure which adjusts expenditures for football ticket prices are considered. UQR is a novel technique which is as yet underexploited within sport economics and confers important methodological advantages over both OLS and quantile regressions. Significant cross quantile variation is found. High spending football fans spend more, even after ticket prices are excluded. Surprisingly, spending effects owing to attendance are strongest for those who overall spend the least, confirming the role of sport as a generator of tourist expenditure unlike most others. Though the attendance effect is smaller for higher aggregate spenders, there is nevertheless a significant impact across the distribution. Distributional expenditure impacts highlight clear differentials between attendance by high and low spenders. Similar analysis is applicable to other global brands such as the National Football League (NFL) in the United States (American football) and the Indian Premier (cricket) League. The EPL’s global popularity can be leveraged for achieving enhanced tourist expenditure.

This paper applies and extends Machado and Mata's parametric quantile decomposition method and a similar nonparametric technique to explore changes in welfare in Zimbabwe between 1995 and 2001. These methods allow us to construct a counterfactual distribution in order to decompose the shift into the part due to changes in endowments and that due to changes in returns. We examine two subsets of a nationally representative dataset and find that endowments had a positive effect but that returns account for more of the difference. In communal farming areas, the effect of returns was positive while, in urban Harare, it was negative. / Master of Science

The application area of Fifth Generation New Radio (5G-NR) called Ultra-Reliable and Low-Latency Communication (URLLC) requires a reliability, the probability of receiving and decoding a data packet correctly, of 1 - 10^5. For this requirement to be fulfilled in a resource-efficient manner, it is necessary to have a good estimation of extremely low quan- tiles of the channel quality distribution, so that appropriate resources can be distributed to users of the network system.  This study proposes and evaluates two methods for estimating extreme quantiles of the downlink channel quality distribution, linear quantile regression and Quantile Regression Neural Network (QRNN). The models were trained on data from Ericsson’s system-level radio network simulator, and evaluated on goodness of fit and resourcefulness. The focus of this study was to estimate the quantiles 10^2, 10^3 and 10^4 of the distribution.  The results show that QRNN generally performs better than linear quantile regression in terms of pseudoR2, which indicates goodness of fit, when the sample size is larger. How- ever, linear quantile regression was more effective for smaller sample sizes. Both models showed difficulty estimating the most extreme quantiles. The less extreme quantile to esti- mate, the better was the resulting pseudoR2-score. For the largest sample size, the resulting pseudoR2-scores of the QRNN was 0.20, 0.12 and 0.07, and the scores of linear quantile regression was 0.16, 0.10 and 0.07 for the respective quantiles 10^2, 10^3 and 10^4.  It was shown that both evaluated models were significantly more resourceful than us- ing the average of the 50 last measures of channel quality subtracted with a fixed back-off value as a predictor. QRNN had the most optimistic predictions. If using the QRNN, theo- retically, on average 43% more data could be transmitted while fulfilling the same reliability requirement than by using the fixed back-off value.

The aim of the work in this thesis is to carry out dimension reduction (DR) for high dimensional (HD) data by using statistical methods for variable selection, feature extraction and a combination of the two. In Chapter 2, the DR is carried out through robust feature extraction. Robust canonical correlation (RCCA) methods have been proposed. In the correlation matrix of canonical correlation analysis (CCA), we suggest that the Pearson correlation should be substituted by robust correlation measures in order to obtain robust correlation matrices. These matrices have been employed for producing RCCA. Moreover, the classical covariance matrix has been substituted by robust estimators for multivariate location and dispersion in order to get RCCA. In Chapter 3 and 4, the DR is carried out by combining the ideas of variable selection using regularisation methods with feature extraction, through the minimum average variance estimator (MAVE) and single index quantile regression (SIQ) methods, respectively. In particular, we extend the sparse MAVE (SMAVE) reported in (Wang and Yin, 2008) by combining the MAVE loss function with different regularisation penalties in Chapter 3. An extension of the SIQ of Wu et al. (2010) by considering different regularisation penalties is proposed in Chapter 4. In Chapter 5, the DR is done through variable selection under Bayesian framework. A flexible Bayesian framework for regularisation in quantile regression (QR) model has been proposed. This work is different from Bayesian Lasso quantile regression (BLQR), employing the asymmetric Laplace error distribution (ALD). The error distribution is assumed to be an infinite mixture of Gaussian (IMG) densities.

The U.S.-American foreclosure crisis and related economic crises have had severe and wide-reaching effects for the global economy, homeowners, and municipalities alike. These negative changes led to federal, state, regional, and local responses intended to prevent and mitigate foreclosures. As of yet, no research has examined the community- and neighborhood-level impacts of local foreclosure responses. This research seeks to determine the economic, physical, social, and political changes that resulted from these responses. A mixed methods case study of Cuyahoga County, Ohio, home to Cleveland, was used to identify local level foreclosure responses—i.e. those carried out at the county level and below—and their effects. The qualitative component was comprised of semi-structured stakeholder interviews, including local governmental representatives, advocacy groups, and neighborhood representatives. Two community subcases were investigated in depth to further examine the mechanisms and effects of foreclosure responses. The quantitative component supplements the qualitative component by means of a quantile regression model that examines relationships between foreclosure responses and changes in property value at the Census tract level, used to approximate communities. The model integrates data for the entire county and estimates coefficients at various quantiles of the dependent variable, which uncovers variations in the associations between the variables along the dependent variable’s distribution. That is, with quantile regression it is possible to determine whether foreclosure responses have different effects depending on community conditions. The results indicate that the national and local context are of particular importance when responding to the foreclosure crisis. Lackluster national level responses necessitated creative and innovative responses at the local level. The Cleveland region is characterized a weak housing market and its concomitant vacancy and abandonment problems. Thus, post-foreclosure responses that deal with blighted property are essential. A wide variety of foreclosure responses took place in Cuyahoga County, in the form of systems reform, foreclosure prevention, targeting, property acquisition and control, legal efforts, and community- and neighborhood-level efforts. Several strategies used in these responses emerged as themes: targeting, addressing blight, strengthening the social fabric, planning for the future, building institutions and organizational capacity, and advocacy. Physical and economic impacts are closely linked and are brought about especially by responses using targeting and blight reduction strategies. Social impacts, such as increased identification with, investment in, and commitment to the community occurred as the result of responses that used the strategies of strengthening the social fabric and planning a shared future for the community. Finally, the strategies of building institutions and organizational capacity and advocacy resulted in increased political power in the form of more local control and additional resources for neighborhoods and communities. These results provide deeper insight into the effects of the foreclosure crisis and local responses to it on neighborhoods and communities. This case study identifies the importance of targeting, blight removal, strengthening social bonds, planning for a shared future, increasing organizational capacity, and advocacy in addressing the foreclosure crisis on the community and neighborhood levels, especially in weak housing market cities where need far outstrips the available resources.

The purpose of this essay is to study what impact immigration has on the Swedish income distribution for the period 1992-2005. This essay uses a two-folded approach to study the income distribution, first, an income inequality measure will be investigated in order to find if the inequality increases or decreases by the increased immigration. Secondly, we estimate a quantile regression for the 10th, 50th and 90th percentiles for the period 1992, 1995, 2000 and2005, together with an OLS regression in order to find the income gap between the immigrants and natives, which is analysed for males and females separately. The study found that the inflow of immigrants increased income inequality in the lower tail of the income distribution. Immigrants at the upper tail of the income distribution are doing relatively better than the immigrants in the lower tail of the income distribution. Conclusively, independently of gender, the income gap between immigrants and natives is almost three times as large in the lower tail of the income distribution relative to the upper tail of the income distribution.