澳大利亚住房负担能力评析

由于住房政策的转变以及经济社会因素的变化,澳大利亚同我国一样正经历着严峻的住房负担能力问题。通过文献研究的方法,从澳大利亚住房负担问题的概念、产生的背景、问题的原因与影响等几个方面对澳大利亚住房负担能力问题的相关研究成果进行了阐述,并通过这些研究成果提出了我国住房负担能力问题研究的新思路和新视角:一是研究方法的借鉴,二是研究领域的扩展,三是应给予住房供给更多关注。     

城市规划对住房可支付性的影响

以场景分析法剖析城市规划与住房可支付性之间的关系,认为城市规划对住房可支付性具有双重影响:其限制作用可能导致住房价格上涨,其再分配作用则可对可支付住房进行补贴。提出通过城市规划提升住房可支付性的两条途径:直接途径为通过增加住房供给来达到提升可支付性的目的;间接途径为通过抽取规划得益来补贴住房,以提升住房的可支付性。在房价与需求双高的条件下,住房市场持续存在经济租金,因此抽取规划得益具有很强的可操作性,有可能成为解决中国住房可支付性问题的现实途径。     

住房负担能力测度方法研究综述

如何测定居民住房负担和价格可支付能力,既是开展住房保障制度建设的重要前提,也是防范住房市场风险的基础工作。对居民住房负担能力研究的重点,需要对其定义有一个十分清晰的界定与把握,选择合适和科学的住房负担能力测度方法。就国际上研究居民住房可支付能力的最主要四种方法:传统的住房支出收入比法、考虑适当住房标准的住房支出收入比法、剩余收入法以及复合测度法等做了研究综述。     

Measuring housing affordability: Looking beyond the median

We draw a distinction between the concepts of purchase affordability (whether a household is able to borrow enough funds to purchase a house) and repayment affordability (the burden imposed on a household of repaying the mortgage). We operationalize this distinction in the context of a new methodology for constructing affordability measures that draws on the value-at-risk concept and takes account of the whole distribution of household income and house prices rather than just the median. Empirically we find that the distinction between purchase and repayment affordability can be pronounced. In the Sydney prime mortgage market over the period 1996–2006, repayment affordability deteriorated very significantly while purchase affordability remained quite stable. This difference can be attributed to the loosening of credit constraints in the mortgage market which it seems has carried through primarily into higher house prices rather than an improvement in purchase affordability. We also show how median house-price-to-income ratio measures of affordability can be extended to take account of the whole distribution of income and house prices, and how as a result of differential skewness in the house price and income distributions the housing affordability problem may be significantly worse for lower income households than suggested by standard median measures.     

Capital market integration in ASEAN: A non-stationary panel data analysis

Motivated by the establishment of ASEAN Economic Community (AEC) at the end of 2015, we examine saving-investment relationship in various subgroups of ASEAN to assess their capital market integration. The results from second generation panel unit-root and cointegration tests that account for cross-sectional dependence as well as estimates of long-run saving-retention rate provide some evidence of market integration in ASEAN. The analysis of short-run dynamics suggests that capital mobility in ASEAN during 1980–2014 appears similar to that in OECD countries during 1970–1999. More importantly, across different panel estimators and subgroups of membership, there is considerable heterogeneity among the member countries. The saving-investment association is very weak, thereby implying very high capital mobility, in more developed members such as Singapore, Malaysia, and Brunei; the association is very strong, implying very low capital mobility, for much less developed members such as Laos, Myanmar, and Cambodia. The results call for renewed effort to develop capital markets in less developed nations and integrate them with the rest of the membership in ASEAN. In this paper, we also address several major shortcomings of the original Feldstein-Horioka framework.     

国外住房可支付能力研究概要

运用文献法和归纳法,介绍了国外对住房可支付能力的内涵界定、测度方法、影响因素、低住房可支付能力带来的后果以及提高住房可支什能力的对策等研究成果,归纳了国外研究的特点;结合国内该领域研究的现状,提出了从视角、内容、方法等方面开展我国住房可支付能力研究的建议.     

Further evidence on finance-growth causality: A panel data analysis

This paper reassesses the causal relationship between financial development and economic growth. Using recently developed panel methods on a data set of 71 developed and developing countries over the period 1960-2004, our study confirms previous results of a bidirectional causality between finance and growth. In addition, we show significant differences among country groups when considering both long-run and short-run causality. While in low and middle income countries there is no supportive evidence of short-run causality between financial development and economic growth, in high income countries economic growth significantly affects financial development.     

A general framework for frontier estimation with panel data

The main objective of the paper is to present a general framework for estimating production frontier models with panel data. A sample of firms i = 1, ..., N is observed on several time periods t = 1, ... T. In this framework, nonparametric stochastic models for the frontier will be analyzed. The usual parametric formulations of the literature are viewed as particular cases and the convergence of the obtained estimators in this general framework are investigated. Special attention is devoted to the role of N and of T on the speeds of convergence of the obtained estimators. First, a very general model is investigated. In this model almost no restriction is imposed on the structure of the model or of the inefficiencies. This model is estimable from a nonparametric point of view but needs large values of T and of N to obtain reliable estimates of the individual production functions and estimates of the frontier function. Then more specific nonparametric firm effect models are presented. In these cases, only NT must be large to estimate the common production function; but again both large N and T are needed for estimating individual efficiencies and for estimating the frontier. The methods are illustrated through a numerical example with real data.     

Imputation of non-ignorable nonresponses for income: analysis of a panel study on Taiwan

In this paper, the two-step generalized estimating equations (GEE) approach developed by Wang and Fitzmaurice (Biom J 2:302–318, 2006) is employed to handle income non-responses in the Panel Study of Family Dynamics survey conducted in Taiwan. In our analysis, we first construct a conditional logit model of the paid work equation by taking the missing patterns into account. We then use the estimation results to impute whether or not the nonresponses were working for pay. For those who were imputed or observed to work for pay, we adopt the two-step GEE method to estimate the income equation. Compared to simply deleting the missing cases, the two-step imputation procedure is found to improve the estimation results.     

Financial development and governance: A panel data analysis incorporating cross-sectional dependence

This study investigates bidirectional causality between governance and financial development using panel data of 101 countries from 1984 to 2013. The financial development–governance nexus is explored using econometric methods robust to cross-sectional dependence, and the relationship between different levels of development and openness is analyzed. Long-run equation estimates show clear evidence that financial development positively affects governance, and this positive impact is found to be robust to three different measures of governance. Further analysis shows that improving governance quality has a positive effect on financial development, while Granger causality tests demonstrate bidirectional causality between financial development and the governance measures. Finally, the impact of financial development on governance is dependent on a country’s level of development and openness. These findings underscore the crucial role of financial development in bringing about good governance reforms and economic growth that, in turn, can further develop the financial sector. As such, a symbiotic and synergistic relationship can persist between good governance, growth, and financial development. The findings provide significant motivation for policymakers to encourage openness and financial sector development to lift the standard of living, especially in emerging economies.     

Habits and heterogeneity in demands: a panel data analysis

We examine demand behaviour for intertemporal dependencies, using Spanish panel data. We present evidence that there is both state dependence and correlated heterogeneity in demand behaviour. Our specific findings are that food outside the home, alcohol and tobacco are habit forming, whereas clothing and small durables exhibit durability. We conclude that demand analyses using cross‐section data that ignore these effects may be seriously biased. On the other hand, the degree of intertemporal dependence is not sufficiently strong to make composite ‘consumption’ significantly habit forming, as has been suggested in some recent analyses. Copyright © 2007 John Wiley & Sons, Ltd.     

Multivariate regression models for panel data

The paper examines the relationship between heterogeneity bias and strict exogeneity in a distributed lag regression of y on x. The relationship is very strong when x is continuous, weaker when x is discrete, and non-existent as the order of the distributed lag becomes infinite. The individual specific random variables introduce nonlinearity and heteroskedasticity; so the paper provides an appropriate framework for the estimation of multivariate linear predictors. Restrictions are imposed using a minimum distance estimator. It is generally more efficient than the conventional estimators such as quasi-maximum likelihood. There are computationally simple generalizations of two- and three-stage least squares that achieve this efficiency gain. Some of these ideas are illustrated using the sample of Young Men in the National Longitudinal Survey. The paper reports regressions on the leads and lags of variables measuring union coverage, SMSA, and region. The results indicate that the leads and lags could have been generated just by a random intercept. This gives some support for analysis of covariance type estimates; these estimates indicate a substantial heterogeneity bias in the union, SMSA, and region coefficients.     

Institutions and economic development: A Granger causality analysis of panel data evidence

This study examines the causal relationship between institutions and economic development using a panel Granger causality test. The study incorporates two institutional datasets, the International Country Risk Guide (ICRG) and World Governance Indicators (WGI). The empirical results based on 60 countries show that there is a bi-directional causality between institutions and economic development. The findings also suggest that causality patterns between institutions and economic performance vary at different stages of income level. Better institutional quality fosters economic development in higher income countries, whereas economic development tends to enhance institutional quality in lower income countries.     

The impact of institutional quality on manufacturing sectors: A panel data analysis

We use data on Russian regions to examine the effect of institutional quality on institutionally dependent manufacturing sectors in terms of both output levels and growth rates. Unlike the existing literature on this topic, which mostly uses cross-sectional or pooled specifications for either country-level or regional data, we use panel data. This approach allows us to distinguish between short-term and long-term impacts and mitigates endogeneity concerns. As an additional contribution to the literature, we estimate the full marginal effects of institutions on manufacturing sectors with different degrees of institutional dependence. In terms of policy recommendations, our results imply that significant institutional improvements would be needed for the Russian economy to diversify away from heavy reliance on oil and natural gas.     

A nonlinear panel data model of cross-sectional dependence

This paper proposes a nonlinear panel data model which can endogenously generate both ‘weak’ and ‘strong’ cross-sectional dependence. The model’s distinguishing characteristic is that a given agent’s behaviour is influenced by an aggregation of the views or actions of those around them. The model allows for considerable flexibility in terms of the genesis of this herding or clustering type behaviour. At an econometric level, the model is shown to nest various extant dynamic panel data models. These include panel AR models, spatial models, which accommodate weak dependence only, and panel models where cross-sectional averages or factors exogenously generate strong, but not weak, cross sectional dependence. An important implication is that the appropriate model for the aggregate series becomes intrinsically nonlinear, due to the clustering behaviour, and thus requires the disaggregates to be simultaneously considered with the aggregate. We provide the associated asymptotic theory for estimation and inference. This is supplemented with Monte Carlo studies and two empirical applications which indicate the utility of our proposed model as a vehicle to model different types of cross-sectional dependence.     

Organized crime and public spending: a panel data analysis

The aim of this paper is to investigate, empirically, what components of public spending imply a decreasing effect on organized crime and what components create opportunities for organized crime, discussing also the role of government efficiency. Using a panel data analysis, the results show a strikingly consistent pattern for the EU Member States. Organized crime mainly operates in the distribution of government spending for local public goods and public provision of private services. There is a decreasing effect on organized crime of the public expenditure devoted to education and social policy. Government efficiency in public spending is beneficial to limit the opportunities of the organized crime.     

Errors in variables in panel data

Panel data based studies in econometrics use the analysis of covariance approach to control for various ‘individual effects’ by estimating coefficients from the ‘within’ dimension of the data. Often, however, the results are unsatisfactory, with ‘too low’ and insignificant coefficients. Errors of measurement in the independent variables whose relative importance gets magnified in the within dimension are then blamed for this outcome.Errors-in-variables models have not been used widely, in part because they seem to require extraneous information to be identified. We show how a variety of errors-in-variables models may be identifiable and estimable in panel data without the use of external instruments and apply it to a relatively simple but not uninteresting case: the estimation of ‘labor demand’ relationships, also known as the ‘short-run increasing returns to scale’ puzzle.     

The importance of efficient use in the adoption of technology: A micro panel data analysis

Even after the introduction of the new rice technology (Green revolution) about more than two decades ago, the food shortage problem has not decreased significantly in many developing countries. Obviously, there can be no dispute over the importance of the new rice technology in bringing about improvements in agricultural productivity. But, there are factors other than the choice of technology which are equally important, and a neglect of these factors may lead to a complete negation of any possible gains in productivity arising from the use of new technology. One such factor is the efficient use of the technology. This paper examines farm-specific application of the new rice technology by using a micro-level panel data from a South Indian district. The results show a wide variation in the efficient use of the technology by the sample farmers. Extension and education were identified as two major constraining factors.The refereeing process of this paper was handled through N.R. Adam.     

Common breaks in means and variances for panel data

This paper establishes the consistency of the estimated common break point in panel data. Consistency is obtainable even when a regime contains a single observation, making it possible to quickly identify the onset of a new regime. We also propose a new framework for developing the limiting distribution for the estimated break point, and show how to construct confidence intervals. The least squares method is used for estimating breaks in means and the quasi-maximum likelihood (QML) method is used to estimate breaks in means and in variances. QML is shown to be more efficient than the least squares even if there is no change in the variances.