最低工资政策的城镇家庭收入分配效应研究

家庭在福利分析中具有重要地位，但家庭收入如何受最低工资政策影响却很少得到关注。本文采用2005-2015年中国综合社会调查（CGSS）数据，利用多时期双重差分和再中心化影响函数回归方法实证考察了最低工资政策对我国城镇家庭收入及其分布的影响。研究发现：①最低工资标准上涨显著提升了处于贫困标准1~2倍家庭的收入水平，对贫困标准以下或高于贫困标准2倍以上的家庭收入没有影响；②最低工资标准提升可有效减缓城镇家庭收入不平等，尤其对家庭收入分布较低分位部分的不平等程度有明显改善；③最低工资政策对家庭收入的最终影响取决于工资溢出效应和就业挤出效应的比较，且其对不同收入等级家庭中成员的收入和就业影响存在明显的异质性。政府在制定最低工资政策时，应针对低收入女性劳动力出台相应的配套措施，以保证各类低收入家庭均能从最低工资标准提升中受益。     

Has the national minimum wage reduced UK wage inequality?

Summary.  The paper investigates the effect on the wage distribution of the introduction, in April 1999, of the national minimum wage (NMW) in the UK. Because of the structure of UK earnings statistics, it is not straightforward to investigate this and various methods for adjusting the published statistics are discussed. The main conclusions are that the NMW does have a detectable effect on the wage distribution and that compliance with the NMW is widespread but the effect is limited because the NMW has been set at a level such that only 6–7% of workers are directly affected and the NMW has had virtually no effect on the pay of workers who are not directly affected. Furthermore, virtually all the changes occurred within 2 months of the introduction in April 1999 and its effect declined over time from April 1999 to September 2001 as the minimum wage was not uprated in line with the increase in average earnings. The more substantial increase in the NMW in October 2001 partially, but not wholly, restored some of this decline in impact.     

分位数回归技术综述

普通最小二乘回归建立了在自变量X=x下因变量Y的条件均值与X的关系的线性模型。而分位数回归（Quantile Regression）则利用自变量X和因变量y的条件分位数进行建模。与普通的均值回归相比，它能充分反映自变量X对于因变量y的分布的位置、刻度和形状的影响，有着十分广泛的应用，尤其是对于一些非常关注尾部特征的情况。文章介绍了分位数回归的概念以及分位数回归的估计、检验和拟合优度，回顾了分位数回归的发展过程以及其在一些经济研究领域中的应用，最后做了总结。     

Conditional Stochastic Dominance Testing

This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional distributions and their moments. We exploit the fact that a conditional distribution dominates the other if and only if the difference between the marginal joint distributions is monotonic in the explanatory variable at each value of the dependent variable. The proposed test statistic compares restricted and unrestricted estimators of the difference between the joint distributions, and it can be implemented under minimal smoothness requirements on the underlying nonparametric curves and without resorting to smooth estimation. The finite sample properties of the proposed test are examined by means of a Monte Carlo study. We illustrate the test by studying the impact on postintervention earnings of the National Supported Work Demonstration, a randomized labor training program carried out in the 1970s.     

Multiply robust inference for statistical interactions

A primary focus of an increasing number of scientific studies is to determine whether two exposures interact in the effect that they produce on an outcome of interest. Interaction is commonly assessed by fitting regression models in which the linear predictor includes the product between those exposures. When the main interest lies in the interaction, this approach is not entirely satisfactory because it is prone to (possibly severe) bias when the main exposure effects or the association between outcome and extraneous factors are misspecified. In this article, we therefore consider conditional mean models with identity or log link which postulate the statistical interaction in terms of a finite-dimensional parameter, but which are otherwise unspecified. We show that estimation of the interaction parameter is often not feasible in this model because it would require nonparametric estimation of auxiliary conditional expectations given high-dimensional variables. We thus consider 'multiply robust estimation' under a union model that assumes at least one of several working submodels holds. Our approach is novel in that it makes use of information on the joint distribution of the exposures conditional on the extraneous factors in making inferences about the interaction parameter of interest. In the special case of a randomized trial or a family-based genetic study in which the joint exposure distribution is known by design or by Mendelian inheritance, the resulting multiply robust procedure leads to asymptotically distribution-free tests of the null hypothesis of no interaction on an additive scale. We illustrate the methods via simulation and the analysis of a randomized follow-up study.     

The internal relocation premium: are migrants positively or negatively selected? Evidence from Italy

This paper analyzes the wage returns from internal migration for recent graduates in Italy. We employ a switching regression model that accounts for the endogeneity of the individual's choice to relocate to get a job after graduation: the omission of this selection decision can lead to biased estimates, as there is potential correlation between earnings and unobserved traits, exerting an influence on the decision to migrate. The empirical results sustain the appropriateness of the estimation technique and show that there is a significant pay gap between migrants and non-migrants; migrants seem to be positively selected and the migration premium is downward biased through OLS estimates. The endogeneity of migration shows up both as a negative intercept effect and as a positive slope effect, the second being larger than the first: bad knowledge of the local labor market and financial constraints lead migrants to accept a low basic wage but, due to relevant returns to their characteristics, they finally obtain a higher wage than the others.     

EXAMPLES OF MINIMUM VARIANCE ESTIMATION1

In this sequel to a previous discussion of minimum variance estimation (Bartlett, 1982), the gain with conditional estimation procedures is illustrated for the location parameter for (i) the rectangular distribution; (ii) a triangular distribution (typifying an asymmetric case). This note concludes with further remarks on the multi-sample and multi-parameter cases.     

劳动力成本上升与中国制造业转移

制造业合理有序的转移对于我国经济的稳定平衡发展具有重要意义。本文使用1998-2013年中国工业企业数据库加总得到的城市层面制造业数据和最低工资代表的劳动力成本数据,首次考察了最低工资与制造业空间分布的因果关系。研究结果表明,最低工资标准的提高降低了城市制造业产值的份额、就业人数份额和规模以上企业数目份额,并推动制造业就近转移。同时,最低工资对制 造业份额的影响存在着明显的异质性,资本密度越高和国有企业占比越多的地区制造业份额受影响越小。进一步的机制检验发现,最低工资标准的上升抑制新企业的设立,促进企业跨区域资本配置和推动企业退出。本文的研究结论有助于厘清劳动力成本上升对制造业空间分布的影响效应,为制造业在区域间的分布和转移提供了重要的政策借鉴。     

Box–Cox power transformation unconditional quantile regressions with an application on wage inequality

This study proposes a semi-parametric estimation method, Box–Cox power transformation unconditional quantile regression, to estimate the impact of changes in the distribution of the explanatory variables on the unconditional quantile of the outcome variable. The proposed method consists of running a nonlinear regression of the recentered influence function (RIF) of the outcome variable on the explanatory variables. We also show the asymptotic properties of the proposed estimator and apply the estimation method to address an existing puzzle in labor economics–why the 50th/10th percentile wage gap has been falling in the USA since the late 1980s. Our results show that declining unionization can explain approximately 10% of the decline in the 50/10 wage gap in 1990–2000 and 23% in 2000–2010.     

The Minimum Wage

Do moderate increases in the minimum wage reduce employment? If not, do they nevertheless raise wages? To examine these questions, we apply techniques of time series analysis and systems estimation that are commonly used in macroeconomics and finance to five panels of data that contain between 11 and 34 low-wage industries. Our answers are “No” and “Yes,” respectively. We find that increases in the federal minimum wage between 1947 and 1997 have raised average wages in many of these industries, especially the lowest wage ones. The effect on employment, however, is mixed and typically nonsignificant, even when average wages have risen.     

面板数据的可加分位回归模型研究与应用

本文在贝叶斯分析的框架下讨论了面板数据的可加模型分位回归建模方法。首先通过低秩薄板惩罚样条展开和个体效应虚拟变量的引进将非参数模型转换为参数模型,然后在假定随机误差项服从非对称Laplace分布的基础上建立了贝叶斯分层分位回归模型。通过对非对称Laplace分布的分解,论文给出了所有待估参数的条件后验分布,并构造了待估参数的 Gibbs抽样估计算法。计算机模拟仿真结果显示,新提出的方法相比于传统的可加模型均值回归方法在估计稳健性上明显占优。最后以消费支出面板数据为例研究了我国农村居民收入结构对消费支出的影响,发现对于农村居民来说,无论是高、中、低消费群体,工资性收入与经营净收入的增加对其消费支出的正向刺激作用更为明显。进一步,相比于高消费农村居民人群,低消费农村居民人群随着收入的增加消费支出上升速度较为缓慢。     

Local Box–Cox transformation on time-varying parametric models for smoothing estimation of conditional CDF with longitudinal data

Nonparametric estimation and inferences of conditional distribution functions with longitudinal data have important applications in biomedical studies, such as epidemiological studies and longitudinal clinical trials. Estimation approaches without any structural assumptions may lead to inadequate and numerically unstable estimators in practice. We propose in this paper a nonparametric approach based on time-varying parametric models for estimating the conditional distribution functions with a longitudinal sample. Our model assumes that the conditional distribution of the outcome variable at each given time point can be approximated by a parametric model after local Box–Cox transformation. Our estimation is based on a two-step smoothing method, in which we first obtain the raw estimators of the conditional distribution functions at a set of disjoint time points, and then compute the final estimators at any time by smoothing the raw estimators. Applications of our two-step estimation method have been demonstrated through a large epidemiological study of childhood growth and blood pressure. Finite sample properties of our procedures are investigated through a simulation study. Application and simulation results show that smoothing estimation from time-variant parametric models outperforms the existing kernel smoothing estimator by producing narrower pointwise bootstrap confidence band and smaller root mean squared error.     

Non‐parametric Estimation of Extreme Risk Measures from Conditional Heavy‐tailed Distributions

In this paper, we introduce a new risk measure, the so‐called conditional tail moment. It is defined as the moment of order a ≥ 0 of the loss distribution above the upper α‐quantile where α ∈ (0,1). Estimating the conditional tail moment permits us to estimate all risk measures based on conditional moments such as conditional tail expectation, conditional value at risk or conditional tail variance. Here, we focus on the estimation of these risk measures in case of extreme losses (where α ↓0 is no longer fixed). It is moreover assumed that the loss distribution is heavy tailed and depends on a covariate. The estimation method thus combines non‐parametric kernel methods with extreme‐value statistics. The asymptotic distribution of the estimators is established, and their finite‐sample behaviour is illustrated both on simulated data and on a real data set of daily rainfalls.     

Model checks for nonparametric regression with missing data: a comparative study

ABSTRACT

This paper analyses the behaviour of the goodness-of-fit tests for regression models. To this end, it uses statistics based on an estimation of the integrated regression function with missing observations either in the response variable or in some of the covariates. It proposes several versions of one empirical process, constructed from a previous estimation, that uses only the complete observations or replaces the missing observations with imputed values. In the case of missing covariates, a link model is used to fill the missing observations with other complete covariates. In all the situations, Bootstrap methodology is used to calibrate the distribution of the test statistics. A broad simulation study compares the different procedures based on empirical regression methodology, with smoothed tests previously studied in the literature. The comparison reflects the effect of the correlation between the covariates in the tests based on the imputed sample for missing covariates. In addition, the paper proposes a computational binning strategy to evaluate the tests based on an empirical process for large data sets. Finally, two applications to real data illustrate the performance of the tests.     

公共部门与非公共部门工资差异的分位数回归分析

本文基于浙江省城镇就业人员的微观调查数据,在考虑了部门选择的内生性后,利用工具变量分位数回归方法研究了公共部门与非公共部门间的工资差异。研究结果表明,未考虑部门选择的内生性会低估公共部门和非公共部门间的工资差异。同时,我们还发现公共部门的工资显著高于非公共部门,在各个分位数上,超出的比例大致分布在30.1%至10.7%之间。但随着分位数 的提高,两部门间的工资差距呈现递减的趋势。     

Synthetic exponential control charts with unknown parameter

The existing synthetic exponential control charts are based on the assumption of known in-control parameter. However, the in-control parameter has to be estimated from a Phase I dataset. In this article, we use the exact probability distribution, especially the percentiles, mean, and standard deviation of the conditional average run length (ARL) to evaluate the effect of parameter estimation on the performance of the Phase II synthetic exponential charts. This approach accounts for the variability in the conditional ARL values of the synthetic chart obtained by different practitioners. Since parameter estimation results in more false alarms than expected, we develop an exact method to design the adjusted synthetic charts with desired conditional in-control performance. Results of known and unknown in-control parameter cases show that the control limit of the conforming run length sub-chart of the synthetic chart should be as small as possible.     

A minimum variance adaptive technique for parameter estimation and hypothesis testing

This report presents numerical results of an approach for parameter estimation and hypothesis testing that does not rely on specific assumptions about the underlying distribution of errors in the measured data. This approach combines robust estimation procedures, the bootstrap method for estimation of parameter uncertainties, permutation techniques for hypothesis testing, and adaptive approaches to estimation in order to obtain the minimum variance estimator or test statistic (within a predefined class) for the data under consideration. The technique produces efficient estimators of central tendency and powerful test statistics, even for small sample sizes. (Portions of this work have been presented in preliminary form (Turkheimer et al., 1996)).     

Estimation of location extremes within general families of scale mixtures

In this paper, we study the estimation of the minimum and maximum location parameters, respectively, representing the minimum guaranteed lifetime of series and parallel systems of components, within a general class of scale mixtures. The conditional or underlying distribution has only the primary restriction of being a location-scale family with positive support. The mixing distribution is also quite general in that we only assume that it has positive support and finite second moment. For demonstrative purposes several special cases are highlighted such as the gamma, inverse-Gaussian, and discrete mixture. Various estimators, including bootstrap bias corrected estimators, are compared with respect to both mean-squared-error and Pitman's measure of closeness.     

样本选择模型截距项的半参数估计及应用———户籍工资差异分解研究

样本选择模型是解决样本选择问题的主要工具,广泛应用于工资差异分解、平均处理效应测算等实证研究。截距项的估计是样本选择模型半参数估计中相对独立且重要的一部分,现有的以无穷处识别为代表的半参数估计方法存在窗宽参数难以选取的问题。为此,本文把无穷处识别等价转化为边界处识别,并基于新的识别关系给出样本选择模型截距项的核估计方法。这种新方法的好处在于将样本选择模型截距项的估计纳入核估计框架中,从而可以采用经验法则解决现有方法的窗宽选取难题。数值模拟结果表明,本文所提出的估计方法在不同设定下均有良好的有限样本表现。把这种新的半参数估计方法应用于户籍工资差异分解后发现,我国劳动力市场目前不存在明显的户籍差别待遇。     

最低工资对企业长、短期杠杆率的异质性影响

本文基于2007-2012年工业企业和全国县区级最低工资的匹配数据,探讨最低工资对企业杠杆率的影响,研究发现最低工资上升会对企业的长、短期杠杆率产生截然相反的影响。具体而言,最低工资标准每上升10%,企业长期杠杆率将上升0.42个百分点,而短期杠杆率将下降0.29个百分点。针对最低工资对企业长、短期杠杆率的异质性影响,本文给出了两种机制进行解释———“要素替代”和“盈利预期”效应。就作用渠道而言,“要素替代”效应下,最低工资上升意味着资本相对劳动更为便宜,这会促使企业更多地以资本替代劳动,表现为债务的增加,且主要反映在长期债务上。“盈利预期”效应下,企业生产成本上升会恶化企业盈利预期,使得企业减少投资,同时整体性地收缩债务,这主要对应于短期债务调整。进一步,将企业按照高/ 低劳动密集度以及高/ 低市场竞争度进行划分时,异质性分析结果表明,“要素替代”所导致的长期杠杆率上升只在高劳动密集度企业才成立,而“盈利预期”效应导致的短期杠杆率下降只在高市场竞争度企业才成立。