Estimation under length-bias and right-censoring: an application to unemployment duration analysis for married women

In this work we analyse unemployment duration for married women in Spain, using the Labour Force Survey (LFS) of the Spanish Institute for Statistics, 1987-1997. Consistent non-parametric estimation of the unemployment survival function is provided. Since the available data are length- biased, a suitable correction of the Kaplan-Meier product-limit estimator is motivated and used for the referred analysis. The accuracy of parametric models is checked by means of goodness-of-fit plots--a graphical tool that requires preliminary estimation of the survival. Structural features of the associated hazard (as monotonicity and unimodality) are explored.     

Choosing between alternative classification criteria to measure the labour force state

Summary.  Labour force counting relies on general guidelines that are set by the International Labour Office to classify individuals into three labour force states: employment, unemployment and inactivity. However, the resulting statistics are known to be sensitive to slight variations in operational definitions which are prima facie consistent with the general guidelines. We consider two interpretations of the general guidelines, operationalized by the criterion that is currently followed by Eurostat and a criterion that was followed by the Italian Statistical Office up to 1992. After showing that the labour force statistics resulting from the two criteria differ considerably, we compare individuals whose classification depends on the criterion that is adopted with individuals whose classification is common between criteria to study the boundary between unemployment and inactivity. An application of our strategy is presented using data from the Italian Labour Force Survey, painting a picture neatly against the criterion that is currently followed by Eurostat.     

Hierarchical Bayes estimation of spatial statistics for rates

The U.S. Bureau of Labour Statistics publishes monthly unemployment rate estimates for its 50 states, the District of Columbia, and all counties, under Current Population Survey. However, the unemployment rate estimates for some states are unreliable due to low sample sizes in these states. Datta et al. (1999) proposed a hierarchical Bayes (HB) method using a time series generalization of a widely used cross-sectional model in small-area estimation. However, the geographical variation is also likely to be important. To have an efficient model, a comprehensive mixed normal model that accounts for the spatial and temporal effects is considered. A HB approach using Markov chain Monte Carlo is used for the analysis of the U.S. state-level unemployment rate estimates for January 2004-December 2007. The sensitivity of such type of analysis to prior assumptions in the Gaussian context is also studied.     

Dynamic factor analysis with non-linear temporal aggregation constraints

Summary.  The paper estimates an index of coincident economic indicators for the US economy by using time series with different frequencies of observation (monthly and quarterly, possibly with missing values). The model that is considered is the dynamic factor model that was proposed by Stock and Watson, specified in the logarithms of the original variables and at the monthly frequency, which poses a problem of temporal aggregation with a non-linear observational constraint when quarterly time series are included. Our main methodological contribution is to provide an exact solution to this problem that hinges on conditional mode estimation by iteration of the extended Kalman filtering and smoothing equations. On the empirical side the contribution of the paper is to provide monthly estimates of quarterly indicators, among which is the gross domestic product, that are consistent with the quarterly totals. Two applications are considered: the first dealing with the construction of a coincident index for the US economy, whereas the second does the same with reference to the euro area.     

Missing data and small area estimation in the UK Labour Force Survey

Summary.  We apply multivariate shrinkage to estimate local area rates of unemployment and economic inactivity by using UK Labour Force Survey data. The method exploits the similarity of the rates of claiming unemployment benefit and the unemployment rates as defined by the International Labour Organisation. This is done without any distributional assumptions, merely relying on the high correlation of the two rates. The estimation is integrated with a multiple-imputation procedure for missing employment status of subjects in the database (item non-response). The hot deck method that is used in the imputations is adapted to reflect the uncertainty in the model for non-response. The method is motivated as a development (improvement) of the current operational procedure in which the imputed value is a non-stochastic function of the data. An extension of the procedure to subjects who are absent from the database (unit non-response) is proposed.     

Ethnic differences in the labour market: a comparison of the samples of anonymized records and Labour Force Survey

The public use sample from the 1991 UK census makes it possible to conduct individual level analyses of ethnic minorities' educational and occupational attainments. Unfortunately, however, the census asked only about higher level qualifications obtained after reaching 18 years of age. A comparison with the Labour Force Surveys (LFSs) shows that the census gives in some respects a misleading impression of qualifications among the first-generation members of ethnic minorities: the LFS data show that ethnic minorities tend to be more polarized in their qualifications than the British-born whites, with relatively large proportions at the two extremes, either with degrees or with no qualifications at all. It follows that the census's treatment of qualifications may tend to exaggerate the scale of disadvantage of ethnic minorities in the labour market, and particularly in access to the salariat where qualifications play a particularly large role in recruitment. Regression analyses of sample of anonymized records and LFS data confirm these expectations although they indicate that the results of the census are not seriously misleading as regards the pattern of ethnic disadvantages in the competition to avoid unemployment. The LFS data also confirm earlier findings that the ethnic penalties are in general of similar magnitude among the second generation to those among the first generation, despite the substantial equalization of educational experience that has taken place. There is some evidence that disadvantages in access to the salariat may have been reduced, but this is counterbalanced by the evidence that disadvantages in the avoidance of unemployment may have deteriorated.     

Predictive Accuracy Gain From Disaggregate Sampling in ARIMA Models

We compare the forecast accuracy of autoregressive integrated moving average (ARIMA) models based on data observed with high and low frequency, respectively. We discuss how, for instance, a quarterly model can be used to predict one quarter ahead even if only annual data are available, and we compare the variance of the prediction error in this case with the variance if quarterly observations were indeed available. Results on the expected information gain are presented for a number of ARIMA models including models that describe the seasonally adjusted gross national product (GNP) series in the Netherlands. Disaggregation from annual to quarterly GNP data has reduced the variance of short-run forecast errors considerably, but further disaggregation from quarterly to monthly data is found to hardly improve the accuracy of monthly forecasts.     

基于行政大数据的失业率估计:以某四百万人口城市为例

我国城镇登记失业率指标稳定在4%左右,难以较为准确反映就业动态;而劳动力调查样本量有限,城镇调查失业率对省以下各级行政区域代表性不足。本文将针对大数据的机器学习算法与针对传统统计数据的核算思想结合起来,基于某四百万人口城市2016—2018年的全样本行政大数据,利用机器学习算法,对每个城镇居民每个月的就业状态进行预测,再利用统计核算方法,估计出该城市的失业率。在个人层面,本文的模型在样本外测试集上的准确率达到96.7%。经过统计核算加总,本文估计的当地失业率在合理区间范围内,并表现出明显的周期性特征,对就业形势动态变化的刻画明显优于当地一年发布一次的登记失业率数据。本文基于个人层面的预测结果,进一步探讨了当地失业人口 的性别与文化程度特征,以及再就业的时间规律。本文针对如何使用行政大数据辅助经济决策提出了新的范式,对大数据时代如何理解经济与制定政策具有参考意义。     

Unemployment duration and the length of entitlement periods for unemployment benefits: do the IAB employment subsample and the German Socio-Economic Panel yield the same results?*

Summary: We compare information on the length of unemployment spells contained in the IAB employment subsample (IABS) and in the German Socio-Economic Panel (GSOEP). Due to the lack of information on registered unemployment in the IABS, we use two proxies of unemployment in the IABS as introduced by Fitzenberger/Wilke (2004). The first proxy comprises all periods of nonemployment after an employment spell which contain at least one period with unemployment compensation transfers. The second proxy includes all episodes between two employment spells during which an individual continuously received unemployment benefits. Estimation of standard duration models indicates that conclusions drawn from the IABS and the GSOEP differ in many cases. While the GSOEP suggests that the hazard rate has a maximum at about 12 months of unemployment, the IABS results suggest that this maximum is at about 20 months. Contrary to our GSOEP results and contrary to many results based on the GSOEP found in the literature, we find a statistically significant association between longer maximum entitlement periods of unemployment benefits (‘Arbeitslosengeld’) and longer unemployment durations for men in the IABS. The results for women do not show such clear patterns. The large sample size of the IABS also allows to trace out statistically significant effects of characteristics such as regional and industry indicators, which is generally not possible in the relatively small GSOEP. * Acknowledgements: We would like to thank the editors of this special issue, Joachim M?ller and Bernd Fitzenberger, two anonymous referees, the participants of the ‘Statistische Woche 2004’ in Frankfurt (in particular Reinhard Hujer, Olaf Hübler and Gerd Ronning), seminar participants at the ZEW Mannheim (especially Fran?ois Laisney and Alexander Spermann) and Jennifer Hunt for their many helpful comments and suggestions. All remaining errors are our own. Financial support of the Deutsche Forschungsgemeinschaft (DFG) through the research project ‘Microeconometric modelling of unemployment durations under consideration of the macroeconomic situation’ is gratefully acknowledged. The data used in this paper were made available by the Institute for Employment Research (IAB) at the Federal Labour Office of Germany, Nürnberg, and the German Socio Economic Panel Study (GSOEP) at the German Institute for Economic Research (DIW), Berlin.     

Model-Based Estimation of Unemployment Rates in Small Areas of Portugal

The high level of unemployment is a major problem in most European countries nowadays. Hence, the demand for small area labor market statistics has rapidly increased over the past few years. The Portuguese Labour Force Survey is the main source of official statistics at the macro level. However, it was not designed to produce reliable design-based statistics at the micro level due to small sample sizes. The goal of this article is to analyze the performance of model-based small area estimators to estimate the unemployment rate at micro level. Our results showed that the temporal estimator is the most suitable.     

Real-Time Forecasting With a Mixed-Frequency VAR

This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies—quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.     

A sequential approach to testing seasonal unit roots in high frequency data

In this paper we introduce a sequential seasonal unit root testing approach which explicitly addresses its application to high frequency data. The main idea is to see which unit roots at higher frequency data can also be found in temporally aggregated data. We illustrate our procedure to the analysis of monthly data, and we find, upon analysing the aggregated quarterly data, that a smaller amount of test statistics can sometimes be considered. Monte Carlo simulation and empirical illustrations emphasize the practical relevance of our method.     

COMBINING HOUSEHOLD SURVEYS USING MASS IMPUTATION TO ESTIMATE POPULATION TOTALS

Pressure is often placed on statistical analysts to improve the accuracy of their population estimates. In response to this pressure, analysts have long exploited the potential to combine surveys in various ways. This paper develops a framework for combining surveys when data items from one of the surveys is mass imputed. The estimates from the surveys are combined using a composite estimator (CE). The CE accounts for the variability due to the imputation model and the surveys’ sampling schemes. Diagnostics for the validity of the imputation model are also discussed. We describe an application of combining the Australian Labour Force Survey and the National Aboriginal and Torres Strait Islander Health Survey to estimate employment characteristics about the Indigenous population. The findings suggest that combining these surveys is beneficial.     

Complete censuses and samples

"Complete decennial censuses are needed for small areas and other domains. Sample surveys yield diverse and timely data. Censuses can also be combined with samples, and sometimes with data from registers, for diverse estimates that are detailed over both space and time, and hence are timely for small domains. Methods of 'postcensal estimates' for small domains are described. We note uses of censuses for improving samples and of samples for improving censuses, and propose a method for cumulating data from 'rolling' (or rotating) periodic (weekly, monthly or quarterly) samples specifically designed to cover the population in detail over designed spans (annual and quinquennial)."     

Adjusted Supremum Score‐Type Statistics for Evaluating Non‐Standard Hypotheses

Supremum score test statistics are often used to evaluate hypotheses with unidentifiable nuisance parameters under the null hypothesis. Although these statistics provide an attractive framework to address non‐identifiability under the null hypothesis, little attention has been paid to their distributional properties in small to moderate sample size settings. In situations where there are identifiable nuisance parameters under the null hypothesis, these statistics may behave erratically in realistic samples as a result of a non‐negligible bias induced by substituting these nuisance parameters by their estimates under the null hypothesis. In this paper, we propose an adjustment to the supremum score statistics by subtracting the expected bias from the score processes and show that this adjustment does not alter the limiting null distribution of the supremum score statistics. Using a simple example from the class of zero‐inflated regression models for count data, we show empirically and theoretically that the adjusted tests are superior in terms of size and power. The practical utility of this methodology is illustrated using count data in HIV research.     

Estimating the underlying change in unemployment in the UK

By setting up a suitable time series model in state space form, the latest estimate of the underlying current change in a series may be computed by the Kalman filter. This may be done even if the observations are only available in a time-aggregated form subject to survey sampling error. A related series, possibly observed more frequently, may be used to improve the estimate of change further. The paper applies these techniques to the important problem of estimating the underlying monthly change in unemployment in the UK measured according to the definition of the International Labour Organisation by the Labour Force Survey. The fitted models suggest a reduction in root-mean-squared error of around 10% over a simple estimate based on differences if a univariate model is used and a further reduction of 50% if information on claimant counts is taken into account. With seasonally unadjusted data, the bivariate model offers a gain of roughly 40% over the use of annual differences. For both adjusted and unadjusted data, there is a further gain of around 10% if the next month's figure on claimant counts is used. The method preferred is based on a bivariate model with unadjusted data. If the next month's claimant count is known, the root-mean-squared error for the estimate of change is just over 10000.     

Sufficiency Revisited: Rethinking Statistical Algorithms in the Big Data Era

The big data era demands new statistical analysis paradigms, since traditional methods often break down when datasets are too large to fit on a single desktop computer. Divide and Recombine (D&R) is becoming a popular approach for big data analysis, where results are combined over subanalyses performed in separate data subsets. In this article, we consider situations where unit record data cannot be made available by data custodians due to privacy concerns, and explore the concept of statistical sufficiency and summary statistics for model fitting. The resulting approach represents a type of D&R strategy, which we refer to as summary statistics D&R; as opposed to the standard approach, which we refer to as horizontal D&R. We demonstrate the concept via an extended Gamma–Poisson model, where summary statistics are extracted from different databases and incorporated directly into the fitting algorithm without having to combine unit record data. By exploiting the natural hierarchy of data, our approach has major benefits in terms of privacy protection. Incorporating the proposed modelling framework into data extraction tools such as TableBuilder by the Australian Bureau of Statistics allows for potential analysis at a finer geographical level, which we illustrate with a multilevel analysis of the Australian unemployment data. Supplementary materials for this article are available online.     

Decline and fall: Unemployment among Britain's non-white ethnic communities 1960–1999

The declining employment fortunes of Britain's non-white communities, relative to whites, are explained. To achieve this comparison, some historical official sources of unemployment data are reviewed. The earliest known official time series on unemployment of non-whites dates back to 1960. This historical context is explored, especially the reluctance to make the early data more widely known. An unemployment series for non-white males and females from 1970 to 1999 is derived in two separate ways by splicing together official sources. These series are compared with unemployment of whites to demonstrate a relative increase in unemployment of non-whites.     

A spatial analysis on Italian unemployment differences

Using spatial econometric models, this paper focuses attention on the spatial structure of provincial unemployment disparities of Italian provinces for the year 2003. On the basis of findings from the economic literature and of the available socio-economic data, various model specifications including supply- and demand-side variables are tested. Further we use ESDA analysis as equivalent to integration analysis on time series; therefore it is applied on each variable, dependent and independent, involved in the statistical model. The suggestions of ESDA lead us to the most adequate statistical model, which estimates indicate that there is a significant degree of neighbouring effect (i.e. positive spatial correlation) among labour markets at the provincial level in Italy; this effect is present notwithstanding we controlled for local characteristics. The unemployment shows a polarized spatial pattern that is strongly connected to labour demand and to a much lesser extent to the share of young population and economic structural composition.     

An Application of Non–Ignorable Non–Response Models for Gross Flows Estimation in the British Labour Force Survey

In previous work, non–response adjustments based on calibration weighting have been proposed for estimating gross flows in economic activity status from the quarterly Labour Force Survey. However, even after adjustment there may be residual non–response bias. The weighting is based on estimates of cross–sectional distributions and so cannot adjust for bias if non–response is associated with individual flows between quarters. To investigate this possibility, it was decided to apply models for estimating gross flows when non–response depends on the flows. This paper has two aims: first to describe the many problems encountered when attempting to implement these models; and second to outline a solution to the major problem that arose, namely, that comparing the model results directly with the weighting results was not possible. A simulation study was used to compare the results indirectly and it was tentatively concluded that non–response is not strongly associated with the flows and that the weighting provides an adequate adjustment.