Transfer Prices: A Financial Perspective

The arguments for and against transfer pricing schemes so far have focused on profit‐seeking approaches based on tax differentials, or on evasion of government enforced goods and fund flow restrictions. This article shifts to a value‐seeking framework where transfer prices act as strategic tools that may enhance value for the multinational with a foreign affiliate by exploiting financial and/or tax arbitrage that also lead to ownership arbitrage. The results show that there is an optimal level of transfer price depending on the specific exchange rate distribution when the cost structure allows for a penalty for overcharging. Moreover, this article introduces a new form of tax arbitrage benefit of transfer prices that is based on present value of tax shields.     

Estimation of Poverty Transition Matrices with Noisy Data

This paper investigates measurement error biases in estimated poverty transition matrices. We compare transition matrices based on survey expenditure data to transition matrices based on measurement‐error‐free simulated expenditure. The simulation model uses estimates that correct for measurement error in expenditure. We find that time‐varying measurement error in expenditure data magnifies economic mobility. Roughly 45% of households initially in poverty at time t ? 1 are found to be out of poverty at time t using data from the Korean Labor and Income Panel Study. When measurement error is removed, this drops to between 26 and 31% of households initially in poverty. Copyright © 2016 John Wiley & Sons, Ltd.     

Differences in principals' leadership behavior in high‐ and low‐performing schools

This study was based on data from the 2008 Take20: Alabama Teaching and Learning Conditions Survey and focused on a comparison of teachers' perceptions of how school principals exercise their role in both high‐ and low‐performing elementary and middle schools that serve high‐poverty student populations. Teachers in the high‐performing schools consistently viewed their principals' behavior more positively than did their counterparts in the lower‐performing schools. Teachers reported less difference in regard to engaging the community to create shared responsibility for student and school success. Very little difference existed in the principal's involvement of teachers in key school decisions; neither population of principals scored high in this area.     

A Comparison of Methods for Poverty Estimation in Developing Countries

Small area estimation is a widely used indirect estimation technique for micro‐level geographic profiling. Three unit level small area estimation techniques—the ELL or World Bank method, empirical best prediction (EBP) and M‐quantile (MQ) — can estimate micro‐level Foster, Greer, & Thorbecke (FGT) indicators: poverty incidence, gap and severity using both unit level survey and census data. However, they use different assumptions. The effects of using model‐based unit level census data reconstructed from cross‐tabulations and having no cluster level contextual variables for models are discussed, as are effects of small area and cluster level heterogeneity. A simulation‐based comparison of ELL, EBP and MQ uses a model‐based reconstruction of 2000/2001 data from Bangladesh and compares bias and mean square error. A three‐level ELL method is applied for comparison with the standard two‐level ELL that lacks a small area level component. An important finding is that the larger number of small areas for which ELL has been able to produce sufficiently accurate estimates in comparison with EBP and MQ has been driven more by the type of census data available or utilised than by the model per se.     

Analysis of Categorical Data for Complex Surveys

This paper reviews methods for handling complex sampling schemes when analysing categorical survey data. It is generally assumed that the complex sampling scheme does not affect the specification of the parameters of interest, only the methodology for making inference about these parameters. The organisation of the paper is loosely chronological. Contingency table data are emphasised first before moving on to the analysis of unit‐level data. Weighted least squares methods, introduced in the mid 1970s along with methods for two‐way tables, receive early attention. They are followed by more general methods based on maximum likelihood, particularly pseudo maximum likelihood estimation. Point estimation methods typically involve the use of survey weights in some way. Variance estimation methods are described in broad terms. There is a particular emphasis on methods of testing. The main modelling methods considered are log‐linear models, logit models, generalised linear models and latent variable models. There is no coverage of multilevel models.     

Incentive schemes for executive officers when forecasts matter

This paper develops a new perspective on results‐based incentive schemes for non‐CEO managers. It shows that it is possible to establish incentive schemes that take into account both the actual output obtained and the forecast figure previously established as a target, without the negative consequences derived from the perverse loop of hiding‐ratchet effects. A general linear two‐staged scheme is proposed. In addition, relevant properties of this incentive system are stated that show how principals (corporate management) may determine the expected forecasting behavior of agents (executive officers) by suitably choosing the scheme parameters according to a simple set of rules. Copyright © 2009 John Wiley & Sons, Ltd.     

Intergenerational Transmission of Fertility Patterns*

Recent studies by economists have focused on cultural transmission from the origin country rather than the origin family. Our paper extends this research by investigating how family‐specific‘cultural transmission’ can affect fertility rates. Following Machado and Santos Silva [Journal of the American Statistical Association (2005) Vol. 100, p. 1226] and Miranda [Journal of Population Economics (2008) Vol. 21, p. 67], we estimate count data quantile regression models using the British Household Panel Survey. We find that a woman's origin‐family size is positively associated with completed fertility in her destination family. A woman's country of birth also matters for her fertility. For a sub‐sample of continuously partnered men and women, both partners’ origin‐family sizes significantly affect destination‐family fertility.     

Quadratic Engel Curves with Measurement Error: Evidence from a Budget Survey*

This paper examines the importance of accounting for measurement error in total expenditure in the estimation of Engel curves, based on the 1994 Ethiopian Urban Household Survey. Using Lewbel's [Review of Economics and Statistics (1996 ), Vol. 78, pp. 718–725] estimator for demand models with correlated measurement errors in the dependent and independent variables, we find robust evidence of a quadratic relationship between food share and total expenditure in the capital city, and significant biases in various estimators that do not correct for correlated measurement errors.     

Reliable inference for the Gini index

Although attention has been given to obtaining reliable standard errors for the plug-in estimator of the Gini index, all standard errors suggested until now are either complicated or quite unreliable. An approximation is derived for the estimator by which it is expressed as a sum of IID random variables. This approximation allows us to develop a reliable standard error that is simple to compute. A simple but effective bias correction is also derived. The quality of inference based on the approximation is checked in a number of simulation experiments, and is found to be very good unless the tail of the underlying distribution is heavy. Bootstrap methods are presented which alleviate this problem except in cases in which the variance is very large or fails to exist. Similar methods can be used to find reliable standard errors of other indices which are not simply linear functionals of the distribution function, such as Sen’s poverty index and its modification known as the Sen–Shorrocks–Thon index.     

The Effect of Income Distribution on the Ability of Growth to Reduce Poverty: Evidence from Rural and Urban African Economies

The present study examines the extent to which income distribution affects the ability of economic growth to reduce poverty, based on 1990s data for a sample of rural and urban sectors of African economies. Using the basic‐needs approach, an analysis‐of‐covariance model is derived and estimated, with the headcount, gap, and squared gap poverty ratios serving as the respective dependent variables, and the Gini coefficient and PPP‐adjusted incomes as explanatory variables. The study finds that the responsiveness of poverty to income growth is a decreasing function of inequality, albeit at varying rates for the three poverty measures: lowest for the headcount, followed by the gap and fastest for the squared gap. The ranges for the income elasticity in the sample are estimated at: 0.02–0.68, 0.11–1.05, and 0.10–1.35, respectively, for these poverty measures. Furthermore while, on average, the responsiveness of poverty to income growth appears to be the same between the rural and urban sectors, there are substantial sectoral differences across countries. The results suggest the need for country‐specific emphases on growth relative to inequality.     

Cash transfers versus jobs programs

This paper uses a microsimulation computer model to compare the work incentive and distributional implications of public service employment programs and direct transfer programs. The simulation findings suggest that a pure direct transfer program would diminish poverty and inequality by somewhat more than an equal cost plan than combined direct transfers with a jobs program. Moreover, the results imply that a redistribution scheme based entirely on direct transfers is at least as effective per dollar of tax expenditure in raising incomes as a combination of direct transfers and jobs. Furthermore, the simulated job and direct transfer combinations were predicted to cause reductions in private sector earnings that are at least as large as those that would result from comparable schemes that relied solely upon direct transfers.     

Nonparametric estimation and inference about the overlap of two distributions

This paper develops methodology for nonparametric estimation of a measure of the overlap of two distributions based on kernel estimation techniques. This quantity has been proposed as a measure of economic polarization between two groups, Anderson (2004) and Anderson et al. (2010). In ecology it has been used to measure the overlap of species. We give the asymptotic distribution theory of our estimator, which in some cases of practical relevance is nonstandard due to a boundary value problem. We also propose a method for conducting inference based on estimation of unknown quantities in the limiting distribution and show that our method yields consistent inference in all cases we consider. We investigate the finite sample properties of our methods by simulation methods. We give an application to the study of polarization within China in recent years using household survey data from two provinces taken in 1987 and 2001. We find a big increase in polarization between 1987 and 2001 according to monetary outcomes but less change in terms of living space.     

Simplified Jackknife Variance Estimates for Fuzzy Measures of Multidimensional Poverty

In this paper, we present a practical methodology for variance estimation for multi‐dimensional measures of poverty and deprivation of households and individuals, derived from sample surveys with complex designs and fairly large sample sizes. The measures considered are based on fuzzy representation of individuals' propensity to deprivation in monetary and diverse non‐monetary dimensions. We believe this to be the first original contribution for estimating standard errors for such fuzzy poverty measures. The second objective is to describe and numerically illustrate computational procedures and difficulties in producing reliable and robust estimates of sampling error for such complex statistics. We attempt to identify some of these problems and provide solutions in the context of actual situations. A detailed application based on European Union Statistics on Income and Living Conditions data for 19 NUTS2 regions in Spain is provided.     

Moment estimation in discrete shifting level model applied to fast array‐CGH segmentation

We develop a mathematical theory needed for moment estimation of the parameters in a general shifting level process (SLP) treating, in particular, the finite state space case geometric finite normal (GFN) SLP. For the SLP, we give expressions for the moment estimators together with asymptotic (co)variances, following, completing, and correcting Cline (Journal of Applied Probability 20, 1983, 322–337); formulae are then made more explicit for the GFN‐SLP. To illustrate the potential uses, we then apply the moment estimation method to a GFN‐SLP model of array comparative genomic hybridization data. We obtain encouraging results in the sense that a segmentation based on the estimated parameters turns out to be faster than with other currently available methods, while being comparable in terms of sensitivity and specificity.     

Optimal Design Approach to GMM Estimation of Parameters Based on Empirical Transforms

Parameter estimation based on the generalized method of moments (GMM) is proposed. The proposed method employs a distance between an empirical and the corresponding theoretical transform. Estimation by the empirical characteristic function (CF) is a typical example, but alternative empirical transforms are also employed, such as the empirical Laplace transform when dealing with non‐negative random variables. D‐optimal designs are discussed, whereby the arguments of the empirical transform are chosen by maximizing the determinant of the asymptotic Fisher information matrix for the resulting estimators. The methods are applied to some parametric models for which classical inference is complicated.     

Combining density forecasts using focused scoring rules

We investigate the added value of combining density forecasts focused on a specific region of support. We develop forecast combination schemes that assign weights to individual predictive densities based on the censored likelihood scoring rule and the continuous ranked probability scoring rule (CRPS) and compare these to weighting schemes based on the log score and the equally weighted scheme. We apply this approach in the context of measuring downside risk in equity markets using recently developed volatility models, including HEAVY, realized GARCH and GAS models, applied to daily returns on the S&P 500, DJIA, FTSE and Nikkei indexes from 2000 until 2013. The results show that combined density forecasts based on optimizing the censored likelihood scoring rule significantly outperform pooling based on equal weights, optimizing the CRPS or log scoring rule. In addition, 99% Value‐at‐Risk estimates improve when weights are based on the censored likelihood scoring rule.     

Leading Indicators for Euro‐area Inflation and GDP Growth*

In this paper, we evaluate the role of a set of variables as leading indicators for Euro‐area inflation and GDP growth. Our leading indicators are taken from the variables in the European Central Bank's (ECB) Euro‐area‐wide model database, plus a set of similar variables for the US. We compare the forecasting performance of each indicator ex post with that of purely autoregressive models. We also analyse three different approaches to combining the information from several indicators. First, ex post, we discuss the use as indicators of the estimated factors from a dynamic factor model for all the indicators. Secondly, within an ex ante framework, an automated model selection procedure is applied to models with a large set of indicators. No future information is used, future values of the regressors are forecast, and the choice of the indicators is based on their past forecasting records. Finally, we consider the forecasting performance of groups of indicators and factors and methods of pooling the ex ante single‐indicator or factor‐based forecasts. Some sensitivity analyses are also undertaken for different forecasting horizons and weighting schemes of forecasts to assess the robustness of the results.     

SEMI‐NONPARAMETRIC ESTIMATION OF CONSUMER SEARCH COSTS

This paper studies the estimation of the distribution of non‐sequential search costs. We show that the search cost distribution is identified by combining data from multiple markets with common search technology but varying consumer valuations, firms' costs, and numbers of competitors. To exploit such data optimally, we provide a new method based on semi‐nonparametric estimation. We apply our method to a dataset of online prices for memory chips and find that the search cost density is essentially bimodal, such that a large fraction of consumers searches very little, whereas a smaller fraction searches a relatively large number of stores. Copyright © 2012 John Wiley & Sons, Ltd.     

Inference of Dynamic Generalized Linear Models: On-Line Computation and Appraisal

The purpose of this paper is to provide a critical discussion on real-time estimation of dynamic generalized linear models. We describe and contrast three estimation schemes, the first of which is based on conjugate analysis and linear Bayes methods, the second based on posterior mode estimation, and the third based on sequential Monte Carlo sampling methods, also known as particle filters. For the first scheme, we give a summary of inference components, such as prior/posterior and forecast densities, for the most common response distributions. Considering data of arrivals of tourists in Cyprus, we illustrate the Poisson model, providing a comparative analysis of the above three schemes.     

Cartel dating

The begin and end dates of cartels are often ambiguous, despite competition authorities stating them with precision. The legally established infringement period(s) from documentary evidence need not coincide with the period(s) of actual cartel effects. In this paper, we show that misdating cartel effects leads to a (weak) overestimation of but‐for prices and an underestimation of overcharges. Total overcharges based on comparing but‐for prices to actual prices are a (weak) underestimation of the true amount overcharged, irrespective of the type and size of the misdating. The bias in antitrust damage estimation based on predicted cartel prices can have either sign. We extend the before‐during‐and‐after method with an empirical cartel dating procedure, which infers structural breaks of unknown number and dates that mark the actual begin and end dates of the collusive effects. Empirical findings in the European Sodium Chlorate cartel corroborate our theoretical results.