Microfoundations of Inflation Persistence in the New Keynesian Phillips Curve

This paper proposes a dynamic stochastic general equilibrium model that endogenously generates inflation persistence. We assume that although firms change prices periodically, they face convex costs that preclude optimal adjustment. In essence, the model assumes that price stickiness arises from both the frequency and size of price adjustments. The model is estimated using Bayesian techniques and the results strongly support both sources of price stickiness in the U.S. data. In contrast with traditional sticky price models, the framework yields inflation inertia, delayed effect of monetary policy shocks on inflation, and the observed "reverse dynamic" correlation between inflation and economic activity.In Inflation Persistence, Phillips Curve, Sticky Prices, Convex Costs

On the Measurement of Privacy as an Attacker's Estimation Error

A wide variety of privacy metrics have been proposed in the literature to evaluate the level of protection offered by privacy enhancing-technologies. Most of these metrics are specific to concrete systems and adversarial models, and are difficult to generalize or translate to other contexts. Furthermore, a better understanding of the relationships between the different privacy metrics is needed to enable more grounded and systematic approach to measuring privacy, as well as to assist systems designers in selecting the most appropriate metric for a given application. In this work we propose a theoretical framework for privacy-preserving systems, endowed with a general definition of privacy in terms of the estimation error incurred by an attacker who aims to disclose the private information that the system is designed to conceal. We show that our framework permits interpreting and comparing a number of well-known metrics under a common perspective. The arguments behind these interpretations are based on fundamental results related to the theories of information, probability and Bayes decision.Comment: This paper has 18 pages and 17 figure

Romania -- Systematic Country Diagnostic: background note-agriculture (English)

Agriculture plays a significant socio-economic role in Romania and its transformation to a modern, vibrant, and market-oriented sector is central to fighting poverty, promoting social inclusion, and reducing the urban/rural development divide. Most of Romania\u27s poor live in rural areas and earn their living from agriculture or agriculture-related activities. In 2016, eight out of ten people who were at risk of poverty or social exclusion lived either in rural areas or in towns and suburbs that were predominately rural. Using microdata from the 2013 Household Budget Survey (HBS), this report finds that individuals living in rural areas are 16.5 percent more likely to be poor than those who live in urban areas. Also, those living in rural areas and working in agriculture are 27 percent more likely to be poor. There are large variations in poverty rates and in the risk of poverty or social exclusion across regions in Romania. The risk of poverty or social exclusion is significantly higher in the northeast, southeast, west Oltenia, south Mutenia, and the west regions compared to that in Bucharest-Ilfov, the northwest, and center regions

Methods for measuring the effect of adjustment policies on income distribution

The effect of adjustment policies on different groups in the population is an important and complex issue. The distributional implications can have a large influence on the sustainability of programs. In particular, there is a need to identify how different groups in society are affected by an adjustment program and how an alternative mix of policies would affect this outcome. This assessment will be useful for developing compensation schemes for those groups that may be negatively affected in the transitional adjustment period, and in generating support for adjustment programs. This paper reviews the methodologies that have been suggested to study the effect of adjustment on distribution and provides examples of each approach. Each method is evaluated and data requirements are described. The review provides a range of approaches to selecta method to apply to a particular country. The paper also reviews recent work on macropolicies and income distribution and provides recommendations and conclusions.Inequality,Achieving Shared Growth,Environmental Economics&Policies,Economic Theory&Research,Health Economics&Finance

p-probabilistic k-anonymous microaggregation for the anonymization of surveys with uncertain participation

We develop a probabilistic variant of k-anonymous microaggregation which we term p-probabilistic resorting to a statistical model of respondent participation in order to aggregate quasi-identifiers in such a manner that k-anonymity is concordantly enforced with a parametric probabilistic guarantee. Succinctly owing the possibility that some respondents may not finally participate, sufficiently larger cells are created striving to satisfy k-anonymity with probability at least p. The microaggregation function is designed before the respondents submit their confidential data. More precisely, a specification of the function is sent to them which they may verify and apply to their quasi-identifying demographic variables prior to submitting the microaggregated data along with the confidential attributes to an authorized repository. We propose a number of metrics to assess the performance of our probabilistic approach in terms of anonymity and distortion which we proceed to investigate theoretically in depth and empirically with synthetic and standardized data. We stress that in addition to constituting a functional extension of traditional microaggregation, thereby broadening its applicability to the anonymization of statistical databases in a wide variety of contexts, the relaxation of trust assumptions is arguably expected to have a considerable impact on user acceptance and ultimately on data utility through mere availability.Peer ReviewedPostprint (author's final draft

Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation

In this paper, some vital methodological issues of spatial microsimulation modelling for small area estimation have been addressed, with a particular emphasis given to the reweighting techniques. Most of the review articles in small area estimation have highlighted methodologies based on various statistical models and theories. However, spatial microsimulation modelling is emerging as a very useful alternative means of small area estimation. Our findings demonstrate that spatial microsimulation models are robust and have advantages over other type of models used for small area estimation. The technique uses different methodologies typically based on geographic models and various economic theories. In contrast to statistical model-based approaches, the spatial microsimulation model-based approaches can operate through reweighting techniques such as GREGWT and combinatorial optimization. A comparison between reweighting techniques reveals that they are using quite different iterative algorithms and that their properties also vary. The study also points out a new method for spatial microsimulation modellingBayesian prediction approach; combinatorial optimisation; GREGWT; microdata; small area estimation; spatial microsimulation