Beaches, Sunshine, and Public-Sector Pay: Theory and Evidence on Amenities and Rent Extraction by Government Workers

The absence of a competitive market may enable public-sector workers to extract rents from taxpayers in the form of high pay, especially when public-sector workers are unionized. On the other hand, this rent extraction may be suppressed by the ability of taxpayers to vote with their feet, leaving jurisdictions where public-sector workers extract high rents. However, although migration of taxpayers may limit rent-seeking, public-sector workers may be able to extract higher rents in regions where high amenities mute the migration response. We develop a theoretical model that predicts such a link between public-sector wage differentials and local amenities, and we test the model’s predictions by analyzing variation in these wage differentials and amenities across states. We find that public-sector wage differentials are, in fact, larger in the presence of high amenities, with the effect stronger for unionized public-sector workers who are likely better able to exercise political power in extracting rents. The implication is that the mobility of taxpayers is insufficient to prevent rent-seeking behavior of public-sector workers from leading to higher public-sector pay.

Beaches, sunshine, and public-sector pay: theory and evidence on amenities and rent extraction by government workers

The absence of a competitive market and the presence and strength of public-sector labor unions make it likely that public-sector pay reflects an element of rent extraction by government workers. In this paper, we test a specific hypothesis that connects such rent extraction to the level of local amenities. Specifically, although migration of taxpayers limits the extent of rent-seeking, public-sector workers may be able to extract higher rents in regions where high amenities mute the migration response. We develop a theoretical model that predicts such a link between public-sector wage differentials and local amenities, and we test the model’s predictions by analyzing variation in these wage differentials and amenities across states. The evidence reveals that public-sector wage differentials are, in fact, larger in the presence of high amenities, with the effect being stronger for unionized public-sector workers, who are likely better able to exercise political power in extracting rents.Public-sector pay, unions, amenities

Is There a Real Estate Bubble in the Czech Republic?

Real estate prices more than doubled in many countries of Central and Eastern Europe from 2003 to 2008. In this paper, I provide one of the first assessments of whether housing prices in this region correspond to rents, i.e. to cash-flows related to an apartment purchase. State-of-the-art panel data stationarity and Granger causality techniques are employed to test the implications of the standard present value model using regional data from the Czech Republic. Apartment prices are only slightly overvalued. In addition, changes in prices are helpful in predicting changes in rents and vice versa.Central and Eastern Europe, Czech Republic, panel data, unit root, bubble, house prices, rents

Sequential Dialogue Context Modeling for Spoken Language Understanding

Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.Comment: 8 + 2 pages, Updated 10/17: Updated typos in abstract, Updated 07/07: Updated Title, abstract and few minor change

VAT tax gap prediction: a 2-steps Gradient Boosting approach

Tax evasion is the illegal evasion of taxes by individuals, corporations, and trusts. The revenue loss from tax avoidance can undermine the effectiveness and equity of the government policies. A standard measure of tax evasion is the tax gap, that can be estimated as the difference between the total amounts of tax theoretically collectable and the total amounts of tax actually collected in a given period. This paper presents an original contribution to bottom-up approach, based on results from fiscal audits, through the use of Machine Learning. The major disadvantage of bottom-up approaches is represented by selection bias when audited taxpayers are not randomly selected, as in the case of audits performed by the Italian Revenue Agency. Our proposal, based on a 2-steps Gradient Boosting model, produces a robust tax gap estimate and, embeds a solution to correct for the selection bias which do not require any assumptions on the underlying data distribution. The 2-steps Gradient Boosting approach is used to estimate the Italian Value-added tax (VAT) gap on individual firms on the basis of fiscal and administrative data income tax returns gathered from Tax Administration Data Base, for the fiscal year 2011. The proposed method significantly boost the performance in predicting with respect to the classical parametric approaches.Comment: 27 pages, 4 figures, 8 tables Presented at NTTS 2019 conference Under review at another peer-reviewed journa

Comparing discrete choice models: some housing market examples

Introduction: Since the mid nineteen seventies there has been strong interest within variolls branches of social science in the adaptation of the discrete choice modeling methodology towards a wide range of research problems. This has required recognition of a wide variety of alternative decision-contexts (Landau et a1. 1982) and behaviour-patterns (Lerman, 1979), and has also raised general issues concerning the variable extent to which individual or subgroup choices may be restricted by spatial and temporal constraints. Further interest has been expressed about the spatial and temporal transferability of alternative discrete choice models (Atherton and Ben-Akiva, 1976: Galbraith and Hensher, 1982). This substantive diversification has been accompanied by a variety of technical and methodological refinements of the multinomiallogit (MNL) and multinomial probit (MNP) models, ranging from new estimation procedures (Hausman and Wise, 1978) to the development of less-restrictive, computationally tractable discrete choice model forms (for example, Williams, 1977: Daly and Zachary, 1978). Faced with both a wider selection of methodological tools and a broader spectrum of substantive enquiry, there exists a clear need for formal comparison procedures which the analyst can call upon to evaluate a given model specification or framework. In this paper, I attempt to review briefly some trends amongst recent housing choice studies which employ discrete choice modeling methods. A new procedure is then presented (Hubert and Golledge, 1981; Halperin et al. 1984) which may be used to compare discrete choice models specified and/or structured in accordance with different a priori hypotheses. It is argued that this method fills a gap between existing discrete choice model comparison-procedures which are inapplicable to 'nonnested' model specifications, that is, to competing discrete choice models which comprise totally different variable specifications and that such procedures can usefully aid selection of the discrete choice model most appropriate to any given decision context

Producer Preference for Land-Based Biological Carbon Sequestration in Agriculture: An Economic Inquiry

This study was intended to develop an understanding of producer preference for land-based carbon sequestration in agriculture. We conducted a mail survey to elicit producer choice to provide marketable carbon offsets by participating in different carbon credit programs characterized by varying practices. Based on a quantitative analysis, we found that: 1) the market price for carbon offsets could increase producer participation in carbon sequestration; 2) producers perceived differentially different but correlated private costs for adopting carbon sequestering practices, depending on production attributes; and 3) relatively high carbon prices would be needed to stimulate producer provision of carbon offsets by land-based carbon sequestration activities. A simulation of producer choice with agricultural census data estimated potential carbon offsets supply in the Northern Great Plains region. This study contributes to the economic understanding of agricultural potential for greenhouse gas mitigation.greenhouse gas, carbon sequestration, producer stated preferences, agriculture, economics, carbon offsets, carbon markets, Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use, Production Economics, Resource /Energy Economics and Policy, Q54, Q52, Q58,