Adjustment costs in labour markets and the distributional effects of trade liberalization: Analytics and calculations for Vietnam

This paper explores the implications of different labour market adjustment formulations for the analysis of trade liberalization across different sectors and households in the Vietnamese economy using computable general equilibrium (CGE) models. The model is calibrated to a model admissible Vietnamese data set for 1997.We use five different adjustment cost treatments in analyzing the effects of trade liberalization in Vietnam. We compare simulation results from each and show how different treatments can significantly affect the distributional impacts of policy reforms, such as the trade liberalization. First, labour is treated as fully mobile across all sectors in the economy. Second, the sectors of economy are broken down into the two blocks of agricultural and industrial-service sectors and labour markets are treated as segmented by sector block. No mobility of labour between blocks is allowed while labour within each sector block remains fully mobile. The third is the same as the second, but movement within each agricultural and industrial-service sector block involves transactions costs. In the fourth, mobility of workers from the agricultural to industrial-service sectors and vice versa is possible with transactions costs. Finally, we calibrate the model with unemployment but no adjustment costs for labour reallocation to explore how model results differ in terms of adjustments in the labour market and welfare effects.Our results indicate significant differences in the impacts from trade liberalization across these cases. The redistributional impact of trade liberalization is sharper against poor rural households with segmented labour markets and with transactions costs, while aggregate efficiency gains are similar to no adjustment cost analyses. The conclusion is the choice of model structure for labour markets is crucially important for the perceived distributional impacts of trade liberalization.     

Partitioning of Dietary Metal Intake—A Metal Dietary Exposure Screening Tool

Detection of heavy metals at trace or higher levels in foods and food ingredients is not unexpected given the widespread unavoidable presence of several metals in nature, coupled with advancement in analytical methods and lowering limits of detection. To assist risk managers with a rapid risk assessment when facing these situations, a metal dietary exposure screening tool (MDEST) was developed. The tool uses food intake rates based on the National Health and Nutrition Examination Survey 2005–2010 consumption data for the U.S. population two+ years and up and for infants age six months to <two years based on the Nestlé Feeding Infants and Toddlers Study, and existing exposure limits for several frequently detected metals (e.g., inorganic arsenic, cadmium, chromium, lead, and mercury). The tool has data entry fields for detected concentrations and includes algorithms that combine metal levels with consumption data to generate screening‐level exposure estimates, which it then compares to MDEST assigned default portions of the exposure limits in the risk characterization module. As a screening‐level tool, the risk assessment output is intentionally conservative, public health protective, and useful for a rapid assessment to set aside issues that are not of concern. Issues that cannot be readily resolved using this screening tool will need to be further evaluated with more refined input data that are tailored to the specific question or situation under consideration.     

Solving the Multidimensional Assignment Problem by a Cross-Entropy method

The Multidimensional Assignment Problem (MAP) is a higher dimensional version of the linear assignment problem, where we find tuples of elements from given sets, such that the total cost of the tuples is minimal. The MAP has many recognized applications such as data association, target tracking, and resource planning. While the linear assignment problem is solvable in polynomial time, the MAP is NP-hard. In this work, we develop a new approach based on the Cross-Entropy (CE) methods for solving the MAP. Exploiting the special structure of the MAP, we propose an appropriate family of discrete distributions on the feasible set of the MAP that allow us to design an efficient and scalable CE algorithm. The efficiency and scalability of our method are proved via several tests on large-scale problems with up to 5 dimensions and 20 elements in each dimension, which is equivalent to a 0–1 linear program with 3.2 millions binary variables and 100 constraints.     

Ethnic Minorities in Northern Mountains of Vietnam: Employment,Poverty and Income

The Italian National Statistical Office is implementing a new project for computing sub-national Purchasing Power Parities (PPPs) on a regular basis, which is based on an appropriate use of existing Consumer Price Index (CPI) data and new sources of data. Concerning the use of CPI data, in this paper the role of the Country Product Dummy (CPD) method for compiling sub-national PPPs at Basic–Heading (BH) level is analysed together with the specific issues that arise in this context, such as the need to take spatial autocorrelation among price relatives into account. The results of various experiments based on CPI data concerning 7 BHs and 19 Italian regional chief towns are presented and discussed with the aim of exploring the performance of various CPD models and analysing to what extent the type and characteristics of the data affect the estimates obtained. The statistical uncertainty associated with the ranks derived from the regional PPPs was then calculated by means of a simulation procedure. Our findings prove to be interesting and confirm that methods and CPI data for spatial comparisons are reciprocally influenced.     

Debt threshold for fiscal sustainability assessment in emerging economies

This paper explores the debt threshold for fiscal sustainability assessment for 14 emerging economies during the period 1999–2016. The threshold point is identified as the level which, if exceeded, promptly raises sovereign risk to an unsustainable level. As such, we employ a panel threshold analysis to the determination of debt limit, which can serve as a distinctive feature from other studies on fiscal sustainability. Our results demonstrate that non-Latin American economies are considered to be sustainable in the short run, as their debts remain below the threshold bounds of 40–55% of GDP. However, the long-run sustainability risk may emerge from a continuous upward trend in debt paths, implying the need for rebuilding fiscal buffers. It is important to emphasize that fiscal sustainability is far more challenging for most Latin-American economies. This is indicated by their debt accumulation beyond the threshold level of roughly 35% of GDP which is relatively lower than that estimated for the other countries. Indeed, during times of high debt, emerging countries in Latin America also face higher default risk since their sovereign risk premium respond more strongly to debt rise. Their paths toward fiscal sustainability, hence, requires an immediate imposition of strict fiscal discipline to relieve the debt pressure.     

Amalgamation in action: Participant perspectives on the Armidale regional council merger process

Under its Fit for the Future reform programme, in May 2016 the New South Wales (NSW) government forcibly merged a number of municipalities, including the Armidale Dumaresq Council and the Guyra Shire Council in the New England region of northern NSW. Whilst scholarly attention has focused on the likely impact of municipal mergers on council performance at the system‐wide level (Bell, Dollery & Drew 2016; Economic Papers: A journal of applied economics and policy, 35, 99), much less effort has been devoted to the analysis of the perspectives of council managers and employees involved in forced consolidation. In order to address this gap in the literature, in this paper we present a case study of compulsory council consolidation of the Armidale and Guyra councils based on interviews with senior managers as well as a survey of council workers.     

Differential association between unemployment status and mental health among veterans and civilians

This study aims to examine the negative effect of unemployment on mental health among 2,762 veterans and 45,095 civilians aged 18 to 50 years. The data were from the 2012 Behavioral Risk Factor Surveillance System (BRFSS) survey. We used ordinary least squares regression to test the interaction association between employment status and veteran/civilian status with mental health status. Findings revealed that long-term unemployed veterans had a significantly greater number of days with poor mental health than long-term unemployed civilians. The study highlights the need for future research concerning the negative effects of long-term unemployment on veterans’ mental health as well as the reciprocal relationships between mental health and occupational functioning for veterans. The findings challenge researchers to question the meanings that are associated with employment status for veterans as compared to those of their civilian peers. In addition to this, the study raises the need for further exploration into the topics of identity, self-perception, and the notion often present in the military/veteran culture that there is “dignity in labor.”     

Subsampling sequential Monte Carlo for static Bayesian models

Statistics and Computing - We show how to speed up sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles...     

Expected utility with lower probabilities

An uncertain and not just risky situation may be modeled using so-called belief functions assigning lower probabilities to subsets of outcomes. In this article we extend the von Neumann-Morgenstern expected utility theory from probability measures to belief functions. We use this theory to characterize uncertainty neutrality and different degrees of uncertainty aversion.We are grateful to Birgit Grodal, Salvatore Modica, David Schmeidler, and an anonymous referee for comments, help, and encouragement. Financial support from the Danish Social Sciences Research Council is acknowledged.     

The Determinants of Economic Growth Versus Genuine Progress in South Korea

This paper examines whether the drivers of economic growth are the same as those for genuine progress in the case of South Korea. Using data covering the period 1970–2005, the paper first constructs a Genuine Progress Indicator (GPI). An empirical model is then specified and estimated using growth in GDP per capita and growth in the GPI per capita as dependent variables. Results indicate that while physical capital, research and development, exports, and inflation are all important in determining growth in GDP per capita, only physical capital is a driver of genuine progress. These findings highlight the need for policymakers to identify and target other determinants of genuine progress to improve the well-being of South Koreans, rather than focus attention on traditional sources of economic growth.