Microsimulation and Policy Analysis

We provide an overview of microsimulation approaches for assessing the effects of policy on income distribution. We focus on the role of tax-benefit policies and review the concept of microsimulation and how it contributes to the analysis of income distribution in general and policy evaluation in particular. We consider the main challenges and limitations of this approach and discuss directions for future developments

Metrics and indicators used to assess health system resilience in response to shocks to health systems in high income countries—A systematic review

Health system resilience has never been more important than with the COVID-19 pandemic. There is need to identify feasible measures of resilience, potential strategies to build resilience and weaknesses of health systems experiencing shocks. The purpose of this systematic review is to examine how the resilience of health systems has been measured across various health system shocks. Following PRISMA guidelines, with double screening at each stage, the review identified 3175 studies of which 68 studies were finally included for analysis. Almost half (46%) were focused on COVID-19, followed by the economic crises, disasters and previous pandemics. Over 80% of studies included quantitative metrics. The most common WHO health system functions studied were resources and service delivery. In relation to the shock cycle, most studies reported metrics related to the management stage (79%) with the fewest addressing recovery and learning (22%). Common metrics related to staff headcount, staff wellbeing, bed number and type, impact on utilisation and quality, public and private health spending, access and coverage, and information systems. Limited progress has been made with developing standardised qualitative metrics particularly around governance. Quantitative metrics need to be analysed in relation to change and the impact of the shock. The review notes problems with measuring preparedness and the fact that few studies have really assessed the legacy or enduring impact of shocks

GINI DP 1: Distributional Consequences of Labor-Demand Adjustments to a Downturn. A Model-Based Approach with Application to Germany 2008-09

Macro-level changes can have substantial effects on the distribution of resources at the household level. While it is possible to speculate about which groups are likely to be hardest-hit, detailed distributional studies are still largely backward-looking. This paper suggests a straightforward approach to gauge the distributional and fiscal implications of large output changes at an early stage. We illustrate the method with an evaluation of the impact of the 2008-2009 crisis in Germany. We take as a starting point a very detailed administrative matched employeremployee dataset to estimate labor demand and predict the effects of output shocks at a disaggregated level. The predicted employment effects are then transposed to household-level microdata, in order to analyze the incidence of rising unemployment and reduced working hours on poverty and inequality. We focus on two alternative scenarios of the labor demand adjustment process, one based on reductions in hours (intensive margin) and close to the German experience, and the other assuming extensive margin adjustments that take place through layoffs (close to the US situation). Our results suggest that the distributional and fiscal consequences are less severe when labor demand reacts along the intensive margin. JEL Classifcation : D58, J23, H24, H60.

Of clerks & cleaners: the heterogeneous impact of monetary policy on the US labor market

In this paper we estimate the effect of monetary policy on the US labor market using disaggregated data based on large scale micro surveys. By employing a Bayesian factor-augmented vector autoregression framework, we investigate the impact of an unanticipated interest rate change on the unemployment rate in 32 occupation groups. Our results on the aggregate level are in line with the literature and point towards a strong influence of monetary policy on economic activity, overall unemployment and investment. A closer look on the disaggregated level reveals heterogeneous impacts across occupation groups. This heterogeneity can partially be explained by the amount of routine tasks and the degree of offshorability of an particular occupation group. These results suggest that workers who are highly vulnerable to medium-term and long-term developments such as automatization and offshoring are also hit disproportionately hard by short-term economic fluctuations.Series: Department of Economics Working Paper Serie

The heterogeneous impact of monetary policy on the US labor market

We empirically investigate the role of central banks in the context of heterogeneous labor markets, jobless recoveries and job polarization. Specifically, we estimate the effect of monetary policy on the US labor market using disaggregated time series based on large scale survey data. The impact of interest rate changes on unemployment in 32 occupation groups is explored in a Bayesian factor-augmented vector autoregression framework. The results suggest largely heterogeneous impacts across various occupation groups. This heterogeneity can be explained by differential task profiles of the workers in their respective occupations. Workers with tasks that are easily automated or offshored as well as workers at the bottom of the skill distribution are disproportionately affected following a monetary policy shock. This implies that labor market participants that are highly vulnerable to structural developments such as skill-biased technological change and the globalization of labor markets are also most sensitive to conventionalmonetary policy measures. From a policy perspective, we conclude that central banks are unlikely to beable to take on a stabilizing role in the context of labor market polarization

Global food price and monetary policy : evidence from oil-importing and oil-exporting countries : a thesis written in fulfilment of the requirements for the program of Master of Agribusiness at Massey University, Palmerston North, New Zealand

This study examines food price vulnerability in the case of oil-importing countries of Singapore and Vietnam and oil-exporting countries of Kuwait and Indonesia. The study is further extended to address the response of domestic food inflation to a sudden shock of a change in global food price, global oil price and monetary policy. Applying the Autoregressive Distributed Lag method to cointegration and Vector Error Correction Model, the relationship between domestic food inflation and macroeconomic variables is analysed using monthly data over the period 2004-2019. Two following key methodologies to measure the volatility of domestic food inflation are applied GARCH and GARCH-ARMA models. The impulse response of domestic food inflation to the monetary shocks is based on the Vector Autoregression. The findings indicate that there exist long-run relationships between domestic food price of four countries and a set of macroeconomic variables. However, there is different impacts of macroeconomic factors, i.e., GDP per capita, the real money supply, the real effective exchange rate, industrial production, global food price and global oil price on food inflation in each case of four countries. The findings also indicate the potential impacts of short-run deviations between domestic food inflation and macroeconomic variables, as well as the behaviour of food price vulnerability in the four sample countries. Given the vital role of macroeconomic factors and global food price in controlling domestic food price volatility, the estimated findings provide various appropriate implications for monetary policies to deal with the issues of stabilising food inflation. The related issue of global financial crisis impacts on economic growth and domestic food inflation is considered of 2007-2008 using the structural break analysis based on monthly data for the period 2004 to 2019. The results show that food price volatility happens during the period of global financial crisis. This study also indicates the important role of monetary policy on reducing food price vulnerability, especially in the case of emerging economies. The findings of the study provide a number of policy implications for policy- makers as well as for the behaviour of the producers and consumers. There are a series of comparisons in the investigating the sources of variations in domestic food price in four sample countries. Thus, the findings are highly important for the future course of food price because it relies on different structural economies and macroeconomic environment

Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlinear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of our machine learning models using a novel framework based on Shapley values, uncovering nonlinear relationships between the predictors and crisis risk. Throughout, the most important predictors are credit growth and the slope of the yield curve, both domestically and globally. A flat or inverted yield curve is of most concern when nominal interest rates are low and credit growth is high

Bayesian dynamic graphical models for high-dimensional flow forecasting in road traffic networks

Congestion on roads is a crucial problem which affects our lives in many ways: As a consequence, there is a strong effort to improve road networks in order to keep the traffic flowing. Flow forecasting models based on the large amount of traffic data, which are now available, can be a very useful tool to support decisions and actions when managing traffic networks. Although many forecasting models have been developed to this end, very few of them capture important features of high dimensional traffic data and, moreover, operating most of these models is a hard task when considering on-line traffic management environments. Dynamic graphical models can be a suitable choice to address the challenge of forecasting high-dimensional traffic flows in real-time. These models represent network flows by a graph, which not only is a useful pictorial representation of multivariate time series of traffic flow data, but it also ensures that model computation is always simple, even for very complex road networks. One example of such a model is the multiregression dynamic model (MDM). This thesis focuses on the development of two classes of dynamic graphical models to forecast traffic flows . Firstly, the linear multiregression dynamic model (LMDM), which is an MDM particular case, is extended to allow important traffic characteristics in its structure, such as the heterocedasticity of daily traffic flows, measurement errors due to malfunctions in data collection devices, and the use of extra traffic variables as predictors to forecast flows. Due to its graphical structure, the MDM assumes independence of flows at the entrances of a road network. This thesis therefore introduces a new class of dynamic graphical models where the correlation across road network entrances is accommodated, resulting in better forecasts when compared to the LMDM. All the methodology proposed in this thesis is illustrated using data collected at the intersection of three busy motorways near Manchester, UK