Tool For Policy Analysis Research Articles

Assessing the spatial impact of policy interventions on real-estate values: an exemplar of the use of the hybrid hedonic/repeat-sales method

This paper sets out to make a contribution to the extensive literature that seeks to develop methods that allow rigorous and robust analysis of the spatial and temporal impacts of public policy interventions on property (real-estate) values. It argues that the hybrid repeat-sales/hedonic method developed in real-estate studies over the last 30 years has considerable, but as yet under-developed, potential as a policy analysis tool. Using data from Glasgow, UK, the empirical analysis illustrates how the technique can be used to understand the spatial spillovers and the dynamic temporal effects of a historic £100 million state-led, area-based, urban-renewal programme, New Life for Urban Scotland. The paper concludes by arguing that, with the rise in the availability of rich geocoded, micro-datasets, this framework is sufficiently flexible to be used to evaluate the real-estate market impacts of a wide range of public policy interventions. Significantly, as the case study demonstrates, the framework overcomes some of the sustained criticisms of the more commonly used hedonic modelling approach. There is, however, still much to do to enhance the technical qualities of the models through further application.

Quantifying the City’s Green Area Potential Gain Using Remote Sensing Data

Information about green spaces available in a city is essential for urban planning. Urban green areas are generally assessed through environmental indicators that reflect the city’s quality of life and urban comfort. A methodology based on 3D measure and analysis of green urban areas at the city scale is presented. Two products are proposed: (1) measuring current vegetation cover at ground level through object-oriented classification of WorldView-2 imagery; and (2) estimating potential green cover at rooftop level using 3D data obtained by LiDAR sensor. The methodology, implemented in Lisbon, Portugal, demonstrates that: (1) remote sensing imagery provides powerful tools for master planning and policy analysis regarding green urban area expansion; and (2) measures of urban sustainability cannot be solely based on indicators obtained from 2D geographical information. In fact, 2D urban indicators should be complemented by 3D modelling of geographic data.

A stock-flow cohort model of the national car fleet

Various regulatory and fiscal policy instruments are in force to reduce the amount of greenhouse gases and local pollutants emitted by private cars. The incentives operate primarily—or exclusively—on the newest generation of cars. But how fast will technological developments affecting new vehicle models penetrate into the car fleet? The speed at which the adverse effects of private car use will be mitigated through the normal vehicle renewal process, or through an accelerated one, carries considerable interest. Suitable modelling tools are needed. This paper aims to demonstrate the usefulness and flexibility of a bottom-up stock-flow modelling approach to private car fleet forecasting and policy analysis. In the BIG model of the Norwegian automobile fleet, the annual stocks and flows characterising the car fleet are specified as matrices of 682 mutually exclusive and exhaustive cells, formed by cross-tabulations between 22 vehicle segments and 31 age classes. New car registrations follow from a disaggregate generic discrete choice model based on two decades of complete sales data for individual passenger car models. Example projections are presented onto the 2050 horizon under a low carbon fiscal policy scenario as well as a business-as-usual scenario. The fiscal policy is seen to make a large difference in terms of long term fuel consumption and CO2 emissions. Stock-flow cohort modelling of the automobile fleet is a powerful and handy tool for policy analysis. Even quite simple and straightforward accounting relations may provide important insights into the dynamics of fleet development. It is possible to incorporate, into the stock-flow modelling framework, interesting and useful behavioural relations, explaining aggregate automobile ownership and travel demand, scrapping and survival rates, or consumer choice in the market for new cars.

Integrating natural capital into system of national accounts for policy analysis: An application of a computable general equilibrium model

Natural capital is an asset that provides natural resource inputs and environmental services for economic production. Regional patterns of production and consumption have direct and indirect effects on sustainability of its natural capital. Traditional gross domestic product (GDP) estimates fail to explicitly and fully account for natural capital in system of national accounts (SNA). To address this gap, this study builds on United Nation's System of Environmental-Economic Accounting (SEEA) and provides a methodological framework to integrate natural capital into system of national accounts, in input-output table. It further illustrates how natural capital-integrated system of national accounts in input-output table can be used as an effective tool for natural resource management and policy analysis in computable general equilibrium (CGE) modeling framework. Results indicate that, without natural capital accounting and integration into system of national accounts, GDP estimates may give misleading signals as a measure of a region's economic performance, where economic growth could be at the expense of natural capital depletion.

Macroeconomics and the reality of mixed frequency data

Many time series are sampled at different frequencies. When we study co-movements between such series we usually analyze the joint process sampled at a common low frequency. This has consequences in terms of potentially mis-specifying the co-movements and hence the analysis of impulse response functions—a commonly used tool for economic policy analysis. We introduce a class of mixed frequency VAR models that allows us to measure the impact of high frequency data on low frequency and vice versa. Our approach does not rely on latent processes/shocks representations. As a consequence, the mixed frequency VAR is an alternative to commonly used state space models for mixed frequency data. State space models are parameter-driven whereas mixed frequency VAR models are observation-driven models as they are formulated exclusively in terms of observable data and do not involve latent processes as well as shocks and thus avoid the need to formulate measurement equations, filtering, etc. We also propose various parsimonious parameterizations, in part inspired by recent work on MIDAS regressions. We also explicitly characterize the mis-specification of a traditional common low frequency VAR and its implied mis-specified impulse response functions. The class of mixed frequency VAR models can also characterize the timing of information releases for a mixture of sampling frequencies and the real-time updating of predictions caused by the flow of high frequency information. Various estimation procedures for mixed frequency VAR models are also proposed, both classical and Bayesian. Numerical and empirical examples quantify the consequences of ignoring mixed frequency data.

A Heterogeneous Agent Macroeconomic Model for Policy Evaluation: Improving Transparency and Reproducibility

This paper provides a detailed description of the Eurace@Unibi model, which has been developed as a versatile tool for economic policy analysis. The model explicitly incorporates the decentralized interaction of heterogeneous agents across different sectors and regions. The modeling of individual behavior is based on heuristics with empirical microfoundations. Although Eurace@Unibi has been applied successfully to different policy domains, the complexity of the structure of the model, which is similar to other agent-based macroeconomic models, has given rise to concerns about the reproducibility and robustness of the obtained insights. This paper addresses these concerns by describing the exact details of all decision rules, interaction protocols and balance sheets used in the model. Furthermore, we discuss the use of a virtual appliance as a tool allowing third parties to reproduce and verify the simulation results. The paper provides a systematic and extensive sensitivity analysis of the simulation output with respect to a set of key parameters. Particular emphasis is put on the question which parameter constellations give rise to strong economic fluctuations and high frequencies of sudden downturns in economic activity.

GTAP-HET: Introducing Firm Heterogeneity into the GTAP Model

Computable General Equilibrium (CGE) models incorporating firm heterogeneity can overcome the shortcomings of traditional Armington-based models in explaining changes in productivity and variety in the wake of reduced trade costs. In this paper, we present a new modeling framework where the firm heterogeneity theory of Melitz is introduced into the Global Trade Analysis Project (GTAP) model and calibrated to the GTAP 8 Data Base. The new mechanisms in the model are demonstrated in a stylized scenario with 3 regions (USA, Japan and the Rest of the World) and 2 sectors (manufacturing and non-manufacturing) where the elimination of tariffs levied by Japan on the import of US manufacturing goods is examined. Results are compared with those under monopolistic competition motivated by Krugman and under perfect competition motivated by Armington. The firm heterogeneity model incorporates endogenous variety, scale, productivity, and fixed cost effects into welfare change in addition to the traditional allocative efficiency and terms of trade effects. We observe that these effects are significant sources of welfare change. GTAP-HET presents the first GTAP implementation of firm heterogeneity. It is a powerful tool for policy analysis with improved abilities in tracing out productivity changes and entry/exit of firms following trade liberalization scenarios.

An Economic and Stakeholder Analysis for the Design of IPP Contracts for Wind Farms

In this paper we introduce a method for quantifying the benefits and costs of implementing a grid-connected onshore wind project that is owned and operated by an independent power producer (IPP). The outcomes and risks are assessed from the perspective of all stakeholders. The proposed policy analysis tool is applied to the appraisal of a wind farm in Santiago Island, Cape Verde. The policy analysis is conducted from the perspectives of the electric utility, the country’s economy, the government and the private sector investor. The key question is whether the design of the power purchase agreement (PPA) will yield a high enough rate of return to the project to be bankable, while at the same time yielding a positive net financial and economic present value to the electric utility and the country respectively. The PPA results in a negative outcome for the economy of Cape Verde in almost all circumstances. In contrast the owners of the IPP are guaranteed a very substantial return for their modest investment under all circumstances.

Application of microsimulation models in the analysis of taxes and social benefits in Croatia

Fiscal system microsimulation models enable the simulation of taxes, social insurance contributions, and social benefits, thus allowing researchers to predict the effects of a potential reform of these fiscal instruments on the distribution of disposable income, work incentives, as well as on budgetary revenue and expenditure. Although microsimulation models are one of the key tools for academic and policy analysis in other EU countries, such models have not been systematically developed and used in Croatia so far. In September 2015 researchers from the Institute of Public Finance therefore launched the research project ”Application of Microsimulation Models in the Analysis of Taxes and Social Benefits in Croatia” (hereinafter: the Project), which is outlined in this paper.

Evaluating Off-Peak Pricing Strategies in Public Transportation with an Activity-Based Approach

Public transportation authorities across the world are implementing various peak and off-peak pricing strategies to manage travel demand and improve the overall system performance. In this study, an activity-based demand framework was used to evaluate two off-peak pricing strategies currently in use in Singapore. These strategies consisted of a free prepeak travel on mass rapid transit (MRT) and an off-peak discount for an integrated transit (public buses and MRT). Smart card data collected before and after the implementation of the first policy were used to calibrate the behavioral models involved, to capture travelers’ preferences and choices properly. To evaluate both pricing strategies, a comprehensive set of key performance indicators was considered and included the changes in peak ridership, average trip fare, operator’s revenue, the number of public transportation trips, and mode share. The results indicate that off-peak discount pricing strategies are a viable policy option for spreading demand peaks and that they are more effective during the afternoon peak period. This study also demonstrates the capabilities and the advantages of an agent-based modeling platform, SimMobility, as a tool for policy analysis.

Modelling Future Composite Indices for Nigerian Millennium Development Goals

This paper synthesized Millennium Development Goals (MDGs) progress by modelling future composite indices from the year 2016 to 2020 base on three out-of-sample results of forecasted models; Trend regression, Holt Exponential Smoothing, and Box-Jenkins. It applied a modified version of Malaysian Quality of Life index. The results revealed that there is aggregate progress across MDG composite indices of the models. Comparatively, MDG composite index of Trend regression is the highest among the models, followed by Holt Exponential Smoothing and then Box-Jenkins. The implication is that there is improvement in the aggregate MDG indicators, but the progression is too low or slow symbolizing oscillations not a straight line as assumed by MDG hypothesis. Consequently, more is needed to be done to fast-track MDG achievement. This work has originated Nigerian MDG composite indices computations, provided useful tools for policy analysis and public communications and basis for comparison among countries.

Microsimulation and interregional input–output modelling as tools for multi-level policy analysis

This article addresses the differentiated impacts of various sectors and branches in a multi-layer spatial system. The key question is whether in an interdependent spatial system – comprising a local, regional and national component – one or more core industries or sectors can be identified that may act as strategic handles for long-range sustainable development of a local economy. As a case study, the Cairngorms National Park in Scotland is used. In this area, policy makers – at different administrative levels – strongly emphasize the need for new sustainable economic development. We use a novel combination of stakeholder analysis (with household questionnaires) and multi-level interregional input–output analysis to identify which critical local key sectors are acting as carriers for local sustainability. The methodological vehicle employed in our study is based on microsimulation, as a tool to cope with limited data availability. This paper demonstrates how, even for small areas such as the Cairngorms National Park in Scotland, survey information combined with secondary data and existing input–output tables can be integrated into a useful policy toolbox for local sustainable development in a broader regional-national context.

Policies and Processes for Social Inclusion: Using EquiFrame and EquIPP for Policy Dialogue Comment on "Are Sexual and Reproductive Health Policies Designed for All? Vulnerable Groups in Policy Documents of Four European Countries and Their Involvement in Policy Development"

The application of EquiFrame in the analysis of sexual and reproductive health policies by Ivanova et al to a new thematic area, their selection of only some of the Core Concepts of human rights in health service provision and the addition of new vulnerable groups relevant to the purpose of their analysis, are all very welcome developments. We also applaud their application of EquiFrame to policies in countries where it has not previously been used, along with their use of interviews with policy-makers to produce a deeper understanding of policy processes. We argue that clear justification for the inclusion of additional, or replacement of some exiting vulnerable groups within EquiFrame should be accompanied by clear definitions of such groups, along with the evidence-base that justifies their classification as a vulnerable or marginalised group. To illustrate the versatility of EquiFrame, we summarise a range of ways in which it has been used across a number of regions; including a brief Case Study of its use to develop the National Health Policy of Malawi. While EquiFrame focuses on policy content, we preview a new policy analysis tool - Equity and Inclusion in Policy Processes (EquIPP) - which assesses the extent of equity and inclusion in broader policy processes. Together, EquiFrame and EquIPP can be used to help governments and civil society ensure that policies are addressing the much stronger emphasis on social inclusion, now apparent in the Sustainable Development Goals (SDGs).

Models of disability support governance: a framework for assessing and reforming social policy

In many developed countries, the provision of disability services has undergone significant transformations, from institutional to community based care, and from oganisational to personalised funding. Yet delivering disability support remains an ongoing challenge for governments. Specifically, the relative success of different types of disability support governance is convoluted and problematic given the diversity and complexity of disability support systems and the people they serve. To enhance the systematic analysis and evaluation of disability support governance, this paper conceptually advances four distinct models based on the locus of control and coordination of such support: uncoordinated; casework governance; dwelling‐based governance; and user‐coordinated. Using illustrations from case studies of individuals receiving care, the identification of these ideal types enables their relative strengths, weaknesses, and the occasions of governance failure to be articulated. No one model is universally applicable to people, nor immune to failure. Furthermore, the paper presents a novel approach to visualising actual disability support arrangements as social networks. The utility of such visualisations for analysing individual and system‐ wide arrangements is outlined. In the context of Australia's developing National Disability Insurance Scheme, these conceptual and analytical developments are argued to be important tools for policy and service analysis and reform.

Social sciences and the politics of knowledge: Inculturation and anthropology of knowledge: American orientalism 1945-1970

Social sciences have now contributed to a better understanding of society and different cultural spaces that make up the world basing themselves on disciplines such as anthropology, sociology and history. These disciplines are involved in the development of a cognitive environment which allows us to define and delimit Orientalism perceived here as an approach. The purpose of this writing is to compare the organization and ways of Orientalism in two distinct cultural environments (Anglo-Saxon and Arabic) through the analysis of interactions and different structures which preceded its identification. If from the 40s to the 60s showed the emergence of Orientalism as a tool for policy analysis, institutionalization of the scientific community with the establishment of departments of research, growth of foundations roles in funding and policy definition of Area Studies equally sored. This leads us to question the real contribution of Areas Studies in the understanding of the Orient.

Evidence-Based Policy Making and the Implementation of Regulatory Impact Assessment in Croatia

Purpose: This study is dealing with the application of evidence-based policy-making in Croatian public administration and the general role of applied policy analysis in the Croatian system of governance. Methodology: This development is illustrated by the peculiarities of introducing regulatory impact assessment (RIA) tools in Croatian public administrative structures. Findings: The author is pointing out various limitations in the usage of policy analysis tools in formulating, implementing and evaluating public policies in Croatia. The crucial role of RIA for enhancing executive capacity of governance structures is particularly stressed in the study. The final part of the article is devoted to the prospects of further development of RIA as an applied policy analysis tool within the Croatian system of governance

Data-driven weights and restrictions in the construction of composite indicators

Composite indicators are increasingly recognized as a useful tool in policy analysis and public communication. However, if poorly constructed, they can send misleading policy messages. Perhaps the most difficult aspect of constructing a composite indicator is choosing weights for the components. The categorization of Croatian territorial units for development policy is based on the value of the composite indicator called the development index. The main goal of this paper is to propose an empirical approach for weight selection. In order to generate the set of non-subjective weights, principal component analysis and linear programming methods have been applied. An application of data envelopment analysis to the field of composite indicators, known as the Benefit-of-the-Doubt approach, has been demonstrated subject to proportional subindicator share restrictions. Additionally, the Monte Carlo simulation of weights was conducted, and confidence intervals for the values of the development index were estimated. Owing to the fact that the examined weighting schemes have resulted in the different categorization of territorial units, use of unit-specific weights and incorporating uncertainty in the construction of a composite indicator looks promising for further work.

SAMBA: Stochastic Analytical Model with a Bayesian Approach

We develop and estimate a DSGE model for the Brazilian economy as part of the macroe-conomic modeling framework of the Central Bank of Brazil. The model combines the building blocks of standard DSGE models (e.g., price and wage rigidities and adjustment costs) with the following features that better describe the Brazilian economy: (i) a fiscal authority pursuing an explicit target for the primary surplus; (ii) administered or regulated prices as part of the consumer price index; (iii) external nance for imports,which amplies the eects of changes in external nancial conditions on the economy;and (iv) imported goods used in the production of intermediate goods. It also includes the presence of nancially constrained households. We estimate the model with Bayesian techniques, using data since 1999, when in ation targeting was implemented in Brazil.Evaluation based on impulse response functions, moment conditions, variance error decomposition, and initial forecasting exercises suggests that the model can be a useful toolfor policy analysis and forecasting.

A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems.

BackgroundIn the design of public health policy, a broader understanding of risk factors for disease across the life course, and an increasing awareness of the social determinants of health, has led to the development of more comprehensive, cross-sectoral strategies to tackle complex problems. However, comprehensive strategies may not represent the most efficient or effective approach to reducing disease burden at the population level. Rather, they may act to spread finite resources less intensively over a greater number of programs and initiatives, diluting the potential impact of the investment. While analytic tools are available that use research evidence to help identify and prioritise disease risk factors for public health action, they are inadequate to support more targeted and effective policy responses for complex public health problems.DiscussionThis paper discusses the limitations of analytic tools that are commonly used to support evidence-informed policy decisions for complex problems. It proposes an alternative policy analysis tool which can integrate diverse evidence sources and provide a platform for virtual testing of policy alternatives in order to design solutions that are efficient, effective, and equitable. The case of suicide prevention in Australia is presented to demonstrate the limitations of current tools to adequately inform prevention policy and discusses the utility of the new policy analysis tool.SummaryIn contrast to popular belief, a systems approach takes a step beyond comprehensive thinking and seeks to identify where best to target public health action and resources for optimal impact. It is concerned primarily with what can be reasonably left out of strategies for prevention and can be used to explore where disinvestment may occur without adversely affecting population health (or equity). Simulation modelling used for policy analysis offers promise in being able to better operationalise research evidence to support decision making for complex problems, improve targeting of public health policy, and offers a foundation for strengthening relationships between policy makers, stakeholders, and researchers.

Bayesian Graphical Models for STructural Vector Autoregressive Processes

SummaryThis paper proposes a Bayesian, graph‐based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous and temporal causal structures of the structural VAR model are represented by two different graphs. We also provide an efficient Markov chain Monte Carlo algorithm to estimate jointly the two causal structures and the parameters of the reduced‐form VAR model. The BGVAR approach is shown to be quite effective in dealing with model identification and selection in multivariate time series of moderate dimension, as those considered in the economic literature. In the macroeconomic application the BGVAR identifies the relevant structural relationships among 20 US economic variables, thus providing a useful tool for policy analysis. The financial application contributes to the recent econometric literature on financial interconnectedness. The BGVAR approach provides evidence of a strong unidirectional linkage from financial to non‐financial super‐sectors during the 2007–2009 financial crisis and a strong bidirectional linkage between the two sectors during the 2010–2013 European sovereign debt crisis. Copyright © 2015 John Wiley & Sons, Ltd.