Conducting Policy Analysis Research Articles

Training on Education Policy Analysis for Education Stakeholders in Brebes

The Brebes district youth education and sports office is one of the districts responsible for making education policies based on local wisdom and innovative policies. In addition, the qualifications needed by the Brebes Youth and Sports Education office are as an evidence-based policy maker based on research analysis for policy, policy analysis, policy implementation, and evaluation, which can ultimately provide policy recommendations. However, only some education personnel in Brebes district's education, youth and sports offices have competence in policy analysis. Based on the situation analysis, it is urgent to conduct policy analysis training for education stakeholders, especially the Brebes Regency youth and sports education office. The target audience in this training is stakeholders in the Brebes district, consisting of school principals, supervisors, and administration/managers. The implementation method includes lectures, discussions/questions and answers, educational games, training with problem-based learning, and (post-test) evaluation of training implementation. The training results showed that the participants gave an assessment related to the review of this education policy analysis training in the "outstanding" category of 90%; all participants gave relatively the same evaluation associated with the response in the training, which was 95%. Participation of training participants who, on average, stated that participation in the training was classified as very good as 90%. In addition, 90% of participants felt empowered during the training. On average, 90% of trainees stated that the training significantly impacted them. Furthermore, 85% of participants noted that the training was relevant to their problems.

Composite property price index forecasting with neural networks

PurposeThe Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.Design/methodology/approachThe goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.FindingsThe authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.Originality/valueResults here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

Office property price index forecasting using neural networks

PurposeThe Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.Design/methodology/approachThe authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.FindingsThe authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.Originality/valueThe results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Social determinants of health in non-communicable diseases prevention policies in South Africa.

The South African government has developed many policies for the prevention and control of non-communicable diseases. However, non-communicable diseases remain among the major causes of morbidity and mortality in South Africa. Although these diseases are linked to interaction of multiple risk factors, many of which are modifiable, they continue to cause much suffering particularly among the marginalised and people from the lower socio-economic status. The objective of this research was to explore and present the inclusion of social determinants of health in the policies meant for the prevention and control of non-communicable diseases in South Africa. The qualitative document analysis approach was used to conduct policy analysis of purposefully selected policies for prevention and control of cancers, obesity and mental and behavioural disorders in South Africa. The analysis revealed that policies for prevention and control of cancers, obesity and mental and behavioural disorders included policy intervention activities that focused on five social determinants of health: (1) governance, (2) social policies, (3) public policies, (4) material circumstances and (5) health system. Excluding most of the important social determinants of health in the policies for prevention and control of non-communicable diseases means that these policies would continue to fail in preventing these diseases from the root causes.Contribution:This article points out weaknesses in the policies meant for prevention and control of obesity, cancers and mental and behavioural disorders. This article further suggests policy improvement strategies that may be considered to effectively address these diseases.

Health Equity in Climate Change Policies and Public Health Policies Related to Climate Change: Protocol for a Systematic Review.

The relationship between risks to health equity and climate change is well established, and various international organizations and literature has called for intersectoral action for tackling these issues. While there is a growing recognition of the importance of equity-focused responses to climate change, if and how health equity measures are incorporated into climate-change-related policy decisions has not yet been adequately explored. There are numerous approaches and frameworks for conducting policy analysis, and for understanding equity in the context of climate change and public health separately; however, a comprehensive framework for exploring the complexity of these interacting factors is hard to find. This review aims to systematically identify policy analysis frameworks and studies focusing on health equity in climate change related policies. Five electronic databases will be searched for peer-reviewed articles in English and from 2000. Articles will be subjected to systematic retrieval and quality assessment, and thematic analysis will be used for data analysis. The study findings will provide insight into different existing policy-analysis frameworks and policy-analysis approaches to understand health equity considerations in climate change policies and in health policies related to climate change.

Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat

SummaryAgricultural commodity price forecasting represents a key concern for market participants. We explore the usefulness of neural network modeling for forecasting problems in datasets of daily prices over periods of greater than 50 years for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat. By investigating different model settings across the algorithm, delay, hidden neuron, and data‐splitting ratio, we arrive at models leading to a decent performance for each commodity, with the overall relative root mean square error ranging from 1.70% to 3.19%. These results have small advantages over no‐change models due to particular price adjustments in the prices considered here. Our results can be used on a standalone basis or combined with fundamental forecasts in forming perspectives of commodity price trends and conducting policy analysis. Our empirical framework should not be diffucult to implement, which is a critical consideration for many decision‐makers and has the potential to be generalized for price forecasts of more commodities.

Thermal coal price forecasting via the neural network

• We build a non-linear auto-regressive neural network model for forecasts of thermal coal prices. • Closing prices recorded on a daily basis of thermal coal traded in China Zhengzhou Commodity Exchange are used for analysis. • The model is simple and leads to performance of good accuracy and stabilities. • The model could serve as technical forecast tools and for policy analysis. Thermal coal price forecasts represent an essential issue to investors and policy makers, given its importance as a strategic energy source. The current work aims at exploring usefulness of non-linear auto-regressive neural networks for this forecast problem based upon a data-set of closing prices recorded on a daily basis of thermal coal traded in China Zhengzhou Commodity Exchange during January 4, 2016 – December 31, 2020, which is an important financial index not sufficiently explored in the literature in terms of its price forecasts. Through testing a variety of model settings over algorithms, delays, hidden neurons, and data splitting ratios, the model that produces performance of good accuracy and stabilities is reached. Particularly, the model has five delays and ten hidden neurons and is constructed with the Levenberg-Marquardt algorithm based on the ratio of 80%–10%–10% of the data for training–validation–testing. It leads to relative root mean square errors of 1.48%, 1.49%, and 1.47% for the training, validation, and testing phases, respectively. Usefulness of neural networks for the price forecast issue of thermal coal is demonstrated. Forecast results here could serve as standalone technical forecasts and be combined with other forecasts when conducting policy analysis that involves forming perspectives of trends in prices.

Soybean and Soybean Oil Price Forecasting through the Nonlinear Autoregressive Neural Network (NARNN) and NARNN with Exogenous Inputs (NARNN–X)

• We develop nonlinear autoregressive neural network (with exogenous inputs) models for daily agricultural commodity price forecasting. • We explore the forecasting issue for soybeans and soybean oil over a period of greater than fifty years. • We construct simple, accurate, and stable models for the two agricultural commodities of economic significance. • The models are useful as technical forecasting tools and for policy analysis. Price forecasting is a key concern for market participants in the agriculture sector. This study explores usefulness of the nonlinear autoregressive neural network (NARNN) and NARNN with exogenous inputs (NARNN–X) for forecasting issues in data sets of daily prices over periods of greater than fifty years for soybeans and soybean oil. The exogenous input in the NARNN–X for prices of soybeans (soybean oil) are prices of soybean oil (soybeans). Through investigating various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, models resulting in accurate and stable performance for these two commodities are arrived at. The overall relative root mean square errors based on the chosen NARNNs (NARNN–Xs) are 1.701% (1.695%) and 1.777% (1.775%) for soybeans and soybean oil, respectively. Usefulness of the machine learning approach for price forecasting issues of the two commodities is demonstrated, as well as potential usefulness of prices of closely related commodities for providing predictive content. Results here could be used on a standalone basis as technical forecasts or combined with fundamental forecasts for forming perspectives of price trends and conducting policy analysis. The empirical framework here should not be difficult to implement, which is an important consideration to many decision makers, and has potential to be generalized for forecasting prices of other commodities.

Rent index forecasting through neural networks

PurposeChinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.Design/methodology/approachTo facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.FindingsThe authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.Originality/valueThe results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

Second-hand house price index forecasting with neural networks

ABSTRACT The house market in China has been growing rapidly for the past decade and price forecasting has become a significant issue to the people and policymakers. We approach this problem by examining neural networks for second-hand house price index forecasting from 10 major cities for March 2012–May 2020. Our purpose is to build simple and accurate neural networks to contribute to pure technical house price forecasting of the Chinese market. We explore various model settings across the algorithm, delay, hidden neuron, and data spitting ratio, and arrive at a rather simple neural network with three delays and eight hidden neurons, which leads to stable performance of 0.8% average relative root-mean-square error across the 10 cities for the training, validation, and testing phases. Results here can be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis.

House price forecasting with neural networks

• We develop neural network models for house price forecasting in China. • We explore the forecasting issue for 100 major Chinese cities. • We construct simple, accurate, and stable models. • The models are useful as technical tools and for policy analysis. The house market has been rapidly growing for the past decade in China, making price forecasting an important issue to the people and policy makers. We approach this problem by exploring neural networks for forecasting of house prices from one hundred major cities for the period of June 2010–May 2019, serving as the first study with such wide coverage for the emerging Chinese market through a machine learning technique. We aim at constructing simple and accurate neural networks as a contribution to pure technical forecasting of house prices. To facilitate the analysis, we investigate different model settings over the algorithm (the Levenberg-Marquardt, scaled conjugate gradient, and Bayesian regularization), delay (from two to six), hidden neuron (two, three, five, and eight), and data spitting ratio (70%–15%–15%, 60%–20%–20%, and 80%–10%–10% for trainingvalidationtesting), and arrive at a rather simple neural network with only four delays and three hidden neurons that leads to stable performance of 1% average relative root mean square error across the one hundred cities for the training, validation, and testing phases. We demonstrate the usefulness of the machine learning approach to the house price forecasting problem in the Chinese market. Our results could be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis. Our empirical framework should not be difficult to deploy, which is an important consideration to many decision makers, and has potential to be generalized for house price forecasting of other cities in China.

Policy Formulation In Global And Digital Economic Dynamics In Indonesia

Digital transaction tax is one of the new types of taxes issued by the government to be able to capture potential taxes in the era of the digital economy. The complexity of the tax application and the lack of global consensus regarding the digital transaction tax are the main reasons for not implementing the digital transaction tax. Due to the complexity and uncertainty, the government, in this case the Directorate General of Taxes, as the fiscal institution of the Indonesian state, has two choices regarding the implementation of digital transaction taxes, namely waiting for global consensus or taking unilateral steps. In the decision-making process, one method that can be used by fiscal institutions in determining the next step is to use SWOT analysis theory. The results of the SWOT analysis are then used as a tool in conducting policy analysis so as to produce decisions in the form of waiting for global consensus. The whole series of policy analysis processes are summarized in the structure of the policy argument. This study was structured using a qualitative approach by triangulating the theory between the results of interviews with other secondary data that has been obtained.

Does the proposed CAP reform allow for a paradigm shift towards a greener policy?

Aim of study: The paper explores whether the legislative proposal for the EU Common Agricultural Policy (CAP) after 2021 and the novelty of comprehensive strategic planning at Member State (MS) level can bring about a greener, more multifunctional policy paradigm. While existing research has explored long-term policy change over the entire decision-making process, this study aims to demonstrate the usefulness of conducting policy analysis at the inception of the legislative procedure.Area of study: The study applies to the European Union.Material and methods: The research employs a qualitative method of policy analysis, using a combination of three theoretical frameworks – social learning, path dependency and intergovernmentalism. Extensive document analysis and in-depth interviews were applied to evaluate the proposed reform and gauge the responses of key interest groups.Main results: The proposal holds potential for a substantial overall greening of the policy but will be strongly dependent on implementation at the MS level; the institutional framework provides space for increased environmental ambition, but does not guarantee it, as the proposed safeguards are too weak.Research highlights: More accountability is required during the formulation and implementation of Strategic Plans. Due to strong elements of path dependency and intergovernmentalism, an overall paradigm shift at EU level is unlikely. Strengthening the role of MSs is weakening the commonality of the policy that guarantees at least minimal environmental standards.

IMPLEMENTASI KEBIJAKAN PENGENDALIAN PENULARAN HIV/AIDS MELALUI HUBUNGAN SEKSUAL

Indonesia is the fourth country with the number of HIV / AIDS incidents increasing from year to year. HIV is a virus that can cause AIDS. The occurrence of AIDS can not only occur in the city but has spread to remote villages. The number of HIV / AIDS incidents at this time is of concern to the world. Until the existence of an HIV / AIDS control policy that refers to the Getting To Zeros global policy, namely: (1) reducing to eliminate new HIV infections, (2) reducing to eliminate deaths caused by conditions related to AIDS, (3) eliminating discrimination PLWHA. Based on global policies, the Indonesian government issued a policy as an effort to prevent the spread of HIV / AIDS. The government also issued a policy which essentially prohibits sexual acts freely, penetration, and sexual relations in similar couples, children, deceased people, and animals. This review aims to conduct policy analysis and implementation in dealing with HIV / AIDS cases through free sexual relations and barriers that affect the success of policies. The results of this literature review are that the policy to prevent HIV / AIDS transmission in Indonesia is still not going well. These obstacles occur in the form of still a dilemma in the law. Evidenced by the existence of problems in translating global and national strategies into packages of (operational) action programs that easily measure success at all levels of service by considering the problem of HIV / AIDS. So far, the action programs carried out by Indonesia are still carried out inconsistently so that the results have not been felt. Thus, HIV / AIDS control programs are not enough to be carried out by health workers alone but must involve other sectors. The community is primarily a key population and the need for government roles and responsibilities.

Market power and price discrimination in the US market for higher education

AbstractWe estimate an equilibrium model of private and state college competition that generates realistic pricing patterns for private colleges using a large national data set from the National Postsecondary Student Aid Study (NPSAS). Our analysis distinguishes between tuition variation that reflects efficient pricing to students who generate beneficial peer externalities and variation that reflects arguably inefficient exercise of market power. Our findings indicate substantial exercise of market power and, importantly, sizable variation in this power along the college quality hierarchy and among students with different characteristics. Finally, we conduct policy analysis to examine the consequences of increased availability of quality public colleges in a state.

Joint Modeling of Traffic Incident Duration Components (Reporting, Response, and Clearance Time): A Copula-Based Approach

The current study develops a trivariate framework that accommodates for inherent dependencies across the various components of incident duration. A unique ordered response model structure (sometimes referred to as grouped ordered response model) is introduced for modeling three durations—reporting time, response time, and clearance time—in this study. Further, as opposed to employing a simulation oriented multivariate model approach, the study proposes and estimates a copula-based methodology that allows for a closed-form probability computation. The approach is the first application of this model for incident duration analysis. The proposed copula framework is estimated to identify factors affecting incident duration components from a host of characteristics including incident characteristics, traffic conditions, roadway, and environmental characteristics. The data for the analysis are obtained from the 2015 Florida Department of Transportation incident database. The model estimates were also augmented by conducting policy analysis by generating a three-dimensional representation of incident frequencies as a function of reporting, response, and clearance time.

What is the Problem Represented to Be: A Research Methodology for Analysing Australias Skilled Migration Policy

Most discourse theories contain no suggestion of a methodology to conduct policy analysis. The view that a prescribed methodology is a restriction on interpretation may account for why specific methodologies are few in policy (and discourse) analysis. However, Fairclough (2001) Critical Discourse Analysis (CDA) demonstrates that a prescribed methodology in discourse analysis can coexist with abstract discourse analysis. Extending Faircloughs argument for prescribed methodology, Bacchi (2009) What is the problem represented to be? What is the Prob-lem Represented to Be (WPR) uses a series of questions to assist in uncovering problem representation within policy settings. This paper applies the WPR approach to an analysis of the 2010 reform of the Australian skilled migration policy. This reform was a watershed in Australian immigration policy in terms of who may immigrate to Australia and on what conditions. The skilled migration program focus shifted from supply to demand driven migration. The WPR approach is applied to determine the motivation behind the policy shift through a series of six questions that uncover the ineffectiveness of human capital theory assumptions that drive the rationale for supply driven migration as the problem representation but fail to address the real and unrepresented problems. The paper demonstrates the effectiveness of the WPR approach as a research methodology in critical policy analysis.

Policy Analysis to Reduce Climate Change-Induced Risks in Urban and Rural Areas in Korea

The purpose of this paper was to project changes in climate change-induced risks over time and to investigate policy alternatives to mitigate the risks from increases in sea level, heavy rains, and heat waves in urban and rural areas. System dynamics simulation was used to build a model and conduct policy analysis for a simulation period over the years 2000–2050. The model was built with a focus on the interaction among three factors: damage restoration costs from heavy rains, heat waves, and sea level rise; the total cost of food imports due to decreases in arable land and agricultural productivity; and changes in the government budget to respond to climate change problems. A policy experiment was conducted with the model under four scenarios mainly based on the government budget for climate change. The results indicated, firstly, that the climate budget needs to be increased to at least 13 trillion Korean Won (US $11.6 billion) per year. Secondly, an earlier budget increase would more effectively reduce the total disaster restoration cost than a delayed budget increase. Third, if an earlier budget increase is difficult, the next best alternative would be to allocate a greater fraction of the climate budget to urban rather than to rural areas. Lastly, an early response to climate change would more effectively reduce food import costs, maintain agricultural productivity, and improve infrastructure for climate change adaptation than a delayed response. In conclusion, an earlier increase in the climate change budget would be more effective than a delayed budget increase of the same amount, and allocating a larger fraction of the climate budget to urban areas could be more cost-effective than increasing the budget, if urban and rural parties could agree on the method of allocation.

Quarterly Projection Model for India: Key Elements and Properties

This paper outlines the key features of the production version of the quarterly projection model (QPM), which is a forward-looking open-economy gap model, calibrated to represent the Indian case, for generating forecasts and risk assessment as well as conducting policy analysis. QPM incorporates several India-specific features like the importance of the agricultural sector and food prices in the inflation process; features of monetary policy transmission and implications of an endogenous credibility process for monetary policy formulation. The paper also describes key properties and historical decompositions of some important macroeconomic variables.

Quarterly Projection Model for India: Key Elements and Properties

This paper outlines the key features of the production version of the quarterly projection model (QPM), which is a forward-looking open-economy gap model, calibrated to represent the Indian case, for generating forecasts and risk assessment as well as conducting policy analysis. QPM incorporates several India-specific features like the importance of the agricultural sector and food prices in the inflation process; features of monetary policy transmission and implications of an endogenous credibility process for monetary policy formulation. The paper also describes key properties and historical decompositions of some important macroeconomic variables.