Structural Reforms and Economic Growth: A Machine Learning Approach PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Structural Reforms and Economic Growth: A Machine Learning Approach PDF full book. Access full book title Structural Reforms and Economic Growth: A Machine Learning Approach by Mr. Anil Ari. Download full books in PDF and EPUB format.
Author: Mr. Anil Ari Publisher: International Monetary Fund ISBN: Category : Business & Economics Languages : en Pages : 32
Book Description
The qualitative and granular nature of most structural indicators and the variety in data sources poses difficulties for consistent cross-country assessments and empirical analysis. We overcome these issues by using a machine learning approach (the partial least squares method) to combine a broad set of cross-country structural indicators into a small number of synthetic scores which correspond to key structural areas, and which are suitable for consistent quantitative comparisons across countries and time. With this newly constructed dataset of synthetic structural scores in 126 countries between 2000-2019, we establish stylized facts about structural gaps and reforms, and analyze the impact of reforms targeting different structural areas on economic growth. Our findings suggest that structural reforms in the area of product, labor and financial markets as well as the legal system have a significant impact on economic growth in a 5-year horizon, with one standard deviation improvement in one of these reform areas raising cumulative 5-year growth by 2 to 6 percent. We also find synergies between different structural areas, in particular between product and labor market reforms.
Author: Mr. Anil Ari Publisher: International Monetary Fund ISBN: Category : Business & Economics Languages : en Pages : 32
Book Description
The qualitative and granular nature of most structural indicators and the variety in data sources poses difficulties for consistent cross-country assessments and empirical analysis. We overcome these issues by using a machine learning approach (the partial least squares method) to combine a broad set of cross-country structural indicators into a small number of synthetic scores which correspond to key structural areas, and which are suitable for consistent quantitative comparisons across countries and time. With this newly constructed dataset of synthetic structural scores in 126 countries between 2000-2019, we establish stylized facts about structural gaps and reforms, and analyze the impact of reforms targeting different structural areas on economic growth. Our findings suggest that structural reforms in the area of product, labor and financial markets as well as the legal system have a significant impact on economic growth in a 5-year horizon, with one standard deviation improvement in one of these reform areas raising cumulative 5-year growth by 2 to 6 percent. We also find synergies between different structural areas, in particular between product and labor market reforms.
Author: Jorge M. Katz Publisher: United Nations Publications ISBN: Category : Business & Economics Languages : en Pages : 164
Book Description
In the last ten to fifteen years, profound structural reforms have moved Latin America and the Caribbean from closed, state-dominated economies to ones that are more market-oriented and open. Policymakers expected that these changes would speed up growth. This book is part of a multi-year project to determine whether these expectation have been fulfilled. Focusing on technological change, the impact of the reforms on the process of innovation is examined. It notes that the development process is proving to be highly heterogenous across industries, regions and firms and can be described as strongly inequitable. This differentiation that has emerged has implications for job creation, trade balance, and the role of small and medium sized firms. This ultimately suggests, amongst other things, the need for policies to better spread the use of new technologies.
Author: Mr.Andrew J Tiffin Publisher: International Monetary Fund ISBN: 1513518305 Category : Computers Languages : en Pages : 30
Book Description
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
Author: Mr.Andrew Tiffin Publisher: International Monetary Fund ISBN: 1513568264 Category : Computers Languages : en Pages : 20
Book Description
Macroeconomic analysis in Lebanon presents a distinct challenge. For example, long delays in the publication of GDP data mean that our analysis often relies on proxy variables, and resembles an extended version of the “nowcasting” challenge familiar to many central banks. Addressing this problem—and mindful of the pitfalls of extracting information from a large number of correlated proxies—we explore some recent techniques from the machine learning literature. We focus on two popular techniques (Elastic Net regression and Random Forests) and provide an estimation procedure that is intuitively familiar and well suited to the challenging features of Lebanon’s data.
Author: Nauro F. Campos Publisher: ISBN: Category : Economic development Languages : en Pages : 52
Book Description
This chapter provides a critical overview of the state of the art in the economics literature on structural reforms. It takes stock of theoretical developments, measurement efforts and of the econometric evidence. We start with a simple theoretical framework for the relationship between structural reforms, economic growth and income inequality. We argue that whether structural reforms have a positive or negative impact depends on various factors. The type of reform, timing, sequence and political constraints play crucial roles in determining the effectiveness of reforms on economic growth and income inequality. We conclude by proposing a 7-point agenda for future research.
Author: Ajay Agrawal Publisher: University of Chicago Press ISBN: 022661347X Category : Business & Economics Languages : en Pages : 643
Book Description
Advances in artificial intelligence (AI) highlight the potential of this technology to affect productivity, growth, inequality, market power, innovation, and employment. This volume seeks to set the agenda for economic research on the impact of AI. It covers four broad themes: AI as a general purpose technology; the relationships between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. It explores the economic influence of machine learning, the branch of computational statistics that has driven much of the recent excitement around AI, as well as the economic impact of robotics and automation and the potential economic consequences of a still-hypothetical artificial general intelligence. The volume provides frameworks for understanding the economic impact of AI and identifies a number of open research questions. Contributors: Daron Acemoglu, Massachusetts Institute of Technology Philippe Aghion, Collège de France Ajay Agrawal, University of Toronto Susan Athey, Stanford University James Bessen, Boston University School of Law Erik Brynjolfsson, MIT Sloan School of Management Colin F. Camerer, California Institute of Technology Judith Chevalier, Yale School of Management Iain M. Cockburn, Boston University Tyler Cowen, George Mason University Jason Furman, Harvard Kennedy School Patrick Francois, University of British Columbia Alberto Galasso, University of Toronto Joshua Gans, University of Toronto Avi Goldfarb, University of Toronto Austan Goolsbee, University of Chicago Booth School of Business Rebecca Henderson, Harvard Business School Ginger Zhe Jin, University of Maryland Benjamin F. Jones, Northwestern University Charles I. Jones, Stanford University Daniel Kahneman, Princeton University Anton Korinek, Johns Hopkins University Mara Lederman, University of Toronto Hong Luo, Harvard Business School John McHale, National University of Ireland Paul R. Milgrom, Stanford University Matthew Mitchell, University of Toronto Alexander Oettl, Georgia Institute of Technology Andrea Prat, Columbia Business School Manav Raj, New York University Pascual Restrepo, Boston University Daniel Rock, MIT Sloan School of Management Jeffrey D. Sachs, Columbia University Robert Seamans, New York University Scott Stern, MIT Sloan School of Management Betsey Stevenson, University of Michigan Joseph E. Stiglitz. Columbia University Chad Syverson, University of Chicago Booth School of Business Matt Taddy, University of Chicago Booth School of Business Steven Tadelis, University of California, Berkeley Manuel Trajtenberg, Tel Aviv University Daniel Trefler, University of Toronto Catherine Tucker, MIT Sloan School of Management Hal Varian, University of California, Berkeley
Author: Markus Faessler Publisher: ISBN: Category : Languages : en Pages :
Book Description
Due to various contradicting empirical studies, this thesis aims to provide new complementary perspectives on the relation and causation of real economic growth, stock market development and stock market participation rate. This was done by applying an unsupervised machine-learning algorithm, an agglomerative hierarchical clustering method. The motivation to use a clustering method was to simplify complex data structures in order "to represent the data in a way which will suggest fruitful hypotheses (Jardine, 1970, p.117)." Once the theoretical background with regard to stock market development, economic growth, equity premium puzzle and methodology was laid out, the findings were presented. The empirical results implied a breakdown of the OECD countries into four clusters which were compared with their respective mean values. The implications of these results may allow for an analysis as to the direction of causation on a cluster-base. Furthermore, as this thesis includes stock market participation rate in the analysis, it suggests an analysis of potentially underlying premises which may influence both stock market participation rate and the relation and causation of economic growth and financial markets development akin.
Author: Tzai-Shuen Chen Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 110
Book Description
This dissertation concentrates on applying machine learning methods to economic policy analysis. When talking about using machine learning or other non-behavioral model to conduct policy analysis, the first question raised by economists is the Lucas critique. A policy intervention would affect the incentive that people face and thus changes the underlying decision-making problem. A predictive model without the component of optimizing behavior might not capture people's reactions to the policy intervention to give a reliable prediction. Even if the quantitative effect of the Lucas critique is not significant, the machine learning method might have no advantage over a well-performed standard econometric model in terms of prediction or time efficiency. The first chapter presents an out-of-sample prediction comparison between major machine learning models and the structural econometric model. To evaluate the benefits of this approach, I use the most common machine learning algorithms, CART, C4.5, LASSO, random forest, and adaboost, to construct prediction models for a cash transfer experiment conducted by the Progresa program in Mexico, and I compare the prediction results with those of a previous structural econometric study. Two prediction tasks are performed in this paper: the out-of-sample forecast and the long-term within-sample simulation. For the out-of-sample forecast, both the mean absolute error and the root mean square error of the school attendance rates found by all machine learning models are smaller than those found by the structural model. Random forest and adaboost have the highest accuracy for the individual outcomes of all subgroups. For the long-term within-sample simulation, the structural model has better performance than do all of the machine learning models. The poor within-sample fitness of the machine learning model results from the inaccuracy of the income and pregnancy prediction models. The result shows that the machine learning model performs better than does the structural model when there are many data to learn; however, when the data are limited, the structural model offers a more sensible prediction. In addition to prediction outcome, machine learning models are more time-efficient than the structural model. The most complicated model, random forest, takes less than half an hour to build and less than one minute to predict. The findings show promise for adopting machine learning in economic policy analyses in the era of big data. The second chapter exploits the predictive power of machine learning algorithms to conduct covariate adjustment for estimating average treatment effects and the log-odds ratio. Previous semi-parametric approaches have proven that baseline covariate adjustment can increase the estimator efficiency and statistical power, compared to an unadjusted estimator. I use random forest model to select predictive covariates and conduct a Monte Carlo simulation to compare the efficiency and statistical power of unadjusted, OLS-based, and random-forest-based approaches in different parameter settings. The simulation result indicates that the random-forest-based estimator is more efficient and has higher statistical power than the other two methods. In addition, I apply this approach to the Zomba Cash Transfer Experiment in Malawi to study the difference in policy effect between conditional and unconditional cash transfers. The third chapter investigates the possibility of using machine learning models to conduct the counterfactual analysis for conditional policies. Conditional Cash Transfer has become a popular tool to alleviate intergenerational poverty in many developing countries due to the success of the Progresa program in Mexico. There are some experiments focused on the implementation details to explore the efficient practice of the policy implementation. The policy analysis, however, still heavily relies on the counterfactual prediction because of the budget and time constraints. Recently, machine learning has been proved successful in many prediction applications. Adopting machine learning model into economic policy analysis might help to increase the prediction performance and hence offer another approach of counterfactual analysis. While it is straightforward to apply machine learning algorithms to conduct counterfactual prediction for the unconditional policy, there is no direct prediction for the conditional policy due to the lack of behavioral description. This chapter uses the Zomba Cash Transfer Experiment in Malawi to examine the error of using an unconditional machine learning approach to prediction the outcome of the conditional policy. The result shows that the error from the conditional-unconditional difference is a minor source of prediction errors, which provides support of exploiting the predictive power of machine learning algorithms to offer policy suggestions for the conditional policy.
Author: Mr. Anil Ari
Author: Mr. Anil Ari
Author: Jorge M. Katz
Author: Mr.Andrew J Tiffin
Author: Mr.Andrew Tiffin
Author: Nauro F. Campos
Author: Ajay Agrawal
Author: Markus Faessler
Author: Anna Rose Bordon
Author: Tzai-Shuen Chen
Author: Sylvester C.W. Eijffinger