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How important is financial development for economic development? A costly state verification model of financial intermediation is presented to address this question. The model is calibrated to match facts about the U.S. economy, such as intermediation spreads and the firm-size distribution for the years 1974 and 2004. It is then used to study the international data, using cross-country interest-rate spreads and per-capita GDP. The analysis suggests that a country like Uganda could increase its output by 140 to 180 percent if it could adopt the world's best practice in the financial sector. Still, this amounts to only 34 to 40 percent of the gap between Uganda's potential and actual output. Charts and tables.
CD-ROM contains: World Bank data.
We develop a micro-founded general equilibrium model with heterogeneous agents to identify pertinent constraints to financial inclusion. We evaluate quantitatively the policy impacts of relaxing each of these constraints separately, and in combination, on GDP and inequality. We focus on three dimensions of financial inclusion: access (determined by the size of participation costs), depth (determined by the size of collateral constraints resulting from limited commitment), and intermediation efficiency (determined by the size of interest rate spreads and default possibilities due to costly monitoring). We take the model to a firm-level data from the World Bank Enterprise Survey for six countries at varying degrees of economic development—three low-income countries (Uganda, Kenya, Mozambique), and three emerging market countries (Malaysia, the Philippines, and Egypt). The results suggest that alleviating different financial frictions have a differential impact across countries, with country-specific characteristics playing a central role in determining the linkages and tradeoffs between inclusion, GDP, inequality, and the distribution of gains and losses.
This SPR Departmental Paper will provide policymakers with a framework for studying changes to national data policy frameworks.
This report is about a huge contribution to our deepening understanding of what the global economy really means and how it is changing. The report helpfully distinguishes elements of an economy that are tradable and the large set that are non-tradable. Clearly the tradables set is expanding with the support of enabling technology. The report argues that connectivity in the networks that define the evolving architecture of GVCs is important. This Global Value Chain Development Report is the result of intensive and detailed work in assembling and analyzing data on the structure of economies and on how they are linked. It creates a much clearer picture of evolving patterns of independence. It also presents a much clearer picture of comparative advantage. --Publisher description.
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
In the aftermath of the recent financial crisis, the federal government has pursued significant regulatory reforms, including proposals to measure and monitor systemic risk. However, there is much debate about how this might be accomplished quantitatively and objectively—or whether this is even possible. A key issue is determining the appropriate trade-offs between risk and reward from a policy and social welfare perspective given the potential negative impact of crises. One of the first books to address the challenges of measuring statistical risk from a system-wide persepective, Quantifying Systemic Risk looks at the means of measuring systemic risk and explores alternative approaches. Among the topics discussed are the challenges of tying regulations to specific quantitative measures, the effects of learning and adaptation on the evolution of the market, and the distinction between the shocks that start a crisis and the mechanisms that enable it to grow.
Abstract: How important is financial development for economic development? A costly state verification model of financial intermediation is presented to address this question. The model is calibrated to match facts about the U.S. economy, such as intermediation spreads and the firm-size distribution for the years 1974 and 2004. It is then used to study the international data, using cross-country interest-rate spreads and per-capita GDP. The analysis suggests a country like Uganda could increase its output by 140 to 180% if it could adopt the world's best practice in the financial sector. Still, this amounts to only 34 to 40% of the gap between Uganda's potential and actual output
This paper examines the empirical relationship between long–run growth and the degree of financial development, proxied by the ratio of bank credit to the private sector as a fraction of GDP. We find that this proxy enters significantly and with a positive sign in growth regressions on a large cross–country sample, but with a negative sign using panel data for Latin America. Our findings suggest that the main channel of transmission from financial development to growth is the efficiency of investment, rather than its volume. We also present a model where the negative correlation between financial intermediation and growth results from financial liberalization in a poor regulatory environment.
As the accelerated technological advances of the past two decades continue to reshape the United States' economy, intangible assets and high-technology investments are taking larger roles. These developments have raised a number of concerns, such as: how do we measure intangible assets? Are we accurately appraising newer, high-technology capital? The answers to these questions have broad implications for the assessment of the economy's growth over the long term, for the pace of technological advancement in the economy, and for estimates of the nation's wealth. In Measuring Capital in the New Economy, Carol Corrado, John Haltiwanger, Daniel Sichel, and a host of distinguished collaborators offer new approaches for measuring capital in an economy that is increasingly dominated by high-technology capital and intangible assets. As the contributors show, high-tech capital and intangible assets affect the economy in ways that are notoriously difficult to appraise. In this detailed and thorough analysis of the problem and its solutions, the contributors study the nature of these relationships and provide guidance as to what factors should be included in calculations of different types of capital for economists, policymakers, and the financial and accounting communities alike.