Boom-bust cycles and financial liberalization高涨萧条周期与金融自由化 mobi 下载 网盘 caj lrf pdf txt 阿里云

The volatility that has hit many middle-income countries (MICs) after liberalizimg their financial markets has prompted critics to call for new policies to stabilize these boom-bust cycles. But, as Aaron Tornell and Frank Westermann point out in this book, over the last two decades most of the developing countries that have experienced lending booms and busts have also exhibited the fastest growth among MICs. Countries with more stable credit growth, by contrast, have exhibited, on average, lower growth rates. Factors that contribute to financial fragility thus appear, paradoxically, to be a source of long-run growth as well. Tornell and Westermann ***yze boom-bust cycles in the developing world and discuss how these cycles are generated by credit market imperfecti***. They explain why the financial liberalization that allows countries to overcome imperfecti*** impeding rapid growth also generates the financial fragility that leads to greater volatility and occasional crises. The conceptual framework they present illustrates this linkage and allows Tornell and Westermann to address normative questi*** regarding liberalization policies.

The authors also characterize key macroeconomic regularities observed across MICs, showing that credit markets play a key role not only in boom-bust episodes but in the strong "credit channel" observed during tranquil times. A theoretical framework is then presented that explains how credit market imperfecti*** can account for these empirical patterns. Finally, Tornell and Westermann provide microeconomic evidence on the credit market imperfecti*** that drive the results of the theoretical framework, finding that asymmetries between tradables and nontradables are key to understanding the patterns in MIC data.

Series Foreword

Acknowledgments

Introduction

2 Road map

3 The long run : liberalization, growth, and crises

4 The short run : boom-bust cycles and the credit channel

5 The conceptual framework

6 Credit market imperfecti*** in middle-income countries

7 Conclusi*** and policy implicati***

Appendix:Data sources and defintit***

Notes

References

List of abbreviati***

Index

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作者介绍：Aaron Tornell

　　Aaron Tornell is Professor of Economics at UCLA, an NBER Research Fellow, and a CESifo Research Fellow.

书籍介绍

The volatility that has hit many middle-income countries (MICs) after liberalizimg their financial markets has prompted critics to call for new policies to stabilize these boom-bust cycles. But, as Aaron Tornell and Frank Westermann point out in this book, over the last two decades most of the developing countries that have experienced lending booms and busts have also exhibited the fastest growth among MICs. Countries with more stable credit growth, by contrast, have exhibited, on average, lower growth rates. Factors that contribute to financial fragility thus appear, paradoxically, to be a source of long-run growth as well. Tornell and Westermann ***yze boom-bust cycles in the developing world and discuss how these cycles are generated by credit market imperfecti***. They explain why the financial liberalization that allows countries to overcome imperfecti*** impeding rapid growth also generates the financial fragility that leads to greater volatility and occasional crises. The conceptual framework they present illustrates this linkage and allows Tornell and Westermann to address normative questi*** regarding liberalization policies.The authors also characterize key macroeconomic regularities observed across MICs, showing that credit markets play a key role not only in boom-bust episodes but in the strong "credit channel" observed during tranquil times. A theoretical framework is then presented that explains how credit market imperfecti*** can account for these empirical patterns. Finally, Tornell and Westermann provide microeconomic evidence on the credit market imperfecti*** that drive the results of the theoretical framework, finding that asymmetries between tradables and nontradables are key to understanding the patterns in MIC data.