作者: Aihua Xu
DOI:
关键词: Government spending 、 Economics 、 Debt 、 Capital (economics) 、 External debt 、 Conditional convergence 、 Investment (macroeconomics) 、 Convergence (economics) 、 Capital accumulation 、 Monetary economics
摘要: Employing closed-economy models, recent cross-country growth literature seems to have confirmed the conditional convergence hypothesis (CCH): holding population and capital accumulation constant, poor countries tend grow faster than rich countries. However, this reveals an empirical puzzle. African Latin American grew systematically slower sample mean during 1970s 1980s. This thesis reexamines CCH under assumption of open economies with imperfect mobility. Two alternative models are constructed. The first shows that can be extended if foreign borrowing used only finance physical (but not human) capital. Furthermore, external variables such as debt openness expected affect growth, either directly, or indirectly by affecting investment share. second model studies a small economy facing credit ceiling internationally. framework classifies one three cases: never- constrained, ever-constrained optimal-regime-switching. key result is paths output, investment, for regime-switching country exhibit kinks different properties. attributes low rates in Africa America excessive late subsequent regime-switching. Empirically, 98 from 1960 1986, we find has positive effect on rate while negative effect, does indeed hold open-economy setting. Secondly, when both treated dependent reestimated simultaneous equation system, significant share, much larger coefficient growth. We also reconfirm two-link chain previously identified literature: positively correlated share therefore growth-promoting. To allow dynamic variations across time, further respecify pooled cross-sectional time-series analysis, producing more efficient parameter estimates. Finally, propose Logit determine probability country's being constrained. debt-service ratio, reserves-to-import average interest loans, lagged rate, government spending important indicators servicing capacity.