Corruption and Growth Under Weak Identification

The goal of this paper is to revisit the influential work of Mauro [1995] focusing on the strength of his results under weak identification. He finds a negative impact of corruption on investment and economic growth that appears to be robust to endogeneity when using two-stage least squares (2SLS). Since the inception of Mauro [1995], much literature has focused on 2SLS methods revealing the dangers of estimation and thus inference under weak identification. We reproduce the original results of Mauro [1995] with a high level of confidence and show that the instrument used in the original work is in fact 'weak' as defined by Staiger and Stock [1997]. Thus we update the analysis using a test statistic robust to weak instruments. Our results suggest that under Mauro's original model there is a high probability that the parameters of interest are locally almost unidentified in multivariate specifications. To address this problem, we also investigate other instruments commonly used in the corruption literature and obtain similar results.

Cool-season turfgrass color and growth calibrated to leaf nitrogen

Tissue N analysis a tool available for N management of turfgrass. However, peer-reviewed calibration studies to determine optimum tissue N values are lacking. A field experiment with a mixed cool-season species lawn and a greenhouse experiment with Kentucky bluegrass (Poa pratensis L.) were conducted across 2 yr, each with randomized complete block design. Treatments were N application rates between 0 and 587 kg N ha-1 yr-1. In the field experiment, clipping samples were taken monthly from May to September, dried, ground, and analyzed for total N. Clippings samples were collected one to two mowings after plots were fertilized. Linear plateau models comparing relative clipping yield, Commission Internationale de l' Eclairage hue, and CM1000 index to leaf N concentrations were developed. In the greenhouse experiment, clipping samples were taken every 2 wk from May to October and composited across sample dates for leaf N analysis. Color and clipping yields were related to leaf N concentrations using linear plateau models. These models indicated small marginal improvements in growth or color when leaf N exceeded 30 g kg-1, suggesting that a leaf N test can separate turf with optimum leaf N concentrations from turf with below optimum leaf N concentrations. Plateaus in leaf N concentrations with increasing N fertilizer rates suggest, however, that this test may be unable to identify sites with excess available soil N when turf has been mowed before tissue sampling.

Fall Fertilization Timing Effects on Nitrate Leaching and Turfgrass Color and Growth

Fall season fertilization is a widely recommended practice for turfgrass. Fertilizer applied in the fall, however, may be subject to substantial leaching losses. A field study was conducted in Connecticut to determine the timing effects of fall fertilization on nitrate N (NO3-N) leaching, turf color, shoot density, and root mass of a 90% Kentucky bluegrass (Poa pratensis L.), 10% creeping red fescue (Festuca rubra L.) lawn. Treatments consisted of the date of fall fertilization: 15 September, 15 October, 15 November, 15 December, or control which received no fall fertilizer. Percolate water was collected weekly with soil monolith lysimeters. Mean log10 NO3-N concentrations in percolate were higher for fall fertilized treatments than for the control. Mean NO3-N mass collected in percolate water was linearly related to the date of fertilizer application, with higher NO3-N loss for later application dates. Applying fall fertilizer improved turf color and density but there were no differences in color or density among applications made between 15 October and 15 December. These findings suggest that the current recommendation of applying N in mid- to late November in southern New England may not be compatible with water quality goals.

Technology Creation, Diffusion, and Growth Cycles

Standard macroeconomic models that assume an exogenous stochastic process for multifactor productivity offer the interpretation that recessions are the result of ''bad news'' (technological regress) and expansions are the result of ''good news'' (technological advancement). The view taken here is that both expansions and recessions are the result of ''good news'' in the sense that in both cases, aggregate production possibilities have increased. Recessions can be thought of as the transition from one technological frontier to the next.