### Table 8: Neighbor Regressions: Two-stage least-squares Dependent variable is growth of per capita real GDP (GYP

"... In PAGE 18: ...ide variables have no direct effect on growth (i.e. other than through the growth contagion channel), which will allow us to test our contagion hypothesis against the policy spillovers hypothesis. Table8 shows two-stage least squares with the neighbors apos; weighted average growth rate included in the core regression that excludes the government surplus. We use the neighbors apos; weighted average right-hand side variables as instruments.... In PAGE 18: ... A test of the overidentifying restrictions that all of the neighbors apos; right-hand side variables have zero direct effect on the country apos;s own growth rate once its neighbors apos; growth is considered fails to reject this set of restrictions. The test statistic is TR2 where T is the number of observations and the R2 is from the regression of the residuals in the regression shown in Table8 on the set of all exogenous variables, 22 With SURPLUS and the neighbor apos;s growth rate both included in the core regression, the Africa dummy remains insignificant, but P-value on the neighbors apos; growth rate falls to 0.06 and the coefficient is reduced to 0.... In PAGE 20: ... Even policies that are bad for growth could be imitated by neighbors if they are demonstrated to be good for creating rent-seeking opportunities or some other non-growth objective that is desired by policy-making elites. We find that our observable policy indicators and the other right-hand side variables from Table8 are indeed highly correlated across neighbors (Table 9). This gives a hint that unobservable government or private sector behavior contained in the residual may be correlated as well.... ..."

### Table 5 Ordinary Least Squares Regression Results with Interactions

"... In PAGE 20: ... Since it establishes interaction terms with mean 0, it in effect allows direct interpretation in the interaction equation of the coefficients of the original variables of interest. The first column of Table5 displays the model with control variables only. As expected, larger banks, branches with more employees, and branches in counties with many branches (generally, densely populated urban areas) paid their branch CSRs more.... In PAGE 21: ... Such measures added no explanatory power beyond the controls we report here and did not alter our results. The results in Column 2 of Table5 suggest that Quality Circles have a positive, main effect on wages, providing support for hypothesis 4 . This effect is substantial, adding over 5% to earnings.... In PAGE 35: ...Figure 1 (Based on Column 3 of Table5 , with all other variables evaluated at their means) Effects of Interaction Between Automation and Discretion on CSR Wages 12000 14000 16000 18000 20000 22000 24000 26000 28000 30000 Minimum Mean Maximum Levels of Automation CSR Annual Wages (Ln Wages Transformed into Dollars) Minimum Discretion Average Discretion Maximum... In PAGE 36: ...Figure 2 (Based on Column 3 from Table5 , with all other variables evaluated at their means) Effects of Interaction Between Automation and QCs on CSR Wages 12000 14000 16000 18000 20000 22000 24000 26000 28000 30000 Minimum Mean Maximum Levels of Automation CSR Annual Wages (Ln Wages Transformed into Dollars) Has No QCs... ..."

### Table 4: Comparison of stochastic frontier and least-squares estimates Stochastic frontier Ordinary least squares

"... In PAGE 4: ... 18 Table 3: Simulated changes in production for selected variables. 19 Table4 : Comparison of stochastic frontier and least-squares estimates 20 Table 5: Estimation results from restricted models. 21 Table 6: Tested restrictions about model specification 22 Table 7: Estimated model parameters from full panel and from balanced panel 23 Figure 1: Area planted to rice and rice production in Bicol for 1978, 1983 and 1994 17 ... ..."

### Table 1: Operators Used in Least-Squares State Esti-

"... In PAGE 2: ... Depending on the operator in question, can either be integer and mean operator multiplicity or just a real parameter. Three examples of pseudodi erential operators used in the literature for designing state es- timators are given in Table1 . The use of the di eren- tial operator P d is classical (e.... ..."

### Table 7. Dependence of the least-squares error on N for sample A. N Least-squares error

### Table 3 Summaryof equations required for each recursive least-squares or constrained least-squares algorithm

2001

"... In PAGE 13: ... 1. Table3 shows a summaryof the equations required byeach algorithm, and Table 4 shows the number of multiplies required for each equation found in Table 3. Table 4 is not absolutelyaccurate because the number of multiplies required for matrix inver-... In PAGE 13: ... 1. Table 3 shows a summaryof the equations required byeach algorithm, and Table 4 shows the number of multiplies required for each equation found in Table3 . Table 4 is not absolutelyaccurate because the number of multiplies required for matrix inver-... ..."

### Table 2: Phosphorescence Emission Decay Parameters for Scallop Myofibrilsa

1999

"... In PAGE 4: ... These curves do not decay to zero in 500 s, due to the long lifetime of phosphorescence emission. The results of three-exponential fits of these curves are shown in Table2 . ATP and/or calcium cause only small changes in amplitudes (Ri) and lifetimes ( i), which are not enough to account for the large differences in anisotropy decays reported below (37, 44).... ..."

### Table 1 Evaluation of the numerical stabilityof the diVTerent recursive least-squares algorithms for I =1,J = 2 and K = 2 ASC system

2001

"... In PAGE 12: ...hat were numericallystable are shown in Fig. 2. Fig. 2 clearlyshows the motivation for the development of numericallystable recursive least-squares algorithms for multichannel ASC=MSD systems, because of the tremendous gain of broadband convergence that they can provide over the least-mean-squares algorithms. Table1 compares the numerical stabilityof the diVTerent recursive least-squares algorithms for mul- tichannel ASC=MSD systems, observed from the FFrst set of simulations. If the algorithm is stable, the steady-state performance of the algorithm is shown, and if the algorithm is unstable, the approximate number of iterations before the algorithm diverges is shown.... In PAGE 12: ... If the algorithm is stable, the steady-state performance of the algorithm is shown, and if the algorithm is unstable, the approximate number of iterations before the algorithm diverges is shown. From Table1 , it is clear that the RLS and FTF algorithms developed for multichannel ASC=MSD systems are numericallyunstable, as previouslyre- ported [3]. Although it is obtained from a slight mod- iFFcation to the numericallyunstable RLS algorithm, the symmetry-preserving RLS algorithm was found to be stable, except for an extreme value of NAK =0:9, which is too small anyway for most applications.... ..."

### Table 3: Regression coefficients for the aboveground biomass model and DAIS spectral bands based on generalised least squares.

"... In PAGE 9: ... Figure 2 shows the section of the DAIS image to which the method was applied as a false colour combination. Table3 shows the regression coefficients for the Peyne area. This method results in a smooth and blurred map, and ignores the spatial variability in the residuals of the regression function.... ..."