### Table 1. pMSE and Bias of Power Estimates for Two-sample t-Statistic with Parametric Bootstrap Critical Values O = 1000, I = 59

2000

"... In PAGE 12: ... We look at two (O; I) combinations, (O = 1000; I = 59) and (O = 596; I = 99), that have about 59000 computations each. Table1 reports estimates of the root mean squared error (pMSE) and bias 1000 of the various power estimates. The standard errors of the estimates are in the range .... In PAGE 12: ...002 for pMSE and around 2 for the bias 1000. The rst and seventh rows (p1) of Table1 give results for power estimates based on the true known t percentiles appropriate for normal data. They are labeled p1 to re ect the fact that resampling with I approaching 1 would give this result.... In PAGE 12: ... They are labeled p1 to re ect the fact that resampling with I approaching 1 would give this result. These of course are unbiased (the nonzero bias results in Table1 just re ect Monte Carlo variation), and here pMSE could have been calculated simply by ppower(1-power)=O. For a given O, p1 represents the best power estimates possible.... In PAGE 12: ... For these raw estimates the (O = 596; I = 99) situation is more e cient in terms of pMSE than (O = 1000; I = 59) for all but = 0:5 because the bias is a large factor except at = 0:5. The other estimators in Table1 are 1. b plin: the simple linear extrapolation method using (5) for the (O = 1000; I = 59) case and (6) for the (O = 596; I = 99) case.... In PAGE 13: ...a;bb) distribution. From Table1 we see that the the linear extrapolation estimators, b plin and b pgls, perform the best and very similarly. Their similarity is likely due to the fact (not displayed) that the estimated covariance matrix of the b pI used as dependent variables in the regressions has nearly equal diagonal elements and nearly equal o -diagonal elements.... ..."

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### Table 3: Firm Wage Inequality and Firm Performance: Within and Between Firm Effects

"... In PAGE 15: ... In order to shed light on these issues we apply panel data methods. The first and third column of Table3 introduce firm fixed effects.17 These estimates refer to the impact of changes in inequality over time within a firm on standardised wages and thus refer to short- term effects.... In PAGE 15: ... In Table 4 we check the robustness of our results. Here the turning point of the hump-shape is very similar to the one found in the OLS- regression, whereas the coefficients in Table3 indicate a positive inequality wage relation over the most part (up to two standard deviations above the mean) of the inequality scale. Especially for blue-collar workers, the fixed effects estimates reveal a limited impact of temporary changes in within-firm wage dispersion on firm outcomes.... In PAGE 16: ...technological differences and other effects of particular industries, we also include in addition the log average real wage of the industry in which the respective firm is operating. Table 3 Columns 2 and 4 of Table3 present the results of the between-firm (group-means) regressions. The estimates are weighted by the number of periods a firm is observed in the sample.... In PAGE 16: ... Here however, the turning point is much later: at a value of firm wage inequality sigma of 0.28 in Table3 . This means, that for the most part of the empirical distribution of the inequality measure sigma, a positive dispersion-wage schedule can be observed.... ..."

### Table 6: Comparing least squares and quantile regression results for selected regressors

1998

"... In PAGE 18: ... The quantile regression techniques proposed by Koenker and Bassett (1978, 1982) and described in Deaton (1997) provide a natural mechanism for doing so. Table6 reports the results of five quantile regressions corresponding to the 10 , th 33 , 50 (median), 67 and 90 centiles as well as, for the purposes of comparison, the rd th th th least squares results reported in Table 4. These are obtained by simultaneously estimating quantile regressions for each specified centile and then obtaining the variance-covariance matrix by bootstrapping (Gould, 1997).... ..."

### Table 3: Two-Stage Least-Squares Estimates

"... In PAGE 9: ... We next conduct a 2SLS analysis in which LNDIST, INITIAL, EDU, REF, LPRIV and SPRIV are used as instruments for NEWENT in a first-stage regression, and the fitted value of NEWENT is combined with the remaining variables (IO, DEFENSE and PRICE) in a second- stage growth regression. The estimates we obtain are reported in Table3 . Consider first the NEWENT regression.... In PAGE 9: ... The coefficients on LNDIST and SPRIV are statistically insignificant, but the remaining variables each appear to have a significant relationship with NEWENT, both statistically (at the 5% level) and quantitatively. To characterize quantitative significance, we report the impact on NEWENT of a one-standard-deviation increase in each variable in the last column of Table3 . For example, a one-standard-deviation increase in initial income (representing an 79% increase in the purchasing power of money income per capita in 1993:IV, as reported in the sixth column of Table 3) corresponds with an additional 0.... In PAGE 9: ... To characterize quantitative significance, we report the impact on NEWENT of a one-standard-deviation increase in each variable in the last column of Table 3. For example, a one-standard-deviation increase in initial income (representing an 79% increase in the purchasing power of money income per capita in 1993:IV, as reported in the sixth column of Table3 ) corresponds with an additional 0.549 additional new enterprises per 1000 inhabitants on average across regions.... In PAGE 10: ... A similar observation holds for the reformist voting proxy. The 2SLS estimates reported in Table3 are of course based on identifying restrictions used to select instruments for NEWENT in the first-stage regression. The restrictions involve the exclusion of the variables used as instruments from the second-stage growth regression.... In PAGE 10: ...586), while the quantitative significance of the remaining variables is negligible by comparison. Thus there is reasonable empirical support for the identifying assumptions upon which the results of Table3 are based. We conclude our analysis with an assessment of the influence of the outlier regions.... ..."

### Table 4 summarizes the results of ordinary least-squares estimation of (1)

1995

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### 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 6: Sum of Squared Difierences Between the Estimates when Dividing the HIV Data in Two Groups of 4 Replicates. The posterior mean reduces the variability by 83% relative to the raw log ratios.

2003

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### Table 2 Least Squares Shape from Motion

1997

"... In PAGE 14: ...To compare precision, we performed the same experiments as in the previous subsection but randomly moved the sensor in 3-D rather than just the plane. The extremes of these data are tabulated in Table2 . Again, shape from motion accuracy (mean) is as good or better while imprecision (standard deviation) is always better.... In PAGE 14: ...-D rather than just the plane. The extremes of these data are tabulated in Table 2. Again, shape from motion accuracy (mean) is as good or better while imprecision (standard deviation) is always better. Figure 8 shows the magnitude of the linear forces of the one-bar experiment from Table2 . Ideally, this should be a straight line with a value of 25.... In PAGE 15: ... These plots are analogous to those with real data in Figure 8. The first line of Table 4 lists the mean and standard deviation data anal- ogous to Table2 with real data. The extra column for load error is the peak error for each component of the load vector used in the least squares technique.... In PAGE 31: ...List of Table Captions Table 1: FINGERTIP SENSOR COMPARISON Table2 : LORD F/T SENSOR COMPARISON Table 3: AVERAGE LORD FORCE ERRORS... ..."

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### Table 1 Least Squares Shape from Motion

1997

"... In PAGE 31: ...List of Table Captions Table1 : FINGERTIP SENSOR COMPARISON Table 2: LORD F/T SENSOR COMPARISON Table 3: AVERAGE LORD FORCE ERRORS... ..."

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