85 is not the proportion of urban population(0-1). I wonder if the problem might be the large ratios between pairs of variables. For example 80354 is roughly 1000 times larger than 85. The ratio of variances will be roughly 10E6. Perhaps significant digits are being lost in the eigenvalue solution. Please fix the data typo and try again.
PCA is not scale free. PCA on a variance-covariance matrix of a set of variables will typically yield different results compared to PCA on the correlation matrix of the same variables.
If you was analyzing correlations, there would be no excuse for the correlations to come out different. So, the likely explanation is some unnoted change in data. Look at all the means and r's.
If you was analyzing variances - which must be considered a mistake -there is no reason for loadings of the two scores to have much resemblance across analyses. And the "number of factors extracted"is not meaningfully related to a cutoff of 1.0, if that was used. When PCA is employed on correlations, the 1.0 represents the amount of variance to be explained for each variable, and "less than one" says that the factor is worth less than a single variable and thus might be ignored for subsequent rotation... assuming you are working from a theory about important latent factors.
Changing the scale of a variable by a multiplicative constant will NOT change the correlations. Since a PCA is solely a function of the correlations, I would attribute your finding of the difference using two data sets to typo(s) (either in calculation or reporting)! Please compare the Means, SDs, Ns and R matrix (recalculate means and SDs based on the different 'scaling').
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