Your mistake is in choosing the Wilcoxon/Mann-Whitney rank-sum tests as your post hoc tests following the rejection of the Kruskal-Wallis. The appropriate pos hoc test is Dunn's test* which properly (1) accounts for pooled variance assumed by the null hypothesis, and (2) uses the same ranks for your data as used in the construction of the Kruskal-Wallis test. The vanilla rank-sum tests entail separate estimates of variance for each pair-wise test, and ignore the rankings of the total data set as performed with a Kruskal-Wallis test.
Dunn's test is implemented for Stata in the dunntest package (within Stata type net describe dunntest, from(https://alexisdinno.com/stata)), and for R in the dunn.test package. Not sure about implementations in SAS.
**Reference**
Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3):241–252.
* There are some far less used alternatives to Dunn's test including the Conover-Iman (like Dunn, but based on the t distribution, rather than the z distribution, implemented for Stata in the conovertest package, and for R in the conover.test package), and the Dwass-Steel-Citchlow-Fligner tests.