This study extends the prior research on first divorce differentials by education and economic activity status in Lithuania by examining divorce risk by occupation and (in progress) branch of industry. The analysis is carried out using a unique in the region census-linked dataset based on the linkages between the 2001 census and all first divorce records for 2001-2003. The final dataset covers all married individuals aged 15-60 years and include 3.18 million person-years of population exposure, and 41 thousand first divorces. Using Poisson regression modelling, the research aims at establishing complex relationships between the risk of first divorce and selected socio-economic variables as well as confounding and interaction effects of available explanatory variables. The originally available ILO-based occupation groups have been allocated into six social classes using the Erikson-Goldthorpe-Portocarero classification scheme. Economically inactive people and people with unknown economic activity status and occupation (included as additional categories of the occupational variable) were also considered in the statistical modeling. Primary results based on Poisson regression models controlling for duration of marriage, marriage cohort, and age at first marriage suggest that there is no statistically significant difference in the risk of first divorce between upper and lower white collar employees (both sexes). However, self-employed males and females showed a higher propensity to divorce than those in the upper white collar category (reference). Divorce probability was lower among self-employed farmers and farm workers, unskilled manual workers, and skilled manual employees (males only). Additional control for education, place of residence, and ethnicity led to a reversal in the effect for self-employed male farmers and farm workers, now showing the highest divorce incidence. A slight advantage of lower divorce among unskilled workers found in the initial model disappeared for males. The excess in first divorce of self-employed females and males remained statistically significant.