Having this in mind let us compare with the evidence gained by the decision tree algorithm (figure 3). Note that if one’s employment sector (WORKSEC) is not mining/quarrying, electricity, gas, water supply etc. than it is agriculture, hunting, forestry, manufacturing, education, and healthcare. It is essential to consider that model’s predictive accuracy is 78% which means out of 100 instances it correctly predicts 78 of them.
If respondent’s work sector is not mining/quarrying, electricity, gas, water supply etc. we turn to the left and get the 86% probability of that person being the part of low income class; in the same fashion if that person belongs to either mining and quarrying, financial intermediation/banking, mass media work sectors then the probability of that person being in the ‘High’ class is 28%. Hence, the highest probability of one being in the ‘High’ income class (100%-55%=45%) is when the person belongs to either mining/quarrying, financial intermediation/banking, mass media work sectors or when his or her employment duration starts earlier than 2012. Most of the abstract rules that the model is suggesting, are quite intuitive and close to our prior determinations. The model hints that the driving factor of one’s income in Armenia is employment.
Based on the findings most of the people in the rural areas are involved in agriculture which is a very low productivity sector; almost twice less productive than services sector. According to the decision tree model people employed in agriculture, hunting, forestry, manufacturing, education, and healthcare are poor. Moreover, 86 out of 100 people of either sector can be identified as low income people.
 The workings have been fully done using R statistical software; the full information about the workings (figures, decision trees) with code can be found here
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