## How the Results were obtained

Firstly the data was sorted in ascending order by the categories ‘male’ and ‘married’. This helped find where the boundaries for the four categories were. It was found that:

* Single males lie between 3976-5664

* Married males lie between 5665-7641

* Single females lie between 1-1863

* Married females lie between 1864-3975

Using the ‘count if’ function within the range of f3976: f5644 the number of cells which contained 1 were counted this displayed the number of single male respondents who were self-employed. In the cell below, for the same, the cells containing 2 were counted this showed the number of single male respondents who were employed. This was completed for all of the 16 cells.

As a check the sum of each column was taken and matched against the number of respondents in each gender and marital status category. As a double check the sum totals were taken and checked against the total number of respondents.

For the second section the distribution of length of Unemployment over Gender and marital status was calculated. Again the data was sorted but this time by ‘male’, ‘married’ and ‘status’ so the unemployed respondents could be easily distinguished. This time the range was different, as it was only information from unemployed respondents being used.

There are 91 days in a quarter. Therefore again using the ‘count if’ function, for single males, the cells in the range of 5112:5271 in the tenure row (row G) were counted if the value contained within was less than or equal to 91. For ‘more than one quarter but less than two quarters’ the cells were counted if they were less than or equal to 182 minus the number in the cell above. For example the formula in cell B7652 is “=COUNTIF(G5112:G5271,”<=182″)-B7651”. This method of counting if less than the number of days in the highest quarter then subtracting everything that was counted before was continued until all the required data was extracted. For example in cell D7658 the number of single females unemployed for more than seven quarters but no more than eight quarters was calculated by the formula “=COUNTIF(G1099:G1190,”<=728″)-COUNTIF(G1099:G1190,”<=637″)”.

The third task was to work out the mean, the standard deviation, and the variance of the duration of unemployment for each of the four gender and marital status sub-samples. The ‘mean’ is the average; calculated by summing all the values in the sub-sample and dividing by the number in the sub-sample. The ‘standard deviation’ is the average deviation from the mean and the ‘variance’ is the average of the square of all deviations. These are calculated by using the ‘mean’, ‘stdev’, and ‘var’ functions using the same ranges as before again for row G.

The results and how they may be interpreted.

Self-Employed

In this sample males were more likely to be self-employed than females. This may be due to men being encouraged to go into business on their own more than females, or it may be the case that banks are discriminating against females who are looking for loans to set up a business. Another point noted from these results was that regardless of gender married people were more likely to be self-employed than single people were. This may be because it is easier to get a business loan as married people usually have more collateral than single people do for example a house or spouse’s income.

Out Of Labour Force.

In the sample 34% of all female respondents were out of the labour force. 36% of single and 32% of married respondents. This may be an area where there is a high number of young single mothers who leave the workforce (or never enter it) too care for their children. A possible reason for married females leaving the work force is that this may be an area where there is a high ratio of people of a race or religion who believe that married women should not work. This may cause a bias in the statistics. Over all the number of respondents who are out of the labour force is very high. This area may have a high population of retired people this could also account for the difference in males and females as the age for retiral for females is 60 while the retiral age for males is 65.

Unemployment and length of current unemployment.

37% of unemployed single males were unemployed for no more than one quarter. This could be due to frictional or cyclical unemployment. Single males may be less frightened to leave a job for the chance of a better job even if it means being unemployed for a short while. This could be because they do not have the same responsibilities as their married counter parts. However there is a high number of respondents who become in the 1st to the 4th quarter. This may be due to a recession in the area. Between the 5th and the 8th quarter there is a very low number of people becoming unemployed so this may have been a boom period.

32% of all unemployed respondents had been unemployed for over eight quarters.

* Single males 25%

* Single females 29%

* Married females 30%

The most startling was that over half the number unemployed married males had been unemployed for more than eight quarters. This is because the longer a person is unemployed the more difficult it becomes to find work due to the loss of skills. Married people may believe they are better off unemployed with their unemployment benefits, their spouse’s income, and any other job they can do on the side.

The mean of the length of unemployment for all cases is greater than eight quarters. So although the table is mutually exclusive and exhaustive it is top heavy. Perhaps it would have shown more information if it continued to 16 quarters. There are also massive deviations from the mean. The tenure varies from < 5 to > 5000. Therefore the mean has little meaning.

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