# Relationship between education and income statistics

is unfair.4 While many factors contribute to income and wealth inequality, the role . The relationship between education and income is strong. It is also hard to obtain any related statistics that might be helpful. Earlier work shows a close relation between education and income Earnings-Education. But it pales compared to the gap between the wages of a family of two college collection of education statistics from around the industrialized world . the correlation between higher levels of education and greater income.

Somaliland being one of these developing countries has not encountered such a research. However, the considerable researches that have been done as far as this topic is concerned will set us a route to follow in conducting such a research in Somaliland. Data was collected from respondents using administered questionnaires where an assigned people helped respondents to fill in and complete questionnaires.

The questionnaires distributed were self-administered to help reduce any misunderstandings that might arise and in order to intensify response rate in consideration of the time factor as well. As part of pretesting before the main survey was conducted, I have selected few people Five participants from the target population.

I have then identified the target areas or fields that best represented the target population. These were first categorized on the basis of governmental and non- governmental. Then, for the non-governmental institutions I have identified sub- categories e.

Earnings-Education Correlation International Horn University Questionnaires were completed and filled by respondents with the help of someone to administer in each selected field. Thanks to friends whom I have requested to help did the job well and as required. As precautionary, questionnaires that were distributed were slightly more than the assigned number in each area. This was important in that replacement could be easily available for incomplete questionnaires.

Hence, no reduction from the calculated sample size. These are the people who earn wage or salary in return of work done being physical or in the form of services rendered.

### The Relationship Between Education and Income | LoveToKnow

However, using the aforementioned criteria the target population was easily identifiable. But, there was no any reliable actual sampling frame representing the population. The most suitable sampling technique I considered for this research was random sampling, a probability sampling method. As in all survey researches in which generalizing sample results to the large population is the underlying factor, I felt that random sampling was the best to represent the population.

## The Relationship Between Education and Income

To ensure that the sample accurately reflects relevant sub-groups in the target population estimate percentages were used. These percentages were based on gender and sector being either public or private. The private sector included both profit seeking and not for profit organizations.

The larger the sample size, the more confidence that results truly reflect the population. However, it also means higher cost and more time. On the other hand, the smaller the Earnings-Education Correlation International Horn University sample size the lower will be the probability of getting real relationship between variables. In determining a fair sample size for the unknown population in this research, this formula was used: Calculating this using the above formula would reveal the need for 96 people in the survey.

The selected sample of the subjects was ordered into governmental and non-governmental employees. Non-governmental institutions included international NGOs, e.

UN Agencies, local NGOs and profit seeking institutions such as telecommunication, media and trading companies. Subjects were categorized in this way because of existing differences in the pay received by governmental and non- governmental employees in the form of salaries.

Thus, earnings-education correlation could be performed separately using these two categories and then compared to the overall correlation of the two. For easy data entry and analysis the text data of the variables were coded, put into quantitative form and then entered into the computer. Earnings-Education Correlation International Horn University As mentioned, data on earnings will be looked at in comparison to education levels.

Education levels as shown by this table have been categorized as follows: These earnings levels are listed in this table: The entire range of schooling levels ranked as shown above was correlated with earning levels. The research approach for this project is more along the lines of the positive influence of education on earnings rather than less education means less income. While this inverse would hold true, it would require broader analysis which is outside the scope of this research.

If there is a correlation between education level and earnings it is a safe assumption that the two are related. Correlations will not prove that education effects earnings, although indications on prior research made hints on this. It would be safe to say education and earnings are not merely arbitrarily related.

Several assumptions were taken in this report. First, as shown by the table in the analysis section, people in which their earnings lie within the same range are assumed to get the same pay on average. Second, the data is based on earnings at the time when research was conducted and is not representative of the patterns in previous or future earnings.

Third, this small sample size of 96 cases is representative of the larger population and that the results would be generalized to that population. Fourth, there is a strong possibility that some people might not show the truth when filling in questionnaires.

However, the data on completed questionnaires was assumed to be the actual one. On the other hand, there are certain limitations that are worth to mention. First, many factors which affect earnings are not covered in this report.

These include continuity of occupation Career pathmotivation and effort put on work, etc. The limited sample of this research precludes their use in the analysis. Occupation, marital status, income requirements and employment rates fall into this category. In addition non-cash or to put it in economics terminology non-labour income is not also considered as part of earnings.

Finally earnings or the amount of monthly pay is greatly influenced by the type of institution rather than education level. The total number of cases were 96, in which 29 of the respondents were governmental employees where as the remaining 67 were non-governmental employees.

In summary, statistics of figures from these two tables gives the following results: Both tests show the existence of positive correlation with correlation coefficients of above 0. These findings indicate that education level is positively correlated with earnings. Considering separately the governmental and non-governmental employees tables 6, 7, 8 and 9 show that schooling-earnings correlation is stronger when the 29 cases for governmental employees are considered separately with a correlation coefficient of around 0.

On the other hand, in non-governmental case, there exists a positive Earnings-Education Correlation International Horn University correlation of just below 0.

Regression analysis tables 10, 11 and 12 further explains the positive relationship between earnings and schooling. The linear regression equation could be summarized as follows: However, correlation between earnings and each of these variables is what we are focusing. Schooling level, type of organization, technical skills and number of years worked show positive correlation of different degrees with earnings. Whereas, the opposite is true with position, age and weekly average hours. From this, we infer that increased years of schooling has a direct effect on earnings.

Earnings differences are not so obvious in lower levels of education such as people with no schooling and those having primary education. One good reason might be the relatively small sample of respondents I have chosen.

Randomly selected respondents of lower levels of education were less in number which might show inconclusive results. However as far as the sample selected is concerned this is the actual results found. Again, the relatively smaller sample is one factor that might impede ultimate true results.

On the other hand, majority of respondents were people in jobs with relatively high pay in international NGOs and local private companies.

I agree that majority of my target population were not workers on such areas. Another important reason that might increased average earnings could be the higher educational attainment as shown by the modal schooling level University graduate. It is more likely that results would have changed if modal class would be lower.

In the findings and analysis discussion, data was categorized as governmental and non- governmental. Such a division was due to existed differences in the pay of the employees. In the case of non-governmental employees payment differs. Thus, it would be more fair and realistic to perform analysis on the basis of these two categories. Correlation was a bit stronger in the case of governmental employees compared to the non-governmental ones.

Lower correlation in the non- Earnings-Education Correlation International Horn University governmental case was due to differences of payments that different institutions pay to their employees. Some institutions pay higher compared to others. Regression analysis further explained the positive relationship between earnings and schooling. The following was the linear equation we found: From the above, schooling has relatively greater effect on earnings compared to the other variables that we have used in this research.

Now let us take other variables into consideration by introducing their effects on earnings. Schooling level showed strongest correlation with average earnings followed by the type of organization; technical skills and number of years worked all showing minor positive correlations. By type of organization we mean governmental or non- governmental. Technical skills as supplementary skills helped more on those who had them. Most of the professional employees, e. Number of years worked or work experience in other words has also slight positive correlation with earnings.

More years of work help job specialization and better performance thus more likely to higher earnings. Position, gender, age, years on current job and weekly average hours on the other hand all showed minor negative correlation with earnings. As expected position should have Earnings-Education Correlation International Horn University positive correlation with earnings but again type of organization appeared to be the limitation.

For example, high position in a governmental employee means less pay than a low position in an international NGO. More realistic figures would have been found if such a problem could be eliminated. Gender also showed slight negative correlation.

In the process of data entry of the SPSS software package that we used for our analysis, we gave female and male value labels of 1 and 0 respectively.

## The Connection Between Education, Income Inequality, and Unemployment

From this, gender-earnings correlation of — 0. This is more realistic as higher positions with higher pay are mainly occupied by men.

The slight negative correlation between age and earnings was mainly due to the large young university undergraduate and graduate population that are now occupying new jobs that need more educational talents. This as a result leaves middle aged and older people jobs with less pay compared to the new generation. As in our findings mostly these group of respondents were below 40 years of age.

Finally, weekly hours also showed minor correlation with earnings.

### Income-education Correlation | Nuraddin Elmi - zolyblog.info

This is mainly due to the fact that most of the jobs are not paid on hourly basis but on monthly basis. Fixed monthly pay regardless of how many hours worked is more common.

A pattern can also be deduced from this where lower earners relatively work more hours than higher earners. The results in this research paper are on the support that education pays.

When I say this I am not importing ideas from other countries in particular the so called developed world in which major researches regarding this subject have been conducted. That is what my paper says to the readers. The only inconclusive data is in regards in those areas where effects of several variables overlap.

This research study which is cross-sectional in nature and subject to changes if any further studies are made in the future showed that increase in earnings would be explained by increases in educational level, having technical skills and more experience, working in non-governmental institution and being male in gender.

Despite the limitations that existed including lack of local references and records I have managed to perform this study which is the first of its kind in Somaliland. It sheds lights on to a field that deserves more scrutiny. If results of this research paper are taken acceptable, this means higher motivation towards further educational attainment and the completion of further years of schooling in Somaliland. While the various results cannot directly answer this question, as previously mentioned they do show that education has strong relationship with earnings.

Determining a causal relationship is also out of the scope of this project, and therefore has not been done, but provides another opportunity for looking into education and its effects on earnings. Indeed, America is in some ways two different countries economically, segregated by educational achievement. Table 1 below shows a significant relationship between income levels and educational attainment.

Basically, the higher the education level, the higher the income. For example, people with professional degrees earned 6x as much as people who did not graduate from high school in However, this is not just an income effect. Table 2 demonstrates that US unemployment rates and educational attainment are also strongly related to each other.

The better educated the group, the lower the unemployment rate -- and this striking result is consistent over a ten-year period and is highly significant. These figures strongly suggest weak demand in our economy -- over a long period -- for less educated workers, and greater demand for more educated workers.

Even assuming an imperfect labor market, this indicates rising wages for workers in demand high educational attainmentand weak-to-flat wages for workers not in demand low educational attainment.

If you have a four-year college degree and at least some graduate school, recessions have been mild -- with current unemployment rates of 4. In many ways, our two economies have created two separate societies. Those with low educational attainment drift permanently between recessions and depressions, with little stability. Those with high educational attainment experience increased wealth, only mild recessions, and interesting projects with personal growth.