Main Problem Presented by Statistics of Employment, Unemployment and Economic Inactivity - Admission/Application Essay Example

Main Problem Presented by Statistics of Employment, Unemployment, underemployment and economic inactivity Statistics gives information on aspects concerning a population under research. This information is essential as it helps determine the role of the population to a country and in this case Ethiopia. It shows the characteristics of the labour force, whether active or not or productive or unproductive. For a population to have better living standards in terms of economic growth, full utilization of the available human resources is needed. In the research study, unemployment is discussed and defined as a situation whereby people in a certain population who can work remain jobless. It is mainly because there are no employment opportunities to absorb all of them despite the fact that they may be ready to work. In addition, the study analyses five problems of statistical figures in analysing the rate of unemployment, underemployment and economic inactivity in developing and developed countries. The problems identified through comparison of different research were identified as definition, classification, accuracy, bias and validity. There is a linkage to the dominance of agriculture and the state of women as perceived by the developing countries. Hence, in conclusion, the rates of unemployment, underemployment and economic inactivity are higher because of domination in agriculture and not because of status of women. Contents Abstract 2 Introduction 3 Definition 4 Introduction This research paper tries to explain whether dominance on agriculture and the low state of women has any significant impact on developing countries’ labour force. Statistics is used to explain the characteristics of the labour market which are the unemployment rate, under-employment rates and the economic inactivity. Does the woman state have an impact on agriculture dominance on developing countries? Consequences and causes will get explained in the study and conclusion drawn on the same. Definition Research on labour force has been carried out by different organization over the years. Each has different research objectives and sample size. It is due to a different definition of the survey carried out in a different organization. For instance, the Labour Force Survey defines a bigger sample compared to the Claimant survey that covered a small size. Hence, the first survey reveals 7.2% unemployment rate while the second survey reveals 6.8% rate of unemployment. This difference in sample size resulted in the difference in information obtained. While Labour Survey statistics indicates a high rate of unemployment, the Claimant Count indicates the rates to be. Thus, definition used in statistics influences the quality of the data. If the data defines different objective and sample size from the other then, there will be different data from the two surveys. Classification Research work is a broad term, and researchers use different classification methods in the survey. It has resulted in invariance in the data obtained in different researches that have been carried out in Ethiopia. The first survey mostly classifies its data in the mean-standard deviation classification and the other survey used equal interval classification. Hence, resulting to different statistics from the two surveys due to different methods of classification. Since the research covers the geographical phenomena, it is not easy to have equal statistics, and the equal interval does not reveal the data distribution along a number line. In addition, constraints disadvantage the mean-standard classification. For example, the Labour Force survey shows a labour force rate of as high as 10.8% while that of Claimant being 9.9%. Also, there were 191,900 people in 2007 aged 15-24 years classified as unemployed unlike to only 98,700 people in the same age group who received Labour-market payment. Hence, different classification methods lead to different statistics. The above chart shows data for Claimant Count and Labour Force Survey. Both have different information. Labour Force survey indicates a high rate of unemployment while Claimant Count indicates a lower rate of unemployment in the UK. Hence, clearly showing the survey obtained different data due to different classification methods. Accuracy Unemployment data presented by statistics cannot be said to be very accurate as there are people who are unemployed, but were not included in the research study while collecting data. The study only accounts for the sampled data which act as a representation of the whole population. The data also does not account in most cases the ‘hidden employment’ who are seasonal in the job market or the discouraged parties who have sought for a job without any success. It may cause people to get discouraged and stop looking for jobs. An example of employment statistics problem is the research carried by two different research institutions. The Claimant Count and Labour force Survey. Both have different mechanisms by which they get data. Claimant count considers those who have reached the appropriate age for the job seeking and had sought for a job, but still unemployed. On the other hand, Labour Force Survey accounts for individuals who have ever sought for a job in the past one month which is the reference period and can work if provided with one. Various institutions get involved in the research study and therefore accuracy of the data has no guarantee but assumed to be accurately collected. In addition, the accuracy of data depends on the method of data collection. For instance, Labour force survey used questionnaire to carry out its analysis. Hence, the respondent may exaggerate or bias information thus, leading to inaccurate data. On the other hand, the Claimant Survey made use of interviews that also encourages bias and exaggeration or the respondent providing false information leading to incorrect data collection. The above chart shows data for Claimant Count and Labour Force Survey. Both have different information. Labour Force survey indicates a high rate of unemployment of up to 10.8% while Claimant Count indicates a lower rate of up to 9.9% unemployment in the UK. The difference in accuracy mainly depends on how the researchers define the variables used in the research. Therefore, research statistics on employment cannot be depended on as they are not accurate and determining which survey is more accurate than the other is problematic. Hence, the difference in accuracy mainly depends on how the researchers define the variables used in the research. Therefore, research statistics on employment cannot be depended on as they are not accurate and determining which survey is more accurate than the other is problematic. Bias Researchers get biased in data collection, and this as a factor alters the degree of accuracy of data collected. Most researchers base their research in urban areas and neglect the rural areas. The two populations i.e. the rural and the urban have different characteristics and in most case the rates of unemployment, underdevelopment and economic inactivity will be different. Previous surveys conducted in the 80’s in Ethiopia on labour force mainly based the research on urban areas. Comparison between urban and rural population became a bit difficult till a recent survey got conducted and included the urban areas. True representation of information for the whole country. The data also does not account in most cases the ‘hidden employment’ which includes those who are seasonal in the job market or the discouraged parties who have sought for a job without any success. It may cause people to get discouraged and stop looking for jobs. Data for such people is left out hence leaving out important information that requires for the research study. In most instances, underemployment data is not accurately collected as the researchers tend to ignore it. It may include people who had no chance of getting full-time job, but did not get one and thus opted to go for a part time offer. The above graph from Explains how data changed from 80’s as researcher’s started to carry out survey in rural areas. Leaving of obvious variables and incomplete coverage of research survey are the major causes of inaccurate data. It is evident from observation that biases have led to inaccuracy of the data collected major problem to labour statistics hence. In result, there are incorrect labour statistics. Validity The behaviour of the respondents on providing information weighs the validity of the research. The information exaggeration and biases make the information more invalid. Variance observed in this study indicates that statistical data on unemployment is invalid. Therefore, the underemployment and economic inactivity are not very accurate as different research gives different figures resulting to varied information. For instance, there were 191,900 people in 2007 aged 15-24 years that get classified as unemployed unlike to only 98,700 people in the same age group who received Labour-market payment. Different surveys and research on unemployment, economic activity and underemployment show different information. It is due to different collection of data methods, bias and exaggeration that result to incorrect and different data. Therefore, making it difficult for the information to gain validity. The chart in < http://tutor2u.net/economics/revision-notes/a2-macro-unemployment.html> shows different information in different period of time hence the statistics lose long-term validity now that it changes as time goes. Hence, the research accuracy, methods of data collection defines the validity of the research. Definition of research variable varies is hence causing the researchers to classify data differently. Different classification of research carried out determines the accuracy of data and thus, its validity. Unemployment It is a situation whereby people in a certain population and can work and remain jobless. Unemployment is a major problem facing the people of Ethiopia as revealed consequently in the paper. For instance, the rates of unemployment averaged in 20.67% in 2012. Though, it is noted that by 2011 most of them get employed. In the research statistics recently carried out by UK to determine the trend in current labour force shows that there is a steady increase in unemployment rates among the people. The research closely observes unemployment rates among different age sets, gender and the population demographics in general. Unemployment in urban and rural areas hinders development growth and has been found to cause both economical and social problems in such areas. The unemployed people’s living standards are normally way below those of other people who have jobs. The population growth of Ethiopia is worrying as it was 15 million in 1950 and 63 million in 1990. The rapid growth in population and the unemployment rates raises eyebrows as this will have negative consequences on the rate of economic development. Though, it is argued that population growth can also increase the labour rate thus, economic development. Therefore, overpopulation in developing countries like Ethiopia leads to growth of unemployment. Underemployment Underemployment is a situation of employment where there in insufficiency in a significant way for an employee, relative to a standard. For example, over-qualification, overstaffing and involuntary part-time work. It is a great problem particularly In countries that are developing where there is low unemployment with most employees doing part-time or subsistence jobs. Researchers observe that mostly the UK women are experiencing the underemployment. For example since 2008, the number of women underemployed increased to 495, 000. Therefore, even though it’s better than unemployment but they do not utilize all their skills they have learnt due to underemployment. Therefore, underemployment significantly leads to poverty. Although employees can find a part-time job, the part-time payment cannot sufficiently cater for their basic needs Economic Inactivity Economic inactivity refers to a situation where many people of working age are not doing any paying job and furthermore are not looking for a job. Such people are inactive as they are not contributing positively in any manner to the economy. This category of people includes those who are sick, students and even the people who retire early from jobs than expected of them. The condition as the study reveals is higher in developing countries as compared to developed countries. The economy workforce get determined by the number of actively working people in the economy. The working population represents the economically active population needed for growth. The majority of the population who are economically inactive according to data collected by the LFS include the young adults and the elderly. Although some of the workers might have retired or are with low education knowledge in comparison to those of developed countries. Though, the figures are increasing in developing countries. For instance, women in UK, who are economic inactive, are about 1, 036, 000, which is twice the number of men. These figures are high in developing countries as compared to developed countries making the dependency ratio to be high. . Problem Statement The statistics show a difference in the rates of unemployment, underemployment and economic inactivity in the developed countries and the underdeveloped countries. The difference arises due to the nature of their respective economic activities that are carried out in the two countries. Two major factors identified in the two countries is the type of economic activities carried out in the two countries. While developed countries depend on industrialization for their rapid economic growth, developing countries are found to rely on agriculture. The nature of labour force in the two countries also differs. In developed countries have more females in their workforce unlike in the developing countries. Unemployment rates as per the 2012 statics in Ethiopia indicate a small drop from 18% to 17.5%. Unemployment rate in U.K according to Labour Force Survey covering from 1971 to 2014 indicate the rate to be 6%. The two countries have a very different rate as seen. Ethiopia is a developing country and by the statistic, it is noted that a large portion of the population main employment (805) is agriculture. It identifies the country to dominantly depend on agriculture unlike their counterparts who rely on industrialization. The rate of economic inactivity in the two countries was also seen to be high with Ethiopia having a slightly higher level. Statistics indicated that the major cause of economic inactivity was that most people were students hence still learning. Some could not get time to go for jobs as either they were looking after the elderly or sick, others were retiring, and some were discouraged and not seeking for employment. Agriculture is the main driving force to the most developing countries economies and acts a critical role in the poverty level of the countries. Developed countries, on the other hand, depend on industries and related activities for the growth of their economy. 80% labour force according to statistics indicated that they depend on agriculture. It, therefore, is a sure indicator that the economies of the two entities differ in very many ways. The common form of employment in under-developed countries is informal as the employment opportunities in such countries are little. It is as a result of depending on agriculture as the practice is seasonal and depends on rain. Another factor is that developing countries only deal with raw materials that they in return export to developed countries with industries for processing. Developed countries like the UK have many industries hence their labour demand is large. It urges for a massive portion of the population securing employment. The unemployment rates in this case will be small as compared to the developing nations who majorly depend on agriculture. Underemployment is also high in developing countries as job cannot secure full-time jobs hence resorting to such options. Many factors other than agriculture were noted to cause high unemployment rates, underemployment rates and economic inactivity. The problem statement that developing countries have higher rates of unemployment, underemployment and economic inactivity than developed countries is vague. According to a survey carried out in Ethiopia in 2004, it was noted that the female population in the country dominates that of the male population in the country. The female population stood at 50.22%. Most of the individuals who have no jobs in the developing countries fall between 15-24 years of age. 25% of the households in Ethiopiahave women as the heads who play as the bread winner. Statistics indicates that the majority of the female population have low education level than their male counterparts hence reducing their chances of being employed. Illiteracy level was noted to be high in rural areas than in urban centres. The labour force needs both male and female and sometimes females perform better than their male counterparts. It has resulted to a high rate of unemployment as most people are not studying or working. The results are high birth rates and an increase in poverty levels. In developed countries, the situation is different as there is equality in the labour work force. The educated but unemployed female population is high, hence contributing to a larger percentage of the unemployed. Most people in the developed countries venture into formal employment unlike in developing countries where they involve themselves in informal activities that are sometimes not paid. These and many other factors can be said to be the result of the high rate of underdevelopment in poor countries. Ethiopia has a female labour force of 47.22% according to research carried out in 2011. It showed a significant increase from 46.66% in 1997.UK has a female labour force of 46.7% with the majority of women holding senior positions. Conclusion Statistical data on labour indicating the unemployment rates, underemployment and economic inactivity are normally based on internationally agreed surveys. Researchers tend to depend on the argument, and this way affect the quality of data collected as it due to the tamper. There are five problems that were found to affect the statistics, and they include problem of definition, classification, accuracy, bias and validity. These problems got identified on a detailed comparison between the research surveys done over the years of UK and Ethiopia. Figures from different research were found to differ. While other survey overestimated one thing the other underestimated. It showed that research statistics cannot be relied on for information as they are not accurate. In conclusion, the rates of unemployment, underemployment and economic inactivity are higher because of domination in agriculture and not because of status of women. People in the developing countries largely depend on agriculture for employment. The practice being seasonal and has minimal employment opportunities to be high. 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