Chapter Summaries - Literature review Example

Chapter Summaries Chapter 15 This chapter discusses the main microeconomic factors shaping the interrelationship between job search and the structureof the labor market. The author argues that there are multifarious reasons for job search and that trends in job search patterns influence patterns in the labor market. In highlighting the impact of job search trends on the labor market, the author firstly refers to the significant increase in the job switching, particularly with the younger demographic. Additionally, it is highlighted that the trend of job switching has also impacted employer organisational practice, where some companies also offer job switching opportunities. Secondly, the author discusses the fact that job search trends do not operate in a vacuum and are intrinsically shaped by microeconomic factors specific to the job seeker. In particular, the chapter highlights the point that the job search market comprises people searching for various types of jobs for multiple reasons that are dependent on age, education, economic status and gender. Furthermore, the job search market is effectively a two tiered structure, namely: 1) External search (job seeker); and 2) Internal search (employer search for employees). As highlighted above, the external search will be motivated by age, education, location and gender. Additionally, Figure 15.1 refers to the example of wage offers, the acceptance wage and unemployment status impacting job acceptance. In contrast, the internal search will be dependent on organisational needs, which can create demand in the labor market. The author argues that the relationship between the external search and internal search ultimately dictates the labor market through the wage structure and nature of the market. Additionally, it is posited that notwithstanding periods of unemployment, the relationship between job search and the structure of the labor market results in a continuous supply of workers and jobs, which in turn creates equilibrium between the supply of jobs and the demand for jobs from the unemployed. Therefore in summary, the central argument in this chapter is that numerous factors influence why people search for jobs and employer demand for specific job roles to be satisfied, and that it is the interrelationship between these factors that dictate the trends and shape of the labor market. Additionally, it is argued that the structure of the labor market creates a constant flow of workers as “stock”, which in turn ensures equilibrium in the supply and demand curves of the labor market. Chapter 16 Summary This Chapter discusses the macroeconomic factors within which the labor market operates and in particular, highlights the impact of personal earning distribution and seeks to evaluate why distribution patterns have become more uneven in the past thirty years. In reviewing earning distribution patterns, the chapter highlights various macroeconomic theories put forward to explain income distribution and focuses on the following theories: 1) Lorenz; 2) Frequency distribution. The frequency distribution method finds the average by considering the earnings by the number of workers. On this basis, the median is found in order to measure below average, average and above average distribution figures. In considering the Lorenz curve, the chapter argues that this formula demonstrates the extent of uneven distribution by looking at the cumulative percentage of annual earnings of full time earners. Additionally, the author refers to the Gini coefficient calculation, which divides the area between the Lorenzo curve and the diagonal total area below. This measure is utilised to evaluate the extent of distribution inequality. According to the Lorenz curve and Gini coefficient measures, there appears to be inequality in income distribution. However, whilst the author acknowledges that the trends clearly indicate an increase in uneven income distribution, it is posited that understanding this solely by reference to the Lorenz curve and Gini coefficient measure is intrinsically flawed due to the limitations of the calculations. For example, Figure 16.4 evaluates the interrelationship between numerous factors affecting personal annual earnings. To this end, Figure 16.4 demonstrates that significant factors include the following: 1. Level of Education; 2. Training, 3. Ability and family background 4. Level of discrimination; 5. Level of movement and mobility of individuals Additionally, the chapter refers to the element of luck that can impact annual earnings and that the Lorenz curve and Gini coefficient measure do not always account for the interrelationship between these complex factors. Therefore in summary, the chapter highlights that the last 30 years has seen a consistent increase in uneven income distribution. Whilst the frequency distribution model and Lorenz Curve are useful aids in confirming the rise of income inequality, they must be viewed with caution as they fail to take account of numerous socio-economic factors impacting income distribution. Additionally, the number of complex background factors that impact income distribution is also interrelated with changes in employment trends and demand in the workplace and therefore it is impossible to attribute one single cause for the continued increase in uneven income distribution and income inequality. Chapter 17 Summary This chapter seeks to further develop the findings and propositions in previous chapters by evaluating long term trends of average wages in order to determine patterns in wage growth in the last century and the cause of a real wage growth rebound since 1995. In evaluating this, the chapter firstly evaluates patterns in productivity and the relationship between productivity and real wage growth. The productivity model explains the interrelationship between the number of goods and services produced (real output), which is divided by the measure of the resource (input). In applying the productivity model to the labor market, the total product is then divided by the number of worker hours to determine labor productivity and the real GDP. This in turn is utilised to find the average labor input into the economy and to calculate a productivity index figure. Additionally, the chapter refers to the Bureau of Labor Statistics Index which is the official US economy index for labor productivity and comments that when comparing the various productivity measures, each one is different depending on the input and the focus should be on labor productivity to determine factors influencing real wage growth in employment. The author argues that out of the various methods for determining productivity growth, the BLS index is simple to follow and from a labor productivity perspective specifically refers to working week hours and hourly pay rates. However, the disadvantage of the BLS index is that it can ignore important output factors and as a result can undermine the overall picture of productivity growth. Additionally, this chapter highlights the inefficiencies in each model of determining productivity and posits that the central factors that are fundamental to determining productivity growth in the labor market are: 1) Capital-labor ratio; 2) Efficiency of the labor and capital inputs; and 3) Quality of workforce (which in turn impacts efficiency). Overall, the chapter highlights that irrespective of the method use to ascertain labor productivity, the intrinsic limitation of applying a formulaic approach to calculate productivity is the failure to account for factors impacting specific industries and the supply/demand curves in the job market. Furthermore, from a macroeconomic perspective significant external factors will influence corporate strategy and growth, which in turn influences internal requirements for specific job roles. The correlation of these background factors all impact pricing strategy and product demand and it is difficult to actually quantify the relationship between productivity patterns and changes in employment practices. For example, the author highlights the underlying influence of the demand factors variable in productivity growth and Figure 17.8 highlights that the changes in employment in certain industries are not always interrelated with changes in productivity. Therefore in summary, Chapter 17 highlights that when considering long term trends, wage growth is linked to productivity however there is no one appropriate formula for calculating actual labor productivity. Additionally, the author highlights that significant background factors impact productivity in certain industries such as technology, consumer demand and corporate strategy. Therefore, whilst the last century saw a significant increase in productivity and wage growth; the existence of numerous complex background factors arguably make it impossible to calculate the exact relationship between productivity and changes in employment practice. Chapter 18 Summary Chapter 18 undertakes a comparative analysis of employment and unemployment statistics in various countries and seeks to evaluate the cause of employment and unemployment trends by considering the aggregate labor market. In particular, this chapter argues that from a macroeconomic policy perspective, employment and unemployment data are commonly viewed as a yardstick against which to measure an economy’s performance. As such, it is argued that it is vital to understand the basis on which employment and unemployment statistics are calculated. There are various methods of measuring employment and unemployment statistics and the US Bureau of Census undertakes a household survey each month of 60,000 households as a representative sample of the US population. This is then utilised to provide estimates on the percentages of the population that are employed, unemployed or not in the workforce and the reasons behind these trends. In using the data sample to calculate employment trends in the population the survey divides the number of people in the sample that are employed by the number of non-institutional population and multiplies that figure by 100. With regard to unemployed people, factors taken into account are people that are available for work and are looking for work. The number of unemployed people is then divided by the number of people in the labor force and multiplied by 100. From a historical perspective the data indicates that in the last forty years the statistics for unemployment have been variable reaching the lowest rate in years by 2000. The figures also demonstrate a significant increase to 5.8% in 2008. However, whilst the statistics are useful as an aid, there are distinct limitations as part time workers are included in the “fully employed” definition and in order to be unemployed, the data sample must be looking for work. The intrinsic flaw in this requirement is that other reports indicate that sometimes people looking for work may subsequently abandon their search if they have been looking for a long time and this group of people are not included in the data sample. As a result, these limitations clearly impact the veracity of the data, which can undermine attempts to determine causality in unemployment and employment trends. For example, it is posited that the limitations of the data can even result in unemployment figures being exaggerated. Additionally, the increase in multi-earner families also undermines the value of the household survey data in examining the social impact of job loss in the contemporary social framework. Furthermore, the household data survey method applies the stock flow model and focuses on the numbers of people that fall into three distinct categories of being either employed, unemployed or not in the labor force. As a result, the data results can be rigid in failing to address causality and reasons for unemployment and the actual length of unemployment. For example, someone may be unemployed for a very short time or someone employed may be temporarily employed. As a result the strict categorisation of data in the household survey does not always mirror actual employment and unemployment rates. For example, figure 18.4 demonstrates the stock flow model of unemployment and highlights that changes in these flows significantly alter the actual unemployment rate figures. Additionally, changes in working patterns and the increase in flexible working mean that it is arguably impossible to specifically calculate unemployment and employment rates. Furthermore, the chapter highlights the fact that within the categories of employment and unemployment, there are various factors influencing trends such as the nature of the industry and working patterns and that it is important to understand this when considering trends in employment and unemployment. To this end, it is posited that understand the supply/demand curve in the labor market also contributes to a better understanding of causality in employment and unemployment. For example, it is highlighted that the supply/demand curve impacts employer demand for certain skills and that the inability to supply this demand can result in structural unemployment. Additionally, lack of supply of jobs in a certain sector or area due to industry changes or economically driven cutbacks can resulted in displaced unemployment. Furthermore, social factors influence unemployment with race, age and gender impacting unemployment rates. In summary, this chapter highlights that employment and unemployment rates are often viewed as being indicative of economic performance and therefore it is important to understand causality in employment and unemployment trends. Whilst the household data survey is useful as an indicator of trends, the strict categorisation can ignore the practical realities of job search and definition of unemployed and employed. As a result, the household data survey can give a distorted view of actual employment and unemployment figures. On this basis, it is argued in the chapter that whilst it is difficult to calculate and determine causality of unemployment and employment trends with complete accuracy; a useful model to apply is the supply/demand curve in the labor market. Read More