Saturday, January 25, 2020
The Labour Force And Unemployment Economics Essay
The Labour Force And Unemployment Economics Essay Every market has buyers and sellers, and the labour market is no exception: the buyers are employers, and the sellers are workers. Some of this participant may not be active at any given moment in the sense of seeking new employees or new jobs, but on any given day, thousands of firms and workers will be in the market trying to transact. The Labour Force and Unemployment The term labour force refers to all those over 16 years of age who are either employed, actively seeking work, or expecting recall from a layoff. Those in the labour force who are not employed for pay are the unemployed. [1] People who are not employed and are neither looking for work nor waiting to be recalled from layoff by their employers are not counted as part of the labour force. The total labour force thus consists of the employed and the unemployed. The number and identities of people in each labour market category are always changing; the flows of people from one category to another are considerable. There are four major flows between labour market states: employed workers become unemployed by quitting voluntarily or being laid off (being involuntarily separated from the firm, either temporarily or permanently), unemployed workers obtain employment by being newly hired or being recalled to a job from which they were temporarily laid off, those in the labour force, whether employed or unemployed, can leave the labour force by retiring or otherwise deciding against taking or seeking work for pay (dropping out), those who have never worked or looked for a job expand the labour force by entering it, while those who have dropped out do so by re-entering the labour force. The ratio of those unemployed to those in the labour force is the unemployment rate. While this rate is crude and has several imperfections, it is the most widely cited measure of labour market conditions. The relation among unemployment, employment, and labour force Analytically, to access the unemployment rate we can use the following equality: where , , and designate respectively the working-age population, the level of employment, the number of unemployed, and the participation rate at period t. Defining the unemployment as , we have Using this equation in logarithm terms at time t and t-1, we get: Assuming that u is a small number, this relation allows us to express the variation of unemployment rate as a function of the growth rates of working-age population, employment, and participation: This decomposition shows that the variation in the rate of unemployment come from variations in the employment rate, the size of the working-age population, and participation rate. Chapter 2 Some facts The different unemployment experience During the last 20 years, the industrialized countries have evolved in very different direction with respect to unemployment. In contradiction to Japan, or the United States, most of European countries showed a high proportion of unemployment. Table 1.1 Rates of unemployment, participation, and employment in 20 OECD countries in 2011 Country Unemployment Rate Participation Rate Employment Rate Australia 5,10 78,8 72,70 Austria 4,14 75,79 72,13 Belgium 7,14 68,88 61,93 Canada 7,45 80,25 71,98 Denmark 7,57 83,19 73,15 Finland 7,77 75,43 69,03 France 9,26 69,34 63,80 Germany 5,92 81,04 72,53 Greece 17,66 68,57 55,55 Ireland 14,39 70,96 59,20 Italy 8,40 63,01 56,98 Japan 4,57 80,61 71,20 Luxembourg 4,90 70,57 64,63 Netherlands 4,44 80,13 74,88 Norway 3,21 80,22 75,30 Portugal 12,74 77,42 64,20 Spain 21,64 75,28 57,68 Sweden 7,54 31,00 74,10 Switzerland 4,04 86,60 79,35 United Kingdom 8,01 76,75 69,48 United States 8,95 64,21 66,65 Euro area (17 countries) 10,07 26,20 64,25 EU (27 countries) 9,59 64,30 OECD Total 7,92 27,80 64,85 Source: OECD Data Table 1.1 summarises the unemployment, participation and employment rates in 20 OECD countries for 2011. We see that unemployment is a phenomenon that touches all the countries, but in different proportions. There are some countries such as Austria, Japan, Luxemburg, the Netherlands, Norway, and Switzerland, have an unemployment rate below 5 per cent. But other countries, such as Greece, Ireland, Portugal, and Spain, have an unemployment rate higher than 10 per cent. For the European Union as a whole (27 countries), the average unemployment rate is the neighbourhood of 10 per cent, 2 points greater than the overall OECD unemployment rate. The third column reports the employment rate, i.e. the ratio of the number of persons employed to the number of person in the population (working-age from 15 to 64 years old). This indicator is very important for the analysis since it can be used as a complement to the data of unemployment, given that the definition of unemployment is necessarily objective. As we can see from table 1.1 countries with high employment rate are also the ones who have low rates of unemployment. So there is a negative relationship among them. The second column also shows that participation rates are highly dispersed, since they vary from 63.01 per cent in Italy to 86.60 per cent in Switzerland. Moreover, countries that face high unemployment rate generally have relatively a weak participation rate. This rapid overview of the rates of unemployment, participation, and employment in different OECD countries suggest that certain countries face a relatively high unemployment rate because of insufficient job creation. Examination of changes over time since the beginning of 1950s in unemployment and employment rate in the United States and selected OECD countries will throw further lights on the origins of unemployment. The US unemployment experience in comparative perspective Table 1.2 summarises the unemployment experience of the United States, selected other countries, and the OECD as a whole from 1950 to 2011. The OECD unemployment rate averaged about 3 per cent during the 1950s and 1960s unemployment throughout the OECD increased sharply in the aftermath of the oil shocks of the 1970s and continued rising the worldwide recession of the early 1980s. The overall OECD unemployment rate more than doubled from 2.8 per cent in the 1960s to 7.0 per cent in the 1980s, and has remained at an even higher rate in the 1990s. Last year the overall OECD unemployment rate was 8.2 per cent. Table 1.2 Unemployment rates in selected OECD countries Country 1950 1960 1970 1980 1990 2000 2011 Australia 1,50 2,00 3,90 7,50 9,10 6,28 5,20 Canada 3,80 4,70 6,60 9,30 9,90 6,82 7,50 France 1,50 1,70 3,80 9,00 11,10 9,4 9,30 Germany 4,90 0,60 1,90 5,70 6,50 7,76 6,00 Italy 7,20 3,80 4,70 7,50 10,20 10,59 8,50 Japan 2,10 1,30 1,70 2,50 2.7 4,72 4,80 Netherlands 1,50 0,90 4,00 9,60 6,90 2,95 4,40 Norway 1,70 1,70 1,60 2,80 5,30 3,33 3,30 New Zeland 0,90 0,90 1,50 4,10 8,10 9,00 6,70 Portugal 2,20 2,40 1,60 7,30 5,80 4,04 13,40 Spain 2,10 2,30 4,20 17,50 20,30 13,92 21,80 Sweden 1,70 1,50 1,80 2,20 7,00 5,4 7,60 United Kingdom 1,70 2,00 4,40 10,10 8,70 5,58 8,00 United States 4,40 4,70 6,10 7,20 6,00 4,00 9,10 OECD 3,50 2,80 4,30 7,00 7,30 6,1 8,2 Source: OECD Data Table 1.2 indicates that major OECD nations shared a pattern of rising unemployment from the 1960s to the 1970s to the 1980s, but the magnitude of the increases vary widely across countries, with the largest increase in Spain. In the 1990s the unemployment experience diverge somewhat, with continued increases from the 1980s in most European countries and Australia, but decline in the United States, United Kingdom, and Portugal. In the 2000s there is a general decrease of unemployment rate among all the countries, except in Italy and Japan. From 2000 to 2011 unemployment is a phenomenon that touches all the countries but in different proportion, with the largest increase in Spain and Portugal. The table highlights the distinctive aspects of the evolution of US unemployment. The United States has moved from having a consistently higher unemployment rate than the OECD as a whole in the 1950s, 1960s and 1970s to having a much lower rate in the 1990s and 2000s, but again a higher unemployment in 2011. The United States is the only major OECD economy with a lower average unemployment rate in 2000s than in 1980s: 4.0 per cent in the 2000s versus 7.2 per cent in 1980s. But the current US unemployment rate of 9.1 per cent is the highest experienced since 1980. The composition of US unemployment also differs substantially from many other OECD nations. The United States has much larger month-to-month flows into and out of employment than most of OECD economies and a much lower incidence of long-term unemployment than any advanced OECD economy. Long-term unemployment (six months and less than one year) as a percentage of total unemployment in 2011 stood at 12.43 per cent in the United States as compared with 9.8 per cent in Canada, 13.48 per cent in Australia, 18.65 per cent in France, 14.71 in Germany, 15.03 in Italy, 17.68 in Greece and 18.66 in Spain. US unemployment rates for the working-age population are particularly low (and employment/population ratios are particularly high) for young workers (those aged to 15 to 24), women and older workers (those aged 55 to 64). Overall, the US labour market does a relatively good job of moving new entrants and women into employment. European labour market institutions (especially employment protect ion laws) seem geared to keeping married males in work, but appear to make it tougher for new entrants to gain steady employment. Cyclical versus Structural unemployment The analytical discussion of unemployment since Friedman (1968) and Phelps (1968) start with the hypothesis that at any given time, a national economy is characterized by a natural rate of unemployment. Aggregate demand expansions can (at least temporarily) push the economy below this rate of unemployment, but at the cost of accelerating inflation. Similarly, shocks that raise unemployment above the natural rate lead to deceleration inflation. As long as the policy-maker avoids explosive inflation or deflation, the economy cannot remain persistently above or below the natural rate of unemployment, but it may fluctuate around it. This hypothesis suggests separating changes in unemployment into cyclical fluctuation around the natural rate and structural movement in the natural rate itself. Figure 1 Unemployment in the US, Australia, Europe and OECD Figure 1 illustrates the time patterns of the unemployment rates for the United States, Australia, Europe, and OECD countries from 1970 to 2011. The figure suggests cyclical unemployment fluctuation around a relatively stable natural rate in the United States until 2008, and a possible upward drift in the natural rate in Europe and Australia. The acceleration in inflation in most European economies in late 1980s, despite much higher unemployment rate than in the 1960s and 1970s, indicates a large rise in natural rate of unemployment. The deceleration of inflation in the 1990s and early 2000s suggests that some cyclical component has played a role in recent high European unemployment. 2 Data and Descriptive statistics I next explore in a more depth, the extent to which a relatively stable natural rate of unemployment since 1970 or so is consistent with the experience of the flexible US labour market. The data for this analysis are taken from Bureau of Labour Statistics from 1970 to 2012 (monthly data). 3 Empirical Methodology and Results For estimating the natural rate of unemployment (un) I am going to use the expectations-augmented (or accelerationist) Phillips Curve (EAPC) in which the rate of growth of price inflation (or more generally the difference between current inflation and expected inflation) depends on the deviation of the unemployment rate from the natural rate: where p is the log of the price level, u is the unemployment rate, is a positive coefficient, equals, and is an error term. Expected inflation is assumed to equal the lagged inflation rate (). A regression of the change in the inflation rate on the unemployment rate yields estimates of the natural rate of unemployment ( = -. The basic idea behind this equation is that price inflation increases when unemployment is below the natural rate and decreases when it is above. Table 2.1 Price inflation and unemployment in the United States, Europe and OECD countries United States Europe OECD (1) (2) (3) (4) (5) Constant 0.397562 0.519119 0.142052 11.87027 12.00131 [6.163198] [8.568430] [1.910330] [7.503319] [5.137325] D80 -0.348037 [0.929960] D90 -0.355382 [0.950040] D00 -0.369512 [0.986341] Unemployment rate (u) -0.006995 -0.026207 0.032498 -0.596646 -0.906432 [0.669781] [2.835975] [2.918381] [3.129660] [2.544017] Observations (n) 511 511 511 41 41 Durbin-Watson Statistic 0.798394 0.828986 0.833514 0.233627 0.304103 R2 0.006191 0.015555 0.016457 0.200734 0.142330 Notes: The US regressions cover 1970 to 2012. The dependent variable in all regressions is the inflation rate (Dp).The numbers in parenthesis are standard errors. p=100*log(CPI), using the Consumer Price Index for the United States and Europe; u is the unemployment rate measured in percentage, D80=1 for the 1980- and 0 otherwise; D90=1 for the 1990- and 0 otherwise; D00=1 for the 2000- and 0 otherwise. Estimation for US unemployment Dependent Variable: P Method: Least Squares Date: 10/04/12 Time: 17:04 Sample (adjusted): 1970M02 2012M08 Included observations: 511 after adjustments Variable Coefficient Std. Error t-Statistic Prob.  C 0.397562 0.064506 6.163198 0.0000 UNEMP -0.006995 0.010444 -0.669781 0.5033 D80 -0.348037 0.374250 -0.929960 0.3528 D90 -0.355382 0.374071 -0.950040 0.3425 D00 -0.369512 0.374629 -0.986341 0.3244 R-squared 0.006191     Mean dependent var 0.353720 Adjusted R-squared -0.001665     S.D. dependent var 0.373392 S.E. of regression 0.373702     Akaike info criterion 0.879023 Sum squared resid 70.66469     Schwarz criterion 0.920475 Log likelihood -219.5904     F-statistic 0.788056 Durbin-Watson stat 0.798394     Prob(F-statistic) 0.533265 Estimation for US male unemployment Dependent Variable: P Method: Least Squares Date: 10/04/12 Time: 17:05 Sample (adjusted): 1970M02 2012M08 Included observations: 511 after adjustments Variable Coefficient Std. Error t-Statistic Prob.  C 0.519119 0.060585 8.568430 0.0000 UNEMPMALE -0.026207 0.009241 -2.835975 0.0048 R-squared 0.015555     Mean dependent var 0.353720 Adjusted R-squared 0.013621     S.D. dependent var 0.373392 S.E. of regression 0.370840     Akaike info criterion 0.857814 Sum squared resid 69.99885     Schwarz criterion 0.874395 Log likelihood -217.1715     F-statistic 8.042753 Durbin-Watson stat 0.828986     Prob(F-statistic) 0.004751 Estimation for US female unemployment Dependent Variable: P Method: Least Squares Date: 10/04/12 Time: 17:07 Sample (adjusted): 1970M02 2012M08 Included observations: 511 after adjustments Variable Coefficient Std. Error t-Statistic Prob.  C 0.142052 0.074360 1.910330 0.0567 UNEMPFEMALE 0.032498 0.011136 2.918381 0.0037 R-squared 0.016457     Mean dependent var 0.353720 Adjusted R-squared 0.014525     S.D. dependent var 0.373392 S.E. of regression 0.370670     Akaike info criterion 0.856897 Sum squared resid 69.93471     Schwarz criterion 0.873478 Log likelihood -216.9373     F-statistic 8.516946 Durbin-Watson stat 0.833514     Prob(F-statistic) 0.003674 Estimation for Europe unemployment Dependent Variable: P2 Method: Least Squares Date: 10/04/12 Time: 17:08 Sample (adjusted): 1970M02 1973M06 Included observations: 41 after adjustments Variable Coefficient Std. Error t-Statistic Prob.  C 11.87027 1.582002 7.503319 0.0000 UNEMPEURO -0.596646 0.190642 -3.129660 0.0033 R-squared 0.200734     Mean dependent var 7.164938 Adjusted R-squared 0.180240     S.D. dependent var 3.481375 S.E. of regression 3.152057     Akaike info criterion 5.181538 Sum squared resid 387.4831     Schwarz criterion 5.265127 Log likelihood -104.2215     F-statistic 9.794774 Durbin-Watson stat 0.233627     Prob(F-statistic) 0.003308 Estimation for Europe unemployment Dependent Variable: P3 Method: Least Squares Date: 10/04/12 Time: 17:09 Sample (adjusted): 1970M02 1973M06 Included observations: 41 after adjustments Variable Coefficient Std. Error t-Statistic Prob.  C 12.00131 2.336102 5.137325 0.0000 UNEMPOECD -0.906432 0.356299 -2.544017 0.0150 R-squared 0.142330     Mean dependent var 6.186970 Adjusted R-squared 0.120338     S.D. dependent var 3.301618 S.E. of regression 3.096597     Akaike info criterion 5.146035 Sum squared resid 373.9676     Schwarz criterion 5.229624 Log likelihood -103.4937     F-statistic 6.472025 Durbin-Watson stat 0.304103     Prob(F-statistic) 0.015033 Conclusion References Literature Ronald G. Ehrenberg, Robert S. Smith Modern Labour Economics. Theory and Public Policy Pearson International Edition, 2009, Tenth Edition Internet Sources http://www.tradingeconomics.com http://www.indexmundi.com/ http://www.statcan.gc.ca/daily-quotidien/120907/dq120907a-eng.htm Eurostat Website: http://ec.europa.eu/eurostat I have a problem with the regression of this model: I have monthly data. But when I estimate it on Eviews, the results I get are not that expected: R-squared is very small (near to zero), the standard errors are all smaller than 1. In order to estimate the model first I have done this: P=100*log(CPI), but Im not sure if is right or not. I can send the data after if this description is not enough.
Friday, January 17, 2020
Individual Behaviour
ORGANISATIONAL BEHAVIOUR MSC 42102 Individual Processes Attitude and Values Organizational Behavior Submitted To :Submitted By : Dr. Pramod PathakAjit Vinod Kujur Manwendra Prakash Anshul Rawat Prateek Purty Prateeksha Maurya Individual Processes Individual behavior is how we as individuals behave ourselves. This behavior is subject to many personal traits as well as habits, values, perceptions, and other qualities and features. People make assumptions about those whom they work with, supervise, or spend time with in leisure activities.To some extent, these assumptions influence the person’s behavior towards others. Effective employees understand what affects their own behavior before attempting to influence the behavior of others. Individual behavior is the foundation of organizational performance. Understanding individual behavior, therefore, is crucial for effective management. Each person is a physiological system composed of a number of subsystems- attitudes, perception, personality, needs, values and feelings. Attitude A tendency to feel & behave in a particular way towards objects, people or events. Characteristics Remain unchanged for a long period – unless influenced by external forces * Evaluative statements – favorable or unfavorable Components * Cognitive – the opinions, values or beliefs of an individual * Affective – the feelings of a person towards something * Behavioral – the intention of a person to behave in a particular way Sources of Attitude * Attitudes are acquired by parents, teachers,& peer group members * Individuals are willing to modify their behavior & shape their attitude – to match with the opinion leader * Attitude can be changed by providing feedbackTypes of Attitude * Job satisfaction * The pleasurable or positive emotional state that results when an individual evaluates his job or job experience. Dimensions * It is an emotional response to a job. * The satisfac tion that an individual derives from his job depends on the extent to which outcomes meet his expectations. * Job satisfaction reflects other attitudes of employee. * 6 dimensions (P. C. Smith, L. M Kendall, C. L. Hulin) i. e. 1) The work 2) The pay 3) Promotion 4) Opportunity 5) Supervision 6) Co-worker 7) Working conditions Job Involvement * The extent to which person identifies himself psychologically with his job, actively participates in it & considers that his performance in the job contribute to his self worth * Organizational Commitment * An employee’s satisfaction with a particular organization & its goals OC is affected by a number of * Personal variables – employee’s age, attitude towards job. * Organizational variables – job design, leadership style of the superior. John P. Meyer & Natalie J. Allen gave 3 component model i. . * Affective commitment – It is concerned with employee’s emotional attachment & involvement with th e organization * Continuance commitment – It is influenced by the costs that could accrue to the employee if he leaves the org * Normative commitment – It refers to the extent to which an employee feels obliged to continue in the organization. Functions of Attitudes * The adjustment function * People modify their attitudes to adjust to their work environment * When fair treatment is given – positive attitude When treatment is not good – negative attitude * Ego-defensive function * Attitudes help employee to defend their self image – when mistake identified – protects their ego * The value expressive function * Values can be expressed through attitudes E. g. if mgr wants employee to work hard – may tell company has a tradition of hard work. * The knowledge function * Attitudes act as a standard of reference which allows people to understand & explain their environment. E. g. Union leader’s attitude towards management – based on past.Attitudes & Consistency * People may change their attitude – do not contradict their action * If any discrepancy arises, individuals will try to bridge the discrepancy by developing a rational explanation for the discrepancy. Cognitive Dissonance Theory – Leon Festinger (1950s) * Cognitive dissonance – Incompatibility that an individual may perceive between 2 or more of his attitudes or between his behavior and attitude. * Emotional dissonance – Conflict between the emotions an individual experiences & emotions he needs to expressAn individual can deal with dissonance either by * Sticking to his attitude * Give up his attitude * Change the attitude Values Values are enduring beliefs that a specific mode of conduct or end state of existence is personally or socially preferable to an opposite or converse mode of conduct or end state of existence. Values in the Workplace * Stable, evaluative beliefs that guide our preferences * Define right or wrong, good or bad * Value system  hierarchy of values Values and Behavior Habitual behavior usually consistent with values, but conscious behavior less so because values are abstract constructs * Decisions and behavior are linked to values when: * Mindful of our values have logical reasons to apply values in that situation * Situation does not interfere Values Congruence * Used where two or more entities have similar value systems * Problems with incongruence * Incompatible decisions * Lower satisfaction/loyalty * Higher stress and turnover Benefits of incongruence * Better decision making (diverse perspectives) * Avoids â€Å"corporate cults†Values across Cultures: Individualism and Collectivism * Degree that people value duty to their group (collectivism) versus independence and person uniqueness (individualism) * Previously considered opposites, but unrelated  i. e. possible to value high individualism and high collectivism Ethical Behavior Ethical Beh avior means acting in ways consistent with one’s personal values and the commonly held values of the organization and society. Qualities Required for Ethical Decision-making * The competence to identify ethical issues and evaluate the consequences of alternative courses of action. The self-confidence to seek out different opinions about the issue and decide what is right in terms of a situation. * Tough-mindedness – the willingness to make decisions when all that needs to be known cannot be known and when the ethical issue has no established, unambiguous solution. Values, Ethics & Ethical Behavior * Value Systems – systems of beliefs that affect what the individual defines as right, good, and fair * Ethics – reflects the way values are acted out * Ethical behavior – actions consistent with one’s values
Thursday, January 9, 2020
Essay about Preventing and Assessing Intensive Care Unit...
Abstract Delirium in the Intensive Care Unit (ICU) has become a genuine phenomenon and can be problematic for the patient and the staff caring for them. Delirium occurs when a patient is placed in an unfamiliar environment and has to endure the stress of not just the hospitalization but the stimuli of the environment, which can cause disturbances in consciousness. Patients can become confused, anxious, and agitated; making this difficult for the staff to correctly diagnosis and care for them. Sleep deprivation and environmental factors along with neurotransmitters are strongly related to the occurrence of ICU delirium. ICU staff needs to become more educated on prevention, detection, and proper treatment for the patient experiencing this†¦show more content†¦This may lead to illusions or hallucinations (Figueroa-Ramos, Arroyo-Novoa, Lee, Padilla, Puntillo, 2009). Some signs of delirium are restlessness, anxiety, hallucinations, agitation, disorientation, and any abnormal behavior. S ome causes of ICU delirium are due to drugs, stress, environmental factors, and sleep deprivation. Studies show a strong connection between sleep deprivation and delirium. Alterations in specific neurotransmitters are the basis of current research (Figueroa-Ramos, Arroyo-Novoa, Lee, Padilla, Puntillo, 2009). Enhanced assessment and nursing implementations to better prevent and detect ICU delirium will bring improved outcomes for this particular patient population. There are many ways to assess for ICU delirium. Two of the most reliable and easiest methods are basic observations from the bedside nurse and The Confusion Assessment Method (CAM). The CAM includes nine different criteria for delirium (1) acute onset and fluctuation, (2) inattention, (3) disorganized thinking, (4) altered level of consciousness, (5) disorientation, (6) memory impairment, (7) perceptual disturbances, (8) psychomotor agitation or retardation, and (9) altered sleep-wake cycle. 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