The Impact of Education, Technology, and Skills on Employment and Wages, Exercises of Economics of Education

Download The Impact of Education, Technology, and Skills on Employment and Wages and more Exercises Economics of Education in PDF only on Docsity! Lecture 10: Why has the rate of return to education increased? What skills are now rewarded in the workplace? Questions from last lecture: Q: Why is unemployment higher in Europe than in the US, considering that the two regions have similarly educated populations and technology? A: We’ll look at this later, especially in the context of rigid labor market institutions. Q: Why are sales jobs to prevalent? A: We will discuss this later on in this lecture. Inductive Rules: How do financial institutions rate mortgage applicants? They use a scoring method to predict how likely it is that an applicant will default. Independent variables in the model- Income, years out of school, whether or not it’s a first house, credit rating. Dependent variable- A dummy variable equal to 1 if the customer defaults within the first 4 years, 0 otherwise. iiiiii ngCreditRatiFirstHouseSchoolYearsOutofincomey εββββα +++++= )()()()( 43210 A mortgage officer can apply the calculated coefficients to a mortgage applicant’s data to get a probability that the applicant will default. The regression equation is an inductive rule that simulates what a mortgage application officer used to do in the past. If we think of wages as a proxy for skill: you’d assume that the greatest substitution of computers for labor would occur for the lowest wage jobs. However, when you incorporate an understanding of the ease of programming different jobs, the jobs that are the easiest to program follow a set of deductive rules, which are the low to middle skilled jobs. Why? Computers don’t substitute for the lowest wage/skilled jobs like janitorial work and other very low skilled jobs that are often trivial for humans because these tasks are outside the bounds of computerization (for now). What does this story imply for human interaction jobs such as sales positions? Sales people are good at persuasion and marketing. Say a sales person tells you: “You look great in those lime green pants you’re wearing.” Is this true? Maybe, but the sales person wants you to buy the pants and has little incentive to tell you the truth. All information is inherently ambiguous. Humans need to put information into context to understand it. docsity.cmo For example, cars salesmen usually don’t have a set desk in the showrooms they work in. However, the salesman always puts a picture of a wife and children on his desk. Why? To make customers trust him more by portraying himself as a family man. Words themselves must be understood in context. Example: bill. What does it mean? A piece of legislation? A man’s name? A part of a duck? It all depends on the context. What is the relationship between the computerization and off-shoring of work? Jobs that can be easily computerized can be easily offshored. Why? Because the process of off-shoring and computerizing are similar: a job is broken down into small, routine, and repeatable tasks. Examples of off-shoring: -Simple tax returns are sent to overseas accountants -Call centers Examples of computerization: -TurboTax and other tax preparation software -Speech recognition software such as the MIT general number speech interface What about software work? The rules of computer programming allow overseas workers to perform these tasks. Overall, there has been growth in high skilled jobs and very low skilled jobs but loss of jobs in low to middle skilled jobs. What does this say about education? Do we still need basic skills? What about reading? Definitely. People need reading more now than ever before. Even the lower skilled jobs require people to be able to read instruction manuals that explain the tasks that they need to perform. Ted Schultz: A Chicago economists who worked on human capital theory. He studied 2 sets of agrarian villages in India during the Green Revolution. Set 1: Results showed that literacy and basic math skills lead to much higher crop yields Set 2: Results showed that literacy and basic math skills lead to no difference in crop yields What account for these results? Perhaps access to information and technology. Perhaps soil quality. In reality, Set 1 had access to new Green Revolution seeds whereas Set 2 did not. These needs required farmers to use a new set of farming techniques. There were only a few farm agents traveling around India to explain and show these new techniques so being literate and math able allowed certain farmers in Set 1 to use docsity.cmo