If the marginal product of labor is always positive, the total revenue will grow with each additional worker. Firms do not continuously hire new workers because:

Amathematical approximation called the rule of 70 tells us that the number of years that it will take something that is growing to double in size is approximately equal to the number 70 divided by its percentage rate of growth. thus, if mexico's real gdp per person is growing at 7 percent per year, it will take about 10 years(=70/7) to double. apply the rule of 70 to solve the following problem. real gdp per person in mexico in 2005 was about $11,000 per person, while it was about $44,000 per person in the u.s. if real gdp per person in mexico grows at the rate of 5 percent per year: how long will it take mexico's real gdp per person to reach the level that the united states was at in 2005? (hint: how many times would mexico's 2005 real gdp per person have to double to reach the u.s.'s 2005 real gdp per person?

Ajustification for job training programs is that they improve worker productivity. suppose that you are asked to evaluate whether more job training makes workers more productive. however, rather than having data on individual workers, you have access to data on manufacturing firms in ohio. in particu- lar, for each firm, you have information on hours of job training per worker (training) and number of nondefective items produced per worker hour (output). (i) carefully state the ceteris paribus thought experiment underlying this policy question. (ii) does it seem likely that a firm’s decision to train its workers will be independent of worker characteristics? what are some of those measurable and unmeasurable worker characteristics? (iii) name a factor other than worker characteristics that can affect worker productivity. (iv) if you find a positive correlation between output and training, would you have convincingly established that job training makes workers more productive? explain.

True or false: a fitted model with more predictors will necessarily have a lower training set error than a model with fewer predictors.