
Subpart (a):
Calculate the member of required labor.
Subpart (a):

Explanation of Solution
Number of workers required to produce one unit of goods can be calculated using the following formula.
Substitute the respective values in Equation (1) to calculate the required number of person to produce one unit of car in U.S.
Required labor to produce one unit of car in U.S. is 0.25.
Table 1 illustrates the workers required to produce a car and a ton of grain in the U.S. and the Japan that obtained by using Equation (1).
Table 1
Workers required to produce | ||
One Car | One Ton of Grain | |
U.S. | 0.25 workers | 0.10 workers |
Japan | 0.25 workers | 0.20 workers |
Concept introduction:
Subpart (b):
Draw the production possibility frontier.
Subpart (b):

Explanation of Solution
Figure 1 shows the productive capacity of two countries.
In Figure 1, the horizontal axis measures the quantity of grains produced by both the countries and the vertical axis measures the quantity of cars produced. If either economy, that is, the U.S. or Japan devotes all of its 100 million workers in producing cars each economy can produce 400 million cars in a year
Concept introduction:
Production Possibility Frontier (PPF): PPF refers to the maximum possible combinations of output of goods or services that an economy can attain by efficiently utilizing and employing full resources.
Subpart (c):
Calculate the opportunity cost.
Subpart (c):

Explanation of Solution
Opportunity cost of a car for the U.S. is calculated as follows.
Thus, the opportunity cost of a car for the U.S. is 2.5 tons of grains.
Opportunity cost of a car for Japan is calculated as follows.
Thus, the opportunity cost of a car for Japan is 1.25 tons of grains.
Opportunity cost of producing a ton of grains in the U.S. is calculated as follows
Thus, the opportunity cost of producing a ton of grains in the U.S. is 0.4 units of cars.
Opportunity cost of producing a ton of grains in Japan is calculated as follows.
Thus, the opportunity cost of producing a ton of grains in Japan is 0.8 units of cars.
The results can be tabulated in Table 2 below.
Table 2
Opportunity Cost | ||
One Car | One Ton of Grain | |
U.S. | 2.5 tons of grains | 0.4 units of car |
Japan | 1.25 tons of grains | 0.8 units of car |
Concept introduction:
Opportunity cost: Opportunity cost is the cost of a foregone alternative, that is, the loss of other alternative when one alternative is chosen.
Subpart (d):
Find the country that has absolute advantage in the production of goods.
Subpart (d):

Explanation of Solution
Neither of these countries has an absolute advantage in producing cars. This is because they are equally productive in the production of a car (4 cars per worker per year). However, in the production of grains, the United States has an absolute advantage because it is more productive than Japan. The U.S. can produce 10 tons of grains per worker per year; whereas Japan can produce only 5 tons of grains per worker per year.
Concept introduction:
Absolute advantage: It is the ability to produce a good using fewer inputs than another producer.
Subpart (e):
Find the country that has absolute advantage in the production of goods.
Subpart (e):

Explanation of Solution
Japan has a
Concept introduction:
Comparative advantage: It refers to the ability to produce a good at a lower opportunity cost than another producer.
Subpart (f):
Calculate the total production before the trade.
Subpart (f):

Explanation of Solution
Without trade and with half the workers in each country producing each of the goods, the United States would produce 200 million cars
Concept introduction:
Trade: The trade refers to the exchange of capital, goods, and services across different countries.
Subpart (g):
Gains from trade for the U.S. and Japan.
Subpart (g):

Explanation of Solution
Firstly, consider the situation without trade in which each country is producing some cars and some grains. Suppose the United States shifts its one worker from producing cars to producing grain, then that worker would produce 4 cars and 10 additional tons of grain. Now suppose, with trade, the United States offers to trade 7 tons of grain to Japan for 4 cars. The United States would encourage this because the cost of producing 4 cars in the United States is 10 tons of grain. So by trading, the United States can gain 4 cars for a cost of only 7 tons of grain. Hence, it is better off by 3 tons of grain.
The same is applicable for Japan, if Japan changes one worker from producing grain to producing cars. That worker would produce 4 more cars and 5 fewer tons of grain. Japan will take the trade because Japan will be better off by 2 tons of grain.
So with the trade and the change of one worker in both the United States and Japan, each country gets the same amount of cars as before but gets additional tons of grain (3 tons of grains for the United States and 2 tons of grains for Japan) making both countries better off.
Concept introduction:
Trade: The trade refers to the exchange of capital, goods, and services across different countries.
Want to see more full solutions like this?
Chapter 3 Solutions
Bundle: Principles of Macroeconomics, Loose-Leaf Version, 7th + Aplia, 1 term Printed Access Card
- Choose all correct answers regarding the estimated multiple linear regression model: ŷ = ߸ + ß‚× + ². Assume that ẞ, and ẞ are both non-zero. 0 1 (a) The estimated regression function is represented by a plane in three-dimensional space. (b) It gives the value of y, for each value of x, such that half the values of y are above the function and half are below. (c) The predicted value of y with x₁ = 1 and x2 = 1 is ẞ + Â₁ + Â₂ 0 (d) If you use robust option in Stata, then ẞ, becomes either smaller or larger in magnitude.arrow_forwardDetermine whether the following statement is true or false. In a simple linear regression, y = ẞo + ẞ₁x+u, if E[ulx] = 0, the estimate of ẞ₁ is statistically significant. Explain your answer in two sentences or fewer.arrow_forward-.5 y .5 0 Choose all the correct answers based on the figure below T T -2 0 2 4 X (a) The coefficient of x is not statistically significant (b) The explained sum of squares is less than the total sum of squares (c) The independent variable is a dummy variable (d) The assumption of homoskedasticity is not likely to holdarrow_forward
- Determine whether the following is true or false: In a multiple linear regression, y = ẞ₁ + ẞ₁x, + ẞ₂×₂ + u, 0 if corr(x,, x) = 1, then ẞ, cannot be estimated, because the numerator in the formula for ß, is zero. Explain your answer in two sentences or fewer.arrow_forwardChoose all correct answers regarding assumptions for B₁ in a simple linear regression. (a) If there is no variation in X, we cannot identify the estimate of ẞ₁, so E[ẞ₁] = ẞ, does not hold. (b) It measures the y-intercept of the estimated regression line. (c) As the sample size increases, ẞ₁ is more likely to be closer to ẞ, assuming E[ß₁] = ß₁. (d) If the sample covariance between x and y is positive, then B₁ > 0.arrow_forwardChoose all correct answers regarding the multiple linear regression model: y = Bo+ B₁x₁ + B2X2 + + BkXk + U. (a) If you add a relevant variable XK+1, but corr(XK, Xk+1) is high, then you must not include xk+1 in the regression equation. (b) Adding a variable XK+₁ may lower the R² value. (c) Adding all the variables in the dataset does not imply that E[u | X1, X2, ... Xk] = 0. (d) We can estimate the value of ẞ₁ by minimizing Σ(û¡)².arrow_forward
- Refer to the Stata output from an analysis of data on Store sales. The data include nominal store-level sales (measured in for the month of June 1996 from 844 stores of a national retail chain. The other variables are: A categorical string variable loctype which indicates the type of store location and takes one of three values: "mall" = 1 if the store is located in a shopping mall (indoor or outdoor) "street" = 1 if the store is located on a street "strip" = 1 if the store is located on a strip or highway ⚫ medhhinc = the median household income of the city where the store is located, measured in $1,000 • empl = the total number of employees working in the store in June 1996 . sqft = the store's size, measured in 1,000 square feet A manager wants to know how average monthly sales varies with the characteristics of a store and its location and uses Stata to perform the regression analysis shown. gen sqftXmedhhinc =sqft*medhhinc . reg sales mall street medhhinc empl sqft sqftXmedhhinc,…arrow_forwardWe would like to examine whether there is a differential effect of study hours on test scores between students whose first language is English and those whose first language is not English by adding an interaction term. gen hoursEng = study_hours *English . reg test_score study_hours English hoursEng Source SS df MS Number of obs 876 Model Residual 136565.839 18952.9115 3 45521.9464 872 21.7349902 F(3, 872) Prob F 2094.41 0.0000 R-squared 0.8781 Total 155518.751 875 177.735715 Adj R-squared = Root MSE 0.8777 = 4.6621 test_score Coef. Std. Err. t P>|t| [95% Conf. Interval] study_hours English hoursEng 1.82049 .0369748 49.24 0.000 8.318905 .8108202 10.26 0.000 .101876 cons 43.52037 .0516819 .5767457 1.97 75.46 0.049 0.000 1.74792 6.727517 .0004404 42.3884 1.89306 9.910292 .2033115 44.65234 (a): What is the predicted test score for an individual who studied two hours and whose first language is not English? (Round to two decimal places) (b): Choose all correct answers (a) The differential…arrow_forwardDetermine whether the following statement is true or false. Suppose you reject the null hypothesis: Ho: ẞ₁ = ẞ₂ = 0. Then, both ẞ₁ = 0 and ẞ2 ± 0. Explain your answer in two sentences or fewer.arrow_forward
- Choose one correct answer: The ---A----- dataset includes 100,000 students from 512 schools observed over 12 months. To control differences between schools and months, we should add a total of What are A and B? (a) A: Time series, B: 524 (b) A: Time series, B: 522 (c) A: Panel, B: 524 (d) A: Panel, B: 522 dummies.arrow_forwardRefer to the Stata output from an analysis of data on Store sales. The data include nominal store-level sales (measured in for the month of June 1996 from 844 stores of a national retail chain. The other variables are: A categorical string variable loctype which indicates the type of store location and takes one of three values: "mall" = 1 if the store is located in a shopping mall (indoor or outdoor) "street" = 1 if the store is located on a street "strip" = 1 if the store is located on a strip or highway ⚫ medhhinc = the median household income of the city where the store is located, measured in $1,000 • empl = the total number of employees working in the store in June 1996 . sqft = the store's size, measured in 1,000 square feet A manager wants to know how average monthly sales varies with the characteristics of a store and its location and uses Stata to perform the regression analysis shown. gen sqftXmedhhinc =sqft*medhhinc . reg sales mall street medhhinc empl sqft sqftXmedhhinc,…arrow_forwardEssay question. In this regression analysis, you are interested in the effect of age on the amount of gasoline used by a car, measured in gallons. Suppose you analyze your dataset and find the following results. Based on the Stata output, how can you improve the analysis? State 3 issues in the figure and table, and possible solution for each problem you can apply based on what you have learned in class. gasoline_use 40 20 100 .reg gasoline_use age Source SS df MS Model Residual 28.1117271 9969.5248 1 28.1117271 23 433.4576 Number of obs F(1, 23) Prob > F R-squared 25 0.06 0.8012 0.0028 a Total 9997.63653 24 416.568189 Adj R-squared Root MSE -0.0405 20.82 gasoline_use Coef. Std. Err. t P>|t| [95% Conf. Interval] age cons -.0688645 .2704114 62.37748 13.86839 -0.25 0.801 4.50 0.000 -.6282532 .4905242 33.68853 91.06643 20 40 60 80 agearrow_forward
- Brief Principles of Macroeconomics (MindTap Cours...EconomicsISBN:9781337091985Author:N. Gregory MankiwPublisher:Cengage LearningEssentials of Economics (MindTap Course List)EconomicsISBN:9781337091992Author:N. Gregory MankiwPublisher:Cengage Learning





