Category: Economics

Run the regression of log(psoda) on prpblck, log(income), and prppov, using only the observations where there are no missing data. The estimated equation is: log(psoda)^ = (-1.4387) + 0.0795 prpblck + 0.1346 log(income) + 0.3688 prppov. True or False: β1^ is statistically different from zero at both the 5% and 1% significance levels against the two-sided alternative. True False
The correlation between log(income) and prppov is approximately (-0.160), indicating some degree of multicollinearity. The two-sided p-value for testing H0: β2^ = 0 is approximately 0, indicating that β2^ is statistically significant at the 5% significance level and is statistically significant at the 1% significance level. The two-sided p-value for testing H0: β3^ = 0 is approximately 0.0076, indicating that β3^ is statistically significant at the 5% significance level and is statistically significant at the 1% significance level.
Run the regression of log(psoda) on prpblck, log(income), prppov, and log(hseval) using only the observations where there are no missing data. The estimated equation is: log(psoda)^ = ( ) + β1 prpblck + β2 log(income) + β3 prppov + β4 log(hseval). The two-sided p-value for testing H0: β4^ = 0 is approximately ( ), indicating that β4^ is statistically significant at the 5% significance level and statistically significant at the 1% significance level.
True or False: After adding the variable log(hseval) into the regression model, the coefficient on prppov is statistically significant from 0 at the 5% significance level, but the coefficient on log(income) is not. True False
Use the test for H0: β2 = 0, β3 = 0 against H1: β2 ≠ 0 OR β3 ≠ 0 to answer the following questions. The F-statistic is ( ). The p-value is approximately ( ). True or False: The variables log(income) and prppov are not jointly significant at the 5% significance level. True False
True or False: The regression model including log(hseval): log(psoda) = β0 + β1 prpblck + β2 log(income) + β3 prppov + β4 log(hseval) + u appears to be more reliable than the regression model excluding log(hseval): log(psoda) = β0 + β1 prpblck + β2 log(income) + β3 prppov + u when it comes to determining whether the racial makeup of a ZIP code influences local fast-food prices. True False

Expectations in Macroeconomics7that you are forming an expectation ‘of what the rate of inflation will be this year. And imagine that the inflation rate you were expecting for last year was 10 per cent. If last year the actual rate of inflation was 10 per cent the adaptive expectations hypothesis predicts that you will not change your expectation about the inflation rate for this year: you will expect 10 per cent for this year too.
But if last year the inflation rate was higher than 10 per cent, say 20 per cent, the hypothesis suggests that you will change your expectation of inflation for this year; more precisely, it suggests that you will raise your expectation above 10 per cent, although not necessarily all the way up to 20 per cent. And if the inflation rate last year was below 10 per cent, at say 4 per cent, it suggests that you will lower your expectation for this year to somewhere between 10 and 4 per cent. The hypothesis does not predict the exact amount by which you raise or lower your expectation: that will differ from case to case and can only be determined empirically. But the general idea is clear enough. People will change their expectation of any variable if there is a difference between what they were expecting it to be last period and what it actually was last period.Specifically, they will raise their expectation if the actual value last period was higher than they were expecting, and they will lower their expectation if the actual value last period was below what they were expecting. If their expectation last period turned out to be correct they will not change their expectation.
This idea that people will adapt or change their expectations in response to last period’s forecast error has a number of attractive features. First, it implies that if, for example, the inflation rate rises from 5 to 20 per cent and remains there, people’s expectations of inflation will gradually rise (if, that is, they were initially expecting inflation to be less than 20 per cent), until they have ‘homed in’ on the new rate of 20 per cent. Similarly, if actual inflation falls back again to 5 per cent and remains there then, once again, people’s expectations will gradually fall until they have homed in on the new rate of 5 per cent. So the adaptive expectations hypothesis has the appealing feature that while people can be fooled temporarily by the type of changes that we have assumed in the inflation rate, they will not be fooled in the longer run. It may, of course, take some time for people to adapt their expectations fully, but eventually they will catch on.wari Photo Copy Delhi school of Economics North Campus University Of Delhi , mob- 9810867

(i) Run the regression of log(psoda) on prpblck, log(income), and prppov, using only the observations where there are no missing data. The estimated equation is: log(psoda)ˆ = logpsoda^ = + prpblck + log(income) + prppov. True or False: β1ˆ is statistically different from zero at the 5% significance level but is not statistically different from zero at the 1% significance level against the two-sided alternative. True False
(ii) The correlation between log(income) and prppov is approximately , indicating some degree of . The two-sided p-value for testing H0: β2ˆ = 0 is approximately , indicating that β2ˆ is statistically significant at the 5% significance level and statistically significant at the 1% significance level. The two-sided p-value for testing H0: β3ˆ = 0 is approximately , indicating that β3ˆ is statistically significant at the 5% significance level and statistically significant at the 1% significance level.
(iii) Run the regression of log(psoda) on prpblck, log(income), prppov, and log(hseval) using only the observations where there are no missing data. The estimated equation is: log(psoda)ˆ = logpsoda^ = + prpblck + log(income) + prppov + log(hseval). The two-sided p-value for testing H0: β4ˆ = 0 is approximately , indicating that β4ˆ is statistically significant at the 5% significance level and statistically significant at the 1% significance level.
(iv) True or False: After adding the variable log(hseval) into the regression model, both the coefficient on log(income) and the coefficient on prppov are not statistically different from 0 at the 5% significance level. True False
Use the test for H0: β2 = 0, β3 = 0 against H1: β2 ≠ 0 OR β3 ≠ 0 to answer the following questions. The F-statistic is . The p-value is approximately . True or False: The variables log(income) and prppov are jointly significant at the 5% significance level. True False
(v) True or False: The regression model including log(hseval): log(psoda) = β0 + β1 prpblck + β2 log(income) + β3 prppov + β4 log(hseval) + u appears to be less reliable than the regression model excluding log(hseval): log(psoda) = β0 + β1 prpblck + β2 log(income) + β3 prppov + u when it comes to determining whether the racial makeup of a ZIP code influences local fast-food prices. True False

Hudson Valley Goods, a family-owned company in upstate New York, had built a reputation for high-quality artisanal food products since its founding in the late 1960s.
By the 2010s, the business had grown into a national supplier, benefiting from a strong economy marked by steady GDP growth, low unemployment, and robust consumer spending. Taking advantage of low interest rates, the company expanded by financing new factories, upgrading equipment, and broadening its product lines.
This period of growth, however, relied on the assumption that favorable economic conditions would continue indefinitely. By the early 2020s, inflation began rising sharply. Costs for raw materials such as dairy and wheat climbed, as did wages due to a tight labor market. Hudson Valley faced difficult decisions: raising prices to cover costs risked alienating price-sensitive customers, while maintaining current prices eroded profit margins.
Consumer spending also shifted as families cut back on discretionary items, including luxury artisanal goods, due to rising living expenses. Simultaneously, a labor shortage strained operations. Despite offering competitive wages, the company struggled to retain workers, many of whom sought less stressful jobs in the gig economy.
The Federal Reserve’s response to inflation—raising interest rates—compounded the company’s difficulties. Hudson Valley’s variable-rate loans, once manageable, became increasingly costly to service, eating into cash reserves. Meanwhile, slowing GDP growth signaled broader economic challenges. Higher interest rates also curtailed access to affordable credit, further limiting the company’s ability to invest or adapt.
By 2023, Hudson Valley Goods was at a crossroads. Mounting debt, shrinking demand, and operational challenges forced tough choices. Leadership explored layoffs, production cuts, asset sales, or merging with a larger competitor. Despite the Federal Reserve’s inflation control measures and limited fiscal policy support, the company’s financial position remained precarious, illustrating the challenges businesses face in adapting to volatile economic conditions.
As Hudson Valley Goods continues to face labor shortages, which government financial policy change could most directly address their hiring challenges?

1. Welfare effects of free trade in an exporting country
The following problem analyzes the Russian market for pumpkins. The graph below shows the domestic supply and demand curves for pumpkins in Russia. Assume that Russia’s government does not currently permit international trade in pumpkins. Use the black point (plus symbol) to denote the equilibrium price of one ton of pumpkins and the equilibrium quantity of pumpkins in Russia without international trade. Next, use the green triangle (triangle symbol) to shade in the area that represents consumer surplus in equilibrium. Finally, use the purple triangle (diamond symbol) to shade in the area that represents producer surplus in equilibrium. Based on the information from the previous graph, absent international trade total surplus is
The following graph shows the same domestic supply and demand curves for pumpkins in Russia. Now, suppose that the Russian government changes its stance on international trade, deciding to allow free trade in pumpkins. The horizontal black line (P
W
) represents the world price of pumpkins at $1,000 per ton. Assume that Russia’s entry into the world market for pumpkins has no effect on the world price and there are no transportation or transaction costs associated with international trade in pumpkins. Also assume that domestic suppliers will satisfy domestic demand as much as possible before any exporting or importing takes place. Use the green triangle (triangle symbol) to shade in the area representing consumer surplus, and then use the purple triangle (diamond symbol) to shade in the area representing producer surplus.
When Russia adjusts its trade policy to allow free trade of pumpkins, the price of one ton of pumpkins in Russia becomes $1,000. At this price, _____ tons of pumpkins will be demanded in Russia, _____ tons will be supplied by domestic suppliers. Therefore, Russia will export _____ tons of pumpkins.
Using the information from the previous tasks, complete the following table to analyze the welfare effect of allowing free trade.
With Free Trade (Dollars)
Without Free Trade (Dollars)
Consumer Surplus
Producer Surplus
When Russia allows free trade, the country’s producer surplus _____ by _____, and consumer surplus by _____ . Therefore, the net effect of allowing international trade on Russia’s total surplus is a _____ of