Fixed Capital (Current U.S. Dollars) Labor Force GDP (Current U.S. Dollars) K Country Name L Y Afghanistan 2,190,816,336.48 8,720,341.85 14,213,670,485.31 Albania 3,489,305,719.30 1,478,120.92 12,118,583,126.00 Algeria 52,938,380,711.04 10,911,506.52 138,119,949,894.70 Angola 11,487,362,683.01 6,842,908.86 75,492,385,927.77 Argentina 64,243,419,938.65 18,357,011.68 307,081,774,895.42 Armenia 3,148,616,218.89 1,421,419.66 8,648,015,305.00 Australia 261,608,094,311.48 11,635,867.07 924,197,418,942.73 79,043,848,450.29 4,332,722.39 381,775,164,862.34 Azerbaijan 8,335,406,817.62 4,510,050.32 44,291,490,420.50 Bahamas, The 1,901,530,000.00 192,896.38 7,717,078,000.00 Bahrain 5,137,288,694.56 649,394.20 19,318,822,540.67 21,778,968,023.26 70,773,866.46 89,359,767,441.86 807,049,292.48 158,052.67 3,595,210,912.50 Belarus 17,689,765,190.89 4,454,425.22 49,271,267,252.20 Belgium 98,879,894,643.69 4,802,611.88 473,419,898,505.65 1,662,801,311.35 3,500,390.31 6,585,116,881.91 Bhutan 522,644,081.80 352,699.45 1,264,687,048.13 Bolivia 2,857,502,564.10 4,467,268.99 17,339,992,191.23 Bosnia and Herzegovina 3,139,727,134.71 1,454,556.29 17,082,889,409.76 Botswana 3,324,687,939.03 1,015,140.57 11,536,926,822.00 293,012,556,839.51 99,958,636.59 1,621,661,507,655.08 1,884,229,341.40 191,134.40 10,732,435,033.69 Bulgaria 14,021,676,263.60 3,597,908.27 48,568,714,011.52 Burundi 341,767,873.27 4,167,504.34 1,815,182,228.41 2,093,453,058.93 7,793,044.94 10,401,935,531.82 285,190,272,067.19 18,793,899.71 1,337,577,639,751.55 Cape Verde 625,393,231.44 218,192.48 1,600,829,245.80 Central African Republic 213,051,902.61 2,014,106.88 1,980,151,889.30 Chad 2,236,347,457.63 4,299,500.76 7,084,745,762.71 Chile 37,489,947,359.41 7,560,947.95 172,590,595,086.12 China 2,293,994,143,484.63 793,880,406.52 4,991,256,406,734.99 Colombia 48,141,831,046.37 21,581,574.80 236,164,279,710.84 Comoros 66,372,056.25 235,799.48 535,336,307.67 Congo, Dem. Rep. 3,217,322,062.60 24,464,251.99 11,204,008,248.87 Congo, Rep. 2,130,145,437.74 1,642,627.76 9,593,536,719.02 Costa Rica 6,486,748,268.65 2,136,018.20 29,397,499,977.25 Cote d'Ivoire 2,617,188,989.08 7,603,567.40 23,041,767,439.10 Croatia 15,781,061,905.03 1,992,009.03 63,435,948,447.17 Cyprus 4,844,401,222.56 571,275.00 23,542,650,736.32 Czech Republic 48,660,283,315.84 5,299,941.18 196,181,794,333.68 Denmark 58,010,360,258.69 2,968,828.14 311,113,494,638.00 6,872,508,361.20 4,363,000.60 46,788,255,295.43 Ecuador 12,599,141,000.00 6,679,446.13 52,021,861,000.00 Egypt, Arab Rep. 35,740,514,075.89 26,427,506.94 188,984,088,127.30 El Salvador 2,775,600,000.00 2,540,514.93 20,661,000,000.00 Equatorial Guinea 7,355,783,232.18 357,159.18 12,232,619,307.83 Estonia 4,129,833,666.51 700,741.40 19,225,808,565.20 Ethiopia 7,183,596,088.28 39,508,742.86 31,963,408,038.48 Finland 47,239,467,495.00 2,692,124.95 239,660,592,657.01 France 510,507,152,254.03 29,904,291.16 2,619,685,000,757.11 Gabon 2,958,757,674.27 570,091.48 10,946,387,018.82 176,764,654.61 727,658.63 983,436,619.72 1,649,421,024.63 2,343,212.28 10,766,836,276.56 568,540,640,111.80 42,179,953.42 3,298,635,952,561.88 Ghana 5,122,228,942.09 10,077,810.35 25,978,537,278.72 Greece 61,320,344,126.43 5,238,437.76 321,795,189,413.53 5,709,104,631.96 5,492,468.05 37,733,791,088.96 Guinea 885,393,854.43 3,979,447.01 4,164,722,264.04 Guyana 519,696,003.92 296,794.02 2,025,565,089.48 3,182,662,799.89 2,889,181.10 14,123,452,608.40 Hong Kong SAR, China 41,638,329,162.26 3,707,358.84 209,283,263,242.09 Hungary 26,169,607,512.05 4,269,883.58 126,631,684,032.78 Iceland 1,676,802,466.18 186,727.25 12,094,665,798.46 India 430,354,325,605.22 471,872,607.01 1,361,057,169,926.85 Indonesia 167,891,365,108.94 116,407,259.69 539,579,959,052.70 Ireland 35,090,625,428.04 2,151,325.78 223,099,498,857.39 Israel 32,546,601,225.75 3,101,261.21 194,866,363,197.11 Italy 409,367,294,218.32 25,141,681.80 2,111,148,008,711.62 2,546,946,006.75 1,228,415.35 12,440,944,881.89 1,047,241,586,788.94 66,472,480.33 5,035,141,567,658.90 5,991,830,985.92 1,537,351.36 23,820,013,058.62 Austria Bangladesh Barbados Benin Brazil Brunei Darussalam Cambodia Canada Dominican Republic Gambia, The Georgia Germany Guatemala Honduras Jamaica Japan Jordan Kazakhstan 32,045,550,508.47 8,646,150.45 115,306,081,355.93 5,850,513,600.85 14,983,191.40 30,580,367,979.31 242,545,793,426.42 24,547,227.60 834,060,441,840.98 19,039,576,882.85 1,316,945.49 105,911,338,608.40 Kyrgyz Republic 1,341,263,366.93 2,485,145.05 4,690,029,460.85 Lao PDR 1,766,525,829.68 3,083,576.71 5,832,882,921.51 Latvia 5,551,378,560.13 1,192,086.19 25,875,781,250.00 Lebanon 11,540,961,857.38 1,428,601.44 34,924,709,784.41 Lesotho 478,823,428.68 876,411.64 1,711,350,115.48 Liberia 216,307,800.00 1,317,799.25 879,300,000.00 Lithuania 6,307,137,681.16 1,638,980.76 36,846,183,172.30 Luxembourg 9,925,497,643.24 235,193.96 51,945,493,670.15 Macao SAR, China 3,995,249,250.09 326,137.31 21,312,049,797.83 Macedonia, FYR 1,856,473,608.07 935,983.38 9,313,573,964.98 Madagascar 2,797,269,390.11 9,812,380.12 8,487,968,571.88 Malawi 1,032,596,383.50 6,506,181.07 4,727,588,360.52 Malaysia 39,012,493,336.37 11,748,309.30 192,911,631,102.08 1,217,070,336.33 176,626.54 8,099,400,960.98 744,690,624.78 1,079,913.57 3,027,020,111.52 2,328,859,781.46 578,459.63 8,825,248,711.74 188,207,418,891.14 47,938,339.12 882,354,745,911.03 Moldova 1,229,117,159.93 1,262,365.73 5,439,439,763.81 Mongolia 1,324,223,670.51 1,152,774.41 4,583,834,427.36 Morocco 28,072,111,207.65 11,324,686.72 90,908,402,631.25 Mozambique 2,004,380,879.14 10,826,464.90 9,674,140,562.54 Namibia 2,277,082,841.63 903,613.59 8,930,729,289.59 Kenya Korea, Rep. Kuwait Malta Mauritania Mauritius Mexico Nepal 2,754,718,705.13 15,591,278.24 12,900,039,159.38 Netherlands 154,311,426,241.56 9,029,032.39 793,429,956,254.35 New Zealand 23,028,198,256.25 2,322,054.74 117,376,308,375.00 1,688,780,820.40 2,313,085.50 6,213,677,482.79 Norway 81,989,971,900.33 2,604,580.84 374,757,527,038.18 Pakistan 26,886,758,489.41 58,041,388.56 161,819,031,346.25 Panama 5,932,000,000.00 1,616,321.60 24,080,100,000.00 Papua New Guinea 1,463,589,073.00 2,952,815.45 7,914,594,202.90 Paraguay 2,160,172,454.76 3,011,276.55 14,295,003,243.21 Peru 29,004,183,278.03 15,220,208.10 126,923,120,548.98 Philippines 32,007,096,949.05 37,791,477.79 168,333,540,385.11 Poland 91,230,729,784.30 17,907,013.79 430,878,337,232.78 Portugal 48,106,878,861.09 5,579,477.93 234,083,821,055.43 9,688,496,000.00 1,371,663.19 95,211,400,000.00 Nicaragua Puerto Rico Qatar 38,684,614,269.73 1,184,647.06 97,583,513,671.18 Romania 48,876,909,249.49 10,147,324.87 161,110,320,401.40 268,765,575,624.68 75,757,632.39 1,222,648,134,225.45 1,134,653,321.56 5,075,365.28 5,252,683,091.64 89,421,333,333.33 9,256,356.16 376,693,333,333.33 Senegal 3,562,562,408.44 5,221,302.30 12,769,040,890.45 Serbia 9,200,091,956.69 3,652,272.97 40,147,697,712.07 285,818,050.63 2,208,537.92 1,856,392,961.88 Singapore 45,659,726,108.08 2,719,444.49 175,934,878,515.68 Slovak Republic 18,095,069,463.74 2,705,653.79 87,239,747,151.99 Slovenia 11,485,872,503.47 1,039,719.45 49,056,152,691.03 South Africa 64,127,082,284.73 18,433,205.05 283,012,416,481.16 349,358,123,924.25 23,106,628.37 1,455,638,243,339.17 Sri Lanka 9,982,947,624.85 8,573,246.05 42,067,965,895.25 St. Lucia 329,461,315.93 88,773.12 1,105,422,525.07 Russian Federation Rwanda Saudi Arabia Sierra Leone Spain St. Vincent and the Grenadines 158,051,851.85 53,454.80 672,305,502.99 11,932,044,403.51 10,517,716.36 54,633,362,293.66 325,372,435.68 359,630.21 2,949,723,996.13 Sweden 72,986,952,920.24 4,932,800.44 405,782,994,635.36 Switzerland 99,448,301,286.60 4,473,946.80 492,261,743,767.35 9,672,222,445.34 5,284,892.43 53,934,534,350.51 855,163,057.91 2,777,018.07 4,978,154,343.79 Tanzania 6,075,055,960.49 21,536,457.19 21,368,165,399.61 Thailand 63,586,524,833.57 38,638,033.98 263,505,029,603.95 Togo 505,133,337.78 2,855,941.58 3,156,082,168.30 Tonga 82,028,423.84 41,479.14 317,974,679.65 Tunisia 10,520,625,046.29 3,749,457.66 43,522,032,141.01 Turkey 103,689,053,248.78 25,574,890.34 614,553,921,823.29 Turkmenistan 9,418,515,789.47 2,116,620.51 18,650,526,315.79 Uganda 3,776,869,644.67 12,994,413.12 15,803,499,656.86 Ukraine 21,517,096,211.11 23,214,637.09 117,227,769,791.56 United Arab Emirates 60,245,610,978.90 4,524,065.67 270,334,929,437.51 325,980,380,702.24 31,693,358.22 2,171,386,109,462.41 2,115,100,000,000.00 157,816,053.52 13,863,600,000,000.00 Uruguay 5,872,119,845.70 1,692,126.10 30,497,048,979.37 Uzbekistan 8,565,192,205.35 11,762,701.04 32,816,828,372.98 72,098,015,370.28 13,072,388.20 329,418,979,506.29 Sudan Swaziland Syrian Arab Republic Tajikistan United Kingdom United States Venezuela, RB Vietnam 33,549,557,820.31 50,190,071.82 97,180,304,813.44 Yemen, Rep. 3,403,833,506.75 6,208,351.73 25,130,088,570.90 Zambia 2,835,778,054.78 5,442,037.28 12,805,027,606.31 142,256,733.11 6,523,869.21 5,836,213,746.00 Zimbabwe Economics 102 Problem Set #4 20 points possible Due on Sunday, February 21 before 10 p.m. in SmartSite Department of Economics UC Davis Professor Siegler Winter 2016 Instructions: Please submit a self-contained and well-formatted Word (or similar) document in SmartSite containing all of your written answers, calculations, and graphs to the seven questions below. Also, submit Excel and GRETL documents containing the background calculations and graphs used in Word. Remember that the problem set will be graded, in part, based on the visual presentation of your results. 1. Academic Honesty (8 points) The results below are from a student survey conducted at the University of North Carolina, Wilmington in 2009, a medium-sized, public, four-year institution. The results were published in the Spring 2013 issue of the American Economist.1 The dependent variable in the ordinary least squares regression is the self-reported number of annual instances of cheating by each student in the sample, which includes both plagiarism and copying work from other students. The explanatory variables are: (1) the age of the student in years, (2) whether a student is female or not, (3) whether a student is a graduate student or not (4) whether a student believes that the penalties for cheating are moderate or severe or not, (5) whether a student believes faculty are vigilant about the detection of cheating or not, (6) whether a student believes faculty are vigilant about confronting students are detection or not, (7) whether academic dishonesty was discussed by faculty at the beginning of the semester or not, (8) whether a student believes their fellow students are vigilant about reporting cheating or not, (9) whether a student believes cheating is a moderate or severe problem on campus or not, and (10) whether a student is undeclared, or (11) a major in the college of arts and letters, (12) a major in the college of nursing, or (13) a major in the college of education (the college of business is the omitted category). 1 Robert T. Burrus, et al., \"It's the Students Stupid: How Perceptions of Student Reporting Impact Cheating,\" American Economist 58, no. 1 (Spring 2013), 51-59. 1 Dependent Variable: Annual Number of Instances of Cheating Explanatory Variables Coefficients (Standard Errors in Parentheses) 1.133 (0.307) -0.031 (0.011) -0.151 (0.162) -0.176 (0.134) -0.476 (0.192) -0.011 (0.228) 0.186 (0.242) Constant Age Female Graduate Student Penalties for Cheating Believed to be Severe Faculty Vigilant About Detection of Cheating Faculty Vigilant About Confronting Students After Detection Academic Honesty Discussed by Faculty at Start of Semester Peers Vigilant About Reporting Cheating Belief that Cheating is a Moderate or Severe Problem on Campus Undeclared Major 0.219 (0.174) -0.496 (0.150) 0.397 (0.144)) 0.536 (0.244) 0.038 (0.146) -0.072 (0.240) 0.003 (0.300) 0.174 229 Arts and Sciences College Nursing College Education College R2 Number of observations A. B. (2 points) Which of the explanatory variables above are NOT dummy variables? Briefly explain. (2 points) Consider the results above. Which of the following coefficients are statistically different from zero at the five-percent level of significance? For the variables that are statistically significant at the five-percent level of significance, is the sign of the coefficient consistent with what you expected or not? Provide an economic and intuitive explanation for the expected signs of each of the statistically significant coefficients. Show your work and briefly explain. 2 C. D. 2. (2 points) Consider the results from the regression. What is the predicted number of annual instances of cheating for an 18-year old male, undergraduate student who is undeclared and believes that cheating is a severe problem on campus but cheating was not discussed by his faculty at the beginning of the semester and he believes that his faculty and peers are not vigilant about detecting or reporting cheating? (2 points) Suppose that you are the provost of a university similar to UNC Wilmington. Write a memo to the president at your university discussing the likely best ways to curb academic dishonesty on your campus, based on the regression results above. Multiple Regression and the 2000 U.S. Presidential Election (5 points) The 2000 U.S. Presidential election was one of the most controversial and disputed elections in American history. In the end, George W. Bush received 271 electoral votes to Al Gore's 266 electoral votes. The election featured the controversy over who won Florida's 25 electoral votes (and thus the presidency). If Florida's popular vote had gone to Gore instead, he would have received Florida's 25 electoral votes and would have been President. The data in the file (election.xls) in SmartSite contains the number of votes for Pat Buchanan in each of the n=67 counties of Florida, before recounting. The county Palm Beach is observation number i=50. The recounts in Florida were motivated in part by possible mistakes by voters in Palm Beach, who wanted to vote for Gore (the second candidate, but third hole punch on the ballot) but by accident first selected Buchanan (second punch hole on the ballot paper). The difference (before recounts) between Bush and Gore in the state of Florida was 975 votes in favor of Bush. A. (1 point) Use GRETL to estimate and report the following OLS multiple regression model with the number of votes for Buchanan (BUCHANAN) as the dependent variable on an intercept (constant) and the following explanatory variables: PALMDUMMY = A dummy (binary) variable set equal to 1 if the county is Palm Beach County and 0 otherwise. You will need to create this variable, which is just a column of zero's for all counties with the exception of Palm Beach which is denoted with a \"1.\" HOUSEHOLDS = The number of households in each county in 2000. FOREIGN BORN = The percent of the population foreign born in each county in 2000. INCOME = Per capita income in each county in 2000. WHITE = Percent of the population that is white in each county in 2000. 3 UNEMPLOYMENT RATE = The unemployment rate in each county in 2000. CRIME RATE = Crime rate per 100,000 population in each country in 2000. DENSITY = The number of persons per square mile in each county in 2000. B. C. 3. The coefficient on the Palm Beach dummy variable is the estimate of the number of votes cast for Buchanan that cannot be explained by the other explanatory variables. If the regression model is correctly specified, the coefficient on PALMDUMMY represents the best estimate of the number of votes mistakenly cast for Buchanan. (2 points) Use a t-test to test the hypothesis that the coefficient on PALMDUMMY is greater than 975 votes. That is, the null hypothesis is 975 against the alternative that > 975. What can you conclude from this test? Should Bush or Gore have won the Florida and U.S. election based on the results of this test at the one-percent significance level? What is the p-value associated with this t-test? What does this p-value mean? Briefly explain. (2 points) Can you conclude that all of explanatory variables, excluding the intercept or constant, help explain the number of votes cast for Pat Buchanan, based on your results above? What are the null and alternative hypotheses? What is value of the test statistic used for the test and what can you conclude at the onepercent level of significance? Explain. Testing General Linear Restrictions in Multiple Regression (7 points) Testing exclusion restrictions is one of the most important applications of F statistics. Sometimes, however, the restrictions implied by theory are more complicated than just excluding some independent variables. However, it is still straightforward to use an F statistic for hypothesis testing. For example, consider the commonly-used Cobb-Douglas production function: = where Y is gross domestic product, A is the level of total factor productivity, K is stock of physical capital, and L is quantity of labor. Typically, in Econ 100 and 101 we assume that this production function exhibits constant returns to scale. That is, if we double the inputs (K and L), we get double the output (Y). The Cobb-Douglas production function exhibits constant returns to scale if: + = 1 We can transform the Cobb-Douglas production function above to test for constant returns to scale by first taking the natural logarithms of the variables (This can be accomplished in GRETL using Add/Logs of selected variables): = + + 4 The file (Cobb-Douglas 2009.xls) contains data on Y, K, and L for 152 countries in 2009 (n=152). While the equation above is deterministic, we can apply it to data by assuming that A is constant, by adding country subscripts for Y, K, and L, and by introducing a disturbance term: = + + + The multiple regression model above is the unrestricted equation since and are determined by the data. We can now impose the restriction of constant returns to scale to derive the restricted multiple regression model: = + + 1 + = + + + = + + The equation directly above is the restricted model, which can be estimated using data from Cobb-Douglas 2009.xls. You can also use GRETL to impose the restriction directly on the unrestricted model. The screen shot below contains my GRETL file with all of the variables you will need for this problem. 5 A. B. C. D. (1 point) Using OLS, estimate the unrestricted model given above and report the results. (2 points) Using OLS, estimate the restricted model given above and report the results. In GRETL, you can compute the variables to estimate the restricted model by using Add/Define new variable. Note that your new dependent variable is and your new explanatory variable is . These can be created by formula in GRETL or Excel. (2 points) Compute the appropriate F statistic to test for constant returns to scale. Can you reject the null hypothesis of constant returns to scale? Show your calculations and explain your results. (2 points) Parts A through C had you do this \"by hand.\" Use GRETL to automatically test this restriction. After you estimated the unrestricted model in Part A, select Tests/Linear restrictions and then type in the restriction that the two coefficients need to sum to one. Report the results below. 6 Florida County Buchanan Votes in 2000 County Buchanan Alachua, FL Baker, FL Bay, FL Bradford, FL Brevard, FL Broward, FL Calhoun, FL Charlotte, FL Citrus, FL Clay, FL Collier, FL Columbia, FL DeSoto, FL Dixie, FL Duval, FL Escambia, FL Flagler, FL Franklin, FL Gadsden, FL Gilchrist, FL Glades, FL Gulf, FL Hamilton, FL Hardee, FL Hendry, FL Hernando, FL Highlands, FL Hillsborough, FL Holmes, FL Indian River, FL Jackson, FL Jefferson, FL Lafayette, FL Lake, FL Lee, FL Leon, FL Levy, FL Liberty, FL Madison, FL Manatee, FL Marion, FL Martin, FL Miami-Dade, FL Monroe, FL Nassau, FL Okaloosa, FL Okeechobee, FL Orange, FL Osceola, FL Palm Beach, FL Pasco, FL Pinellas, FL Polk, FL Putnam, FL St. Johns, FL St. Lucie, FL Santa Rosa, FL Sarasota, FL Seminole, FL Sumter, FL Suwannee, FL Taylor, FL Union, FL Volusia, FL Wakulla, FL Walton, FL Washington, FL 263 73 248 65 570 788 90 182 270 186 122 89 36 29 652 502 83 33 38 29 9 71 23 30 22 242 127 847 76 105 102 29 10 289 305 282 67 39 29 271 563 112 560 47 90 267 43 446 145 3407 570 1013 532 148 311 305 194 229 124 114 108 27 37 496 46 120 88 Number of Households, 2000 Foreign Born (percent) in 200 Households Foreign Born 87,509 7.3 7,043 1.1 59,597 3.6 8,497 1.8 198,195 6.5 654,445 25.3 4,468 2.2 63,864 8.0 52,634 4.9 50,243 4.5 102,973 18.3 20,925 2.3 10,746 18.7 5,205 2.0 303,747 5.9 111,049 3.7 21,294 9.9 4,096 1.9 15,867 4.1 5,021 1.7 3,852 7.9 4,931 2.1 4,161 2.3 8,166 17.5 10,850 24.0 55,425 5.3 37,471 9.1 391,357 11.5 6,921 1.7 49,137 8.1 16,620 1.5 4,695 1.2 2,142 6.6 88,413 5.1 188,599 9.2 96,521 4.7 13,867 2.6 2,222 2.1 6,629 2.0 112,460 8.4 106,755 5.2 55,288 8.1 776,774 50.9 35,086 14.7 21,980 2.7 66,269 5.3 12,593 11.5 336,286 14.4 60,977 14.0 474,175 17.4 147,566 7.0 414,968 9.5 187,233 6.9 27,839 3.4 49,614 4.9 76,933 10.5 43,793 3.0 149,937 9.3 139,572 9.1 20,779 5.5 13,460 4.7 7,176 1.7 3,367 2.1 184,723 6.4 8,450 1.5 16,548 3.2 7,931 2.5