From Theory to Empirics A central question in development economics is why some nations are rich and other poor? An- swering this question has important implications for development polices, which aim to advance human wellbeing and eliminate poverty. Let's see how economics tackles this question (of course I cannot talk about every economic theory addressing this question here). 2.1 Production Function and Capital endowment The total production in country i depends on labor L, capital, K, and raw materials, R. We use the famous Cobb-Douglas function to model the relationship between production output and inputs. First lets define the value added of production as the value of total output Q minus the value of raw material, R: V = Q - R. Then we write the Cobb-Douglas using V. Vi = K L-a This equation tells us that the value-added measure of output in country i, Vi depends on K and L in country i. a is a positive parameter less than one. Usually, when we compare the economic perforce between countries we use GDP per capita (or value added per capita). Hence, we would like to write the Cobb Douglas in terms of value added per capital. Ki Vi KL L; v; = ko (1) VA where vi = and ki = L Li 1. If equation 1 hods perfectly how would the scatter diagram plotting v against k look like? 2. In reality we know that equation 1 does not hold perfectly. Rewrite equation 1 to include all other variables that might affect v in addition to k. 3. Suppose you have data about v and k, how would you estimate equation 1. 4. Use the dataset hjoines.dta to plot a scatter diagram between v and k. 5. Estimate your model in (3). Interpret the coefficient of k 6. Add the regression line to the scatter figure. Make sure you label the scatter points in the diagram. (use mlabel option). 7. How much investment for capital per capita is needed in Tanzania to be as rich as USA? 8. Could you think of a method to test the normality assumption (u ~ N(0,02)). Hint: obtain from the above regression and plot a histogram for . You might want check the commands: pnormal and qnormal 9. Assumption 5 states that Var(u|X) = E(u2|X) = 02 ( constant variance (homeskdacity)). Could you think of a way to test this empirically. Hint: the distribution of u should not be correlated with X or Y. You might want try the command rufplot. 10. Discuss whether the model can tell why some countries are rich and others poor (hint: does the regression provide causal relationship between v and k). 11. Add human capital to your regression and estimate the model (note: use number of years of education divided by L as a measure of human capital). 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O 11 20232 0.704 1 1 1 1 1 1 0.66 15 82804 15.83146 0 O 0.001 0.003 003 OM2 0.01 06 0 0012 1154 14 2543 2.400 09 2515 O 0536 NIGER 0 O 20 Nigeria Reunion 4 NGERIA REUNION WWANDA SENEGAL SEYCHELLES SIERRA LEONE 0 0.357 i 1 0.327 0 10 3266 01002 Docs 0.00 0.061 0.003 17.01 12.342 14.93 14.57867 10.2014 14.1847 15.00 16.31631 TE SED 0.64 O REU EWA SEN SYC SI SOM ZA SON SWE TZA TGO TUN o O 2014 -2012 14.772 466 8.395 0.611 -2.13 14043 0.10 10 1097 SOMALIA Senegal Seychelles Sierra Leone Somalia South africa Sudan Swaziland Tanzania Topo 0.32 0.00 0 0.00 1.135 2.701 2.136 2.395 12.65879 03 0308 0.602 0.551 O 1 1 1 1 1 1 1 1 1 1 1 1 LOT 0.006S O O 214 1 7.44511 8.21654061142 019621 66345 16 9.57263 0.2922 191338 0.52439 10 1732245 0.84 011156 7.500 7839 76029292 239 17025 1 34 9.41304 023735 2.22702 0.248.40257 7.72941 6.1593 0.78505 172 0.23048 8.28399 1 7.45231 7.50918 0.06844228781 0.30657 7.0773 O 9.90849040907 4.95 0.631 32 1 7.76558 8.31243 027343 0.91 0.12194 7.37021 1 8.66 9.00675 016853 3.77 0.50515 7.99596 7.02414 7.0657 0.02073 2.23 6. 7.25061 789372 0.32156 213 E6436 8.948429.28947 0.17052 2.48 3.44556 O 7.02369589581 -0.56334 1.92 0.25728 2.05 1 7.05455 656787024324 2:24 0.30016 6.99743 0749617 877629064036 4.35 0.57135628445 7.74836 8.43456 0.341 263 0.35242705284 1 9.5687 9.836 01336 743 08.54757 0 9270 0 10.41442 1131986 0.45272 1037 110116 6604 3 9.11803 97223030043 5.33 0.6703314537 4 016711 5 0.53117 0.2767 1 0.2273 4 . 0.33733 3 0.26178 2 2. 1725 3.171 LIGA 1454251 1471480 15.78421 16.0688 14.04 1523019 0.099 0.001 0.04 0204 0.06 0.006 0 0 1036 28 59 ande 2 ZAR IMS ZWE 2114 0434 CVETO HUO 0 2 O 2492 014 0.865 1 it.i..SHIBIS.............. 0 247 1 MENO 5 0.000 BRB 1 0.739 0.61 O 1000 PEDO 16.37839 SOUTH AFRICA SUDAN SWAZILAND TANZANIA TOGO TUNISIA UGANDA ZARE ZAMBIA DIMLARWE BAHAMAS MARBADOS BELGE CANADA COSTA RICA DOMINICA DOMINICAN REP EL SALVADOR GRENADA GUATEMALA HATI HONDURAS JAMAICA MEXICO NICARAGUA PANAMA PUERTO RICO 4097 471 AN . 3151 CAN 5 5 0.96578 1 054611 0956 0053 5 0.001 0.004 0 1 1 0 1 1 0 1 0.016 0.26 0001 0 1 1 2541 30185 1108 0906 1054 OH w 15 9.341 15.455 18.561 13.75 Zimbabwe Bahamas Barbados Belar Canada Costa rica Dominica Dominican rep El salvador Grenada Guatemala Hati Honduras Jamaica Mexico Nicaragua Panama Puerto rico DUA DOM SLV GRD GTM HTI LEO 0432 14.55682 14 31394 10.59015 14.72302 14.77 14.1977 O 0 0 3.093 0588 0.005 14 19 0.001 0.002 0014 0.00 OD 02571 0863 03128 357.47338 11673 69338 8.91335 895836 0. 35478 7.6053 7.40046010292 160.21708 749234 3.4336 8.73022 014758 3.56 0.47704 7.80844 8.43284 9.45961 051118 4.16 055216 9.63754 10.25586030916 4.42 OST.74955 1 4028 9.07821 0.33547 1.78 0.50652 7.5563 1 9.99013 0.45313 0.7687.74567 1 10 13436 30.65694 0.26129462541 099917 9 TI 0.236 0:434 0544 2017 HIND 7.36 30.3678 1 011783 303361 5 0.43311 3 1 0.433 3 011445 0432 02 DR O 0 1.509 1 1 1 1 1 1 17.000 MEX NIC PAN PRI WHO 3.36 041 116512 1195737 0.001 GO 0.895 1 SET KNA YST TO W USA se NETISCH WE 1 1 STRES SE VICENTE TIDAD TOBAGO USA ARGENTINA BOUVIA BRE CHE COLOMBIA ECUADOR GUYANA PARAGUAY BSS 1 SO 8. $ IEI Swee BRA CHL COL TCU SUSTE 1 1 SEED Gars 0.0 OM 07 TO - NA ON 1723 2007 40.43 47 45 Com Bruder 14005 0913 017 2 . 0:00 . ........ 09 0991 - 11 A BOSELE O 067140 00 PER 12:57 1412 OST 202 74 O 14 Suriname uruguay O TE GEMEE LED VE TO WOO 1 1 1 1 1 0.005 7170 5.0.7187914 2100019 LIRUOWAY VENEZUELA 1 URY VEN SHR BGD 0.49 D 1114413 11912 9.50 10.00 0.3323 9730 0.00 02 2014 0.001 011 0.355 ho OV 9 0 0 0 D 0 15.2 0 1 2012 BANGLADESH BHUTAN . 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EO 15.37232 12.39767 34.21071 14.38005 5 -32,219 1 1 1 1 0 0.015 Gods 0.00 0.00 0.011 0.192 0.01 0.005 0.01 0.593 0.452 0.931 0.611 0985 0.08 0.008 0.00 2.921 20 0 50 SED 0.015 Newzealand Papua Solomon Tongs Vanuatu Western Samos S100 97 NEW ZEALAND PAPUA N GUINEA SOLOMON TONGA VANLATU WESTERN SAMOA 5 TON VUT WSM 0 5 2.315 3224 3.79 0 21.173 O 0 30.71054 10.8312 0.011 0 . 349 13.633 Please I need to know the comand for stata From Theory to Empirics A central question in development economics is why some nations are rich and other poor? An- swering this question has important implications for development polices, which aim to advance human wellbeing and eliminate poverty. Let's see how economics tackles this question (of course I cannot talk about every economic theory addressing this question here). 2.1 Production Function and Capital endowment The total production in country i depends on labor L, capital, K, and raw materials, R. We use the famous Cobb-Douglas function to model the relationship between production output and inputs. First lets define the value added of production as the value of total output Q minus the value of raw material, R: V = Q - R. Then we write the Cobb-Douglas using V. Vi = K L-a This equation tells us that the value-added measure of output in country i, Vi depends on K and L in country i. a is a positive parameter less than one. Usually, when we compare the economic perforce between countries we use GDP per capita (or value added per capita). Hence, we would like to write the Cobb Douglas in terms of value added per capital. Ki Vi KL L; v; = ko (1) VA where vi = and ki = L Li 1. If equation 1 hods perfectly how would the scatter diagram plotting v against k look like? 2. In reality we know that equation 1 does not hold perfectly. Rewrite equation 1 to include all other variables that might affect v in addition to k. 3. Suppose you have data about v and k, how would you estimate equation 1. 4. Use the dataset hjoines.dta to plot a scatter diagram between v and k. 5. Estimate your model in (3). Interpret the coefficient of k 6. Add the regression line to the scatter figure. Make sure you label the scatter points in the diagram. (use mlabel option). 7. How much investment for capital per capita is needed in Tanzania to be as rich as USA? 8. Could you think of a method to test the normality assumption (u ~ N(0,02)). Hint: obtain from the above regression and plot a histogram for . You might want check the commands: pnormal and qnormal 9. Assumption 5 states that Var(u|X) = E(u2|X) = 02 ( constant variance (homeskdacity)). Could you think of a way to test this empirically. Hint: the distribution of u should not be correlated with X or Y. You might want try the command rufplot. 10. Discuss whether the model can tell why some countries are rich and others poor (hint: does the regression provide causal relationship between v and k). 11. Add human capital to your regression and estimate the model (note: use number of years of education divided by L as a measure of human capital). 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Burundi OMR Cameroon CPV Cape Verdes CA CAR TCD Chad Cou COS DE EGY Cert TH Ethiopia GAB Gabon GMB Gambia GHA GIN GNB CV Ivory Coast KEN LSO Lesotho LBR Liberia MDG Madagascar MWI MU MRT Mauritania MUS Mauritius MAR Morocco MOE Morambique NAM Namibia NER Nger O EGYPT ETHIOPIA GABON GAMBIA GHANA GUINEA 0315 OS OG LIVE 2006 LED 0 - 1 1 4 1 1 2016 0.222 0222 02 o O 3955 29 3336 11 257 SEN 067 0 GUINEA IVORY COAST 12.6139 15 61004 141638 12.99862 15.90041 36.13396 13.58411 POS MO 090 0 1222 O KUNTA 1 1 . 0 0.002 5050818 1 0.10731 5 1 0.2755 0019931 5 20663 50 . 10.38128 5035135 1 025672 5 03122 50.11106 5055154 5 1 5 1 0.23328 30.20054 5 0.15191 3 0.5072 1 0.26 3 0.365 5 0.253 2.08 2.524 10 16.80153 812542 0.33805 5.0974 064585 8.43273 1 162177 10.05713071468 22546022 760052 7.447709654 0.17609 04011256 751226 7.5 7.10093 0.21008 122 0.431487.2017 1 7.32935 6.90098 021119 21595031619 72144 7.23673 7.8141029869 0.56 07504 1 8.14367 8.44026 0.1429292846 0.39241 760297 7.597878163 0.15847 0.414067.000 3 7.71105 783589 0.06212 3.51 0.47034 71779 1 7.2735 5.7622 7545 3.97152 0.52218 749678 1 6954 7.07719009142 2.58 0.57 6.54722 7.113 6.72355 02 0.10988 7.19837 1 7464 8.30818 0.41087 251012 0.3346 1 91145 4.59 0.550 0022 3.74085 0.0271507048 0.64412 1 7.21750 602818 0.6032 0.14472 1 8.83613 10 1921 067798 141427 0.45751 770053 O 7049 006562 054 0.02.16 0.005 0. 061 017 0.436 500 0 1 1 0.007 0 . 0 0 3 2393 1 18 0 LESOTHO UBERIA MADAGASCAR MALAWI MALI MAURITANIA MAURITIUS MOROCCO MOZAMBIQUE MAMIA 15647 15.0480 15.01141 13.55016 011 0 D . 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