------------------------------------------------------------------------------------------------------------------------------------------- name: log: C:\Users\AN.4271\Dropbox\HHS 651\Assignments\Assignment 2\assignment2log.log log type: text opened on: 10 Oct 2017, 10:07:58 . set more off . . ****************************************************************************** . ************ HHS 651: Assignment 2 ********************* . *************** Stata Solutions - Andrew Proctor ********************* . ****************************************************************************** . . . ***** Import Data . wbopendata, indicator(SG.GEN.PARL.ZS; TM.TAX.MRCH.WM.AR.ZS; /// > NY.GDP.TOTL.RT.ZS; SE.SEC.CMPT.LO.ZS; SH.H2O.SAFE.ZS; SH.ANM.ALLW.ZS; /// > IC.LGL.DURS; SE.SEC.ENRR; NY.GDP.PCAP.KD ) clear Metadata: SG.GEN.PARL.ZS ------------------------------------------------------------------------------------------------------------------------------------------- Name: Proportion of seats held by women in national parliaments (%) ----------------------------------------------------------------------------------------------------------------------------------------- Source: World Development Indicators ------------------------------------------------------------------------------------------------------------------------------------------ Source Note: Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women. ------------------------------------------------------------------------------------------------------------------------------------------ Source Organization: Inter-Parliamentary Union (IPU) (www.ipu.org). ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Public Sector ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Gender ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Social Development ------------------------------------------------------------------------------------------------------------------------------------------ Metadata: TM.TAX.MRCH.WM.AR.ZS ------------------------------------------------------------------------------------------------------------------------------------------- Name: Tariff rate, applied, weighted mean, all products (%) ----------------------------------------------------------------------------------------------------------------------------------------- Source: World Development Indicators ------------------------------------------------------------------------------------------------------------------------------------------ Source Note: Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Divisions Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. ------------------------------------------------------------------------------------------------------------------------------------------ Source Organization: World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Developments Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database. ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Private Sector ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Trade ------------------------------------------------------------------------------------------------------------------------------------------ Metadata: NY.GDP.TOTL.RT.ZS ------------------------------------------------------------------------------------------------------------------------------------------- Name: Total natural resources rents (% of GDP) ----------------------------------------------------------------------------------------------------------------------------------------- Source: World Development Indicators ------------------------------------------------------------------------------------------------------------------------------------------ Source Note: Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. ------------------------------------------------------------------------------------------------------------------------------------------ Source Organization: Estimates based on sources and methods described in The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium (World Bank, 2011). ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Energy & Mining ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Environment ------------------------------------------------------------------------------------------------------------------------------------------ Metadata: SE.SEC.CMPT.LO.ZS ------------------------------------------------------------------------------------------------------------------------------------------- Name: Lower secondary completion rate, total (% of relevant age group) ----------------------------------------------------------------------------------------------------------------------------------------- Source: World Development Indicators ------------------------------------------------------------------------------------------------------------------------------------------ Source Note: Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. ------------------------------------------------------------------------------------------------------------------------------------------ Source Organization: United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics. ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Education ------------------------------------------------------------------------------------------------------------------------------------------ Metadata: SH.H2O.SAFE.ZS ------------------------------------------------------------------------------------------------------------------------------------------- Name: Improved water source (% of population with access) ----------------------------------------------------------------------------------------------------------------------------------------- Source: World Development Indicators ------------------------------------------------------------------------------------------------------------------------------------------ Source Note: Access to an improved water source refers to the percentage of the population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection). ------------------------------------------------------------------------------------------------------------------------------------------ Source Organization: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation ({browse "http://www.wssinfo.org/). ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Climate Change ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Infrastructure ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Health ------------------------------------------------------------------------------------------------------------------------------------------ Metadata: SH.ANM.ALLW.ZS ------------------------------------------------------------------------------------------------------------------------------------------- Name: Prevalence of anemia among women of reproductive age (% of women ages 15-49) ----------------------------------------------------------------------------------------------------------------------------------------- Source: World Development Indicators ------------------------------------------------------------------------------------------------------------------------------------------ Source Note: Prevalence of anemia among women of reproductive age refers to the combined prevalence of both non-pregnant with haemoglobin levels below 12 g/dL and pregnant women with haemoglobin levels below 11 g/dL. ------------------------------------------------------------------------------------------------------------------------------------------ Source Organization: Stevens GA et al. Global, regional, and national trends in hemoglobin concentration and prevalence of total and severe anemia in children and pregnant and non-pregnant women for 1995-2011: a systematic analysis of population-representative data. The Lancet Global Health 2013;1:e16-e25. ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Health ------------------------------------------------------------------------------------------------------------------------------------------ Metadata: IC.LGL.DURS ------------------------------------------------------------------------------------------------------------------------------------------- Name: Time required to enforce a contract (days) ----------------------------------------------------------------------------------------------------------------------------------------- Source: World Development Indicators ------------------------------------------------------------------------------------------------------------------------------------------ Source Note: Time required to enforce a contract is the number of calendar days from the filing of the lawsuit in court until the final determination and, in appropriate cases, payment. ------------------------------------------------------------------------------------------------------------------------------------------ Source Organization: World Bank, Doing Business project ({browse "http://www.doingbusiness.org/). ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Private Sector ------------------------------------------------------------------------------------------------------------------------------------------ Metadata: SE.SEC.ENRR ------------------------------------------------------------------------------------------------------------------------------------------- Name: Gross enrolment ratio, secondary, both sexes (%) ----------------------------------------------------------------------------------------------------------------------------------------- Source: World Development Indicators ------------------------------------------------------------------------------------------------------------------------------------------ Source Note: Total enrollment in secondary education, regardless of age, expressed as a percentage of the population of official secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition. ------------------------------------------------------------------------------------------------------------------------------------------ Source Organization: UNESCO Institute for Statistics ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Education ------------------------------------------------------------------------------------------------------------------------------------------ Metadata: NY.GDP.PCAP.KD ------------------------------------------------------------------------------------------------------------------------------------------- Name: GDP per capita (constant 2010 US$) ----------------------------------------------------------------------------------------------------------------------------------------- Source: World Development Indicators ------------------------------------------------------------------------------------------------------------------------------------------ Source Note: GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2010 U.S. dollars. ------------------------------------------------------------------------------------------------------------------------------------------ Source Organization: World Bank national accounts data, and OECD National Accounts data files. ------------------------------------------------------------------------------------------------------------------------------------------ Topics: Economy & Growth ------------------------------------------------------------------------------------------------------------------------------------------ . . /* Only if you import using the CSV file: > import delimited using "./WDI_csv/WDIData.csv", varnames(1) clear > > **** Keep only the relevant variables for analysis" > keep if (inlist(indicatorcode, "SG.GEN.PARL.ZS", "TM.TAX.MRCH.WM.AR.ZS", /// > "NY.GDP.TOTL.RT.ZS", "SE.SEC.CMPT.LO.ZS") /// > | inlist(indicatorcode, "SH.H2O.SAFE.ZS", "SH.ANM.ALLW.ZS", /// > "IT.CEL.SETS", /// > "IC.LGL.DURS", "SE.SEC.ENRR", "NY.GDP.PCAP.KD")) > */ . . . ****************************************************************************** . ******************** Data Preparation ******************** . ****************************************************************************** . . . ***** Drop Supranational areas . *** Create list of countrycodes to drop . local dropregions ARB CSS CEB EAR EAS EAP TEA EMU ECS ECA TEC EUU FCS HPC HIC /// > IBD IBT IDB IDX IDA LTE LCN LAC TLA LDC LMY LIC LMC MEA MNA TMN MIC NAC /// > OED OSS PSS PST PRE SST SAS TSA SSF SSA TSS UMC WLD INX . . *** Drop the countrycodes in 'dropregions' . foreach region in `dropregions' { 2. drop if countrycode=="`region'" 3. } (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) (9 observations deleted) . . **** Change indicator codes into readable names for variables . replace indicatorcode = "pcgdp" if (indicatorcode=="NY.GDP.PCAP.KD") (217 real changes made) . replace indicatorcode = "contractenftime" if (indicatorcode=="IC.LGL.DURS") (217 real changes made) . replace indicatorcode = "womenlegislseats" if (indicatorcode=="SG.GEN.PARL.ZS") (217 real changes made) . replace indicatorcode = "tariff" if (indicatorcode=="TM.TAX.MRCH.WM.AR.ZS") (217 real changes made) . replace indicatorcode = "natresource" if (indicatorcode=="NY.GDP.TOTL.RT.ZS") (217 real changes made) . replace indicatorcode = "secondeduc" if (indicatorcode=="SE.SEC.CMPT.LO.ZS") (217 real changes made) . replace indicatorcode = "waterq" if (indicatorcode=="SH.H2O.SAFE.ZS") (217 real changes made) . replace indicatorcode = "anemia" if (indicatorcode=="SH.ANM.ALLW.ZS") (217 real changes made) . replace indicatorcode = "secondenroll" if (indicatorcode=="SE.SEC.ENRR") (217 real changes made) . . . *** Explore the relationship between the Stata assign variable name and the year . drop countryname . . . ***** Reshaping the Data . /* Only if you import using the CSV file: > *** Drop empty variables > drop v62' > */ . . *** Reshape year as a single column . reshape long yr, i(countrycode indicatorcode ) j(year) (note: j = 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 198 > 5 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 > 2013 2014 2015 2016) Data wide -> long ----------------------------------------------------------------------------- Number of obs. 1953 -> 111321 Number of variables 63 -> 8 j variable (57 values) -> year xij variables: yr1960 yr1961 ... yr2016 -> yr ----------------------------------------------------------------------------- . /* Only if you import using the CSV file: > replace year = year + 1955 if !missing(year) > */ . *** Drop early years . drop if year < 1970 (19,530 observations deleted) . . *** Change v prefix to value . rename yr value . /* If you import using the CSV file, this should instead be: > rename v value > */ . . drop indicatorname // Drop superfluous variable . . *** Reshape Indicator column so that indicators are each different variables . reshape wide value, i(countrycode year) j(indicatorcode) string (note: j = anemia contractenftime natresource pcgdp secondeduc secondenroll tariff waterq womenlegislseats) Data long -> wide ----------------------------------------------------------------------------- Number of obs. 91791 -> 10199 Number of variables 7 -> 14 j variable (9 values) indicatorcode -> (dropped) xij variables: value -> valueanemia valuecontractenftime ... valuewomenlegislseats ----------------------------------------------------------------------------- . rename value* * // Remove variables . . ***** Get log of GDP for analysis . gen lnpcgdp =log(pcgdp) (2,442 missing values generated) . . ***** Set the Panel . encode countrycode, gen(countryval) /* Encode country code so it can be used for > xtset. */ . xtset countryval year // Tell Stata the panel structure. panel variable: countryval (strongly balanced) time variable: year, 1970 to 2016 delta: 1 unit . . ***** Create Lower Secondary Completion Average . /* Create a variable equal to secondary school education if nonmissing, > otherwise 0. */ . gen compsecforavg = secondeduc (6,686 missing values generated) . replace compsecforavg = 0 if missing(secondeduc) (6,686 real changes made) . . gen compsectotal = 0 // Sum of completion variable . gen nonmissing_compsec = 0 // Count of nonmissing completion variable . . *** Compute sum and count . forval i=3/15 { 2. replace nonmissing_compsec = nonmissing_compsec + 1 if /// > !missing(l`i'.secondeduc) 3. replace compsectotal = (compsectotal + l`i'.compsecforavg) 4. } (3,268 real changes made) (3,919 real changes made, 651 to missing) (3,140 real changes made) (3,357 real changes made, 217 to missing) (3,013 real changes made) (3,230 real changes made, 217 to missing) (2,885 real changes made) (3,102 real changes made, 217 to missing) (2,761 real changes made) (2,978 real changes made, 217 to missing) (2,632 real changes made) (2,849 real changes made, 217 to missing) (2,510 real changes made) (2,727 real changes made, 217 to missing) (2,396 real changes made) (2,613 real changes made, 217 to missing) (2,290 real changes made) (2,507 real changes made, 217 to missing) (2,177 real changes made) (2,394 real changes made, 217 to missing) (2,066 real changes made) (2,283 real changes made, 217 to missing) (1,963 real changes made) (2,180 real changes made, 217 to missing) (1,858 real changes made) (2,075 real changes made, 217 to missing) . *** Generate average . gen compsecavg = compsectotal / nonmissing_compsec (5,782 missing values generated) . . . . . ****************************************************************************** . ******************** Analysis ******************** . ****************************************************************************** . . ***** Exploratory Analysis . *** Check for missing observations in variables . mdesc Variable | Missing Total Percent Missing ----------------+----------------------------------------------- countrycode | 0 10,199 0.00 year | 0 10,199 0.00 anemia | 5,123 10,199 50.23 contracten~e | 7,700 10,199 75.50 natresource | 2,295 10,199 22.50 pcgdp | 2,442 10,199 23.94 secondeduc | 6,686 10,199 65.56 secondenroll | 4,253 10,199 41.70 tariff | 7,227 10,199 70.86 waterq | 5,147 10,199 50.47 womenlegis~s | 6,475 10,199 63.49 iso2code | 188 10,199 1.84 region | 188 10,199 1.84 regioncode | 188 10,199 1.84 lnpcgdp | 2,442 10,199 23.94 countryval | 0 10,199 0.00 compsecfor~g | 0 10,199 0.00 compsectotal | 3,255 10,199 31.91 nonmissing~c | 0 10,199 0.00 compsecavg | 5,782 10,199 56.69 ----------------+----------------------------------------------- . . *** Get Descriptive Statistics . summarize Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- countrycode | 0 year | 10,199 1993 13.56533 1970 2016 anemia | 5,076 31.7168 13.43998 7.7 69.6 contracten~e | 2,499 626.4693 304.9142 120 1800 natresource | 7,904 6.814033 10.98016 0 89.16611 -------------+--------------------------------------------------------- pcgdp | 7,757 11205.74 16498.95 115.7941 144246.4 secondeduc | 3,513 60.62882 32.47474 .23964 206.6042 secondenroll | 5,946 63.8026 34.11878 0 166.8085 tariff | 2,972 6.967079 7.907766 0 254.58 waterq | 5,052 84.41205 18.20072 13.2 100 -------------+--------------------------------------------------------- womenlegis~s | 3,724 15.71765 10.91741 0 63.8 iso2code | 0 region | 0 regioncode | 0 lnpcgdp | 7,757 8.290893 1.529593 4.751814 11.87928 -------------+--------------------------------------------------------- countryval | 10,199 109 62.64491 1 217 compsecfor~g | 10,199 20.88333 34.54399 0 206.6042 compsectotal | 6,944 242.0472 327.2191 0 1474.215 nonmissing~c | 10,199 3.231591 4.150613 0 13 compsecavg | 4,417 55.51052 32.16627 1.238303 136.2766 . . . ***** Regressional Analysis . *** 3a: Basic Regression . reg lnpcgdp compsecavg contractenftime anemia waterq tariff womenlegislseats natresource Source | SS df MS Number of obs = 1,494 -------------+---------------------------------- F(7, 1486) = 513.55 Model | 2323.10998 7 331.872855 Prob > F = 0.0000 Residual | 960.296185 1,486 .646228927 R-squared = 0.7075 -------------+---------------------------------- Adj R-squared = 0.7062 Total | 3283.40617 1,493 2.19920038 Root MSE = .80388 ---------------------------------------------------------------------------------- lnpcgdp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- compsecavg | .0158533 .0013284 11.93 0.000 .0132476 .018459 contractenftime | .0000537 .0000725 0.74 0.459 -.0000886 .0001959 anemia | -.0259523 .0026344 -9.85 0.000 -.0311198 -.0207847 waterq | .0350172 .0024943 14.04 0.000 .0301245 .0399098 tariff | -.0356248 .0055111 -6.46 0.000 -.0464351 -.0248144 womenlegislseats | .0066975 .0021143 3.17 0.002 .0025503 .0108448 natresource | .0227171 .0020578 11.04 0.000 .0186807 .0267536 _cons | 4.989517 .2345285 21.27 0.000 4.529475 5.449559 ---------------------------------------------------------------------------------- . . *** 3b: Regression with country and year fixed-effects . reg lnpcgdp compsecavg contractenftime anemia waterq tariff womenlegislseats /// > natresource i.countryval i.year Source | SS df MS Number of obs = 1,494 -------------+---------------------------------- F(178, 1315) = 3340.02 Model | 3276.15978 178 18.405392 Prob > F = 0.0000 Residual | 7.24638839 1,315 .005510562 R-squared = 0.9978 -------------+---------------------------------- Adj R-squared = 0.9975 Total | 3283.40617 1,493 2.19920038 Root MSE = .07423 ---------------------------------------------------------------------------------- lnpcgdp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- compsecavg | .0020907 .0004033 5.18 0.000 .0012995 .0028819 contractenftime | -.0001974 .0000374 -5.28 0.000 -.0002707 -.0001241 anemia | .0040422 .0014602 2.77 0.006 .0011777 .0069067 waterq | .0123581 .0011737 10.53 0.000 .0100555 .0146607 tariff | -.0052052 .0010451 -4.98 0.000 -.0072554 -.003155 womenlegislseats | -.0011545 .000554 -2.08 0.037 -.0022413 -.0000676 natresource | -.001318 .0006405 -2.06 0.040 -.0025745 -.0000615 | countryval | AGO | 1.881584 .0566396 33.22 0.000 1.77047 1.992697 ALB | 1.060734 .0806293 13.16 0.000 .9025577 1.21891 ARE | 3.446473 .0823091 41.87 0.000 3.285001 3.607944 ARG | 2.089608 .0784686 26.63 0.000 1.935671 2.243545 ARM | .8398783 .083083 10.11 0.000 .6768886 1.002868 ATG | 2.251362 .0859328 26.20 0.000 2.082781 2.419942 AUT | 3.521298 .0842777 41.78 0.000 3.355964 3.686632 AZE | 1.505392 .0826737 18.21 0.000 1.343205 1.667579 BDI | -1.180143 .0615891 -19.16 0.000 -1.300967 -1.059319 BEL | 3.520507 .081951 42.96 0.000 3.359739 3.681276 BEN | -.11219 .0728871 -1.54 0.124 -.2551778 .0307977 BFA | -.5054266 .0786791 -6.42 0.000 -.6597768 -.3510764 BGD | -.1707197 .0631697 -2.70 0.007 -.2946441 -.0467954 BGR | 1.629957 .0794352 20.52 0.000 1.474124 1.785791 BHR | 2.669008 .0890381 29.98 0.000 2.494336 2.84368 BHS | 2.800538 .086586 32.34 0.000 2.630676 2.970399 BLR | 1.425366 .0882517 16.15 0.000 1.252236 1.598495 BLZ | 1.35647 .073505 18.45 0.000 1.21227 1.500669 BOL | .5855312 .0718453 8.15 0.000 .4445873 .7264751 BRA | 2.20117 .0768447 28.64 0.000 2.050418 2.351921 BRB | 2.614535 .0826983 31.62 0.000 2.4523 2.77677 BTN | .4501493 .095006 4.74 0.000 .2637695 .6365291 BWA | 1.625918 .0764004 21.28 0.000 1.476038 1.775798 CAF | -.5146051 .0692688 -7.43 0.000 -.6504946 -.3787156 CHE | 3.995399 .0829501 48.17 0.000 3.83267 4.158128 CHL | 2.313282 .0818909 28.25 0.000 2.152631 2.473933 CHN | 1.219794 .0757707 16.10 0.000 1.071149 1.368438 CIV | .2535742 .0753563 3.37 0.001 .1057425 .4014059 CMR | .4415028 .0622057 7.10 0.000 .3194695 .5635361 COD | -.727125 .0713762 -10.19 0.000 -.8671486 -.5871013 COG | 1.101752 .0829192 13.29 0.000 .939084 1.264421 COL | 1.851263 .0644154 28.74 0.000 1.724895 1.977632 COM | -.3730015 .0764234 -4.88 0.000 -.5229265 -.2230765 CPV | 1.069102 .0765503 13.97 0.000 .9189282 1.219276 CRI | 2.039899 .072312 28.21 0.000 1.898039 2.181758 CYP | 3.081007 .078651 39.17 0.000 2.926712 3.235302 CZE | 2.664074 .0806327 33.04 0.000 2.505891 2.822257 DEU | 3.490165 .0821308 42.50 0.000 3.329043 3.651287 DJI | .4531545 .0690167 6.57 0.000 .3177597 .5885493 DMA | 1.639805 .0924651 17.73 0.000 1.45841 1.8212 DNK | 3.735135 .0863851 43.24 0.000 3.565667 3.904603 DOM | 1.545186 .0732874 21.08 0.000 1.401413 1.688959 DZA | 1.455358 .0742877 19.59 0.000 1.309622 1.601093 ECU | 1.571834 .0679943 23.12 0.000 1.438445 1.705224 EGY | .7236802 .0769104 9.41 0.000 .5727996 .8745607 ERI | -.2442172 .0948188 -2.58 0.010 -.4302299 -.0582046 ESP | 3.183714 .0816288 39.00 0.000 3.023577 3.343851 EST | 2.425293 .0835965 29.01 0.000 2.261296 2.58929 ETH | -.5120867 .0611257 -8.38 0.000 -.6320012 -.3921721 FIN | 3.521189 .0861131 40.89 0.000 3.352255 3.690123 FRA | 3.409975 .0836959 40.74 0.000 3.245783 3.574167 GAB | 2.245004 .0896139 25.05 0.000 2.069202 2.420805 GEO | .7171521 .0842494 8.51 0.000 .5518743 .88243 GHA | .1746909 .0828345 2.11 0.035 .0121887 .337193 GIN | -.5751421 .0933784 -6.16 0.000 -.7583289 -.3919552 GMB | -.8348798 .0947525 -8.81 0.000 -1.020762 -.6489972 GNB | -.18301 .0534366 -3.42 0.001 -.2878404 -.0781796 GNQ | 3.397682 .087106 39.01 0.000 3.2268 3.568564 GRC | 3.105868 .074468 41.71 0.000 2.959779 3.251957 GRD | 1.832218 .0777909 23.55 0.000 1.67961 1.984825 GTM | 1.169966 .0618031 18.93 0.000 1.048723 1.29121 GUY | .9130189 .0810407 11.27 0.000 .7540358 1.072002 HND | .7391563 .064582 11.45 0.000 .6124614 .8658512 HRV | 2.329857 .08169 28.52 0.000 2.1696 2.490114 HTI | -.1426838 .0690514 -2.07 0.039 -.2781467 -.0072209 HUN | 2.248218 .0863616 26.03 0.000 2.078796 2.41764 IDN | 1.01342 .0711248 14.25 0.000 .8738899 1.152951 IND | .1848951 .075097 2.46 0.014 .0375721 .3322182 IRL | 3.701657 .0795381 46.54 0.000 3.545622 3.857693 IRN | 1.624774 .0860929 18.87 0.000 1.455879 1.793668 ISL | 3.485413 .0836354 41.67 0.000 3.32134 3.649487 ISR | 3.198172 .0771382 41.46 0.000 3.046845 3.3495 ITA | 3.440335 .0741765 46.38 0.000 3.294818 3.585852 JAM | 1.391444 .0798677 17.42 0.000 1.234762 1.548126 JOR | 1.020907 .0786352 12.98 0.000 .8666432 1.175172 JPN | 3.473803 .0891975 38.95 0.000 3.298818 3.648788 KAZ | 1.958092 .0836747 23.40 0.000 1.793942 2.122242 KGZ | -.3057205 .0782139 -3.91 0.000 -.4591582 -.1522829 KHM | -.0573585 .0693762 -0.83 0.409 -.1934586 .0787415 KOR | 2.740273 .0869296 31.52 0.000 2.569738 2.910809 KWT | 3.523655 .0857852 41.08 0.000 3.355364 3.691946 LAO | .2982254 .066762 4.47 0.000 .1672537 .4291971 LBN | 1.748961 .0786869 22.23 0.000 1.594595 1.903326 LBR | -.6970602 .07192 -9.69 0.000 -.8381506 -.5559698 LCA | 1.76113 .0772736 22.79 0.000 1.609537 1.912723 LKA | .9362005 .0669825 13.98 0.000 .8047964 1.067605 LSO | .2748861 .0622629 4.41 0.000 .1527406 .3970316 LTU | 2.235695 .0824247 27.12 0.000 2.073996 2.397393 LUX | 4.294516 .0863536 49.73 0.000 4.12511 4.463921 LVA | 2.156176 .0855252 25.21 0.000 1.988396 2.323957 MAR | 1.009477 .0715468 14.11 0.000 .8691191 1.149836 MDA | .3056021 .0755379 4.05 0.000 .1574142 .4537901 MDG | -.3120563 .0510356 -6.11 0.000 -.4121764 -.2119361 MDV | 1.576904 .0935807 16.85 0.000 1.393321 1.760488 MEX | 2.065461 .077345 26.70 0.000 1.913728 2.217194 MKD | 1.231023 .0792073 15.54 0.000 1.075636 1.386409 MLI | -.1672504 .0721199 -2.32 0.021 -.3087331 -.0257676 MLT | 2.791095 .0848953 32.88 0.000 2.62455 2.95764 MMR | .2329129 .0721573 3.23 0.001 .0913568 .374469 MNG | 1.176479 .0721802 16.30 0.000 1.034878 1.31808 MOZ | -.3764403 .0551024 -6.83 0.000 -.4845386 -.268342 MRT | .6371556 .0712036 8.95 0.000 .4974706 .7768406 MUS | 1.807714 .0778603 23.22 0.000 1.65497 1.960458 MWI | -.796405 .071704 -11.11 0.000 -.9370717 -.6557383 MYS | 1.942328 .0798416 24.33 0.000 1.785697 2.098958 NAM | 1.547456 .0720529 21.48 0.000 1.406105 1.688808 NER | -.6506018 .0652296 -9.97 0.000 -.7785673 -.5226363 NGA | 1.001468 .0708653 14.13 0.000 .8624462 1.140489 NIC | .4877776 .0689844 7.07 0.000 .352446 .6231092 NLD | 3.731026 .08012 46.57 0.000 3.573849 3.888203 NOR | 4.189833 .0858593 48.80 0.000 4.021397 4.358269 NPL | -.507902 .0667949 -7.60 0.000 -.6389381 -.3768659 OMN | 2.7252 .0817084 33.35 0.000 2.564906 2.885493 PAK | -.0125436 .0781972 -0.16 0.873 -.1659484 .1408612 PAN | 1.954282 .0743044 26.30 0.000 1.808514 2.10005 PER | 1.461365 .0722552 20.23 0.000 1.319617 1.603113 PHL | .6483113 .0682934 9.49 0.000 .5143353 .7822872 PNG | .8758755 .0654053 13.39 0.000 .7475654 1.004186 POL | 2.258459 .0755455 29.90 0.000 2.110257 2.406662 PRT | 3.008297 .0850938 35.35 0.000 2.841363 3.175232 PRY | 1.029933 .0720271 14.30 0.000 .8886327 1.171234 QAT | 3.907215 .0836492 46.71 0.000 3.743114 4.071315 ROU | 1.829531 .0787875 23.22 0.000 1.674968 1.984093 RUS | 2.107241 .0840267 25.08 0.000 1.9424 2.272082 RWA | -.4097135 .0765557 -5.35 0.000 -.5598981 -.2595289 SAU | 2.726925 .0909916 29.97 0.000 2.548421 2.90543 SEN | .1264025 .0736379 1.72 0.086 -.0180582 .2708631 SLB | .2797031 .0889694 3.14 0.002 .1051658 .4542405 SLE | -.5799796 .0708857 -8.18 0.000 -.719041 -.4409182 SLV | 1.218882 .0676109 18.03 0.000 1.086245 1.351519 SUR | 2.296507 .0733794 31.30 0.000 2.152553 2.44046 SVK | 2.456901 .0808482 30.39 0.000 2.298295 2.615506 SVN | 2.993768 .0727084 41.17 0.000 2.851131 3.136406 SWE | 3.633559 .0890032 40.83 0.000 3.458955 3.808163 SWZ | 1.570807 .0532517 29.50 0.000 1.466339 1.675274 SYC | 2.30476 .0847078 27.21 0.000 2.138583 2.470937 TCD | .33403 .0619978 5.39 0.000 .2124047 .4556554 TGO | -.4098398 .0673259 -6.09 0.000 -.5419178 -.2777618 THA | 1.363881 .0847582 16.09 0.000 1.197605 1.530157 TJK | -.2195515 .0714084 -3.07 0.002 -.3596383 -.0794646 TLS | .1044352 .0889022 1.17 0.240 -.0699703 .2788408 TON | .918905 .087528 10.50 0.000 .7471953 1.090615 TTO | 2.782115 .06944 40.07 0.000 2.64589 2.918341 TUN | 1.247639 .0776614 16.07 0.000 1.095285 1.399993 TUR | 2.089798 .0830693 25.16 0.000 1.926835 2.252761 TZA | .040556 .0614799 0.66 0.510 -.0800534 .1611655 UGA | -.3402252 .0637982 -5.33 0.000 -.4653825 -.2150679 UKR | .7891186 .0837501 9.42 0.000 .6248201 .953417 URY | 2.233643 .0760216 29.38 0.000 2.084506 2.38278 UZB | .015804 .0897519 0.18 0.860 -.1602686 .1918765 VCT | 1.614673 .0803445 20.10 0.000 1.457056 1.77229 VEN | 2.51807 .0759567 33.15 0.000 2.369061 2.66708 VNM | .1205131 .0755144 1.60 0.111 -.0276288 .268655 VUT | 1.045267 .0750326 13.93 0.000 .8980707 1.192464 WSM | .9078097 .0879645 10.32 0.000 .7352436 1.080376 YEM | .3680235 .0846832 4.35 0.000 .2018947 .5341524 ZAF | 1.854645 .0738143 25.13 0.000 1.709838 1.999452 ZMB | .5920214 .0600777 9.85 0.000 .4741628 .70988 ZWE | -.12698 .0984984 -1.29 0.198 -.3202113 .0662513 | year | 2004 | .0331114 .0113513 2.92 0.004 .0108428 .05538 2005 | .0692521 .0109964 6.30 0.000 .0476796 .0908245 2006 | .1027079 .0110173 9.32 0.000 .0810945 .1243214 2007 | .1458974 .011324 12.88 0.000 .1236823 .1681124 2008 | .1583023 .0115663 13.69 0.000 .1356119 .1809926 2009 | .1319771 .011801 11.18 0.000 .1088263 .1551278 2010 | .1549212 .0120616 12.84 0.000 .1312591 .1785833 2011 | .1744766 .0123191 14.16 0.000 .1503094 .1986439 2012 | .1820987 .0126048 14.45 0.000 .157371 .2068263 2013 | .1950298 .0130268 14.97 0.000 .1694744 .2205853 2014 | .1990976 .0137118 14.52 0.000 .1721983 .2259969 2015 | .2207319 .0138447 15.94 0.000 .1935717 .2478921 | _cons | 5.689903 .1093353 52.04 0.000 5.475413 5.904394 ---------------------------------------------------------------------------------- . . /* Commentary: > The sign of the effect changes for contract enforcement time, anemia, > women's seats in legislature, and natural resources. The magnitude > of the effects change significantly, while contract enforcement time > becomes highly statistical significant (from non-significance), while > women's legislative seats and natural resources become less > significant. > */ . . *** Part 3c: Test for heteroskedasticity . hettest, iid Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of lnpcgdp chi2(1) = 0.09 Prob > chi2 = 0.7587 . . /* Commentary: > The p-value for the generalized Breusch-Pagan test is 0.7587, hence > we fail to reject the null hypothesis of homoskedasticity. Note that > this is not the same thing as accepting homoskedasticity. > */ . . *** Part 3d: Repeat the regression with heteroskedasticity-robust standard errors . reg lnpcgdp compsecavg anemia waterq tariff womenlegislseats natresource /// > i.countryval i.year, robust Linear regression Number of obs = 1,969 F(184, 1783) = . Prob > F = . R-squared = 0.9960 Root MSE = .09714 ---------------------------------------------------------------------------------- | Robust lnpcgdp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- compsecavg | -.0000496 .0003853 -0.13 0.898 -.0008053 .0007061 anemia | .0044579 .0017894 2.49 0.013 .0009483 .0079675 waterq | .0100826 .0016479 6.12 0.000 .0068505 .0133146 tariff | -.0062376 .0010524 -5.93 0.000 -.0083017 -.0041735 womenlegislseats | -.0001998 .0006289 -0.32 0.751 -.0014333 .0010337 natresource | -.000553 .0008556 -0.65 0.518 -.0022312 .0011252 | countryval | AGO | 1.917819 .0609381 31.47 0.000 1.798301 2.037337 ALB | 1.535588 .0966885 15.88 0.000 1.345953 1.725223 ARE | 3.905502 .1113673 35.07 0.000 3.687077 4.123926 ARG | 2.536864 .08996 28.20 0.000 2.360426 2.713302 ARM | 1.339655 .1048207 12.78 0.000 1.134071 1.545239 ATG | 2.846099 .10604 26.84 0.000 2.638123 3.054075 AUT | 4.06986 .092504 44.00 0.000 3.888432 4.251288 AZE | 1.911269 .1243607 15.37 0.000 1.667361 2.155177 BDI | -.9637307 .0702807 -13.71 0.000 -1.101572 -.8258895 BEL | 4.068534 .0946166 43.00 0.000 3.882963 4.254105 BEN | .1425856 .0907371 1.57 0.116 -.0353768 .3205479 BFA | -.2145568 .0852803 -2.52 0.012 -.3818167 -.0472969 BGD | .0373365 .090808 0.41 0.681 -.1407649 .2154378 BGR | 1.996659 .0994844 20.07 0.000 1.80154 2.191777 BHR | 3.208143 .1163214 27.58 0.000 2.980002 3.436283 BHS | 3.378059 .1060367 31.86 0.000 3.170089 3.586028 BLR | 1.91471 .1093254 17.51 0.000 1.70029 2.129129 BLZ | 1.786574 .0939462 19.02 0.000 1.602318 1.970831 BOL | 1.024048 .0815683 12.55 0.000 .8640686 1.184028 BRA | 2.634537 .0943941 27.91 0.000 2.449402 2.819672 BRB | 3.081819 .1017598 30.29 0.000 2.882237 3.2814 BTN | .8502036 .1125376 7.55 0.000 .6294842 1.070923 BWA | 2.046567 .0974821 20.99 0.000 1.855376 2.237758 CAF | -.2270487 .0789447 -2.88 0.004 -.3818825 -.0722148 CHE | 4.530783 .0929983 48.72 0.000 4.348385 4.71318 CHL | 2.819844 .0897343 31.42 0.000 2.643849 2.99584 CHN | 1.552843 .1016332 15.28 0.000 1.353511 1.752176 CIV | .5618468 .096494 5.82 0.000 .3725935 .7511001 CMR | .7047183 .0758099 9.30 0.000 .5560326 .853404 COD | -.5355844 .0642509 -8.34 0.000 -.6615994 -.4095694 COG | 1.397741 .0974307 14.35 0.000 1.206651 1.588831 COL | 2.116307 .0876555 24.14 0.000 1.944389 2.288225 COM | -.0036395 .0843986 -0.04 0.966 -.16917 .161891 CPV | 1.488484 .0856186 17.39 0.000 1.320561 1.656407 CRI | 2.349125 .0849725 27.65 0.000 2.182469 2.515781 CUB | 1.912143 .0935036 20.45 0.000 1.728755 2.095531 CYP | 3.635536 .1009941 36.00 0.000 3.437456 3.833615 CZE | 3.143654 .0983177 31.97 0.000 2.950824 3.336484 DEU | 4.015925 .091167 44.05 0.000 3.83712 4.19473 DJI | .6655584 .0870415 7.65 0.000 .4948443 .8362726 DMA | 2.193904 .0976854 22.46 0.000 2.002314 2.385494 DNK | 4.26123 .0893979 47.67 0.000 4.085894 4.436566 DOM | 1.942355 .0870494 22.31 0.000 1.771625 2.113085 DZA | 1.823093 .0884413 20.61 0.000 1.649634 1.996553 ECU | 1.965704 .0735442 26.73 0.000 1.821462 2.109946 EGY | 1.114436 .1022638 10.90 0.000 .9138667 1.315006 ERI | .1608271 .1106633 1.45 0.146 -.0562163 .3778705 ESP | 3.713916 .0962581 38.58 0.000 3.525125 3.902706 EST | 2.895303 .0997864 29.02 0.000 2.699592 3.091013 ETH | -.3447448 .0706336 -4.88 0.000 -.4832781 -.2062115 FIN | 4.101658 .0950131 43.17 0.000 3.915309 4.288006 FRA | 3.99821 .0958313 41.72 0.000 3.810257 4.186164 GAB | 2.638299 .1335826 19.75 0.000 2.376304 2.900294 GEO | 1.206699 .1037954 11.63 0.000 1.003126 1.410273 GHA | .5577518 .0945838 5.90 0.000 .372245 .7432587 GIN | -.2814191 .0784416 -3.59 0.000 -.4352663 -.1275719 GMB | -.3963736 .1160695 -3.41 0.001 -.6240203 -.1687269 GNB | -.0898112 .0803342 -1.12 0.264 -.2473703 .067748 GNQ | 3.603472 .0738303 48.81 0.000 3.458668 3.748275 GRC | 3.544465 .1011279 35.05 0.000 3.346123 3.742806 GRD | 2.302943 .0942665 24.43 0.000 2.118059 2.487828 GTM | 1.372968 .0805781 17.04 0.000 1.21493 1.531005 GUY | 1.35708 .0974128 13.93 0.000 1.166025 1.548135 HND | 1.056076 .0752266 14.04 0.000 .9085345 1.203618 HRV | 2.819475 .0972994 28.98 0.000 2.628642 3.010308 HTI | .1883682 .0731424 2.58 0.010 .0449144 .331822 HUN | 2.782791 .0986629 28.21 0.000 2.589284 2.976298 IDN | 1.418479 .0765281 18.54 0.000 1.268385 1.568574 IND | .3913276 .1048296 3.73 0.000 .1857258 .5969293 IRL | 4.223426 .0935879 45.13 0.000 4.039873 4.40698 IRN | 2.07579 .0990468 20.96 0.000 1.88153 2.27005 ISL | 4.021722 .0925584 43.45 0.000 3.840187 4.203256 ISR | 3.646757 .0926538 39.36 0.000 3.465036 3.828479 ITA | 3.89767 .1005619 38.76 0.000 3.700439 4.094902 JAM | 1.940257 .0980033 19.80 0.000 1.748044 2.13247 JOR | 1.493303 .1003695 14.88 0.000 1.296449 1.690157 JPN | 4.114505 .1021575 40.28 0.000 3.914144 4.314866 KAZ | 2.468166 .097301 25.37 0.000 2.27733 2.659002 KGZ | .1723502 .0821571 2.10 0.036 .0112159 .3334845 KHM | .2166096 .0659696 3.28 0.001 .0872238 .3459954 KOR | 3.308496 .0929467 35.60 0.000 3.1262 3.490792 KWT | 3.997584 .1108763 36.05 0.000 3.780123 4.215045 LAO | .5927349 .0636968 9.31 0.000 .4678067 .7176631 LBN | 2.276284 .1049722 21.68 0.000 2.070403 2.482166 LBR | -.5433462 .0813354 -6.68 0.000 -.7028689 -.3838234 LCA | 2.271713 .0954476 23.80 0.000 2.084512 2.458914 LKA | 1.253701 .0904251 13.86 0.000 1.076351 1.431051 LSO | .5468686 .0737574 7.41 0.000 .4022085 .6915286 LTU | 2.681548 .0957067 28.02 0.000 2.493839 2.869258 LUX | 4.893985 .0921343 53.12 0.000 4.713282 5.074687 LVA | 2.58327 .1038201 24.88 0.000 2.379648 2.786892 MAR | 1.378392 .0794102 17.36 0.000 1.222645 1.534139 MDA | .7550643 .08696 8.68 0.000 .5845101 .9256185 MDG | -.1168496 .0625186 -1.87 0.062 -.239467 .0057677 MDV | 2.079475 .1146054 18.14 0.000 1.8547 2.30425 MEX | 2.587964 .0860826 30.06 0.000 2.41913 2.756797 MKD | 1.706334 .0929169 18.36 0.000 1.524097 1.888572 MLI | .1000283 .0865945 1.16 0.248 -.069809 .2698657 MLT | 3.293585 .0939437 35.06 0.000 3.109333 3.477836 MMR | .3985895 .0888147 4.49 0.000 .2243976 .5727815 MNG | 1.550028 .0823276 18.83 0.000 1.388559 1.711496 MOZ | -.3087306 .0573566 -5.38 0.000 -.4212239 -.1962374 MRT | .8702238 .0658578 13.21 0.000 .7410572 .9993904 MUS | 2.240449 .0953548 23.50 0.000 2.05343 2.427467 MWI | -.3646322 .0888739 -4.10 0.000 -.5389402 -.1903243 MYS | 2.427828 .0937037 25.91 0.000 2.244048 2.611609 NAM | 1.925847 .0780879 24.66 0.000 1.772693 2.079 NER | -.4122832 .0659917 -6.25 0.000 -.5417123 -.2828541 NGA | 1.286348 .0747472 17.21 0.000 1.139747 1.43295 NIC | .8662283 .0708851 12.22 0.000 .7272016 1.005255 NLD | 4.197635 .090996 46.13 0.000 4.019165 4.376106 NOR | 4.75261 .09276 51.24 0.000 4.57068 4.934539 NPL | -.2153031 .0875337 -2.46 0.014 -.3869825 -.0436237 OMN | 3.144452 .10444 30.11 0.000 2.939614 3.349289 PAK | .2495983 .1038255 2.40 0.016 .0459659 .4532308 PAN | 2.283285 .0939712 24.30 0.000 2.09898 2.46759 PER | 1.889631 .0789536 23.93 0.000 1.734779 2.044482 PHL | 1.005288 .0849904 11.83 0.000 .8385968 1.171979 PNG | 1.057775 .0669228 15.81 0.000 .92652 1.189031 POL | 2.647429 .0967352 27.37 0.000 2.457702 2.837155 PRT | 3.481083 .0914824 38.05 0.000 3.301659 3.660507 PRY | 1.46819 .0818344 17.94 0.000 1.307688 1.628691 QAT | 4.404252 .1013021 43.48 0.000 4.205568 4.602935 ROU | 2.282406 .0904781 25.23 0.000 2.104952 2.45986 RUS | 2.586585 .0945355 27.36 0.000 2.401173 2.771997 RWA | -.1837633 .0773142 -2.38 0.018 -.3353993 -.0321273 SAU | 3.159151 .1117846 28.26 0.000 2.939909 3.378394 SEN | .3636067 .089257 4.07 0.000 .1885473 .5386661 SLB | .6563101 .0832133 7.89 0.000 .4931042 .819516 SLE | -.3226779 .0663917 -4.86 0.000 -.4528917 -.1924641 SLV | 1.635174 .0797819 20.50 0.000 1.478698 1.79165 SUR | 2.416828 .0894786 27.01 0.000 2.241334 2.592321 SVK | 2.883499 .1013867 28.44 0.000 2.68465 3.082349 SVN | 3.340431 .0970159 34.43 0.000 3.150155 3.530708 SWE | 4.159553 .0881972 47.16 0.000 3.986573 4.332534 SWZ | 1.834456 .0612706 29.94 0.000 1.714286 1.954625 SYC | 2.816642 .0979733 28.75 0.000 2.624487 3.008796 TCD | .4539211 .0737844 6.15 0.000 .3092082 .5986341 TGO | -.1163241 .0732534 -1.59 0.112 -.2599955 .0273474 THA | 1.859095 .0945483 19.66 0.000 1.673658 2.044532 TJK | .2221596 .0728931 3.05 0.002 .0791947 .3651245 TLS | .3191211 .0686235 4.65 0.000 .1845303 .453712 TON | 1.477204 .095929 15.40 0.000 1.289059 1.665349 TTO | 2.989828 .0951833 31.41 0.000 2.803146 3.176511 TUN | 1.673531 .0906246 18.47 0.000 1.495789 1.851273 TUR | 2.561519 .0972358 26.34 0.000 2.37081 2.752227 TZA | .2508499 .0578771 4.33 0.000 .1373358 .364364 UGA | -.0776686 .061006 -1.27 0.203 -.1973194 .0419822 UKR | 1.314001 .0974828 13.48 0.000 1.122809 1.505194 URY | 2.649881 .0960114 27.60 0.000 2.461574 2.838187 UZB | .5201667 .1071611 4.85 0.000 .3099921 .7303412 VCT | 2.091299 .0899327 23.25 0.000 1.914914 2.267684 VEN | 2.961188 .0890602 33.25 0.000 2.786515 3.135861 VNM | .5703063 .0811607 7.03 0.000 .4111262 .7294863 VUT | 1.441828 .0835838 17.25 0.000 1.277895 1.60576 WSM | 1.455965 .1007987 14.44 0.000 1.258269 1.653661 YEM | .649224 .0968193 6.71 0.000 .4593328 .8391153 ZAF | 2.287086 .084082 27.20 0.000 2.122177 2.451996 ZMB | .8738124 .0573995 15.22 0.000 .7612351 .9863897 ZWE | .8112446 .1926173 4.21 0.000 .4334653 1.189024 | year | 1997 | .1316556 .0334894 3.93 0.000 .065973 .1973382 1998 | .1610742 .0347732 4.63 0.000 .0928738 .2292747 1999 | .1638944 .0331132 4.95 0.000 .0989496 .2288392 2000 | .1991168 .0322172 6.18 0.000 .1359293 .2623044 2001 | .2072131 .0318235 6.51 0.000 .1447979 .2696283 2002 | .2262189 .0313076 7.23 0.000 .1648155 .2876223 2003 | .2567339 .0311544 8.24 0.000 .1956309 .3178368 2004 | .2862697 .0313085 9.14 0.000 .2248644 .3476749 2005 | .3216881 .0315465 10.20 0.000 .2598161 .3835601 2006 | .3576887 .0316906 11.29 0.000 .295534 .4198433 2007 | .4016396 .0322928 12.44 0.000 .338304 .4649753 2008 | .4156951 .0328476 12.66 0.000 .3512714 .4801189 2009 | .3932491 .0332342 11.83 0.000 .3280671 .4584311 2010 | .4168931 .0334057 12.48 0.000 .3513748 .4824115 2011 | .4386499 .033972 12.91 0.000 .3720207 .5052791 2012 | .445284 .034286 12.99 0.000 .378039 .5125289 2013 | .4608867 .0349855 13.17 0.000 .3922698 .5295036 2014 | .4616302 .0357186 12.92 0.000 .3915755 .531685 2015 | .4871996 .0361472 13.48 0.000 .4163043 .5580949 | _cons | 5.216596 .1474708 35.37 0.000 4.927362 5.50583 ---------------------------------------------------------------------------------- . . /* Commentary: > The parameter estimates do not change, but the standard errors all > increase except for water quality and tariffs. Women's legislative > seats and natural resources lose significance. > */ . . *** Part 3e: Repeat the regression allowing for correlation in errors. . reg lnpcgdp compsecavg contractenftime anemia waterq tariff womenlegislseats /// > natresource i.countryval i.year, cluster(countryval) Linear regression Number of obs = 1,494 F(18, 159) = . Prob > F = . R-squared = 0.9978 Root MSE = .07423 (Std. Err. adjusted for 160 clusters in countryval) ---------------------------------------------------------------------------------- | Robust lnpcgdp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- compsecavg | .0020907 .0009773 2.14 0.034 .0001604 .0040209 contractenftime | -.0001974 .0001137 -1.74 0.085 -.000422 .0000273 anemia | .0040422 .0045504 0.89 0.376 -.0049449 .0130293 waterq | .0123581 .0037751 3.27 0.001 .0049024 .0198138 tariff | -.0052052 .0018432 -2.82 0.005 -.0088455 -.001565 womenlegislseats | -.0011545 .0010821 -1.07 0.288 -.0032915 .0009826 natresource | -.001318 .001009 -1.31 0.193 -.0033108 .0006747 | countryval | AGO | 1.881584 .0767149 24.53 0.000 1.730072 2.033095 ALB | 1.060734 .2450721 4.33 0.000 .5767175 1.54475 ARE | 3.446473 .2470873 13.95 0.000 2.958476 3.934469 ARG | 2.089608 .2301976 9.08 0.000 1.634968 2.544247 ARM | .8398783 .2553511 3.29 0.001 .3355609 1.344196 ATG | 2.251362 .2561882 8.79 0.000 1.745391 2.757332 AUT | 3.521298 .2541645 13.85 0.000 3.019324 4.023272 AZE | 1.505392 .2360129 6.38 0.000 1.039268 1.971517 BDI | -1.180143 .1480205 -7.97 0.000 -1.472483 -.8878031 BEL | 3.520507 .2453653 14.35 0.000 3.035912 4.005103 BEN | -.11219 .1928148 -0.58 0.561 -.4929986 .2686185 BFA | -.5054266 .2106856 -2.40 0.018 -.9215298 -.0893233 BGD | -.1707197 .1681845 -1.02 0.312 -.5028834 .1614439 BGR | 1.629957 .2404459 6.78 0.000 1.155078 2.104837 BHR | 2.669008 .274521 9.72 0.000 2.12683 3.211186 BHS | 2.800538 .2464169 11.37 0.000 2.313865 3.28721 BLR | 1.425366 .2652316 5.37 0.000 .9015342 1.949197 BLZ | 1.35647 .2137154 6.35 0.000 .9343824 1.778557 BOL | .5855312 .2067371 2.83 0.005 .1772262 .9938362 BRA | 2.20117 .2269786 9.70 0.000 1.752888 2.649452 BRB | 2.614535 .2135693 12.24 0.000 2.192736 3.036333 BTN | .4501493 .2637177 1.71 0.090 -.0706923 .9709908 BWA | 1.625918 .2300977 7.07 0.000 1.171476 2.08036 CAF | -.5146051 .1619048 -3.18 0.002 -.8343665 -.1948438 CHE | 3.995399 .2520901 15.85 0.000 3.497522 4.493276 CHL | 2.313282 .2475597 9.34 0.000 1.824353 2.802212 CHN | 1.219794 .2221029 5.49 0.000 .7811412 1.658446 CIV | .2535742 .2071012 1.22 0.223 -.1554499 .6625983 CMR | .4415028 .1527857 2.89 0.004 .1397517 .7432539 COD | -.727125 .1414283 -5.14 0.000 -1.006445 -.4478046 COG | 1.101752 .2181263 5.05 0.000 .6709538 1.532551 COL | 1.851263 .1785705 10.37 0.000 1.498587 2.20394 COM | -.3730015 .2125109 -1.76 0.081 -.7927097 .0467067 CPV | 1.069102 .2189026 4.88 0.000 .6367705 1.501434 CRI | 2.039899 .2045847 9.97 0.000 1.635844 2.443953 CYP | 3.081007 .2328972 13.23 0.000 2.621036 3.540978 CZE | 2.664074 .24631 10.82 0.000 2.177613 3.150535 DEU | 3.490165 .2480117 14.07 0.000 3.000343 3.979987 DJI | .4531545 .1684306 2.69 0.008 .1205046 .7858044 DMA | 1.639805 .2366598 6.93 0.000 1.172403 2.107207 DNK | 3.735135 .246751 15.14 0.000 3.247803 4.222467 DOM | 1.545186 .2099112 7.36 0.000 1.130612 1.95976 DZA | 1.455358 .2076523 7.01 0.000 1.045245 1.86547 ECU | 1.571834 .1895889 8.29 0.000 1.197397 1.946272 EGY | .7236802 .2296415 3.15 0.002 .270139 1.177221 ERI | -.2442172 .1505278 -1.62 0.107 -.5415091 .0530746 ESP | 3.183714 .2431957 13.09 0.000 2.703404 3.664025 EST | 2.425293 .2565309 9.45 0.000 1.918646 2.931941 ETH | -.5120867 .1329794 -3.85 0.000 -.7747205 -.2494528 FIN | 3.521189 .2594686 13.57 0.000 3.00874 4.033639 FRA | 3.409975 .2565794 13.29 0.000 2.903231 3.916718 GAB | 2.245004 .2403411 9.34 0.000 1.770331 2.719676 GEO | .7171521 .2574576 2.79 0.006 .2086744 1.22563 GHA | .1746909 .2295157 0.76 0.448 -.2786018 .6279835 GIN | -.5751421 .2104084 -2.73 0.007 -.9906978 -.1595863 GMB | -.8348798 .2755523 -3.03 0.003 -1.379095 -.290665 GNB | -.18301 .1100027 -1.66 0.098 -.4002649 .0342449 GNQ | 3.397682 .1559418 21.79 0.000 3.089697 3.705666 GRC | 3.105868 .2195426 14.15 0.000 2.672272 3.539464 GRD | 1.832218 .2257037 8.12 0.000 1.386454 2.277982 GTM | 1.169966 .1639561 7.14 0.000 .8461535 1.493779 GUY | .9130189 .2373051 3.85 0.000 .4443423 1.381696 HND | .7391563 .1757607 4.21 0.000 .3920296 1.086283 HRV | 2.329857 .24636 9.46 0.000 1.843297 2.816417 HTI | -.1426838 .1603523 -0.89 0.375 -.4593791 .1740115 HUN | 2.248218 .2652083 8.48 0.000 1.724433 2.772003 IDN | 1.01342 .2023712 5.01 0.000 .6137379 1.413103 IND | .1848951 .2173561 0.85 0.396 -.2443823 .6141725 IRL | 3.701657 .239901 15.43 0.000 3.227854 4.175461 IRN | 1.624774 .245251 6.62 0.000 1.140404 2.109144 ISL | 3.485413 .2509007 13.89 0.000 2.989885 3.980941 ISR | 3.198172 .2265071 14.12 0.000 2.750822 3.645523 ITA | 3.440335 .2155056 15.96 0.000 3.014712 3.865957 JAM | 1.391444 .2337032 5.95 0.000 .929881 1.853007 JOR | 1.020907 .2387249 4.28 0.000 .5494266 1.492388 JPN | 3.473803 .2706125 12.84 0.000 2.939344 4.008261 KAZ | 1.958092 .2471851 7.92 0.000 1.469902 2.446282 KGZ | -.3057205 .2286574 -1.34 0.183 -.7573181 .145877 KHM | -.0573585 .1717017 -0.33 0.739 -.3964687 .2817517 KOR | 2.740273 .26367 10.39 0.000 2.219526 3.26102 KWT | 3.523655 .2548831 13.82 0.000 3.020262 4.027048 LAO | .2982254 .1606507 1.86 0.065 -.0190592 .6155099 LBN | 1.748961 .2264775 7.72 0.000 1.301669 2.196253 LBR | -.6970602 .1318121 -5.29 0.000 -.9573885 -.4367319 LCA | 1.76113 .2268312 7.76 0.000 1.313139 2.209121 LKA | .9362005 .1883859 4.97 0.000 .5641391 1.308262 LSO | .2748861 .1621019 1.70 0.092 -.0452646 .5950368 LTU | 2.235695 .2499547 8.94 0.000 1.742035 2.729354 LUX | 4.294516 .2570329 16.71 0.000 3.786877 4.802155 LVA | 2.156176 .260348 8.28 0.000 1.64199 2.670363 MAR | 1.009477 .1987931 5.08 0.000 .6168617 1.402093 MDA | .3056021 .2216445 1.38 0.170 -.132145 .7433492 MDG | -.3120563 .0915204 -3.41 0.001 -.4928088 -.1313038 MDV | 1.576904 .2699063 5.84 0.000 1.043841 2.109968 MEX | 2.065461 .2290933 9.02 0.000 1.613003 2.51792 MKD | 1.231023 .237339 5.19 0.000 .7622792 1.699766 MLI | -.1672504 .1853228 -0.90 0.368 -.5332622 .1987614 MLT | 2.791095 .2459847 11.35 0.000 2.305276 3.276914 MMR | .2329129 .1614538 1.44 0.151 -.0859576 .5517835 MNG | 1.176479 .1854394 6.34 0.000 .8102371 1.542721 MOZ | -.3764403 .102025 -3.69 0.000 -.5779392 -.1749414 MRT | .6371556 .1565946 4.07 0.000 .3278818 .9464294 MUS | 1.807714 .2349293 7.69 0.000 1.343729 2.271698 MWI | -.796405 .1952979 -4.08 0.000 -1.182117 -.4106924 MYS | 1.942328 .2427148 8.00 0.000 1.462967 2.421689 NAM | 1.547456 .2043384 7.57 0.000 1.143889 1.951024 NER | -.6506018 .1503813 -4.33 0.000 -.9476043 -.3535993 NGA | 1.001468 .1769077 5.66 0.000 .6520755 1.35086 NIC | .4877776 .19516 2.50 0.013 .1023374 .8732178 NLD | 3.731026 .2373397 15.72 0.000 3.262281 4.199771 NOR | 4.189833 .2602575 16.10 0.000 3.675825 4.703841 NPL | -.507902 .1835012 -2.77 0.006 -.8703161 -.1454879 OMN | 2.7252 .237021 11.50 0.000 2.257084 3.193315 PAK | -.0125436 .2197448 -0.06 0.955 -.4465387 .4214515 PAN | 1.954282 .2120917 9.21 0.000 1.535402 2.373162 PER | 1.461365 .2055206 7.11 0.000 1.055463 1.867267 PHL | .6483113 .1935901 3.35 0.001 .2659715 1.030651 PNG | .8758755 .1332048 6.58 0.000 .6127966 1.138954 POL | 2.258459 .2257485 10.00 0.000 1.812607 2.704312 PRT | 3.008297 .2395233 12.56 0.000 2.53524 3.481355 PRY | 1.029933 .2096887 4.91 0.000 .6157988 1.444068 QAT | 3.907215 .2540738 15.38 0.000 3.40542 4.40901 ROU | 1.829531 .2387738 7.66 0.000 1.357953 2.301108 RUS | 2.107241 .2538083 8.30 0.000 1.605971 2.608512 RWA | -.4097135 .1832098 -2.24 0.027 -.7715522 -.0478748 SAU | 2.726925 .2639113 10.33 0.000 2.205701 3.248149 SEN | .1264025 .1938612 0.65 0.515 -.2564727 .5092777 SLB | .2797031 .2080101 1.34 0.181 -.1311161 .6905223 SLE | -.5799796 .1588175 -3.65 0.000 -.8936436 -.2663156 SLV | 1.218882 .1924229 6.33 0.000 .8388475 1.598916 SUR | 2.296507 .1779417 12.91 0.000 1.945073 2.647941 SVK | 2.456901 .2472335 9.94 0.000 1.968615 2.945186 SVN | 2.993768 .2125113 14.09 0.000 2.574059 3.413477 SWE | 3.633559 .2492219 14.58 0.000 3.141346 4.125771 SWZ | 1.570807 .120366 13.05 0.000 1.333084 1.808529 SYC | 2.30476 .224615 10.26 0.000 1.861146 2.748373 TCD | .33403 .1275494 2.62 0.010 .0821204 .5859397 TGO | -.4098398 .1655608 -2.48 0.014 -.7368217 -.0828579 THA | 1.363881 .2452896 5.56 0.000 .8794352 1.848327 TJK | -.2195515 .1820224 -1.21 0.230 -.5790449 .139942 TLS | .1044352 .1214819 0.86 0.391 -.135491 .3443615 TON | .918905 .2600852 3.53 0.001 .4052377 1.432572 TTO | 2.782115 .1901623 14.63 0.000 2.406546 3.157685 TUN | 1.247639 .2263437 5.51 0.000 .8006111 1.694667 TUR | 2.089798 .2522872 8.28 0.000 1.591532 2.588064 TZA | .040556 .1381731 0.29 0.770 -.2323354 .3134474 UGA | -.3402252 .1608867 -2.11 0.036 -.6579757 -.0224747 UKR | .7891186 .253942 3.11 0.002 .287584 1.290653 URY | 2.233643 .2277689 9.81 0.000 1.7838 2.683486 UZB | .015804 .2553321 0.06 0.951 -.488476 .5200839 VCT | 1.614673 .2336886 6.91 0.000 1.153139 2.076207 VEN | 2.51807 .2215092 11.37 0.000 2.080591 2.95555 VNM | .1205131 .2206964 0.55 0.586 -.3153615 .5563877 VUT | 1.045267 .2099019 4.98 0.000 .630712 1.459823 WSM | .9078097 .2568607 3.53 0.001 .4005107 1.415109 YEM | .3680235 .2032483 1.81 0.072 -.0333911 .7694382 ZAF | 1.854645 .2131014 8.70 0.000 1.433771 2.27552 ZMB | .5920214 .1417637 4.18 0.000 .3120385 .8720042 ZWE | -.12698 .1839751 -0.69 0.491 -.4903301 .2363701 | year | 2004 | .0331114 .007844 4.22 0.000 .0176195 .0486033 2005 | .0692521 .0122369 5.66 0.000 .0450843 .0934198 2006 | .1027079 .0139226 7.38 0.000 .0752109 .130205 2007 | .1458974 .0165958 8.79 0.000 .1131207 .178674 2008 | .1583023 .0188581 8.39 0.000 .1210575 .195547 2009 | .1319771 .0201126 6.56 0.000 .0922547 .1716995 2010 | .1549212 .0220051 7.04 0.000 .1114612 .1983812 2011 | .1744766 .0243725 7.16 0.000 .1263411 .2226122 2012 | .1820987 .0255173 7.14 0.000 .1317021 .2324953 2013 | .1950298 .0278159 7.01 0.000 .1400935 .2499661 2014 | .1990976 .0285072 6.98 0.000 .142796 .2553992 2015 | .2207319 .029752 7.42 0.000 .1619718 .2794919 | _cons | 5.689903 .3331527 17.08 0.000 5.031928 6.347879 ---------------------------------------------------------------------------------- . . /* Commentary: > Once again, allowing for autocorrelation in standard errors will not > affect point estimates. The standard errors increase even more in > this case, with anemia statistically significant and contract > enforcement time significant only at the p=0.1 level. > */ . . *** Part 3f: Basic fixed effects regression. . xtreg lnpcgdp compsecavg contractenftime anemia waterq tariff womenlegislseats /// > natresource i.year, fe Fixed-effects (within) regression Number of obs = 1,494 Group variable: countryval Number of groups = 160 R-sq: Obs per group: within = 0.6005 min = 1 between = 0.5099 avg = 9.3 overall = 0.5111 max = 13 F(19,1315) = 104.02 corr(u_i, Xb) = 0.6137 Prob > F = 0.0000 ---------------------------------------------------------------------------------- lnpcgdp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- compsecavg | .0020907 .0004033 5.18 0.000 .0012995 .0028819 contractenftime | -.0001974 .0000374 -5.28 0.000 -.0002707 -.0001241 anemia | .0040422 .0014602 2.77 0.006 .0011777 .0069067 waterq | .0123581 .0011737 10.53 0.000 .0100555 .0146607 tariff | -.0052052 .0010451 -4.98 0.000 -.0072554 -.003155 womenlegislseats | -.0011545 .000554 -2.08 0.037 -.0022413 -.0000676 natresource | -.001318 .0006405 -2.06 0.040 -.0025745 -.0000615 | year | 2004 | .0331114 .0113513 2.92 0.004 .0108428 .05538 2005 | .0692521 .0109964 6.30 0.000 .0476796 .0908245 2006 | .1027079 .0110173 9.32 0.000 .0810945 .1243214 2007 | .1458974 .011324 12.88 0.000 .1236823 .1681124 2008 | .1583023 .0115663 13.69 0.000 .1356119 .1809926 2009 | .1319771 .011801 11.18 0.000 .1088263 .1551278 2010 | .1549212 .0120616 12.84 0.000 .1312591 .1785833 2011 | .1744766 .0123191 14.16 0.000 .1503094 .1986439 2012 | .1820987 .0126048 14.45 0.000 .157371 .2068263 2013 | .1950298 .0130268 14.97 0.000 .1694744 .2205853 2014 | .1990976 .0137118 14.52 0.000 .1721983 .2259969 2015 | .2207319 .0138447 15.94 0.000 .1935717 .2478921 | _cons | 7.159495 .1234928 57.98 0.000 6.917231 7.40176 -----------------+---------------------------------------------------------------- sigma_u | 1.3267145 sigma_e | .07423316 rho | .99687907 (fraction of variance due to u_i) ---------------------------------------------------------------------------------- F test that all u_i=0: F(159, 1315) = 1072.31 Prob > F = 0.0000 . *** 3g: Test for autocorrelation using the Inoue and Solo (2006) Test . xtistest (8,705 missing values generated) Inoue and Solo (2006) LM-test as postestimation Panelvar: countryval Timevar: year p (lags): 2 --------------------------------------------------------------------------------------+ Variable | IS-stat p-value | N maxT | balance? | ------------------------------+-----------------------+----------------+--------------| Post Estimation + 81.09 0.000 + 160 13 + gaps | --------------------------------------------------------------------------------------+ Notes: Under H0, LM ~ chi2(p*T-p(p+1)/2) H0: No auto-correlation of any order. Ha: Auto-correlation up to order 2. . . /* Commentary: > With a p-value of 0.000, we reject the null of no autocorrelation. > */ . . *** Part 3h: Fixed effects regression allowing for autocorrelated errors. . xtreg lnpcgdp compsecavg contractenftime anemia waterq tariff womenlegislseats /// > natresource i.year, fe robust Fixed-effects (within) regression Number of obs = 1,494 Group variable: countryval Number of groups = 160 R-sq: Obs per group: within = 0.6005 min = 1 between = 0.5099 avg = 9.3 overall = 0.5111 max = 13 F(19,159) = 19.24 corr(u_i, Xb) = 0.6137 Prob > F = 0.0000 (Std. Err. adjusted for 160 clusters in countryval) ---------------------------------------------------------------------------------- | Robust lnpcgdp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- compsecavg | .0020907 .0009231 2.26 0.025 .0002675 .0039139 contractenftime | -.0001974 .0001074 -1.84 0.068 -.0004096 .0000148 anemia | .0040422 .004298 0.94 0.348 -.0044464 .0125308 waterq | .0123581 .0035656 3.47 0.001 .005316 .0194002 tariff | -.0052052 .0017409 -2.99 0.003 -.0086436 -.0017669 womenlegislseats | -.0011545 .001022 -1.13 0.260 -.003173 .000864 natresource | -.001318 .000953 -1.38 0.169 -.0032002 .0005642 | year | 2004 | .0331114 .0074089 4.47 0.000 .0184789 .0477439 2005 | .0692521 .011558 5.99 0.000 .046425 .0920791 2006 | .1027079 .0131503 7.81 0.000 .0767362 .1286796 2007 | .1458974 .0156752 9.31 0.000 .1149389 .1768558 2008 | .1583023 .017812 8.89 0.000 .1231236 .1934809 2009 | .1319771 .0189969 6.95 0.000 .0944582 .1694959 2010 | .1549212 .0207844 7.45 0.000 .1138721 .1959703 2011 | .1744766 .0230205 7.58 0.000 .1290113 .219942 2012 | .1820987 .0241018 7.56 0.000 .1344978 .2296996 2013 | .1950298 .0262729 7.42 0.000 .143141 .2469186 2014 | .1990976 .0269258 7.39 0.000 .1459192 .2522759 2015 | .2207319 .0281015 7.85 0.000 .1652314 .2762323 | _cons | 7.159495 .3886965 18.42 0.000 6.391821 7.927169 -----------------+---------------------------------------------------------------- sigma_u | 1.3267145 sigma_e | .07423316 rho | .99687907 (fraction of variance due to u_i) ---------------------------------------------------------------------------------- . /* Commentary: > A 1 percentage point change in the average lower secondary school completion rate > is estimate to increase GDP per capita by 0.209 percentage points (significant > at p=0.05 level). > > A increase in the average time to enforcce a contract in court > of 1 day is estimated to reduce GDP by 0.01974 percentage points (significant at > p=0.1 level). > > A 1 percentage point increase in the women aged 15-49 is estimated > to increase GPD per capita by 0.40422 percentage points, but this is not > statistically significant. > > A 1 percentage point increase in the % of the population > with acess to an improved water source is estimate to increase GDP per capita by > 1.23581 percentage points (significant at p=0.01 level). > > A 1 percentage point > increase in the weight mean tariff rate rates for all products is estimated to > decrease GDP per capita by 0.52052 percentage points (significant at p=0.01 level). > > A 1 percentage point increase in the proportion of seats held by women in the > national parliament/legislature is estimated to decrease GDP per capita by 0.11545 > percentage points, but this is not statistically significant. > > A 1 percentage point increase in > natural resource rents as a share of GDP is estimated to reduce GDP per capita by > 0.1318 percentage points but this is not statistically significant. > > Comparing this to the model in 3f, we see that whereas all parameter estimates are > statistically significant at the p=0.05 level (and significant at p<0.01), now > estimates for anemaia rates, women's share of seats in legislature, and natural > resource rents are all not statistically significant. > */ . . *** Part 3i: Discussion of Fixed Effects Assumptions in Model . /* > Commentary: > In short, this model is quite problematic. As with any fixed effects or > difference-in-differences model, we are able to control for only those factors > that are fixed within a country over time, or which are common to all countries > in a given time period (year). This panel notably contains the 2008 recession, > which hit different countries in very different ways. Additionally, however, > one might note the following two observations (in addition to the concern in 3j): > > (1) We should very much question what is the basis for the variation in our > explanatory variables. To the extent that these variables are relevant in the > fixed effects regression, this indicates that the variables change both between > regions and over time - and without a strong a priori basis for thinking these > changes are random. In truth, these variables are likely endogenous to other > characteristics of the country that are also not captured by the year or country > fixed effects. > > (2) The explanatory variables are likely to have persistent effects (i.e. an effect > not only in the current period, but period (t+1,t+2,...). Each of the issues listed > above indicate a violation of the strict exogeneity assumption: E[u_it|X_it, a_t]=0. > > */ . . *** Part 3j: Concern with Fixed Effects in this context . . /* > Commentary: > Because determinants of growth are likely to change only slowly, the fixed effects are > likely to absorb much of the effects of the true underlying features captured by the > proxy variables we choose to include. In this case, instead of identifying the effects > of, for example, (relatively fixed) natural resource endowments, we instead capture the > effects of the change in value of these fixed resource endowments. This change in value > is likely to occur for several reasons (and hence may be endogenous to a number of other > issues). In any case, it is unclear that estimates based on these minor fluctuations in > relatively static determinants will identify the same effects as estimates based on the > underlying variables themselves. > */ . . *** Part 4a: Inclusion of lagged dependent variable in fixed effects model . xtreg lnpcgdp L.lnpcgdp compsecavg contractenftime anemia waterq tariff womenlegislseats /// > natresource i.year, fe robust Fixed-effects (within) regression Number of obs = 1,494 Group variable: countryval Number of groups = 160 R-sq: Obs per group: within = 0.9367 min = 1 between = 0.9994 avg = 9.3 overall = 0.9991 max = 13 F(20,159) = 524.24 corr(u_i, Xb) = 0.9675 Prob > F = 0.0000 (Std. Err. adjusted for 160 clusters in countryval) ---------------------------------------------------------------------------------- | Robust lnpcgdp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- lnpcgdp | L1. | .8794047 .0209687 41.94 0.000 .8379916 .9208178 | compsecavg | .0003352 .0002357 1.42 0.157 -.0001303 .0008006 contractenftime | -.0000369 .0000193 -1.91 0.058 -.000075 1.22e-06 anemia | -.0015506 .0006705 -2.31 0.022 -.0028749 -.0002263 waterq | .0015618 .0006702 2.33 0.021 .0002381 .0028856 tariff | -.0005795 .0004654 -1.25 0.215 -.0014986 .0003397 womenlegislseats | -.000019 .0002542 -0.07 0.941 -.000521 .000483 natresource | .0003827 .0003135 1.22 0.224 -.0002364 .0010018 | year | 2004 | .0110729 .0042857 2.58 0.011 .0026086 .0195372 2005 | .0123409 .0041745 2.96 0.004 .0040964 .0205855 2006 | .0193883 .0049827 3.89 0.000 .0095476 .0292291 2007 | .0243832 .0056939 4.28 0.000 .0131378 .0356286 2008 | .0067951 .0060496 1.12 0.263 -.0051528 .0187431 2009 | -.0338128 .0077973 -4.34 0.000 -.0492126 -.0184131 2010 | .0076526 .0072857 1.05 0.295 -.0067366 .0220419 2011 | .006421 .0072582 0.88 0.378 -.0079139 .0207558 2012 | .0024062 .0080366 0.30 0.765 -.0134661 .0182786 2013 | .0058373 .0087218 0.67 0.504 -.0113883 .0230629 2014 | .0089794 .0089784 1.00 0.319 -.0087529 .0267118 2015 | .0086924 .0099075 0.88 0.382 -.0108748 .0282597 | _cons | .9449181 .1794213 5.27 0.000 .5905617 1.299275 -----------------+---------------------------------------------------------------- sigma_u | .1393468 sigma_e | .02954875 rho | .95696893 (fraction of variance due to u_i) ---------------------------------------------------------------------------------- . . *** Part 4b: Discussion of lagged dependent variable . /* Commentary: See theory solution for Stock and Watson question , part (g). */ . . *** Part 4d: Discussion of inclusion of alternative explanatory variables . /* Commentary: > > Each of these variables are proxies for determinants of growth. > > Variables like sanitation rate, tuburculosis rate, and female mortality > rate address issues related to the health environment of a country, which may help > determine it's human capital stock. Each of these variables address different types > of health problems, consequentially producing different estimated effects and implicating > different types of endogeneity concerns. > > Domestic credit and the tax rate address the financial institutional setting of a country, > which one might consider related to the tariff rate, although 'openness,' 'competitiveness,' > and 'financial access' or all somewhat different. Once again, each of these concerns > implicate different types of endogeneity/OVB, but are also each likely to be sources of OVB in > the original FE regression. > > Tertiary enrollment captures a human capital effect similar to the lower secondary school > completion rate, but it is also different in that the effects of basic and higher educational > are likely quite different. > > Finally, cellphone usage rates proxies communication and infrastructure development, which has > generally not been addressed up by variables in the main regressions. > > The different but correlated effects of these additional variables relative to the original > variables suggests we might want to include them in the analysis, but the fundamental concern > that we fail to isolate exogenous variation in the explanatory variables is still a major issue. > > Access to captial for example is likely a major determinant of growth, but it is likely > endogenous to income/wealth (e.g. GDP) and demand for capital, expectations about the likelihood > of repayment (including features likely likelihood of conflict or recession), as well as to the > political and historical context of the banking sector. > > In similar fashion, the tax rate is likely a response to issues like income, inequality, and > changes in the macroeconomic environemtn, as well as a host of politica and cultural/historical > determinants of the size of government and scope of redistribution. We could continue listing > such concerns for each of these variables. */ . . *** Part 4e: Discussion of the effect of including additional explanatory variables on number of . *** repeated observations and implications . . /* Commentary: > > Including additional variables in this case will often make the number of observations > (years) per group (country) quite small, which will tend to increase standard errors and > reduce the power of hypothesis tests. > > */ . . end of do-file