SUPPLEMENTARY ONLINE APPENDIX FOR: TECHNOLOGY AND COLLECTIVE ACTION: THE EFFECT OF CELL PHONE COVERAGE ON POLITICAL VIOLENCE IN AFRICA
|
|
|
- Dylan Dawson
- 5 years ago
- Views:
Transcription
1 SUPPLEMENTARY ONLINE APPENDIX FOR: TECHNOLOGY AND COLLECTIVE ACTION: THE EFFECT OF CELL PHONE COVERAGE ON POLITICAL VIOLENCE IN AFRICA
2 1. CELL PHONES AND PROTEST The Afrobarometer survey asks whether respondents never, less than once a month, a few times a month, a few times a week or every day use a cell phone. In addition, the survey includes a question on protest behavior, i.e whether respondents have or would have participated in a demonstration or protest march in the last year, ranging from No, would never do this to Yes, often on a 0-4 ordinal scale. Table 1 presents the cross tabulation of both variables for respondents that chose to answer each question. Table 2 shows a simple linear regression, controlling for the respondent s employment status, age, education and internet usage. 2
3 TABLE 1. Cross Tab for Cell Phone Use and Protest Behavior Protest / Cell Phone Usage Never Less than once a month A few times a month A few times a week Every day Total Never % 51.66% 51.90% 51.43% 49.03% No, but would % 34.48% 35.94% 35.93% 35.20% Yes, once or twice % 7.73% 6.96% 6.64% 8.28% Yes, several times % 3.68% 3.66% 3.81% 4.86% Yes, often % 2.45% 1.55% 2.19% 2.63% Total % 100% 100% 100% 100% TABLE 2. Cell Phone Usage and Protest Behavior (Intercept) ( ) Employment Status (2.567) Age ( 2.004) Education (0.821) Internet Usage (7.747) Cell Phone Usage (8.495) AIC BIC Deviance Log-likelihood N
4 2. SUMMARY STATISTICS 4
5 TABLE 3. Summary Statistics Variable Mean Std.Dev Min Max pre-2000 Conflict Pct Mountainous Border Distance Capital Distance Population in Pct Irrigation GDP pc in Cell Phone Coverage in Conflict Dummy in Conflict 2008 Count
6 3. ADDITIONAL TABLES 6
7 TABLE 4. Spatial Binary Models Logit, robust SE Re-Logit, robust SE Mixed Logit Mixed Logit OLS FE, robust SE (Intercept) ( ) ( ) ( ) ( ) ( 1.695) Spatial Lag (16.517) (16.436) (19.002) (17.579) (11.359) pre-2000 Conflict (2.204) (2.182) (2.950) (3.626) (2.408) Border Distance (0.475) (0.496) (0.495) ( 0.467) ( 2.198) Capital Distance (1.822) (1.821) (1.888) (1.121) (0.614) Population (3.705) (3.577) (4.583) (4.865) (2.451) Pct Mountainous (4.904) (4.914) (4.853) (5.148) (4.228) Pct Irrigation ( 1.438) ( 1.336) ( 1.613) ( 2.123) ( 2.803) GDP pc ( 3.889) ( 3.834) ( 4.088) ( 3.678) ( 0.257) Cell Phone Coverage (1.572) (1.608) (1.516) (4.476) (3.074) Mean Cell Coverage ( 5.803) Country Fixed Effects No No No No Y es AIC BIC Deviance Log-likelihood N TABLE 5. Count DV Models Poisson, robust SE Negative Binomial, robust SE Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( ) ( ) ( ) pre-2000 Conflict (6.151) (36.757) (0.843) (24.258) Border Distance (1.585) ( 0.555) (1.357) ( 0.696) Capital Distance (1.394) (3.010) (0.560) (3.350) Population (4.222) (13.285) (3.290) (10.216) Pct Mountainous (7.596) (4.994) (2.877) (5.065) Pct Irrigation ( 2.580) ( 1.934) ( 2.192) ( 0.685) GDP pc ( 3.077) ( 5.404) ( 3.208) ( 5.601) Cell Phone Coverage (3.845) (1.898) (2.852) (3.431) Spatial Lag (8.635) (30.193) AIC BIC Deviance Log-likelihood N
8 TABLE 6. Binary DV Models, Natural Resources Logit, robust SE Re-Logit, robust SE Mixed Effects Logit Mixed Effects Logit Fixed Effects OLS, robust SE (Intercept) ( ) ( 20.87) ( ) ( ) ( 1.639) pre-2000 Conflict (1.952) (1.931) (5.948) (6.465) (3.048) Border Distance (0.585) (0.621) (0.620) ( 0.640) ( 2.801) Capital Distance (2.234) (2.245) (2.302) (1.571) (0.089) Population (2.820) (2.693) (4.174) (4.587) (2.494) Pct Mountainous (8.537) (8.537) (8.610) (8.907) (5.341) Pct Irrigation ( 2.183) ( 1.992) ( 2.126) ( 2.667) ( 3.573) GDP pc ( 4.304) ( 4.269) ( 6.042) ( 4.531) ( 0.546) Diamonds e ( ) (1.112e+06) ( 0.024) ( 0.024) ( 2.262) Oil (3.679) (3.745) (4.387) (3.783) (1.238) Cell Phone Coverage (2.773) (2.808) (2.745) (7.164) (5.758) Mean Cell Coverage ( 8.348) Country Fixed Effects No No No No Y es AIC BIC Deviance Log-likelihood N
9 TABLE 7. Count DV Models, Natural Resources Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( ) pre-2000 Conflict (5.920) (36.612) Border Distance (1.437) ( 0.699) Capital Distance (1.371) (2.944) Population (3.859) (12.546) Pct Mountainous (7.737) (5.062) Pct Irrigation ( 2.714) ( 2.140) GDP pc ( 3.041) ( 5.634) Diamond ( ) ( ) Oil (2.156) (2.194) Cell Phone Coverage (3.631) (2.150) AIC BIC Deviance Log-likelihood N
10 TABLE 8. Spatial Binary and Count Models, Natural Resources Logit, robust SE Re-Logit, robust SE Mixed Logit Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( ) ( ) ( ) ( ) Spatial Lag (15.645) (15.55) (16.828) (7.528) (29.382) pre-2000 Conflict (2.399) (2.361) (3.785) (1.739) (25.297) Border Distance (0.553) (0.572) ( 0.345) (1.633) ( 0.846) Capital Distance (1.526) (1.530) (0.906) ( 0.209) (3.348) Population (3.623) (3.514) (4.647) (3.075) (10.328) Pct Mountainous (5.032) (5.039) (5.169) (3.156) (5.131) Pct Irrigation ( 1.654) ( 1.552) ( 2.284) ( 2.401) ( 0.885) GDP pc ( 4.216) ( 4.152) ( 3.704) ( 2.299) ( 5.897) Diamonds e ( ) (1.429e+06) ( 0.024) ( ) ( ) Oil (1.840) (1.896) (1.922) (0.396) (0.374) Cell Phone Coverage (1.601) (1.635) (4.525) (2.828) (3.507) Mean Cell Coverage ( 5.841) AIC BIC Deviance Log-likelihood N
11 TABLE 9. Binary DV Models, Excluded Ethnicity Logit, robust SE Re-Logit, robust SE Mixed Effects Logit Mixed Effects Logit Fixed Effects OLS, robust SE (Intercept) ( ) ( ) ( ) ( 3.412) ( 1.682) pre-2000 Conflict (1.683) (1.666) (5.306) (8.725) (2.982) Border Distance ( 0.032) ( 0.004) ( 0.041) ( 2.868) ( 2.821) Capital Distance (1.323) (1.332) (1.719) ( 1.048) ( 0.322) Population (2.609) (2.490) (4.580) (4.606) (2.518) Pct Mountainous (7.064) (7.058) (8.110) (4.382) (4.719) Pct Irrigation ( 1.804) ( 1.614) ( 1.906) ( 1.766) ( 2.867) GDP pc ( 2.901) ( 2.864) ( 4.591) ( 2.087) ( 3.436) Ethnicities excluded (3.150) (3.134) (4.229) (2.710) (1.604) Cell Phone Coverage (3.020) (3.043) (3.657) (4.806) (5.811) Mean Cell Coverage ( 1.109) Country Fixed Effects No No No No Y es AIC BIC Deviance Log-likelihood N
12 TABLE 10. Count DV Models, Excluded Ethnicity Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( ) pre-2000 Conflict (5.918) (33.957) Border Distance (0.940) ( 1.751) Capital Distance (0.839) (1.516) Population (4.085) (11.993) Pct Mountainous (7.435) (7.143) Pct Irrigation ( 2.524) ( 2.454) GDP pc ( 2.378) ( 4.074) Ethnicities excluded (3.451) (5.496) Cell Phone Coverage (4.097) (2.741) AIC BIC Deviance Log-likelihood N
13 TABLE 11. Spatial Binary and Count Models, Ethnicity Excluded Logit, robust SE Re-Logit, robust SE Mixed Logit Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( ) ( ) ( ) ( ) Spatial Lag (13.589) (13.481) (13.803) (8.815) (20.782) pre-2000 Conflict (3.020) (2.979) (4.369) (1.895) (27.742) Border Distance (0.813) (0.840) (0.105) (1.883) ( 1.075) Capital Distance (0.644) (0.657) (0.568) ( 0.331) (3.315) Population (3.610) (3.458) (4.415) (2.741) (9.326) Pct Mountainous (5.239) (5.235) (5.048) (3.802) (10.130) Pct Irrigation ( 1.653) ( 1.511) ( 2.188) ( 2.310) ( 2.278) GDP pc ( 2.957) ( 2.912) ( 2.504) ( 2.632) ( 6.132) Ethnicities excluded (0.804) (0.807) (0.252) (1.508) (3.123) Cell Phone Coverage (2.715) (2.735) (4.764) (3.568) (4.930) Mean Cell Coverage ( 4.531) AIC BIC Deviance Log-likelihood N
14 TABLE 12. Spatial Binary and Count Models, Ethnicity Excluded Logit, robust SE Re-Logit, robust SE Mixed Logit Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( ) ( ) ( ) ( ) Spatial Lag (14.078) (13.993) (15.999) pre-2000 Conflict (2.006) (1.966) (3.629) (1.883) (22.487) Border Distance (0.286) (0.300) ( 0.431) (1.046) ( 0.979) Capital Distance (1.173) (1.176) (1.208) ( 0.328) (2.844) Population (3.494) (3.377) (4.749) (3.171) (10.058) Pct Mountainous (4.308) (4.313) (4.734) (3.611) (6.430) Pct Irrigation ( 1.461) ( 1.348) ( 2.213) ( 2.231) ( 1.273) GDP pc ( 2.833) ( 2.777) ( 3.069) ( 1.818) ( 4.911) Ethnicities excluded (0.891) (0.895) ( 0.194) (2.557) (3.091) Cell Phone Coverage (1.387) (1.410) (4.375) (2.915) (3.597) Mean Cell Coverage ( 5.794) Spatial Lag (7.810) (21.030) AIC BIC Deviance Log-likelihood N
15 TABLE 13. Binary DV Models, Precise UCDP Logit, robust SE Re-Logit, robust SE Mixed Effects Logit Mixed Effects Logit Fixed Effects OLS, robust SE (Intercept) ( ) ( ) ( ) ( ) ( 1.446) pre-2000 Conflict (1.910) (1.822) (3.235) (3.339) (1.775) Border Distance ( 1.050) ( 1.018) ( 1.087) ( 2.150) ( 4.108) Capital Distance (1.348) (1.371) (1.406) (0.575) ( 0.009) Population (2.559) (2.463) (4.632) (4.713) (2.508) Pct Mountainous (8.852) (8.863) (8.150) (8.695) (4.575) Pct Irrigation ( 1.351) ( 1.112) ( 1.370) ( 1.826) ( 2.866) GDP pc ( 3.735) ( 3.670) ( 4.873) ( 3.604) (0.095) Cell Phone Coverage (3.445) (3.475) (3.320) (6.938) (5.355) Mean Cell Coverage Country Fixed Effects No No No No Y es AIC BIC Deviance Log-likelihood N TABLE 14. Count DV Models, Precise UCDP Poisson, robust SE Negative Binomial, robust SE (Intercept) ( 9.955) ( ) pre-2000 Conflict (5.097) (31.386) Border Distance ( 0.528) ( 1.885) Capital Distance (0.825) (2.648) Population (4.486) (15.973) Pct Mountainous (6.549) (5.234) Pct Irrigation ( 1.518) ( 0.220) GDP pc ( 2.610) ( 4.147) Cell Phone Coverage (4.992) (2.380) AIC BIC Deviance Log-likelihood N
16 TABLE 15. Spatial Binary and Count Models, Precise UCDP Logit, robust SE Re-Logit, robust SE Mixed Logit Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( ) ( ) ( ) ( ) Spatial Lag (13.206) (13.121) (14.631) (5.019) (22.375) pre-2000 Conflict (1.984) (2.075) (2.250) (2.698) (5.761) Border Distance ( 0.402) ( 0.390) ( 1.506) ( 0.340) ( 1.624) Capital Distance (0.240) (0.259) ( 0.404) ( 0.472) (2.539) Population (3.179) (3.044) (4.556) (3.082) (10.777) Pct Mountainous (4.936) (4.939) (5.236) (2.315) (4.476) Pct Irrigation ( 1.135) ( 0.968) ( 1.570) ( 1.262) (0.403) GDP pc ( 3.137) ( 3.050) ( 2.771) ( 1.976) ( 4.514) Cell Phone Coverage (2.146) (2.171) (4.832) (3.972) (2.922) Mean Cell Coverage ( 5.611) AIC BIC Deviance Log-likelihood N
17 TABLE 16. Binary DV Models, ACLED Logit, robust SE Re-Logit, robust SE Mixed Effects Logit Mixed Effects Logit Fixed Effects OLS, robust SE (Intercept) ( ) ( ) ( ) ( ) (0.330) pre-2000 Conflict (2.518) (2.478) (5.039) (5.037) (2.540) Border Distance ( 2.846) ( 2.829) ( 3.290) ( 3.298) ( 4.423) Capital Distance (1.003) (1.007) (1.131) (1.085) (3.411) Population (9.318) (9.271) (13.305) (13.275) (4.462) Pct Mountainous (5.031) (5.047) (5.230) (5.229) (3.923) Pct Irrigation ( 1.068) ( 1.047) ( 1.615) ( 1.633) ( 0.469) GDP pc ( 2.610) ( 2.577) ( 3.414) ( 3.269) ( 0.175) Cell Phone Coverage (7.238) (7.243) (7.449) (6.861) (6.351) Mean Cell Coverage ( 0.259) Country Fixed Effects No No No No Y es AIC BIC Deviance Log-likelihood N TABLE 17. Count DV Models, ACLED Poisson, robust SE Negative Binomial, robust SE (Intercept) ( 7.104) ( 3.708) pre-2000 Conflict (11.293) (28.269) Border Distance ( 2.405) ( 2.205) Capital Distance (1.190) (0.652) Population (6.147) (15.101) Pct Mountainous (3.846) (1.084) Pct Irrigation ( 1.654) ( 0.229) GDP pc ( 1.429) ( 2.275) Cell Phone Coverage (6.103) (1.680) AIC BIC Deviance Log-likelihood N
18 TABLE 18. Spatial Binary and Count Models, ACLED Logit, robust SE Re-Logit, robust SE Mixed Logit Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( ) ( ) ( 7.882) ( 3.348) Spatial Lag (17.868) (17.835) (19.070) (10.167) (14.043) pre-2000 Conflict (2.590) (2.509) (4.042) (8.841) (15.794) Border Distance ( 0.715) ( 0.695) ( 0.910) ( 1.790) ( 1.520) Capital Distance (0.863) (0.867) (0.723) (1.464) (0.404) Population (6.945) (6.900) (9.507) (5.963) (7.177) Pct Mountainous (3.926) (3.942) (4.158) (0.095) (0.564) Pct Irrigation ( 0.344) ( 0.317) ( 0.634) ( 1.465) (0.879) GDP pc ( 2.300) ( 2.266) ( 2.293) ( 1.136) ( 1.749) Cell Phone Coverage (5.360) (5.365) (5.458) (4.753) (1.619) Mean Cell Coverage ( 1.122) AIC BIC Deviance Log-likelihood N
19 TABLE 19. Binary DV Models, SCAD Logit, robust SE Re-Logit, robust SE Mixed Effects Logit Mixed Effects Logit Fixed Effects OLS, robust SE (Intercept) ( ) ( ) ( ) ( ) ( 2.927) pre-2000 Conflict (1.530) (1.496) (2.960) (3.105) (1.943) Border Distance ( 2.616) ( 2.576) ( 2.641) ( 2.859) ( 2.653) Capital Distance (0.245) (0.298) (0.290) ( 0.037) (1.906) Population (9.636) (9.582) (9.751) (9.902) (6.073) Pct Mountainous ( 0.042) (0.034) ( 0.043) (0.064) ( 1.441) Pct Irrigation (0.119) (0.093) (0.137) ( 0.203) ( 0.445) GDP pc ( 0.642) ( 0.247) ( 0.613) ( 0.437) ( 0.287) Cell Phone Coverage (5.078) (5.030) (4.959) (5.374) (1.906) Mean Cell Coverage ( 1.881) Country Fixed Effects No No No No Y es AIC BIC Deviance Log-likelihood N TABLE 20. Count DV Models, SCAD Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( 9.905) pre-2000 Conflict (4.261) (6.920) Border Distance ( 1.494) ( 1.921) Capital Distance ( 1.300) ( 0.174) Population (5.585) (15.695) Pct Mountainous (0.119) ( 0.792) Pct Irrigation ( 0.370) (1.354) GDP pc ( 0.701) ( 0.398) Cell Phone Coverage (6.464) (3.460) AIC BIC Deviance Log-likelihood N
20 TABLE 21. Spatial Binary and Count Models, SCAD Logit, robust SE Re-Logit, robust SE Mixed Logit Poisson, robust SE Negative Binomial, robust SE (Intercept) ( ) ( ) ( ) ( ) ( 9.920) Spatial Lag (2.371) (2.418) (2.711) (3.648) (2.093) pre-2000 Conflict (1.548) (1.529) (2.956) (4.445) (6.157) Border Distance ( 2.407) ( 2.355) ( 2.604) ( 1.434) ( 1.754) Capital Distance (0.405) (0.459) (0.138) ( 1.117) ( 0.249) Population (8.595) (8.517) (9.201) (5.944) (14.367) Pct Mountainous ( 0.146) ( 0.074) ( 0.043) (0.044) ( 0.886) Pct Irrigation ( 0.093) ( 0.117) ( 0.473) ( 0.634) (1.354) GDP pc ( 0.666) ( 0.351) ( 0.431) ( 0.711) ( 0.359) Cell Phone Coverage (4.936) (4.902) (5.279) (6.219) (3.284) Mean Cell Coverage ( 1.969) AIC BIC Deviance Log-likelihood N
21 4. MATCHING Alternative to the estimation of parametric models, we also explore the effect of cell phone coverage using matching methods. In particular, we rely on Coarsened Exact Matching (CEM) (Iacus, King & Porro 2012). CEM bins observations into coarsened strata and matches based on the new groupings. This matching approach reduces imbalance in the sample based on all properties of the covariate distributions, not just differences of means or similar univariate statistics (Iacus, King & Porro 2012). We use the cem library in R to implement this matching algorithm and estimate the sample average treatment effect for the cells treated (SATT) with cell phone coverage after matching. We match on our baseline set of pre-treatment covariates. The original sample contains 5,628 untreated and 3,715 treated grid cells with an overall L 1 imbalance score of After matching we retain 4,882 control and 2,794 treated units, with a L 1 imbalance score of 0.728, a moderately sized imbalance reduction of over 8%. We then use a logit model with and without additional balance adjustment through covariates to obtain the estimated treatment effect. Without additional control variables, the estimated SATT is 0.45 with a 95% CI of [0.14, 0.76]. Including control variables in the estimation produces a SATT estimate of 0.30 and a 95% CI of [ 0.03, 0.65]. Both estimates are very similar in magnitude to our original estimates and confirm the main finding. 21
22 TABLE 22. Imbalance Statistics, Unmatched Sample Variable Diff-in-Means L 1 Diff-in-Means CEM L 1 CEM pre-2000 Conflict Pct Mountainous Border Distance Capital Distance Population in Pct Irrigation GDP pc in
23 5. INSTRUMENTAL VARIABLES 23
24 TABLE 23. Bivariate Probit Model, Cell Phone Coverage Instrumented (1) (2) (3) (4) Robust SE Robust SE Clustered SE Clustered SE (Intercept) *** *** *** *** (-16.42) (-15.49) (-9.15) (-8.06) pre-2000 Conflict *** 0.007* * (4.49) (1.96) (2.45) (1.17) Border Distance (0.75) (0.83) (0.32) (0.45) Capital Distance 0.000*** 0.000** (4.38) (3.16) (1.56) (1.36) Population 0.000* 0.000** 0.000* 0.000** (2.46) (3.16) (2.24) (3.08) Pct Mountainous 0.502*** 0.318** 0.502** (5.04) (2.72) (2.96) (1.63) Pct Irrigation (-0.62) (0.67) (-0.56) (0.57) GDP per capita *** ** * (-4.59) (-3.15) (-2.04) (-1.62) Spatial Lag 2.732*** 2.732*** (12.89) (8.93) Cell Phone Coverage 0.590*** ** (3.58) (1.81) (3.05) (1.31) N t statistics in parentheses p < 0.10, * p < 0.05, ** p < 0.01, *** p <
25 6. PANEL DATA 25
26 TABLE 24. Panel Data, Country and Year Fixed Effects (1) (2) OLS, clustered SE OLS, clustered SE Cell Phone Coverage ** 0.121** ( ) (0.0390) pre-2000 Conflict ( ) ( ) Border Distance ( ) ( ) Capital Distance ( ) ( ) Population 2.36e e-08 (1.32e-08) (8.70e-08) Pct Mountainous (0.0350) (0.229) Pct Irrigation ( ) ( ) GDP pc 8.82e e-09 (3.49e-09) (1.15e-08) Country & Year Effects Yes Yes Observations Adjusted R F Clustered standard errors in parentheses p < 0.10, * p < 0.05, ** p < 0.01, *** p <
27 TABLE 25. Panel Data, Additional Count Models (1) (2) log(count+1), OLS, clustered SE Poisson Fixed Effects Cell Phone Coverage ** 1.136* ( ) (0.502) Cell & Year Effects Yes Yes Observations Adjusted R F Standard errors in parentheses p < 0.10, * p < 0.05, ** p < 0.01, *** p <
28 7. MODEL FIT While the focus on statistical and substantive significance of individual covariates is warranted given our theoretical interests, overall model fit and predictive capabilities of the model should not go without any consideration (Ward et al. 2010). Below we present a heat map of predicted conflict probabilities that suggest a fairly accurate identification of conflict hot spots. A better and intuitive graphical method to assess model fit for models with binary dependent variables is the separation plot (Greenhill et al. 2011). A separation plot orders observations according to their predicted probabilities derived from the model and plots the predicted probability curve. For each observation realized events in the data are then plotted with red vertical lines. A good model fit shows a clustering of actual events for higher predicted probabilities, whereas an inferior model fit shows a haphazard pattern. Below we show separation plots for the in-sample and out-of-sample (predicting 2009 conflict) fit for several models. Overall, the models do quite well in classifying grid cells correctly, especially when the spatial lag is included, strengthening the overall credibility or our models and findings. 28
29 FIGURE 1. Predicted Probabilities Heat Map, darker colors signify higher probability, In-Sample 29
30 (a) Logit (b) Logit with Spatial Lag FIGURE 2. Separationplots In-Sample (a) Logit (b) Logit with Spatial Lag FIGURE 3. Separationplots Out-of-Sample
31 8. COMPARISON OF UCDP AND WORLD BANK DATA FOR SIERRA LEONE 31
32 under over 80 (a) Household Member Injured or Maimed unde over (b) Household Member Made Refugee (a) Source: Sacks and Larizza 2012, p.41 under over 80 (c) House Burned Down under over 80 (d) Household Member Fled Figure 4: These maps show the mean percentage of respondents for each of Sierra Leone s 165 chiefdoms who were victims of the civil war. 41 (b) UCDP-GED F IGURE 4. Violence in Sierra Leone s Civil War 32
33 REFERENCES Greenhill, Brian, Ward, Michael D., & Sacks, Audrey The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models. American Journal of Political Science, 55(4), Ward, Michael D., Greenhill, Brian, & Bakke, Kristin The Perils of Policy by P-value: Predicting Civil Conflicts. Journal of Peace Research, 47,
STA 4504/5503 Sample questions for exam True-False questions.
STA 4504/5503 Sample questions for exam 2 1. True-False questions. (a) For General Social Survey data on Y = political ideology (categories liberal, moderate, conservative), X 1 = gender (1 = female, 0
yuimagui: A graphical user interface for the yuima package. User Guide yuimagui v1.0
yuimagui: A graphical user interface for the yuima package. User Guide yuimagui v1.0 Emanuele Guidotti, Stefano M. Iacus and Lorenzo Mercuri February 21, 2017 Contents 1 yuimagui: Home 3 2 yuimagui: Data
Online Appendices for
Online Appendices for From Made in China to Innovated in China : Necessity, Prospect, and Challenges Shang-Jin Wei, Zhuan Xie, and Xiaobo Zhang Journal of Economic Perspectives, (31)1, Winter 2017 Online
Simulating the Effect of Peacekeeping Operations
Simulating the Effect of Peacekeeping Operations 2010 2035 Håvard Hegre 1,3, Lisa Hultman 2, and Håvard Mokleiv Nygård 1,3 1 University of Oslo 2 Swedish National Defence College 3 Centre for the Study
Motivation for research question
Motivation for research question! Entrepreneurial exits typically equated with success!! Exit (liquidity event) as a key performance metric for venture capital-backed start-ups (equity investments illiquid
APPENDIX to Pyramidal Ownership and the Creation of New Firms
APPENDIX to Pyramidal Ownership and the Creation of New Firms This Appendix reports additional results that we discuss but do not tabulate in the main text of the paper. The content is summarized below,
Ordinal Multinomial Logistic Regression. Thom M. Suhy Southern Methodist University May14th, 2013
Ordinal Multinomial Logistic Thom M. Suhy Southern Methodist University May14th, 2013 GLM Generalized Linear Model (GLM) Framework for statistical analysis (Gelman and Hill, 2007, p. 135) Linear Continuous
Do School District Bond Guarantee Programs Matter?
Providence College DigitalCommons@Providence Economics Student Papers Economics 12-2013 Do School District Bond Guarantee Programs Matter? Michael Cirrotti Providence College Follow this and additional
Intro to GLM Day 2: GLM and Maximum Likelihood
Intro to GLM Day 2: GLM and Maximum Likelihood Federico Vegetti Central European University ECPR Summer School in Methods and Techniques 1 / 32 Generalized Linear Modeling 3 steps of GLM 1. Specify the
Crafting Mass Partisanship at the Grass Roots
Crafting Mass Partisanship at the Grass Roots (Supplemental Information) Cesar Zucco Rutgers University zucco@polisci.rutgers.edu David Samuels University of Minnesota dsamuels@umn.edu This document includes
MANAGEMENT SCIENCE doi /mnsc ec
MANAGEMENT SCIENCE doi 10.1287/mnsc.1100.1159ec e-companion ONLY AVAILABLE IN ELECTRONIC FORM informs 2010 INFORMS Electronic Companion Quality Management and Job Quality: How the ISO 9001 Standard for
Multiple Regression and Logistic Regression II. Dajiang 525 Apr
Multiple Regression and Logistic Regression II Dajiang Liu @PHS 525 Apr-19-2016 Materials from Last Time Multiple regression model: Include multiple predictors in the model = + + + + How to interpret the
Analysis of Microdata
Rainer Winkelmann Stefan Boes Analysis of Microdata Second Edition 4u Springer 1 Introduction 1 1.1 What Are Microdata? 1 1.2 Types of Microdata 4 1.2.1 Qualitative Data 4 1.2.2 Quantitative Data 6 1.3
The Effect of Community-Based Programs on Elephant Populations in Africa
The Effect of Community-Based Programs on Elephant Populations in Africa Anomitro Chatterjee Georgia State University Camp Resources 2017: Research Sketch August 7, 2017 Anomitro Chatterjee (GSU) CBNRM
Supplementary Appendix. July 22, 2016
For Online Publication Supplementary Appendix News Shocks In Open Economies: Evidence From Giant Oil Discoveries July 22, 2016 1 Supplementary Appendix C: Model Graphs -.06-.04-.02 0.02.04 Sector 1 Output
Session 5. Predictive Modeling in Life Insurance
SOA Predictive Analytics Seminar Hong Kong 29 Aug. 2018 Hong Kong Session 5 Predictive Modeling in Life Insurance Jingyi Zhang, Ph.D Predictive Modeling in Life Insurance JINGYI ZHANG PhD Scientist Global
Financial Liberalization and Neighbor Coordination
Financial Liberalization and Neighbor Coordination Arvind Magesan and Jordi Mondria January 31, 2011 Abstract In this paper we study the economic and strategic incentives for a country to financially liberalize
Acemoglu, et al (2008) cast doubt on the robustness of the cross-country empirical relationship between income and democracy. They demonstrate that
Acemoglu, et al (2008) cast doubt on the robustness of the cross-country empirical relationship between income and democracy. They demonstrate that the strong positive correlation between income and democracy
ONLINE APPENDIX (NOT FOR PUBLICATION) Appendix A: Appendix Figures and Tables
ONLINE APPENDIX (NOT FOR PUBLICATION) Appendix A: Appendix Figures and Tables 34 Figure A.1: First Page of the Standard Layout 35 Figure A.2: Second Page of the Credit Card Statement 36 Figure A.3: First
the display, exploration and transformation of the data are demonstrated and biases typically encountered are highlighted.
1 Insurance data Generalized linear modeling is a methodology for modeling relationships between variables. It generalizes the classical normal linear model, by relaxing some of its restrictive assumptions,
Development, Democracy, and. Corruption - Online Appendix
Development,, and Corruption - Online Appendix Natascha S. Neudorfer, Ph.D. July 1, 2014 1 Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) ICRG_TT O NMA SA WCE ESE CA SoA NA A AO GDP per Capita -0.0539***
Sociology 704: Topics in Multivariate Statistics Instructor: Natasha Sarkisian. Binary Logit
Sociology 704: Topics in Multivariate Statistics Instructor: Natasha Sarkisian Binary Logit Binary models deal with binary (0/1, yes/no) dependent variables. OLS is inappropriate for this kind of dependent
Public Employees as Politicians: Evidence from Close Elections
Public Employees as Politicians: Evidence from Close Elections Supporting information (For Online Publication Only) Ari Hyytinen University of Jyväskylä, School of Business and Economics (JSBE) Jaakko
Does FinTech Affect Household Saving Behavior? Findings from a Natural Experiment. Gregor Becker Philadelphia, September 29 th 2017
Does FinTech Affect Household Saving Behavior? Findings from a Natural Experiment. Gregor Becker Philadelphia, September 29 th 2017 Contents The Economic Problem of Under-saving and Over-consumption Does
Does Manufacturing Matter for Economic Growth in the Era of Globalization? Online Supplement
Does Manufacturing Matter for Economic Growth in the Era of Globalization? Results from Growth Curve Models of Manufacturing Share of Employment (MSE) To formally test trends in manufacturing share of
Stock Liquidity and Default Risk *
Stock Liquidity and Default Risk * Jonathan Brogaard Dan Li Ying Xia Internet Appendix A1. Cox Proportional Hazard Model As a robustness test, we examine actual bankruptcies instead of the risk of default.
Internet Appendix for Did Dubious Mortgage Origination Practices Distort House Prices?
Internet Appendix for Did Dubious Mortgage Origination Practices Distort House Prices? John M. Griffin and Gonzalo Maturana This appendix is divided into three sections. The first section shows that a
Happy Voters. Exploring the Intersections between Economics and Psychology. Federica Liberini 1, Eugenio Proto 2 Michela Redoano 2.
Exploring the Intersections between Economics and Psychology Federica Liberini 1, Eugenio Proto 2 Michela Redoano 2 1 ETH Zurich, 2 Warwick University and IZA 3 Warwick University 29 January 2015 Overview
Online Appendix for Does mobile money affect saving behavior? Evidence from a developing country Journal of African Economies
Online Appendix for Does mobile money affect saving behavior? Evidence from a developing country Journal of African Economies Serge Ky, Clovis Rugemintwari and Alain Sauviat In this document we report
Model fit assessment via marginal model plots
The Stata Journal (2010) 10, Number 2, pp. 215 225 Model fit assessment via marginal model plots Charles Lindsey Texas A & M University Department of Statistics College Station, TX lindseyc@stat.tamu.edu
Appendix. A.1 Independent Random Effects (Baseline)
A Appendix A.1 Independent Random Effects (Baseline) 36 Table 2: Detailed Monte Carlo Results Logit Fixed Effects Clustered Random Effects Random Coefficients c Coeff. SE SD Coeff. SE SD Coeff. SE SD Coeff.
Supporting Information for:
Supporting Information for: Can Political Participation Prevent Crime? Results from a Field Experiment about Citizenship, Participation, and Criminality This appendix contains the following material: Supplemental
XLSTAT TIP SHEET FOR BUSINESS STATISTICS CENGAGE LEARNING
XLSTAT TIP SHEET FOR BUSINESS STATISTICS CENGAGE LEARNING INTRODUCTION XLSTAT makes accessible to anyone a powerful, complete and user-friendly data analysis and statistical solution. Accessibility to
Internet Appendix: High Frequency Trading and Extreme Price Movements
Internet Appendix: High Frequency Trading and Extreme Price Movements This appendix includes two parts. First, it reports the results from the sample of EPMs defined as the 99.9 th percentile of raw returns.
Panel Data with Binary Dependent Variables
Essex Summer School in Social Science Data Analysis Panel Data Analysis for Comparative Research Panel Data with Binary Dependent Variables Christopher Adolph Department of Political Science and Center
Investment and Capital Constraints: Repatriations Under the American Jobs Creation Act
Investment and Capital Constraints: Repatriations Under the American Jobs Creation Act Online Appendix: Additional Results I) Description of AJCA Repatriation Restrictions. This is a more complete description
Step 1: Load the appropriate R package. Step 2: Fit a separate mixed model for each independence claim in the basis set.
Step 1: Load the appropriate R package. You will need two libraries: nlme and lme4. Step 2: Fit a separate mixed model for each independence claim in the basis set. For instance, in Table 2 the first basis
Income inequality and the growth of redistributive spending in the U.S. states: Is there a link?
Draft Version: May 27, 2017 Word Count: 3128 words. SUPPLEMENTARY ONLINE MATERIAL: Income inequality and the growth of redistributive spending in the U.S. states: Is there a link? Appendix 1 Bayesian posterior
Abadie s Semiparametric Difference-in-Difference Estimator
The Stata Journal (yyyy) vv, Number ii, pp. 1 9 Abadie s Semiparametric Difference-in-Difference Estimator Kenneth Houngbedji, PhD Paris School of Economics Paris, France kenneth.houngbedji [at] psemail.eu
Logistic Regression. Logistic Regression Theory
Logistic Regression Dr. J. Kyle Roberts Southern Methodist University Simmons School of Education and Human Development Department of Teaching and Learning Logistic Regression The linear probability model.
Keywords Akiake Information criterion, Automobile, Bonus-Malus, Exponential family, Linear regression, Residuals, Scaled deviance. I.
Application of the Generalized Linear Models in Actuarial Framework BY MURWAN H. M. A. SIDDIG School of Mathematics, Faculty of Engineering Physical Science, The University of Manchester, Oxford Road,
TABLE I SUMMARY STATISTICS Panel A: Loan-level Variables (22,176 loans) Variable Mean S.D. Pre-nuclear Test Total Lending (000) 16,479 60,768 Change in Log Lending -0.0028 1.23 Post-nuclear Test Default
Web Appendix Figure 1. Operational Steps of Experiment
Web Appendix Figure 1. Operational Steps of Experiment 57,533 direct mail solicitations with randomly different offer interest rates sent out to former clients. 5,028 clients go to branch and apply for
Online Appendix: The Effect of Terrorism on Employment and Consumer Sentiment: Evidence from Successful and Failed Terror Attacks
Online Appendix: The Effect of Terrorism on Employment and Consumer Sentiment: Evidence from Successful and Failed Terror Attacks Abel Brodeur November 22, 2017 Abstract This paper examines the economic
The Impact of International Patent Systems: Evidence from Accession to the European Patent Convention. November 2017
The Impact of International Patent Systems: Evidence from Accession to the European Patent Convention Bronwyn H. Hall November 2017 Christian Helmers Motivation There are no international patents Patents
Does FinTech Affect Household Saving Behavior? Findings from a Natural Field Experiment.
Does FinTech Affect Household Saving Behavior? Findings from a Natural Field Experiment. Gregor Becker Goethe University, Frankfurt June 12, 2017, Working Paper Abstract Using data from a natural field
Using New SAS 9.4 Features for Cumulative Logit Models with Partial Proportional Odds Paul J. Hilliard, Educational Testing Service (ETS)
Using New SAS 9.4 Features for Cumulative Logit Models with Partial Proportional Odds Using New SAS 9.4 Features for Cumulative Logit Models with Partial Proportional Odds INTRODUCTION Multicategory Logit
What special purposes make Ireland attractive for debt funding by international banks? 1
IFC-National Bank of Belgium Workshop on "Data needs and Statistics compilation for macroprudential analysis" Brussels, Belgium, 18-19 May 2017 What special purposes make Ireland attractive for debt funding
Lecture 1: Logit. Quantitative Methods for Economic Analysis. Seyed Ali Madani Zadeh and Hosein Joshaghani. Sharif University of Technology
Lecture 1: Logit Quantitative Methods for Economic Analysis Seyed Ali Madani Zadeh and Hosein Joshaghani Sharif University of Technology February 2017 1 / 38 Road map 1. Discrete Choice Models 2. Binary
Regional Market Integration and City Growth in East Africa: Local but no Regional Effects?
Public Disclosure Authorized Regional Market Integration and City Growth in East Africa: Local but no Regional Effects? * & Alexander Moradi * University of Sussex a.eberhard@sussex.ac.uk losure Authorized
Online Appendix for Unemployment Insurance as a Housing Market Stabilizer
Online Appendix for Unemployment Insurance as a Housing Market Stabilizer By JOANNE W. HSU, DAVID A. MATSA, AND BRIAN T. MELZER * Appendix A. Using LPS to calculate extended benefits effect on the probability
Modelling Bank Loan LGD of Corporate and SME Segment
15 th Computing in Economics and Finance, Sydney, Australia Modelling Bank Loan LGD of Corporate and SME Segment Radovan Chalupka, Juraj Kopecsni Charles University, Prague 1. introduction 2. key issues
Do Democracies Attract More Foreign Direct Investment? A Meta Regression Analysis
Do Democracies Attract More Foreign Direct Investment? A Meta Regression Analysis Quan Li, Erica Owen and Austin Mitchell Texas A&M University IPES Annual Meeting November 11, 2016 1 / 31 Introduction
Basic Procedure for Histograms
Basic Procedure for Histograms 1. Compute the range of observations (min. & max. value) 2. Choose an initial # of classes (most likely based on the range of values, try and find a number of classes that
WP 3 - Innovation and Access to Finance Project Steering Meeting and Stakeholders Meeting September 2016
WP 3 - Innovation and Access to Finance Project Steering Meeting and Stakeholders Meeting 29-30 September 2016 Venue: Ekonomski institut, Zagreb (EIZ)/The Institute of Economics, Zagreb Michele CINCERA,
Applied Econometrics for Health Economists
Applied Econometrics for Health Economists Exercise 0 Preliminaries The data file hals1class.dta contains the following variables: age male white aglsch rheuma prheuma ownh breakhot tea teasug coffee age
*9-BES2_Logistic Regression - Social Economics & Public Policies Marcelo Neri
Econometric Techniques and Estimated Models *9 (continues in the website) This text details the different statistical techniques used in the analysis, such as logistic regression, applied to discrete variables
Dan Breznitz Munk School of Global Affairs, University of Toronto, 1 Devonshire Place, Toronto, Ontario M5S 3K7 CANADA
RESEARCH ARTICLE THE ROLE OF VENTURE CAPITAL IN THE FORMATION OF A NEW TECHNOLOGICAL ECOSYSTEM: EVIDENCE FROM THE CLOUD Dan Breznitz Munk School of Global Affairs, University of Toronto, 1 Devonshire Place,
CHAPTER 8 EXAMPLES: MIXTURE MODELING WITH LONGITUDINAL DATA
Examples: Mixture Modeling With Longitudinal Data CHAPTER 8 EXAMPLES: MIXTURE MODELING WITH LONGITUDINAL DATA Mixture modeling refers to modeling with categorical latent variables that represent subpopulations
Online Appendix A: Verification of Employer Responses
Online Appendix for: Do Employer Pension Contributions Reflect Employee Preferences? Evidence from a Retirement Savings Reform in Denmark, by Itzik Fadlon, Jessica Laird, and Torben Heien Nielsen Online
Mixed models in R using the lme4 package Part 3: Inference based on profiled deviance
Mixed models in R using the lme4 package Part 3: Inference based on profiled deviance Douglas Bates Department of Statistics University of Wisconsin - Madison Madison January 11, 2011
The Impact of a $15 Minimum Wage on Hunger in America
The Impact of a $15 Minimum Wage on Hunger in America Appendix A: Theoretical Model SEPTEMBER 1, 2016 WILLIAM M. RODGERS III Since I only observe the outcome of whether the household nutritional level
Generalized Multilevel Regression Example for a Binary Outcome
Psy 510/610 Multilevel Regression, Spring 2017 1 HLM Generalized Multilevel Regression Example for a Binary Outcome Specifications for this Bernoulli HLM2 run Problem Title: no title The data source for
Online Appendix. Moral Hazard in Health Insurance: Do Dynamic Incentives Matter? by Aron-Dine, Einav, Finkelstein, and Cullen
Online Appendix Moral Hazard in Health Insurance: Do Dynamic Incentives Matter? by Aron-Dine, Einav, Finkelstein, and Cullen Appendix A: Analysis of Initial Claims in Medicare Part D In this appendix we
A Comparison of Univariate Probit and Logit. Models Using Simulation
Applied Mathematical Sciences, Vol. 12, 2018, no. 4, 185-204 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ams.2018.818 A Comparison of Univariate Probit and Logit Models Using Simulation Abeer
Table IA.1 CEO Pay-Size Elasticity and Increased Labor Demand Panel A: IPOs Scaled by Full Sample Industry Average
Table IA.1 CEO Pay-Size Elasticity and Increased Labor Demand Panel A: IPOs Scaled by Industry Average (1) (2) (3) (4) (5) Ln(Market Value) 0.423 0.419 0.423 0.423 0.255 (33.29) (30.84) (33.29) (33.29)
ONLINE APPENDIX. Concentrated Powers: Unilateral Executive Authority and Fiscal Policymaking in the American States
ONLINE APPENDIX Concentrated Powers: Unilateral Executive Authority and Fiscal Policymaking in the American States As noted in Note 13 of the manuscript document, discrepancies exist between using Thad
Negative Binomial Model for Count Data Log-linear Models for Contingency Tables - Introduction
Negative Binomial Model for Count Data Log-linear Models for Contingency Tables - Introduction Statistics 149 Spring 2006 Copyright 2006 by Mark E. Irwin Negative Binomial Family Example: Absenteeism from
The current study builds on previous research to estimate the regional gap in
Summary 1 The current study builds on previous research to estimate the regional gap in state funding assistance between municipalities in South NJ compared to similar municipalities in Central and North
Financial Innovation and Borrowers: Evidence from Peer-to-Peer Lending
Financial Innovation and Borrowers: Evidence from Peer-to-Peer Lending Tetyana Balyuk BdF-TSE Conference November 12, 2018 Research Question Motivation Motivation Imperfections in consumer credit market
Home Energy Reporting Program Evaluation Report. June 8, 2015
Home Energy Reporting Program Evaluation Report (1/1/2014 12/31/2014) Final Presented to Potomac Edison June 8, 2015 Prepared by: Kathleen Ward Dana Max Bill Provencher Brent Barkett Navigant Consulting
Supplementary Appendix
Supplementary Appendix This appendix has been provided by the authors to give readers additional information about their work. Supplement to: Sommers BD, Musco T, Finegold K, Gunja MZ, Burke A, McDowell
Supporting information for. Mainstream or niche? Vote-seeking incentives and the programmatic strategies of political parties
Supporting information for Mainstream or niche? Vote-seeking incentives and the programmatic strategies of political parties Thomas M. Meyer, University of Vienna Markus Wagner, University of Vienna In
F. ANALYSIS OF FACTORS AFFECTING PROJECT EFFICIENCY AND SUSTAINABILITY
F. ANALYSIS OF FACTORS AFFECTING PROJECT EFFICIENCY AND SUSTAINABILITY 1. A regression analysis is used to determine the factors that affect efficiency, severity of implementation delay (process efficiency)
A Joint Credit Scoring Model for Peer-to-Peer Lending and Credit Bureau
A Joint Credit Scoring Model for Peer-to-Peer Lending and Credit Bureau Credit Research Centre and University of Edinburgh raffaella.calabrese@ed.ac.uk joint work with Silvia Osmetti and Luca Zanin Credit
Forecasting & Futurism
Article from: Forecasting & Futurism December 2013 Issue 8 PREDICTIVE MODELING IN INSURANCE Modeling Process By Richard Xu In the July 2013 issue of the Forecasting & Futurism Newsletter, we introduced
Longitudinal Logistic Regression: Breastfeeding of Nepalese Children
Longitudinal Logistic Regression: Breastfeeding of Nepalese Children Scientific Question Determine whether the breastfeeding of Nepalese children varies with child age and/or sex of child. Data: Nepal
Illustration 1: Determinants of Firm Debt
Illustration 1: Determinants of Firm Debt Consider the file CentralBalancos-BP.dta, which comprises accounting data for Portuguese firms. The aim is to explain the proportion of debt in the firm s capital
Regression with a binary dependent variable: Logistic regression diagnostic
ACADEMIC YEAR 2016/2017 Università degli Studi di Milano GRADUATE SCHOOL IN SOCIAL AND POLITICAL SCIENCES APPLIED MULTIVARIATE ANALYSIS Luigi Curini luigi.curini@unimi.it Do not quote without author s
Country Fixed Effects and Unit Roots: A Comment on Poverty and Civil War: Revisiting the Evidence
The University of Adelaide School of Economics Research Paper No. 2011-17 March 2011 Country Fixed Effects and Unit Roots: A Comment on Poverty and Civil War: Revisiting the Evidence Markus Bruckner Country
FOR ONLINE PUBLICATION ONLY. Supplemental Appendix for:
FOR ONLINE PUBLICATION ONLY Supplemental Appendix for: Perceptions of Deservingness and the Politicization of Social Insurance: Evidence from Disability Insurance in the United States Albert H. Fang Yale
Appendix for Beazer, Quintin H. & Byungwon Woo IMF Conditionality, Government Partisanship, and the Progress of Economic Reforms
Appendix for Beazer, Quintin H. & Byungwon Woo. 2015. IMF Conditionality, Government Partisanship, and the Progress of Economic Reforms This appendix contains the additional analyses that space considerations
Technical Appendices to Extracting Summary Piles from Sorting Task Data
Technical Appendices to Extracting Summary Piles from Sorting Task Data Simon J. Blanchard McDonough School of Business, Georgetown University, Washington, DC 20057, USA sjb247@georgetown.edu Daniel Aloise
Computational Statistics Handbook with MATLAB
«H Computer Science and Data Analysis Series Computational Statistics Handbook with MATLAB Second Edition Wendy L. Martinez The Office of Naval Research Arlington, Virginia, U.S.A. Angel R. Martinez Naval
Does Inequality Reduce Happiness? Evidence from the States of the USA from the 1970s to the 1990s
For Milan, March 2003. To be presented by Andrew Oswald, Warwick University. Email: andrew.oswald@warwick.ac.uk Does Inequality Reduce Happiness? Evidence from the States of the USA from the 1970s to the
Import Protection, Business Cycles, and Exchange Rates:
Import Protection, Business Cycles, and Exchange Rates: Evidence from the Great Recession Chad P. Bown The World Bank Meredith A. Crowley Federal Reserve Bank of Chicago Preliminary, comments welcome Any
Subject CS1 Actuarial Statistics 1 Core Principles. Syllabus. for the 2019 exams. 1 June 2018
` Subject CS1 Actuarial Statistics 1 Core Principles Syllabus for the 2019 exams 1 June 2018 Copyright in this Core Reading is the property of the Institute and Faculty of Actuaries who are the sole distributors.
ANDREW YOUNG SCHOOL OF POLICY STUDIES
ANDREW YOUNG SCHOOL OF POLICY STUDIES INTERNATIONAL CENTER FOR PUBLIC POLICY In International Center for Public Policy Working Paper 16-09 October 2016 The Effects of Media Campaigns on Individual Attitudes
Online Appendix for Taxation and the International Mobility of Inventors
Online Appendix for Taxation and the International Mobility of Inventors Ufuk Akcigit, University of Chicago and NBER Salomé Baslandze, EIEF Stefanie Stantcheva, Harvard and NBER May 2016 A Data Construction
Internet Appendix for: Does Going Public Affect Innovation?
Internet Appendix for: Does Going Public Affect Innovation? July 3, 2014 I Variable Definitions Innovation Measures 1. Citations - Number of citations a patent receives in its grant year and the following
Does Privatized Health Insurance Benefit Patients or Producers? Evidence from Medicare Advantage
Does Privatized Health Insurance Benefit Patients or Producers? Evidence from Medicare Advantage Marika Cabral, UT Austin and NBER Michael Geruso, UT Austin and NBER Neale Mahoney, Chicago Booth and NBER
Appendix A: Descriptive Statistics
Supplementary appendices for Graeme T. Boushey and Robert J. McGrath. N.d. (forthcoming). Experts, Amateurs, and Bureaucratic Influence in the American States. Journal of Public Administration Research
Generalized Linear Models
Generalized Linear Models Scott Creel Wednesday, September 10, 2014 This exercise extends the prior material on using the lm() function to fit an OLS regression and test hypotheses about effects on a parameter.
Marital Disruption and the Risk of Loosing Health Insurance Coverage. Extended Abstract. James B. Kirby. Agency for Healthcare Research and Quality
Marital Disruption and the Risk of Loosing Health Insurance Coverage Extended Abstract James B. Kirby Agency for Healthcare Research and Quality jkirby@ahrq.gov Health insurance coverage in the United
1. Modification algorithm
Internet Appendix for: "The Effect of Mortgage Securitization on Foreclosure and Modification" 1. Modification algorithm The LPS data set lacks an explicit modification flag but contains enough detailed
Dot Plot: A graph for displaying a set of data. Each numerical value is represented by a dot placed above a horizontal number line.
Introduction We continue our study of descriptive statistics with measures of dispersion, such as dot plots, stem and leaf displays, quartiles, percentiles, and box plots. Dot plots, a stem-and-leaf display,
Hierarchical Generalized Linear Models. Measurement Incorporated Hierarchical Linear Models Workshop
Hierarchical Generalized Linear Models Measurement Incorporated Hierarchical Linear Models Workshop Hierarchical Generalized Linear Models So now we are moving on to the more advanced type topics. To begin
The Comovements Along the Term Structure of Oil Forwards in Periods of High and Low Volatility: How Tight Are They?
The Comovements Along the Term Structure of Oil Forwards in Periods of High and Low Volatility: How Tight Are They? Massimiliano Marzo and Paolo Zagaglia This version: January 6, 29 Preliminary: comments
Module 4 Bivariate Regressions
AGRODEP Stata Training April 2013 Module 4 Bivariate Regressions Manuel Barron 1 and Pia Basurto 2 1 University of California, Berkeley, Department of Agricultural and Resource Economics 2 University of
Why Are Japanese Firms Still Increasing Cash Holdings?
Why Are Japanese Firms Still Increasing Cash Holdings? Abstract Japanese firms resumed accumulation of cash to the highest cash holding levels among developed economies after the 2008 financial crisis.