Tarefa causcibench_real.q160

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Pergunta feita ao analista

Does local nonprofit communities have a causal effect on violent crime?

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Trajetória

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1. 0Início

   Does local nonprofit communities have a causal effect on violent crime?

   sharkey_community (agent)

   This study examines community organizations and public safety patterns across major US cities during a period of significant social change from 1990 to 2013. The dataset encompasses comprehensive information on 264 of the largest cities in the United States, capturing various aspects of urban life, including organizational presence, demographic characteristics, economic conditions, and public safety indicators. The analysis draws on multiple data sources, including the FBI's Uniform Crime Reports, the National Center for Charitable Statistics, and US Census data. The dataset represents one of the most comprehensive longitudinal examinations of urban organizational infrastructure and community safety patterns during a critical period in American urban development. Cities range from major metropolitan centers to smaller urban areas, providing broad geographic and demographic representation across all major US regions during the 1990s to 2010s period. Variable Descriptions: poprank: Population-based ranking of cities within the national urban hierarchy agency: Law enforcement agency responsible for crime reporting in each jurisdiction place_name: Official name of the municipal entity place_id: Unique numerical identifier for each city place_id_str: String version of the unique place identifier state_ab: Two-letter state postal abbreviation state_id: Numerical state identifier state_name: Full state name region: Census region classification code region_str: Census region name designation division: US Census Bureau division classification year: Calendar year of observation (1990-2013) year_str: Year designation in string format years_crime: Total number of years for which crime data is available for each city first_crime: Earliest year of available crime data last_crime: Most recent year of available crime data y_1990 through y_2012: Binary indicators for each individual year in the dataset year_region: Combined year and region identifier year_region_num: Numerical encoding of year-region combinations year_reg_1 through year_reg_120: Individual year-region interaction indicators totpop: Total resident population count pop2010: Population count as recorded in the 2010 Census pop1990: Population count as recorded in the 1990 Census pop2000: Population count as recorded in the 2000 Census popweights: Statistical weights based on population size area: Overall geographic area of the municipal boundary popdens: Population density per square unit of area logpop: Natural logarithm of the 1990 population logpopdens: Natural logarithm of population density largest50: Binary indicator for cities ranking among the 50 most populous in the US largest100: Binary indicator for cities ranking among the 100 most populous in the US viol: Absolute count of violent crimes reported annually viol_r: Violent crime rate per 100,000 residents murd: Absolute count of homicides reported annually murd_r: Murder rate per 100,000 residents rape: Absolute count of reported sexual assaults rape_r: Sexual assault rate per 100,000 residents robbery: Absolute count of reported robberies robbery_r: Robbery rate per 100,000 residents assault: Absolute count of reported aggravated assaults assault_r: Aggravated assault rate per 100,000 residents prop: Absolute count of property crimes reported annually prop_r: Property crime rate per 100,000 residents burglary: Absolute count of reported burglaries burglary_r: Burglary rate per 100,000 residents larceny: Absolute count of reported larceny-theft incidents larceny_r: Larceny-theft rate per 100,000 residents autotheft: Absolute count of reported motor vehicle thefts autotheft_r: Motor vehicle theft rate per 100,000 residents community_new: Count of newly established community-focused nonprofit organizations community_cml: Cumulative count of active community-focused nonprofit organizations crime_new: Count of newly established crime prevention organizations crime_cml: Cumulative count of active crime prevention organizations housing_new: Count of newly established housing-related organizations housing_cml: Cumulative count of active housing-related organizations jobs_new: Count of newly established workforce development organizations jobs_cml: Cumulative count of active workforce development organizations nhood_new: Count of newly established neighborhood development organizations nhood_cml: Cumulative count of active neighborhood development organizations reentry_new: Count of newly established prisoner reentry support organizations reentry_cml: Cumulative count of active prisoner reentry support organizations substance_new: Count of newly established substance abuse prevention organizations substance_cml: Cumulative count of active substance abuse prevention organizations youth_new: Count of newly established youth-related organizations youth_cml: Cumulative count of active youth-related organizations Various _r suffixed variables: Rate calculations expressing organizational counts per 100,000 residents (for example, substance_cml_r represents the cumulative count of substance-abuse prevention organizations per 100,000 residents) all_new_r: Rate of all newly established nonprofits per 100,000 residents all_cml_r: Rate of all cumulative active nonprofits per 100,000 residents white: Percentage of residents identifying as white (non-Hispanic) black: Percentage of residents identifying as Black or African American asian: Percentage of residents identifying as Asian hispanic: Percentage of residents identifying as Hispanic or Latino other: Percentage of residents identifying with other racial/ethnic categories fborn: Percentage of foreign-born residents male1524: Percentage of male residents aged 15 to 24 years female1524: Percentage of female residents aged 15 to 24 years lesshs: Percentage of residents 25 years and older without a high school diploma college: Percentage of residents 25 years and older with a bachelor's degree or higher hsdropout: High school dropout rate unemployed: Overall unemployment rate unemployed_male: Male-specific unemployment rate unemployed_female: Female-specific unemployment rate notlabforce: Percentage of residents not participating in the labor force notlabforce_male: Percentage of males not participating in the labor force notlabforce_female: Percentage of females not participating in the labor force manufacturing: Percentage of employed residents working in manufacturing industries poverty: Percentage of residents living below the federal poverty line vacancy: Housing vacancy rate incarceration: Incarceration Rate log_incarceration: Natural logarithm of incarceration rate swornftime_r: Number of sworn police officers per capita iv_cml: Cumulative number of new non-profits focusing on the arts, medical research, and environmental protectionism iv_cml_r: Cumulative number of new non-profits focusing on the arts, medical research, and environmental protectionism per 100,000 residents log_viol_r: Natural logarithm of violent crime rate log_prop_r: Natural logarithm of property crime rate log_murd_r: Natural logarithm of murder rate Multiple lagged difference variables (log_viol_r_1ld through log_viol_r_5ld): Historical differences in log violent crime rates over 1-5 year periods Multiple lagged growth variables (log_viol_r_1lg through log_viol_r_5lg): Historical growth patterns in log violent crime rates over 1-5 year periods; Similar lagged difference and growth variables for property crime (log_prop_r_Xld/lg) and murder rates (log_murd_r_Xld/lg)

   file a9f721b6ac4dc365e59b6317eec13c501a81614bbc3fe13448a09f19a9a5a1b5 · frame —
2. 1Turno do modelo
   call_ca3bbd12referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: iv

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "sharkey_community",
       "endog": [
         "all_cml_r"
       ],
       "exog": [
         "popdens",
         "asian",
         "black",
         "hispanic",
         "other",
         "lesshs",
         "college",
         "fborn",
         "male1524",
         "poverty",
         "unemployed",
         "manufacturing"
       ],
       "instruments": [
         "iv_cml_r"
       ],
       "y": "log_viol_r_1ld"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2slsv0.1.0call_c8ba6567decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: iv

   Argumentos

   {
     "cluster": null,
     "dataset": "sharkey_community",
     "endog": [
       "all_cml_r"
     ],
     "exog": [
       "popdens",
       "asian",
       "black",
       "hispanic",
       "other",
       "lesshs",
       "college",
       "fborn",
       "male1524",
       "poverty",
       "unemployed",
       "manufacturing"
     ],
     "instruments": [
       "iv_cml_r"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "log_viol_r_1ld"
   }

   Figuras call_c8ba6567

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.all_cml_r 2SLS effect of all_cml_r on log_viol_r_1ld	0,010702	0.010702019928228806	0,00081330.0008132995539676548	0,00910766 – 0,0122964

   Diagnósticos

   • first_stage_F.all_cml_r = 901,758901.7577950247841
   • partial_r2.all_cml_r = 0,4389180.438917800463404
   • weak_instrument = false
   • n_obs = 6.0436043
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 5.7610947344787204,
         "ci_low": 5.507351372908102,
         "coef": 5.634223053693411,
         "p": 0,
         "se": 0.06471864354609393,
         "t": 87.05718700177336,
         "term": "Intercept"
       },
       {
         "ci_high": 0.000014179659142565796,
         "ci_low": 0.0000037709973164529077,
         "coef": 0.000008975328229509352,
         "p": 0.0007273178213672686,
         "se": 0.000002654786593613239,
         "t": 3.3808096858337975,
         "term": "popdens"
       },
       {
         "ci_high": 0.011022250067275221,
         "ci_low": 0.005348240734675776,
         "coef": 0.008185245400975498,
         "p": 1.620868215096147e-8,
         "se": 0.001447187367585636,
         "t": 5.6559679722958505,
         "term": "asian"
       },
       {
         "ci_high": 0.010635345611567365,
         "ci_low": 0.007479181116606707,
         "coef": 0.009057263364087036,
         "p": 4.44061457590569e-29,
         "se": 0.0008049971579861358,
         "t": 11.251298559544761,
         "term": "black"
       },
       {
         "ci_high": -0.00802991652872584,
         "ci_low": -0.011481671015318806,
         "coef": -0.009755793772022323,
         "p": 2.896310826017989e-28,
         "se": 0.0008803890152778192,
         "t": -11.081230686350334,
         "term": "hispanic"
       },
       {
         "ci_high": 0.005219793225683772,
         "ci_low": -0.018857327520027507,
         "coef": -0.006818767147171867,
         "p": 0.2668851440057683,
         "se": 0.006141002410911475,
         "t": -1.1103671177617718,
         "term": "other"
       },
       {
         "ci_high": 0.05517016444684785,
         "ci_low": 0.045956227539755624,
         "coef": 0.05056319599330174,
         "p": 5.421667857243199e-99,
         "se": 0.0023500654151314307,
         "t": 21.51565469954117,
         "term": "lesshs"
       },
       {
         "ci_high": -0.011221847066484932,
         "ci_low": -0.016647948051017096,
         "coef": -0.013934897558751014,
         "p": 1.1655503556051437e-23,
         "se": 0.0013839569764086744,
         "t": -10.068880605603537,
         "term": "college"
       },
       {
         "ci_high": -0.005965936409805553,
         "ci_low": -0.011447595751365246,
         "coef": -0.008706766080585399,
         "p": 5.060698071272838e-10,
         "se": 0.0013981274417253424,
         "t": -6.2274481000393775,
         "term": "fborn"
       },
       {
         "ci_high": 0.03238087082208307,
         "ci_low": 0.010327469072560892,
         "coef": 0.02135416994732198,
         "p": 0.0001482513450646556,
         "se": 0.005624841721854903,
         "t": 3.7964037040103626,
         "term": "male1524"
       },
       {
         "ci_high": 0.012944671496367436,
         "ci_low": 0.0027050575395205587,
         "coef": 0.007824864517943998,
         "p": 0.0027455570742840143,
         "se": 0.0026116700024026002,
         "t": 2.996115324962775,
         "term": "poverty"
       },
       {
         "ci_high": -0.025171650232151437,
         "ci_low": -0.057768768158380945,
         "coef": -0.04147020919526619,
         "p": 6.273182685086e-7,
         "se": 0.008314074672296431,
         "t": -4.987952457710089,
         "term": "unemployed"
       },
       {
         "ci_high": -0.011383345572841517,
         "ci_low": -0.01770656885786624,
         "coef": -0.014544957215353878,
         "p": 2.5169427602258334e-19,
         "se": 0.0016127729660111094,
         "t": -9.018601825480802,
         "term": "manufacturing"
       },
       {
         "ci_high": 0.01229637784026274,
         "ci_low": 0.009107662016194873,
         "coef": 0.010702019928228806,
         "p": 5.205510867081311e-39,
         "se": 0.0008132995539676548,
         "t": 13.158767733265508,
         "term": "all_cml_r"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.4476681398059404,
         "ci_low": 0.39280102548866064,
         "coef": 0.4202345826473005,
         "endog": "all_cml_r",
         "instrument": "iv_cml_r",
         "p": 9.334395044509709e-185,
         "se": 0.013994160051807173,
         "t": 30.029282292868473
       }
     ],
     "reduced_form": [
       {
         "ci_high": 0.00507011254394611,
         "ci_low": 0.003924605212098538,
         "coef": 0.004497358878022324,
         "instrument": "iv_cml_r",
         "p": 1.795778612345081e-52,
         "se": 0.0002921679614804335,
         "t": 15.393059715493523
       }
     ]
   }

   Digests dos dados

   sharkey_community 7c840019293bf407ffc95312c730c3f49b210fdd3cd5f257ce6625a547d94b95

   sha256 do resultado

   f4c15496135ca1fb7634e66d91c1b812b52ca8990ab001066ba90176f4a83190

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_5d820785referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.iv_2sls): coef.all_cml_r = 0,01070.",
       "caveats": [],
       "figure_refs": [
         "call_c8ba6567:coef.all_cml_r"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   0,01070

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.iv_2sls): coef.all_cml_r = 0,01070.

   Figuras citadas: call_c8ba6567:coef.all_cml_r · Parada: answered

   Figura da resposta: call_c8ba6567:coef.all_cml_r = 0,0107020.010702019928228806 (se 0,00081330.0008132995539676548)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória0.01070201993 vs -0.006

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1falhoupontuada(exibindo)

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