Tarefa crb_papers.q067

Veredito

Pergunta feita ao analista

What is the causal effect of receiving earlier Progresa coverage on precinct-level turnout in the 2000 presidential election for the subpopulation of precincts whose early-coverage status was shifted by the program's enrollment timing?

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   What is the causal effect of receiving earlier Progresa coverage on precinct-level turnout in the 2000 presidential election for the subpopulation of precincts whose early-coverage status was shifted by the program's enrollment timing?

   ds_0037 (agent)

   | Variable Name | Description | |---------------|-------------| | cve_entIFE | Numeric code for the federal entity (state) as recorded by Mexico's electoral authority (IFE). | | cve_mpioIFE | Numeric municipality code (IFE) corresponding to the locality's municipality. | | seccion | Electoral "sección" (precinct) identifier used by IFE. | | pobtot1994 | Total population in the locality/precinct from the 1994 census (count). | | avgpoverty | Average poverty index for the locality (census-derived index, higher values indicate greater deprivation). | | pobelegiblep | Proportion of the precinct population living in localities deemed eligible for the program (share between 0 and 1). | | treatment | Precinct assignment indicator: 1 when the precinct contains an experimental village assigned to earlier enrollment; 0 for the delayed-assignment comparison group. This is an assignment label, not an indicator that every household or village in the precinct enrolled early. | | numerotreated | Count of evaluation localities within the precinct that were flagged for earlier rollout phases (integer). | | numerocontrol | Count of evaluation localities within the precinct that were flagged for later rollout phases (integer). | | one_random | Legacy sample-selection flag, equal to 1 throughout this extract; it does not give the total number of experimental villages in a precinct. See numerotreated and numerocontrol for the separate counts. | | two_random | Legacy sample-selection flag retained from the source data. It should not be interpreted as a direct count of experimental villages; see numerotreated and numerocontrol. | | early_progresa_p | Number of households enrolled during the first four program-expansion phases divided by the total number of program-enrolled households by the eleventh phase. It measures early enrollment among program recipients, not among all precinct households. | | villages | Total number of localities (villages) aggregated into the electoral precinct (count). | | villages2 | Secondary village-count / grouping field included in the original data (alternate village categorization; sometimes missing). | | pri1994 | Raw count of votes for the PRI in the 1994 election (integer). | | votos_totales1994 | Total number of votes cast in the precinct in the 1994 election (integer). | | pan1994 | Raw count of votes for the PAN in the 1994 election (integer). | | prd1994 | Raw count of votes for the PRD in the 1994 election (integer). | | t2000 | Total votes cast in the precinct in 2000 divided by its voting-age population. Values can exceed 1, for example where special polling places admit voters from outside the precinct. | | t1994 | Total votes cast in the precinct in 1994 divided by its voting-age population; some recorded values exceed 1. | | pri2000s | PRI vote count converted to a share of the voting-age population in 2000 (vote-share metric). | | pri1994s | PRI vote count converted to a share of the voting-age population in 1994 (vote-share metric). | | pan2000s | PAN vote count converted to a share of the voting-age population in 2000 (vote-share metric). | | pan1994s | PAN vote count converted to a share of the voting-age population in 1994 (vote-share metric). | | prd2000s | PRD vote count converted to a share of the voting-age population in 2000 (vote-share metric). | | prd1994s | PRD vote count converted to a share of the voting-age population in 1994 (vote-share metric). | | rep_total | Total number of party observers/representatives recorded at the precinct/polling station (count). | | rep_PAN | Number of PAN party observers present at the precinct (count). | | rep_PRD | Number of PRD party observers present at the precinct (count). | | rep_PRI | Number of PRI party observers present at the precinct (count). | | X_Ivillages_2 | Dummy = 1 if the precinct has 2 villages (indicator capturing number-of-villages category). | | X_Ivillages_3 | Dummy = 1 if the precinct has 3 villages (indicator capturing number-of-villages category). | | X_Ivillages_4 | Dummy = 1 if the precinct has 4 villages (indicator capturing number-of-villages category). | | X_Ivillages_5 | Dummy = 1 if the precinct has 5 villages (indicator capturing number-of-villages category). | | X_Ivillages_6 | Dummy = 1 if the precinct has 6 villages (indicator capturing number-of-villages category). | | X_Ivillages_7 | Dummy = 1 if the precinct has 7 villages (indicator capturing number-of-villages category). | | X_Ivillages_8 | Dummy = 1 if the precinct has 8 villages (indicator capturing number-of-villages category). | | X_Ivillages_9 | Dummy = 1 if the precinct has 9 villages (indicator capturing number-of-villages category). | | X_Ivillages_10 | Dummy = 1 if the precinct has 10 villages (indicator capturing number-of-villages category). | | X_Ivillages_11 | Dummy = 1 if the precinct has 11 villages (indicator capturing number-of-villages category). | | X_Ivillages_12 | Dummy = 1 if the precinct has 12 villages (indicator capturing number-of-villages category). | | X_Ivillages_13 | Dummy = 1 if the precinct has 13 villages (indicator capturing number-of-villages category). | | X_Ivillages_14 | Dummy = 1 if the precinct has 14 villages (indicator capturing number-of-villages category). | Data Description: This precinct-level dataset combines official electoral returns and observer counts from Mexico's Federal Electoral Institute (IFE), population and poverty indicators from census/CONAPO sources, and Progresa program-enrollment records around the late 1990s and the 2000 presidential election. The released file contains 420 precinct observations. The program evaluation initially assigned 506 eligible rural villages in seven Mexican states to different enrollment dates: 320 villages to earlier enrollment beginning in September 1998 and 186 to delayed enrollment beginning in January 2000. Assignment was randomized at the village level. Each village had a 60% probability of assignment to the earlier group. Eligible households in early-assigned villages were offered enrollment; the delayed group began receiving benefits later. Electoral outcomes are recorded for precincts rather than individual villages. Experimental villages were geographically linked to precincts and then to election returns, demographic data, and program-enrollment records. A precinct can contain several villages, including villages outside the experiment. Consequently, assignment and actual coverage are different measures: some households in early-assigned precincts enrolled later, and some households in delayed-assigned precincts enrolled early through the broader rollout. Some released precincts contain both early- and delayed-assigned experimental villages. treatment records the precinct's assignment grouping, while early_progresa_p records actual early coverage among program recipients. t2000 is total votes cast in 2000 divided by the precinct's voting-age population. Geographic identifiers, population and poverty measures, village counts, historical and recent votes, turnout, party-observer counts, and village-count indicators describe the precincts and their composition.

   file 5cd9b9d22abe96d9e8187990bc85716754b5da614d1968270be4026369c4bebb · frame —

   ref_iv (reference)

   file 5cd9b9d22abe96d9e8187990bc85716754b5da614d1968270be4026369c4bebb · frame —

   1. select {"columns":["t2000","early_progresa_p","treatment","avgpoverty","pobtot1994","votos_totales1994","pri1994","pan1994","prd1994","villages"]}
   2. dropna {"columns":["t2000","early_progresa_p","treatment","avgpoverty","pobtot1994","votos_totales1994","pri1994","pan1994","prd1994","villages"]}
   3. dummies {"column":"villages","drop_first":true,"prefix":"villages"}
2. 1Transformação
   ref_iv · select {"columns":["t2000","early_progresa_p","treatment","avgpoverty","pobtot1994","votos_totales1994","pri1994","pan1994","prd1994","villages"]}

   420 → 420 linhas

   Frame antes → depois: 44fb6dccc652 → f5e40a57e0c3
3. 2Transformação
   ref_iv · dropna {"columns":["t2000","early_progresa_p","treatment","avgpoverty","pobtot1994","votos_totales1994","pri1994","pan1994","prd1994","villages"]}

   420 → 417 linhas

   Frame antes → depois: f5e40a57e0c3 → 6c5588b9ded5
4. 3Transformação
   ref_iv · dummies {"column":"villages","drop_first":true,"prefix":"villages"}

   417 → 417 linhas

   Frame antes → depois: 6c5588b9ded5 → b404b85da47c
5. 4Turno do modelo
   call_281c9bc6referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   IV2SLS.from_formula('t2000 ~ 1 + controls + C(villages) + [early_progresa_p ~ treatment]').fit(cov_type='robust', debiased=True)

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "ref_iv",
       "endog": [
         "early_progresa_p"
       ],
       "exog": [
         "avgpoverty",
         "pobtot1994",
         "votos_totales1994",
         "pri1994",
         "pan1994",
         "prd1994",
         "villages_2.0",
         "villages_3.0",
         "villages_4.0",
         "villages_5.0",
         "villages_6.0",
         "villages_7.0",
         "villages_8.0",
         "villages_9.0",
         "villages_10.0",
         "villages_11.0",
         "villages_12.0",
         "villages_13.0",
         "villages_14.0"
       ],
       "instruments": [
         "treatment"
       ],
       "se": "HC1",
       "y": "t2000"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
6. 5Ferramenta
   infer.iv_2slsv0.1.0call_25590d9bdecidido pelo modeloreexecutável

   Motivo: IV2SLS.from_formula('t2000 ~ 1 + controls + C(villages) + [early_progresa_p ~ treatment]').fit(cov_type='robust', debiased=True)

   Argumentos

   {
     "cluster": null,
     "dataset": "ref_iv",
     "endog": [
       "early_progresa_p"
     ],
     "exog": [
       "avgpoverty",
       "pobtot1994",
       "votos_totales1994",
       "pri1994",
       "pan1994",
       "prd1994",
       "villages_2.0",
       "villages_3.0",
       "villages_4.0",
       "villages_5.0",
       "villages_6.0",
       "villages_7.0",
       "villages_8.0",
       "villages_9.0",
       "villages_10.0",
       "villages_11.0",
       "villages_12.0",
       "villages_13.0",
       "villages_14.0"
     ],
     "instruments": [
       "treatment"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "t2000"
   }

   Figuras call_25590d9b

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.early_progresa_p 2SLS effect of early_progresa_p on t2000	0,156302	0.15630190259608176	0,08920980.08920981043804706	-0,0190821 – 0,331686

   Diagnósticos

   • first_stage_F.early_progresa_p = 153,285153.28536386250093
   • partial_r2.early_progresa_p = 0,3091320.3091315841996508
   • weak_instrument = false
   • n_obs = 417417
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 1.012435999365226,
         "ci_low": 0.3915071462679472,
         "coef": 0.7019715728165866,
         "p": 0.000011419959623471234,
         "se": 0.1579189990802194,
         "t": 4.445136917692851,
         "term": "Intercept"
       },
       {
         "ci_high": 0.0460689136575205,
         "ci_low": -0.0831843579548889,
         "coef": -0.018557722148684206,
         "p": 0.5727099659750712,
         "se": 0.032872602358643525,
         "t": -0.5645346220605695,
         "term": "avgpoverty"
       },
       {
         "ci_high": -0.0000054077590026015995,
         "ci_low": -0.0000194687570048806,
         "coef": -0.0000124382580037411,
         "p": 0.000560869274055357,
         "se": 0.000003576092042611178,
         "t": -3.4781705435800188,
         "term": "pobtot1994"
       },
       {
         "ci_high": 0.00010971750173705122,
         "ci_low": -0.000860600546010981,
         "coef": -0.00037544152213696486,
         "p": 0.1289643740918659,
         "se": 0.0002467781197886056,
         "t": -1.5213728123813188,
         "term": "votos_totales1994"
       },
       {
         "ci_high": 0.000996843693666522,
         "ci_low": -0.00007976279589669131,
         "coef": 0.0004585404488849154,
         "p": 0.09478934048192389,
         "se": 0.0002738101448934527,
         "t": 1.674665666837679,
         "term": "pri1994"
       },
       {
         "ci_high": 0.0019049205479480542,
         "ci_low": -0.000682957747363085,
         "coef": 0.0006109814002924846,
         "p": 0.35381384463082083,
         "se": 0.0006581674343178473,
         "t": 0.9283069450644147,
         "term": "pan1994"
       },
       {
         "ci_high": 0.001034207586529851,
         "ci_low": -0.0003774711625219253,
         "coef": 0.00032836821200396283,
         "p": 0.3609565626784886,
         "se": 0.0003590280818181707,
         "t": 0.9146031428546152,
         "term": "prd1994"
       },
       {
         "ci_high": 0.028656117144096105,
         "ci_low": -0.16511638723789374,
         "coef": -0.06823013504689882,
         "p": 0.16698641819698365,
         "se": 0.049281588041258574,
         "t": -1.3844954628851753,
         "term": "villages_2.0"
       },
       {
         "ci_high": 0.010680838085365657,
         "ci_low": -0.16278291752969032,
         "coef": -0.07605103972216233,
         "p": 0.08551224111964477,
         "se": 0.04411652402168823,
         "t": -1.723867448958007,
         "term": "villages_3.0"
       },
       {
         "ci_high": 0.06722348974867899,
         "ci_low": -0.15521272392074614,
         "coef": -0.04399461708603357,
         "p": 0.4372214320479887,
         "se": 0.05657154446383282,
         "t": -0.7776810320983918,
         "term": "villages_4.0"
       },
       {
         "ci_high": 0.17749536467517527,
         "ci_low": -0.15764093133981025,
         "coef": 0.009927216667682504,
         "p": 0.9073392270884517,
         "se": 0.08523422314512279,
         "t": 0.11646984393557593,
         "term": "villages_5.0"
       },
       {
         "ci_high": 0.0480093054593977,
         "ci_low": -0.16362468749313563,
         "coef": -0.05780769101686896,
         "p": 0.2834729334314719,
         "se": 0.0538242476117925,
         "t": -1.074008343485023,
         "term": "villages_6.0"
       },
       {
         "ci_high": 0.07669401079929723,
         "ci_low": -0.1548864720594284,
         "coef": -0.03909623063006558,
         "p": 0.5072015254051901,
         "se": 0.05889717940653404,
         "t": -0.6638048039653364,
         "term": "villages_7.0"
       },
       {
         "ci_high": -0.0377872348789441,
         "ci_low": -0.2700854809754532,
         "coef": -0.15393635792719865,
         "p": 0.009517875967037756,
         "se": 0.05907972600832577,
         "t": -2.605569936216449,
         "term": "villages_8.0"
       },
       {
         "ci_high": 0.3113667094234982,
         "ci_low": -0.1850970322906362,
         "coef": 0.06313483856643101,
         "p": 0.6173374155726117,
         "se": 0.12626415535377583,
         "t": 0.5000218659803756,
         "term": "villages_9.0"
       },
       {
         "ci_high": -0.015300789836504458,
         "ci_low": -0.28406348071651577,
         "coef": -0.14968213527651011,
         "p": 0.029120054345348343,
         "se": 0.06835362042231984,
         "t": -2.189820149272352,
         "term": "villages_10.0"
       },
       {
         "ci_high": -0.005096172641462116,
         "ci_low": -0.23565980331489425,
         "coef": -0.12037798797817818,
         "p": 0.04074070601500736,
         "se": 0.05863856639714805,
         "t": -2.052880815040405,
         "term": "villages_11.0"
       },
       {
         "ci_high": 0.09514867875547262,
         "ci_low": -0.17672415402669234,
         "coef": -0.040787737635609854,
         "p": 0.555600616749071,
         "se": 0.06914461361539763,
         "t": -0.5898903110874705,
         "term": "villages_12.0"
       },
       {
         "ci_high": 0.39834121701103054,
         "ci_low": -0.15142267339937185,
         "coef": 0.12345927180582936,
         "p": 0.3777782882558956,
         "se": 0.13981982455960482,
         "t": 0.8829883186786506,
         "term": "villages_13.0"
       },
       {
         "ci_high": 0.013667178757696283,
         "ci_low": -0.252211262730884,
         "coef": -0.11927204198659387,
         "p": 0.07852735288664738,
         "se": 0.06762007780351491,
         "t": -1.763855438515838,
         "term": "villages_14.0"
       },
       {
         "ci_high": 0.33168594632404325,
         "ci_low": -0.019082141131879726,
         "coef": 0.15630190259608176,
         "p": 0.08053563921871394,
         "se": 0.08920981043804706,
         "t": 1.75207078491247,
         "term": "early_progresa_p"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.39409813441257724,
         "ci_low": 0.28609006333483944,
         "coef": 0.34009409887370834,
         "endog": "early_progresa_p",
         "instrument": "treatment",
         "p": 5.5059150457351315e-30,
         "se": 0.02746937333013473,
         "t": 12.380846653702685
       }
     ],
     "reduced_form": [
       {
         "ci_high": 0.11130923113367165,
         "ci_low": -0.004994521702350638,
         "coef": 0.05315735471566051,
         "instrument": "treatment",
         "p": 0.07307838156352417,
         "se": 0.029579189540835264,
         "t": 1.7971200543637151
       }
     ]
   }

   Digests dos dados

   ref_iv b404b85da47cd17ecc0f48608fa67a115542e841ac1b45a7c956032413471471

   sha256 do resultado

   ebed3783a088eb67f94042dbf2ce43504ce8afbd95133b4d0b585522197fbe6b

   Resultado mostrado ao modelo
7. 6Turno do modelo
   call_cb1797c4referenceFim: 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.early_progresa_p = 0,1563. Reference path transcribed from the item's own estimation script.",
       "caveats": [],
       "figure_refs": [
         "call_25590d9b:coef.early_progresa_p"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
8. 7Guarda
   resposta aceita

   Números encontrados

   0,1563

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
9. 8Resposta

   Estimativa (infer.iv_2sls): coef.early_progresa_p = 0,1563. Reference path transcribed from the item's own estimation script.

   Figuras citadas: call_25590d9b:coef.early_progresa_p · Parada: answered

   Figura da resposta: call_25590d9b:coef.early_progresa_p = 0,1563020.15630190259608176 (se 0,08920980.08920981043804706)
10. 9Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
11. 10Pontuação
    #1figura da resposta próximapassouobrigatória0.1563019026 in [-0.01854932586, 0.3311531311]?
12. 11Pontuação
    #2figura da resposta próximapassouregistrada0.1563019026 vs 0.15630190259612853
13. 12Pontuação
    #3figura da resposta próximapassouregistrada0.08920981044 vs 0.08920981043805275

Épocas × verificações

Tentativas

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

• época 1, tentativa 1passoupontuada(exibindo)

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