Tarefa paper_repro.mmv_2011.tab20_1_c1.design

Veredito

Pergunta feita ao analista

Do government cash transfers buy political goodwill? In Uruguay's PANES programme, households qualified for monthly transfers when their income measure fell below an eligibility cutoff. Using the households near that cutoff, estimate the effect of receiving the transfers on the household's backing for the government.

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Trajetória

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1. 0Início

   Do government cash transfers buy political goodwill? In Uruguay's PANES programme, households qualified for monthly transfers when their income measure fell below an eligibility cutoff. Using the households near that cutoff, estimate the effect of receiving the transfers on the household's backing for the government.

   gov_transfers (agent)

   Households near the income cutoff of Uruguay's PANES cash-transfer programme, the extract of the survey of Manacorda, Miguel and Vigorito (2011), 'Government Transfers and Political Support', distributed with the causaldata R package and used in chapter 20 of Huntington-Klein's 'The Effect': one row per household (1,948 rows), only households whose income measure is within 0.02 of the cutoff. Households below the cutoff were eligible for the transfers. Columns: rownames, the row number; Income_Centered, the household's income measure minus the eligibility cutoff (negative values are below the cutoff, hence eligible); Income_Below, minus Income_Centered, added by the task author: how far below the cutoff the household's income measure is (positive values are eligible); Education, average years of education of household members aged 16 and older (missing for some households); Age, average age of household members; Participation, 1 if the household participated in the transfer programme; Support, the household's support for the government (0, 0.5 or 1; higher means more support).

   file 51312ecd5b3cb22b5dc027966a81e0a8579f24a708ce4b476674fc527652ebd0 · frame —

   1. derive {"args":{"column":"Income_Centered"},"name":"Income_Below","op":"negate"}
2. 1Transformação
   gov_transfers · derive {"args":{"column":"Income_Centered"},"name":"Income_Below","op":"negate"}

   1948 → 1948 linhas

   Frame antes → depois: 2600b9da8866 → ee25bc867239
3. 2Turno do modelo
   call_530931e7referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Table 20.1, column 'Quadratic': OLS of support on participation with a quadratic in centred income interacted with participation, on every household of the extract (all lie within 0.02 of the cutoff), unweighted. The treated side is below the cutoff, so the running variable is the declared negation Income_Below and treatment is Income_Below >= 0 (Participation equals that indicator on every row).

   Chamadas pedidas

   • infer.rdd

     {
       "bandwidth": 0.02,
       "cutoff": 0,
       "dataset": "gov_transfers",
       "kernel": "uniform",
       "polynomial": 2,
       "running": "Income_Below",
       "y": "Support"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
4. 3Ferramenta
   infer.rddv0.1.0call_170b1d94decidido pelo modeloreexecutável

   Motivo: Table 20.1, column 'Quadratic': OLS of support on participation with a quadratic in centred income interacted with participation, on every household of the extract (all lie within 0.02 of the cutoff), unweighted. The treated side is below the cutoff, so the running variable is the declared negation Income_Below and treatment is Income_Below >= 0 (Participation equals that indicator on every row).

   Argumentos

   {
     "bandwidth": 0.02,
     "cluster": null,
     "cutoff": 0,
     "dataset": "gov_transfers",
     "kernel": "uniform",
     "level": 0.95,
     "polynomial": 2,
     "running": "Income_Below",
     "subset": null,
     "y": "Support"
   }

   Figuras call_170b1d94

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in Support at the cutoff of Income_Below	0,0928553	0.09285534103880423	0,04363360.04363357378600273	0,00733511 – 0,178376

   Diagnósticos

   • bandwidth = 0,020.02
   • bandwidth_method = "user"
   • n_left = 821821
   • n_right = 1.1271127
   • kernel = "uniform"
   • polynomial = 22
   • bandwidth_sensitive = true
   • n_obs = 1.9481948
   • se_type = "HC1"

   Tabelas (1)

   {
     "sensitivity": [
       {
         "bandwidth": 0.01,
         "ci_high": 0.09793649228155744,
         "ci_low": -0.16157308291363565,
         "estimate": -0.031818295316039094,
         "multiplier": 0.5,
         "n_left": 400,
         "n_right": 537,
         "se": 0.06620263873269394
       },
       {
         "bandwidth": 0.02,
         "ci_high": 0.17837557417614058,
         "ci_low": 0.007335107901467866,
         "estimate": 0.09285534103880423,
         "multiplier": 1,
         "n_left": 821,
         "n_right": 1127,
         "se": 0.04363357378600273
       },
       {
         "bandwidth": 0.04,
         "ci_high": 0.17837557417614058,
         "ci_low": 0.007335107901467866,
         "estimate": 0.09285534103880423,
         "multiplier": 2,
         "n_left": 821,
         "n_right": 1127,
         "se": 0.04363357378600273
       }
     ]
   }

   Digests dos dados

   gov_transfers ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113

   sha256 do resultado

   68870848bc7c75ef6aaf29cf6f299bc254aac896676d945514af1968daa000c1

   Resultado mostrado ao modelo
5. 4Turno do modelo
   call_5c894e44referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.rdd): effect = 0,09286.",
       "caveats": [],
       "figure_refs": [
         "call_170b1d94:effect"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
6. 5Guarda
   resposta aceita

   Números encontrados

   0,09286

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
7. 6Resposta

   Estimativa (infer.rdd): effect = 0,09286.

   Figuras citadas: call_170b1d94:effect · Parada: answered

   Figura da resposta: call_170b1d94:effect = 0,09285530.09285534103880423 (se 0,04363360.04363357378600273)
8. 7Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
9. 8Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_170b1d94:effect (infer.rdd)
10. 9Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.rdd
11. 10Pontuação
    #3argumento igualpassouobrigatóriainfer.rdd.y = 'Support'
12. 11Pontuação
    #4argumento igualpassouobrigatóriainfer.rdd.running = 'Income_Below'
13. 12Pontuação
    #5argumento igualpassouobrigatóriainfer.rdd.cutoff = 0.0
14. 13Pontuação
    #6argumento igualpassouregistradainfer.rdd.y = 'Support'
15. 14Pontuação
    #7argumento igualpassouregistradainfer.rdd.running = 'Income_Below'
16. 15Pontuação
    #8argumento igualpassouregistradainfer.rdd.cutoff = 0.0
17. 16Pontuação
    #9argumento igualpassouregistradainfer.rdd.kernel = 'uniform'
18. 17Pontuação
    #10argumento igualpassouregistradainfer.rdd.polynomial = 2
19. 18Pontuação
    #11diagnósticopassouregistrada821 vs 821
20. 19Pontuação
    #12diagnósticopassouregistrada1127 vs 1127
21. 20Pontuação
    #13figura da resposta próximapassouregistrada0.09285534104 vs 0.09285534103880216
22. 21Pontuação
    #14figura da resposta próximapassouregistrada0.04363357379 vs 0.04363357378599472
23. 22Pontuação
    #15figura da resposta próximapassouregistrada0.09285534104 vs 0.093
24. 23Pontuação
    #16figura da resposta próximapassouregistrada0.04363357379 vs 0.044

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• época 1, tentativa 1passoupontuada(exibindo)

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