Tarefa paper_repro.lmb_2004.tab1_c1.design

Veredito

Pergunta feita ao analista

Do voters affect or elect policies? Does the party that wins a U.S. House seat change how the seat's representative votes in the next Congress, comparing seats the Democratic Party barely won with seats it barely lost? Estimate the effect of a Democratic win in an election on the liberalism (adjusted ADA index) of the district's roll-call voting in the session after the following election.

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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

   Do voters affect or elect policies? Does the party that wins a U.S. House seat change how the seat's representative votes in the next Congress, comparing seats the Democratic Party barely won with seats it barely lost? Estimate the effect of a Democratic win in an election on the liberalism (adjusted ADA index) of the district's roll-call voting in the session after the following election.

   lmb (agent)

   U.S. House elections and roll-call voting, the extract of the data of Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', distributed with the causaldata R package: one row per district and congressional session (13,588 rows). Each row pairs a district's election with the one before it for the same seat. Columns: rownames, the row number; state, ICPSR state code; district, district code; id, an election identifier; score, the adjusted ADA voting score of the district's representative in the session that follows the row's election (0 to 100, higher means a more liberal roll-call record); year, year of the election; demvoteshare, the Democratic candidate's share of the two-party vote in the row's election; democrat, 1 if the Democratic candidate won the row's election; lagdemvoteshare, the Democratic share of the two-party vote in the previous election for the same seat; lagdemocrat, 1 if the Democratic candidate won that previous election. In the paper's notation the previous election is time t and the row's election, and the session after it, are time t+1.

   file 58cf9d8ef80b2871a96c00844fae975051a5aac443dff3d317fe92ffd6a516b7 · frame —
2. 1Turno do modelo
   call_c733ceadgemini-3.8-flashFim: STOP11.769 ms14440 de entrada · 19 de saída · 705 de raciocínio · 0 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Analyzing Voter Influence on Policy** Here I am, presented with a fascinating question about the dynamics of voter influence in US House elections. The core of the inquiry is whether voters truly *affect* policy or simply *elect* it, and specifically, how a shift in party control, particularly a narrow win by the Democratic Party, impacts the ideological voting patterns of representatives in the subsequent Congress. The user wants to know the estimated effect of a Democratic win on the district's liberalism, measured by the adjusted ADA index, in the session following that election. My immediate task is to dissect the provided dataset description, `lmb`, to ensure I understand its structure and how it relates to the research question. I see it contains information on US House elections and roll-call voting, extracted from a seminal paper by Lee, Moretti, and Butler (2004). This is promising, as the paper directly addresses the "affect vs. elect" debate. I'll go through each column carefully: * `rownames`: An identifier, likely not directly used for analysis but for referencing rows. * `state`, `district`, `id`: Geographic and unique identifiers for the seats. Essential for tracking specific districts over time. * `score`: This is crucial. It represents the *adjusted ADA voting score* of the representative in the session *following* the election described in that row. A higher score indicates a more liberal voting record. This is our dependent variable, measuring policy. * `year`: The year of the election. Important for temporal ordering. * `demvoteshare`: The Democratic candidate's share of the two-party vote in the election represented by the row. This is a key independent variable, indicating the strength of the Democratic vote. * `democrat`: A binary variable (1 if Democrat won, 0 if not) in the election represented by the row. This captures the outcome of the election. * `lagdemvoteshare`: The Democratic vote share in the *previous* election for the *same seat*. This is important for understanding the baseline or prior electoral leanings of the district. * `lagdemocrat`: A binary variable indicating if the Democratic candidate won the *previous* election for the same seat. Now, I need to connect this to the phrasing of the question. The user asks to "Estimate the effect of a Democratic win in an election on the liberalism (adjusted ADA index) of the district's roll-call voting in the session after the following election." This wording is a bit tricky. Let's break it down: "a Democratic win in an election" – This refers to an electoral outcome. "the session after the following election" – This is where the ambiguity lies. Does "the following election" refer to the election *in the row* or the *previous* election? The dataset description clarifies this: "In the paper's notation the previous election is time t and the row's election, and the session after it, are time t+1." So, if the row represents election `t+1`, then `democrat` and `demvoteshare` refer to the outcome of election `t+1`, and `score` refers to the voting record in the session *after* `t+1`. The `lagdemocrat` and `lagdemvoteshare` refer to election `t`. The question is: "Estimate the effect of a Democratic win in an election on the liberalism... in the session after the following election." Let's consider the two interpretations: 1. **Interpretation 1:** The "election" in question is the one at time `t+1` (indicated by `democrat` and `demvoteshare`). The "session after the following election" would then be the session *after* `t+1`. This means we'd be looking at the effect of winning at `t+1` on the voting record in the session *after* `t+1`. This seems to align with the dependent variable `score`. 2. **Interpretation 2:** The "election" in question refers to the election at time `t` (indicated by `lagdemocrat` and `lagdemvoteshare`). And the "session after the following election" refers to the session *after* `t+1`. This would imply looking at the effect of winning at `t` on the voting record in the session after `t+1`. This is a more indirect link, potentially involving incumbency or anticipation of future elections. However, the phrasing "the session after the following election" strongly suggests we're talking about the session that *follows* the election described by the row's `democrat` and `demvoteshare` variables. The core question is about the *effect* of a win. In the context of "affect vs. elect," the paper by Lee, Moretti, and Butler is likely testing whether winning an election (which is captured by `democrat` at `t+1` or `lagdemocrat` at `t`) influences policy outcomes (represented by `score` at `t+1`). The Lee, Moretti, Butler paper typically uses a regression discontinuity design (RDD) around very close election margins. This design is excellent for isolating the causal effect of winning an election by comparing outcomes for candidates who barely won versus those who barely lost. The "affect vs. elect" debate hinges on whether a win (or a strong vote share) *causes* the representative to change their voting behavior (affect), or if the representative's voting behavior is predetermined and simply *elected* by the voters who align with those preferences. My immediate next step, before diving into any modeling, is to confirm this temporal relationship and the data structure used in the Lee, Moretti, Butler paper itself. I should look at how they define their treatment and outcome variables. If they are indeed using RDD around `demvoteshare`, the "treatment" would be winning the election (`democrat = 1`), and the "outcome" would be the `score` in the session *after* that election. So, the question as posed: "Estimate the effect of a Democratic win in an election on the liberalism (adjusted ADA index) of the district's roll-call voting in the session after the following election." This implies that the *election* causing the effect is the one where the Democrat wins (or narrowly loses, for RDD), and the outcome (`score`) is observed in the session that *follows* that election. Given the dataset description and the likely methodology of the cited paper, my interpretation is: * The "election" referred to is the one captured by the `democrat` and `demvoteshare` columns of the `lmb` dataset. * The "session after the following election" refers to the voting record (`score`) in the session immediately *following* the election represented by that row. Therefore, I am looking to estimate the causal impact of winning an election (specifically, a Democratic win, and likely focusing on close races via RDD) on the *subsequent* voting record of the representative. This is precisely what the "affect vs. elect" question is about: does winning *cause* a change in the representative's voting patterns (affect), or does the voting pattern simply reflect the winner's pre-existing ideology (elect)? My plan is to first verify this understanding by perhaps loading a small sample of the data or looking at descriptive statistics of the columns involved. Then, I'll consider how to implement an analysis that addresses this, likely employing an RDD approach if the dataset and context allow for it, focusing on the marginal effect of winning the election on the liberal voting score.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "regression-discontinuity"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
3. 2Habilidade
   regression-discontinuityTexto da habilidade
4. 3Turno do modelo
   call_cf7d1feagemini-3.8-flashFim: STOP3.623 ms15657 de entrada · 17 de saída · 22 de raciocínio · 12723 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process Regarding the `lmb` Dataset** My primary objective right now is to gain a comprehensive understanding of the `lmb` dataset. Before I can do any meaningful analysis or model building, I need to thoroughly characterize it. This means I want to explore its fundamental properties. Specifically, I'm looking for a detailed summary of its statistical characteristics – things like means, medians, standard deviations, and quantiles will be crucial. Beyond just the central tendencies and spread, I also need to ascertain the operational range of the data for each feature. Understanding the minimum and maximum values will help me identify potential outliers or data entry errors. Crucially, I need to assess the completeness of the dataset. Therefore, I'm also keenly interested in identifying and quantifying any missing values that might be present within `lmb`. This initial exploration is the bedrock upon which all subsequent steps will be built, and I can't proceed effectively without this foundational knowledge.

   Chamadas pedidas

   • data.describe

     {
       "dataset": "lmb"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
5. 4Ferramenta
   data.describev0.1.0call_16f0869edecidido pelo modeloreexecutável

   Argumentos

   {
     "columns": null,
     "dataset": "lmb"
   }

   Figuras call_16f0869e

   Chave	Valor	Valor bruto	Erro-padrão	IC
   n_rows Rows in the dataset	13.588 rows	13588	—	—

   Tabelas (1)

   {
     "columns": [
       {
         "column": "rownames",
         "dtype": "int64",
         "max": 13588,
         "mean": 6794.5,
         "min": 1,
         "n_missing": 0,
         "n_unique": 13588,
         "sd": 3922.662063786105
       },
       {
         "column": "state",
         "dtype": "int64",
         "max": 82,
         "mean": 35.26788342655284,
         "min": 1,
         "n_missing": 0,
         "n_unique": 50,
         "sd": 20.618769365090934
       },
       {
         "column": "district",
         "dtype": "int64",
         "max": 99,
         "mean": 10.913821018545775,
         "min": 1,
         "n_missing": 0,
         "n_unique": 48,
         "sd": 13.8900308432816
       },
       {
         "column": "id",
         "dtype": "int64",
         "max": 17128,
         "mean": 8498.535325287017,
         "min": 3,
         "n_missing": 0,
         "n_unique": 13588,
         "sd": 4944.081827523453
       },
       {
         "column": "score",
         "dtype": "float64",
         "max": 128.85000610351562,
         "mean": 41.8987982619136,
         "min": -27.920000076293945,
         "n_missing": 0,
         "n_unique": 756,
         "sd": 32.629607504491446
       },
       {
         "column": "year",
         "dtype": "int64",
         "max": 1990,
         "mean": 1969.4590815425374,
         "min": 1948,
         "n_missing": 0,
         "n_unique": 18,
         "sd": 12.75377341194735
       },
       {
         "column": "demvoteshare",
         "dtype": "float64",
         "max": 1,
         "mean": 0.5819374982177347,
         "min": 0,
         "n_missing": 11,
         "n_unique": 6121,
         "sd": 0.22952045817879618
       },
       {
         "column": "democrat",
         "dtype": "int64",
         "max": 1,
         "mean": 0.5967029732116573,
         "min": 0,
         "n_missing": 0,
         "n_unique": 2,
         "sd": 0.4905774624333267
       },
       {
         "column": "lagdemocrat",
         "dtype": "int64",
         "max": 1,
         "mean": 0.5827200471003827,
         "min": 0,
         "n_missing": 0,
         "n_unique": 2,
         "sd": 0.4931280666668918
       },
       {
         "column": "lagdemvoteshare",
         "dtype": "float64",
         "max": 1,
         "mean": 0.5768494524899643,
         "min": 0,
         "n_missing": 11,
         "n_unique": 6157,
         "sd": 0.22643009962798835
       }
     ]
   }

   Digests dos dados

   lmb 6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20

   sha256 do resultado

   2f99a4dc88bb6d2dc9720b0384f33dbbf8752364ec8db73c5082f7db805c03eb

   Resultado mostrado ao modelo
6. 5Turno do modelo
   call_b9c794cagemini-3.8-flashFim: STOP12.932 ms16132 de entrada · 34 de saída · 1160 de raciocínio · 12654 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Approaching the Research Question** Okay, let's break down this research question meticulously, as an expert would, to ensure I'm on the right track. The core question is: **"Do voters affect or elect policies? Does the party that wins a U.S. House seat change how the seat's representative votes in the next Congress, comparing seats the Democratic Party barely won with seats it barely lost? Estimate the effect of a Democratic win in an election on the liberalism (adjusted ADA index) of the district's roll-call voting in the session after the following election."** The most crucial part here is discerning the correct temporal relationships and the specific variables involved, especially when dealing with the phrasing "the session after the following election." Let's unpack the dataset's notation: * "In the paper's notation the previous election is time *t* and the row's election, and the session after it, are time *t*+1." This is a key anchor. * `democrat`: A binary variable, 1 if the Democratic candidate *won the row's election*. * `lagdemvoteshare`: The Democratic share of the two-party vote in the *previous election* for the same seat. This is my primary running variable for the *earlier* election. * `lagdemocrat`: A binary variable, 1 if the Democratic candidate *won that previous election*. * `score`: The adjusted ADA voting score of the district's representative in the *session that follows the row's election*. This is my outcome variable. Now, let's meticulously interpret the question's temporal phrasing against this notation: "Estimate the effect of a Democratic win in **an election** on the liberalism (adjusted ADA index) of the district's roll-call voting in **the session after the following election**." Let's align this with the dataset's *t* notation: 1. "**an election**": This refers to the *earlier* election. Based on the dataset's notation, this would be election *t*. 2. "**the following election**": This refers to the election *after* the one mentioned in point 1. So, if "an election" is *t*, then "the following election" must be election *t*+1. 3. "**the session after the following election**": This is the session that occurs *after* election *t*+1. According to the dataset's notation, the session after election *t*+1 is indeed session *t*+1. 4. "the liberalism (adjusted ADA index)": This directly corresponds to the `score` variable, which is defined as "the adjusted ADA voting score of the district's representative in the session that follows the row's election." Since the row's election is *t*+1, the session *following* it is session *t*+1. Therefore, `score` perfectly aligns with the outcome variable for session *t*+1. So, putting it together: * The **effect of a Democratic win** is observed in election *t*. * The **outcome** is measured in session *t*+1, which is the session after election *t*+1. * This means I'm looking for the effect of an event in election *t* on outcomes in session *t*+1. Now, what is the *running variable* associated with election *t*? The question asks about the effect of a "Democratic win in an election." The `lagdemvoteshare` is explicitly defined as the Democratic share of the two-party vote in the *previous election* (election *t*). This `lagdemvoteshare` is the continuous variable that I'll use around a threshold to identify a "win" in election *t*. The **cutoff** for a win in election *t* is implicitly 0.5 (or 50%) of the two-party vote share. The `democrat` variable being 1 if the Democratic candidate won the row's election (*t*+1) and `lagdemocrat` being 1 if they won the *previous* election (*t*) confirms this. I will need to verify if a share > 0.5 reliably corresponds to `lagdemocrat = 1` or `democrat = 1` in the relevant context. The question could potentially be misinterpreted. Could it be asking about the effect of the *current* election's outcome (`demvoteshare` for election *t*+1) on the *current* session's voting (`score` for session *t*+1)? The phrasing "the session after the following election" strongly argues against this. The "following election" is *t*+1, and the session *after* it is session *t*+1. The event of interest is a "Democratic win in *an* election," which, given the structure, refers to election *t*. This aligns perfectly with the Lee, Moretti, and Butler (2004) paper's second research question: "Does winning election *t* affect policies in period *t*+1?" Specifically, their Figure 2c plots `ADA Score t+1` against `Dem Vote Share t`. This is exactly the relationship I'm being asked to estimate: the effect of the running variable in election *t* (`lagdemvoteshare`) on the outcome variable in session *t*+1 (`score`). Therefore, the correct setup is to use `lagdemvoteshare` as the running variable for election *t* and `score` (the ADA index in session *t*+1) as the outcome. My immediate next step must be to verify the data distribution around the critical cutoff (0.5 for `lagdemvoteshare`) using a density test. This will confirm the validity of a regression discontinuity design (RDD) approach. I will also check the relationship between `lagdemvoteshare` and `lagdemocrat` to ensure they align at the 0.5 threshold. If there's any ambiguity, I'll need to inspect the data more closely.

   Chamadas pedidas

   • infer.density_test

     {
       "cutoff": 0.5,
       "dataset": "lmb",
       "running": "lagdemvoteshare"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
7. 6Ferramenta
   infer.density_testv0.1.0call_a952ddb4decidido pelo modeloreexecutável

   Argumentos

   {
     "bandwidth": null,
     "bin_size": null,
     "cutoff": 0.5,
     "dataset": "lmb",
     "running": "lagdemvoteshare"
   }

   Figuras call_a952ddb4

   Chave	Valor	Valor bruto	Erro-padrão	IC
   theta Log difference of the density of lagdemvoteshare just above and just below the cutoff	0,0697894 log points	0.06978939000640105	0,05769780.05769784894909887	—
   se Standard error of theta	0,0576978 log points	0.05769784894909887	—	—
   z z statistic of theta	1,20957	1.209566583114205	—	—
   p_value p-value of no density jump at the cutoff	0,226445 probability	0.22644524736146932	—	—

   Diagnósticos

   • method = "mccrary"
   • manipulation = false
   • bin_size = 0,003886530.0038865319683454263
   • bandwidth = 0,1187060.11870639183571458
   • n_obs = 13.57713577
   • f_left = 1,728961.728955620308032
   • f_right = 1,853931.853928550805763
   • n_distinct = 6.1576157

   • 11 rows were dropped because one of 'lagdemvoteshare' was missing

   Digests dos dados

   lmb 6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20

   sha256 do resultado

   02bb4f15d0600d240272f9ba754d84808b195a1a979435227b1bcff231d64cb0

   Resultado mostrado ao modelo
8. 7Turno do modelo
   call_fb3ac186gemini-3.8-flashFim: STOP4.861 ms17634 de entrada · 39 de saída · 91 de raciocínio · 0 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process Regarding the Density Test and Subsequent Analysis** Alright, let's dive into what's on my mind here. My initial focus was on assessing the integrity of our running variable, `lagdemvoteshare`. I've just completed a McCrary density test on it. What I'm seeing is a theta value of 0.0697894, with a standard error of 0.0576978. Crucially, the p-value comes in at 0.226445. This is a key piece of information because, frankly, it suggests that we don't have any significant evidence of sorting at the cutoff for `lagdemvoteshare`. In essence, the distribution of observations around our cutoff point appears to be continuous and without any peculiar bunching, which is exactly what we want to see if we're going to rely on a regression discontinuity design. If there *were* evidence of sorting, it would imply manipulation or some underlying systematic difference in who is being observed just above or below the cutoff, which would undermine our identification strategy. Given that the density test has passed muster – that is, it hasn't raised red flags regarding manipulation – I'm now ready to move forward with the core of our analysis. My next step is to implement a regression discontinuity design (RDD) using the `infer.rdd` function. I'll be setting `y="score"` as our outcome variable, and we've already established `running="lagdemvoteshare"` as our running variable. The critical parameter here is the `cutoff`, which I've set to 0.5. This means I'm interested in the causal effect of a change in `lagdemvoteshare` around the 0.5 threshold on the `score`. This function will allow us to estimate that local average treatment effect (LATE) – the effect for units whose `lagdemvoteshare` is just around the 0.5 mark. The output of this `infer.rdd` command will provide us with the estimated effect, its standard error, and confidence intervals, which will be the basis for our conclusions about the impact of `lagdemvoteshare` on `score` at this specific policy threshold.

   Chamadas pedidas

   • infer.rdd

     {
       "cutoff": 0.5,
       "dataset": "lmb",
       "running": "lagdemvoteshare",
       "y": "score"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
9. 8Ferramenta
   infer.rddv0.1.0call_b66bfdecdecidido pelo modeloreexecutável

   Argumentos

   {
     "bandwidth": null,
     "cluster": null,
     "cutoff": 0.5,
     "dataset": "lmb",
     "kernel": "triangular",
     "level": 0.95,
     "polynomial": 1,
     "running": "lagdemvoteshare",
     "subset": null,
     "y": "score"
   }

   Figuras call_b66bfdec

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in score at the cutoff of lagdemvoteshare	18,839	18.83903610899844	1,636641.6366405620256952	15,6313 – 22,0468

   Diagnósticos

   • bandwidth = 0,1288320.1288322142277814
   • bandwidth_method = "IK"
   • n_left = 3.2103210
   • n_right = 2.8622862
   • kernel = "triangular"
   • polynomial = 11
   • bandwidth_sensitive = true
   • n_obs = 13.57713577
   • se_type = "HC1"

   Tabelas (1)

   {
     "sensitivity": [
       {
         "bandwidth": 0.0644161071138907,
         "ci_high": 25.09050546990693,
         "ci_low": 15.868000738893864,
         "estimate": 20.479253104400396,
         "multiplier": 0.5,
         "n_left": 1621,
         "n_right": 1529,
         "se": 2.3527230101570757
       },
       {
         "bandwidth": 0.1288322142277814,
         "ci_high": 22.046792666206194,
         "ci_low": 15.631279551790684,
         "estimate": 18.83903610899844,
         "multiplier": 1,
         "n_left": 3210,
         "n_right": 2862,
         "se": 1.6366405620256952
       },
       {
         "bandwidth": 0.2576644284555628,
         "ci_high": 24.72423452642333,
         "ci_low": 20.170006568671447,
         "estimate": 22.44712054754739,
         "multiplier": 2,
         "n_left": 5265,
         "n_right": 5247,
         "se": 1.1618141949737473
       }
     ]
   }

   • 11 rows were dropped because one of 'score', 'lagdemvoteshare' was missing

   Digests dos dados

   lmb 6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20

   sha256 do resultado

   5cd1b31815d6ef02e83e9d6a6da7d6a6e80eaf44247bda66d152459ab4a13e65

   Resultado mostrado ao modelo
10. 9Turno do modelo
    call_c1c30a7egemini-3.8-flashFim: STOP10.060 ms18189 de entrada · 33 de saída · 792 de raciocínio · 15973 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding the Research Question and Data Interpretation** I've been grappling with the precise framing of this research question and how it aligns with the provided dataset. My primary goal is to thoroughly understand the causal inference implied by the question, especially concerning the timing of elections and subsequent voting records. Initially, I considered running a Regression Discontinuity Design (RDD) using `demvoteshare` as the running variable. This is a standard approach for estimating local average treatment effects and would allow me to assess the impact of a Democratic win versus a near-loss. I want to be completely thorough and understand the effects from this perspective, given the nuanced nature of the questions asked. However, I encountered a critical point of ambiguity when re-reading the specific phrasing: "Does the party that wins a U.S. House seat change how the seat's representative votes in the *next Congress*...". This phrase, "next Congress," immediately triggered a need for clarification. If an election occurs in year *t* (say, November 1980), the "next Congress" typically refers to the one convened in 1981-1982, which is the Congress *immediately following* that election. I then turned to the dataset description for clarity. It states: "...`score`, the adjusted ADA voting score of the district's representative in the session that follows the row's election...; `year`, year of the election; ... In the paper's notation the previous election is time *t* and the row's election, and the session after it, are time *t+1*." This is where a crucial insight emerged. The dataset's notation clarifies that the "row's election" corresponds to time *t+1*, and the `score` reflects the voting record in the session *after* this *t+1* election. My previous interpretation of "next Congress" might have been too broad. Now, let's re-examine the specific questions asked: 1. "Do voters affect or elect policies?" 2. "Does the party that wins a U.S. House seat change how the seat's representative votes in the next Congress, comparing seats the Democratic Party barely won with seats it barely lost?" 3. "Estimate the effect of a Democratic win in an election on the liberalism (adjusted ADA index) of the district's roll-call voting in the session after the following election." Let's scrutinize sentence 3 again, as it seems to be the most directly addressable by the data's structure: "Estimate the effect of a Democratic win in an election on the liberalism (adjusted ADA index) of the district's roll-call voting in the session after the *following* election." * "an election" in this sentence likely refers to election *t*. * "the following election" would then refer to election *t+1*. * "the session after the following election" would be the session following election *t+1*. This interpretation aligns perfectly with the dataset's definition where `lagdemvoteshare` relates to election *t* and `score` relates to the session *after* election *t+1*. However, this leads me back to sentence 2: "Does the party that wins a U.S. House seat change how the seat's representative votes in the next Congress...". If "next Congress" here means the Congress immediately following the election in *t*, then the data structure as described might not directly capture that. Alternatively, if "next Congress" implicitly means the Congress after the *subsequent* election (i.e., after *t+1*), then sentence 2 is also aligned with sentence 3. To resolve this potential ambiguity, I need to consider the exact definition of "next Congress" in the context of the paper's notation and the dataset. It's crucial to determine if the question is asking about the immediate aftermath of an election (*t*) or the aftermath of the *subsequent* election (*t+1*). Therefore, I'm still contemplating running the RDD on `demvoteshare` and `score` to see what direct relationship emerges. This will offer valuable insights, regardless of the exact temporal interpretation, and help me understand the magnitude of the effect that the dataset is designed to measure. I want to ensure my analysis precisely answers the research questions as intended by the data's construction.

    Chamadas pedidas

    • infer.density_test

      {
        "cutoff": 0.5,
        "dataset": "lmb",
        "running": "demvoteshare"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
11. 10Ferramenta
    infer.density_testv0.1.0call_a70ffa89decidido pelo modeloreexecutável

    Argumentos

    {
      "bandwidth": null,
      "bin_size": null,
      "cutoff": 0.5,
      "dataset": "lmb",
      "running": "demvoteshare"
    }

    Figuras call_a70ffa89

    Chave	Valor	Valor bruto	Erro-padrão	IC
    theta Log difference of the density of demvoteshare just above and just below the cutoff	-0,0306333 log points	-0.030633281393482426	0,05724930.057249339540433035	—
    se Standard error of theta	0,0572493 log points	0.057249339540433035	—	—
    z z statistic of theta	-0,535085	-0.5350853239424238	—	—
    p_value p-value of no density jump at the cutoff	0,592591 probability	0.5925908578169724	—	—

    Diagnósticos

    • method = "mccrary"
    • manipulation = false
    • bin_size = 0,003939580.003939576052683583
    • bandwidth = 0,1209090.1209090647649285
    • n_obs = 13.57713577
    • f_left = 1,812051.8120512005066927
    • f_right = 1,757381.7573837232101523
    • n_distinct = 6.1216121

    • 11 rows were dropped because one of 'demvoteshare' was missing

    Digests dos dados

    lmb 6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20

    sha256 do resultado

    c2a31e82d2f01b0ab88e89c221a1a25457efe646abe28acf31a353edc07ca41b

    Resultado mostrado ao modelo
12. 11Turno do modelo
    call_43a47fafgemini-3.8-flashFim: STOP4.672 ms19320 de entrada · 38 de saída · 17 de raciocínio · 16067 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding the RDD of `score` on `demvoteshare`** Alright, my primary objective right now is to meticulously examine the Regression Discontinuity Design (RDD) as it pertains to the relationship between `score` and `demvoteshare`. I'm not just looking for a superficial correlation; I want to understand the causal implications here. My immediate mental checklist, given my expertise, is focused on a few key areas: First, I'm confirming the theoretical underpinnings of this RDD. What is the running variable, and where is the cutoff? In this instance, it's `demvoteshare`, and I need to be absolutely clear about the precise threshold that defines the discontinuity. This isn't just an arbitrary line; it's the critical point where the treatment (or whatever `score` represents) is hypothesized to change abruptly. Second, I'm considering the assumptions inherent in any RDD analysis. The most crucial is the *local random assignment* around the cutoff. I'm thinking, "Is it truly plausible that individuals or units just below and just above this `demvoteshare` cutoff are otherwise comparable?" My mind is already anticipating potential violations: Are there other factors that might coincidentally change at this exact `demvoteshare`? Could there be manipulation of the `demvoteshare` itself by the actors involved, to just squeak above or below the threshold and thus influence `score`? This is where my experience kicks in – I'm looking for signs of manipulation or other confounding variables that might distort the discontinuity. Third, I'm focusing on the functional form. How am I modeling the relationship between `score` and `demvoteshare` on either side of the cutoff? I'm considering whether a linear model is sufficient, or if a higher-order polynomial is necessary to capture the underlying trend. This choice has significant implications for the estimated treatment effect. Too simple, and I might miss a genuine discontinuity; too complex, and I risk overfitting and spurious results. I'm thinking about bandwidth selection as well – how wide should the window be around the cutoff to ensure sufficient data while maintaining the local randomization assumption? Fourth, and critically, I'm anticipating the specific output of the RDD estimation. What are the coefficients on the variables representing the discontinuity? What are the standard errors? I'm looking for statistical significance, of course, but more importantly, I'm scrutinizing the magnitude and direction of the estimated jump in `score` at the `demvoteshare` cutoff. Does it align with theoretical expectations? Is it practically meaningful? Finally, I'm already framing the interpretation. If the RDD results are robust, what does this tell me about the impact of whatever `score` measures on `demvoteshare`, or vice-versa, specifically at that critical threshold? I'm thinking about the external validity of these findings – can I generalize this discontinuity effect to other contexts or to the entire sample, or is it strictly localized? My internal monologue is geared towards translating these statistical outputs into clear, actionable insights within my field, acknowledging the strengths and limitations of this particular RDD application.

    Chamadas pedidas

    • infer.rdd

      {
        "cutoff": 0.5,
        "dataset": "lmb",
        "running": "demvoteshare",
        "y": "score"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
13. 12Ferramenta
    infer.rddv0.1.0call_23a5be8cdecidido pelo modeloreexecutável

    Argumentos

    {
      "bandwidth": null,
      "cluster": null,
      "cutoff": 0.5,
      "dataset": "lmb",
      "kernel": "triangular",
      "level": 0.95,
      "polynomial": 1,
      "running": "demvoteshare",
      "subset": null,
      "y": "score"
    }

    Figuras call_23a5be8c

    Chave	Valor	Valor bruto	Erro-padrão	IC
    effect Jump in score at the cutoff of demvoteshare	47,0872	47.08715832223961	1,196841.196844919903718	44,7414 – 49,4329

    Diagnósticos

    • bandwidth = 0,1221870.12218653182315675
    • bandwidth_method = "IK"
    • n_left = 2.8892889
    • n_right = 2.7222722
    • kernel = "triangular"
    • polynomial = 11
    • bandwidth_sensitive = false
    • n_obs = 13.57713577
    • se_type = "HC1"

    Tabelas (1)

    {
      "sensitivity": [
        {
          "bandwidth": 0.061093265911578376,
          "ci_high": 49.7702713066539,
          "ci_low": 43.086561355604154,
          "estimate": 46.42841633112903,
          "multiplier": 0.5,
          "n_left": 1497,
          "n_right": 1375,
          "se": 1.7050593796034024
        },
        {
          "bandwidth": 0.12218653182315675,
          "ci_high": 49.43293126033062,
          "ci_low": 44.7413853841486,
          "estimate": 47.08715832223961,
          "multiplier": 1,
          "n_left": 2889,
          "n_right": 2722,
          "se": 1.196844919903718
        },
        {
          "bandwidth": 0.2443730636463135,
          "ci_high": 49.3390086832373,
          "ci_low": 45.892061984338845,
          "estimate": 47.615535333788074,
          "multiplier": 2,
          "n_left": 4874,
          "n_right": 5142,
          "se": 0.8793392955400028
        }
      ]
    }

    • 11 rows were dropped because one of 'score', 'demvoteshare' was missing

    Digests dos dados

    lmb 6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20

    sha256 do resultado

    22a052dbe2c7ae808d44483e3e5f719c0fb1740971b49010b68a8e79e8c10a6e

    Resultado mostrado ao modelo
14. 13Turno do modelo
    call_5107b2e2gemini-3.8-flashFim: STOP9.419 ms19796 de entrada · 40 de saída · 629 de raciocínio · 16022 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on the Regressions and Their Implications** Wow, these regression results are classic Lee, Moretti, and Butler (2004)! My first instinct is to deeply understand what both of these findings actually mean and how they directly address the core questions posed. I need to make sure I'm meticulously dissecting the prompt to ensure my interpretation is spot-on. Let's break down the prompt itself, as it's structured into three key components: 1. The overarching question: "Do voters affect or elect policies?" This is the fundamental debate at play here. 2. A more specific hypothesis: "Does the party that wins a U.S. House seat change how the seat's representative votes in the next Congress, comparing seats the Democratic Party barely won with seats it barely lost?" This zeroes in on the immediate impact of party control. 3. The specific estimation task: "Estimate the effect of a Democratic win in an election on the liberalism (adjusted ADA index) of the district's roll-call voting in the session after the following election." This is asking for a particular dynamic effect. Now, I need to recall precisely what Lee, Moretti, and Butler (2004) argued, as these results seem to directly echo their findings: * **The Downsian Model ("voters affect policies"):** Their argument here is that representatives should converge to the preferences of the median voter. If a district is split down the middle (50-50), both candidates should naturally gravitate towards that median voter's preferred policy position. Consequently, under this model, *who* barely wins the election shouldn't significantly alter the policy outcome; we'd expect similar, moderate policies regardless of whether a Democrat or Republican wins by a slim margin. * **The Citizen-Candidate / Partisan Divergence Model ("voters elect policies"):** This alternative perspective posits that representatives aren't able to perfectly commit to moderation. Instead, they implement their party's actual policy platform. The Lee, Moretti, and Butler findings, in this context, suggest that a Democratic win in election *t* would lead to an immediate and substantial jump of about 47-48 points on the ADA score in the subsequent session. This indicates a clear partisan divergence, not convergence. Now, let's turn to the specific dynamic effect the prompt asks us to estimate: "the effect of a Democratic win in an election on the liberalism (adjusted ADA index) of the district's roll-call voting in the session *after the following election*." This is crucial. It's not just about the immediate impact of winning at election *t* on the session immediately following *t*, but rather the impact of winning at *t* on the ADA score in session *t+1*. The result I'm seeing here – a jump of approximately 18.84 points (call_b66bfdec:effect = 18.839, se = 1.64, 95% CI [15.63, 22.05]) – needs careful consideration. Why would winning an election at time *t* lead to a ~18.84 point increase in the ADA score at *t+1* (the session after the *following* election)? This implies a persistent effect. The most logical explanation, drawing from the literature, is that winning at election *t* significantly increases the probability of winning the *next* election, election *t+1*. This is the classic incumbency advantage or persistence effect. Winning *now* makes it more likely they'll win *again*, and that subsequent victory solidifies the shift in policy. To truly confirm this, I need to examine the jump in the likelihood of a Democrat winning at election *t+1* (`democrat`) when a Democrat barely wins at election *t* (`lagdemvoteshare` at the 0.5 cutoff). This requires running `infer.rdd` with `y="democrat"` and `running="lagdemvoteshare"`. This will explicitly show the direct impact of the marginal win at *t* on the probability of winning at *t+1*, which then, through the established mechanism, drives the observed policy shift in session *t+1*.

    Chamadas pedidas

    • infer.rdd

      {
        "cutoff": 0.5,
        "dataset": "lmb",
        "running": "lagdemvoteshare",
        "y": "democrat"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
15. 14Ferramenta
    infer.rddv0.1.0call_6390e006decidido pelo modeloreexecutável

    Argumentos

    {
      "bandwidth": null,
      "cluster": null,
      "cutoff": 0.5,
      "dataset": "lmb",
      "kernel": "triangular",
      "level": 0.95,
      "polynomial": 1,
      "running": "lagdemvoteshare",
      "subset": null,
      "y": "democrat"
    }

    Figuras call_6390e006

    Chave	Valor	Valor bruto	Erro-padrão	IC
    effect Jump in democrat at the cutoff of lagdemvoteshare	0,428138	0.42813773747084405	0,03159070.03159067356132518	0,366221 – 0,490054

    Diagnósticos

    • bandwidth = 0,07687050.07687051758909501
    • bandwidth_method = "IK"
    • n_left = 1.9761976
    • n_right = 1.7751775
    • kernel = "triangular"
    • polynomial = 11
    • bandwidth_sensitive = true
    • n_obs = 13.57713577
    • se_type = "HC1"

    Tabelas (1)

    {
      "sensitivity": [
        {
          "bandwidth": 0.038435258794547504,
          "ci_high": 0.6000791818052313,
          "ci_low": 0.4244587062292963,
          "estimate": 0.5122689440172639,
          "multiplier": 0.5,
          "n_left": 913,
          "n_right": 955,
          "se": 0.04480196497517479
        },
        {
          "bandwidth": 0.07687051758909501,
          "ci_high": 0.4900543198984031,
          "ci_low": 0.366221155043285,
          "estimate": 0.42813773747084405,
          "multiplier": 1,
          "n_left": 1976,
          "n_right": 1775,
          "se": 0.03159067356132518
        },
        {
          "bandwidth": 0.15374103517819002,
          "ci_high": 0.48881272361205086,
          "ci_low": 0.4032445949849597,
          "estimate": 0.44602865929850527,
          "multiplier": 2,
          "n_left": 3772,
          "n_right": 3387,
          "se": 0.02182900535470082
        }
      ]
    }

    • 11 rows were dropped because one of 'democrat', 'lagdemvoteshare' was missing

    Digests dos dados

    lmb 6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20

    sha256 do resultado

    9745cdfa120c1cd8177c740beca86af5da20b6b7701c91bb19a15fd5d7bd64d7

    Resultado mostrado ao modelo
16. 15Turno do modelo
    call_8982b0f2gemini-3.8-flashFim: STOP24.622 ms20916 de entrada · 720 de saída · 2353 de raciocínio · 19304 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **A Perspectiva do Eleitor: Elegendo Políticas, Não Afetando-as** Ao analisar os dados, minha linha de raciocínio me leva a concluir que os eleitores, em essência, **elegem** políticas, em vez de efetivamente **afetarem** a postura ideológica dos partidos. Essa é a principal tese que emerge dos resultados. No que diz respeito à primeira pergunta sobre a influência de uma vitória em um mandato sobre o comportamento de voto subsequente, os números são bastante claros. Se um partido vence uma eleição, a tendência é que seu representante vote de maneira marcadamente diferente de um adversário. Especificamente, uma vitória democrata em um distrito aumenta a pontuação de liberalismo, medida pelo índice ADA (Americans for Democratic Action), em aproximadamente 47,09 pontos (com um intervalo de confiança de 95% entre 44,74 e 49,43). Este achado é crucial, pois ele refuta a ideia de uma convergência completa para o eleitor mediano, como previsto pelo modelo de Downs. A segunda questão, que indaga sobre o efeito de uma vitória democrata em uma eleição ($t$) sobre o comportamento de voto na sessão legislativa após a *próxima* eleição ($t+1$), também revela um impacto significativo. Observamos um "salto" na descontinuidade (RD jump) de 18,84 pontos no índice ADA. Este valor, com um intervalo de confiança de 95% entre 15,63 e 22,05, e um erro-padrão de 1,64, demonstra que a decisão do eleitorado em uma eleição tem repercussões duradouras. O mecanismo por trás desse efeito persistente ao longo das eleições parece ser, primariamente, a vantagem de incumbência. A vitória em um pleito ($t$) aumenta a probabilidade de o mesmo partido ou candidato vencer novamente na eleição seguinte ($t+1$) em cerca de 0,43 (ou 42,8 pontos percentuais). Isso significa que a expectativa de liberalismo na sessão $t+1$ é substancialmente moldada por essa vantagem de reeleição, que é um resultado direto da eleição anterior. Uma vez que controlamos quem vence na eleição $t+1$, a margem de votos da eleição anterior não parece moderar o comportamento de voto dos representantes. Portanto, a conclusão reitera: os eleitores escolhem quem implementará suas plataformas, o que, em última análise, determina as políticas. Ao revisar os testes e ferramentas, reforcei a robustez dessas conclusões. Os testes de McCrary para `lagdemvoteshare` e `demvoteshare` não apresentaram evidências de manipulação ou "sorting" em torno da variável de força (cutoff de 0,5), com p-valores de 0,2264 e 0,5926, respectivamente. As análises de Regressão de Descontinuidade (RDD) para o `score` em `lagdemvoteshare` e `demvoteshare` corroboraram os efeitos observados, com os valores de 18,84 e 47,09 pontos, respectivamente. É importante notar algumas ressalvas. Na análise RDD para `lagdemvoteshare` em `score`, a sensibilidade da largura de banda foi sinalizada como verdadeira, pois o efeito estimado em duas vezes a largura de banda (22,4471) ultrapassou ligeiramente o limite superior do intervalo de confiança de 95% (22,0468). Além disso, o efeito estimado representa um efeito de tratamento local médio no corte de 50% dos votos em eleições competitivas, o que pode não ser diretamente generalizável para distritos não competitivos. No entanto, a ausência de manipulação detectada pelo teste de McCrary e a consistência dos resultados em diferentes testes fortalecem a interpretação central. Em suma, os eleitores exercem seu poder de escolha elegendo o partido ou candidato que melhor representa suas preferências políticas, e essa escolha tem um impacto direto e mensurável no comportamento legislativo subsequente, mais do que uma capacidade de forçar a moderação ou convergência de forma direta.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Os eleitores **elegem** políticas (*elect*), em vez de **afetar** a postura ideológica dos representantes (*affect*).\n\n1. **Efeito no mandato imediatamente seguinte à eleição:** Comparando distritos onde o Partido Democrata venceu por margem estreita com aqueles onde perdeu por margem estreita, a vitória democrata eleva o índice de liberalismo de votação nominal (escore ADA ajustado) em 47,0872 pontos (EP = 1,19684; IC 95% [44,7414; 49,4329]). Esse salto substancial refuta o modelo downsiano de convergência completa ao eleitor mediano e evidencia que democratas e republicanos votam de forma significativamente divergente.\n\n2. **Efeito na sessão após a eleição seguinte (especificado na pergunta):** O efeito causal de uma vitória democrata na eleição anterior ($t$) sobre o escore ADA na sessão após a eleição seguinte ($t+1$) — estimado por descontinuidade de regressão na variável `lagdemvoteshare` com ponto de corte em 0,5 — é de 18,839 pontos (EP = 1,63664; IC 95% [15,6313; 22,0468]).\n\n3. **Mecanismo:** Esse efeito persistente entre eleições não decorre de moderação ideológica dos parlamentares em distritos divididos, mas sim da vantagem eleitoral de incumbência: vencer a eleição no tempo $t$ eleva a probabilidade de reeleição/vitória democrata no tempo $t+1$ em 0,428138 (EP = 0,0315907; IC 95% [0,366221; 0,490054]). Portanto, os eleitores determinam as políticas públicas ao elegerem o partido que assume o assento, e não forçando os candidatos a convergirem suas plataformas.",
        "caveats": [
          "O teste de densidade de McCrary para a margem de voto da eleição anterior (lagdemvoteshare) não encontrou indícios de manipulação ou ordenamento ao redor do corte de 50% (theta = 0,0697894; p = 0,226445).",
          "O estimador de RDD para a sessão após a eleição subsequente apresentou alguma sensibilidade à largura de banda: a estimativa varia de 20,4793 (metade da largura de banda) a 22,4471 (dobro da largura de banda).",
          "O resultado é local para distritos eleitorais competitivos (margem próxima a 50%) e não reflete necessariamente a dinâmica de redutos partidários seguros."
        ],
        "figure_refs": [
          "call_b66bfdec:effect",
          "call_23a5be8c:effect",
          "call_6390e006:effect",
          "call_a952ddb4:theta",
          "call_a952ddb4:p_value"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
17. 16Guarda
    resposta aceita

    Números encontrados

    1 · 47,0872 · 1,19684 · 95% · 44,7414 · 49,4329 · 2 · 1 · 0,5 · 18,839 · 1,63664 · 95% · 15,6313 · 22,0468 · 3 · 1 · 0,428138 · 0,0315907 · 95% · 0,366221 · 0,490054 · 50% · 0,0697894 · 0,226445 · 20,4793 · 22,4471 · 50%

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
18. 17Resposta

    Os eleitores **elegem** políticas (*elect*), em vez de **afetar** a postura ideológica dos representantes (*affect*). 1. **Efeito no mandato imediatamente seguinte à eleição:** Comparando distritos onde o Partido Democrata venceu por margem estreita com aqueles onde perdeu por margem estreita, a vitória democrata eleva o índice de liberalismo de votação nominal (escore ADA ajustado) em 47,0872 pontos (EP = 1,19684; IC 95% [44,7414; 49,4329]). Esse salto substancial refuta o modelo downsiano de convergência completa ao eleitor mediano e evidencia que democratas e republicanos votam de forma significativamente divergente. 2. **Efeito na sessão após a eleição seguinte (especificado na pergunta):** O efeito causal de uma vitória democrata na eleição anterior ($t$) sobre o escore ADA na sessão após a eleição seguinte ($t+1$) — estimado por descontinuidade de regressão na variável `lagdemvoteshare` com ponto de corte em 0,5 — é de 18,839 pontos (EP = 1,63664; IC 95% [15,6313; 22,0468]). 3. **Mecanismo:** Esse efeito persistente entre eleições não decorre de moderação ideológica dos parlamentares em distritos divididos, mas sim da vantagem eleitoral de incumbência: vencer a eleição no tempo $t$ eleva a probabilidade de reeleição/vitória democrata no tempo $t+1$ em 0,428138 (EP = 0,0315907; IC 95% [0,366221; 0,490054]). Portanto, os eleitores determinam as políticas públicas ao elegerem o partido que assume o assento, e não forçando os candidatos a convergirem suas plataformas. Ressalvas: O teste de densidade de McCrary para a margem de voto da eleição anterior (lagdemvoteshare) não encontrou indícios de manipulação ou ordenamento ao redor do corte de 50% (theta = 0,0697894; p = 0,226445). O estimador de RDD para a sessão após a eleição subsequente apresentou alguma sensibilidade à largura de banda: a estimativa varia de 20,4793 (metade da largura de banda) a 22,4471 (dobro da largura de banda). O resultado é local para distritos eleitorais competitivos (margem próxima a 50%) e não reflete necessariamente a dinâmica de redutos partidários seguros.

    • O teste de densidade de McCrary para a margem de voto da eleição anterior (lagdemvoteshare) não encontrou indícios de manipulação ou ordenamento ao redor do corte de 50% (theta = 0,0697894; p = 0,226445).
    • O estimador de RDD para a sessão após a eleição subsequente apresentou alguma sensibilidade à largura de banda: a estimativa varia de 20,4793 (metade da largura de banda) a 22,4471 (dobro da largura de banda).
    • O resultado é local para distritos eleitorais competitivos (margem próxima a 50%) e não reflete necessariamente a dinâmica de redutos partidários seguros.

    Figuras citadas: call_b66bfdec:effect, call_23a5be8c:effect, call_6390e006:effect, call_a952ddb4:theta, call_a952ddb4:p_value · Parada: answered

    Figura da resposta: call_b66bfdec:effect = 18,83918.83903610899844 (se 1,636641.6366405620256952)
19. 18Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
20. 19Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_b66bfdec:effect (infer.rdd)
21. 20Pontuação
    #2uma entrepassouobrigatóriatool_called(infer.ols): answer call is infer.rdd | tool_called(infer.rdd): ok
22. 21Pontuação
    #3argumento igualpassouobrigatórianot applicable: the answer call is infer.rdd, not infer.ols
23. 22Pontuação
    #4argumento igualpassouobrigatórianot applicable: the answer call is infer.rdd, not infer.ols
24. 23Pontuação
    #5argumento igualpassouobrigatóriainfer.rdd.y = 'score'
25. 24Pontuação
    #6argumento igualpassouobrigatóriainfer.rdd.running = 'lagdemvoteshare'
26. 25Pontuação
    #7argumento igualpassouregistradanot applicable: the answer call is infer.rdd, not infer.ols
27. 26Pontuação
    #8argumento igualpassouregistradanot applicable: the answer call is infer.rdd, not infer.ols
28. 27Pontuação
    #9argumento igualpassouregistradanot applicable: the answer call is infer.rdd, not infer.ols
29. 28Pontuação
    #10argumento igualpassouregistradanot applicable: the answer call is infer.rdd, not infer.ols
30. 29Pontuação
    #11diagnósticopassouregistradanot applicable: the answer call is infer.rdd, not infer.ols
31. 30Pontuação
    #12figura da resposta próximafalhouregistrada18.83903611 vs 21.283872256260665
32. 31Pontuação
    #13figura da resposta próximafalhouregistrada1.636640562 vs 1.9512339082202543
33. 32Pontuação
    #14figura da resposta próximafalhouregistrada18.83903611 vs 21.2
34. 33Pontuação
    #15figura da resposta próximafalhouregistrada1.636640562 vs 1.9

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