Tarefa crb_papers.q008

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Pergunta feita ao analista

Among countries that adopted a parliamentary gender quota in 1997, how did their public-health spending share change from 1997 to 1998 relative to countries that never adopted a quota?

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Trajetória

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

1. 0Início

   Among countries that adopted a parliamentary gender quota in 1997, how did their public-health spending share change from 1997 to 1998 relative to countries that never adopted a quota?

   ds_0005 (agent)

   | Variable Name | Description | | --- | --- | | year | Calendar year of observation (1995–2012). Each row is a country–year. | | percenthealth | Share of total general government expenditure allocated to public health (%). Drawn from the World Bank’s World Development Indicators (sectoral spending as a fraction of total government spending). | | country | Country name corresponding to the national polity whose lower or single parliamentary chamber is referenced for legislative variables. | | ever_high_adopt_treat | Indicator equal to 1 if the country ever experienced a large first-year jump in women’s parliamentary seat share associated with the first election using a quota (above the sample mean “shock”) during 1995–2012; 0 otherwise. Time-invariant within a country. | | time_trend | Numeric time index equal to the calendar year, included to capture linear secular change. | | time_trend_2 | Square of the numeric time index (year^2), allowing flexible nonlinear trends over time. | | demlag | Democracy indicator lagged one year: 1 if the prior year’s Polity IV combined score was ≥ 6; 0 otherwise. | | dem_cumlag | Cumulative years of democracy since 1945 up through the previous year (count based on Polity IV threshold ≥ 6). | | autoclag | Autocracy indicator lagged one year: 1 if the prior year’s Polity IV combined score was ≤ −6; 0 otherwise. | | leftlag | Indicator lagged one year for whether the governing/prime minister’s party was coded as left-of-center in the Database of Political Institutions. | | left_cumlag | Cumulative years of left-leaning executive rule since 1945 up through the previous year (based on DPI coding). | | lnODAlag | Natural log of official development assistance received as a percentage of GDP, lagged one year (World Bank). | | lnOillag | Natural log of oil rents as a percentage of GDP, lagged one year (World Bank). Oil rents reflect the value of crude oil production minus costs as a share of the economy. | | lnTradelag | Natural log of total trade as a percentage of GDP, lagged one year (World Bank). Total trade is exports plus imports of goods and services. | | lnFDIlag | Natural log of foreign direct investment, net inflows, as a percentage of GDP, lagged one year (World Bank). | | increaselag | Percentage-point increase in women’s share of seats in the lower/single parliamentary chamber at the first election conducted under a gender quota (“quota shock”), lagged one year. Calculated as the difference between the last pre-quota election and the first quota-based election; coded 0 where no implementation occurred in the period. | | adopt_quotalag | Indicator for the presence of a parliamentary gender quota from the year of adoption onward, lagged one year. Adoption covers reserved-seat provisions, candidate-list gender requirements, and voluntary party quotas that collectively cover at least ~30% of lower-house seats; compiled from the International IDEA Quota Project and harmonized scholarly sources. | Dataset Context: This dataset is a country–year panel spanning 1995–2012, a period marked by widespread introduction and use of electoral gender quota policies across regions. Fiscal variables come from the World Bank’s World Development Indicators, which report sectoral expenditures as shares of total general government spending. The health-spending series used here captures the proportion of national budgets allocated to public health, including both recurrent and capital outlays, enabling consistent comparison across countries and years. Information on quota policies—adoption dates, first election under a quota, and the magnitude of changes in women’s legislative representation—was compiled from public administrative and research sources, notably the International IDEA Quota Project, and cross-checked with scholarly datasets and case documentation. Adoption covers three institutional designs: reserved seats for women, legal candidate-list requirements, and voluntary party quotas that meet a threshold of parliamentary seat coverage. In many systems, there is a lag between legal adoption and first use in an election; some adopters did not functionally implement quotas within the 1995–2012 window. Legislative composition is measured for the lower or single parliamentary chamber. The dataset includes a derived “quota shock” measure that records the first percentage-point jump in women’s seat share at the initial quota-based election; this value reflects the immediate numerical change produced by the reform in each country. To align political and economic covariates with subsequent budgeting, several variables are lagged one year. Regime characteristics (democracy/autocracy status and cumulative democratic experience) are drawn from Polity IV. Governing party ideology is coded using the Database of Political Institutions. Macroeconomic indicators—official development assistance, oil rents, trade openness, and foreign direct investment—come from the World Bank and are expressed as percentages of GDP with natural-log transformations. The panel also includes linear and quadratic time indices to summarize long-run temporal patterns. Coverage emphasizes countries with available fiscal and political data in the post-1995 period. Some early quota adopters prior to 1995 are outside scope to maintain consistent timing and measurement. As with most cross-national administrative series, occasional missingness may reflect gaps in national reporting or differences in when reforms occurred relative to fiscal years. Overall, the file provides a harmonized basis for examining how legislative reforms and macro-political contexts coincide with evolving public budget compositions worldwide.

   file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —

   ref_a_pre (reference)

   file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —

   1. dropna {"columns":["percenthealth","year","country"]}
   2. filter {"query":"year == 1997 and adopt_quotalag == 0"}
   3. collapse {"aggs":[{"column":"adopt_quotalag","fn":"count","name":"a_pre"}],"by":["country"]}

   ref_c_post (reference)

   file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —

   1. dropna {"columns":["percenthealth","year","country"]}
   2. filter {"query":"year == 1998 and adopt_quotalag == 0"}
   3. collapse {"aggs":[{"column":"adopt_quotalag","fn":"count","name":"c_post"}],"by":["country"]}

   ref_never (reference)

   file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —

   1. dropna {"columns":["percenthealth","year","country"]}
   2. collapse {"aggs":[{"column":"adopt_quotalag","fn":"max","name":"amax"}],"by":["country"]}
   3. derive {"args":{"column":"amax","default":1,"keep_unmapped":false,"mapping":[{"source":1,"target":0}]},"name":"never","op":"map"}

   ref_panel (reference)

   file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —

   1. dropna {"columns":["percenthealth","year","country"]}
   2. filter {"query":"year in [1997, 1998]"}
   3. merge {"how":"left","on":["country"],"with":"ref_a_pre"}
   4. merge {"how":"left","on":["country"],"with":"ref_t_post"}
   5. merge {"how":"left","on":["country"],"with":"ref_c_post"}
   6. merge {"how":"left","on":["country"],"with":"ref_never"}
   7. filter {"query":"a_pre == a_pre and (t_post == t_post or (c_post == c_post and never == 1))"}
   8. derive {"args":{"cmp":"ge","column":"t_post","value":1},"name":"treat","op":"indicator"}
   9. derive {"args":{"column":"treat","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treat","op":"map"}
   10. derive {"args":{"cmp":"eq","column":"year","value":1998},"name":"post","op":"indicator"}
   11. derive {"args":{"columns":["treat","post"]},"name":"treat_post","op":"interaction"}

   ref_t_post (reference)

   file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —

   1. dropna {"columns":["percenthealth","year","country"]}
   2. filter {"query":"year == 1998 and adopt_quotalag == 1"}
   3. collapse {"aggs":[{"column":"adopt_quotalag","fn":"count","name":"t_post"}],"by":["country"]}
2. 1Transformação
   ref_a_pre · dropna {"columns":["percenthealth","year","country"]}

   2481 → 2474 linhas

   Frame antes → depois: cabc9e8f74fe → 65e201d72a85
3. 2Transformação
   ref_a_pre · filter {"query":"year == 1997 and adopt_quotalag == 0"}

   2474 → 127 linhas

   Frame antes → depois: 65e201d72a85 → 039cb16c48d6
4. 3Transformação
   ref_a_pre · collapse {"aggs":[{"column":"adopt_quotalag","fn":"count","name":"a_pre"}],"by":["country"]}

   127 → 127 linhas

   Frame antes → depois: 039cb16c48d6 → fa1152072e11
5. 4Transformação
   ref_t_post · dropna {"columns":["percenthealth","year","country"]}

   2481 → 2474 linhas

   Frame antes → depois: cabc9e8f74fe → 65e201d72a85
6. 5Transformação
   ref_t_post · filter {"query":"year == 1998 and adopt_quotalag == 1"}

   2474 → 15 linhas

   Frame antes → depois: 65e201d72a85 → 722feca91c53
7. 6Transformação
   ref_t_post · collapse {"aggs":[{"column":"adopt_quotalag","fn":"count","name":"t_post"}],"by":["country"]}

   15 → 15 linhas

   Frame antes → depois: 722feca91c53 → c938aa930cd9
8. 7Transformação
   ref_c_post · dropna {"columns":["percenthealth","year","country"]}

   2481 → 2474 linhas

   Frame antes → depois: cabc9e8f74fe → 65e201d72a85
9. 8Transformação
   ref_c_post · filter {"query":"year == 1998 and adopt_quotalag == 0"}

   2474 → 120 linhas

   Frame antes → depois: 65e201d72a85 → b2b88a07da49
10. 9Transformação
    ref_c_post · collapse {"aggs":[{"column":"adopt_quotalag","fn":"count","name":"c_post"}],"by":["country"]}

    120 → 120 linhas

    Frame antes → depois: b2b88a07da49 → e7f1cad92f7c
11. 10Transformação
    ref_never · dropna {"columns":["percenthealth","year","country"]}

    2481 → 2474 linhas

    Frame antes → depois: cabc9e8f74fe → 65e201d72a85
12. 11Transformação
    ref_never · collapse {"aggs":[{"column":"adopt_quotalag","fn":"max","name":"amax"}],"by":["country"]}

    2474 → 139 linhas

    Frame antes → depois: 65e201d72a85 → be8251e9423b
13. 12Transformação
    ref_never · derive {"args":{"column":"amax","default":1,"keep_unmapped":false,"mapping":[{"source":1,"target":0}]},"name":"never","op":"map"}

    139 → 139 linhas

    Frame antes → depois: be8251e9423b → a7eb1c0a0267
14. 13Transformação
    ref_panel · dropna {"columns":["percenthealth","year","country"]}

    2481 → 2474 linhas

    Frame antes → depois: cabc9e8f74fe → 65e201d72a85
15. 14Transformação
    ref_panel · filter {"query":"year in [1997, 1998]"}

    2474 → 271 linhas

    Frame antes → depois: 65e201d72a85 → cf5330b5955d
16. 15Transformação
    ref_panel · merge {"how":"left","on":["country"],"with":"ref_a_pre"}

    271 → 271 linhas

    Frame antes → depois: cf5330b5955d → 30e8f3432884
17. 16Transformação
    ref_panel · merge {"how":"left","on":["country"],"with":"ref_t_post"}

    271 → 271 linhas

    Frame antes → depois: 30e8f3432884 → fd9f09528d54
18. 17Transformação
    ref_panel · merge {"how":"left","on":["country"],"with":"ref_c_post"}

    271 → 271 linhas

    Frame antes → depois: fd9f09528d54 → 5ed3ded1cf37
19. 18Transformação
    ref_panel · merge {"how":"left","on":["country"],"with":"ref_never"}

    271 → 271 linhas

    Frame antes → depois: 5ed3ded1cf37 → 30785699eaba
20. 19Transformação
    ref_panel · filter {"query":"a_pre == a_pre and (t_post == t_post or (c_post == c_post and never == 1))"}

    271 → 168 linhas

    Frame antes → depois: 30785699eaba → fb777bcb0c93
21. 20Transformação
    ref_panel · derive {"args":{"cmp":"ge","column":"t_post","value":1},"name":"treat","op":"indicator"}

    168 → 168 linhas

    Frame antes → depois: fb777bcb0c93 → 2131cd8eec35
22. 21Transformação
    ref_panel · derive {"args":{"column":"treat","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treat","op":"map"}

    168 → 168 linhas

    Frame antes → depois: 2131cd8eec35 → 69314e7caf6c
23. 22Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":1998},"name":"post","op":"indicator"}

    168 → 168 linhas

    Frame antes → depois: 69314e7caf6c → 3e4a37118563
24. 23Transformação
    ref_panel · derive {"args":{"columns":["treat","post"]},"name":"treat_post","op":"interaction"}

    168 → 168 linhas

    Frame antes → depois: 3e4a37118563 → 5696e461ab52
25. 24Turno do modelo
    call_ca072f1creferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    OLS of patsy 'percenthealth ~ treat + post + treat:post + controls', cluster by country

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "country",
        "controls": [
          "treat",
          "post",
          "demlag",
          "dem_cumlag",
          "autoclag",
          "leftlag",
          "left_cumlag",
          "lnODAlag",
          "lnOillag",
          "lnTradelag"
        ],
        "dataset": "ref_panel",
        "x": [
          "treat_post"
        ],
        "y": "percenthealth"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
26. 25Ferramenta
    infer.olsv0.1.0call_a38fb89fdecidido pelo modeloreexecutável

    Motivo: OLS of patsy 'percenthealth ~ treat + post + treat:post + controls', cluster by country

    Argumentos

    {
      "cluster": "country",
      "controls": [
        "treat",
        "post",
        "demlag",
        "dem_cumlag",
        "autoclag",
        "leftlag",
        "left_cumlag",
        "lnODAlag",
        "lnOillag",
        "lnTradelag"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "treat_post"
      ],
      "y": "percenthealth"
    }

    Figuras call_a38fb89f

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treat_post Coefficient on treat_post in the regression of percenthealth	0,273275	0.2732748071816379	0,607840.6078400610971946	-0,935695 – 1,48224

    Diagnósticos

    • n_obs = 168168
    • k = 1212
    • r2 = 0,1764240.17642383390560767
    • df_resid = 156156
    • se_type = "cluster"
    • n_clusters = 8484

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 15.792152185315583,
          "ci_low": 1.3925521672052783,
          "coef": 8.59235217626043,
          "p": 0.019922382441193976,
          "se": 3.619882151875934,
          "t": 2.37365522294907,
          "term": "Intercept"
        },
        {
          "ci_high": 1.4822442414831714,
          "ci_low": -0.9356946271198955,
          "coef": 0.2732748071816379,
          "p": 0.6541827439868247,
          "se": 0.6078400610971946,
          "t": 0.44958340963633986,
          "term": "treat_post"
        },
        {
          "ci_high": 4.488601668794637,
          "ci_low": -2.2670080691146666,
          "coef": 1.1107967998399848,
          "p": 0.5148731383869016,
          "se": 1.6982771107906351,
          "t": 0.6540727616135932,
          "term": "treat"
        },
        {
          "ci_high": 0.16523004336906927,
          "ci_low": -0.6285384973749413,
          "coef": -0.23165422700293603,
          "p": 0.24900242707610012,
          "se": 0.19954363800008704,
          "t": -1.1609201341855608,
          "term": "post"
        },
        {
          "ci_high": 3.3859861184970583,
          "ci_low": -0.6239018321415228,
          "coef": 1.3810421431777677,
          "p": 0.17437161767645304,
          "se": 1.0080364597129865,
          "t": 1.37003193671287,
          "term": "demlag"
        },
        {
          "ci_high": 0.020711208931593207,
          "ci_low": -0.08269907994132206,
          "coef": -0.03099393550486443,
          "p": 0.23655905252766815,
          "se": 0.025996073400691943,
          "t": -1.1922545004061826,
          "term": "dem_cumlag"
        },
        {
          "ci_high": 1.111274362569888,
          "ci_low": -3.270080811295686,
          "coef": -1.0794032243628988,
          "p": 0.32992832470297895,
          "se": 1.1014187460039335,
          "t": -0.9800116697478508,
          "term": "autoclag"
        },
        {
          "ci_high": 1.5497766905990116,
          "ci_low": -2.1868194380601214,
          "coef": -0.318521373730555,
          "p": 0.7353963467999134,
          "se": 0.9393342605273951,
          "t": -0.339092682036019,
          "term": "leftlag"
        },
        {
          "ci_high": 0.09201582851587228,
          "ci_low": -0.10630060100937969,
          "coef": -0.0071423862467536944,
          "p": 0.8864280032163686,
          "se": 0.04985430864463905,
          "t": -0.1432651748851748,
          "term": "left_cumlag"
        },
        {
          "ci_high": 0.06199317944946378,
          "ci_low": -0.21700800485584928,
          "coef": -0.07750741270319275,
          "p": 0.2723186386612988,
          "se": 0.07013746257874108,
          "t": -1.1050786534539607,
          "term": "lnODAlag"
        },
        {
          "ci_high": 0.0296950723638124,
          "ci_low": -0.26144595091915235,
          "coef": -0.11587543927766998,
          "p": 0.11717225812022665,
          "se": 0.07318926862797717,
          "t": -1.5832299112957071,
          "term": "lnOillag"
        },
        {
          "ci_high": 1.9090424383902296,
          "ci_low": -1.2032832766459027,
          "coef": 0.35287958087216337,
          "p": 0.6531500166715646,
          "se": 0.7824003647680694,
          "t": 0.45102174891849534,
          "term": "lnTradelag"
        }
      ]
    }

    Digests dos dados

    ref_panel 5696e461ab52687b737da17533e25f93b9ef4fd19c6a4fb4bd1607647632e277

    sha256 do resultado

    4afe0aafb813ae804286527fea48a7c2adc272116b39bc9e1b4ef0b8abe6718b

    Resultado mostrado ao modelo
27. 26Turno do modelo
    call_ac512b36referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Estimativa (infer.ols): coef.treat_post = 0,2733. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_a38fb89f:coef.treat_post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
28. 27Guarda
    resposta aceita

    Números encontrados

    0,2733

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
29. 28Resposta

    Estimativa (infer.ols): coef.treat_post = 0,2733. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_a38fb89f:coef.treat_post · Parada: answered

    Figura da resposta: call_a38fb89f:coef.treat_post = 0,2732750.2732748071816379 (se 0,607840.6078400610971946)
30. 29Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
31. 30Pontuação
    #1figura da resposta próximapassouobrigatória0.2732748072 in [-0.9180917126, 1.464641327]?
32. 31Pontuação
    #2figura da resposta próximapassouregistrada0.2732748072 vs 0.27327480718163333
33. 32Pontuação
    #3figura da resposta próximapassouregistrada0.6078400611 vs 0.6078400610971942

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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