Tarefa crb_papers.q051

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Pergunta feita ao analista

How did logged district capital expenditures per capita change in 2010 in districts holding a district head election that year versus districts that did not hold an election that year (restricting to districts without an election the year prior)?

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Trajetória

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1. 0Início

   How did logged district capital expenditures per capita change in 2010 in districts holding a district head election that year versus districts that did not hold an election that year (restricting to districts without an election the year prior)?

   ds_0027 (agent)

   | Variable Name | Description | | --- | --- | | year | Calendar year of observation for the district-level fiscal and political records (2001–2012). | | kode_neil | Harmonized district identifier that tracks administrative units across boundary changes and splits to maintain longitudinal comparability. | | personnel_sh | Share of total realized district expenditures allocated to personnel (wages, salaries, allowances); expressed as a fraction between 0 and 1. | | capital_sh | Share of total realized district expenditures allocated to capital investment (e.g., infrastructure, equipment); expressed as a fraction between 0 and 1. | | ltotalexp_all_pc | Natural log of total realized district government expenditures per capita (rupiah per resident). | | lcapital_exp_pc | Natural log of realized district capital expenditures per capita (rupiah per resident). | | incumbency | Indicator for whether the sitting district head is a candidate in that year (1=yes, 0=no); relevant in years when district head elections are held. | | services_provision | Standardized index summarizing local service delivery (sanitation access, clean water access, school enrollment, skilled birth attendance, and road quality); higher values indicate better provision. | | lgdppc_l | Natural log of district GDP per capita from official statistics, lagged one year. | | lpop_l | Natural log of the district’s population, lagged one year. | | rev_total_pc2_l | Total district revenue per capita from all sources (including general transfers and revenue sharing), lagged one year; measured in rupiah per resident. | | rev_natural_pc_l | Shared natural resource revenues (intergovernmental revenue sharing tied to extractive resources) per capita, lagged one year; measured in rupiah per resident. | | poverty_pc_l | Fraction of the district population below the official poverty line, lagged one year (0–1 scale). | | gini_l | Gini index of consumption inequality from household survey data, lagged one year (0–100 scale; higher = more unequal). | | elected_leader_l | Indicator that the district head position is filled by direct popular election (1) versus selection by the local legislature (0), lagged one year; direct elections were rolled out beginning in 2005. | | golkar_share_all | Vote share of the Golkar party in the district’s legislative elections (compiled from 1999 and 2004 results); expressed as a fraction between 0 and 1. | | pdip_share_all | Vote share of the Indonesian Democratic Party of Struggle (PDI–P) in the district’s legislative elections (compiled from 1999 and 2004 results); expressed as a fraction between 0 and 1. | | enp_all | Effective number of parties in the district legislature (Laakso–Taagepera index); higher values indicate more fragmented party systems. | | election_year | Indicator that a district head election is held in the current calendar year (1=yes, 0=no); reflects the local executive election calendar. | | election_year_l | Indicator for the year immediately preceding a district head election (lead-up year; 1=yes, 0=no). | | election_year_lead1 | Indicator for the year immediately following a district head election (post-election year; 1=yes, 0=no). | Dataset Context: This panel assembles district-by-year information for Indonesia’s post-decentralization era, covering 2001–2012. Fiscal figures are realized (executed) budgets from the Ministry of Finance and use the national economic classification that separates outlays into capital investment, goods and services, and personnel. Revenue items distinguish general-purpose transfers and shared natural resource revenues alongside locally generated revenue. Where noted, variables are expressed per capita using contemporaneous district population counts, and several key covariates are provided in natural logs to reduce skew. Political characteristics capture the local partisan environment and institutional arrangements. District-level party vote shares are compiled from the 1999 and 2004 legislative elections to describe the baseline partisan landscape. The effective number of parties quantifies legislative fragmentation. Indonesia shifted from indirect selection of district heads by local legislatures (1999–2004) to direct popular elections beginning in 2005. The initial schedule of local executive contests was phased according to pre-existing appointment terms, producing staggered election years across districts. Indicators mark the election year itself as well as the years immediately before and after. Socioeconomic and service delivery measures come from official statistics agencies and large-scale household surveys (including Susenas). The services index combines access and quality proxies—sanitation, clean water, school enrollment, skilled birth attendance, and road conditions—standardized so that higher values indicate stronger provision. Inequality (Gini) and poverty rates are derived from household consumption data. Variables with the “_l” suffix are lagged one calendar year to reflect prior conditions relative to the current budget year. The administrative map of Indonesia evolved substantially during this period due to district proliferation. The dataset harmonizes identifiers to follow units through splits, yielding an unbalanced panel by design. Users should note that national statistical agencies implemented notable survey and weighting changes around 2011; while the panel extends through 2012, comparability for certain survey-based indicators across this redesign may warrant caution. District governments largely finance spending through intergovernmental transfers and shared revenues, as local borrowing was highly restricted during these years. Overall, the dataset provides a consolidated view of local public finance, political structure, and socioeconomic context during Indonesia’s early decentralization and direct local election era.

   file e02ae6b363d47cf0f76a5fc37d88e1f8ffc08c6b9f51eee68d0b5d6b12c2d727 · frame —

   ref_panel (reference)

   file e02ae6b363d47cf0f76a5fc37d88e1f8ffc08c6b9f51eee68d0b5d6b12c2d727 · frame —

   1. select {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}
   2. dropna {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}
   3. filter {"query":"year in [2009, 2010]"}
   4. merge {"how":"inner","on":["kode_neil"],"with":"ref_pre"}
   5. merge {"how":"inner","on":["kode_neil"],"with":"ref_post"}
   6. filter {"query":"t0 == 0 and (t1 == 1 or t1 == 0)"}
   7. derive {"args":{"cmp":"eq","column":"t1","value":1},"name":"TreatGroup","op":"indicator"}
   8. derive {"args":{"cmp":"eq","column":"year","value":2010},"name":"Post","op":"indicator"}
   9. derive {"args":{"columns":["TreatGroup","Post"]},"name":"DiD","op":"interaction"}

   ref_post (reference)

   file e02ae6b363d47cf0f76a5fc37d88e1f8ffc08c6b9f51eee68d0b5d6b12c2d727 · frame —

   1. select {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}
   2. dropna {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}
   3. filter {"query":"year == 2010"}
   4. select {"columns":["kode_neil","election_year"]}
   5. rename {"map":{"election_year":"t1"}}

   ref_pre (reference)

   file e02ae6b363d47cf0f76a5fc37d88e1f8ffc08c6b9f51eee68d0b5d6b12c2d727 · frame —

   1. select {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}
   2. dropna {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}
   3. filter {"query":"year == 2009"}
   4. select {"columns":["kode_neil","election_year"]}
   5. rename {"map":{"election_year":"t0"}}
2. 1Transformação
   ref_pre · select {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}

   6887 → 6887 linhas

   Frame antes → depois: 34e6aab5839b → 685626007f35
3. 2Transformação
   ref_pre · dropna {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}

   6887 → 5046 linhas

   Frame antes → depois: 685626007f35 → 84a435f06eb1
4. 3Transformação
   ref_pre · filter {"query":"year == 2009"}

   5046 → 356 linhas

   Frame antes → depois: 84a435f06eb1 → a666104f7b02
5. 4Transformação
   ref_pre · select {"columns":["kode_neil","election_year"]}

   356 → 356 linhas

   Frame antes → depois: a666104f7b02 → 781d079a9033
6. 5Transformação
   ref_pre · rename {"map":{"election_year":"t0"}}

   356 → 356 linhas

   Frame antes → depois: 781d079a9033 → 261ea2716c80
7. 6Transformação
   ref_post · select {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}

   6887 → 6887 linhas

   Frame antes → depois: 34e6aab5839b → 685626007f35
8. 7Transformação
   ref_post · dropna {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}

   6887 → 5046 linhas

   Frame antes → depois: 685626007f35 → 84a435f06eb1
9. 8Transformação
   ref_post · filter {"query":"year == 2010"}

   5046 → 490 linhas

   Frame antes → depois: 84a435f06eb1 → 5aab255c8d31
10. 9Transformação
    ref_post · select {"columns":["kode_neil","election_year"]}

    490 → 490 linhas

    Frame antes → depois: 5aab255c8d31 → 651fae5d4afa
11. 10Transformação
    ref_post · rename {"map":{"election_year":"t1"}}

    490 → 490 linhas

    Frame antes → depois: 651fae5d4afa → a61cf426cdda
12. 11Transformação
    ref_panel · select {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}

    6887 → 6887 linhas

    Frame antes → depois: 34e6aab5839b → 685626007f35
13. 12Transformação
    ref_panel · dropna {"columns":["lcapital_exp_pc","election_year","kode_neil","year"]}

    6887 → 5046 linhas

    Frame antes → depois: 685626007f35 → 84a435f06eb1
14. 13Transformação
    ref_panel · filter {"query":"year in [2009, 2010]"}

    5046 → 846 linhas

    Frame antes → depois: 84a435f06eb1 → d8f6e311a5b7
15. 14Transformação
    ref_panel · merge {"how":"inner","on":["kode_neil"],"with":"ref_pre"}

    846 → 704 linhas

    Frame antes → depois: d8f6e311a5b7 → 7d629d9760e4
16. 15Transformação
    ref_panel · merge {"how":"inner","on":["kode_neil"],"with":"ref_post"}

    704 → 696 linhas

    Frame antes → depois: 7d629d9760e4 → 45c2df66c471
17. 16Transformação
    ref_panel · filter {"query":"t0 == 0 and (t1 == 1 or t1 == 0)"}

    696 → 632 linhas

    Frame antes → depois: 45c2df66c471 → f30253389fab
18. 17Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"t1","value":1},"name":"TreatGroup","op":"indicator"}

    632 → 632 linhas

    Frame antes → depois: f30253389fab → 5eea420c9556
19. 18Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2010},"name":"Post","op":"indicator"}

    632 → 632 linhas

    Frame antes → depois: 5eea420c9556 → 6ac9e69c8527
20. 19Transformação
    ref_panel · derive {"args":{"columns":["TreatGroup","Post"]},"name":"DiD","op":"interaction"}

    632 → 632 linhas

    Frame antes → depois: 6ac9e69c8527 → 0a07692d6239
21. 20Turno do modelo
    call_0756ab60referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(y, add_constant([TreatGroup, Post, DiD])).fit(cov_type='cluster', groups=kode_neil) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "kode_neil",
        "controls": [
          "TreatGroup",
          "Post"
        ],
        "dataset": "ref_panel",
        "x": [
          "DiD"
        ],
        "y": "lcapital_exp_pc"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
22. 21Ferramenta
    infer.olsv0.1.0call_b30e38dadecidido pelo modeloreexecutável

    Motivo: sm.OLS(y, add_constant([TreatGroup, Post, DiD])).fit(cov_type='cluster', groups=kode_neil) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "kode_neil",
      "controls": [
        "TreatGroup",
        "Post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "DiD"
      ],
      "y": "lcapital_exp_pc"
    }

    Figuras call_b30e38da

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.DiD Coefficient on DiD in the regression of lcapital_exp_pc	-0,175996	-0.17599592472724745	0,06591230.06591232006923621	-0,30568 – -0,0463119

    Diagnósticos

    • n_obs = 632632
    • k = 44
    • r2 = 0,08736010.08736010450065912
    • df_resid = 628628
    • se_type = "cluster"
    • n_clusters = 316316

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 13.722029107598201,
          "ci_low": 13.40151025725802,
          "coef": 13.56176968242811,
          "p": 4.177683545219556e-309,
          "se": 0.08145235499795468,
          "t": 166.4994177610783,
          "term": "Intercept"
        },
        {
          "ci_high": -0.04631188396270372,
          "ci_low": -0.3056799654917912,
          "coef": -0.17599592472724745,
          "p": 0.00797507973248248,
          "se": 0.06591232006923621,
          "t": -2.6701521740150587,
          "term": "DiD"
        },
        {
          "ci_high": 0.17326151637540804,
          "ci_low": -0.29779955163623795,
          "coef": -0.06226901763041496,
          "p": 0.6033109452956122,
          "se": 0.11970913191744383,
          "t": -0.5201693190236995,
          "term": "TreatGroup"
        },
        {
          "ci_high": -0.47003358064254386,
          "ci_low": -0.6472936533768602,
          "coef": -0.558663617009702,
          "p": 4.941858757829338e-29,
          "se": 0.04504649369606437,
          "t": -12.401933450782906,
          "term": "Post"
        }
      ]
    }

    Digests dos dados

    ref_panel 0a07692d62394106dfc25570fd5084a6fe2f87641078e23ec94d298f0b3388f1

    sha256 do resultado

    a1602f4bb5f8a3a78a9d5cfa6da7f550ecb6c817ebbb32b43e4fccf11bbbb2a6

    Resultado mostrado ao modelo
23. 22Turno do modelo
    call_384263a0referenceFim: 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.DiD = -0,1760. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_b30e38da:coef.DiD"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
24. 23Guarda
    resposta aceita

    Números encontrados

    -0,1760

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
25. 24Resposta

    Estimativa (infer.ols): coef.DiD = -0,1760. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_b30e38da:coef.DiD · Parada: answered

    Figura da resposta: call_b30e38da:coef.DiD = -0,175996-0.17599592472724745 (se 0,06591230.06591232006923621)
26. 25Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
27. 26Pontuação
    #1figura da resposta próximapassouobrigatória-0.1759959247 in [-0.3051840721, -0.04680777739]?
28. 27Pontuação
    #2figura da resposta próximapassouregistrada-0.1759959247 vs -0.17599592472724762
29. 28Pontuação
    #3figura da resposta próximapassouregistrada0.06591232007 vs 0.06591232006923611

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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