{
  "paper": {
    "rank": 37,
    "paper_id": "aeraej-42",
    "title": "Communication Infrastructure and Stabilizing Food Prices: Evidence from the Telegraph Network in China",
    "authors": "Pei Gao; Yu-Hsiang Lei",
    "year": 2021,
    "journal": "American Economic Journal: Applied Economics",
    "doi": "10.1257/app.20190416",
    "package_bytes": 5569761,
    "source_specification": "Table 3"
  },
  "mapping": {
    "paper_id": "aeraej-42",
    "match": true,
    "outcome": "max_price_R1",
    "treatment": "ind_telegraph",
    "unit": "pref_id",
    "time": "year_month",
    "cluster": "pref_id",
    "extra_rhs": "~ factor(prov_id):year_month",
    "note": "The source province-specific linear trends are retained alongside prefecture and month effects.",
    "assessment_status": "backend_limit",
    "assessment_reason": "The source coefficient reproduces with province-specific trends retained. This large cohort-by-month design exceeds the dense backend's size limit, while the sparse backend does not yet support those covariates. Trends are not dropped to force a fit."
  },
  "status": "backend_limit",
  "source_fit": {
    "formula": "list(linear = max_price_R1 ~ ind_telegraph, fixef = ~year_month +      pref_id + prov_id + prov_id[[year_month]])",
    "coefficients": {
      "ind_telegraph": -9.21559040156812
    },
    "se": [
      3.56804814200302
    ],
    "n": 47436
  },
  "source_check": {
    "source_term": "ind_telegraph",
    "source_beta": -9.21559040156812,
    "template_source_sample_beta": -9.21559040156821,
    "template_source_sample_n": 47436,
    "template_source_sample_se": 3.5677066284551806,
    "same_point_specification": true,
    "dropped_missing_identifiers": 0,
    "controls": {
      "x001": "factor(prov_id)2:year_month",
      "x002": "factor(prov_id)7:year_month",
      "x003": "factor(prov_id)10:year_month",
      "x004": "factor(prov_id)11:year_month",
      "x005": "factor(prov_id)13:year_month",
      "x006": "factor(prov_id)17:year_month",
      "x007": "factor(prov_id)18:year_month",
      "x008": "factor(prov_id)21:year_month",
      "x009": "factor(prov_id)23:year_month"
    },
    "absolute_coefficient_difference": 9.059419880941277e-14
  },
  "reason": "The source coefficient reproduces with province-specific trends retained. This large cohort-by-month design exceeds the dense backend's size limit, while the sparse backend does not yet support those covariates. Trends are not dropped to force a fit."
}