change_analysis

Fetch a JOLTS change-analysis TXT table (estimated rate/level changes + significance).

Examples

from openbb import obb
# National JOLTS Table 1 — Job openings SA over-the-month.
obb.bls.jolts.change_analysis(provider='bls')
# National Table 10 — Quits NSA over-the-year.
obb.bls.jolts.change_analysis(provider='bls', table_number=10)
# State Table 1 — Job openings by state SA over-the-month.
obb.bls.jolts.change_analysis(provider='bls', scope='state', table_number=1)

Parameters

• standard
• bls

scope: str | None
Default: national
JOLTS change-analysis family to load.

table_number: int | None
Default: 1
Numbered change-analysis table to load.

---

Returns

results: BlsJoltsChangeAnalysis

Serializable results.

provider: str | None

Provider name.

warnings: Optional[list[Warning_]]

List of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

---

Data

• standard
• bls

table_id: str
Stable identifier for the source table.

table_title: str
Full BLS table title.

scope: str
National or state scope of the table.

table_number: int
Source table number within the scope.

measure: str
JOLTS measure published in this table.

period: str
Comparison window for the table.

seasonally_adjusted: bool | None
Whether the table is seasonally adjusted.

period_start: date | None
First-of-month start date of the comparison window.

period_end: date | None
First-of-month end date of the comparison window.

source_date: date | None
Release date stamped in the TXT footer.

row_index: int
Sequential ordering index preserving the source row order.

section: str | None
Section header the row appears under in the source TXT.

indent_spaces: int
Leading-space count from the source TXT encoding hierarchy depth.

label: str
Row label — industry, region, or state name.

rate_change: float | None
Estimated rate change in percentage points over the comparison window.

rate_min_significant: float | None
Minimum rate change in percentage points needed to reach 90-percent significance.

rate_passes_significance: bool | None
Whether the rate change is statistically significant at 90 percent.

level_change_thousands: float | None
Estimated level change in thousands over the comparison window.

level_min_significant_thousands: float | None
Minimum level change in thousands needed to reach 90-percent significance.

level_passes_significance: bool | None
Whether the level change is statistically significant at 90 percent.