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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Failed to parse string: 'American Economic Review' as a scalar of type double
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in cast_table_to_schema
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2247, in <listcomp>
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2103, in cast_array_to_feature
return array_cast(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1798, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1950, in array_cast
return array.cast(pa_type)
File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Failed to parse string: 'American Economic Review' as a scalar of type double
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1436, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1053, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1898, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Unnamed: 0
int64 | y
string | x
string | control_variables
string | data_source
string | method
string | other_requirements
string | answer
string | level
null | journal
null | article
null | notation
string | tags
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
0
|
dbrwt
|
tobacco
|
rectype, csex, dmar, pldel3, pre4000, preterm, alcohol, dmage, dmeduc, dlivord, monpre, nprevist, dplural, birattnd, cntocpop, ormoth, mrace3, adequacy, delmeth5
|
ps1_24S_cleaned.dta
|
OLS
|
birattnd, cntocpop, ormoth, mrace3, adequacy, delmeth5 are multi-class categorical variables
|
{'coefficient': -212.9425,'standard_error': 4.579,'p_value': 0.0}
| null | null | null |
ps1_B4
| null |
1
|
dbrwt
|
tobacco
|
rectype, csex, dmar, pldel3, pre4000, preterm, alcohol, dmage, dmeduc, dlivord, monpre, nprevist, dplural, birattnd, cntocpop, ormoth, mrace3, adequacy, delmeth5
|
ps1_24S_cleaned.dta
|
propensity score regression
|
birattnd, cntocpop, ormoth, mrace3, adequacy, delmeth5 are multi-class categorical variables
|
{'coefficient': -209.835,'standard_error': 5.044,'p_value': 0.0}
| null | null | null |
ps1_C1 & ps1_C2
| null |
2
|
log_fatality_rate
|
primary
| null |
ps2_24S_cleaned.dta
|
panel OLS
|
need to constuct log_fatality_rate as the dependent variable, by taking the direct logarithm of fatality_rate. Compute the robust standard errors
|
{'coefficient': -0.1429,'standard_error': 0.0284,'p_value': 0.0}
| null | null | null |
ps2_A2_1
|
data processing, robust standard error
|
3
|
log_fatality_rate
|
primary
| null |
ps2_24S_cleaned.dta
|
panel OLS
|
need to constuct log_fatality_rate as the dependent variable, by taking the direct logarithm of fatality_rate. add state fixed effect and cluster standard error by state & year
|
{'coefficient': -0.2055,'standard_error': 0.0331,'p_value': 0.0}
| null | null | null |
ps2_B1_1
|
data processing, cluster standard error, fixed effect
|
4
|
log_fatality_rate
|
primary
| null |
ps2_24S_cleaned.dta
|
Staggered DID Event Study
|
for the event study setting, set the see-back length as 4 and see-forward length as 3. Need to constuct log_fatality_rate as the dependent variable, by taking the direct logarithm of fatality_rate. Add state fixed effect, year fixed effect and cluster standard error by state. Output the result of coefficient towards "Lag_D1".
|
{'coefficient': -0.0430,'standard_error': 0.0176,'p_value': 0.0147}
| null | null | null |
ps2_B2
|
data processing, cluster standard error, fixed effect
|
0
|
lwklywge
|
educ
|
race, smsa, married, yob
|
IV_Birth_Quarter/Birth_Quarter_1980_30_39.dta
|
IV 2SLS Regression
|
yob is a multi-class categorical variable, and should first be constructed into dummies as the control variables; qob is also a multi-class categorical variable, construct into dummies and use these dummies as instrument variables
|
{'coefficient': 0.0989901,'standard_error': 0.0206919,'p_value': 0.000}
| null | null | null |
IV_Birth_Quarter
|
data processing
|
1
|
loggdp
|
risk
|
latitude, asia, africa, other
|
IV_Colonial_Origins/Colonial_Origins_Reply.dta
|
IV 2SLS Regression
|
use logmort0 as the instrument variable; use robust standard error
|
{'coefficient': 1.073909,'standard_error': 0.4835807,'p_value': 0.026}
| null | null | null |
IV_Colonial_Origins
|
robust standard error
|
2
|
lngini
|
_intra
| null |
DID_BBB/macro_workfile.dta
|
Staggered DID Event Study
|
need to construct lngini by taking the direct logarithm of gini; for the event study setting, set the see-back length as 10 and see-forward length as 15. Add state fixed effect towards statefip, year fixed effect towards wrkyr, and cluster standard error by state. Output the result of coefficient towards the parameter "Lag_D1".
|
{'coefficient': -0.0005764,'standard_error': 0.0076168,'p_value': 0.940}
| null | null | null |
DID_BBB
|
data processing, cluster standard error, fixed effect
|
3
|
rebel
|
canal
| null |
DID_Canal/cao_chen_cleaned_3.dta
|
Static DID Regression
|
canal denotes if an entity receives treatment, and post denote if an entity in the treatment group has received treatement;add entity fixed effect towards county, time fixed effect towards year, and cluster standard error by county. Output the result of coefficient towards the interaction term parameter "treatment_group_treated".
|
{'coefficient': 0.0380143,'standard_error': 0.016621,'p_value': 0.023}
| null | null | null |
DID_Canal
|
fixed effect
|
4
|
fte
|
treated
| null |
DID_MinWage/cardkrueger1994_cleaned.dta
|
Static DID Regression
|
treated denotes if an entity receives treatment, and t denote if an entity in the treatment group has received treatement;add entity fixed effect towards id, time fixed effect towards t, and cluster standard error by id. Output the result of coefficient towards the interaction term parameter "treatment_group_treated".
|
{'coefficient': 2.986941,'standard_error': 1.33263,'p_value': 0.026}
| null | null | null |
DID_MinWage
|
fixed effect, cluster standard error
|
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