{"part":"task","text":"# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"ds_0016\",\n \"rows\": 400,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| l.multi | Natural log of the number of multifamily housing units authorized by building permits in the city and year, computed as log(units + 1) to accommodate zero-permit years. Counts come from the U.S. Census Bureau\u2019s Building Permits Survey and refer to residential structures with two or more units. |\\n| treat | Electoral system indicator for the city-year: 1 if city council seats were elected from single-member districts, 0 if elected at-large. Many California cities transitioned during the 2010s in response to state voting-rights litigation and related commitments. |\\n| seg.low.third | Indicator that the city falls in the lowest tercile of residential segregation within the study sample, based on Theil\u2019s H index calculated from population distributions across neighborhoods. Lower values indicate less segregation. This tercile classification is based on baseline (pretreatment) measures and is constant across years for a city. |\\n| max.high.third | Indicator that the council-dominant group\u2019s population share places the city in the highest tercile of \u201cmajority size\u201d within the sample at baseline. Used to summarize where the dominant group constitutes a relatively large share of residents. |\\n| population.th | Total resident population of the city (in thousands), taken from the American Community Survey (ACS) 5-year estimates aligned to each year. |\\n| own_rate | Homeownership rate: share of occupied housing units that are owner-occupied, from ACS 5-year estimates. |\\n| vacancy_rate | Residential vacancy rate: share of housing units that are vacant, from ACS 5-year estimates. |\\n| med_value.th | Median value of owner-occupied housing units (in thousands of dollars), from ACS 5-year estimates. |\\n| med_income.th | Median household income (in thousands of dollars), from ACS 5-year estimates. |\\n| prop_min_elected_past12 | Proportion of city council seats won over the prior 12 years by the study-identified underrepresented minority group in that city. Constructed from administrative election records and updated each year using a rolling 12-year window. |\\n| year | Calendar year of observation (2010\u20132019). |\\n| location | Municipality name (city identifier). |\\n| mht | Indicator derived from baseline council representation: equals 1 for cities in the highest tercile of \u201cmajority control\u201d (dominant group\u2019s past seat share scaled by its population share), summarizing overrepresentation on council. |\\n\\nDataset Context:\\nThis panel assembles city-year observations for a subset of California municipalities during 2010\u20132019 to study how local political institutions and neighborhood context align with the production of housing. The housing measures come from the U.S. Census Bureau\u2019s Building Permits Survey, an annual administrative program covering jurisdictions that issue residential building permits. The survey reports counts of authorized dwelling units by structure type; multifamily units refer to buildings with two or more dwellings. Because permitting can be zero in some years, logged measures add one unit before transformation.\\n\\nDemographic and housing-market indicators\u2014population, income, home values, homeownership, and vacancy\u2014are drawn from the American Community Survey (ACS) 5-year estimates, which pool multiple years of responses to improve precision for cities. Values are scaled where noted (e.g., thousands of residents or dollars) to ease interpretation.\\n\\nElectoral context is captured using municipal sources and statewide administrative election records that list candidates and winners for city council races. These records are used to construct rolling measures of representation over the prior 12 years for racial and ethnic groups within each city. The dataset also summarizes each city\u2019s baseline residential segregation using Theil\u2019s H index and classifies cities into terciles (low, middle, high). Similarly, tercile-based indicators describe the size of the council-dominant group in the population and the degree to which that group\u2019s past council seat share exceeds its population share. These tercile classifications are based on pretreatment baselines and remain fixed across years for a given city.\\n\\nThe municipal institutions captured here reflect a period when many California cities shifted from at-large to single-member district council elections following the California Voting Rights Act and related legal actions. The sample focuses on cities meeting size and demographic thresholds commonly considered in that policy environment. Observations are indexed by city name and year, providing a consistent framework to link building permits, ACS measures, and electoral structure over time.\",\n \"columns\": {\n  \"l.multi\": \"float64\",\n  \"treat\": \"int64\",\n  \"seg.low.third\": \"float64\",\n  \"max.high.third\": \"float64\",\n  \"population.th\": \"float64\",\n  \"own_rate\": \"float64\",\n  \"vacancy_rate\": \"float64\",\n  \"med_value.th\": \"float64\",\n  \"med_income.th\": \"float64\",\n  \"prop_min_elected_past12\": \"float64\",\n  \"year\": \"int64\",\n  \"location\": \"str\",\n  \"mht\": \"float64\"\n }\n}\n{\n \"id\": \"ref_first\",\n \"rows\": 32,\n \"description\": \"Each location's first year with treat == 1 (gfirst)\",\n \"columns\": {\n  \"location\": \"str\",\n  \"gfirst\": \"int64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 74,\n \"description\": \"Location rows of 2015 and 2017: cohort 2016 and locations never or not yet treated by 2017, observed in both years\",\n \"columns\": {\n  \"l.multi\": \"float64\",\n  \"treat\": \"int64\",\n  \"year\": \"int64\",\n  \"location\": \"str\",\n  \"mht\": \"float64\",\n  \"population.th\": \"float64\",\n  \"own_rate\": \"float64\",\n  \"vacancy_rate\": \"float64\",\n  \"med_value.th\": \"float64\",\n  \"med_income.th\": \"float64\",\n  \"prop_min_elected_past12\": \"float64\",\n  \"gfirst\": \"float64\",\n  \"nyears\": \"int64\",\n  \"TreatGroup\": \"float64\",\n  \"Post\": \"int64\",\n  \"D\": \"float64\"\n }\n}\n{\n \"id\": \"ref_years\",\n \"rows\": 40,\n \"description\": \"How many of 2015 and 2017 each location has a row in (missing values included)\",\n \"columns\": {\n  \"location\": \"str\",\n  \"nyears\": \"int64\"\n }\n}"}