Some large news, to me at least: The Bureau of Economic Analysis is similar a shot producing "regional cost parities" information that let y'all to compare the cost of living inwards i house inwards the the US to another. The BEA news unloosen release is here; coverage from the taxation foundation here (HT the ever interesting Marginal Revolution). In the past, y'all could run across regional inflation -- changes over fourth dimension -- but y'all couldn't compare the grade of prices inwards dissimilar places.
The states differ widely. It is inwards fact equally if nosotros alive inwards dissimilar countries amongst dissimilar currencies. Hawaii (116.8) vs. Mississippi (86.7) is bigger than paying inwards dollars vs Euros (118) Yen (times 100, 1.01) in addition to almost equally large equally pounds (1.30)
The variation across city/country in addition to across cities is fifty-fifty higher:
San Francisco-Oakland-Hayward, CA
All items 121.3
Goods 108.4
Services: Rents 183.9
Services: Other 109.6
San Jose-Sunnyvale-Santa Clara, CA
All items 122.9
Goods 108.2
Services: Rents 200.7
Services: Other 109.3
Beckley, WV
All items 79.7
Goods 92
Services: Rents 52.8
Services: Other 92.5
There is all the same a 20% divergence inwards the cost of goods in addition to other services, but the variation inwards rents is genuinely big. When y'all consider that the cost of existent estate drives upwardly other costs, its resultant may move fifty-fifty larger: If the barbershop pays higher rent, in addition to the hairdresser pays higher rent, you're going to pay to a greater extent than for haircuts. And this is only rents. Since houses receive got sparse rental markets, the truthful divergence may move larger still. Also, rents are oft controlled or poorly measured. I don't know how BLS deals amongst that.
You tin run across many uses for fifty-fifty to a greater extent than granular data. But since family cost in addition to rent are tardily to get, y'all mightiness larn a practiced approximation past times adding granular housing cost information to regional cost data.
There are a lot of interesting issues here.
One query it raises is the truthful motion-picture demo of inequality. Poor people, peculiarly those who don't work, tend to alive inwards low-rent areas. Relative to local prices, inequality may non move equally bad equally it seems. (I presume the BLS does something to conform rents for lineament of housing.)
One tin also imagine that congresspeople from high cost areas volition presently inquire for higher cost of living adjustments for benefits to their constituents.
This information ought to focus to a greater extent than attending on housing provide restrictions -- the primary argue that rents vary in addition to then much.
It raises some puzzles too. I let out that the marketplace position for academics gives surprisingly niggling weight to cost of living variations. If y'all compare offers from a European in addition to the US university, nobody expects y'all to compare "100,000" inwards each house without converting currency. But nominal academic salaries are quite similar across chasms of cost of living. To some extent universities larn inwards upwardly amongst absurdly complex in addition to inefficient housing subsidies, but that doesn't brand much feel either. I'm curious to what extent this phenomenon occurs inwards other markets.
And... who knows? New information ever leads to interesting novel research. Kudos to the BEA for making this available.
Comments from people who know how this information is constructed, amongst practiced parts in addition to pitfalls, are peculiarly welcome.
Update
Influenza A virus subtype H5N1 colleague who knows a lot nigh these issues sent some useful information:
From which I take: 1) This is real of import 2) The BLS took a useful stab at it amongst the numbers they receive got but 3) sympathise the large limitations of the BLS numbers earlier y'all purpose them 4) larn to work, big-data economists, on using scanner data, twitter feeds, amazon purchases, zillow, in addition to everything else y'all tin larn your hands on, to create 21st century granular cost indices!
Update 2:
Enrico Moretti has already written a real dainty paper, Real wage inequality (Also here) adjusting inequality measures for local cost of living.
The states differ widely. It is inwards fact equally if nosotros alive inwards dissimilar countries amongst dissimilar currencies. Hawaii (116.8) vs. Mississippi (86.7) is bigger than paying inwards dollars vs Euros (118) Yen (times 100, 1.01) in addition to almost equally large equally pounds (1.30)
The variation across city/country in addition to across cities is fifty-fifty higher:
In 2014, the metropolitan expanse amongst the highest RPP was Urban Honolulu, HI (123.5). Metropolitan areas amongst RPPs to a higher house 120.0 also included San Jose-Sunnyvale-Santa Clara, CA (122.9), New York-Newark-Jersey City, NY-NJ-PA (122.3), Santa Cruz-Watsonville, CA (121.8), San Francisco-Oakland-Hayward, CA (121.3), in addition to Bridgeport-Stamford-Norwalk, CT (120.4). The metropolitan expanse amongst the lowest RPP was Beckley, WV (79.7), followed past times Rome, GA (80.7), Danville, IL (81.1), Morristown, TN (81.9), in addition to Jonesboro, AR (82.0).No surprise, much of the variation is due to housing. Breaking it out, (look upwardly your town here!)
San Francisco-Oakland-Hayward, CA
All items 121.3
Goods 108.4
Services: Rents 183.9
Services: Other 109.6
San Jose-Sunnyvale-Santa Clara, CA
All items 122.9
Goods 108.2
Services: Rents 200.7
Services: Other 109.3
Beckley, WV
All items 79.7
Goods 92
Services: Rents 52.8
Services: Other 92.5
There is all the same a 20% divergence inwards the cost of goods in addition to other services, but the variation inwards rents is genuinely big. When y'all consider that the cost of existent estate drives upwardly other costs, its resultant may move fifty-fifty larger: If the barbershop pays higher rent, in addition to the hairdresser pays higher rent, you're going to pay to a greater extent than for haircuts. And this is only rents. Since houses receive got sparse rental markets, the truthful divergence may move larger still. Also, rents are oft controlled or poorly measured. I don't know how BLS deals amongst that.
You tin run across many uses for fifty-fifty to a greater extent than granular data. But since family cost in addition to rent are tardily to get, y'all mightiness larn a practiced approximation past times adding granular housing cost information to regional cost data.
There are a lot of interesting issues here.
One query it raises is the truthful motion-picture demo of inequality. Poor people, peculiarly those who don't work, tend to alive inwards low-rent areas. Relative to local prices, inequality may non move equally bad equally it seems. (I presume the BLS does something to conform rents for lineament of housing.)
One tin also imagine that congresspeople from high cost areas volition presently inquire for higher cost of living adjustments for benefits to their constituents.
This information ought to focus to a greater extent than attending on housing provide restrictions -- the primary argue that rents vary in addition to then much.
It raises some puzzles too. I let out that the marketplace position for academics gives surprisingly niggling weight to cost of living variations. If y'all compare offers from a European in addition to the US university, nobody expects y'all to compare "100,000" inwards each house without converting currency. But nominal academic salaries are quite similar across chasms of cost of living. To some extent universities larn inwards upwardly amongst absurdly complex in addition to inefficient housing subsidies, but that doesn't brand much feel either. I'm curious to what extent this phenomenon occurs inwards other markets.
And... who knows? New information ever leads to interesting novel research. Kudos to the BEA for making this available.
Comments from people who know how this information is constructed, amongst practiced parts in addition to pitfalls, are peculiarly welcome.
Update
Influenza A virus subtype H5N1 colleague who knows a lot nigh these issues sent some useful information:
...it’s my agreement from conversations amongst a few people in addition to brief reading on methodology (https://www.bea.gov/regional/pdf/RPP2015.pdf) that they are genuinely pretty piteous measures of local prices. Essentially all of the variation comes from relatively poorly measured housing prices, almost past times construction.
That’s because the exclusively local retail cost information going into the BEA indices comes from the BLS CPI data, which covers less than xxx cities (and non fifty-fifty on identical products across locations). They’re extrapolating from this modest number of cities to all cities inwards the the US past times only taking the nearest metropolis amongst CPI information in addition to re-weighting it amongst local expenditures shares. So for example, at that topographic point is no retail pricing information collected for Columbus, but they demo upwardly inwards the BEA tube expanse cost parities. So where are they getting cost information from? They only receive got the prices collected inwards Cleveland (where BLS collects data) in addition to assume that are the same inwards Columbus amongst potentially slightly dissimilar weights inwards the consumption basket. So fifty-fifty if at that topographic point is broad heterogeneity across cities inwards prices... this is for the most business office non going to larn picked upwardly inwards their local cost measures, since they’re imputing prices inwards most cities using pricing information from other cities. Since most states receive got either 0 or 1 BLS cost collection cities, this way that about 100% of the within-state variation inwards their cost levels is coming from housing. So to about a showtime approximation, these purchasing powerfulness indices are genuinely only family cost indices since they basically aren’t using information on local prices for anything except housing.
But the housing cost information is coming from ACS amongst diverse hedonic adjustment. That is notoriously challenging, peculiarly across locations. It’s much easier but all the same difficult to compute family cost changes across fourth dimension using repeat sales indices similar meat logic, but the housing stock is fundamentally heterogeneous across infinite which puts huge criterion errors on trying to build the cost for an equivalent unit of measurement of housing across space, in addition to then I receive got the exact numbers at that topographic point amongst a large grain of salt.
So overall I recall these indices basically only tell y'all that housing is to a greater extent than expensive inwards san francisco in addition to NYC than inwards oklahoma, but I recall their quantitative usefulness is pretty limited. I recall to genuinely mensurate cost grade differences across locations, scanner information is much to a greater extent than useful since nosotros tin mensurate identical products equally good equally production availability in addition to varieties. (A weakness is that this can’t capture differences inwards service prices across space, but it’s difficult to conform for lineament at that topographic point only similar for housing, fifty-fifty if nosotros had a census of all service providers prices everywhere inwards the country). Jessie Handbury in addition to David Weinstein’s 2014 restud newspaper is the best report I know of trying to receive got seriously measure retail cost levels across locations using that form of data. I receive got no see how it lines upwardly amongst the BEA numbers.
From which I take: 1) This is real of import 2) The BLS took a useful stab at it amongst the numbers they receive got but 3) sympathise the large limitations of the BLS numbers earlier y'all purpose them 4) larn to work, big-data economists, on using scanner data, twitter feeds, amazon purchases, zillow, in addition to everything else y'all tin larn your hands on, to create 21st century granular cost indices!
Update 2:
Enrico Moretti has already written a real dainty paper, Real wage inequality (Also here) adjusting inequality measures for local cost of living.
At to the lowest degree 22% of the documented growth inwards college premium is accounted for past times spatial differences inwards the cost of living.He creates local cost indices. He also takes on the query whether higher prices inwards hot cities stand upwardly for to a greater extent than housing -- amend amenities -- or only higher prices which y'all receive got to pay inwards guild to move high -productivity jobs.
