El análisis costo/beneficio en los proyectos de desarrollo rural

The State of Georgia provides homestead exemptions of property taxes based on a

number of criteria and also allows local jurisdictions to implement their own homestead property

tax exemptions. To become binding, these local exemptions must be approved by the state

legislature, signed by the governor of Georgia, and then passed by a local referendum. While

local jurisdictions can propose any exemption, many of the local homestead exemptions follow

what the state government provides relatively closely. Therefore, it is important to understand

both the state and local exemptions and how they interact.

The data are organized around the following conceptual framework. If there were no

exemptions, the total ad valorem property tax for a household type i living in jurisdiction j would

be:

𝑇

_𝑖,𝑗

= (𝜏

_𝑗𝑀𝑂

+ 𝜏

_𝑗𝐵

)𝛽

_𝑗

𝑉

( 32 )

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In this expression, V is the fair market value, 𝛽 is the assessment ratio (generally 0.4 in Georgia),

and the total tax rate, 𝜏, can be divided into two rates, a tax rate for bonds, 𝜏

𝐵

, and a tax rate on

maintenance and operations, 𝜏

𝑀𝑂

. Incorporating various exemptions into Equation (1), the ad

valorem tax for a household type i living in jurisdiction j is the following:

𝑇

_𝑖,𝑗

= 𝜃

_𝑖,𝑗𝑀𝑂

𝜏

_𝑗𝑀𝑂

(𝜙

_𝑖,𝑗𝑀𝑂

𝛽

_𝑗

𝑉 − (𝛿

_{𝑆,𝑖,𝑗}𝑀𝑂

+ 𝛿

_{𝐿,𝑖,𝑗}𝑀𝑂

)) + 𝜃

_𝑖,𝑗𝐵

𝜏

_𝑗𝐵

(𝜙

_𝑖,𝑗𝐵

𝛽

_𝑗

𝑉 − (𝛿

_{𝑆,𝑖,𝑗}𝐵

+ 𝛿

_{𝐿,𝑖,𝑗}𝐵

))

( 33 )

This expression uses the following notation:

𝜃 ∈ [0,1] is the amount the millage rate is prorated (0 being a full exemption),

𝜙 is a proportionate adjustment to the assessment ratio,

𝛿

𝑆

is the dollar amount of the state exemption, which in some cases may differ by

jurisdiction and individual,

𝛿

_𝐿

is the dollar amount of an applicable local exemption.

Using these definitions, 𝛽

_𝑗

𝑉 is the assessed value, and 𝜙

_𝑖,𝑗𝑀𝑂

𝛽

_𝑗

𝑉 − (𝛿

_𝑆𝑀𝑂

+ 𝛿

_{𝐿,𝑖,𝑗}𝑀𝑂

) is the net

assessed value.

We use equation ( 34 ) to build

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a database that provides unique information about state

and local property tax data as it affects households. Until now, the only data available has been

that from the Georgia Tax Digest

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. The digest provides aggregate data on property tax revenues,

number of assessed properties, and aggregate assessed values by jurisdiction. Default millage

rates are also available since 1990. However, those data provide only a partial picture of the

property tax landscape in Georgia. In particular, they do not provide data on the variability in

homestead exemptions across local jurisdictions, nor do they provide data on such exemptions as

they vary within a jurisdiction by individual characteristics include age, disability status, veteran

status, and income.

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See Mickey, Banzhaf, and Patrick (2015) for a description of how the data set was built.

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This unique data set provides this information for the state and for local jurisdictions

from 1938 to 2013; however, the first local homestead exemption didn't appear on the books

until 1948 in Muscogee County. In practice, it covers an even longer period, as we verified that

there were no state or local exemptions before 1938, at least back to 1913. Merged with data on

millage rates, this database allow one to simulate how much property tax an individual

household of a given demographic category would pay in property taxes in a given jurisdiction in

a given year in a property with a specified assessed value (assuming the household takes

advantage of all exemptions available). The local homestead exemptions data by jurisdiction,

includes all counties, all school districts, and a select number of municipalities. A municipality

was automatically included if it had an independent school district for at least one year after

1990, was one of the 30 most populated cities in Georgia, or was one of the top 100 most

populated cities and had no local homestead exemptions

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. A small number of additional cities

were also included. Appendix C contains a table 34 that shows the top 100 most populated cities

in Georgia and indicates whether the city has ever had an exemption and whether it is coded in

the database.

The local homestead exemptions in Georgia vary across space and time. The state of

Georgia does not limit the type, amount, or eligibility requirements of local homestead

exemptions which lead to a wide variety of exemptions. The exemptions range from an

exemption from the full value of the assessed value which can save thousands of dollars to a few

thousand dollars off of the assessed value of a homestead which saves homeowners a few

hundred dollars. For example, Cobb County exempts homeowners who are over 62 years old

from all school property taxes. In 2013, the millage rate in Cobb County for school purposes was

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The laws for other cities have been gathered, but they have not been coded into the data. They are available from the authors upon request.

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18.9 mills. A homeowner who is over 62 years old with an assessed value of 100,000 dollars

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would save approximately 1,890 dollars from the exemption. In contrast, Coweta County

exempts homeowners who are 65 and older from 8,000 dollars of their assessed value for school

purposes; with the 2013 millage rate of 18.59 they only save 148.72 dollars

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. While tax

jurisdictions often copy the state or each other, they adopt the jurisdictions at different points in

time as well.

4.

Data and Methodology

The basic methodology follows the empirical strategy outlined in Banzhaf and Lavery

(2010) and also closely follows Banzhaf and Walsh (2008) and Card, Mas, and Rothstein (2008).

In particular, we will estimate the following difference-in-difference-in-differences model which

identifies the effect of property tax exemptions for the elderly off of differences from pre-

existing trends relative to control jurisdictions:

Δ𝐷

_{𝑗𝑡,𝑡−𝑇}

= 𝛿𝐴𝐸

_𝑗𝑡

+ 𝛼

_𝑗

+ 𝛽

_𝑡

+ 𝛾𝑋

_{𝑗𝑡−𝑇}

+ 𝜀

_𝑖𝑗𝑡

( 35 )

where Δ𝐷

𝑗𝑡,𝑡−1

is the change between t and 𝑡 – 𝑇 in dependent variable of interest

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, AE is the

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The home is valued at $250,000. In the absence of the full exemption and ignoring other exemptions the household may be eligible, the homeowner would pay the following:

Ti,j= τjMO(βjV − (δS,i,jMO+ δMOL,i,j)) + τjB(βjV − (δS,i,jB + δL,i,jB )) Ti,j= 0.0189(. 4 ∗ 250,000) = $1,890

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With the $8,000 exemption, a homeowner 65 and older would pay the following property tax for school purposes in Coweta County:

Ti,j= τjMO(βjV − (δS,i,jMO+ δMOL,i,j)) + τjB(βjV − (δS,i,jB + δL,i,jB )) Ti,j= 0.01859(. 4 ∗ 250,000 − 8,000) = $1,710.28

Without the exemption and ignoring other exemptions the household could be eligible, the same household would pay the following in Coweta County:

Ti,j= τjMO(βjV − (δS,i,jMO+ δMOL,i,j)) + τjB(βjV − (δS,i,jB + δL,i,jB )) Ti,j= 0.01859(. 4 ∗ 250,000) = $1,859

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T is ten years for the data using the decennial Censuses (1970, 1980, 1990, and 2000). Beginning in 2005, the Census Bureau discontinued the decennial long-form in favor of a yearly survey called the American Community

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treatment variable, 𝛼

𝑗

is a jurisdiction fixed effect, 𝛽

𝑡

is a time trend, 𝑿

𝑗𝑡−1

is a vector of lagged

demographic and location controls,and 𝜀

_𝑗𝑡

is a normally distributed error. We will cluster the

standard errors at the school district level to account for within school district correlation of the

error and estimate each model with and without population weighting.

We will use at least two different dependent variables for the sorting results, and one

dependent variable to estimate the effect on housing consumption by older households. The ideal

variable to estimate the sorting effects is the change (and percentage change) in the number of

home-owning households with heads 65 years old and older (older households). We are also

interested in how the proportion of older households

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changes in levels and percentage terms in

a jurisdiction as the results of an older-household exemption. While this ratio may seem

redundant to using the number of older households, it could give us at least two additional

insights. First, it will help us distinguish between a school district being relatively attractive to

just older households from being attractive to all household types. Second, it could be possible

that a school district is losing households, but older households are leaving at a slower rate than

younger households because of a tax exemption. Changes in population levels would not capture

this subtle effect, but the proportion of older households may.

We can also adjust the definitions of the dependent variables two different ways. The first

adjustment we can make is to the demographic variable used in the dependent variable. Since

owner-occupied houses are the target group of these local homestead exemptions, our ideal

variable is owner-occupied households with heads 65 and older. As a robustness check, we will

use the total population number of individuals 65 and over living in owner-occupied houses.

Survey. The American Community Survey accounts for roughly 1% of the population. Therefore, we may need to use either a 3-year or 5-year release. I used T to reflect that the time period may not always be 1 years.

90_{𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛 𝑜𝑓 𝑂𝑙𝑑𝑒𝑟 𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠 =} 𝑂𝑙𝑑𝑒𝑟 𝐻𝑜𝑢𝑠𝑒𝑜𝑙𝑑𝑠

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Finally, the total population 65 and older regardless of tenure will be used to look at the effect on

the whole population. Second, we will want to test for the sensitivity of using different age

cutoffs. Sixty-five seems to be the ideal cutoff because the majority of age-targeted exemptions

begin at that age, but many jurisdictions also have exemptions beginning at 62 years old while

other exemptions begin at 70 years old. We also need to be concerned about households moving

to a jurisdiction prior to being eligible for a local homestead exemption in anticipation of

eligibility which makes using younger age cutoffs important. We will explore these differences;

unfortunately, the actual cutoffs we use will be limited by the demographic data available.

The second question that this paper addresses is essentially whether older households will

consume more housing than younger households as a result of local age-related homestead

exemptions. Therefore, the natural dependent variable to use is changes (and percentage

changes) in a ratio of housing consumption by age, or a proxy for housing consumption for older

households to younger households. Reported house values may be a logical proxy for housing

consumption, but this could be misleading if the two household types report house value

differently. Another proxy candidate is the number of rooms or the number of bedrooms in a

house.

Note that we haven’t said much about the policy variable of interest; this is because the

nature of local homestead exemptions complicates things. Recall that no two exemptions are

exactly alike since Georgia allows its jurisdictions to create their own homestead exemptions

with little oversight or stipulation. The intensity of the exemption can vary widely from a few

thousand dollars off the assessed value to a complete property tax exemption, and the eligibility

requirements for income differ significantly between school districts. In addition to the

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depends on the millage rate applied by that jurisdiction. A complete exemption is worth more in

a jurisdiction with a high property tax rate opposed to a jurisdiction with a much lower property

tax rate. In light of these issues, we will use three separate treatment variables. First, we will use

a simple binary indicator that equals unity if the jurisdiction has a local homestead exemption for

older households and 0 otherwise. Second, we will calculate an index for the typical millage rate

paid by residents of a school district to estimate the typical savings amount for residents resulting

from homestead exemptions, and the final treatment will use the actual millage rates for each

jurisdiction from the Georgia tax digest to find typical savings from the relevant exemptions. We

will have to restrict the analysis to 1990 to 2013 for the final treatment variable because of data

constraints.

The unit of analysis will be at the school district level. Conveniently, a constitutional

amendment was passed in Georgia in 1945 that effectively limited the number of school districts.

It prohibited the creation of new municipal-controlled school districts except through the

consolidations of county school districts and grand-fathered municipal districts or other county

systems. Therefore, we do not have to worry about school districts splitting in the sample.

Unfortunately, this did not prevent school district boundaries from expanding or contracting

through annexation and consolidations, and we will need to account for these changes in the

analysis.

Finally, we need to control for a how the population and districts changed economically

and socially through time by adding controls. We will want to control for the characteristics of

the population living in each district by controlling for the average education levels, race,

employment, marriage outcomes, tenure decisions, home values, and average rent depending on

the specification and dependent variable of interest. We will also want to control for

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improvements in school quality, crime rates, pollution, and other location characteristics at the

school district even though the school district fixed effect will control for any unobserved spatial

amenities that are time invariant.

One major concern of estimating models of this kind is reverse causality. Our story is that

the generous property tax exemptions attracted older households to the places that have them. In

contrast, it is plausible that older households lobby the government for local homestead

exemptions after moving to a certain community because of some other idiosyncratic reason. We

may want to estimate to use lagged treatment effects to make sure that location decisions are

happening after new local homestead exemptions.

We will use aggregate decennial Census for the years 1970, 1980, 1990, and 2000 and

data from American Community Survey for years starting in 2005 for our analysis. The National

Historical Geographic Information System (Minnesota Population Center 2011) possess most, if

not all the variables, that we desire from the Census Bureau. I am in the process of gathering the

necessary data.

In document Horacio Roura y Horacio Cepeda (página 149-155)