Asymmetry in Local Government Responses in Growing vs. Shrinking Counties: The Case of Education Finance

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Asymmetry in Local Government Responses in Growing vs.
Shrinking Counties: The Case of Education Financea
Abstract
Spending for k-12 education in the United States increased by more than 220% between 1972
and 2012, faster than can be explained by population growth (a 48% increase), growth in median
household income (a 32% increase), or changes in other economic, demographic, and
institutional variables. Importantly, school spending nearly doubled in places that experienced
ongoing population decline. In this paper, analysis reveals asymmetric responses in school
spending to changes in school age population growing and shrinking counties. This research
increases understanding of why education spending tends not to shrink in the face of ongoing
declines in school age population, a situation that exists in about 25% of counties.
Key Words: Education finance; asymmetry, declining; growing
JEL Code: H71
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1. Introduction
Between 1972 and 2012 spending for k-12 education in the United States (US) grew by 220%,
much faster than can be accounted for by changes in income and demographics. Over the past
several decades numerous researchers have sought to explain the underlying factors that drive
the growth of government in industrialized countries. Berry, et al. (2012) conducted a detailed
empirical analysis of US local government spending growth over the 1962-2002 period using
data aggregated to the county level. The authors demonstrated that economic, demographic, and
institutional factors explain a significant portion of growth. Despite this, their evaluation reveals
that these factors do not fully explain growth in government over this period. In this sense,
Berry, et al. (2012) is similar to earlier empirical studies in that the typical socio-economic
variables motivated by models of government (Median Voter—Bowen and Black, 1957;
Leviathan—Brennan and Buchanan, 1980) as well as other considerations do not fully explain
the US local government growth experience. Interestingly, Berry, et al. (2012) also show that the
unexplained growth phenomenon exists even in places experiencing population decline.
The purpose of this paper is to offer an examination of the US local government growth
experience with a focus on k-12 education finances over the 1972-2012 period, where I test for
potential asymmetries in how education spending is influenced by changes in population and
school age population in counties where population is shrinking, stable, and growing. As a
prelude to the full analysis, I find significant asymmetries in how education spending responds to
changes in the proportion of school age children in the population, while controlling for a range
of economic, demographic and institutional factors. Education spending in growing places is
much more responsive to changes in school age population than in shrinking places. That is,
spending tends to increase rapidly with growth in school age population, but is unresponsive to
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decreases in school age population. The evaluation offers insight for both urban core and rural
places experiencing long-term chronic decline, where local leaders must make difficult choices
in maintaining quality educational services affordably.
The next section offers a review of the most relevant literature on the growth of
government with a focus on education finance. Section 3 discusses the data and empirical
approach used in this evaluation. Section 4 presents the empirical analysis and findings, and
section five concludes.
2. Literature Review
In this section, I offer a review of research on the growth of government, emphasizing the
experience of local governments in the United States. I also discuss several of the most relevant
articles from the education finance literature. I conclude the section by offering a summary of
two primary explanations for why we might observe asymmetric responses to population change
in growing and shrinking places: 1) wages and employment tend to be unresponsive to the
downward pressures associated with population decline; and 2) upward pressure by bureaucrats
to increase spending during periods of growth and resistance to budget reductions during periods
of decline. Consider first the literature on local government growth.
2.1 Growth in Local Government
Economists often frame the demand for government services in the context of the median
voter model. Starting with Bowen (1943) and Black (1958), economists asserted that a
community’s choice of public services under majority rule depends on the median of the
individual demands: Under restrictive conditions, majority rule generates a political equilibrium
that reflects the preferences of the median voter. This general framework was used by
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Borcherding and Deacon (1972), Bergstrom and Goodman (1973), and many others to
demonstrate that a jurisdiction’s demand for public services depends upon the income of the
median voter, the median (tax) price of the public good, and the preferences of the median voter,
as well as other variables that capture the demand side of the political process. A wide range of
empirical research has usefully applied the median voter model to examine government spending
levels and priorities. Changing community economic and demographic forces ought to play a
primary role in changing government spending levels and priorities.
The present work follows this general line of thinking by considering a number of socioeconomic
and demographic variables in an effort to explain education revenue/expenditure
growth, including median household income, household income in the top 10th percentile,
poverty rate, the proportion of adults with a BA degree, county population, the share of county
households with a single female head, the share of county population over the age of 65 and
under 18, and the share of county population that is white/Caucasian. Rising median incomes as
well as the rising incomes of the top 10% of income earners and higher levels of education may
lead to greater demand for educational services, and vice versa. Increasing single female-headed
households are expected to reduce education spending. Population change, as well as the share of
the population under the age of 18, is expected to be positively related to education spending,
whereas the share of the population over the age of 65 is expected to be negatively related to
education spending. I have no a priori expectation regarding the how the share of the population
that is Caucasian is related to spending.
Brennan and Buchanan (1980) offer another framework for thinking about growth of
government that is worthy of consideration. According to Brennan and Buchanan (2012),
government may have “leviathan” powers, and thus citizens call for legal constraints to limit
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government power to tax and issue debt.1 Since the 1970s, legislative and referenda processes
have been used extensively across the states to enact new limitations on local governments’
ability to tax and spend.2 It is, therefore, important to include explanatory variables that
characterize the imposition of newly imposed constraints on local government spending.
However, as noted by Blankenau and Skidmore (2002), the imposition of tax and expenditure
limits (TEL) often coincides with school finance reform (SFR), which significantly reduced local
control over education spending and increased reliance on intergovernmental transfers. In fact, a
number of new TELs on schools were imposed with the specific purpose of reducing local
control over education taxes and spending. Taking these developments into consideration, I
incorporate information on TELs as well changes in school finance that occurred during the
period of analysis. Public sector employees may also seek to increase bargaining power over
citizens and thus create “leviathan” powers through the support of strong public sector unions.
To counteract such pressures, a number of states have enacted “Right to Work” (RTW) laws,
which weaken the negotiating power of public sector unions; state and local government
employees are not required to pay union dues in RTW states (Reed 2003). As discussed in more
detail in the next section, I control for these three institutional features as well as changes in the
number of school districts when analyzing the growth of k-12 revenues and spending. While this
body of research informs the types of variable that help to explain government growth, it does
not offer context for assessing the asymmetry issue, which is the focus of the present paper.
Of particular interest is the idea that the responsiveness of local government spending
may differ in shrinking places relative to growing places. Berry, et al. (2012) have documented
the tendency for local governments to grow, even when population is in decline. Further, there
1 See Mueller, chapter 21 (2003) and Oates (1989) for more detailed discussions.
2 See Skidmore (1999) for a review of the literature on TELs.
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are numerous cases across the country in which this tendency has resulted in dire fiscal
conditions. The goal of the present research is to improve understanding of this asymmetry: Why
is it that shrinking places often fail to correspondingly reduce the size and scope of government?
One possible explanation is proposed by Niskanen (1975): Bureaucrats seek to maximize their
own personal benefits by seeking ever-larger budgets. In this context, bureaucrats may place
upward pressure to increase spending during periods of growth, and to resist budget reductions
during periods of decline. Baumal’s “cost disease” (1993) may also be a contributing factor in
driving the costs of education services higher, even in shrinking places.
The present research expands our understanding of this phenomenon by: 1) Considering a
wide array of socioeconomic factors within the long-term 1972-2012 timeframe, with a focus on
changes in population and school age population, 2) examining the growth of five education
revenues and expenditure categories, and 3) using a flexible empirical specification that allows
coefficient estimates on total population and school age population to differ across shrinking,
stable, and growing counties. Before turning to the data and empirical analysis, it important to
consider the several elements of the more specific literature on education finance.
2.2 Education Finance
The discussion here focuses on two aspects of an expansive education finance literature:
1) Effects of changing demographic factors on education spending, and 2) the effects of
changing institutions such as tax and expenditure limits and school finance reform on education
spending. While it is beyond the scope of this paper to offer a comprehensive review of this large
literature, I discuss a subset of research that is most relevant to the present work.
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Several articles examine the implications of changing the demographic make-up of
communities on education spending. 3 For example, Harris, et al. (2001) consider the role of the
changing age structure of the population in education spending. Using a panel of public school
districts, they find that an increasing proportion of the elderly have modest negative effects on
local education spending. Epple, Romano, and Sieg (2012), Figlio and Fletcher (2011) also
examined the role of demographic change in school spending. Epple, et al. (2012) focus on
intergenerational conflict, emphasizing the importance of the older generation’s mobility. Figlio
and Fletcher (2011) also consider the role of the growing elderly population in school spending,
finding that increases in the number of the elderly aging in place is associated with reduced
education spending. The majority of studies such as these focus on the impact of an aging
population on education spending, though they consider other changing demographic trends as
well.
Imazeki and Reschovsky (2003) discuss the challenges of financing education in rural
areas, given the small size and often shrinking populations in rural school districts. They estimate
cost functions across rural and non-rural places in Wisconsin and Texas, concluding that, though
the cost structures are similar across rural and non-rural school districts, small district size, high
poverty rate, and a high burden of special needs all lead to higher costs in many rural areas.
Finally, Corcoran and Evans (2010) consider the role of income inequality in the support of
public education, finding that 12% to 22% of the increase in school spending over the 1970-2000
period was attributable to rising income inequality.
There is also a large literature on how changing institutions affect education spending.
First, there is a body of research on how the “tax revolt” and the emergence of new limitations
3 See, for example, Poterba (1998), Harris, Evans, and Schwab (2001), Ladd and Murray (2001), and Grob and
Wolter (2007).
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on local government tax and spending powers beginning in the 1970s affected education
spending. Much of this literature is summarized in the aforementioned article by Blankenau and
Skidmore (2002), as well as Mullins and Wallin (2004).4 While there is a significant challenge in
identifying causal relationships between the imposition of tax and expenditure limitations (TELs)
and changes in education spending, research generally supports the idea that the imposition of
new TELs on local governments corresponds with reductions in local broad-based taxes
(property taxation) and increased reliance on state aid, as well as other types of revenue such as
user charges.
Beginning in the 1970s, the majority of states experienced legal challenges to their school
finance systems on the basis that inequities in funding violated state constitutions. Beginning
with a major ruling in California (Serranno v. Priest, 1971 and 1976), a series of court rulings
across the nation regarding equity in school finance led to significant changes in school funding.
The primary goal of the rulings was to reduce disparities in funding per pupil across school
districts. Generally, existing research concludes that school finance reforms (SFR) led to
reductions in reliance on local property taxes, and to increased reliance on state government
resources in funding local schools.5 In addition, researchers such as Evans, Murray, and Schwab
(1998) show that SFR significantly reduced disparities in per pupil spending across school
districts. However, as noted by Yinger (2004) and Hoxby (1998) the nature of reforms and their
impacts differ greatly across the states. Researchers such as Fahy (2008) also examined the role
of education finance reform in determining education spending in particular states. Fahy (2008)
4 Kenyon (2008) offers an excellent discussion of the interrelationships between, and evolution of, property taxes
and school finance.
5 See Yinger (2004) for an excellent summary of the impacts of school finance reforms across the nation.
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considered the role of state aid in improving equity across schools, finding limited overall
effectiveness.
Within the context of the changing landscape of education finance, a relevant and open
question is the degree to which changes in education spending affected school performance and
longer-term student outcomes. The recent works of Jackson, Johnson, Persico (2016), Hyman
(2017), and Lafortune, Rothstein, Schanzenbach (2018) offer compelling evidence using
exogenous variation in school spending to show significant positive effects on student outcomes.
Increases in school spending appear to result in significant improvements in student outcomes.
With school finance reforms, increases in education spending tended to occur in lower spending
school districts, and was the result of state level redistribution of resources. This discussion may
be of particular relevance to the present paper in that shrinking rural counties have increased
stressors associated with maintaining public service levels such as education—one policy option
that may help to avert negative educational outcomes in declining places is to ensure an adequate
level of school spending from higher levels of government.
With the exceptions of Berry, et al. (2012) and Das and Skidmore (2018), researchers
have not considered potential asymmetry in local government spending across growing and
shrinking places. Nevertheless, there is a rationale for the idea that we ought to observe
asymmetries. My primary hypothesis is that education spending is less responsive to declines in
school-age population than to school-age population growth. When overall and school age
population is growing, both operating and capital spending increase in order to meet increased
demand for educational services. However, when overall and school-age population (and thus the
demand for educational services) is in decline, operational spending, such as labor costs, may
become unresponsive as the number of households and students shrink. Further, capital
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maintenance costs cannot easily be cut without risking depreciation/neglect. There may also be
other types of inertia that limit cuts to spending in shrinking places. For example, wages are
sticky downward, and eliminating excess labor is often difficult. Baumal’s cost disease
framework suggests that increasing costs on educational service provision, even in shrinking
places. Further, the work of Niskanen (1975) suggests that bureaucrats would push for spending
increases during periods of growth, but resist cuts during periods of decline. Thus, the
responsiveness of education spending to population change in shrinking places is likely to be less
than in growing places. For similar reasons, asymmetry is also expected with changes in the
overall population. For these reasons, I hypothesize that the analysis will demonstrate asymmetry
in responses to changes in population and school-age population in places that are expanding
versus places that are in decline. While the empirical analysis does not explicitly measure the
degree to which the aforementioned factors are driving the asymmetry, it is able to document the
degree to which asymmetry is present, which offers a significant contribution of our
understanding of this phenomena.
3. Data and Empirical Approach
Data on local government education revenues and expenditures come from the United States
Census of Governments. Local school fiscal data from independent school districts are
aggregated to the county level.6 In total, 2,752 counties are included in the analysis. The data are
6 School districts sometimes overlap multiple counties, and multiple school districts are often found within a single
county. Regarding school districts that overlap multiple counties, the Census of Governments makes no attempt to
pro-rate the data based on government boundaries. Instead, data for a school district is assigned to the county where
it is headquartered. Due to the nature of the data collected from The Historical Finance Data Base of Individual
Local Governments, only independent school districts are included in the analysis; these are a type of school district
that operates as an independent entity separate from county, municipality, township, special district, and state
governments. They possess their own taxing authority and provide local government finance data separate from
other government types. Therefore, we are unable to separate dependent school district revenue and expenditures,
especially intergovernmental revenues to schools in counties and municipalities that have direct authority over
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available in five-year intervals (1972, 1977, 1982, 1987, 1992, 1997, 2002, 2007, and 2012. To
examine asymmetry in the impacts of the explanatory variables on education revenues and
expenditures, I create three indicator variables: The variable Shrink identifies counties with
declining population over the 1972-2012 period (about 24% of counties); the variable Stable
identifies all counties that had between -5% and +5% growth over the period (10% of counties),
and the variable Grow identifies counties with positive population growth greater than 5% over
the period (66% of counties).7 These indicator variables are then interacted with the population
and school age population variables . A limitation of using county level data is that the analysis
is unable to capture intra-county variation in education spending across school districts. Further,
we are not able to capture factors such as the advent of charter schools and school choice on
school spending; researchers such as Buerger and Bifulco (2019) and other have shown the
introduction of charter schools and school choice to have meaningful effects on both student
composition and costs; controlling or county trends in the first-difference specification should
help to address potential omitted factors. An advantage of the county level data, however, is that
the examination is nationwide, and is conducted over a long period of time. Further, we are able
to include a wide range of explanatory variables not available if one were to use school district
level data. Last, although we could potentially define growing, stable, and shrinking over shorter
periods, my primary objective is to examine the long-term responses to growth vs. decline. There
are trade-offs in decisions to use certain types of data and periods of analyses; despite the
inherent limitations of county level data, the analysis offers new insights into the dynamics of
school spending across space and over time.
school districts. In total 291 counties with dependent school districts, many of which are in Virginia, Tennessee, and
North Carolina are not included in the analysis, nor are dependent school districts that can be found within counties
that predominantly have independent school districts.
7 Eleven counties were omitted because their data were missing for some of the years included in the analysis.
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The logarithmic specifications are based on the following equation:
Δ𝑅𝑒𝑣𝑖𝑡𝑗 = 𝑆ℎ𝑟𝑖𝑛𝑘 ∗ Δ𝑃𝑜𝑝𝑖𝑡𝛼1 + 𝑆𝑡𝑎𝑏𝑙𝑒 ∗ Δ𝑃𝑜𝑝𝑖𝑡𝛼2 + 𝐺𝑟𝑜𝑤 ∗ Δ𝑃𝑜𝑝𝑖𝑡𝛼3 + 𝑆ℎ𝑟𝑖𝑛𝑘 ∗
Δ𝑆𝑐ℎ𝑜𝑜𝑙𝐴𝑔𝑒𝑖𝑡𝛼4 + 𝑆𝑡𝑎𝑏𝑙𝑒 ∗ Δ𝑆𝑐ℎ𝑜𝑜𝑙𝐴𝑔𝑒𝑖𝑡𝛼5 + 𝐺𝑟𝑜𝑤 ∗ Δ𝑆𝑐ℎ𝑜𝑜𝑙𝐴𝑔𝑒𝑖𝑡𝛼5 +
Δ𝐷𝑒𝑚𝑖𝑡𝛽1 + Δ𝐸𝑐𝑜𝑛𝑖𝑡𝛽2 + Δ𝐼𝑛𝑠𝑡𝑖𝑡𝛽3 + 𝑐𝑖 + 𝑡𝑡 + 𝑒𝑖𝑡 (1)
where ΔRev represents the change in the natural logarithm of the education revenue (or
expenditure) for county i between periods t and t-1 for revenue (expenditure) category j, ΔPop
represents changes in the natural logarithm of population, ΔSchoolAge represents changes in the
proportion of school age population, ΔDem represents a vector of other demographic variables
that include the percentage of households headed by a single female, the percentage of the
population over the age of 65, and percentage of the population that is white, ΔEcon represents a
vector of economic variables that include the change in natural logarithm of median household
income, the change in the natural logarithm of the income of the top 10% of households, and the
change in the poverty rate, and ΔInst is a vector of institutional variables which includes
variables that indicate change in RTW status, the change in the number of tax and expenditure
limitations (TEL), the change in number of school finance reform efforts (SFR), and the change
in the number of school districts.8 t is vector of time indicator variables, and c represents a vector
of county fixed effects, which accounts for unobserved community trends that have effects on
education spending. This is a first-difference specification that controls for county-specific
trends with county fixed effects as well as overall national trends with time indicator variables.
Data sources and definitions are provided in Appendix Table A. Summary statistics for all
explanatory variables are presented in Tables 1 and 2 for declining and growing counties
8 Caution is warranted in the interpretation of the coefficients on the TEL, SFR, and RTW variables because policy
changes such as these are potentially endogenously determined. Unfortunately, identifying valid instruments within
a panel data framework is challenging. TEL, SFR, and RTW are included in the analysis primarily as control
variables because previous research demostrates their importance in determining the education spending growth.
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respectively. Tables 3 and 4 present summary statistics of the education finance variables for
shrinking and growing counties.
Note that, because this is a first-difference estimation, the coefficient estimates are
formed by the within-county variation in the independent variables. Thus, it is the within-county
changes in the independent variables upon which the coefficients are generated. In the case of the
institutional variables, the coefficients are being estimated by the changes in the status of the
institutions; over this long period of time we have many changes in RTW, TEL, SFR, and the
number of school districts across the states. It should also be recognized that the nature of TELs
and SFR differ considerably from state to state. Amiel, et al. (2009) and Mullins and Wallin
(2004) catalog TELs and the major characteristics that define them for all states over time. The
approach I use is to identify every new TEL that is imposed on schools in every state. While I
identify every change in the status to TELs over time, this measure does not capture the various
TEL characteristics, and thus measures the average effect of TELs on school revenue and
spending growth. I also include the variable “State TEL”, which again measures every new TEL
on state government that is imposed in each state. To clarify, two TEL variables are included in
the analysis: State TEL and School TEL. Similarly, the SFR variable includes every courtordered
and legislative change in SFR status, but it does not capture the important differences
across states in SFR characteristics as cataloged by researchers such as Yinger (2004) and Hoxby
(2001). This variable also measures the average effect of SFR across the states and over time.
Note that these variables are primarily used as controls, though the estimates may reveal useful
interesting coefficient estimates.
To assess the differences in the effects of the population and school age population
variables on the dependent variables, I interact each variable with the Shrink, Stable, and Grow
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indicator variables. More specifically, Shrink is an indicator equal to 1 if the county experienced
population decline of more than -5% over the period of analysis and zero otherwise. Stable is an
indicator variable equal to 1 if population change was between -5% and +5% over the period,
and zero otherwise. Grow is an indicator equal to 1 if the county experienced positive population
growth of more than 5% over the period of analysis, and zero otherwise. With this framework,
one can determine whether the coefficients for population and school age population differ
across shrinking, stable, and growing counties. The regression estimates use a technique in which
the standard errors are clustered at the county level to address both temporal autocorrelation and
cross-sectional correlation.9 Education expenditure/revenue categories included in j are: Total
education expenditure/revenue from all overlying jurisdictions (Table 5, column 1), own source
revenues (Table 5, column 2), intergovernmental transfers from state and federal governments
(Table 5, columns 3), expenditure on current operations (Table 5, column 4), and expenditures
on capital outlays (Table 5, column 5). These regressions enable one to see how the changing
population and school age population education finances differ across shrinking, stable, and
growing counties, while controlling for a wide range of economic, demographic, and institutional
factors.
Before turning to the econometric analysis, consider Figures 1 and 2, which illustrate
trends over time in per-capita local government revenue, own-source revenue, intergovernmental
transfers, median household income, population, and school age population. From the graphs it is
clear that median household income grew more slowly across both growing and shrinking
counties than did education revenues/expenditures. In 2002 median household income began to
fall in both growing and shrinking counties. Growth in education spending slowed greatly
9 The Stata procedure for panel corrected standard errors is used.
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between 2007 and 2012 across the nation. Finally, population declined in shrinking counties, but
grew elsewhere. Figure 3 offers a map of population change in shrinking, stable, and growing
counties. As one might expect, many of the shrinking counties are found in the rural mid-section
of the country, whereas the growing counties are in the south and along the coasts. However,
with the exception of California, Florida, Utah and a few of the small east coast states, shrinking
counties exist in every state across the nation. Note that most shrinking counties experienced
growth in education revenues and expenditures despite experiencing reductions in population
and school age population, and only modest growth in median income over the period. This
descriptive summary information provides context for understanding the estimates generated
from the regression analysis, which is discussed next.
4. Empirical Analysis
Before considering the regression results, some caution is warranted in assigning causality to the
coefficient estimates due to potential endogeniety of the regressors. Changes in school spending
could very well lead to changes in population and school age population, or the imposition of
new fiscal rules. For reference, in specifications not presented but available upon request, I
estimated regressions similar to those presented except population and school age population
were introduced as lagged terms. These estimates are similar to those presented in the paper. In
sum,the evaluation offers a useful and informative evaluation of important trends across
shrinking, stable, and growing counties. Consider the estimates presented in Table 5, which
include regressions for total education revenues/expenditures (column 1), intergovernmental
revenue (column 2), own source revenue (column 3), operating expenditures (column 4), and
capital expenditure (column 5). The regressions explain between 4% and 25% of the variation in
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the regressions. Note that a low adjusted R2 is not uncommon in this type of regression model.
The data are first differenced and then estimated using the fixed effects technique so that average
growth (decline) in each county is captured with the county fixed effects. The variables in the
regressions capture the remaining variation in growth (decline), and thus the low adjusted R2. An
advantage of this approach is that it offers very robust coefficient estimates that are unlikely to
be biased by omitted factors or spurious correlations.
In the total education revenue/expenditure regression, the coefficient on population is
similar for shrinking, stable, and growing counties, and this is generally true in the
intergovernmental revenue, own-source revenue, operating expenditures, and capital expenditure
regressions. This finding suggests that revenues and expenditures are not responding
differentially to changes in population across shrinking, stable, and growing counties. However,
we observe significant differentials across shrinking, stable, and growing places in response to
changes in school age population. Total revenue/expenditure is very responsive to changes in
school population within stable and growing places, but is unresponsive in shrinking places. The
drivers of this result appear to be own-source revenues and capital spending. In these regressions,
own-source revenue and capital spending appear to increase when school age population
declines. The negative coefficient on school age population for shrinking counties in the ownsource
regression is counter balanced by the positive coefficient (though not statistically
significant) on the same variable in the intergovernmental revenue regression. These findings
confirm the hypothesis that there is significant asymmetry in responses to changes in school age
population in shrinking vs. stable and growing counties. The findings regarding the school-aged
proportion of the population are consistent with the a priori expectations.
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Consider coefficients on the control variables. In general, increases in median income
correspond with increases in education spending. However, this result appears to be driven by
associated increases in intergovernmental transfers. Growth in the income of the top 10% also
drives spending increases, but here it seems to be the result of increased own source or local
spending. Controlling for income neither increases poverty nor the percent of adults with at least
a college degree are significant determinants of education spending. However, increases in the
number of female-headed households and the elderly are associated with reductions in spending.
Turning to the institutional variables, the adoption of right-to-work laws is associated with
reduced spending. TELs imposed on state governments reduce intergovernmental transfers to
schools, but lead to increased own-source spending; however, the net effect on education
spending is negative. On the other hand, new TELs imposed on school districts reduce ownsource
spending but are associated with increases in intergovernmental transfers. The net effect
on total education spending is negligible. School finance reforms lead to increases in
intergovernmental transfers and reductions in own-source spending, but the net effect on overall
spending is positive. Again, I stress that researchers such as Hoxby (2001) have shown
significant differential effects depending on the nature of the reforms; our evaluation only offers
an estimate of the average effect. Finally, changes in the number of school districts are positively
associated with changes in spending.
The findings presented in the paper are robust to alternative specifications and estimation
methods. In the reported estimations, the Shrink, Stable, and Grow variables are not time
varying, rather they are based on population change over the entire period of analysis. In
estimations that are available upon request, Shrink, Stable, and Grow are allowed to vary over
Census periods; these estimates are similar to those presented and are therefore not discussed
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further. I also considered estimates in which there were three separate categories of counties—
shrinking, moderate growth, and high growth. These estimates are consistent with those
presented. In terms of estimation methods, in addition to the first-difference specification—with
the added county indicator variables to control for county specific growth trends—I also
estimated a straight first-difference specification as well as a two-way fixed effects specification
with county-specific time trends. These regressions are generally consistent with the primary
estimates discussed here. For reference, I report the two-way fixed effects regressions with
county-specific time trends in Appendix B. The estimates generated from the other approaches
are available from the author upon request.
5. Implications and Conclusions
This study offers an examination of the growth of K-12 education revenues and spending over
the 1972-2012 period using detailed fiscal data for most counties in the United States. A key
objective of the analyses is to increase understanding of why school spending continues to grow
even in the face of declining population and school age population. Over the period of analysis,
about 10% of US counties experienced population decline of more than -5%, where most
declining counties are rural. The evaluation presented in this paper offers some new insight.
First, education revenue/expenditure is more responsive to changes in school age population in
growing places than in declining places. This finding is consistent with the theoretical
discussion; eagerness in expansion and then resistance in making cuts to labor and capital make
it easier to increase spending during periods of growth, but more difficult to cut spending in the
face of decline.
19
Overall, the analysis offers new information that increases our understanding of the
dynamics of education finance in growing and shrinking counties. Are the asymmetries
identified in this analysis an efficient pattern of education finance dynamics? Should education
spending fall when school age population declines as rapidly as it increases during periods of
growth? While the empirical analysis does not offer clear answers to these questions, it sheds
light on important education spending patterns that have been masked within standard regression
analysis thus far. Considerations such as difficulties in cutting wages and employment to the
downside, eagerness of bureaucracies to expand during periods of growth but resistance to cuts
during periods of decline, and intergovernmental assistance formulae help to explain some of the
observed asymmetries.
Local leaders in declining areas must deal with an ever-present tension. On the one hand,
they may feel compelled to devote public resources in order to ensure the children in their
communities receive high quality education. On the other hand, they must be careful not to overburden
decreasing numbers of households with rising taxes to pay for those services, which
could further hasten the decline. The evaluation shows that, on average, local leaders were
willing to increase spending as the number of school age children declined, presumably in an
effort to maintain the quality of educational services. This tension between maintaining the
quality of educational services and being sensitive to tax burdens is always present in shrinking
communities; creativity and perhaps some intergovernmental assistance are required to
affordably maintain essential public services during periods of chronic decline.
20
Figure 1: Declining Population Variable Trends
Figure 2: Growing Population Variable Trends
21
Figure 3: Percent Change in Real Per-Capita Total Education Revenue from 1972 to 2012
22
Table 1: Declining Population Control Variables
1972 1982 1992 2002 2012
Economic
Median Income 30,740 35,152 39,366 44,638 39,230
(7,211) (6,538) (7,033) (7,383) (7,246)
Top Ten Income 65,558 70,382 81,362 104,265 142,669
(11,824) (9,111) (11,855) (14,458) (18,048)
Poverty Rate 0.167 0.138 0.139 0.127 0.168
(0.096) (0.069) (0.072) (0.063) (0.066)
Pct BA Degree 0.071 0.103 0.122 0.149 0.171
(0.023) (0.029) (0.034) (0.044) (0.054)
Demographic
Population 48,575 46,630 44,365 43,910 42,595
(246,942) (232,161) (225,892) (229,363) (222,006)
Female HH Rate 0.067 0.076 0.088 0.097 0.105
(0.031) (0.039) (0.049) (0.051) (0.056)
Pct Over 65 0.131 0.151 0.170 0.172 0.176
(0.037) (0.038) (0.038) (0.035) (0.035)
Pct Under 18 0.337 0.287 0.267 0.249 0.230
(0.039) (0.032) (0.029) (0.026) (0.027)
Pct White 0.915 0.902 0.891 0.871 0.862
(0.164) (0.166) (0.171) (0.177) (0.179)
Institutional
Right to Work 0.584 0.605 0.613 0.640 0.657
(0.493) (0.489) (0.487) (0.480) (0.475)
State TELs 0 0.147 0.260 0.405 0.461
– (0.355) (0.526) (0.623) (0.737)
School TELs 0.923 1.545 1.914 2.017 1.506
(0.458) (0.707) (0.892) (0.932) (0.766)
School Finance Reform 0.171 0.537 1.327 1.932 2.363
(0.377) (0.641) (1.096) (1.178) (1.368)
School Districts 5.917 5.241 4.877 4.295 4.098
(8.319) (7.609) (7.313) (6.994) (7.624)
Standard deviation in parentheses. Adjusted to 2009 dollars.
23
T able 2: Growing Population Control Variables
1972 1982 1992 2002 2012
Economic
Median Income 33,043 38,094 45,010 50,780 44,277
(8,450) (8,240) (10,964) (11,890) (11,503)
Top Ten Income 68,018 75,057 93,707 120,682 146,496
(12,596) (12,301) (17,832) (22,482) (17,373)
Poverty Rate 0.162 0.122 0.121 0.116 0.168
(0.087) (0.059) (0.063) (0.054) (0.060)
Pct BA Degree 0.084 0.122 0.145 0.175 0.199
(0.045) (0.060) (0.073) (0.085) (0.092)
Demographic
Population 65,186 76,596 87,944 100,363 110,383
(203,921) (231,462) (272,542) (305,225) (328,984)
Female HH Rate 0.078 0.088 0.101 0.108 0.122
(0.025) (0.028) (0.032) (0.034) (0.039)
Pct Over 65 0.107 0.118 0.129 0.131 0.140
(0.035) (0.035) (0.036) (0.034) (0.036)
Pct Under 18 0.339 0.291 0.266 0.252 0.236
(0.038) (0.034) (0.034) (0.032) (0.034)
Pct White 0.889 0.878 0.866 0.841 0.828
(0.146) (0.141) (0.143) (0.148) (0.151)
Institutional
Right to Work 0.517 0.538 0.555 0.580 0.611
(0.500) (0.499) (0.497) (0.494) (0.488)
State TELs 0 0.231 0.411 0.624 0.719
– (0.421) (0.599) (0.669) (0.823)
School TELs 0.834 1.594 1.985 2.225 1.594
(0.583) (1.004) (1.071) (1.213) (0.974)
School Finance Reform 0.064 0.584 1.363 2.024 2.408
(0.245) (0.666) (1.178) (1.230) (1.470)
School Districts 5.683 5.526 5.405 5.170 5.304
(7.378) (7.163) (6.933) (6.675) (6.917)
Standard deviation in parentheses. Adjusted to 2009 dollars.
24
Table 3: Declining Population Local Government Education Spending
1972 1982 1992 2002 2012
Total Revenue 50,358 50,840 65,758 82,495 93,206
(292,916) (262,250) (348,497) (440,048) (524,989)
Own-Source Revenue 29,327 25,674 33,449 37,608 42,584
(199,381) (144,470) (222,724) (265,181) (304,513)
Intergovernmental Revenue 21,031 25,166 32,309 44,887 50,622
(97,818) (120,434) (132,799) (185,455) (233,862)
Current Operations 41,547 42,242 55,848 67,530 74,505
(211,608) (207,846) (274,882) (346,760) (410,416)
Capital Outlays 3,162 2,146 3,926 8,302 7,424
(18,321) (8,127) (18,944) (50,847) (40,030)
Standard deviation in parentheses. Adjusted to 2009 dollars, in thousands.
Table 4: Growing Population Local Government Education Spending
1972 1982 1992 2002 2012
Total Revenue 59,773 74,302 117,966 179,262 206,626
(215,303) (240,112) (383,564) (591,457) (640,240)
Own-Source Revenue 32,122 32,789 52,807 75,628 90,314
(132,313) (95,120) (158,018) (238,219) (271,658)
Intergovernmental Revenue 27,651 41,513 65,159 103,634 116,313
(85,897) (162,563) (259,152) (388,536) (411,449)
Current Operations 49,891 61,065 97,440 144,708 167,260
(182,095) (197,928) (313,193) (466,797) (500,710)
Capital Outlays 5,236 4,632 10,956 22,979 17,252
(16,823) (14,035) (35,174) (81,975) (63,091)
Standard deviation in parentheses. Adjusted to 2009 dollars, in thousands.
25
Table 5: Local Government Education Regressions: First Differenced Variables with Fixed Effects
Total
Revenue
Own-Source
Revenue
Intergovernmental
Revenue
Current
Operations
Capital
Outlays
ln(Population)
Declining Units 0.931*** 1.250*** 0.640*** 0.685*** 2.501***
(0.140) (0.150) (0.231) (0.0870) (0.770)
Stable Units 0.909*** 1.322*** 0.663*** 0.797*** 0.984
(0.167) (0.265) (0.232) (0.125) (0.876)
Growing Units 1.008*** 1.395*** 0.654*** 0.886*** 2.537***
(0.0535) (0.0840) (0.0827) (0.0414) (0.270)
Pct Under 18
Declining Units -0.119 -1.316** 0.879 0.369 -3.969**
(0.324) (0.516) (0.726) (0.287) (1.835)
Stable Units 1.826*** 0.0497 3.661*** 1.448*** 7.885***
(0.430) (0.688) (0.765) (0.371) (2.569)
Growing Units 1.946*** 3.005*** 2.233*** 1.755*** 6.839***
(0.213) (0.430) (0.399) (0.186) (1.173)
Other
ln(Median Income) 0.122** -0.000512 0.250** 0.0353 0.668**
(0.0586) (0.0411) (0.106) (0.0246) (0.279)
ln(Top Ten Income) 0.106*** 0.170*** -0.0120 0.0373* 0.815***
(0.0279) (0.0446) (0.0531) (0.0206) (0.152)
Poverty Rate -0.0718 0.0347 0.194 0.00640 -0.141
(0.147) (0.188) (0.252) (0.0919) (0.757)
Pct BA Degree 0.0771 0.108 0.161 0.0827 0.256
(0.135) (0.115) (0.208) (0.0588) (0.641)
Female HH Rate -0.134*** -0.245** -0.0503* -0.131*** -0.671
(0.0478) (0.115) (0.0288) (0.0229) (0.538)
Pct Over 65 -1.115*** -0.619 -1.152** -1.404*** -0.571
(0.325) (0.437) (0.499) (0.289) (1.555)
Pct White -0.494*** 0.0554 -0.562*** -0.424*** -1.160**
(0.0815) (0.148) (0.146) (0.0683) (0.528)
Right to Work -0.0468*** 0.0393*** -0.109*** -0.0532*** -0.197**
(0.00867) (0.0139) (0.0107) (0.00796) (0.0797)
State TEL’s -0.00704* 0.0177** -0.0301*** -1.16e-05 -0.0756***
(0.00422) (0.00818) (0.00715) (0.00312) (0.0282)
School TEL’s -0.00222 -0.0572*** 0.0419*** 0.00108 0.00210
(0.00194) (0.00365) (0.00371) (0.00142) (0.0133)
School Finance Reform 0.0270*** -0.0105** 0.0635*** 0.00981*** 0.0157
(0.00235) (0.00426) (0.00422) (0.00182) (0.0162)
School District Number 0.0176*** 0.0130*** 0.0170*** 0.0193*** 0.00913
(0.00310) (0.00340) (0.00461) (0.00298) (0.00970)
Constant 0.102*** 0.0126 0.207*** 0.127*** 0.0832*
(0.00897) (0.0147) (0.0157) (0.00734) (0.0474)
Observations 21,976 21,976 21,973 21,975 21,867
R-squared 0.205 0.070 0.153 0.255 0.042
Number of Units 2,752 2,752 2,751 2,752 2,752
Dependent variables in log form. Cluster-robust standard errors in parentheses. Time fixed effects included.
***p<0.01, **p<0.05, *p<0.1
26
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29
Appendix A: Data Variables, Definitions, Sources and Methods
Variable Definition
1 Total Education Revenue Total revenue received by k-12 schools aggregated to the county level.
1 Own-Source Revenue Revenue raised directly by k-12 schools aggregated to the county level.
1 Intergovernmental Revenue
Revenue received by k-12 schools from other governmental units (primarily state and federal
governments) aggregated to the county level.
1 Operational Expenditures Expenditures used by a k-12 schools to operate its normal operations aggregated to the
county level.
1 Capital Expenditures Expenditures used by a governmental unit to acquire or upgrade capital assets aggregated to
the county level.
2 Population Total number of persons inhabiting a county.
2 Median Income Income level that divides the income distribution into two equal groups for a county.
2 Top Ten Income Income level that defines the lower bound of the top ten percent income bracket for a county.
2 Female HH Rate Percentage of households that are female-headed in a county.
2 Poverty Rate Percentage of households with income below the poverty line in a county.
2 Pct Over 65 Percentage of the population aged 65 years or older in a county.
2 Pct Under 18 Percentage of the population aged 18 years or younger in a county.
2 Pct BA Degree Percentage of the population that have earned a bachelor’s degree in a county.
2 Pct White Percentage of the population of the White race in a county.
3 Right to Work
Statute that prohibits union security agreements. This variable equal 1 if a RTW law exists 1
a state, and 0 otherwise
4,5 State TELs
Statutes that restrict the level of growth, or spending of a state governmental unit. This
variable increases by 1 every time a new TEL is imposed, and is reduced by 1 if a TEL is
eliminated.
4,6,7 School TELs
Statutes that restrict the level of growth, or spending of local education governmental units.
This variable increases by 1 every time a new TEL is imposed, and is reduced by 1 if a TEL
is eliminated.
8 School Finance Reform
Judicial or legislative acts that reform school funding rules. This variable increases by 1
every time a new SFR is imposed, and is reduced by 1 if a SFR is eliminated.
2 Independent School Districts The number of independent school districts within each county.
1 United States Census Bureau. “State and Local Government Finance Data” from Census of Government Finances and
Annual Survey of Local Government Finances.
2 Minnesota Population Center. National Historical Geographic Information System (NHGIS): Version 2.0. Minneapolis, MN:
University of Minnesota 2011.
3 United States Department of Labor. “State Right-to-work Laws and Constitutional Amendments in Effect as of January 1,
2009 With Year of Passage”.
4 Significant Features of the Property Tax. http://datatoolkits.lincolninst.edu/subcenters/significant-features-propertytax/
Report_Tax_Limits.aspx. Lincoln Institute of Land Policy and George Washington Institute of Public Policy.
5 National Conference of State Legislatures. Prepared by Bert Wasisanen. “State Tax and Expenditure Limits – 2010.”
6 Advisory Commission on Intergovernmental Relations. “Tax and Expenditure Limits on Local Governments”. Publication
M-195: 1995.
7 Amiel, Lindsay, Deller, S.C., and Stallman, J.I. “The Construction of a Tax and Expenditure Limitation Index for the US.”
University of Wisconsin-Madison, Staff Paper Series No. 536: 2009.
8 Jackson, C. Kirabo, Johnson, R., Persico, C. “The Effect of School Finance Reforms on the Distribution of Spending,
Academic Achievement, and Adult Outcomes.” National Bureau of Eonomic Research Working Paper No. 20118: 2014.
30
Appendix A (continued)
Variable Description and Method
Top Ten Income
Top ten income is defined as a top 10% (or 90th percentile) income level of U.S.
households. As the U.S. Census does not provide the full income distribution at
the local level, we restore an (approximate) income distribution using the
reported number of households in each of 10 income categories. First, the upper
limits of income distribution for each sample periods are estimated using the
historical national-level household income trends. Assuming households are
distributed uniformly within each income category, we get households
distribution function across income levels and using this function we calculate
the top ten percent income by targeting the income level where the area under
the households’ income distribution function above that income level is equal to
0.10*total households in a county.
Right to Work
Right to work statutes are defined as a dummy variable: 1 if a state has enacted a
statute or constitutional amendment, and 0 if the state has not. The dummy
variable applies to all types of local government units within a state.
Tax and Expenditure Limits
TELs are defined as account variables that capture the number of statutory
limitation changes that affect a government unit over the period. The type of
TEL or specific limits are not considered. The starting point in 1972 is 0. School
TELs apply to counties with independent local school districts; counties without
independent local school districts operate through counties, municipalities,
townships, and special districts, therefore the TELs imposed on these
jurisdictions are applied instead. State TELs apply to the state government.
School Finance Reform
The School Finance Reform variable is defined as a count variable that captures
the number of legislative or judicial reforms within a given state. The starting
point in 1972 is 0.
Independent School Districts
The School District variable is the number of independent school districts within
a county, not the number of schools; the reporting methods of these counties and
school districts vary. States, counties and municipalities that operate school
districts as part of their own expenditures, rather than as independent school
districts, are set to 0 because local government finance data does not provide this
information.
31
Appendix B: Local Government Education Regressions – Two Way Fixed Effects with County-specific Time Trends
Total
Revenue
Own-Source
Revenue
Intergovernmental
Revenue
Current
Operations
Capital
Outlays
ln(Population)
Declining Units 0.7675*** 1.1347*** 0.4890* 0.5980*** 1.2311
(0.1813) (0.2016) (0.2732) 0.0980 (0.8762)
Stable Units 0.7415*** 1.1848*** 0.8569** 0.8252*** 0.1353
(0.1826) (0.2690) (0.4101) 0.1811 (0.8305)
Growing Units 0.9765*** 1.4211*** 0.5989*** 0.8954*** 1.6803***
(0.0547) (0.0980) (0.0961) 0.0442 (0.2409)
Pct Under 18
Declining Units 1.0497*** -1.2744** 2.4011*** 1.1323*** 0.2994
(0.3798) (0.5802) (0.7984) 0.3281 (1.9342)
Stable Units 2.9049*** 0.2525 5.8002*** 2.5403*** 8.8420***
(0.4395) (0.7691) (0.8839) 0.3943 (2.6188)
Growing Units 2.8128*** 3.4711*** 3.5745*** 2.5047*** 7.8884***
(0.2471) (0.5268) (0.4623) 0.2071 (1.1503)
Other
ln(Median Income) 0.0867 -0.0369 0.2125** 0.0126 0.4919**
(0.0601) (0.0485) (0.1036) 0.0310 (0.2175)
ln(Top Ten Income) 0.1040*** 0.1767** -0.0530 0.0612** 0.7256***
(0.0310) (0.0557) (0.0567) 0.0250 (0.1502)
Poverty Rate -0.2286 0.0271 -0.1181 -0.0607 -1.0818
(0.1577) (0.2334) (0.2601) 0.1118 (0.6936)
Pct BA Degree 0.0221 0.1208 -0.1345 0.0824 0.0861
(0.1324) (0.1643) (0.2426) 0.0755 (0.6754)
Female HH Rate -0.0775 -0.2172 0.0251 -0.0400 -0.4786
(0.0688) (0.1530) (0.0591) 0.0309 (0.7477)
Pct Over 65 -1.0142*** -1.2482** -0.4746 -1.1242*** -2.0351
(0.3407) (0.5483) (0.7001) 0.2867 (1.4828)
Pct White -0.4251*** 0.1190 -0.4260** -0.4634*** -1.0874**
(0.0990) (0.1654) (0.1904) 0.0815 (0.4790)
Right to Work -0.0105 0.1605*** -0.1003*** -0.0200** -0.3007***
(0.0116) (0.0193) (0.0160) 0.0097 (0.0769)
State TELs 0.0060 0.0157 -0.0288*** 0.0063 -0.0834***
(0.0053) (0.0117) (0.0101) 0.0045 (0.0269)
School TELs -0.0093*** -0.0614*** 0.0290*** -0.0038* -0.0297**
(0.0025) (0.0052) (0.0052) 0.0020 (0.0135)
School Finance Reform 0.0235*** -0.0336*** 0.0656*** 0.0169 -0.0176
(0.0026) (0.0047) (0.0046) 0.0022*** (0.0153)
School District Number 0.0212*** 0.0094** 0.0268*** 0.0218 0.0216**
(0.0032) (0.0041) (0.0048) 0.0031 (0.0091)
Constant -9.681*** -61.021*** 16.271*** -20.263*** 42.478***
(1.662) (3.137) (2.891) (1.367) (7.309)
Observations 24,733 24,733 24,731 24,732 24,659
R-squared 0.993 0.981 0.985 0.996 0.834
Number of Units 2,754 2,754 2,754 2,754 2,753
Dependent variables in log form. Cluster-robust standard errors in parentheses. Time fixed effects included.
***p<0.01, **p<0.05, *p<0.1

Private School Choice and Character: More Evidence from Milwaukee

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Private School Choice and Character: More Evidence from Milwaukee
Corey A. DeAngelis, Ph.D.
Patrick J. Wolf, Ph.D.
February 26, 2019
EDRE Working Paper 2019-03
The University of Arkansas, Department of Education Reform (EDRE) working paper series is intended to widely disseminate and make easily accessible the results of EDRE faculty and students’ latest findings. The Working Papers in this series have not undergone peer review or been edited by the University of Arkansas. The working papers are widely available, to encourage discussion and input from the research community before publication in a formal, peer reviewed journal. Unless otherwise indicated, working papers can be cited without permission of the author so long as the source is clearly referred to as an EDRE working paper.
Electronic copy available at: https://ssrn.com/abstract=3335162
Private School Choice and Character: More Evidence from Milwaukee*
Corey A. DeAngelis, Ph.D.
Center for Educational Freedom
Cato Institute
Corey.DeAngelis@gmail.com
ORCID: 0000-0003-4431-9489
Patrick J. Wolf, Ph.D.
Department of Education Reform,
University of Arkansas
pwolf@uark.edu
February 26, 2019
Final for release
*Direct all correspondence to Corey A. DeAngelis, 1000 Massachusetts Ave NW, Washington, DC 20001, Corey.DeAngelis@gmail.com. The content of the report is solely the responsibility of the authors and does not necessarily represent the views of the Cato Institute or the University of Arkansas. The datasets generated during and/or analyzed during the current study are not publicly available due to student privacy but may be available from the corresponding author on reasonable request with permission from the institutional review board. We thank Kathleen Wolf for writing suggestions that improved the quality of the paper. Electronic copy available at: https://ssrn.com/abstract=3335162
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Abstract
We examine the effects of Milwaukee’s school voucher program on adult criminal activity and paternity suits. Using matched student-level data, we find that exposure to the program in 8th or 9th grade predicts lower rates of conviction for criminal activity and lower rates of paternity suits by ages 25 to 28. Specifically, exposure to the MPCP is associated with a reduction of around 53 percent in drug convictions, 86 percent in property damage convictions, and 38 percent in paternity suits. The program effects tend to be largest for males and students with lower levels of academic achievement at baseline.
Keywords: bottom-up reform; school violence; character education; school voucher; private schooling; religious schooling; school choice
JEL Classifications: I28, I20
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Introduction
Private school choice programs are government initiatives that directly or indirectly provide financial support that allows parents to enroll their child in a private school of choice. These programs use government-financed school vouchers, tax-credit financed scholarships, or K-12 Education Savings Accounts to fund access to private schooling largely for students with low incomes or disabilities. Fifty-six private school choice programs are operating in 29 states plus the District of Columbia, enrolling over 482,000 students in 2018-19 (EdChoice, 2019).1
Most evaluations of private school choice programs have examined their effects on standardized test scores. A recent meta-analysis of 19 experimental studies of 11 different programs around the world finds that private school vouchers have null or small positive effects on student achievement (Shakeel, Anderson, & Wolf, 2016). However, test-score outcomes vary significantly across evaluations based on each individual study’s research methodology, academic subject, and age. The achievement effects of vouchers tilt positively in studies that are experimental, focused on reading, and were published prior to 2012. They tilt negatively in studies that are quasi-experimental, focused on math, and were published after 2012 (Wolf & Egalite, 2018). Recent experimental evaluations report negative effects on math scores in the first two years of the D.C. Opportunity Scholarship Program (Dynarski et al., 2017; Dynarski et al., 2018) and negative effects on both math and reading scores in the first two years of the Louisiana Scholarship Program (Mills, 2015; Mills & Wolf, 2017a; Abdulkadiroglu, Pathak, & Walters, 2018) which turned to null after three years (Mills & Wolf, 2017b). Two recent quasi-
1 These totals include “town-tuitioning” voucher programs in the rural areas of Maine, New Hampshire and Vermont but exclude nine tax provisions that merely provide deductions or partial credits for a parent’s personal private school expenses because they are not “programs” per se. Electronic copy available at: https://ssrn.com/abstract=3335162
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experimental evaluations find mostly negative effects of voucher programs on student test scores in Ohio and Indiana over four-year periods (Figlio & Karbownik, 2016; Waddington & Berends, 2018).
Standardized test scores, however, do not fully capture society’s goals for education (Macedo & Wolf, 2004; Zimmer et al., 2009). Tests measure the effects of schools and teachers on the cognitive performance of students; however, schools are also social institutions that aim to improve non-cognitive skills such as grit, persistence, conscientiousness, and social functioning (Arthur & Davidson, 2000; Duckworth, Peterson, Matthews, & Kelly, 2007; Egalite, Mills, & Greene, 2016; Hitt, Trivitt, & Cheng, 2016). While some studies find a link between teachers’ effects on standardized tests and their effects on long-term outcomes (Chetty, Friedman, & Rockoff, 2014), two recent reviews of the literature find that the effects of school choice interventions, schools and teachers on student test scores do not consistently predict the effects of those factors on long-term outcomes such as high school graduation, college enrollment, employment, and health (DeAngelis, 2018; Hitt, McShane, & Wolf, 2018). Improving student non-cognitive character skills can lead to higher levels of educational attainment and better life outcomes as measured by lifetime earnings, employment, and citizenship (Reynolds, Temple, & Ou, 2010).
Do private school choice programs affect students’ character skills? In theory, private school choice programs could improve character skill development through market pressure, strong-culture organizations, and exposure to peers who discourage risky behaviors. In this study, we use student-level data to estimate the effects of exposure to the longest-standing modern-day voucher program in the United States – the Milwaukee Parental Choice Program
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(MPCP) – on adult criminal activity and paternity suits. We find that exposure to the program in 8th or 9th grade is associated with lower rates of conviction for criminal activity and lower rates of paternity suits by the time students are 25 to 28 years old. The benefits associated with program participation tend to be largest for males and students with lower levels of academic achievement at baseline.
Theory
Schools should teach people to be responsible citizens, increase social cohesion, and boost democratic participation (Mann, 1855; Dewey, 1916; Tooley, 2000). Throughout U.S. history, one of the main arguments for allocating additional resources to schooling is to reduce crime (West, 1965). Additional years of educational attainment improve the economic prospects of young adults, providing them with an incentive to stay out of trouble (Rouse, 2005). Crimes have large negative impacts on society. McCollister, French, and Fang (2010) find that each instance of vandalism and robbery costs society $5,457 and $47,500, respectively, in 2016 U.S. dollars. Access to higher quality schools, or more school choices in general, could dissuade young adults from engaging in risky behavior.
Private school choice might improve character skills. Many parents expect schools to shape positively the character of their children (Zeehandelaar & Winkler, 2013; Stewart & Wolf, 2014; Erickson, 2019). When families choose their children’s school, competitive pressure from the fear of losing students may provide an additional incentive for schools to develop the non-cognitive skills of students that parental customers desire (Chubb & Moe, 1988; Friedman, 1997; DeAngelis & Erickson, 2018). Electronic copy available at: https://ssrn.com/abstract=3335162
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What specific mechanisms might schools of choice use to enhance character skills? Schools of choice involve voluntary associations of people attracted by a common set of values who can build social capital and a strong sense of community (Coleman & Hoffer, 1987; Hill, Foster, & Gendler, 1990; Brandl, 1998). Such “voluntary associations not only generate ‘social capital’…they presuppose it.” (Berkowitz, 1996, 47) Sustained exposure to a voluntary, and therefore value-intensive, educational environment should increase student levels of personal responsibility and conscientiousness.
Similarly, when allowed to be autonomous, schools of choice tend to be more distinctive than traditional public schools (Fox & Buchanan, 2014; DeAngelis & Burke, 2017). Students interested in the distinctive mission of their schools and its curricula may be less likely to engage in risky behaviors out of boredom (Wurmser, 1974).
Religious schools have explicit moral commitments to serve the community and develop student character (Bellah et al., 1985; Bryk, Lee, & Holland, 1993; Johnson, 2011; Jeynes, 2012). Although sectarian private schools participating in choice programs tend to be funded at lower levels than neighboring public schools (Wolf & McShane, 2013; Egalite, 2015; Lueken 2018), “sectarian schools are communities generating and dispensing inspiration and nurture that accomplishes much more that money cannot buy.” (Brandl, 2006, 32). Since most schools participating in choice programs are sectarian (e.g. Sude, DeAngelis, & Wolf, 2018), and these religious schools teach students that God always and everywhere is watching and evaluating what they do, private schools of choice might be expected to improve the subsequent behavior of their charges. Electronic copy available at: https://ssrn.com/abstract=3335162
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Finally, because private schools are typically located in more-affluent and lower-crime areas, access to private school choice could decrease risky behaviors simply by relocating students away from negative influences (DeAngelis & Dills, 2018). Peer pressure at more-advantaged schools may discourage the negative activities of students (Akerlof & Kranton, 2002). Similarly, police may be more familiar with rebellious students in public schools simply because public schools are more likely than private schools to have police officers on campus (Shakeel & DeAngelis, 2018).
For the above reasons, we expect that access to a private school through the MPCP improves character skills, leading to fewer risky behaviors that result in criminal convictions and paternity suits. It is possible that private school choice programs have differential effects on a student’s character skills depending on the student’s gender or initial achievement level. Young male adults are more likely to engage in criminal activity than young female adults. Since males are more at risk of negative behavioral outcomes, we hypothesize that exposure to private schooling would have a larger effect on criminal outcomes for males. Since every paternity suit in our sample includes a male and a female, we expect no difference in the effect of the MPCP on paternity suits by gender. Finally, lower achieving students are less likely to feel optimistic regarding their prospects for success in legitimate pursuits and, therefore, a life of crime if more tempting. As with males, we hypothesize that exposure to the MPCP will have a greater effect on this more vulnerable subgroup of lower achievers.
Specifically, we hypothesize:
1. Exposure to the MPCP reduces adult criminal convictions and paternity suits. Electronic copy available at: https://ssrn.com/abstract=3335162
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2. The effects of the MPCP are largest for the most at-risk subpopulations of students:
a. Males experience a larger reduction in criminal outcomes than females;
b. Students with lower levels of academic achievement at baseline experience a larger reduction in both criminal and paternity outcomes than students with higher levels of academic achievement at baseline; and,
c. Male lower-performers will demonstrate the largest programmatic effects of any student subgroup.
Literature Review
The research literatures on the topics of both school choice and crime are extensive. Unfortunately, the intersection between those two robust literatures is minimal.
Few studies of private school choice examine its effects on outcomes besides test scores. Five of six evaluations of the attainment effects of private school choice find that choice increases rates of high school graduation and college enrollment (Cowen et al., 2013; Wolf et al., 2013; Chingos & Peterson, 2015; Chingos & Kuehn, 2017; Chingos, 2018; Wolf, Witte, & Kisida, 2018). In a review, DeAngelis (2017) identifies eleven studies indicating that school choice program participation had null to positive effects on political participation, volunteering, and charitable giving (e.g. Bettinger & Slonim, 2006; Campbell, 2008; Fleming, 2014; DeAngelis & Wolf, 2018). In his review of 21 studies, Wolf (2007) finds generally positive effects of both private and public school choice on the civic outcomes of students. Swanson’s
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(2017) review of eight U.S. studies reports that school-level racial integration is either unaffected or improved by private school choice.
Avoiding the criminal justice system is critical to the life success of low-income urban youth. Criminal records have negative effects on subsequent employment opportunities, especially for young Black men (Freeman, 1987; Pager, 2003; Apel & Sweeten, 2010). Pager, Western and Sugie (2009) randomly assigned criminal records and races to otherwise equivalent resumes in New York City. After sending these resumes to employers, the authors find that criminal records significantly reduce the likelihood that job-seekers are interviewed. The negative effects are larger for Black applicants. Agan and Starr (2017) performed a similar field experiment and find that employer callback rates were 5.1 percentage points lower (about 38 percent lower) for resumes that were randomly assigned a felony conviction. Waldfogel (1994) reports that first-time convictions reduce the likelihood of employment by 5 percentage points and reduce lifetime income by up to 30 percent.
Most studies focusing on schooling impacts on criminal activity ignore school choice, instead evaluating the crime effects of drop-out rates and broad schooling laws (Luallen, 2006; Lochner, 2010; Anderson, 2014). Other studies examine school desegregation’s impacts on crime (Weiner, Lutz, & Ludwig, 2009; Billings, Deming, & Rockoff, 2013), or how educational attainment can affect criminal activity (Machin, Marie, & Vujić, 2011). These evaluations find that higher levels of education do lead to lower crime rates, however, these same studies do not examine the effects of school type. School choice studies tend to ignore crime and crime studies tend to ignore school choice. Electronic copy available at: https://ssrn.com/abstract=3335162
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Only five studies examine the intersection of school choice and crime. In a study of Charlotte-Mecklenburg County, North Carolina, in 2002, Deming (2011) compares the crime rates of the students who won charter school lotteries to the rates of the students who lost lotteries. He finds that winning a charter school lottery significantly decreases the likelihood of a high-risk student committing a crime. Dobbie and Fryer (2015) perform a similar experimental evaluation and find that winning a lottery to attend a public charter school in the Harlem Children’s Zone eliminates the chance of incarceration for males while reducing the likelihood of a teen pregnancy by 59 percent for females. Dills and Hernández-Julián (2011) use national data to determine that a one standard deviation increase in residential school choice is associated with a reduction in juvenile crime of about 40 percent. Brinig and Garnett (2014) examine the systemic effect of Catholic school closings on crime rates in communities, finding that crime tends to increase when Catholic schools in urban areas shut their doors. In contrast, the increased availability of non-religious schools of choice, specifically public charter schools, has no significant effect on crime in the inner-city, they determine. Brinig and Garnett (2014) argue for increased access to private school choice programs to allow more Catholic schools to generate positive communal effects on crime reduction in American cities.
Only one study exists of the effect of a private school choice program on the criminal behavior of young adults (DeAngelis & Wolf, 2016). Using student-level data from a longitudinal evaluation of the MPCP, the authors find that sustained participation in the MPCP reduces the likelihood of a student engaging in criminal activity by age 22 to 25. Because most significant effects in that analysis are dependent on students’ persistence in the choice program, and that persistence might be driven by unmeasured student and family characteristics correlated
Electronic copy available at: https://ssrn.com/abstract=3335162
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with the likelihood of committing crimes, the researchers cannot conclusively rule out post-match selection bias as the reason for their results.
We build on the previous study in at least five important ways: (1) we look up the cumulative record of risky behaviors over three years later than the original study – in the fall of 2018 – when the students were 25 to 28 years old; (2) we use “exposure to the program in 2006” as our variable of interest in an intent-to-treat analysis that is free of post-match selection bias; (3) instead of simply examining the changes in probabilities of being convicted of any crimes, we track the counts of each type of criminal behavior to use a more holistic approach with more analytic power; (4) we include additional categories of outcomes such at the total amount of fees students were assessed by the state and the total number of paternity suits the students experienced by the fall of 2018; and, (5) we examine heterogeneous effects based on gender and initial academic ability.
We proceed by describing the voucher program on which our evaluation is based and the data and analytical procedures we employ. Next, we present the results from the estimation of statistical models that predict different types of character outcomes, including the role of private schooling through the MPCP. We conclude with a discussion of what our results mean for future research on school choice.
Description of the Program
The MPCP launched in 1990 as a pilot program to test the concept of private school vouchers for low-income urban students. Program enrollment was capped at 1.5 percent of Milwaukee Public Schools (MPS) enrollment, or about 500 students, and only seven non-religious private schools Electronic copy available at: https://ssrn.com/abstract=3335162
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were allowed to participate (Witte et al., 2008). Starting in 1996, the enrollment cap was raised repeatedly, until it was eliminated in 2012. Religious schools were permitted to enroll voucher students starting in 1998. These policy decisions, allowing both demand and supply to grow, resulted in the program enrolling 25 percent of all K-12 students in Milwaukee by 2014-15. Charter schooling and open enrollment programs also spread throughout the city. Perhaps because of this proliferation of public and private school choice in Milwaukee, research indicates that competitive pressures in the Milwaukee education sector have led to better test score outcomes for children left behind in traditional public schools (Greene & Forster, 2002; Hoxby, 2003; Carnoy et al., 2007; Chakrabarti, 2008; Greene & Marsh, 2009). These positive competitive effects of school choice may explain why the latest evaluation of the program found only limited evidence of test score gains for actual voucher participants (Witte et al., 2012; 2014).
The MPCP is a government-run school voucher program. To qualify, applicants have to be entering grades K-12 and reside in the city of Milwaukee. Prior to 2012, students had to have a family income at or below 175 percent of the poverty level, an amount slightly below the federal lunch program ceiling, in order to qualify for the program. That same year, the income ceiling was raised to 300 percent of poverty. The students in our study, who all joined the program before the income eligibility ceiling was raised, tend to be disadvantaged relative to the average MPS student regarding family income and initial test scores but advantaged relative to their public school peers in their parent’s level of education (Fleming, Cowen, Witte & Wolf, 2015). Electronic copy available at: https://ssrn.com/abstract=3335162
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Students first enroll in a participating private school of their choosing and then, through the school, apply to the Wisconsin Department of Public Instruction for tuition assistance. This sequencing of events – choice of school first and voucher second – distinguishes the MPCP from other voucher programs in Cleveland, Ohio; Washington, DC; as well as the states of Indiana and Ohio, where students first are awarded vouchers and then choose their private school.
In the baseline study year of 2006, the voucher was worth up to $6,501, about 40 percent less than the average per pupil expenditure in MPS (Costrell, 2008). By 2011, the final year of the original evaluation that produced these data, the voucher maximum had been cut to $6,442 or 57 percent less than the average per pupil expenditure in MPS (McShane et al., 2012). Financial reports indicate that the private schools subsidized their voucher pupils by an average of $962 per student in 2006 (Kisida, Jensen, & Wolf, 2009) and $1,250 in 2011 (McShane et al., 2012).
In general, private schools participating in the MPCP must not charge tuition above the voucher amount for eligible students; however, since 2012, parents of students in grades 9-12 with an income greater than 220 percent of the federal poverty level may be charged additional tuition above the voucher amount. Participating private schools must administer state standardized tests, be accredited by the state within three years of program participation, allow students to opt out of religious activities, require all teachers and administrators to have a teaching license or a bachelor’s degree, and must admit voucher-eligible students on a random basis (EdChoice, 2019).
Data and Matching Procedure
Since the Milwaukee program expanded in 1998, vouchers rarely have been assigned to students Electronic copy available at: https://ssrn.com/abstract=3335162
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via lottery (Cowen et al., 2013). Although schools in the program must admit students by lottery when a specific grade in a school is oversubscribed, schools tend to recruit voucher-eligible students only until that ceiling is reached. Therefore, an experimental evaluation of the MPCP has not been possible since the expansion.
To generate comparable groups for our analysis, we matched MPCP (i.e. voucher) students with MPS students based on grade, neighborhood, race, gender, English Language Learner (ELL) status as well as math and reading test scores (Witte et al., 2008). The census of 801 MPCP students in 9th grade in the fall of 2006 and a randomly-selected sample of 290 MPCP students in 8th grade that year were combined into a total program sample of 1091. Researchers matched those voucher students to the set of MPS students in their same grade within the same neighborhood census tract who also were in the same 5 percent bandwidth of 2006 test scores. The specific MPS student who served as the match for each MPCP student was selected based on the student’s nearest-neighbor propensity score informed by student race, gender, ELL status, and test score.
All but two students in the program sample were successfully matched, resulting in a treatment group of 1089 students exposed to the voucher program in 2006 and a matched group of 1089 highly similar comparison students in MPS in 2006, for a total analytic sample of 2178. The two matched groups of students do not differ regarding most key characteristics. Table 1 suggests that the students participating in the MPCP had higher baseline reading scores and had more highly educated parents than their counterparts; however, the MPCP students had lower baseline math test scores and came from households with lower income levels than their matched
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MPS peers. Put differently, the direction of selection bias, if any exists after the match, is unclear. All observable differences are controlled for in our model estimations.
[Table 1 about here] Education evaluations employing propensity score matching that prioritize “geography” (i.e. neighborhood) tend to closely replicate experimental results in within-study comparisons (e.g. Heckman, Ichimura, & Todd, 1997; Cook, Shadish, & Wong, 2008; Bifulco, 2012). Census tracts define neighborhoods in Milwaukee. Families who live in the same neighborhood tend to share similar unmeasured factors such as motivation and moral values that, if not balanced across our samples, might bias our quasi-experimental analysis of school choice effects (Ahlbrandt, 2013). The prioritization of neighborhood location in our propensity score matching protocol represents an advance in matching techniques in school choice research that is more likely to capture unmeasured values-based factors that otherwise threaten our study’s internal validity.
After students were matched, their parents were surveyed by telephone about important family background data such as income, mother’s and father’s education, and whether both parents lived in the home (Witte et al., 2008). A total of 69 percent of parents in the sample responded, which is a very high response rate for a telephone survey. The rate for MPCP parents was 73 percent while the rate for MPS parents was 66 percent. Response weights in our analysis correct for any baseline differences between the two groups of respondents. For our more complete model estimations we use the subsample of 1506 students whose parents were survey respondents, in order to control for family characteristics that otherwise might bias our results. Electronic copy available at: https://ssrn.com/abstract=3335162
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Our dependent variables were drawn from the Wisconsin Court System Circuit Court Access.2 By law, every criminal charge and conviction in Wisconsin is entered into this searchable public database. Researchers searched for records using student first name, last name and date of birth. The searchers were unaware of each person’s status as a member of the MPCP or MPS sample. We used ten different categories for dependent variables. Each judicial record matched to a student in our sample generated a count of “1” for each category for which it documented a conviction for: a felony, a misdemeanor, a drug-related offense, property damage, disorderly conduct, battery, theft, or a traffic-related offense. The record generated a count of “1” for a paternity suit if that was the subject of the court case. These categories are not all mutually exclusive. Misdemeanors are mutually exclusive of felonies, while traffic crimes are generally mutually exclusive of both. Drug and theft crimes, however, could be felonies or misdemeanors, depending on the severity of the crime. Thus, a single judicial record could produce multiple codings of “1” across the various behavioral indicators, a single “1” or all zeroes (e.g. if it represented a charge but not a conviction). Multiple judicial records for a given student in the study could produce multiple counts of convictions for a single outcome category or multiple counts of “1” across different categories. We also noted all fines (in current dollars) that were assessed. By law, the data were restricted to outcomes for adults age 18 or older. Because we searched the database during the fall of 2018, the students were 25-28 years old when we looked up their records, and thus experienced 7 to 10 years of adulthood in which they might have been convicted of one or more crimes or might have been a party to one or more paternity suits.
2 Wisconsin Court System Circuit Court Access (2017). Retrieved from https://wcca.wicourts.gov/simpleCaseSearch.xsl. Electronic copy available at: https://ssrn.com/abstract=3335162
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Table 2 summarizes our sample of 2178 students and their characteristics. On average, each person in our dataset was convicted of 0.19 felonies, 0.27 misdemeanors, 0.11 drug-related offenses, 0.01 damages to property, 0.07 disorderly conduct offenses, 0.03 batteries, 0.05 thefts, 0.73 traffic-related offenses, and had 0.11 paternity suits. On average, the students were assessed a total of $526 in crime-related fees. With little variation in our dependent variables, it may be difficult to detect actual differences across our comparison groups for most types of crime. In order to reduce the risk of such Type II errors, we include p < .10 as a threshold for marginal statistical significance of any group differences.
[Table 2 about here] Methods
Our basic model 1 uses an ordinary least squares regression approach of the form:
𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑖= 𝛽0+𝛿1𝑀𝑃𝐶𝑃06𝑖+𝜀𝑖𝑡 (1)
where for each outcome of interest (conviction for felonies, misdemeanors, drug-related offenses, property damage, disorderly conduct, batteries, thefts, traffic-related offenses, total fines (in current dollars), and paternity suits), 𝛿1is the difference associated with exposure to MPCP (enrolled in the MPCP in 2006). Each outcome observation is coded as non-negative integer values for each category besides “total fines” because the data are counts of cases. The category for total fines (in dollars) is also non-negative, but is a continuous variable rather than a count. We obtain robust standard errors of 𝜀𝑖𝑡 by clustering the individual errors “i” by census tract “t” because students within the same geographic region tend to be similar on unobservable characteristics that otherwise might generate spatially auto-correlated error terms. As our sample Electronic copy available at: https://ssrn.com/abstract=3335162
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of 2,178 students comes from only 194 different census tracts, clustering the errors increases their size, thereby leading to more conservative program effect estimates.
We use an Intent-to-Treat (ITT) approach, as all of the students in the MPCP group are coded “1” for MPCP regardless of how long they subsequently persisted in the program. Our analysis estimates the effect of mere “exposure” to the MPCP (for whatever duration of time starting in the fall of 2006) on subsequent criminal behavior, further making our estimates conservative. We also use this ITT approach in our analysis because non-random sorting of students across sectors took place after the 2006 baseline match year (Cowen et al., 2012) that otherwise might bias our estimates of the program’s effect.
Our preferred model 2 adds student controls to the estimation. Since we have complete data on all the student control variables, adding those variables does not reduce our analytic sample. Our preferred model takes the form:
𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑖= 𝛽0+𝛿1𝑀𝑃𝐶𝑃06𝑖+𝛽1𝑋𝑖+𝛽2𝑡𝑒𝑠𝑡2006+𝜀𝑖𝑡 (2)
where the outcome and MPCP exposure variables, as well as the error term, are the same as described for model 1. Added to this equation are vector X of student race, gender, and baseline grade (8th or 9th) indicators; and 𝑡𝑒𝑠𝑡2006, a vector of student math and reading test scores in 2006, standardized to have a mean of zero and a standard deviation of one. Because we control for student 2006 test scores, any effect that the MPCP has on improving character skills by boosting student test scores prior to that date would be captured by that variable, making our independent estimate of the effect of the MPCP on character skills conservative.
Model 2 is our preferred vehicle for estimating the effects of the MPCP on student risky behaviors because it controls for student characteristics known to be predictive of irresponsible
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behavior (e.g. academic ability, gender, relative age) while preserving all of the observations in our analytic sample. More extensive statistical models can control for family background variables which also might predict criminal activity, but they bring with them the disadvantage of reducing the size of the analytic sample by more than one-third, thereby decreasing our study power. Since the nearly 800 observations excluded by adding family variables is likely a non-random subgroup of our sample, adding those variables also risks introducing survey non-response bias into our analysis.
With those cautions in mind we also estimate model 3 as a robustness test of our analytically preferred model 2. Model 3 takes the form:
𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑖= 𝛽0+𝛿1𝑀𝑃𝐶𝑃06𝑖+𝛽1𝑋𝑖+𝛽2𝑡𝑒𝑠𝑡2006+𝛽3𝑍𝑖+𝜀𝑖𝑡 (3)
where for each outcome of interest, 𝛿1is the difference associated with exposure to MPCP after accounting for the same vector X of student characteristics and 𝑡𝑒𝑠𝑡2006 described above, but adding vector Z of parent income levels, education levels, churchgoing activity, and whether or not both parents lived at home. The sample size drops to 1385 in the parental controls models because not all parents responded to the surveys. Because of the count nature of our data, we also use Poisson regression and negative binomial regression as robustness checks for all results.
Results
Table 3 presents the results for three different statistical models: the MPCP indicator variable with no control variables, the MPCP indicator with student controls, and the MPCP indicator with both student and parental controls. A negative coefficient represents a reduction in criminal convictions or paternity disputes and therefore a beneficial effect of exposure to the private Electronic copy available at: https://ssrn.com/abstract=3335162
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school choice program. Exposure to the MPCP is correlated with a reduction in 9 of the 10 negative behavior measures. The only effect estimates that are positively signed are for theft and none of those three estimates are even close to statistically significant. The effects of the MPCP on negative behavioral outcomes do vary in statistical significance across the indicators. Exposure to the MPCP has a highly statistically significant effect on reducing the number of drug convictions across all three statistical models. Specifically, for the model with all controls, exposure to the MPCP is associated with a reduction of about 0.1 drug-related offenses. This result is equivalent to around a 90 percent reduction relative to the mean and around an 18 percent of a standard deviation reduction in drug-related offenses. The program also has a statistically significant effect on reducing property damage convictions and paternity suits, at least in the uncontrolled model and the model with student controls. The effects of the MPCP on property damage convictions and paternity suits are not statistically significant in the model with all of the control variables included. That difference appears partly due to a coefficient that is about one-third smaller regarding property crimes and one-sixth smaller regarding paternity suits. The main reason why the property damage and paternity effects are non-significant in the all controls model, however, is that the loss of almost 800 observations leads the standard errors of those estimates, and therefore the p-values, to increase dramatically.
Our preferred model 2 includes the complete sample with student-level controls. Three of the ten results are statistically significant at the p < 0.05 level or better. Specifically, exposure to the MPCP is associated with a reduction of around 0.06 drug-related offenses, 0.01 property damage offenses, and 0.04 paternity suits. These estimates are equivalent to a 53 percent reduction in drug convictions, an 86 percent reduction in property damage convictions, and a 38
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percent reduction in paternity suits. In terms of generalizable programmatic effect sizes, the estimates are equivalent to an 11 percent of a standard deviation reduction in drug-related offenses, an 8 percent of a standard deviation reduction in property damage offenses, and an 11 percent of a standard deviation reduction in paternity suits. Each of these results is robust to Poisson regression and negative binomial regression as alternative functional forms. Our Hypothesis 1 that exposure to private schooling through a choice program reduces subsequent risky behavior is confirmed for three of our 10 outcome measures. For the other seven measures we are left with uncertainty regarding whether or not MPCP exposure had an effect.
[Table 3 about here] Statistically significant control variables behave as expected. Females are less likely to commit all types of crimes and are assessed less in fines than males; however, the number of paternity suits does not vary by gender. Blacks are more likely to commit crimes, but do not receive higher fines and do not have more or less paternity suits than whites, on average. Asians are generally less likely to commit crimes than whites, and they are less likely to go through paternity suits. Students with higher baseline achievement commit fewer crimes and are assessed less in fees but do not have more or less paternity suits as students with lower achievement levels.
The statistically significant parent-level control variables indicate that more-advantaged students are less likely to engage in risky behaviors. Students from families with higher incomes, with higher levels of parental education, and two-parent households are less likely to commit various crimes but do not differ on their number of paternity suits, all else being equal.
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Heterogeneous Effects
We now explore the possibility of heterogeneous effects of the MPCP by initial student characteristics. We interact indicator variables for membership in various student subgroups with the indicator variable for exposure to the MPCP. Doing so allows us to calculate the effect of MPCP exposure on crime and paternity outcomes for specific subgroups of students and also signals which of those subgroup effects, if any, are truly heterogeneous. For example, the effect of the MPCP on a specific crime outcome might be statistically significant for the subgroup of males and not for the subgroup of females, but the difference in those two subgroup effects itself might not be statistically significant. In such cases, we can say with confidence that the program significantly reduced the criminal outcome for males, but we cannot say with confidence that the effect of the program on males was different than the effect of the program on females.
Generally, gender and initial math ability appear to be stronger sources of heterogeneity in the effects of the MPCP on risky behavior than initial reading levels. In our preferred model with student-level controls (Table 4), males exposed to the MPCP commit 0.12 fewer drug-related offenses, 0.02 fewer property damage offenses, and are listed in 0.05 fewer paternity suits than their MPS peers. The difference between the effect of the MPCP on males and its effect on females is statistically significant for drug and property damage convictions largely because females experienced little to no reduction in convictions for those crimes due to MPCP exposure, while the effects for males were substantial. Females exposed to the MPCP experience 0.04 fewer paternity suits than their MPS peers, a 34 percent reduction from the mean. This finding is consistent with, but somewhat smaller than, the experimental finding that females who won a charter school lottery in Harlem Children’s Zone were 59 percent less likely to experience a teen
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pregnancy than females who lost the lottery (Dobbie & Fryer, 2015). As expected, the reductions in paternity suits for males and females due to the MPCP are not statistically different from one another. Students exposed to the MPCP experience about the same decline in paternity disputes regardless of their gender. Finally, the effect of exposure to the MPCP on misdemeanor convictions is different for males compared to females, but we cannot say with confidence that the MPCP reduced misdemeanors for either of those gender subgroups when compared to their subgroup peers.
Both the higher and lower baseline achievement subgroups demonstrate statistically significant effects of the MPCP on reducing negative behavioral outcomes compared to their MPS subgroup peers– four based on initial reading levels and four based on baseline math levels. For reading, however, in only one case was the effect of the MPCP on crime reduction significantly different due to student baseline achievement subgroup. Students in the higher reading subgroup at baseline experienced a large reduction in total criminal fines, averaging nearly $200, compared to their similarly higher reading MPS peers. That subgroup effect of the MPCP was significantly different from the program’s effect on total fines for the lower reading subgroup, which was positive (an average increase of $111) but not statistically significant.
Initial math ability was as strong a source of heterogeneity in MPCP effects as was gender. For two outcomes, thefts and traffic offenses, exposure to the MPCP had a significantly greater effect on reducing negative behavioral outcomes for the higher performing baseline math subgroup than for the lower performing one. Only in the case of paternity disputes did the subgroup results based on initial math performance play out as we hypothesized, as students with lower initial math ability experienced a significantly larger reduction in paternity suits due to the Electronic copy available at: https://ssrn.com/abstract=3335162
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MPCP than did students with higher initial math ability. When we combine gender interactions with initial ability interactions, we see that gender differences appear to drive the results. The subgroups become individually very small, as the total sample is divided into four subgroups; however, males with lower baseline math scores clearly experienced a larger reduction in drug offenses due to exposure to the MPCP than did females with higher baseline math scores. Conversely, males with higher baseline math achievement realized a significantly larger reduction in drug crimes due to the MPCP than females with lower baseline math achievement.
[Table 4 about here] The pattern of heterogeneity in our results based on gender and initial achievement is similar when parent-level controls are added to the statistical model (Table 5). Where there are differences in the crime-suppressing effects of exposure to the MPCP based on gender, males realize a greater benefit than females. The only difference in the effects of the MPCP based on initial reading ability is a bigger reduction in total criminal fines experienced by the higher baseline reading group than by the lower one. Lower math achievers at baseline experience a larger reduction in drug crimes and paternity suites due to exposure to the MPCP than do higher math achievers at baseline. When gender and initial math ability indicators are used to parse the sample into four different subgroups, being male more consistently leads to a bigger reduction in negative behavioral outcomes from experiencing the MPCP than does being a lower math performer at baseline. All statistically significant results are robust to Poisson regression and negative binomial regression except one: the result for property damage crimes is only robust to ordinary least squares regression.
[Table 5 about here] Electronic copy available at: https://ssrn.com/abstract=3335162
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Discussion
Our results suggest that private school choice is associated with either equal or better demonstrated character skills in the long-run. Students who participated in the MPCP are less likely to commit drug and property crimes and experience paternity suits than their peers in MPS, all else being equal. We conducted 10 statistical tests of Hypothesis 1, that even limited exposure to the MPCP would reduce negative behavioral outcomes of young adults. Three of those tests permitted us to reject the null hypothesis of no significant effect, while the other seven did not allow us to reject the null. Our results regarding heterogeneity in those effects based on gender and initial academic ability were more mixed. Our results generally confirmed Hypothesis 2a, that males would experience larger effects than females. Those results generally did not confirm Hypothesis 2b, that lower initial achievers would experience larger effects than higher initial achievers. When initial test score performance mattered, and it seldom did in the case of reading scores, study participants in the lower baseline achievement group sometimes experienced larger effects from the program, as we hypothesized, but participants in the higher baseline achievement group also sometimes benefited more from the school choice experience. When gender and initial math performance were both factored into generating subgroup comparisons, males consistently benefited more than females from exposure to the MPCP, whether they were lower or higher performing at baseline. Our results do not confirm Hypothesis 2c.
An important limitation of our study is that the students examined in the longitudinal evaluation of the program were not randomly assigned vouchers to attend private schools. If our
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baseline matching procedure does not fully establish equivalence on important unobservable characteristics that predict subsequent risky behavior, our results may be merely correlational rather than causal. However, we use a sophisticated matching procedure shown to replicate experimental results in other school choice evaluations (Bifulco, 2012). In addition, the baseline differences that we observe indicate evidence of both positive and negative selection into the MPCP, meaning that the direction of the overall selection bias, if any exists, is unclear.
Much more research on this topic is needed. Only three evaluations of public school choice examine the important outcome of criminal activity. This study is only the second evaluation linking private school choice to adult criminal activity and the first analysis connecting private school choice to paternity suits. Only two of the evaluations linking public school choice to crime use random-assignment and there are no random-assignment studies that connect private school choice to crime. Furthermore, both evaluations linking private school choice to adult crime examine the MPCP, which is a voucher program that differs from other school choice programs in a few important ways. It is the longest-standing modern-day voucher program in the United States. It is arguably the most heavily regulated program in the United States (Stuit & Doan, 2013). It is located in a large urban area that experiences high crime rates relative to the rest of the country, and students are admitted to private schools before they apply for the voucher. For these reasons, the results observed in this study should not be extrapolated with high confidence to other locations. Additional studies of private school choice programs that are different than the MPCP are needed before we can conclude that choice consistently reduces drug crimes, property crimes and paternity suits. Research on exactly how and why Electronic copy available at: https://ssrn.com/abstract=3335162
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parental school choice may reduce the proclivity of students to engage in such undesirable behaviors as young adults would also be especially welcome.
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Table 1: Statistics on Key Covariates of Matched Groups
MPS in 2006
MPCP in 2006
N Female 0.53 0.57* 2178
Black
0.70
0.70
2178 Hispanic 0.18 0.19 2178
Asian
0.04
0.03
2178 White 0.07 0.07 2178
Grade in 2006
8.73
8.74
2178 Math in 2006 0.04* -0.03 2178
Reading in 2006
0.02
0.13***
2178 Parent Completed College 0.12 0.16** 1506
Parent Some College
0.31
0.35
1506 Income over 50k 0.17*** 0.05 1401
Income under 25k
0.54***
0.59
1401
* p < 0.10, ** p < 0.05, *** p < 0.01
Electronic copy available at: https://ssrn.com/abstract=3335162
41
Table 2: Descriptive Statistics of All Variables Used in Analysis
Variable
N
Mean
Std. Dev.
Min
Max Student Characteristics
MPCP 2006
2178
0.50
0.50
0
1 Black 2178 0.70 0.46 0 1
Hispanic
2178
0.18
0.39
0
1 Asian 2178 0.04 0.19 0 1
White
2178
0.07
0.26
0
1 Female 2178 0.55 0.50 0 1
Grade in 2006
2178
8.74
0.44
8
9 Math Z Score 2178 0.00 0.87 -3.13 3.00
Read Z Score
2178
0.07
0.90
-2.97
2.54 Parent Characteristics
Income>50
1401
0.11
0.31
0
1 35<Income<50 1401 0.14 0.35 0 1
25<Income<35
1401
0.18
0.39
0
1 Income>25 1404 0.31 0.46 0 1
Parent HS Grad
1506
0.29
0.45
0
1 Parent Some College 1506 0.33 0.47 0 1
Parent Completed College
1506
0.15
0.35
0
1 Both Parents in HH 1502 0.34 0.47 0 1
Parent Frequent Churchgoer
1500
0.58
0.49
0
1 Outcomes
Felonies
2178
0.19
0.79
0
16 Misdemeanors 2178 0.27 0.96 0 17
Drug Crime
2178
0.11
0.54
0
12 Property Damage 2178 0.01 0.13 0 3
Disorderly Conduct
2178
0.07
0.34
0
4 Batteries 2178 0.03 0.21 0 3
Thefts
2178
0.05
0.35
0
7 Traffic 2178 0.73 1.80 0 21
Fines (in Current Dollars)
2178
526.05
1,843.96
0
37,717.84 Paternity Disputes 2178 0.11 0.37 0 3
Electronic copy available at: https://ssrn.com/abstract=3335162
42
Table 3: Effects of the MPCP on Character (Three Different Statistical Models)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Felonies
Misdems.
Drugs
Property
Disorder
Battery
Thefts
Traffic
Fines
Paternity
1. MPCP (no
-0.023
-0.042
-0.065***
-0.011**
-0.007
-0.009
0.004
-0.112
-45.737
-0.042**
controls)
(0.524)
(0.376)
(0.007)
(0.041)
(0.641)
(0.324)
(0.822)
(0.218)
(0.602)
(0.016)
R-Squared
0.0002
0.0005
0.0036
0.0017
0.0001
0.0005
0.0000
0.0010
0.0002
0.0031
N
2178
2178
2178
2178
2178
2178
2178
2178
2178
2178
2. MPCP
-0.007
-0.032
-0.058***
-0.011**
-0.005
-0.010
0.007
-0.094
-8.599
-0.042**
(Student controls)
(0.846)
(0.473)
(0.009)
(0.039)
(0.735)
(0.306)
(0.695)
(0.292)
(0.920)
(0.015)
R-Squared
0.0629
0.0666
0.0560
0.0152
0.0207
0.0104
0.0112
0.0278
0.0472
0.0127
N
2178
2178
2178
2178
2178
2178
2178
2178
2178
2178
3. MPCP (All
-0.024
-0.042
-0.099***
-0.007
-0.009
-0.008
0.017
-0.093
-27.918
-0.034
controls)
(0.589)
(0.417)
(0.001)
(0.235)
(0.613)
(0.561)
(0.391)
(0.283)
(0.830)
(0.138)
R-Squared
0.0744
0.0966
0.0652
0.0174
0.0485
0.0124
0.0196
0.0397
0.0556
0.0183
N
1385
1385
1385
1385
1385
1385
1385
1385
1385
1385
P-values in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. Results are average marginal effects. All models use ordinary least squares regression with robust standard errors clustered by census tract. Student controls are for race, gender, grade, and baseline math and reading test scores. All controls include student controls and parental income, parental education, whether parents are frequent churchgoers, and whether both parents reside in the household. Coefficients for control variables available from the authors by request. Statistically significant results are robust to Poisson regression and negative binomial regression functional forms.
Electronic copy available at: https://ssrn.com/abstract=3335162
43
Table 4: Heterogeneous Effects (Student Controls)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Felonies
Misdems.
Drugs
Property
Disorder
Battery
Thefts
Traffic
Fines
Paternity
Male
-0.029 -0.071 -0.124*** -0.024**
0.003
-0.017
-0.002
-0.126
-67.074
-0.045**
(0.690) (0.440) (0.010) (0.030)
(0.921)
(0.272)
(0.950)
(0.348)
(0.698)
(0.026)
Female
0.012 -0.000 -0.003 0.000
-0.012
-0.004
0.014
-0.067
39.586
-0.040*
(0.421) (0.996) (0.629) (0.981)
(0.344)
(0.710)
(0.329)
(0.488)
(0.475)
(0.078)
Difference
-0.041 -0.071* -0.121** -0.024**
0.015
-0.013
-0.016
-0.059
-106.66
-0.005
(0.580) (0.093) (0.011) (0.032)
(0.660)
(0.469)
(0.656)
(0.689)
(0.549)
(0.875)
Low Read
-0.022
-0.060
-0.082**
-0.014
-0.019
-0.008
0.004
-0.090 111.601
-0.056***
(0.705)
(0.395)
(0.041)
(0.118)
(0.448)
(0.563)
(0.861)
(0.501) (0.371)
(0.007)
High Read
-0.015
-0.024
-0.045**
-0.008
0.003
-0.012
0.001
-0.131 -197.450**
-0.034
(0.671)
(0.551)
(0.045)
(0.209)
(0.813)
(0.322)
(0.971)
(0.217) (0.038)
(0.201)
Difference
-0.007
-0.036
-0.037
-0.007
-0.022
0.004
0.004
0.041 309.10**
-0.022
(0.920)
(0.650)
(0.429)
(0.538)
(0.439)
(0.828)
(0.893)
(0.812) (0.042)
(0.478)
Low Math
-0.025
-0.036
-0.097***
-0.017*
0.003
-0.020 0.025 0.021
68.686 -0.071***
(0.626)
(0.562)
(0.009)
(0.076)
(0.881)
(0.131) (0.305) (0.860)
(0.517) (0.002)
High Math
-0.010
-0.053
-0.024
-0.004
-0.025
0.004 -0.026 -0.279**
-164.142 -0.013
(0.800)
(0.229)
(0.312)
(0.407)
(0.144)
(0.734) (0.111) (0.020)
(0.158) (0.597)
Difference
-0.015
0.017
-0.073
-0.013
0.028
-0.024 0.051* 0.300*
232.83 -0.059*
(0.822)
(0.808)
(0.102)
(0.246)
(0.327)
(0.170) (0.064) (0.063)
(0.121) (0.059)
Electronic copy available at: https://ssrn.com/abstract=3335162
44
Table 4 (Continued): Heterogeneous Effects (Student Controls) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Felonies Misdems. Drugs Property Disorder Battery Thefts Traffic Fines Paternity
Low Math Male
-0.073
-0.078 -0.208***
-0.035*
0.015
-0.041*
0.025
0.085
37.702
-0.069**
(0.498)
(0.507) (0.009)
(0.074)
(0.736)
(0.066)
(0.557)
(0.681)
(0.859)
(0.015)
High Math Female
-0.001
0.007 -0.004
0.004
-0.019
-0.003
-0.006
-0.137
-59.891
-0.005
(0.957)
(0.815) (0.655)
(0.431)
(0.225)
(0.854)
(0.608)
(0.279)
(0.275)
(0.866)
Difference
-0.072
-0.085 -0.204*
-0.039
0.033
-0.039
0.032
0.222
97.593
-0.063
(0.609)
(0.717) (0.075)
(0.497)
(0.572)
(0.104)
(0.401)
(0.218)
(0.603)
(0.752)
Low Math Female
0.027
0.013 -0.008
0.000
-0.003
-0.001
0.025
-0.034
106.151
-0.075**
(0.234)
(0.717) (0.193)
(0.857)
(0.889)
(0.958)
(0.318)
(0.764)
(0.176)
(0.044)
High Math Male
-0.027
-0.135 -0.052
-0.015
-0.034
0.013
-0.052
-0.461**
-302.485
-0.021
(0.761)
(0.170) (0.274)
(0.183)
(0.307)
(0.486)
(0.101)
(0.025)
(0.222)
(0.487)
Difference
0.054
0.148 0.060*
0.015
0.031
-0.013
0.077
0.427
408.636
-0.054
(0.609)
(0.717) (0.075)
(0.497)
(0.572)
(0.104)
(0.401)
(0.218)
(0.603)
(0.752)
R-Squared
0.0630
0.0670
0.0591
0.0172
0.208
0.0106
0.0114
0.0279
0.0474
0.0127
N
2178
2178
2178
2178
2178
2178
2178
2178
2178
2178
P-values in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. Results are average marginal effects for the defined subgroup. All models use ordinary least squares regression with robust standard errors clustered by census tract. All models control for student race, gender, grade, and baseline math and reading test scores. Coefficients for control variables available from the authors by request. “Low reading” and “low math” refers to students with baseline test scores at or below the 50th percentile. “Difference” indicates the difference between the coefficients located in the two preceding rows. Subgroup effects and differences are shaded in gray if the subgroup effects themselves are significantly different form each other. Statistically significant results are robust to Poisson regression and negative binomial regression. The null result for property damage crime reduction for students with low reading scores is statistically significant at the p < 0.10 level when negative binomial regression is used. The null result for fine reduction for males with high math scores is statistically significant at the p < 0.10 level when negative binomial regression is used.
Electronic copy available at: https://ssrn.com/abstract=3335162
45
Table 5: Heterogeneous Effects (All Controls)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Felonies
Misdems.
Drugs
Property
Disorder
Batteries
Thefts
Traffic
Fines
Paternity
Male
-0.062
-0.074 -0.211*** -0.019*
0.001
-0.008
0.021
-0.162
-133.197
-0.045
(0.508)
(0.472) (0.002) (0.068)
(0.985)
(0.684)
(0.566)
(0.256)
(0.610)
(0.123)
Female
0.007
-0.016 -0.011 0.003
-0.016
-0.008
0.015
-0.038
54.535
-0.024
(0.739)
(0.637) (0.253) (0.394)
(0.249)
(0.637)
(0.425)
(0.708)
(0.549)
(0.434)
Difference
-0.069
-0.058 -0.199*** -0.022**
0.017
0.001
0.006
-0.124
-187.73
-0.021
(0.466)
(0.579) (0.003) (0.028)
(0.665)
(0.970)
(0.878)
(0.470)
(0.481)
(0.611)
Low Read
-0.040
-0.034
-0.120**
-0.003
-0.022
0.006
0.022
-0.092 163.641
-0.056*
(0.576)
(0.708)
(0.029)
(0.741)
(0.447)
(0.750)
(0.542)
(0.519) (0.357)
(0.059)
High Read
-0.032
-0.073*
-0.096***
-0.012*
0.000
-0.024
0.002
-0.108 -298.418*
-0.015
(0.511)
(0.056)
(0.001)
(0.057)
(0.989)
(0.145)
(0.827)
(0.345) (0.063)
(0.639)
Difference
-0.007
0.040
-0.024
0.010
-0.022
0.031
0.019
0.017 462.08**
-0.041
(0.932)
(0.684)
(0.694)
(0.329)
(0.483)
(0.214)
(0.610)
(0.931) (0.043)
(0.300)
Low Math
-0.068
-0.072 -0.155***
-0.012*
-0.012
-0.012
0.031
0.028
50.479 -0.070**
(0.318)
(0.374) (0.005)
(0.081)
(0.668)
(0.489)
(0.335)
(0.839)
(0.741) (0.039)
High Math
0.005
-0.028 -0.048*
-0.001
-0.011
-0.004
-0.011
-0.264**
-194.072 0.007
(0.924)
(0.514) (0.075)
(0.888)
(0.456)
(0.838)
(0.378)
(0.020)
(0.289) (0.817)
Difference
-0.073
-0.044 -0.107*
-0.011
0.000
-0.008
0.042
0.293
244.55 -0.076*
(0.383)
(0.623) (0.084)
(0.303)
(0.989)
(0.746)
(0.196)
(0.126)
(0.273) (0.081)
Electronic copy available at: https://ssrn.com/abstract=3335162
46
Table 5 (Continued): Heterogeneous Effects (All Controls) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Felonies Misdems. Drugs Property Disorder Batteries Thefts Traffic Fines Paternity
Low Math Male
-0.153
-0.108 -0.316***
-0.025*
0.014
-0.026
0.025
0.148
-11.038
-0.080*
(0.279)
(0.502) (0.005)
(0.071)
(0.808)
(0.357)
(0.686)
(0.558)
(0.970)
(0.065)
High Math Female
-0.004
0.009 -0.010
0.007
0.004
-0.019
-0.021
-0.027
-67.114
0.015
(0.881)
(0.823) (0.503)
(0.345)
(0.774)
(0.480)
(0.142)
(0.825)
(0.437)
(0.718)
Difference
-0.148
-0.117 -0.306*
-0.032
0.011
-0.007
0.045
0.175*
56.077
-0.094
(0.271)
(0.970) (0.087)
(0.798)
(0.289)
(0.148)
(0.625)
(0.055)
(0.719)
(0.996)
Low Math Female
0.022
-0.020 -0.003
0.000
-0.028
0.001
0.041 -0.080
116.249
-0.063
(0.408)
(0.696) (0.760)
(0.881)
(0.202)
(0.949)
(0.148) (0.582)
(0.318)
(0.219)
High Math Male
0.010
-0.086 -0.108**
-0.013
-0.035
0.016
0.000 -0.585***
-373.787
-0.003
(0.927)
(0.282) (0.045)
(0.405)
(0.234)
(0.430)
(0.983) (0.003)
(0.354)
(0.934)
Difference
0.012
0.066 0.105*
0.013
0.006
-0.015
0.041 0.505*
490.035
-0.060
(0.271)
(0.970) (0.087)
(0.798)
(0.289)
(0.148)
(0.625) (0.055)
(0.719)
(0.996)
R-Squared
0.0749
0.0969
0.0722
0.0201
0.0486
0.0124
0.0196
0.0400
0.0560
0.0179
N
1385
1385
1385
1385
1385
1385
1385
1385
1385
1385
P-values in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. Results are average marginal effects. All models use ordinary least squares regression with robust standard errors clustered by census tract. All models control for student race, gender, grade, and baseline math and reading test scores. All models also control for parental income, education, whether parents are frequent churchgoers, and whether both parents reside in the household. Coefficients for control variables available from the authors by request. “Low reading” and “low math” refers to students with baseline test scores at or below the 50th percentile. “Difference” indicates the difference between the coefficients located in the two preceding rows. Subgroup effects and differences are shaded in gray if the subgroup effects themselves are significantly different form each other. All statistically significant results are robust to Poisson regression and negative binomial regression except one: the result for misdemeanors for low math males is only robust to ordinary least squares regression.
Electronic copy available at: https://ssrn.com/abstract=3335162

Centrally Planned Schooling is Price-less

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John Merrifield
john.merrifield@utsa.edu

Abstract

In Friedrich von Hayek’s Nobel Prize acceptance speech, he urged increased attention to
‘gardening’ – effort towards improving underlying conditions – and less focus on engineering
better solutions from existing incentive/constraint regimes. This paper explains what ignoring
Hayek’s advice has meant in K-12 education, and what increased attention to ‘gardening’ might
mean for K-12 education; that is, how might we expand the scope for much-needed improvement
through creative introduction of price signals as a cornerstone for deciding what is taught, where,
how, and to whom. Continue reading