Diminishing Corporate Taxes and Critical Shortfalls in State Budgets

Via Center for Effective Government:

WASHINGTON, March 27, 2014—A new report published today by Center for Effective Government and National People’s Action (see PDF) uncovers how the shrinking corporate tax base is driving critical budget shortfalls and service cuts at the state and federal level. The report outlines exactly how much revenue has been lost due to a precipitous decline in corporate income tax rates and an explosion of loopholes. The report shows that since the recession, corporate income tax revenues have shrunk considerably, despite soaring profits, leaving individuals to pick up the slack.

Reference

Center for Effective Government. (2014). The Disappearing Corporate Tax Base.

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Why Inequality Matters: Associations of Inequality with Social Problems

In this post I present a number of graphs depicting the association of inequality with a number of social problems. In part this study was motivated by the work of epidemiologists, Richard Wilkinson and Kate Pickett, documented in their book, “The Spirit Level: Why Greater Equality Makes Society Stronger.” The difference in their work and mine is that Wilkinson and Pickett’s unit of analysis was countries whereas I use U.S. counties as the unit of analysis.

In the graphs that follow each social problem is represented on the vertical axis with the Gini coefficient, capturing inequality, on the horizontal axis. Greater levels of inequality are represented by higher Gini coefficients. Each graph is a scatter plot (each dot represents a county) and a line of best fit is provided with confidence intervals shaded in gray.  At the end of this post is an appendix with a correlation table of the Gini coefficient with each social problem and a reference to the data sources. Recall the caveat: correlations do not necessarily represent causation.

Association of Inequality with Injury Death Rate

Association of Inequality with High School Graduation Rate

Association of Inequality with Air Pollution

Association of Inequality with Teen Birth Rate

Association of Inequality with Percent Violent Crime Rate

Association of Inequality with Percent Children in Poverty

Association of Inequality with Percent Diabetic

Association of Inequality with Percent Excessive Drinkers

Association of Inequality with Percent Long Commute & Drive Alone

Association of Inequality with Percent Alcohol Impaired Driving Deaths

Association of Inequality with Percent Single Households

Association of Inequality with Percent Uninsured

Association of Inequality with Percent Unemployed

Association of Inequality with 'Percent Some College'

Association of Inequality with Percent Smokers

Association of Inequality with Percent Low Birth Weight

Association of Inequality with Percent Severe Housing

Association of Inequality with Percent Physically Inactive

Association of Inequality with Percent Obesity

Association of Inequality with Percent No Emotional Support

Appendix

Correlations
with Gini Coefficient
Variable Gini
pct_obese 0.0705*
pct_smoke 0.0907*
pct_low_birth _rate 0.4454*
pct__phyically_inactive 0.1633*
pct_excessive_drinkers -0.2220*
pct_alcohol_impaired_deaths -0.0478
teen_birth_rate_x100 0.3583*
pct_uninsured 0.3779*
pct_diabetic -0.1492*
high_school_grad_rate -0.2621*
pct_some_college -0.1921*
pct_unemployed 0.2424*
pct_children_poverty 0.5346*
pct_no_emotional_support 0.3450*
pct_sningle_household 0.4379*
violent_crime_rate 0.3512*
injury_death_rate 0.1798*
air_poll -0.0538
pct_severe_housing_problems 0.3834*
pct_long_commute_drive_alone -0.1089*

* Represents .05 level of significance

Data sources:

1) County Health Rankings from Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute , and
2) Gini coefficients (2009-11) from American Community Survey.

Related posts

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Democracy Does Cause Growth

Abstract

We provide evidence that democracy has a significant and robust positive effect on GDP. Our empirical strategy relies on a dichotomous measure of democracy coded from several sources to reduce measurement error and controls for country fixed effects and the rich dynamics of GDP, which otherwise confound the effect of democracy on economic growth. Our baseline results use a linear model for GDP dynamics estimated using either a standard within estimator or various different Generalized Method of Moments estimators, and show that democratizations increase GDP per capita by about 20% in the long run. These results are confirmed when we use a semiparametric propensity score matching estimator to control for GDP dynamics. We also obtain similar results using regional waves of democratizations and reversals to instrument for country democracy. Our results suggest that democracy increases future GDP by encouraging investment, increasing schooling, inducing economic reforms, improving public good provision, and reducing social unrest. We find little support for the view that democracy is a constraint on economic growth for less developed economies.

Reference

Acemoglu, Daron, et al. (2014). Democracy Does Cause Growth. National Bureau of Economic Research. NBER Working Paper No. 20004.

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States Most & Least Dependent on the Federal Government and Worst States to be a Taxpayer

Via WalletHub:

I. States Most & Least Dependent on the Federal Government
Scroll over a state with your cursor to identify its rank.

II. Worst States to be a Taxpayer

WalletHub

III. Correlation Analysis: Tax Rates with Dependency on Federal Government

WalletHub

Note:
Dependency on Federal Government (1 is lowest, 51 is highest)
Tax Rates (1 is lowest, 51 is highest)
Courtesy of WalletHub

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Finally, A Good Social Mobility Study Focusing on Female Mobility

If you follow social mobility studies it won’t come as a surprise to learn the great majority of those studies rely on father-son comparisons, ignoring to a large degree women’s mobility. A much needed intergenerational social mobility study is found in the recent work by Venator and Reeves (2014). Their research compares Baby Boomer women to their parents in terms of wages, hours worked, and where they end up in the income distribution. Six findings are summarized:

1. Today’s working women (henceforth described as “daughters”) have higher wages than their mothers – but do not have higher wages than their fathers. Men have higher wages than both their fathers and their mothers.

2. The poorest women are doing best. 80% of daughters raised in the bottom quintile have higher wages than their fathers did.

3. “Men’s wages remain more important to increasing couples’ family income,” despite “women’s significant generational gains.” Brad Wilcox talks about this finding in greater detail in the Atlantic, arguing that the class gaps in marriage explain why women’s higher individual incomes at the bottom haven’t translated to family incomes at the bottom being higher.

4. Women who grew up in households where their mother did not work actually have the highest family incomes today—but not because they themselves earn more. Daughters’ individual incomes do not vary significantly by mother’s work status, but family income does—suggesting that daughters whose mothers didn’t work have higher earning husbands. (Catherine Rampell discovered this by asking Pew to split out their analyses by mothers’ labor choices.) Perhaps those raised in more traditional settings are more likely to replicate a traditional division of labor?

5. There’s a lot of stickiness at the top and bottom of income distribution—but women suffer more than men at both endsWomen born at the bottom are more likely to remain in the bottom two quintiles than men, but men born at the top are more likely to stay at the top than woman born at the top.

6. More work for women has been good for mobility, particularly for those at the bottom. Pew looked as what would happen to mobility if women’s wages increased as they have over the past forty years, but women’s labor force participation (i.e., hours worked) were the same as in their mothers’ generation. They found that increased work hours resulted in an 11 percentage point increase in upward mobility for women born at the bottom.

Reference

Venator Joanna, and Reeves, Richard V. (2014). Women and Social Mobility: Six Key Facts. Brookings Institution.

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Predicting Low-Birth Weight Outcomes: A Strategy for Intervention

There are some intractable social problems which are extremely difficult to ameliorate but low-birth weight shouldn’t be one of them for a country as rich as ours. It would appear a prudent approach for mediating the adverse effects of low-birth weight outcomes is to know where to look for such outcomes in order to mobilize and deploy sufficient resources to address the problem. In this study I exploit two datasets I merged to understand the association of several county-wide variables with low-birth rates. Before we move to the study’s findings let’s ask why we should be concerned about low-birth weight.

Why is Low-Birth Weight a Major Problem?

The adverse consequences of low-birth weight have been known for a long-time. Low-birth weight outcomes have been correlated with deviant behavior, difficulties in language development, poor academic achievement, lower intelligence, high school dropouts,  hyperkinesis, autism, involvement in childhood accidents, poor cognitive function, slow physical growth, respiratory disease, high blood pressure, diabetes, and heart disease. In addition, women with low income, have low levels of educational attainment, are Black and younger than 17 or older than 35 are more likely to give birth to low-birth weight babies (Bhutta, et al., 2002; Caputo andWallace, 1970;  Hack, et al., 1991; Hack, et al., 1995; Paneth, 1995; March of Dimes, 2014). The first line of defense associated with low-birth weight outcomes should be prevention (Reichman, 2005).

Data, Variables and Hypotheses

The focus of this study is county variations in low-birth weight outcomes in the US. Two datasets were merged: 1) Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, County Health Ranking and, 2) American Community Survey, Demographics – Poverty; Social Determinants of Health – Employment.

With the merged dataset the following variables were available:

Low birth weight variables

The specific research hypotheses are:

  1. The percent low-birth weight outcomes is directly related to the percent of county-wide adults who report no leisure time physical activity.
  2. The percent of low-birth weight outcomes is directly related to the percent of county-wide children who live in single-parent households.
  3. The percent of low-birth weight outcomes is directly related to the percent of county-wide adults who report not getting social/emotional support.
  4. The percent of low-birth weight outcomes is inversely related to county-wide high school graduation rate.
  5. The percent of low-birth weight outcomes is directly related to the county-wide inequality measure, the Gini coefficient. (The Gini coefficent varies between 0, representing complete equality, and 100, representing complete inequality.)
  6. The percent of low-birth weight outcomes is related to regions of the country: South, Northeast, Midwest and West.

Statistical Method

As the dependent variable, low-birth rate, is a continuous variable and the covariates are a mixture of continuous and one categorical variable, the statistical tool of choice is multiple regression.

Findings

The multiple regression produced the following output.

regression output low-birth weight

The full model is statistically significant, Prob > F = 0.0000, as well as all covariates. The model accounts for a large amount, 64%, of the variance in low-birth weights among counties in the United States.

I could spend a few paragraphs interpreting each coefficient with a phrase something like this: With all other variables controlled, a one unit increase in variable x is associated with a y (coefficient) increase/decrease (depending on the sign of the coefficient) in the percent low-birth weight outcomes.

One problem with this approach to interpretation is some of the covariates are measured in different scales. A scale-free method is provided by using elasticity coefficients (Wooldridge, 2002). Below is a table of elasticity coefficients associated with this regression analysis. Elasticity coefficients indicate a proportional change in the dependent variable is associated with a given proportional change in the independent variable.

elasticities - low-birth weight

Controlling for the influence of all other covariates, we can interpret each elasticity coefficient in the following manner:

  1. A 1% increase in the ‘percent of adults whot report no leisure time physical activity’ is associated with a 0.231% increase in babies categorized as low-birth weight. The research hypothesis is supported.
  2. A 1% increase in the ‘percent of children who live in single-parent households’ is associated with a 0.287% increase in low-birth weight babies. The research hypothesis is supported.
  3. A 1% increase in the ‘percent of adults who report not getting social/emotional support’ is associated with a 0.125% increase in low-birth weight outcomes. The research hypothesis is supported.
  4. A 1% increase in ‘Gini coefficient, measuring inequality,’ is associated with a 0.239% increase in low-birth weight babies. The research hypothesis is supported.
  5. A 1% increase in ‘high school graduation rate’ is associated with a 0.145% decrease in low birth weight outcomes. The research hypothesis is supported.

With the regression formula available we can utilize the regression coefficients to predict each county’s expected low-birth weight mean. By sorting the predictions we can identify the top ten counties with the most favorable low-birth weight outcomes and the worst ten counties with the least favorable low-birth weight outcomes.

Ten counties with the most favorable low-birth weight outcomes are:

Lowest low-birth weight counties

Colorado, Utah and Minnesota dominate US counties in producing the best low-birth weight outcomes.

Ten counties with the worst low-birth weight outcomes are:

Ten worst counties with high low-birth babies

Counties in Southern states exhibit some of the worst low-birth weight outcomes. Mississippi has the unenviable distinction of having 6 of the 10 counties with the least favorable low-birth outcomes.

The impact of the Southern region on low-birth outcomes is highlighted in the following  graphs.

Regional Associations with Predicted Low-Birth Weight Outcomes

As the Gini coefficient increases, capturing greater levels of economic inequality, the percent of low-birth outcomes increases in all regions but Southern counties exhibit higher levels of low-birth weight outcomes throughout the Gini coefficient distribution.

Regional Associations with Predicted Low-Birth Weight Outcomes By Percent Single Household

As the percent of children who live in single-parent households increase the percent of low-birth weight babies increase but the regions of the Northeast, Midwest and West exhibit lower predicted low-birth weight means throughout the ‘percent of single household’ distribution.

Regional Associations with Predicted Low-Birth Weight Outcomes By 'Percent No Emotional Support'

An increase in the ‘percent of adults who report not getting social/emotional support’ is associated with an increase in low-birth weight outcomes but again Southern counties exhibit higher means throughout the ‘no social/emotional support’ distribution.

Regional Associations with Predicted Low-Birth Weight Outcomes By Percent Physically Inactive

Low-birth weight outcomes are directly related to the ‘percent of county-wide adults who report no leisure time physical activity’ in all regions of the US but Southern counties have higher predicted low-birth means than the other US regions.

Regional Associations with Predicted Low-Birth Weight Outcomes By High School Graduation Rate

As hypothesized there is an inverse relationship between low-birth weight outcomes and high school graduation rates. While this relationship holds for all US regions we nevertheless observe Southern counties with higher, unenviable low-birth predicted means throughout the graduation rate distribution.

The observant reader will have noticed that the predicted low-birth weight means appear to be very close in the above graphs for the West, Midwest and Northeast regions.  There are differences in the predicted means but are they significantly different?

Let’s begin by using Stata’s ‘contrast’ command to test the overall effect of the factor variable, region_cat.

Contrasting regions against the reference group - South

The above table demonstrates the overall effect of region_cat is significant (P>F = 0.000). We can say that region_cat deserves to be in the model.

What about the contrasts between the other regions? To answer this question we use Stata’s ‘pwcompare’ command, producing the following table.

Pairwise regional contrasts - low birth weights

The first three contrasts duplicate the results we just interpreted. However, the last three contrasts reveal non-significant differences in low-birth weight means between West, Midwest and Northeast regions. The only significant mean difference contrasts are those which involve the South. The other contrasts are not significantly different at an conventionally used level of significance.

Conclusion and Policy Implications

This analysis has demonstrated low-birth weight outcomes can successfully be predicted by county-wide variables. If we can successfully predict where low-birth weight outcomes are more likely to occur it then becomes prudent to mobilize and deploy resources in those areas where known medical and social interventions can mediate the adverse effects of low-birth weight.

Reference

Bhutta, Adnan T., et al. (2002). “Cognitive and behavioral outcomes of school-aged children who were born preterm: A meta-analysis.” Journal of the American Medical Association. Volume 288, Number 6: 728–37.

Caputo, Daniel V.; Mandell, Wallace. (1970). “Consequence of low birth weight.” Developmental Psychology, Vol 3(3, Pt.1), 363-383. doi: 10.1037/h0030030

Hack,Maureen; et al. (1995). “Long-term developmental outcomes of low birth weight infants.” The Future of Children. Vol. 5, No. 1, pp. 176-196.

Hack, Maureen; et al.  (1991). “Effect of very low birth weight and subnormal head size on cognitive abilities at school age.” New England Journal of Medicine. 325:231-237/ doi: 10.1056/NEJM199107253250403

March of Dimes. (2014). Working Together for Stronger, Healthier Babies.

Paneth, Nigel. (1995). “The problem of low birth weight.”  The Future of Children. Vol 5, No. 1: 19–34.

Reichman, Nancy E. (2005). “Low birth weight and school readiness.” The Future of Children. Volume 15 Number 1.

Wooldridge J. M.  (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.

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Where is Income Inequality the Greatest in the US?

Here’s an income inequality map I created from data from Health Indicators Warehouse. It depicts variances in income inequality among the States, captured by the Gini coefficient. The Gini coefficient varies from 0, representing complete equality to 1, representing complete inequality. (In this data the coefficient is multiplied by 100.) The data was entered into an application, “Generate Your Own Map,” from NCHEMS.

Income Inequality Variances by State, 2009-11.png

Here are the 2009-2011 Gini coefficients, sorted from smallest to highest levels of income inequality.

State Gini
Alaska 41.2
Wyoming 41.4
Utah 41.9
Idaho 42.9
Iowa 43.0
New
Hampshire
43.0
Hawaii 43.1
Montana 43.4
Vermont 43.4
Wisconsin 43.4
Delaware 43.8
Maine 43.9
Nebraska 43.9
South
Dakota
43.9
Indiana 44.0
Minnesota 44.1
Washington 44.2
North
Dakota
44.3
Kansas 44.5
Maryland 44.6
Nevada 44.6
Oregon 45.0
Michigan 45.4
Missouri 45.5
Ohio 45.5
Arizona 45.6
Colorado 45.6
Oklahoma 45.8
Virginia 45.9
Pennsylvania 46.1
Arkansas 46.2
West
Virginia
46.2
South
Carolina
46.4
Rhode
Island
46.5
New
Jersey
46.6
New
Mexico
46.6
North
Carolina
46.6
Kentucky 46.7
Illinois 46.8
Tennessee 46.9
Georgia 47.1
Alabama 47.2
Mississippi 47.2
Texas 47.3
California 47.4
Massachusetts 47.4
Florida 47.5
Louisiana 47.7
Connecticut 48.3
New
York
50.0

 

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Who Owns Financial Assets in the United States?

The distribution of financial assets, according to the Board of Governors of the Federal Reserve System 2010 Survey of Consumer Finances, demonstrates the extremely high level of concentration of financial assets among the richest 20%.

Who Owns Financial Assets in the United States

Reference

Board of Governors of the Federal Reserve System.  (2012). 2010 Survey of Consumer Finances.

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For Gender Inequality Deniers, Here’s the Contrary Evidence

The gap between male and female compensation has been in the news this week so I decided to exam the 2012 American Community Survey sample dataset, downloaded from IPUMS USA.  Here are a couple graphs depicting the total income distribution of males and females, aged 24 to 65.

First, here’s the log income distribution.

Comparing Income Totals for Males and Females

It’s clear females are overrepresented at lower levels of the income distribution and underrepresented at higher income levels.

Let’s “zoom in” by forgoing the log distribution and examine the gender gap for incomes less than or equal to $100,000 in the dollar metric.  This allows us to analyze the distribution of total income for approximately 90% of the sample.

Focusing on Total Incomes Less than or Equal to $100,000The earlier interpretation of female overrepresentation at lower incomes and underrepresentation at higher income levels is clearly evident in the above graph.

So, just what’s wrong with equal pay for equal work? If you re not motivated out of a sense of fairness then step up to the plate for your mother, wife, sister, sister-in-law, grandmother, niece, aunt, or girl friend.

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More Employed Private Guards than Teachers and It’s Correlated with Inequality

A colleague of mine, Bob Koop – former President of the University of Northern Iowa, warned us of an inverse relationship between state funding for prisons and higher education funding. He was right.

Now, Bowles and Jayadev (2014) offer evidence indicating the U.S. now employs as many private security guards as teachers.

“And that’s just a small fraction of what we call “guard labor.” In addition to private security guards, that means police officers, members of the armed forces, prison and court officials, civilian employees of the military, and those producing weapons: a total of 5.2 million workers in 2011. That is a far larger number than we have of teachers at all levels.”

They also demonstrate the proportion of “protective services employees” is associated with growing levels of inequality. The following graph depicts the dubious first place position of America.

Inequality and guard labor

On the horizontal axis — inequality, as measured by the Gini coefficient — and the vertical axis — the number of “protective service workers”* employed per 10,000 workers  – the United States leads 16 other developed countries on both counts.

Do high levels of inequality lead to a garrison-type society? There’s no evidence to suggest cause and effect here, but the question begs for an answer.

Reference

Bowles, Samuel,  and Jayadev, Arjun. (2014). One Nation under Guard. New York Times.

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