Looked at another way, over the last five years, the taxable value of residential and commercial land has risen 25% while the taxable value of ag land has risen 93%.
Of course, when we apply the tax levies of Senate Bill 35, we tax residential property at 2.2 times the rate we tax agricultural land, and we tax commercial land at 4.6 times the ag rate. Multiply those levies by valuations, and we find commercial property bearing 45.2% of the K-12 burden, residential 34.0%, and ag 20.8%.
In 2012, if I’m reading the levies correctly from that year’s SB 49, those levy × valuation burdens would have been 47.8% commercial, 34.6% residential, and 17.7% agricultural. In five years, we’ve slid 2.6 percentage points of K-12 tax burden off commercial property owners and 0.6 percentage points off homeowners and slid them onto ag land owners.
Historical Employment Notes: Monthly unemployment in South Dakota peaks every January: DOL stats since 1990 show that South Dakota off-loads an average of 11,900 jobs from the normal October peak to January hibernation time, leaving an average of 4,300 South Dakotans looking for work in January than were looking in October. Our unemployment rate jumps an average of 1.1 percentage points from October to January.
Looked at from the positives, the numbers of employed and available workers peak every summer, dip when school resumes, resurge a bit at harvest time, then tank in winter.
The problem, according to BEA data, is that our GDP gain is all relative. Our Q3 2016 GDP is actually down 0.3% from Q3 2015. We saw declines in GDP of 9.8% in Q1 and 1.0% in Q2, so as of Q3, we were still in the hole for the year.
Even with low crop prices, agriculture still generated 2.01 percentage points of our Q3 gain, followed by finance and insurance with 1.69 points, wholesale trade at 0.75, durable-goods manufacturing at 0.40, and government (ah, sweet government!) at 0.39. Real estate/rentals and educational services sandbagged us a bit, posting the only sector declines (0.12 and 0.02 points, respectively).
I’ve run the numbers and discovered the following facts about how SB 141 affects the child support table:
Two separated parents making net monthly income of $1,050 or less (that’s $12,600 or less a year) see their child support obligation drop. SB 141 decreases the child support obligation for the poorest parents of two or more children by over 70%.
SB 141 imposes a 10.73% increase on child support obligation on no parents making less than $22,151 a month ($265,812 per year).
There are two exceptions to that statement, one based on two inverted digits in the current table, one based on two inverted digits in SB 141, which I have pointed out to Senator Rusch in hopes of a correcting amendment in committee tomorrow.
The average increase across all income brackets under $20K ranges from 2.72% for parents of six children to 3.31% for parents of one child.
The current table caps child support obligations beyond the $20K bracket. that means parents with monthly net of $21K, $25K, $50K, and so on have the same child support dollar obligation as parents making $20K. SB 141 raises the cap bracket to $30K.
Parents making more than $20K thus see larger increases than parents making less. The average increase in monthly child support obligation for parents netting $20,001–$30,000 a month ranges from 22.29% among parents of five children to 23.01% for parents of three children.
If we average figures for parents netting $2,000 or less a month ($24,000 or less a year), SB 141 lowers the share of net monthly income obliged to chld support.
For parents of one child in that income range, the share drops from 24.34% of monthly net to 23.41%.
For parents of three children in that income range, the share drops from 38.57% to 34.80%.
Almost no parents jointly making less than $3,200 a month ($38,400 a year) see SB 141 increase their child support obligation as a share of their net income by more than half a percentage point.
As made clear in the Child Support Commission’s December 2016 report, SB 141’s changes are based on objective economic analysis. As shown by the numbers above, the new formula spares many parents the full cost-of-living adjustment that a straight reading of the Consumer Price Index would justify.
We know that things are only getting worse under Obamacare. This is about people paying higher premiums every year and feeling powerless to stop it. It’s about families paying deductibles that are so high, it doesn’t even feel like you have health insurance in the first place. And in many parts of the country, as you’ve all heard, even if you want to look for better coverage, you are stuck with one option.
One choice is not a choice—it’s a monopoly. The health care system has been ruined—dismantled—under Obamacare [Speaker Paul Ryan, press conference, 2017.01.04].
With Republicans in charge and Democrats possibly unable to stop a repeal of the Affordable Care Act, it’s easy to retreat from the ACA debate and let Republicans own the real disaster that will follow. But even if we can’t peel away three Republicans to stymie the Trump wing’s plan to blow up the ACA and the deficit, we can still point out that Rounds and Ryan are lying about the ACA. It has made health insurance and health care in the United States better.
Per-person health care spending grew at markedly slower annual rates than they did in the decade before Obamacare. For Medicare recipients, per-person spending actually decreased (except for prescription drugs, which we can go back and talk to George W. Bush about).
Premiums for employer-based health insurance rose 5.6% a year in the decade before the ACA. Workers were seeing their contributions rise 7.2% a year. Because the ACA lowered the number of uninsured people whose costs ended up passed on to policyholders, employer-based insurance premiums grew at only 3.1% a year post-passage of Obamacare. At that rate, a worker making a $1,000 monthly contribution toward family coverage from her employer would be paying $1,238 a month in 2017. At the pre-ACA 7.2% annual rate, that same worker would be contributing $1,627 a month for the same policy. Over ten years, Obamacare saves the typical employer-insured worker $37,000.
Dr. McBride exhorts us to “Tell the whole story” on the Affordable Care Act. In this case, the whole story is that Mike Rounds, Paul Ryan, and Donald Trump are about to kill a program that has insured more Americans and saved us a lot of money. The only people for whom the Affordable Care Act has been a disaster are the Republicans who can’t stand the fact that Obamacare (which probably would have been the same plan a President Romney would have passed) was and is a good program that should be expanded, not killed, and certainly not replaced with the status quo ante.
South Dakota households and individuals on food stamps have trended down each year since 2013. The most recent data from October show about 95,000 South Dakotans on SNAP. That’s one in nine of our neighbors getting our help in putting food on the table.
Record-breaking cold like Sunday’s isn’t stopping folks from moving to South Dakota. According to new Census data posted by Governing, South Dakota is tied with Maine for the 18th-highest net migration rate. For the 12 months ending July 1, 2016, South Dakota saw net migration of 3.0 people per 1,000 population. The top destination states were Florida (16.0 per 1,000), Nevada (14.4) and Oregon (14.0). The biggest losers were North Dakota (–6.2), Illinois (–6.5), and Wyoming (–6.5).
South Dakota had the second-best migration rate (assuming you like folks moving here, which I know some of my neighbors don’t) in the seven-state region. Montana beat us; Minnesota was right behind us:
Over the last five years, net migration has picked up in Montana and Minnesota, tapered off in South Dakota, and plunged in North Dakota and Wyoming (think oil and coal prices). Nationwide, Americans are moving around at the lowest rate on record. 11.2% of Americans moved in the 12 months preceding July 1, 2016.
The 16% of American counties that voted for Clinton generated 64% of American economic activity in 2015. Trump’s third of the map includes far more tiny boxes; I can slide 49 counties over from Clinton’s side and completely cover all 2,650 of Trump’s counties.
I wrote on first viewing that this split between high-output and low-output counties appears to align with the split between new-economy counties and old-economy counties. Donald Trump doesn’t even understand that divide; his business is wheeling-dealing for land and buildings, not providing goods or services. He doesn’t even know what “the cyber” is, let alone how it has fundamentally reshaped economic activity. He doesn’t grasp the global economy; he wants to throttle it and have us retreat back into some fantasy shell of isolationism that will tank the entire U.S. economy and leave other nations free to trade and innovate and grow, leaving us to become the next Portugal. Trump isn’t promising anything to put his lower-GDP counties in a better situation; he’s just promising to drag the bigger-GDP counties down.
Sharing my concern, the Brookings Institution’s Mark Muro and Sifan Liu write that a Presidential focus on boosting the GDP of those low-output counties is fine, if the Administration can look forward and accept change:
Given the election map we revealed, the Trump administration will likely feel pressure to respond most to the desires and frustrations of the nation’s struggling hinterland, and discount the priorities and needs of the nation’s high-output economic base.
On one hand, more attention to the economic and health challenges of rural and small-city Rustbelt America could be welcome, especially if it focuses on the right things: realism about current economic trends, adjustment to change, improving rural education and skills training, and enhancing linkages to nearby metropolitan centers. However, Trump’s promises to “bring back” the coal economy and “bring back” millions of manufacturing jobs (that now don’t exist thanks to automation) don’t speak wisely to real-world trends in low-output America. They look backwards and speak instead to local frustrations [Mark Muro and Sifan Liu, “Another Clinton-Trump Divide: High-Output America vs Low-Output America,” Brookings Institution, 2016.11.29].
Muro and Liu worry that focusing on economic development in lagging rural areas could leave high-output urban areas on their own in sustaining and building the education, research, and infrastructure that makes them successful. I’ve advocated a similar focus in South Dakota economic development: let our big, rich towns take care of themselves while focusing government intervention in places where the free market isn’t meeting economic needs, like the reservations.
But in a way, we already do that. Last March, WalletHub measured state dependency on the federal government, based on federal funding as a percentage of state revenue, the ratio of federal funding to IRS collections in each state, and the share of federal jobs in each state.
Of the ten states most dependent on federal funding and jobs, only New Mexico went for Clinton, by a margin of eight percentage points. The other nine most Uncle Sam-dependent states all went for Trump by double digits (19 points in Missouri to 42 points in West Virginia). Of the ten states least dependent on federal funding and jobs, nine went for Clinton (though it was close in New Hampshire, Minnesota, and Nevada). Only one of those more self-sufficient states, Kansas, went for Trump, by 21 points.
Real Republicans and Libertarians could read these maps and conclude is not that we need to understand the emotions and fears of the Trump voters but that we need to get them off their couches and off the dole and get them contributing to the new economy instead of dreaming that the Trumpist government will give them the things they used to work for.
Related Reading: Loren Collingwood of the Washington Post tests county data and finds a distinct correlation between Trump votes and loss of manufacturing jobs from 2000 to 2014. However, Collingwood finds a slightly stronger correlation between Trump’s vote share and change in Hispanic population over the same period:
I also found evidence consistent with the “racial threat” hypothesis. As shown by the orange dotted line in the graph, Trump’s vote was higher in counties where the number of Latinos has increased significantly since 2000. This suggests that some voters may have supported Trump as a way of expressing white identity in an increasingly diverse nation [Loren Collingwood, “The County-by-County Data on Trump Voters Shows Why He Won,” Washington Post, 2016.11.19].
Collingwood finds even stronger negative predictors of Trump turnout in percentage of blacks, percentage of Hispanics, and, the strongest, percentage of residents with college degrees.
Trump may have played to economic anxiety, but he played more to uneducated white fright.