White House Attitudes toward Fraud: Evidence from 75 ERPs

Judging from their Economic Reports, few Presidents have given much thought to the problems of fraud.  When they do, typically private sector fraud is cited as a reason for government regulation.  Prior to 2019, ERPs rarely included analysis of incentives to prevent fraud, and never explained why those incentives would be different when the victim of fraud is a private entity as opposed to taxpayers.  Does this reflect a (noneconomic) view that fraud is a consequence of bad people rather than poor incentives?

ERPs hardly mentioned fraud before Clinton.  His ERPs cite financial fraud (especially credit card fraud, which had grown with the industry itself) and healthcare providers that fraudulently miscode treatments in order to enhance their receipts from government and other insurance.

George W. Bush has two interesting chapters on "The Tort System" (2004, explaining how the threat of future tort damages is a disincentive for fraud) and, following the Enron scandal, a chapter on "Corporate Governance" (2003).  These are the two exceptions where incentives are noted, although the analysis is not applied to frauds perpetrated against taxpayers.  Bush's ERPs also discuss fraud in the growing ecommerce industry.

The Affordable Care Act was sold on many false pretenses, one of which is that it would be cracking down on fraud.  President Obama's ERP repeated this talking point in 2010, 2011, and 2013 without an analysis of what the ACA was actually doing to incentives to perpetrate fraud or to incentives to prevent it.  Here is a clip of President Obama himself bragging about "cracking down on fraud."

A prime example of what was missing: the fact that the states, which administer eligibility for Medicaid, would have hardly any financial responsibility for the new parts of Medicaid (by no coincidence, the new parts require more effort to police eligibility).  Are we surprised that in reality "Improper Medicaid Payments have Soared Since Obamacare"?  More well known is the "epidemic of identity theft" that followed the opening of ACA insurance applications.

In a chapter about the Economics of Socialism, the 2019 ERP discusses the incentives associated with  "spending other people's money on other people."  On this basis, government health insurance programs are not expected to put much effort into policing fraud -- turning down a legitimate claim makes for political embarrassment whereas quietly paying a fraudulent claim falls on the taxpayer who has no part in managing the plan.  While they brag about "low administrative costs," the government plans are implicitly acknowledging how little effort they put into administration as compared to plans with a profit motive or that must attract voluntary consumers with low premiums.

Although it does not discuss the incentives, the 2016 ERP offers an empirical observation along these lines.  Several pages discuss the lightly regulated "On-Demand Economy," and compliments private industry for innovative ways (especially, rating systems) of reducing fraud against the consumer.

The 2018 ERP included a popular chapter about cyberthreats.  Another half chapter followed in 2019.  The 2021 ERP looked at the role of trade agreements with China in encouraging them to partner with the U.S. in preventing cyber-theft.  It also looked ahead to infrastructure investment, including attention to cyberthreats.

President Biden's economic team may not be in a good position to consider fraud.  So far it has emphasized setting records on metrics like the size of the weekly unemployment benefit, the speed of delivering stimulus payments, and the number of people participating in the programs.  Nigerian criminals have found Biden's appointment for administering federal UI to be an especially incapable gatekeeper.  She will have near veto power over anything Biden's economic team publishes on this subject.

2021 has begun with another epidemic of identity theft, especially in blue states.  President Biden's economic team can help, if they are willing and able.

[Some economists may say that fraud is just a transfer and therefore that policing fraud is a social waste (from a worldwide perspective).  But the criminals also use resources in their craft, not to mention that the funds they steal must be extracted from taxpayers which involves another deadweight cost.] 

Economic Theory in the White House: An Index of 67 Instances in One Year

It is difficult to exaggerate the usefulness of Chicago Price Theory for economic analysis in the White House.  Below is an index of 67 instances that I can remember where Chicago Price Theory was directly and specifically applied to analysis (usually publicly released) of economic issues over a one year time frame.  As an example of what I mean by "directly and specifically," compare Chicago Price Theory's Figure 19-3 to Figure 7-2 in the 2019 Economic Report of the President.

Figure 19-3 from Chicago Price Theory

From the 2019 Economic Report of the President

(the second derivative of the after-AI demand curve is part of the discussion in both sources).

Economic issue analyzed by CEA	CPT pages
ACA employer mandate	75	-	75
Agency Compliance with Circular A-4	131	-	132
Artificial Intelligence and the labor market	120	-	121
Artificial Intelligence and the labor market	132	-	133
Artificial Intelligence and the labor market	147	-	148
Artificial Intelligence and the labor market	176	-	179
Artificial Intelligence and the labor market	182	-	183
Artificial Intelligence and the labor market	186	-	186
Artificial Intelligence and the labor market	189	-	191
Artificial Intelligence and the labor market	195	-	196
CEA's sample of 20 deregulatory actions	7	-	9
CEA's sample of 20 deregulatory actions	59	-	61
CEA's sample of 20 deregulatory actions	131	-	132
CEA's sample of 20 deregulatory actions	135	-	138
CEA's sample of 20 deregulatory actions	140	-	144
Corporate-income taxation	184	-	185
Corporate-income taxation	185	-	186
Corporate-income taxation	210	-	210
Green New Deal	116	-	119
Green New Deal	131	-	132
Health insurance deregulation	59	-	61
Health insurance deregulation	102	-	103
Health insurance deregulation	131	-	132
Health insurance deregulation	150	-	150
HHS Removal of Safe Harbor for Rebates	66	-	72
HHS Removal of Safe Harbor for Rebates	140	-	144
Highly socialist countries	135	-	138
Highly socialist countries	150	-	150
Macro effects of trade policy	32	-	32
Macro effects of trade policy	176	-	179
Measuring Rx drug prices	48	-	55
Measuring Rx drug prices	55	-	57
Medicare for All	157	-	159
Medicare for All	160	-	161
Medicare for All	168	-	170
Medicare for All	176	-	179
Medicare for All	209	-	209
Opportunity Zones	98	-	99
Pandemic Innovation Values	206	-	206
Telecommunications deregulation	7	-	9
Telecommunications deregulation	79	-	81
Telecommunications deregulation	102	-	103
Telecommunications deregulation	140	-	144
Telecommunications deregulation	150	-	150
The "doubling effect" of switching from reg to dereg	106	-	107
The cumulative impact of regulation (conceptual)	116	-	119
The cumulative impact of regulation (conceptual)	120	-	121
The cumulative impact of regulation (conceptual)	131	-	132
The cumulative impact of regulation (conceptual)	132	-	133
The cumulative impact of regulation (conceptual)	135	-	138
The cumulative impact of regulation (conceptual)	147	-	148
The cumulative impact of regulation (conceptual)	176	-	179
The opioid epidemic	7	-	9
The opioid epidemic	44	-	45
The opioid epidemic	66	-	72
The opioid epidemic	74	-	75
The opioid epidemic	128	-	129
The opioid epidemic	131	-	132
The opioid epidemic	135	-	138
The opioid epidemic	204	-	206
USMCA	96	-	98
Wage growth	48	-	55
[redacted regulatory impact analysis]	48	-	55
[redacted regulatory impact analysis]	157	-	159
[redacted regulatory impact analysis]	186	-	188
[redacted trade deregulation]	131	-	132
[redacted trade deregulation]	135	-	138

I suspect that this is historically unusual.  For example, the neoclassical growth model (standard training in Economics PhD programs and on pages 176-196 of Chicago Price Theory) had never been mentioned in an Economic Report of the President until 2018.  In 2018 and 2019 that model was used to address several policy questions, especially those cited above. 

Who recognizes economic history first: politicians or economists?

Figure 1 is the familiar chart showing the “Laffer curve” relationship between a tax rate and the net revenue from the tax.  A small tax on, say, wireless internet service is expected to provide more revenue (point B) than would be obtained without any tax on wireless internet service (point A).  It is conceivable that the wireless internet tax rate could get so high that further increases in the rate actually reduce revenue (point C) as consumers take steps to evade taxation altogether.  At point C, economics gets really interesting because many of the difficult public policy marginal tradeoffs disappear.

When it comes to various taxes in the United States, at least, we economists typically expect that the operative point is B.  E.g., the Federal payroll tax is probably at a point where further increases in the rate would raise at least some revenue, albeit less than static scores that make little distinction between points A and B.

The statutory Federal corporate rate is an interesting case, especially three years ago when it was well above rates elsewhere in the world.  Arguably cutting that rate increased Federal revenue as at point C (combined revenues from payroll, personal income, and corporate income).  But other reasonable experts could opine that point B was and is the operative point for the corporate tax rate.  And even these opposing experts would likely agree that the operative point (B or C) is above point A where the tax is abolished.

My only point here is that we would be at a unique chapter in economic history if a tax were obviously at point C or beyond.  So turn now to Figure 2, especially its point D where the government receives more revenue by abolishing the tax.  This was the case with the Affordable Care Act’s tax on uninsurance (a.k.a., individual mandate tax).

(I cannot say for sure how the path evolves between points A and D, e.g., perhaps the path never crosses above the horizontal axis, but that issue is not important for what follows.)

The first chapter of my ACA book explained what was happening, using the story of Pastor Ben Winslett who described how the ACA “has placed an enormous financial burden on normal, everyday people quite literally forcing us onto government assistance we didn’t need before.”  In other words, the individual mandate penalized people for turning down government assistance!  Mick Mulvaney explains here.

The government saves money by reducing the punishment it imposes on people who turn down subsidies because more people turn down the subsidies.

You don’t have to believe me.  Look at Jonathan Gruber’s 2010 analysis of repealing the individual mandate, where he projected (p. 4) that repealing it would reduce Federal spending by about $46 billion per year, while sacrificing much less than that in terms of mandate collections.  Or the Congressional Budget Office projection that repealing the mandate would reduce Federal spending by about $34 billion per year, while sacrificing much less than that in terms of mandate collections.  I (and the current CEA) think that those two estimates are exaggerated, but if Gruber and CBO stand by their qualitative analysis then all four of us must agree that point D is the operative point.

Having a tax at point D easily makes the highlights of economic history. Neither my book (which focused on the subsidies and the employer mandate), Gruber’s report, nor the CBO’s report put their findings on the individual mandate in the context of a Laffer curve let alone follow up with an estimate of the massive economic damage that comes with pushing a tax down to point D.  It should be no surprise that, in doing the necessary work, the current CEA found massive net benefits of moving from point D to point A even after considering the various benefits of expanding health insurance coverage.

President Trump and Congressional Republicans recognized the historical damage done by the individual mandate well before economists did, even while it is economists who specialize in such matters.  President Trump reached the (important and correct) conclusion sooner because he reasons differently on issues like this.  Simulated annealing is a close analogy that I’ll write about later.  He did not get ahead of us by “playing 3 dimensional chess” or drawing Figure 2: that would be the kind of deductive reasoning that is prevalent in economics and proved slower at reaching the answer.  Many critiques of the President assume that deduction is the only method and thereby entirely miss the point of simulated annealing, which is that he would try both criticizing the mandate and (albeit briefly) praising it and then closely monitor the feedback.  I suspect that members of Congress did something similar (President Obama also recognized -- just privately until he left office -- that there was more to the individual mandate than the technocrats were telling him).

Health regulation is just one area of Federal policy where some of the most interesting economic history is happening now….

Honey, Who Shrank the Economic Pie?

Copyright, TheHill.com

Last week the White House released the latest Economic Report of the President that, following both statute and tradition, begins with a short letter to Congress from President Trump, followed by the detailed annual report of his Council of Economic Advisers.

The period from 2010 should have had relatively rapid growth as the economy recovered from the 2008-09 recession, but it did not. Both the president's letter and the CEA annual report blame the slow growth on federal policy failures during the previous administration.

A lot of research backs up their claim and suggests that higher growth rates are forthcoming if only some of those failures are reversed and not too many new ones are created.

First is the high statutory corporate tax rate that had prevailed for decades prior to 2017 (yes, the "effective rate" was not as high, but a low effective rate is just a symptom of some of the growth-retarding effects of corporate-income taxation.)

The Obama administration agreed that America would benefit if the federal statutory rate were reduced, saying that a reduction would be "as close to a free lunch as tax reformers will ever get."

But they were not enough interested in corporate tax reform to reach a deal with Congress, so the long-overdue rate cut has President Trump's signature on it and is expect to add to economic growth over the next several years.

Second was the so-called federal "stimulus" law of 2009 that was supposed to jumpstart the recovery. But, unlike the stimulus laws in some other countries, such as the United Kingdom, our stimulus did not expand the economic pie by enhancing incentives.

Instead, our stimulus was a redistribution exercise that eroded incentives to work and earn income by expanding food stamps, mortgage subsidies, health insurance assistance and unemployment assistance primarily for people who were unemployed or otherwise had low incomes (the end of the Bush administration did some of this too). The result was less work and less national income for as long as the stimulus lasted.

Third, very soon after the stimulus expired, a new permanent redistribution began to take effect in the form of the Affordable Care Act (ACA). Indeed, the health-insurance assistance provisions of the ACA were presaged in the 2009 stimulus.

As its authors attempt to target assistance to people who they thought needed it most, the ACA unleashes a host of unintended consequences that shrink the economic pie in the process of redistributing it.

Businesses are encouraged to forgo hiring in order to keep their employment below 50 full-time equivalents and to cut workers' hours in order to keep the workweek less than 30 hours. Productive and knowledgeable employees are encouraged to retire early in order to be eligible for taxpayer-funded assistance.

The ACA is also remarkably uneven in its treatment of different sectors, regions and workplace circumstances. Another result is therefore a misallocation of resources away from the most penalized activities to the most favored ones, thereby depressing productivity; i.e., the amount of value that workers create in the marketplace.

Most businesses and households did not react to these incentives because other considerations were dominant, but it only takes a small percent of them who do to make a noticeable dent in the growth rate. If and when the federal government can repeal the ACA or relax its growth-retarding provisions, that will add to the growth rate.

Fourth, other regulations came into effect during the Obama years. Perhaps the leading instance is the 2010 Dodd-Frank financial regulation law. The law is so remarkably complicated that research on its effects will be ongoing for years, and massive complexity is hardly the leading ingredient for economic growth.

But it is likely that new financial regulations have reduced the willingness of banks to lend to small and medium-sized businesses, which further restrains economic activity.

The national economic pie has been smaller due to Obama-era policies, leaving opportunities for subsequent federal policies to enhance economic performance.

Casey Mulligan is a professor of economics at the University of Chicago. His recent research has focused on non-pecuniary incentives to save and work and how the economy affects policy. His two recent books are, "The Redistribution Recession: How Labor Market Distortions Contracted the Economy," and "Side Effects: The Economic Consequences of the Health Reform."

The ACA's Employer Penalty is Distorting Business

Taxes and regulations are known to affect the size distribution of businesses, due to the fact that smaller businesses are less subject to enforcement.  Large informal sectors are an obvious result in developing countries, but measurement challenges have hindered quantifying the size distortions’ impact on developed-country employment and productivity.  This paper uses new and unique data that is readily linked to a specific regulation: the 2010 Affordable Care Act’s (ACA) employer mandate.  The mandate’s size provision took effect in 2015 and is especially interesting, not only due to its notoriety, but because of its bright-line threshold and enforcement by monetary penalty.  This paper quantifies the size incentive of that penalty, develops a framework for combining evidence on size with evidence on voluntary compliance, and uses a new survey of businesses to quantify the number of businesses that changed from large to small as a consequence of the law.

The key size threshold in the ACA is 50 full-time equivalent employees (FTEs), which establishes the legal definition of a “large” business that is subject to the employer mandate.  Momentarily ignoring the distinction between FTEs and total employment, I display in Figure 1 a time series of the share of employment by small businesses, by a 50-total-employees criterion, among private businesses sized 25-99.  The data is sourced from the tables prepared by the Agency for Healthcare Research and Quality from the insurance/employer component of the Medical Expenditure Panel Survey. Both the 2015 and 2016 shares are well outside the range observed in the recent history 2008-14, and in the direction to be expected given that large employers were subject to a new regulation.

Garicano, Lelarge, and Van Reenen (2016) show how the distortionary effects of size-dependent regulations appear muted when the observer uses a different measure of size than regulators do.  This is the case in Figure 1, which looks at total employment as opposed to the full-time equivalents specified by the ACA and has total employment binned rather broadly (25-49 and 50-99).  Both Garicano, Lelarge, and Van Reenen (2016) and Gurio and Roys (2014) therefore obtain size measures that are especially close to regulator measures and find large size distortions in the French economy.  They do not link the distortions to specific regulations, but instead focus on France where there are many size-dependent regulations thought to be binding.  One of their estimation methods is to compare the actual firm size distribution to a Pareto distribution and measure the nonmonotonicity of the actual distribution in the neighborhood of the threshold.

The Mercatus-Mulligan data used in this paper has five measurement advantages.  First, it separately measures full- and part-time employment and therefore can produce good proxies for FTEs.  Second, the size distortion can be linked to a specific and relatively new regulation, which permits a before-after analysis as shown in Figure 1.  Third, voluntary compliance – that is, offering employer-sponsored health insurance (ESI) even when exempt from the mandate – can be measured.  This allows the measurement of size distortions to focus on businesses for which the employer mandate is binding.  Fourth, the survey was not conducted at the corporate level and therefore did not require any corporation’s approval to publish results.  Rather, individuals were confidentially surveyed, and these individuals happened to be managers at businesses.  If the sample aggregate happens to reveal politically-incorrect business practices, such a finding cannot impugn any particular business.  Fifth, the managers of the sample businesses were asked whether and how the law changed their hiring practices, with answers that can be compared to size and compliance.

Before-after comparisons between the Census Bureau business survey and the Mercatus-Mulligan survey show little change in the size distribution of businesses between 2012 and 2016, except among businesses in the total-employment range 40-74.  Among the latter businesses, the employment percentage of those with less than fifty employees has increased from 37 to 45, and this does not count the fact that a number of 49ers reduce employment below 50 full-time-equivalent employees (FTEs) without reducing their total employment below 50.  Annual time series from the MEPS-IC show an extraordinary jump in the employment percentage of those with less than fifty employees, beginning in 2015, which is the same year when the large-employer designation began its 50-FTE threshold.

            The size distortion is closely linked with whether a business offers employer-sponsored health insurance (ESI) to its employees.  Even by comparison with businesses employing fewer than 30 full-time workers, the propensity to offer ESI is low among employers with 30-49 full-time employees.  The size of this dip in the ESI propensity indicates the prevalence of 49er businesses: they do not offer ESI and thereby keep employment low enough to avoid the ACA’s large-employer designation.  The cross-section finding is my second and strongest piece of evidence that the ACA’s employer mandate is pushing a significant number of businesses below the 50-FTE threshold.

My point estimate is that the United States has 38,327 49er businesses that collectively employ 1.7 million people.  This translates to roughly 250,000 positions that are absent from 49er businesses because of the ACA, but the Mercatus-Mulligan sample by itself is not well suited for accurately assessing the average number of positions that the 38,327 49er businesses eliminated.  The sample also indicates that businesses continue to adjust their employment over time.  For example, many of them reported that, because of the ACA, they hire fewer workers or at least fewer full-time workers, but tried not to adjust the situations of their existing employees.  If the ACA and its employer mandate remains in place, perhaps the prevalence of 49er businesses will increase over time.

            By definition, the 49er businesses have less than 50 FTEs and do not offer ESI.  But it appears that a majority of them had been offering it in the prior year.  Employers with 30-49 FTEs are also disproportionately likely to report that they hire less or have shorter work schedules because of the ACA.  This is my third finding pointing toward an economically significant effect of the ACA on the size distribution of businesses.  To my knowledge, this is the first paper to find a business-size distortion that is readily visible in aggregate U.S. data.  It is also remarkable that the distortion can be linked to a specific regulation with a precisely known penalty for violations.

            Individual-based surveys of businesses are rarely used in economics, but that is bound to change as the survey industry is becoming more efficient (i.e., cheaper for the researcher).  It is worth noting the contrast between the Mercatus-Mulligan survey design and in-depth studies of a particular business (e.g., (Einav, Knoepfle, Levin, & Sundaresan, 2014; Handel & Kolstad, 2015)).  The former design has the advantage of representing a wide range of industries and geographic areas.  Moreover, this study is not sponsored by any business and therefore does not require a corporation’s approval for its release.  Corporate approval is a concern for studies of a particular business, especially when the topic involves public-relations-sensitive issues such as distorting business practices to lessen the cost of well-intended federal regulations.  Another dividend from using a professional survey research firm is that every respondent completed the survey.

            This paper does not put its estimates into an equilibrium framework. Future research needs to estimate the number of eliminated positions at 49er businesses that resulted in jobs created at businesses that compete with 49ers in product or labor markets.  To the extent that the employer mandate shifts employment from 49ers to other businesses, future research needs to assess the aggregate productivity loss from the shifts, recognizing that the ACA’s large-employer definition is just a vivid example of a more general pre-existing enforcement phenomenon.  Even without the ACA, businesses are taxed and regulated, and understand that adding to their payroll tends to increase the enforcement of those rules, albeit not discretely at 50 FTEs (Bigio & Zilberman, 2011; Bachas & Jensen, 2017).  One ingredient in such productivity calculations would be the number of positions shifted, which I found to be roughly 250,000.

From the equilibrium perspective, another interpretation of my cross-section finding – the nonmonotonic relationship between ESI and employer size around the threshold – is that businesses below the threshold did not adjust their size but merely dropped their coverage, in which case, I have mislabeled them as 49ers.  Indeed, I find that such businesses are disproportionately likely to have dropped their coverage in the past year.  However, this alternative explanation does not by itself explain why (i) so many businesses were added to the 25-49 (total employment) size category, (ii) so few were added 50-99, or (iii) coverage rates are not particularly low for businesses with less than 30 FTEs.

The implementation of the employer penalty in January 2015 coincides with a sudden slowdown in the post-recession recovery in aggregate work hours per capita, with 2016 national employment about 800,000 below the trend prior to the implementation of the employer penalty (Mulligan, 2016).  This paper’s estimates permit us to gauge the aggregate importance of the 49er phenomenon, not counting the marginal employment impact on non-ESI businesses that continue to employ 50 or more FTEs.  If 250,000 positions were the aggregate employment effect of 49ers (see the equilibrium caveat above), that would be about one third of the recovery slowdown.  Perhaps more important would be the social value of those positions, given that employment and income are substantially taxed by payroll, income, and sales taxes even without the ACA thereby creating a wedge between the positions’ social and private values.  If that wedge were $20,000 annually, that would be $5 billion of lost annual social value, plus the usual Harberger triangle, which is 38,327 businesses in the quantity dimension and up to $68,987 annually in the price dimension (about $1 billion annually).

Stanford University findings on the ACA and the labor market

Stanford Economics Professor Mark Duggan was quoted as concluding that

"While the Affordable Care Act had a significant effect on health insurance coverage, it did not have a substantial effect on the U.S. labor market as many had expected" and

"the Affordable Care Act has not had the negative effect on jobs the law’s critics claimed it would."

He was referring to a working paper distributed by the National Bureau Economic of Research, which states that

“labor market outcomes in the aggregate were not significantly affected.”

Theirs is a working paper and I'm sure that the authors are eager to add data and analysis, so I understand the above conclusions to have been modestly offered. With that said, it is worth recognizing that the above conclusions are not what the working paper shows.

1. Table 4 (the paper's first table on labor market outcomes) shows that the ACA reduced nationwide labor force participation by 349,190 in 2016, plus however much the ACA reduced labor force participation in a geographic area that was fully insured before the ACA, which I call the HFIA (Hypothetically Fully Insured Area).  This effect is economically significant and, when combined with items (2) and (3) below, is easily in line with "the negative effect on jobs that the law's critics claimed it would be."

   [Admittedly, the 349,190 is probably not statistically significant by the usual criteria, but the quotes above are not claiming that either side could be correct. Rather they claim to decisively reject "critics" who made claims right in line with the Duggan-Goda-Jackson point estimate. See below for the derivation of the 349,190]

2. The paper assumes, without much explanation, that the HFIA part of the ACA's impact is zero.  But other work has shown that near-elderly insured people were given a tremendous incentive to retire early.  In other words, basic economics tells us that the HFIA part is likely positive (i.e., in the same direction as the 349,190) and we should not assume it to be close to zero until we have further measurement.

3. The empirical methods in the paper, which emphasize differences among geographic areas such as Medicaid expansion states versus other states, are not designed to detect effects of the employer penalty.  The employer penalty is the same amount throughout the nation.  The penalty creates large labor-market distortions; those distortions that have been measured in other studies have proven to be similar across geographic areas.  Moreover, the employer penalty did not apply until the 2016 coverage year, whereas 8/9 of the working paper's data is before that date. This is an especially serious problem for the low-income population, where the employer penalty in effect has them working 50-60 days per year for the government, on top of the implicit and explicit employment/income taxes they would pay even without the ACA (this fact is nowhere mentioned in the paper). For this reason, the authors' claim than that "lower income individuals were actually incentivized to work more" is especially incredible.

To derive the 349,190, look at the first "Out of the labor force" column of that table.  The first row says that the M variable reduces out of the force by 0.0847 on average for each working-age person in the U.S.  The second row says that the E variable increases out of the labor force by 0.0962.  The mean of the M and E variables are, respectively, 0.073 and 0.086 (p. 11 of the paper). So, relative to the HFIA, their regression says that the U.S. has increased out of the labor force by 0.0021 per working-age person:

0.0021 = 0.073*(-0.0847) + 0.086*(.0962)

To get a number of people, multiply by the number of working age people (difference between these two), and you get 349,190.