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  • Apr 11 / 2016
  • 0
Banks, Congress, Culture, Personal Finance, Real Estate

Strategic default and unemployment: What factors affect the likelihood that homeowners will default on their home mortgages?

Buying a new home has long been considered a quintessential part of the American dream. However, the process can be a source of both excitement and stress, as new buyers must balance managing a new property, performing household tasks and coping with mortgage payments. Homeownership can be challenging, as we saw during the mortgage crisis of 2007-2009, when many households found themselves unable to pay their mortgages on newly purchased homes. The crisis dovetailed with the Great Recession and resulted in many foreclosures, leaving lingering effects during the years that followed.

The effects of the crisis are still playing out, with mixed evidence of recovery. U.S. homeownership rates dipped between 2009 and 2015, according to the U.S. Census Bureau. During the fourth quarter of 2009, 67.2 percent of households owned their homes. That number dropped to 63.8 percent in the fourth quarter of 2015. A 2015 report from the Harvard Joint Center for Housing Studies, “The State of the Nation’s Housing, 2015” offers additional insight into the nation’s housing recovery. The report examines falling homeownership rates among various age groups and a growing demand for rental units, especially among individuals aged 45-64 and higher-income households. Meanwhile, an analysis from the Federal Reserve Bank of St. Louis indicates that mortgage delinquency rates have declined considerably since 2010.

In some cases, however, homeowners are refusing to pay their mortgages and allowing their homes to go into foreclosure even when they can afford to pay. This strategy, known as “strategic default,” is generally limited to individuals whose homes have lost value in recent years and, as a result, they owe more on their mortgages than the homes are worth.

A group of researchers led by the Federal Reserve Bank of Atlanta sought to better understand what makes some homeowners more likely to default. In a 2015 working paper for the National Bureau of Economic Research, titled “Can’t Pay or Won’t Pay? Unemployment, Negative Equity, and Strategic Default,” the authors examine the interplay between household finances and mortgage decisions. They add to previous literature on the topic by providing an analysis with more complete data. Whereas prior research relied on aggregate data, such as state unemployment figures, this study uses household-level data from the Panel Study of Income Dynamics (PSID) to assess how job loss, negative home equity and other types of “financial shock” influence homeowner decisions about whether or not to default on mortgages.

The key findings include:

  • More than 30 percent of households that were at least two payments behind on their home mortgage had experienced a loss of employment. The vast majority — 80 percent — of households that had fallen behind in payments “experienced a major shock to their cash flow, including job loss, a severe income loss, divorce, or hospitalization.”
  • Heads of household who had defaulted on their loans exhibited a 21 percent unemployment rate compared to an overall unemployment rate of 6 percent. Spouses in these households had a 31 percent unemployment rate, compared to 13 percent in households that paid their mortgages.
  • Unemployed households with negative home equity — they had a loan-to-value (LTV) ratio of more than 100 percent — had a default rate nearly five times higher than employed households with negative equity. Unemployment has a more pronounced effect when households have more negative home equity.
  • Approximately 19 percent of households that fell into the category of “can’t pay” — meaning that the head of household was unemployed and the household had less than a month’s worth of mortgage payments available in stocks, bonds, or liquid assets – were in default. But the remaining approximately 81 percent of this group managed to remain current on their loans.
  • Strategic default is rare. Less than 1 percent of households that had the ability to pay their mortgages were in default.

The authors note several areas for future research, including a further examination of why households wrestling with unemployment and very limited funds continue to pay their mortgages. The authors state that their research could be used to inform economic policy and improve the process through which mortgage lenders work with homeowners to resolve loans that are in default. “We show that the size of a payment or principal reduction that a lender is willing to offer to a distressed homeowner is increasing in the probability of that borrower defaulting,” the authors state. “Thus, low default probabilities among distressed borrowers reduce the ability of the lender to mitigate foreclosures.”

Related Research: A 2016 study published in the Journal of Housing Economics, “The Perceived Moral Reprehensibility of Strategic Mortgage Default,” examines the conditions under which the public is more and less accepting of defaulting borrowers. A 2015 report from the Joint Center for Housing Studies of Harvard University and Enterprise Community Partners Inc. looks at trends among households that pay more than one-half of their income on rent. A 2014 report from the Congressional Research Center considers how the federal government’s home mortgage interest deduction (MID) varies among states.


Keywords: housing, mortgage, home loan, unemployment, jobless, recession,  homeowner, strategic default

    • Apr 29 / 2014
    • 0
    Budget, Congress, Inequality, Personal Finance, Taxes

    The mortgage interest deduction: Its geographic distribution and policy implications

    Like much of the U.S. tax code, the home mortgage interest deduction (MID) has a complex history. Introduced in 1913, its original purpose was to simplify businesses’ tax returns — almost all interest payments a century ago were business-related, so no distinction was needed between professional and personal costs. More than half of Americans rented at the time, and continued to do so until the creation of the Federal Housing Administration in 1934 and its subsequent mortgage-guarantee program. It was only when suburbanization took root in the 1950s that the MID came to be perceived as an essential part of enabling the “American dream” of home ownership, an association encouraged by presidents and partisans alike. Not bad for an “accidental deduction,” as it is described in a 2010 paper from U.C. Davis.

    Much has changed since 1913, of course, in particular the tax code. All consumer interest was once deductible, but that went away with the 1986 Tax Reform Act, signed by President Ronald Reagan. Yet MID hangs on, in part thanks to fierce support by the National Association of Realtors and other industry groups. But in an age of high unemployment, falling tax receipts and increasing concerns about Social Security and Medicare, the mortgage-interest deduction has become very expensive: According to a 2013 report from the nonpartisan Center on Budget Priorities, the MID cuts into government revenue by roughly $70 billion a year, but “appears to do little to achieve the goal of expanding homeownership.”

    Part of the problem is that the higher one’s tax bracket, the more benefit the deduction provides. And to even claim it, taxpayers must itemize their returns, yet few lower-income Americans do. As a consequence of these and other factors, more than 75% of the MID’s benefits go to households with incomes over $100,000, and often for second homes, according to the CBP. Given the sharp rise in income inequality in the United States, the desire to address the growing federal deficit, and other issues, Congress has shown a willingness to revisit what has been called the “third rail” of the U.S. tax code. In February 2014, Dave Camp, chairman of the House Ways and Means Committee, unveiled a plan that would have cut the mortgage limit in half, to $500,000. While the plan’s reception was chilly, some adjustment to the MID seems inevitable in the long term, particularly within a larger tax reform.

    A 2014 report from the Congressional Research Service (CRS), An Analysis of the Geographic Distribution of the Mortgage Interest Deduction,” looks at variations in the deduction’s use from state to state. The purpose of the report, authored by Mark P. Keightley, is to help Congress better understand the potential impact of any tax-reform plan.

    The study’s findings include:

    • Because the current mortgage-interest benefit is structured as a deduction, higher-income taxpayers gain more than lower-income ones. For example, someone with a 25% marginal tax bracket would get a tax reduction of $2,500 from $10,000 in mortgage interest; however, higher-income individual in the 35% bracket could deduct $3,500 for the same mortgage.
    • Based on figures from the Joint Committee on Taxation, the CRS estimates that the mortgage-interest deduction cut federal tax revenues by $68.5 billion in the 2012 fiscal year. The U.S. per-capita average was $219.

    Mortgage interest expenditure per captia, 2012 (CRS)

    • The states that were the smallest per-capita beneficiaries were Mississippi and West Virginia, just under $90 per person. Residents of Washington, D.C., and Maryland benefited the most, with benefits of $426 and $414 per person, respectively — nearly five times as much.
    • On average, 25% of U.S. taxpayers claim the mortgage-interest deduction. Residents of South and North Dakota had the lowest claim rates, under 15%, while the highest rates were found in Connecticut and Maryland, where approximately a third of those filing taxes deducted mortgage interest costs.
    • Less than half of all U.S. homeowners claim the deduction, 48%. The reasons can include not having a mortgage, mortgage payments too low to make filing worthwhile or living in a state without an income tax. Louisiana, Mississippi, North and South Dakota and West Virginia had the lowest proportions of homeowners claiming the deduction, while those in California, Colorado, Utah and along the Northeast Corridor had the highest claim rates.
    • A related 2013 report from the Pew Center on the States provides some additional specificity on where MID benefits are most frequently claimed and the amount of interest deducted. In particular, there’s a notable “doughnut hole” pattern visible around big cities, indicating that claims in suburbs are both more frequent and larger than those in other areas of the country.
    • The average homeownership rate in the United States was 65% in 2011, varying between 41% and 73%. High rates of ownership were observed in states such as Wyoming, Minnesota and Iowa, while California, Nevada and New York had low rates.
    • While states with higher homeownership rates should expect to see higher claims rates, that was not found to be the case. Factors in state-to-state variability include home prices, state and local taxes and individual income. Ultimately, “all else equal, markets with higher incomes should be expected to have a higher claim rate.”

    The Congressional Research Service Report looks at four policy options considered for the mortgage interest deduction. They are:

    • Retain the current deduction. While the deduction is understandably popular with higher-income homeowners and industry groups and is thought to promote homeownership, that is not supported by the evidence: Research “generally suggests that the deduction does not achieve the often-stated policy objective of increasing homeownership,” yet it costs the federal government nearly $70 billion per year.
    • Eliminate the deduction. “Elimination of the deduction could improve the overall performance of the economy if the deduction is currently leading labor and capital to be allocated to less-productive uses in the owner-occupied housing sector…. Elimination of the deduction would be a step in the direction of creating more uniformity in the tax treatment of various sectors, which would assist in a more efficient allocation of resources across the economy. The increase in federal revenue from eliminating the deduction could also improve the long-term budgetary situation of the United States, implying less reliance on deficits to finance spending.”
    • Limit the deduction. Given the deduction’s oft-stated goal of increasing homeownership, its effectiveness could be increased if it were limited to mortgage amounts more typical of first-time homebuyers. “In 2009, the Congressional Budget Office (CBO) estimated the revenue effect of gradually reducing the maximum mortgage amount on which interest can be deducted from $1.1 million to $500,000 [and found that] this option would raise a total of $41.4 billion between enactment (2013) and 2019.”
    • Replace the deduction with a credit. Because the benefit is a structured as a deduction, it provides more assistance to higher-income homeowners; a credit, on the other hand, would have the same dollar value of all taxpayers regardless of income. The CRS report analyzes five options, all of which limit the deduction to the principal residence. “Four out of the five would allow a 15% credit rate. Three of the five credit options would be nonrefundable. Two of the options would limit the size of the mortgage eligible for the credit to $500,000, while one would limit eligible mortgages to no greater than $300,000 (with an inflation adjustment). Another option would limit the maximum eligible mortgage to 125% of the area median home prices. And still another would place no cap on the maximum eligible mortgage, but would limit the maximum tax credit one could claim to $25,000.”

    “It is important to note that any change to the mortgage interest deduction would likely require careful consideration of how to transition to the new policy so as to minimize disruptions to the housing market and overall economy,” the author writes. “Depending on its design, a policy modification could result in a more evenly distributed benefit to homeowners. The author cites research that suggests that if the deduction were phased out over 15 to 20 years, it would have no effect on demand or prices.

    Related research: A 2013 study from the Peterson Institute for International Economics, “Does High Home-Ownership Impair the Labor Market?” found that increasing rates of home-ownership in a state were eventually followed by higher unemployment. The reasons: lower levels of labor mobility, longer commutes and reduced rates of businesses creation. Also of interest is a 2010 paper from the University of California, Davis, “The Accidental Deduction: A History and Critique of the Tax Subsidy for Mortgage Interest.” It found that the MID had undesirable effects such as “distorting the cost of owner-occupied housing relative to other investments, contributing to overinvestment in the asset class and misallocation of capital stock [and] artificially raising housing prices.”

    Keywords: homeownership, suburbanization, inequality, consumer affairs, land use, sprawl

      • Mar 14 / 2013
      • 0
      Atlanta, abandoned houses (Wikimedia)
      Inequality, Municipal, Personal Finance, Race, Real Estate

      Improving low-income and minority access to mortgage credit after the housing bust

      Although year-over-year levels of U.S. mortgage foreclosures are trending downward, February 2013 saw another increase, with default notices reported on more than 150,000 properties and foreclosure starts beginning on more than 70,000 homes. This news comes despite a 2012 agreement between the major banks and the government that aimed to curb lending abuses and help homeowners; as the New York Times notes, many homeowners are still facing default, despite nominal help from lending institutions.

      The 2007-2008 financial crisis in the United States is often referred to as the “great recession,” the implication being that while severe, it wasn’t as profound an economic contraction as the Great Depression, which lasted from 1929 to 1939. Yet the collapse of the U.S. housing market, beginning in 2006, cut 33% off prices, more than the 31% lost in the 1930s. As of 2011, 23% of Americans had no liquid assets and 23% had houses that were “under water” — worth less on the market than the money owed on the mortgage. This stress has resulted in fewer Americans owning their own homes — by the start of 2012, just 65% did, the lowest level since 1997. Of course, lower-income households and racial minorities have been particularly hard hit.

      A December 2012 report published by the Joint Center for Housing Studies at Harvard University, “Getting on the Right Track: Improving Low-Income and Minority Access to Mortgage Credit after the Housing Bust,” examines the impact of government housing policies on home-ownership rates of low-income and minority individuals and also how the financial crisis affected such disadvantaged communities.

      Key findings include:

      • The U.S. government began to make strong efforts in the 1990s to expand homeownership to minorities. As a result, between 1993 and 2001 home purchase lending to Hispanic borrowers grew by 159% and to African Americans by 93%, “while lending to Whites grew just 29%.”
      • Despite government policies, and the mortgage boom in the 1990s, there was still a significant racial and ethnic gap in homeownership. Although African-Americans experienced a 7% increase in homeownership between 1995 and 2005, their rates of ownership were still 27 percentage points less than that for whites. The gap between Hispanics and Whites was similar, at 26 percentage points.
      • “High-cost lenders disproportionately targeted minority (particularly African-American) borrowers and communities, resulting in a notable lack of prime loans among even the highest-income minority borrowers.”
      • “In 2001, only 70.8% of refinancing for African Americans with incomes above 120 percent of area median, living in predominantly high-income African-American neighborhoods, were prime loans…. In contrast, the share for lower-income white borrowers living in predominantly white lower-income communities was 83.1%.”
      • What actually fueled the growth of low-income and minority lending was a dual mortgage delivery system. The mortgage products these groups were given typically had higher interest rates and less favorable terms than conventional “prime mortgages.”
      • The profitability of subprime loans led to a surge in mortgage-backed securities and the “mass securitization of nontraditional mortgage products.”
      • Lower-income individuals and minorities “bore a disproportionate share of the aftershock” after the housing market collapsed because they were the primary market for subprime loans.

      To help repair the housing market, the authors suggest that U.S. government should focus on eliminating “vestiges of the discriminatory lending practices that played such a prominent role in the build-up to the recent crisis.” They suggest this be done through “developing liquidity that provides broad access to mortgage credit that borrowers understand and have the ability to repay” in which the government’s role is smaller and more defined. In addition, “mortgage markets should be free from the counterparty risks present in large systemically important financial institutions and the problems associated with ‘too big to fail.'”

      A related 2013 report from the Congressional Budget Office, “Snapshot of Guarantees of New Residential Mortgages,” notes that the percentage of federally-guaranteed mortgage loans will drop sharply in coming years under current law, as the effects of the financial crisis continue to subside and the private mortgage market recovers.

      Tags: financial crisis, subprime loans, African-American, Hispanic, Latino

        • Jun 07 / 2012
        • 1
        Personal Finance, Real Estate

        Mortgage distress: How U.S. families are handling savings, mortgages and other debts

        The U.S. mortgage crisis that triggered the Great Recession was caused in part by subprime lending, and researchers have continued to study the dynamics of how such mortgages affect real estate markets and home ownership rates. Other research has examined how families respond to losing their homes.

        A May 2012 report from researchers at the University of Michigan’s Institute for Social Research, “Mortgage Distress and Financial Liquidity: How U.S. Families are Handling Savings, Mortgages and Other Debts,” analyzed data from the Panel Study of Income Dynamics (PSID), which surveyed roughly 9,000 representative households in 2009 and 2011.

        The report’s findings include:

        • In 2009, 37.9% of families were living in rented properties; in 2011, that figure was 36.9%. Overall, this implies that the rate of home ownership stayed roughly constant at 63%.
        • In 2009, among families who were not renting, “19.7% were homeowners and did not have a mortgage. This rate increased in 2011 to 22.3%.”
        • In 2009, 2.2% of families were behind on mortgage payments; this figure had fallen to 1.9% by 2011. Among all families, a “total of 3.5% of families were homeowners and behind on their mortgage payments in either or both 2009 and 2011.” This means that “approximately 4.1 million were homeowners and behind on their mortgage in 2009 and/or 2011.”
        • Conditions seem to be improving: “In 2009, 6% of families stated that they thought it was very likely or somewhat likely that they would fall behind. This rate moved downward by 2011 to 4.6%.”
        • Of those families who stated they were behind on their mortgages in 2009, two years later 19% had become renters, “45.1% stated that they were no longer behind on their mortgage,” and “9.3% [stated] that they were homeowners but no longer had a mortgage.”
        • Credit card and other non-collateralized debt stayed nearly constant between 2009 and 2011 among participating families, but financial liquidity decreased: “As of 2009, 18.5% of families had no liquid assets, and by 2011 this had grown to 23.4% of families.”

        The authors conclude: “Families are making their way through the economic conditions of 2008-2012 and there appear to be some financial improvements, after a decline in the overall rate of home ownership established in recent Census Data…. Many have responded to the economic conditions by modifying their mortgages or simply becoming owners with no mortgage, or voluntarily or otherwise moving from owning to renting. We can see that families with mortgage difficulties in 2009 were more likely to end up as renters in 2011. Looking forward to 2013, we see that there is some modest reduction in the percent of families expecting to experience payment problems.”

        Tags: economy, financial crisis, consumer affairs

          • Dec 22 / 2009
          • 1
          Banks, Finance, Lobbying, Personal Finance, Real Estate

          Case for banning subprime mortgages

          Between 1994 and 2006, subprime lending grew from $35 billion to $600 billion a year in the United States, amounting to 23% of all mortgage dollars lent. The ensuing subprime mortgage market crash led to severe disruptions in the global financial markets.  By the third quarter of 2007, about 25% of subprime loans were either delinquent or in foreclosure.

          A 2008 paper by the Valparaiso University School of Law, “The Case for Banning Subprime Mortgages,” looks at the potential regulation of mortgage interest rates and the restoration of the Federal Housing Administration (FHA) as the primary provider of mortgages to first-time, low and moderate-income home buyers.

          The paper’s key conclusions are:

          • The extent to which subprime mortgages increased homeownership is overstated. Most subprime mortgages were made to existing homeowners to refinance debt. Roughly 1.4 million out of 5 to 6 million first-time buyers obtained financing from the subprime mortgage market.
          • The key welfare costs of subprime lending include 2 million home foreclosures, losses in property value, social and fiscal impact on cities where subprime mortgages were concentrated, and higher interest rates incurred by minorities.
          • A recurrence of the subprime crisis could be prevented by limiting first mortgage interest rates to a reasonable range above prime rates or an appropriate index.

          The author asserts that banning subprime mortgages does not mean excluding borrowers with marginal credit or no credit history from the mortgage market. Rather, a more prudent and less predatory form of mortgage lending should be made available for these communities.

          Tags: economy

            • Sep 19 / 2016
            • 0
            Banks, Business, Personal Finance

            Do payday loans exploit poor people? Research review

            Half of Americans have almost no savings, according to a May 2016 survey by the Federal Reserve. For such people, car trouble or a toothache can trigger financial ruin.

            Payday loans are instant, short-term cash advances against someone’s next paycheck. They can help in emergencies, but can also leave borrowers indebted for years. They target people without credit cards — often those with the worst credit — and charge these riskiest borrowers much higher interest rates. Annualized rates are about 390 percent, according to the Consumer Financial Protection Bureau (CFPB), a federal consumer watchdog. (At that rate, a $1,000 loan would cost over $4,000 to repay after one year.) By contrast, credit card interest rate averages tend to hover between 12 and 20 percent.

            The market for payday loans grew quickly in the 1990s and 2000s. According to a Federal Reserve estimate, almost 11 million Americans use payday loans each year, spending, on average, over $500 in fees.

            States’ attempts to regulate the sector have had limited success. “Confusion reigns as to legal jurisdiction,” note Keith Lowe and Cassandra Ward of Jacksonville State University in a 2016 paper.

            In June 2016, the CFPB proposed a new federal rule that would require lenders such as CashAdvance.com, CashNetUSA, OneClickLoan and MyPaydayLoan to determine customers’ ability to pay back high-cost loans while forbidding them from offering new loans to pay off the old ones.

            According to the CFPB, more than 80 percent of such loans are rolled over within a month — that is, borrowers borrow more money to pay off the principle, circling deeper into debt. For every five borrowers who offer their cars as collateral, one loses the vehicle, the CFPB says.

            Critics argue that the fees are exorbitant and amount to predatory lending. “It’s much like getting into a taxi just to ride across town and finding yourself stuck in a ruinously expensive cross-country journey,” said Richard Cordray, the CFPB’s director, in a June 2016 statement. “Consumers are being set up to fail with loan payments that they are unable to repay.”

            The proposed regulation is still under review and could be challenged in the courts. Groups like the Community Financial Services Association of America are lobbying against the rule with their Credit Strengthens Communities campaign. The Center for Responsible Lending is lobbying for more regulation over the industry. Whatever the ethical concerns, proponents say payday loans fill a much-needed gap in services.

            What the research says

            Researchers are generally split on the impact of payday loans. A 2016 study by Christine Dobridge of the Federal Reserve illustrates the paradox: She finds that payday loans support families during times of extreme misfortune, such as after a natural disaster, “helping households keep food on the table and pay the mortgage.” But in general, “access to payday credit reduces well-being” by encouraging borrowers to over-consume and spend less on such vitals as rent and food.

            Writing in the Review of Financial Studies in 2014, Jonathan Zinman of Dartmouth College and Scott Carrell of the University of California at Davis find payday loans negatively impact job performance and retention in the U.S. Air Force. (Under the 2006 Military Lending Act, active-duty service members and their dependents cannot be charged more than 36 percent; the Obama administration has tried to close some outstanding loopholes.)

            James Barth of Auburn University and colleagues observe that payday lenders congregate in neighborhoods with higher rates of poverty, lower education and minority populations — sustaining concerns that payday lenders target the vulnerable.

            However, Chintal Desai at Virginia Commonwealth University and Gregory Elliehausen of the Federal Reserve find that a Georgia ban on payday loans hurts locals’ ability to pay other debts. They conclude that payday loans “do not appear, on net, to exacerbate consumers’ debt problems” and call for more research before new regulations are imposed.

            Mehrsa Baradaran, a law professor at the University of Georgia, wrote in the Washington Post in June 2016 that the loans can be ruinous, but they fill a “void created by banks,” which don’t make small loans to the poor because they are not profitable. She suggests the Post Office take on public banking with federally subsidized interest rates, much the way Washington already subsidizes or guarantees loans for two things primarily geared toward the middle class: houses and college.

            Other resources:

            Journalist’s Resource has reviewed research on helping disadvantaged consumers access traditional banking.

            Some useful studies:

            “Do State Regulations Affect Payday Lender Concentration?”
            Bartha, James R; et al. Journal of Economics and Business, 2016. doi: 10.1016/j.jeconbus.2015.08.001.

            Abstract: “Ten states and the District of Columbia prohibit payday loan stores, and 31 other states have imposed regulatory restraints on their operations, ranging from limits on fees and loan amounts to the number of rollovers and renewals allowed a borrower. Given the importance of payday lenders to significant segments of the population and the wide variation among state regulatory regimes, our paper examines the extent to which the concentration of payday lenders in counties throughout the country is related to the regulatory environment as well as to various financial and demographic factors. The analysis is based on a unique dataset that has been obtained directly from each state’s appropriate regulatory authority.”

            “Payday Lending, Bankruptcy, and Insolvency.”
            Hynes, Richard. Washington and Lee Law Review, 2012.

            Abstract: “Economic theory suggests that payday lending can either increase or decrease consumer welfare. Consumers can use payday loans to cushion the effects of financial shocks, but payday loans may also increase the chance that consumers will succumb to temptation or cognitive errors and seek instant gratification. Both supporters and critics of payday lending have alleged that the welfare effects of the industry can be substantial and that the legalization of payday lending can even have measurable effects on proxies for financial distress, such as bankruptcy, foreclosure, and property crime. Critics further allege that payday lenders target minority and military communities, making these groups especially vulnerable. If the critics of payday lending are correct, we should see an increase (decrease) in signs of financial distress after the legalization (prohibition) of payday lending, and these changes should be more pronounced in areas with large military or minority populations. This article uses county-level data to test this theory. The results, like those of the existing literature, are mixed. Bankruptcy filings do not increase after states legalize payday lending, and filings tend to fall in counties with large military communities. This result supports the beneficial view of payday lending, but it may be due to states’ incentives in enacting laws. This article tests the effect of a change in federal law that should have had a disparate impact according to the prior choice of state law. This second test does not offer clear support for either the beneficial or detrimental view of payday lending.”

            “For Better and for Worse? Effects of Access to High-Cost Consumer Credit.”
            Dobridge, Christine L. Finance and Economics Discussion Series: Board of Governors of the Federal Reserve System, 2016. http://dx.doi.org/10.17016/FEDS.2016.056.

            Abstract: “I provide empirical evidence that the effect of high-cost credit access on household material well-being depends on if a household is experiencing temporary financial distress. Using detailed data on household consumption and location, as well as geographic variation in access to high-cost payday loans over time, I find that payday credit access improves well- being for households in distress by helping them smooth consumption. In periods of temporary financial distress — after extreme weather events like hurricanes and blizzards — I find that payday loan access mitigates declines in spending on food, mortgage payments, and home repairs. In an average period, however, I find that access to payday credit reduces well-being. Loan access reduces spending on nondurable goods overall and reduces housing- and food-related spending particularly. These results highlight the state-dependent nature of the effects of high-cost credit as well as the consumption-smoothing role that it plays for households with limited access to other forms of credit.”

            “The Effect of State Bans of Payday Lending on Consumer Credit Delinquencies.”
            Desai, Chintal A.; Elliehausen, Gregory. The Quarterly Review of Economics and Finance, 2016. doi: 10.1016/j.qref.2016.07.004.

            Abstract: “The debt trap hypothesis implicates payday loans as a factor exacerbating consumers’ financial distress. Accordingly, restricting access to payday loans would be expected to reduce delinquencies on mainstream credit products. We test this implication of the hypothesis by analyzing delinquencies on revolving, retail, and installment credit in Georgia, North Carolina, and Oregon. These states reduced availability of payday loans by either banning them outright or capping the fees charged by payday lenders at a low level. We find small, mostly positive, but often insignificant changes in delinquencies after the payday loan bans. In Georgia, however, we find mixed evidence: an increase in revolving credit delinquencies but a decrease in installment credit delinquencies. These findings suggest that payday loans may cause little harm while providing benefits, albeit small ones, to some consumers. With more states and the federal Consumer Financial Protection Bureau considering payday regulations that may limit availability of a product that appears to benefit some consumers, further study and caution are warranted.”

            “An Examination of Payday Lenders: Issues Surrounding State and Federal Regulations.”
            Lowe, S. Keith; Ward, Cassandra L. Proceedings of the American Society of Business and Behavioral Sciences, 2016.

            Abstract: “Payday lenders as a source of small dollar, short-term loans has expanded exponentially over the past two decades. Starting out as simple storefront outlets in approximately 200 locations in the early 1990s, the industry grew more than twelve-fold by the end of 2014. While the growth of this payday loan industry is obvious, there is no general consensus on whether the product offered is beneficial to those who borrow through this medium and the industry’s long-term effect upon society. The majority of policies, legislation, and restrictions within the payday loan industry is administered at the state level. Presently, 13 states prohibit payday lenders to operate within their respective state boundaries through various legislation and statutes. Of the 33 states that allow payday loan operations, most restrict them in some manner through maximum interest rates, loan amounts, and payback periods. Beyond state-based legislations, some Federal oversight does exist in governing the payday loan industry. Most of the federal oversight was created through past Congressional action such as the Truth in Lending Act and through governmental agencies such as the Federal Trade Commission. However, federal reach is growing through newly created groups such as the Consumer Financial Protection Bureau. Payday lending continues to evolve beyond traditional geographical boundaries and into areas such as internet-based lenders. This creates an environment in which confusion reigns as to legal jurisdiction. Because of the uncertainty of existing laws and how they apply to the payday lending, evolving legislation will continue into the foreseeable future.”

            “Banks and Payday Lenders: Friends or Foes?”
            Barth, James R.; Hilliard, Jitka; Jahera, John S. International Advances in Economic Research, 2015. doi: 10.1007/s11294-015-9518-z.

            Abstract: “This paper investigates the geographic distribution of payday lenders and banks that operate throughout the United States. State-level data are used to indicate differences in the regulatory environment across the states. Given the different constraints on interest rates and other aspects of the payday loan products, we empirically examine the relationship between the number of payday lender stores and various demographic and economic characteristics. Our results indicate that number of stores is positively related to the percentage of African-American population, the percentage of population that is aged 15 and under and the poverty rate. The number of stores is also negatively related to income per capita and educational levels.”

            “Payday Loan Choices and Consequences.”

            Bhutta, Neil; Skiba, Paige Marta; Tobacman, Jeremy. Journal of Money, Credit and Banking, 2015. doi: 10.1111/jmcb.12175.

            Abstract: “High-cost consumer credit has proliferated in the past two decades, raising regulatory scrutiny. We match administrative data from a payday lender with nationally representative credit bureau files to examine the choices of payday loan applicants and assess whether payday loans help or harm borrowers. We find consumers apply for payday loans when they have limited access to mainstream credit. In addition, the weakness of payday applicants’ credit histories is severe and longstanding. Based on regression discontinuity estimates, we show that the effects of payday borrowing on credit scores and other measures of financial well-being are close to zero. We test the robustness of these null effects to many factors, including features of the local market structure.”

            “The Effect of Payday Lending Restrictions on Liquor Sales.”
            Cuffe, Harold E; Gibbs, Christopher G. Victoria University of Wellington Working Paper, 2015.

            Abstract: “We exploit a change in lending laws to estimate the causal effect of restricting access to payday loans on liquor sales. Leveraging lender- and liquor store-level data, we find that the changes reduce sales, with the largest decreases at stores located nearest to lenders. By focusing on states with state-run liquor monopolies, we account for supply-side variables that are typically unobserved. Our results are the first to quantify how credit constraints affect spending on liquor, and suggest mechanisms underlying some loan usage. These results illustrate that the benefits of lending restrictions extend beyond personal finance and may be large.”

            “‘In a Perfect World It Would Be Great if They Didn’t Exist’: How Australians Experience Payday Loans.”

            Banks, M.; et al. International Journal of Social Welfare, 2014. doi: 10.1111/ijsw.12083.

            Abstract: “In the last few decades, payday lending has mushroomed in many developed countries. The arguments for and against an industry which provides small, short-term loans at very high interest rates have also blossomed. This article presents findings from an Australian study to contribute to the international policy and practice debate about a sector which orients to those on a low income. At the heart of this debate lies a conundrum: Borrowing from payday lenders exacerbates poverty, yet many low-income households rely on these loans. We argue that the key problem is the restricted framework within which the debate currently oscillates.”

            “In Harm’s Way? Payday Loan Access and Military Personnel Performance.”

            Zinman, Jonathan; Carrell, Scott. Review of Financial Studies, 2014. doi: 10.1093/rfs/hhu034.

            Abstract: “Does borrowing at 400% APR do more harm than good? The U.S. Department of Defense thinks so and successfully lobbied for a 36% APR cap on loans to servicemen. But existing evidence on how access to high-interest debt affects borrowers is inconclusive. We estimate effects of payday loan access on enlisted personnel using exogenous variation in Air Force rules assigning personnel to bases across the United States, and within-state variation in lending laws over time. Airmen job performance and retention declines with payday loan access, and severely poor readiness increases. These effects are strongest among relatively inexperienced and financially unsophisticated airmen.”

            “Payday Loans and Consumer Financial Health.”
            Bhutta, Neil. Journal of Banking & Finance, 2014. doi: 10.1016/j.jbankfin.2014.04.024.

            Abstract: “The annualized interest rate for a payday loan often exceeds 10 times that of a typical credit card, yet this market grew immensely in the 1990s and 2000s, elevating concerns about the risk payday loans pose to consumers and whether payday lenders target minority neighborhoods. This paper employs individual credit record data, and census data on payday lender store locations, to assess these concerns. Taking advantage of several state law changes since 2006 and, following previous work, within-state-year differences in access arising from proximity to states that allow payday loans, I find little to no effect of payday loans on credit scores, new delinquencies, or the likelihood of overdrawing credit lines. The analysis also indicates that neighborhood racial composition has little influence on payday lender store locations conditional on income, wealth and demographic characteristics.”

            “Can Voluntary Price Disclosures Fix the Payday Lending Market?”
            Hawkins, Jim. Harvard Business Law Review, 2016.

            Abstract: “This response discusses Eric J. Chang’s article, ‘www.PayDayLoans.gov: A Solution for Restoring Price-Competition to Short-Term Credit Loans.’ It offers some evidence from recent empirical research to suggest that the federally operated online exchange that Chang proposes for payday lending markets is unlikely to succeed in facilitating price competition. It argues that lenders are unlikely to voluntarily participate in the exchange and that, even if they did, many borrowers are unlikely to use the exchange.”


            Tags: finance, borrowing, loans, poverty, usury, predatory lending, alternative banking

              • Jun 26 / 2015
              • 0
              Gender, Public Health

              Same-sex marriage and big research questions behind the debate: Useful studies

              After years of growing support for gay marriage at the state level, on June 26, 2015, the U.S. Supreme Court ruled that the Constitution guarantees the right to same-sex marriage throughout the United States. Prior to the ruling, 36 states and the District of Columbia authorized gay marriage. The favorable ruling from the Court compels all 50 states to do so.

              According to a UCLA School of Law analysis of preliminary 2014 data, there are an estimate 350,000 married same-sex couples in the United States, a figure that may have doubled since 2013. The world of academic research provides some additional ideas for ways of approaching questions relating to same-sex marriage — and fresh ways of looking at these issues.

              While more than 600 studies relating to LGBT issues have been funded by the National Institutes of Health over the past 25 years, better data and more research continue to be needed, experts say, to derive stronger conclusions on a variety of questions. Research can also provide insights on the deeper structural forces that are shaping the political debate.

              Below is a sampling of research questions and links to associated papers, with both human-interest and political angles, that can help analysts and reporters go beyond the headlines:

              • Same-sex couples and their families. A 2013 literature review by UCLA’s Mignon R. Moore and Michael Stambolis-Ruhstorfer, “LGBT Sexuality and Families at the Start of the Twenty-First Century,” takes a sweeping look at the state of research, which they say has largely focused on four themes: “who counts as family and how/whether changing definitions of family incorporate households formed by lesbian, gay, bisexual, and transgender (LGBT) people; the biological, social, and legal obstacles that influence family formation for this population; the outcomes for youth raised with lesbian or gay parents; and family dynamics, relationship quality, and relationship dissolution in same-sex couple and transgender partner households.”
              • Health benefits. The University of Minnesota’s Gilbert Gonzales and Lynn A. Blewett published a 2014 analysis in the American Journal of Public Health that examines the relationship between state-level policies and employer-sponsored insurance for same-sex partners. Also see the authors’ related study on the implications for children of same-sex parents. (Overall issues of health status were comprehensively reviewed by the U.S. Institute of Medicine in 2011.)
              • What are the tax effects? A 2014 study published in the Journal of Policy Analysis and Management, “Revisiting the Income Tax Effects of Legalizing Same-Sex Marriages,” provides the first comprehensive look at this issue. New data sources allow the authors — James Alm of Tulane, Sebastian Leguizamon of Vanderbilt, and Susane Leguizamon of the University of Kentucky — to use household level information including income, number of children and mortgage payments, to construct their estimates.
              • Children of same-sex parents. A significant volume of social science and psychological research continues to be published examining the question of how a child’s general well-being is affected by same-sex caregivers versus those growing up in a more traditional family. (The American Sociological Association recently reviewed the related research toward an amicus brief for a U.S. Supreme Court case.)
              • Same-sex parents and differences in childcare. How do gay or lesbian parents differ in their approaches, versus heterosexual parents? There are many ways to examine the question. In a 2015 study published in Demography, Kate C. Prickett and Robert Crosnoe at the University of Texas at Austion and Alexa Martin-Storey look at differences in time spent with children. They find “few differences between same- and different-sex couples in child-focused time use.”
              • What do survey data hide? Polls show a remarkable historical trend of growing support for same-sex couples. But how much do we really know? A 2013 paper for the National Bureau of Economic Research, “The Size of the LGBT Population and the Magnitude of Anti-Gay Sentiment are Substantially Underestimated,” looks at the possibility that discrimination may be more pervasive than previously understood — and that the extent of the U.S. gay population remains under-counted. Researchers Katherine B. Coffman and Lucas C. Coffman of Ohio State University and Keith M. Marzilli Ericson of Boston University provide insights using a “veiled report” survey method.
              • Public opinion data and anti-gay marriage arguments. Those who oppose same-sex marriage frequently cite societal opinion and underlying values as the basis for their views. Brian Powell and Natasha Yurk Quadlin of Indiana University and Oren Pizmony-Levy of Columbia University argue that survey data frequently do support certain kinds of legal arguments advanced by opponents.
              • Why states legalize gay marriage. It’s not just one overriding cause or factor, says Rebekah Herrick of the Oklahoma State University. She draws on a wealth of new scholarship to examine geographical “policy diffusion,” the presence of gay state legislators, local attitudes and even the way judges are appointed or elected.


               Keywords: gay issues, civil rights, same-sex unions, LGBT

                • Apr 22 / 2015
                • 0
                Hackathon (llnl.gov)
                Internet, Municipal, News Media

                Open data, government and citizen perceptions: First national survey, by the Pew Research Center 2015

                The push for open government and open data by federal officials, as well as authorities across many states and cities, can seem an unmitigated good. Talk to journalists, however, and there are myriad areas where they believe government at all levels is still being less than transparent — and less than helpful in revealing facts that the public is entitled to know.

                Scholars who study transparency initiatives point out the ambiguities inherent in many transparency projects, the modest real outcomes in most cases, and even the potential downsides of “naked government” or the possibility of using the banner of transparency to whitewash or conceal. For example, the Obama administration has said it has a “lot to brag about” in terms of responding to Freedom of Information Act (FOIA) requests, and in 2014 alone it “processed 647,142 FOIA requests, and over 91% of those requests resulted in the release of either some or all of the requested records,” according to press secretary Josh Earnest. However, the Associated Press did its own analysis and concluded that the Obama administration set a “new record” for denying records in 2014. (This debate comes as the federal government continues to field more and more requests from citizens and firms of all kinds, in addition to journalists.)

                Meanwhile, open-data initiatives such as Data.gov have been criticized by journalists for the perceived lack of useful datasets, even if businesses have leveraged some of the data streams. And at all levels, it is unclear if merely releasing datasets meaningfully contributes to public knowledge or understanding. Part of the cognitive dissonance might be explained by the fact that even the original “open data” agencies, such as the federal government’s principal statistical agencies (the Census Bureau, for example), have struggled to keep up with the pace of Web innovation, from user-centric design and interactive applications to search and mobile optimization.

                Finally, it is also worth noting that recent research has found that members of the public often demonstrate an unconscious (and negative) bias in their evaluations of public sector performance, irrespective of the underlying reality or truth.

                A 2015 report from the Pew Research Center, “Americans’ Views on Open Government Data,” by John B. Horrigan and Lee Rainie, is based on the first national survey testing how these government openness projects, and the wider movement around them, is filtering down into public consciousness. Pew Research polled some 3,200 persons in late 2014 through Web surveys and mail. The margin of sampling error is plus or minus 2.3 percentage points.

                The report’s findings include:

                • A majority of survey respondents said it was either “very easy” or “somewhat easy” to find out how federal (56%), state (61%) or local government (62%) are performing.
                • When asked about how federal and state governments share data with the public, just 5% of those surveyed said they share “very effectively.” However, 39% said the federal government was sharing “somewhat effectively,” and those evaluations were even higher for state (44%) and local (45%) governments.
                • When asked if they had used the Internet or an app to access government information or data over the past year, only about one-third said they had accessed federal government information online; likewise, about a one-third said they had accessed state or local information/data.
                • A combined 65% said they had either used the Internet or an app to get information about the state, local, or federal government, or conducted online activities/transactions such as renewing a driver’s license, reporting a problem or paying a fine.
                • The report notes that most people are “still largely engaged in ‘e-Gov 1.0’ online activities, with far fewer attuned to ‘Data-Gov 2.0’ initiatives that involve agencies sharing data online for public use.”
                • In terms of monitoring services, 20% said they had accessed government data about teacher or student performance, while 17% had looked for information about hospitals or health care providers.
                • Citizens were split on the question of whether or not publicizing data helps improve government services, though 56% said they believe it helps journalists cover government more thoroughly.

                SAttitudes on impact of open data (pew.org)

                • On issues of privacy versus openness, the public had varying views: 60% said they were comfortable with government providing online information about individual teacher performance; 62% were comfortable with providing online criminal records for individuals; 82% were comfortable with government posting online health information about local restaurants. Yet only 22% of respondents were comfortable with providing online information about individuals’ mortgages, and only 54% were even comfortable with putting real estate transaction information online.
                • The authors separate the respondents into four rough categories, reflecting their level of attention and relative optimism about open data and government: “Some 17% of all adults — Ardent Optimists — have truly signed on to government data initiatives…. Another 20% are steady users of online government resources, but are skeptical that they will have any payoff to government performance. These Committed Cynics use the internet to find the government information they need and have relatively high levels of awareness of when governments do a good job sharing information about data…. Some 27% like the idea that data can improve how government performs, but these Buoyant Bystanders are not likely to use the tools that the data enables to connect with government…. Finally, 36% of the general population can be called Dormant Doubters.”

                “The survey unearthed relatively high levels of broad and simple engagement with government data as people use the Internet to access government services and information,” the authors conclude. “At the same time, the survey shows relatively low levels of public awareness of government initiatives to open the data vaults for the public and entrepreneurs. There is optimism among many citizens that government data can improve government accountability (against some caution that open data can improve government performance), along with some level of concern about government sharing data that may hit too close to home.”

                Keywords: data journalism, open government, big data, open data, journalism, local reporting

                  • Apr 07 / 2015
                  • 25
                  margin of error (Wikimedia)

                  Statistical terms used in research studies: A primer for media

                  When assessing academic studies, media members are often confronted by pages not only full of numbers, but also loaded with concepts such as “selection bias,” “p-value” and “statistical inference.”

                  Statistics courses are available at most universities, of course, but are often viewed as something to be taken, passed and quickly forgotten. However, for media members and public communicators of many kinds it is imperative to do more than just read study abstracts; understanding the methods and concepts that underpin academic studies is essential to being able to judge the merits of a particular piece of research. Even if one can’t master statistics, knowing the basic language can help in formulating better, more critical questions for experts, and it can foster deeper thinking, and skepticism, about findings.

                  Further, the emerging field of data journalism requires that reporters bring more analytical rigor to the increasingly large amounts of numbers, figures and data they use. Grasping some of the academic theory behind statistics can help ensure that rigor.

                  Most studies attempt to establish a correlation between two variables — for example, how having good teachers might be “associated with” (a phrase often used by academics) better outcomes later in life; or how the weight of a car is associated with fatal collisions. But detecting such a relationship is only a first step; the ultimate goal is to determine causation: that one of the two variables drives the other. There is a time-honored phrase to keep in mind: “Correlation is not causation.” (This can be usefully amended to “correlation is not necessarily causation,” as the nature of the relationship needs to be determined.)

                  Another key distinction to keep in mind is that studies can either explore observed data (descriptive statistics) or use observed data to predict what is true of areas beyond the data (inferential statistics). The statement “From 2000 to 2005, 70% of the land cleared in the Amazon and recorded in Brazilian government data was transformed into pasture” is a descriptive statistic; “Receiving your college degree increases your lifetime earnings by 50%” is an inferential statistic.

                  Here are some other basic statistical concepts with which journalism students and working journalists should be familiar:

                  • A sample is a portion of an entire population. Inferential statistics seek to make predictions about a population based on the results observed in a sample of that population.
                  • There are two primary types of population samples: random and stratified. For a random sample, study subjects are chosen completely by chance, while a stratified sample is constructed to reflect the characteristics of the population at large (gender, age or ethnicity, for example). There are a wide range of sampling methods, each with its advantages and disadvantages.
                  • Attempting to extend the results of a sample to a population is called generalization. This can be done only when the sample is truly representative of the entire population.
                  • Generalizing results from a sample to the population must take into account sample variation. Even if the sample selected is completely random, there is still a degree of variance within the population that will require your results from within a sample to include a margin of error. For example, the results of a poll of likely voters could give the margin of error in percentage points: “47% of those polled said they would vote for the measure, with a margin of error of 3 percentage points.” Thus, if the actual percentage voting for the measure was as low as 44% or as high as 50%, this result would be consistent with the poll.
                  • The greater the sample size, the more representative it tends to be of a population as a whole. Thus the margin of error falls and the confidence level rises.
                  • Most studies explore the relationship between two variables — for example, that prenatal exposure to pesticides is associated with lower birthweight. This is called the alternative hypothesis. Well-designed studies seek to disprove the null hypothesis — in this case, that prenatal pesticide exposure is not associated with lower birthweight.
                  • Significance tests of the study’s results determine the probability of seeing such results if the null hypothesis were true; the p-value indicates how unlikely this would be. If the p-value is 0.05, there is only a 5% probability of seeing such “interesting” results if the null hypothesis were true; if the p-value is 0.01, there is only a 1% probability.
                  • The other threat to a sample’s validity is the notion of bias. Bias comes in many forms but most common bias is based on the selection of subjects. For example, if subjects self-select into a sample group, then the results are no longer externally valid, as the type of person who wants to be in a study is not necessarily similar to the population that we are seeking to draw inference about.
                  • When two variables move together, they are said to be correlated. Positive correlation means that as one variable rises or falls, the other does as well — caloric intake and weight, for example. Negative correlation indicates that two variables move in opposite directions — say, vehicle speed and travel time. So if a scholar writes “Income is negatively correlated with poverty rates,” what he or she means is that as income rises, poverty rates fall.
                  • Causation is when change in one variable alters another. For example, air temperature and sunlight are correlated (when the sun is up, temperatures rise), but causation flows in only one direction. This is also known as cause and effect.
                  • Regression analysis is a way to determine if there is or isn’t a correlation between two (or more) variables and how strong any correlation may be. At its most basic, this involves plotting data points on a X/Y axis (in our example cited above, vehicle weight and fatal accidents) looking for the average causal effect. This means looking at how the graph’s dots are distributed and establishing a trend line. Again, correlation isn’t necessarily causation.
                  • The correlation coefficient is a measure of linear association or clustering around a line.
                  • While causation is sometimes easy to prove, frequently it can often be difficult because of confounding variables (unknown factors that affect the two variables being studied). Studies require well-designed and executed experiments to ensure that the results are reliable.
                  • When causation has been established, the factor that drives change (in the above example, sunlight) is the independent variable. The variable that is driven is the dependent variable.
                  • Elasticity, a term frequently used in economics studies, measures how much a change in one variable affects another. For example, if the price of vegetables rises 10% and consumers respond by cutting back purchases by 10%, the expenditure elasticity is 1.0 — the increase in price equals the drop in consumption. But if purchases fall by 15%, the elasticity is 1.5, and consumers are said to be “price sensitive” for that item. If consumption were to fall only 5%, the elasticity is 0.5 and consumers are “price insensitive” — a rise in price of a certain amount doesn’t reduce purchases to the same degree.
                  • Standard deviation provides insight into how much variation there is within a group of values. It measures the deviation (difference) from the group’s mean (average).
                  • Be careful to distinguish the following terms as you interpret results: Average, mean and median. The first two terms are synonymous, and refer to the average value of a group of numbers. Add up all the figures, divide by the number of values, and that’s the average or mean. A median, on the other hand, is the central value, and can be useful if there’s an extremely high or low value in a collection of values — say, a Wall Street CEO’s salary in a list of people’s incomes. (For more information, read “Math for Journalists” or go to one of the “related resources” at right.)
                  • Pay close attention to percentages versus percentage points — they’re not the same thing. For example, if 40 out of 100 homes in a distressed suburb have “underwater” mortgages, the rate is 40%. If a new law allows 10 homeowners to refinance, now only 30 mortgages are troubled. The new rate is 30%, a drop of 10 percentage points (40 – 30 = 10). This is not 10% less than the old rate, however — in fact, the decrease is 25% (10 / 40 = 0.25 = 25%).
                  • In descriptive statistics, quantiles can be used to divide data into equal-sized subsets. For example, dividing a list of individuals sorted by height into two parts — the tallest and the shortest — results in two quantiles, with the median height value as the dividing line.  Quartiles separate data set into four equal-sized groups, deciles into 10 groups, and so forth. Individual items can be described as being “in the upper decile,” meaning the group with the largest values, meaning that they are higher than 90% of those in the dataset.

                  Note that understanding statistical terms isn’t a license to freely salt your stories with them. Always work to present studies’ key findings in clear, jargon-free language. You’ll be doing a service not only for your readers, but also for the researchers.

                  Related: See this more general overview of academic theory and critical reasoning courtesy of MIT’s Stephen Van Evera. A new open, online course offered on Harvard’s EdX platform, “Introduction to Statistics: Inference,” from UC Berkeley professors, explores “statistical ideas and methods commonly used to make valid conclusions based on data from random samples.”

                  There are also a number of free online statistics tutorials available, including one from Stat Trek and another from Experiment Resources. Stat Trek also offer a glossary that provides definitions of common statistical terms. Another useful resource is “Harnessing the Power of Statistics,” a chapter in The New Precision Journalism.


                  A special thanks to Sudhakar Raju of Rockhurst University for his invaluable contributions to this article. Keywords: training

                    • Mar 04 / 2015
                    • 0
                    Criminal Justice, Personal Finance

                    Rates of fraud, identity theft and scams across the 50 states: FTC data

                    The Federal Trade Commission (FTC) documents consumer complaints about fraud and commercial abuse of all kinds — from violations of the “Do Not Call Registry” rules to identity theft, impersonating the IRS, fake sweepstakes, and credit-card fraud. For journalists, one of the more underutilized federal data sources is the FTC’s Consumer Sentinel Network, which gathers data from a wide variety of sources, including state attorneys general, the FBI and more local entities such as Better Business Bureaus. The Sentinel Network data itself is available only to law enforcement, but top-level findings and state- and metro area-level data are published on an annual basis.

                    The FTC’s latest report, “Consumer Sentinel Network Data Book for January-December 2014 ” was released in February 2015, and shows evolving patterns of complaints across the United States. Reporters looking to assess complaints in their state and specific local areas can refer to individual statistical breakouts, beginning on page 21. The FTC makes clear that these are “unverified” consumer complaints and are not based on a representative survey or sample. Nevertheless, the data are revealing and can serve as the basis for reporting in the field.

                    The FTC report’s findings include:

                    • During 2014, there were more than 2.58 million complaints (excluding do-not-call complaints), representing a 16% increase over 2013.
                    • Of these, fraud complaints were the most common (60%), followed by identity theft complaints (13%); debt collection (11%); impostor scams (11%); telephone and mobile services (7%); banks and lenders (5%); prizes, sweepstakes and lotteries (4%); auto-related complaints (3%); shop-at-home and catalog sales (3%); television and electronic media (2%); and Internet services (2%).

                    Consumer Sentinel Network complaint count (FTC)

                    • Among the fraud complaints, the average amount lost by alleged victims was approximately $2,000, with a median figure of about $500. In total, approximately $1.7 billion was lost by self-reported victims of fraud. The most common methods of initial contact by fraud perpetrators was telephone (54%) and e-mail (23%).
                    • “Government documents/benefits fraud (39%) was the most common form of reported identity theft, followed by credit-card fraud (17%), phone or utilities fraud (13%) and bank fraud (8%). Other significant categories of identity theft reported by victims were employment-related fraud (5%) and loan fraud (4%).”
                    • The impression can be that scams often originate in foreign countries, but the data reveal that the vast majority of reported frauds are based in the United States (96%). Fraud schemes were reportedly of Nigerian origin in 1% of cases, while Chinese entities were accused in less than 1%. (More data on cross-border complaints can be found at econsumer.gov.)
                    • While it is commonly assumed that seniors and the elderly might be more susceptible and therefore targeted, victims’ ages were fairly uniform across all cohorts, with those in their 20s representing 18% of complaints and those in their 60s representing 13% of complaints. (Those over 70 represented just 7% of complaints, but these are self-reported figures and the number for seniors may reflect a lack of reporting to government, and not necessarily a diminished rate of fraud.)
                    • Florida was overwhelmingly the leader in per-capita complaints, with 1,007 for every 100,000 state residents. Georgia, Nevada, Delaware and Michigan rounded out the top five. Florida was also the leader in rates of identity theft reported, with 186.3 complaints per 100,000 people; Washington, Oregon, Missouri and Georgia followed on that list.
                    • The metropolitan areas with the most fraud complaints were: Sierra Vista-Douglas, Ariz.; Homosassa Springs, Fla.; Colorado Springs, Colo.; Weirton-Steubenville, W.V.-Ohio; Bellingham, Wash.

                    Given privacy concerns, it remains unclear how much of this data — particularly the names of entities and businesses accused of fraud — might be subject to the Freedom of Information Act (FOIA), and a 2014 Washington, D.C., District Court legal decision possibly opens up one new avenue for inquiry. For its part, the Consumer Financial Protection Bureau, which feeds data into the Consumer Sentinel Network, is now releasing the specific names of banks, mortgage companies and other businesses that are the subject of consumer complaints. And it is possible that the state agencies providing the underlying data to the Sentinel Network might be open to FOIA requests.

                    Related research: A 2014 study published in Psychological Science, “Contrary to Psychological and Popular Opinion, There Is No Compelling Evidence That Older Adults Are Disproportionately Victimized by Consumer Fraud,” gathers evidence relating to questions about age cohorts and relative levels of susceptibility. There is also an evolving academic literature on how warnings and other techniques might reduce susceptibility among consumers.


                    Keywords: fraud, crime, aging, the elderly