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Monday, August 08, 2005

Electronic Warfare 
Here's an idea from the comments section to the blog.

For warfare against cellphone based threats, we have 3 choices. The first, is to do nothing, and let our men die. The second, fly over with the EA6B Prowler and radiate enough electromagnetic energy that the all civilian radios will be burned out. The third, do something similar with a microwave radiation source on a vehicle. The third, use the cell phone technology to our advantage: Improvise, Adapt, Overcome as Gunny Highway would say. Cell phones and the towers communicate to know which cell phone is in which cell. Put your cell phone next to your AM radio and every minute or so, you'll hear "clicka clicka clicka" and that is the phone and the cell tower exchanging communications and ID numbers. So, if the enemy is using cell phones, our forces should put up a "spoof" cell phone tower, and send out the message "all cell phones register now". This is done by your cell phone company anyway. Hackers do it also to steal phone numbers in the US. Then, have an automated dialer call every number that is there, at 3am. If you hear a boom, the bomb went off. And it might have gone off on the enemy's workbench. heh heh heh.....

The Isrealis do this. I have heard on the news that the Russians do something similar in Chechnya.

Also, when the cell phones register in, that is when radio direction finders can locate the position of each phone. If the position of the phone is the highway that some troops will be traveling on, it might suggest that a bit more investigation be done.

In terms of ground penetrating radar, think of a system similar in concept to how the P3 aircraft would find Ivan's subs out in the ocean. A map that was made of the earths magnetic field could be compared against a new map made in realtime. If there is a difference, and there is no surface ship present, then Ivan is found. The radar could be used to map the ground under the roads. The next time a check is done, if there is something different, then be afraid. The radar is used on construction to find pipes already, and to map out construction areas before the earth is moved. Lots of computers are needed to work this.


Now, we do have a vehicle-mounted system that does have some electronic capabilities. I don't know the specifics about how it works and I'd keep them to myself if I did. I'm not a technical geek though. There was some talk recently about the insurgency using infrared detonation devices specifically to circumvent US countermeasures. And maybe the force behind this is that the US electronic measures are having some effect?

I know it would frustrate me if I were a terrorist to go through the time and expense of building an IED, risking my neck to go emplace it, wait half the night for a convoy to roll through, and then hit "dial" on my cell phone only to have nothing happen. What a demotivator!

But if US countermeasures weren't effective, maybe they wouldn't use an IR detonator, which requires line of sight, and for that reason may expose the bombers to direct fire.

Any subject matter experts know anything about this? Can we force all cell phones on the network to register, and cause some premature detonations?

Can we magnetic map MSRs to detect changes in the earth's magnetic field beneath an improved road? (Remember, pipes usually extend beyond the street in either direction. A sub-road IED doesn't.)

If that technology can be mounted on a UAV or helicopter, it would not be a problem to put into use. Interesting stuff.

Now, if it works, you also have a synchronization issue: You do not want to force register every cell-phone for miles while you have an EOD team out defusing a bomb in a residential neighborhood. And I would hate to see a whole apartment building blow up because we've set off two 155 shells on someone's kitchen table. Then again, these Iraqi communities are going to have to police themselves. They can solve that problem ahead of time by turning these guys in.

Splash, out

Jason

Comments:
Jason, are you giving me a soapbox? ;-)

I’ve been thinking more about the IEDs, cellphones, our troops and so forth. Uh Oh…..
And it is not just about electronic warfare, it is also about “information warfare”.

Well, “all’s fair in love and war”…. My wife convinced me of at least part of that…

But here’s the major point I’m thinking: supercomputing has applications at the tactical unit level, and it hasn’t been applied yet (to my knowledge). Obviously it (supercomputing) is doing great things for aerospace designers, strategic surveillance systems, and missile interceptors. “Patriot vs. Scud!”

The costs are falling dramatically, and networking technology is enabling new designs. Look what computing researchers put together with a bunch of Playstations. Yes, Playstations, mounted in racks, networked together, and they’d fit in an enclosure on the back of a full size pickup truck. Intro: http://arrakis.ncsa.uiuc.edu/ps2/ Picture: http://arrakis.ncsa.uiuc.edu/ps2/cluster.php

This setup at the time it was made in 2003 cost $50K, and it was one of the 500 most powerful computers in the world. The price of the PS2 has fallen since then. And computing power has increased.

OK, so what. How does that help the soldier?

Well, good intelligence helps the soldier. And good intelligence can be derived from the signals transmitted by cell phones. But, it needs to be analyzed in real-time. What I’d envision is that 3 or more signal monitoring sites would be set up outside of a place like Mosul. Each would monitor cell phone ID communications and signal strength. These monitoring sites (obviously also with some form of computer control) would relay info of signal identity, strength, and time (based off of the GPS master clock). And based upon the data coming in, the supercomputer could then derive the positions of each cell phone in the region, its number, and whether it just dialed a call. That database would then be filtered in accordance with the needs of the mission planners. “Give me the list of phones that haven’t moved in the last day, for which the calculated position happens to be a roadway.” “Give me a list of phones that dialed a number 10 seconds before that last IED blew up.” “Give me a list of the phones that dialed within 15 minutes of our troops losing track of the terrorists that fired on them at 2:30AM while on recon”. “Give me a list of the numbers common to my last three search requests”. And if you knew the nom-de-guerre of the local terrorist leader, build in the software the ability to also compile a list of phone numbers upon which the signal processing equipment detected that name. Call that last one a feature for the upgrade to the system.

As an application, for instance, in Fallujah of last November, perhaps each cell phone position would indicate either that a terrorist, or a scared woman with kids was at a certain location. Either would be good to know. And think of the psychological effect on the terrorist, if he has a phone, to get the call on it, telling him “surrender or die” as the US forces move in to position around his location. And if he was unsophisticated enough to have brought in the phone from his home in Saudi Arabia, he might even have the phone in his own name, and if our guys at the three letter agencies have a dossier on this guy, we could have a three way conference call on the battlefield with the guy and his mom back in Mecca. Mom might express a different idea about her boy meeting the 72 virgins in this manner. Just daydreaming I am, out of my ignorance about this topic.

As the terrorists realized that they could not use a phone more than once and live to tell about it, they’d adapt. And the signal monitoring guys would have to do something new to adapt, like “give me in realtime a notification that a new cell number was just activated in this region”, or “give me the positions of all cell phones that have been activated in the last month, but which never have made an outgoing call”.

Some of what I am thinking about is based upon what is reported by the German police in busting the Baader-Meinhof terrorist group in the ‘70s. They put together a daily database of all electrical consumption on a per-electric meter basis, and began to see a correlation to kidnappings, bombings, murders, and changes in electrical use in the apartments that were used as safe-houses by the gang. The police staked out these apartments, and caught the suspects.

The mobility of cell phones certainly adds a twist to this cat and mouse game.




Guess who provides cell-phone service in Iraq? Turns out to be Worldcom, of the Bernie Ebbers $11 Billion accounting scandal. http://www.computerworld.com/mobiletopics/mobile/story/0,10801,81345,00.html
I’m hoping our intelligence guys are plugged in to the network control centers. ;-)
(And that we have good eavesdropping equipment in place someplace, and Arabic language specialists listening in…)


The FRS and GMRS radios don’t have the unique message identifiers built in to their datastream, so more thought would have to go in to dealing with them. But I bet you give a guy/gal with a PhD a research grant, and a 90 day limit, and something would come out. Since the FRS and GMRS radios don’t transmit anything when in receive mode, like the IED seen at http://michaelyon.blogspot.com/2005/08/jungle-law_10.html, something different would have to be done to detect them. The new radio designs don’t give off the electromagnetic noise like things did in the past. Maybe just jamming these things is the best we can do, or jamming with enough power to damage the devices.


Yes, I like Tom Clancy and the X-Files.

I’ve read of how Homeland security funds are spent. $80K for one small town to buy gym equipment for the part-time fire department, $12K for a plasma TV, a town on Martha’s Vineyard (where Clinton’s vacation) got $260K for bio-response equipment, Concord (home of the Minuteman statue) got $1.7M for ‘a anti-terrorism first responder program’. Well, anyway, we have troops in harm’s way at present, I question the priority here.

Maybe, just maybe, some similar funding has been found to get a truck mounted supercomputer and software system made to work in the military environment, that will help our guys in the realtime it is needed.

Matt
 
Here's the questions I got.

1) Why aren't the forces in Iraq using mine sweepers and sweeping devices before convoy's roll through an area?

2) I like Jason's "spoof" tower idea. I was thinking about that black box that kills all cell phones in its zone. There was an article in the Washington Post a while back that talked about this outlawed technology that's available in Japan where cell phones can't send or receive within its radius. The Japanese use it in restaurants and movie theaters but it is outlawed in America. Increase its power and mount it in the first, middle and last vehicle, and now you got a rolling black hole.
 
check this out:

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YO!
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A payday loan is a short-term loan that you promise to pay back from your next pay cheque. A payday loan is sometimes also called a payday advance.

Normally, you have to pay back a payday loan on or before your next payday (usually in two weeks or less). The amount you can borrow is usually limited to 30 percent of the net amount of your pay cheque. The net amount of your pay cheque is your total pay, after any deductions such as income taxes. For example, if your pay cheque is $1,000 net every two weeks, your payday loan could be for a maximum of $300 ($1,000 x 30%).

Before giving you a payday loan, lenders will ask for proof that you have a regular income, a permanent address and an active bank account. Some payday lenders also require that you be over the age of 18.

To make sure you pay back the loan, all payday lenders will ask you to provide a postdated cheque or to authorize a direct withdrawal from your bank account for the amount of the loan, plus all the different fees and interest charges that will be added to the original amount of the loan. The combination of multiple fees and interest charges are what make payday loans so expensive (Click here for an explanation of the various fees associated with these types of loans.

The lender should also ask you to sign a loan agreement. If the lender does not offer to give you a copy of the loan agreement, ask for one. Read this document carefully before signing it, and keep a copy for your records

How and when do I pay back the loan?
A payday loan agreement usually says that you must pay the total amount you owe for the loan on or before the date stated in your loan agreement. This includes the amount you borrowed, plus interest and any additional fees and charges.

Some lenders will cash your postdated cheque or process your direct withdrawal on the day the loan is due. However, some lenders may require that you pay the loan in cash, on or before the due date.

If you have not paid the loan in cash by the due date, some lenders may cash your cheque or process the direct withdrawal you signed on the day after your loan's due date, and charge you another fee. Ask the lender what the most inexpensive way is for you to repay your loan.

How does a payday loan affect my credit report?
Credit-reporting agencies collect information on whether or not you make your payments on time. This information, also called your "credit history", is part of your credit report and is used to calculate your credit score.

Making payments on time can help improve your credit score by demonstrating that you are able to manage your debt. Even if you have poor credit, you can rebuild it by using a credit card or other type of credit and paying back the money you owe on time.

This is not the case with payday loans. Since payday lenders are not currently members of the main credit-reporting agencies, getting a payday loan and paying it off on time will not improve your credit score. However, if you do not pay your loan back on time and it is sent to a collection agency, this will likely be reported to a credit-reporting agency and could have a negative impact on your credit report.

How much will a payday loan cost?
A payday loan is much more expensive than most other types of loans offered by financial institutions such as banks or credit unions. Before you apply for a payday loan, find out about all the fees and charges you will have to pay — including the fees you will be charged if you cannot repay the loan on time. The fees may not be easy to see right away, so read the agreement carefully before signing it. If you do not receive an explanation of all of the fees, charges and interest that will apply to the loan, or if you are not satisfied with the explanation you receive, do not sign the loan agreement.

How does the cost of a payday loan compare with other credit products?
Payday loans are much more expensive than other types of loans, including credit cards. But how much are you really paying? How does the cost of a payday loan compare with taking a cash advance on a credit card, using overdraft protection on your bank account or borrowing on a line of credit?

Let's compare the cost of using different types of loans. We'll assume that you borrow $300, for 14 days. Note the considerable difference in the cost of each type of loan.

Things to consider before you apply for a payday loan
Even if you think you may be turned down, ask your bank or credit union for overdraft protection on your bank account, or a line of credit. These are relatively inexpensive ways of obtaining access to extra funds, for short-term use.


If you are turned down for any of these credit options, ask why. If the reason is that you have a poor credit history, contact the three credit-reporting agencies to get a copy of your credit report. Read the reports carefully to make sure that all of the information in it is correct. If you find any errors, contact the credit-reporting agency to find out how you can have the information corrected. The three major credit-reporting agencies in Canada are Equifax Canada, TransUnion Canada and Northern Credit Bureaus. All three of these agencies will give you a copy of your credit report for free if you request that it be sent to you by regular mail.


Ask yourself if you really need to take out a loan, or whether you can get by until your next pay cheque. If you need the money immediately, try to make other arrangements. For example, you may be able to cash in vacation days. Or you might consider getting a short-term loan from a family member or a friend.


If you find that you need to apply for a payday loan because you have no alternative, only borrow an amount that you are 100 percent sure you can repay on the due date of the loan.


Don't borrow more than you need.

Things to consider if you take out a payday loan
Don't be afraid to ask a lot of questions. Read carefully — and take home with you — a copy of the loan agreement that you are being asked to sign. Don't feel pressured to sign the loan agreement right away if you have questions and want more time to read through the agreement on your own. If the lender does not want to give you a copy of the agreement, look for another lender.


Be sure to ask about all the fees, charges and interest that apply when you first get the loan, and what other charges you will owe if you can't pay the loan back on time.


If you are taking out a payday loan at another location to pay back the first payday loan, or you are extending or "rolling over" the loan that you had with the same lender, you could find yourself in serious financial difficulty. The fees, charges and interest will add up quickly on these types of loans, which can put you into serious debt.
How can I figure out the cost of each type of loan?
To estimate the total cost of a loan, including the annual cost of the loan expressed as a percentage of the amount borrowed, follow the steps below.

Step 1:

Determine how much interest you will pay. First, find out the annual interest rate that applies to the loan (if there is one). Figure out the daily interest rate by dividing the annual interest rate of the loan by 365 days. Then, multiply that rate by the length of time you are taking the loan. Finally, multiply the result by the amount you will borrow, in dollars:


Amount of interest

= Annual interest rate

--------------------------------------------------------------------------------
365 days × Length of the loan
(number of days) × Amount of the loan

Step 2:

Determine the total cost of the loan by adding any fees that may apply to the interest you will have to pay. Find out what fees apply to the loan and add them to the cost of the interest, found in Step 1:


Total cost of the loan = Amount of interest + Total fees


Step 3:

Estimate the annual cost of the loan, expressed as a percentage of the amount borrowed. First, divide the total cost of the loan, found in Step 2, by the amount of the loan. Then, divide this rate by the length of time you are taking the loan (in days) and multiply it by 365 (the number of days in the year):


Annual cost of the loan (%)

= Cost of the loan

--------------------------------------------------------------------------------
Amount of the loan ÷ Length of the loan
(number of days) × 365 days

Let's find out the cost of a $300 payday loan, taken for 14 days.

We'll assume that the lender charges you a one-time set-up fee of $10 and a service fee of $40, which includes interest on the loan.


Step 1:

Determine how much interest you will pay. In this case, there is no interest fee. The interest is therefore $0.


Step 2:

Figure out the cost of the loan by adding together any fees that apply and the interest you will have to pay. In this case, you would add the $10 set-up fee and the $40 service fee together:

$10 + $40 = $50


Step 3:

Estimate the total annual cost of the loan, expressed as a percentage of the amount borrowed:


Annual cost of the loan (%)

= Cost of the loan

--------------------------------------------------------------------------------
Amount of the loan ÷ Length of the loan
(number of days) × 365 days
= $50
———— ÷ 14 days × 365 days
$300
= 4.35 or approximately 435%

The total cost of the payday loan would be $50 with an annual cost of 435 percent of the amount borrowed.






Information asymmetries are common in credit market models, but the usual assumption,

at least in commercial lending, is that borrowers are the better informed party and that

lenders have to screen and monitor to assess whether

firms are creditworthy. The opposite
asymmetry, as we assume here, does not seem implausible in the context of consumer lending.

"Fringe" borrowers are less educated than mainstream borrowers (Caskey 2003), and many

are

first-time borrowers (or are rebounding from a failed first foray into credit). Lenders
know from experience with large numbers of borrowers, whereas the borrower may only have

their own experience to guide them. Credit can also be confusing; after marriage, mortgages

are probably the most complicated contract most people ever enter. Given the subtleties

involved with credit, and the supposed lack of sophistication of sub-prime borrowers, our

assumption that lenders know better seems plausible.

While lenders might deceive households about several variables that in

fluence household
loan demand, we focus on income. We suppose that lenders exaggerate household's future

income in order boost loan demand. Our borrowers are gullible, in the sense that they can

be fooled about their future income, but they borrow rationally given their beliefs. Fooling

borrowers is costly to lenders, where the costs could represent conscience, technological costs

(of learning the pitch), or risk of prosecution. The upside to exaggerating borrowers' income

prospects is obvious—they borrow more. As long as the extra borrowing does not increase

default risk too much, and as long as deceiving borrowers is easy enough, income deception

and predatory—welfare reducing—lending may occur.

After de

fining predatory lending, we test whether payday lending fi ts our definition. Payday
lenders make small, short-term loans to mostly lower-middle income households. The

business is booming, but critics condemn payday lending, especially the high fees and frequent

loan rollovers, as predatory. Many states prohibit payday loans outright, or

indirectly,
via

usury limits.
To test whether payday lending quali

fies as predatory, we compared debt and delinquency
rates for households in states that allow payday lending to those in states that do not. We

focus especially on di

fferences across states households that, according to our model, seem
more vulnerable to predation: households with more income uncertainly or less education.

We use smoking as a third, more ambiguous, proxy for households with high, or perhaps

hyperbolic, discount rates. In general, high discounters will pay higher future costs for a

given, immediate, gain in welfare. Smokers' seem to

fit that description. What makes the
smoking proxy ambiguous is that smokers may have hyperbolic, not just high, discount rates.

Hyperbolic discount rates decline over time in a way that leads to procrastination and selfcontrol

problems (Laibson 1997). The hyperbolic discounter postpones quitting smoking,

or repaying credit. Without knowing whether smokers discount rates are merely high, or

hyperbolic, we will not be able to say whether any extra debt for smokers in payday states

is welfare reducing.

2
Given those proxies, we use a di

fference-in-difference approach to test whether payday
lending

fits our definition of predatory. First we look for diff erences in household debt
and delinquency across payday states and non-payday states, then we test whether those

di

fference are higher for potential prey. To ensure that any such differences are not merely
state e

ffects, we difference a third time across time by comparing whether those di fferences
changed after the advent of payday lending circa 1995. That triple di

fference identifies any
di

fference in debt and delinquency for potential prey in payday states after payday lending
was introduced.

Our

findings seem mostly inconsistent with the hypothesis that payday lenders prey on,
i.e., lower the welfare of, households with uncertain income or households with less education.

Those types of households who happen to live in states that allow unlimited payday loans

are less likely to report being turned down for credit, but are

not more likely, by and large,
to report higher debt levels, contrary to the overborrowing prediction of our model. Nor are

such households more likely to have missed a debt payment in the previous year. On the

contrary, households with uncertain income who live in states with unlimited payday loans

are

less likely to have missed a debt payment over the previous year. The latter result is
consistent with claims by defenders of payday lending that some households borrow from

2

Consistent with a high discount rate, Munasinghe and Sicherman (2000) discover that smokers have
fl

atter wage profiles and they are willing to trade more future earnings for a given increase in current earnings.
Gruber and Mulainathan (2002)

find that high cigarette taxes make smokers "happier," consistent with
hypberbolic discount rates (because taxes help smokers commit to quitting). DellaVigna and Malmendier

(2004) show how credit card lenders can manipulate hyperbolic discounters by front-loading bene

fits and
back-loading costs.

payday lenders to avoid missing payments on other debt. On the whole, our results seem

consistent with the hypothesis that payday lending represents a legitimate increase in the

supply of credit, not a contrived increase in credit demand.

We

find some interesting differences for smokers, but those diff erences are harder to
interpret in relation to the predatory hypothesis without knowing

apriori whether smokers
are hyperbolic, or merely high, discounters.

We also

find, using a small set of data from different sources, that payday loan rates
and fees decline signi

ficantly as the number of payday lenders and pawnshops increase.
Reformers often advocate usury limits to lower payday loan fees but our evidence suggests

that competition among payday lenders (and pawnshops) works to lower payday loan prices.

Our paper has several cousins in the academic literature. Ausubel (1991) argues that

credit card lenders exploit their superior information about household credit demand in their

marketing and pricing of credit cards. The predators in our model pro

fit from their information
advantage as well. Our concept of income delusion or deception also has a behavioral

fl

avor, as well, hence our use of smoking as a proxy for self-control problems. Brunnermeier
and Parker (2004), for example, imagine that households

choose what to expect about future
income (or other outcomes). High hopes give households' current "felicity," even if it

distorts borrowing and other income-dependent decisions. Our households have high hopes

for income, and they make bad borrowing decisions, but we do not count the current felicity

from high hopes as an o

ffset to the welfare loss from overborrowing.
Our costly falsi

fication (of household income prospects) and costly verification (by counselors)
resemble Townsend's (1979) costly state veri

fication and Lacker andWeinbergs' (1989)
costly state falsi

fication. The main difference here is that the falsifying and verifying comes
before income is realized, not after.

More importantly, we hope our

findings inform the current, very real-world debate,
around predatory lending. The stakes in that debate are high: millions of lower income

households borrow regularly from thousands of payday loan o

ffices around the country. If
payday lenders raise household welfare by relaxing credit constraints, anti-predatory legislation

may lower it.

Payday lenders make small, short-term loans to households. The typical loan is about $300

for two weeks. The typical fee is $15 per $100 borrowed. Lenders require two recent pay

stubs (as proof of employment), and a recent bank account statement. Borrowers secure

the loan with a post-dated personal check for the loan amount plus fees. When the loan

matures, lenders deposit the check.

Payday lending evolved from check cashing much like bank lending evolved from deposit

taking. For a fee, check cashiers turn personal paychecks into cash. After cashing several

paychecks for the same customer, lending against

f uture paychecks was a natural next step.
High

finance charges is the main criticism against payday lenders. The typical fee of $15
per $100 per two weeks implies an annual interest rate of 15

x365/14, or 390 percent. Payday
lenders are also criticize for overlending, in the sense that borrowers often re

finance their
loans repeatedly, and for "targeting" women making the transition from welfare-to-work

(Fox and Mierzewski 2001) and soldiers (Graves and Peterson 2004).

Despite their critics, payday lending has boomed. The number of payday advance o

ffices
grew from 0 in 1990 to 14

, 000 in 2003 (Stegman and Harris 2003). The industry originated
$8 to $14 billion in loans in 2000, implying 26-47 million individual loans. Rapid entry

suggests the industry is pro

fitable.
Payday lenders present sti

ff competition for pawnshops, even though the internet, namely
E-bay, signi

ficantly foreclosure costs for pawnshops (Caskey 2003). The number of pawn
shops in the U.S. grew about six percent per year between 1986 and 1996, but growth

essentially stalled from 1997 to 2003. Prices of shares in EZCorp, the largest, publicly

traded pawn shop holder, were essentially

flat or declining between 1994 and 2004, while
Ace Cash Express share prices, a retail

financial firm selling check cashing and payday loans,
rose substantially over that period (Figure 4). EZCorp CEO, Joseph Rotunday, blamed

payday lenders for pawnshops' dismal performance:

The company had been progressing very nicely until the late 1990s.... (when)

a new product called payroll advance/payday loans came along and provided our

customer base an alternative choice. Many of them elected the payday loan over

the traditional pawn loan. (Quoted by Caskey (2003) p.14).

Payday lending is heavily regulated (Table 1). As of 2001, eighteen states e

ffectively
prohibited payday loans

via usury limits, and most other states prices, loan size, and loan
frequency per customer (Fox and Mierzwinski 2001). Note that the payday loan limit ranges

from 0 (where payday loans are illegal) to 1250. Nine states allow unlimited payday loans.

Payday lenders have circumvented usury limits by a

ffiliating with national or state
chartered banks, but the Comptroller of the Currency—the overseer of nationally chartered

banks–recently banned such a

ffiliations. The Federal Deposit Insurance Corporation still
permits payday lenders to a

ffiliate with state banks, but recently restricted those partnerships
(Graves and Peterson 2005).

Regulatory risk—the threat of costly or disabling legislation in the future—looms large for

Payday lenders. The Utah legislature is reconsidering its permissive laws governing payday

lending. North Carolina recently drove payday lenders from the state by expressly outlawing

the practice.

Heavy regulation increases the cost of payday lending. High regulatory risk increases limits

entry into the industry and increases the expected return required by industry investors.

Driving up costs and driving away investors may be exactly what regulators intended if they

view payday lending as predatory.
We de

fine predatory lending as a welfare reducing provision of credit. Households can be
made worse o

ff by borrowing if lenders can deceive households into borrowing more than is
optimal. Excess borrowing reduces household welfare, and may increase default risk.

We illustrate our concept of predatory lending in a standard model of household borrowing.

Before we get to predatory lending, we review basic principles about welfare

improving
lending, the type that lets households maintain their consumption despite

fluctuations in
their income.

The model has two periods: today (period zero) and payday (period one. Household income

goes up and down periodically, but not randomly (for now): income equals zero today

and

y on payday. If households consume Ct in period t, their utility is U (Ct) .Household welfare
is the sum of utility over both periods:

U (C0)+ δU (C1), where δ equals the household's
time rate of discount. Households with high

δ value current consumption highly relative to
future consumption. In other words, high discounters are impatient.

A digression here on discount rates serves later discussion. In classical economics

δ is
constant. If

δ changes over time, so does household behavior, even if nothing else changes.
If

δ(t) is hyperbolic, households will postpone unpleasant tasks until current consumption
does not seem so precious relative to future consumption (Laibson 1997). With hyperbolic

discounting, that day never arrives, so hyperbolic discounters have behavioral problems: they

procrastinate. They may never repay debt, much less begin saving. Hyperbolic discounters

who start smoking may never quit.

Returning to the model, if the marginal utility of consumption (

U 0) is diminishing, households
will demand credit to reduce

fluctuations in their standard of living. Households
without credit, however, must fend for themselves (autarky). Welfare under autarky equals



U

(0)+δU (y). The fluctuations in consumption for households without credit make autarky
a possible worst case, and hence, a good benchmark for comparing cases

with credit.
If households borrow

B at interest rate r, welfare equals U (B) + δU (y − (1 + r)B).
Borrowing increases utility in period zero, when the proceeds are consumed, but lowers utility

in period one, when households pay for their borrowing. Rational, informed households trade

o

ff the good and bad side of borrowing; they borrow until the marginal utility of consuming
another unit today just equals the marginal, discounted

disutility of repaying the extra debt
on payday:



U

0(B) = δ(1 + r)U 0(y − (1 + r)B). (1)
Equation (1) determines household loan demand as a function of their income, their

discount rate, and the market interest rate:

B(y, δ, r). For standard utility functions,
household loan demand is increasing in income and decreasing in the discount factor and

interest rate:

By > 0; B δ < 0; Br < 0. Household welfare with optimal borrowing equals


U

(B(y, r, d))+δU (y − (1+r)B( y, r, δ)). As long as households follow (1), their welfare with
positive borrowing must be higher than without (autarky).

The welfare gain from borrowing depends on the cost of credit production. Suppose the

cost of lending $

B to a particular household equals (1 + ρ)B + f, where ρ represents the
opportunity cost per unit loaned and

f is the fixed cost per loan. Think of f as the cost
of record-keeping and credit check required for each loan, however large or small the loan

may be. If the going price for loans is (1+

r) per unit borrowed, the lenders' profits equal
(

r − ρ)B − f.


With perfect competition among lenders, the loan interest rate is competed down until

it just covers the costs of the loan:

r = ρ + f /B. Equilibrium r and B are determined
where that credit supply curve equals demand (1).

Equilibrium in the payday credit market is illustrated in Figure (3). If

fixed costs per loan
are prohibitively high, the market may not exist. Perhaps the payday lending technology

lowered the

fixed cost per loan enough to make the business viable.3 Before the advent of
payday lending, households who applied to banks for a very small, short-term loan may have

been denied.

Fixed costs per loan imply that smaller loans will cost more per dollar borrowed than

larger loans. That means households with low credit demand will pay higher rates than

households with high loan demand. Loan demand is increasing in income, so high income

households who demand larger quantities of credit will enjoy a "quantity" discount, while

lower income households will pay a "small lot" premium, or penalty. That price "discrimination"

is not invidious, however; the higher cost of smaller loans re

flects the fixed costs of
lending. The high price of payday loans may partly re

flect the combination of fixed costs
and small loan amounts (Flannery and Samolyk 2005).

A usury limit lowers household welfare. Suppose the maximum legal interest rate is

r.


At that maximum rate, the minimum loan that lenders' cost is

f /(r− ρ) = B. Low income
households with loan demand less than

B face a beggar's choice: borrow B at r or do not
borrow at all. Such households would be willing to pay more to to avoid going without

credit, so raising the usury limit would raise welfare for those households.

Competition is another key determinant of how much households gains from borrowing.



3

Alternatively, or additionaly, the demand for small, short term loans may have increased in the mid
1990s. The welfare reform then almost certainly increased demand for such credit as households who once

"worked" at home for the government were forced to go to work in the market.

Even with no competition — monopoly—households cannot be worse o

ff than under autarky.
The monopolist raises interest rates until the marginal revenue from higher rates equals the

marginal cost from lower loan demand:



B

(y, r) = −(r − ρ)Br(y, r) . (2)
At that monopoly interest rate,

rm, household loan demand equals B(y, rm).Household welfare
under monopoly equals

U (Br(y, r m))+δU (y −(1+ rm)Br(y, r m)). Welfare is lower under
monopoly because credit costs more and their standard of living

fluctuates more (because
costly credit reduces their demand for credit) If households borrow from the monopolist,

however, they must better o

ff than without credit.
In sum, welfare for rational households is highest if credit is available at competitive

prices. If households choose to borrow, they must be at least as well o

ff as they were
without credit. Limiting loan rates cannot raise household welfare and may reduce it.

Monopoly lenders lower household welfare, but even with a monopolist, households cannot

be worse o

ff than without credit.
The high cost of payday lending may partly re

flect fixed costs per loan. Before payday
lending, those

fixed costs may have been prohibitive; very small, short-term loans may not
have been worthwhile for banks. The payday lending technology may have lowered those



fi

xed costs, thus increasing the supply of credit to low income households demanding small
loans. That version of the genesis of payday lending suggests the innovation was welfare

improving, not predatory.





In the textbook model household welfare cannot be lower than under autarky because households

are fully informed and rational. Here we show households how can be made worse o

ff


than without credit if predatory lenders can delude households about their (households')

future income.

Suppose that by spending

C(τ ), lenders can convince a prospective borrower that her
income on payday will be

y +τ. The cost C can be interpreted variously as the cost of a guilty

 
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