Tag Archives: MIT Sloan

Why the Internet did not kill RadioShack

See the original article posted on Fortune Insider here>>

Although the electronics retailer is the latest victim in the rise of e-commerce, several other missteps led to its demise.

We’ve seen the downfall of many bricks and mortar stores over the last decade, including Borders, Circuit City, and most recently, RadioShack — to name just a few. As e-commerce continues to rise, it’s seemingly becoming more difficult for traditional stores to stay in business.

It’s true that online shopping has significantly grown over the last 10 years. Even in the last year, we’ve seen a noticeable uptick. According to the U.S. Census, total e-commerce sales for 2014 in the U.S. were estimated at $304.9 billion, which is a 15.4% increase from 2013. However, plenty of bricks and mortar stores are still healthy. Is it fair to blame e-commerce for every store closing and bankruptcy?

As a U.S. bankruptcy judge on Tuesday said he would approve a plan by the electronics retailer to sell 1,740 of its stores to the Standard General hedge fund and exit bankruptcy, it’s worth taking a closer look at why RadioShack failed. E-commerce wasn’t the only culprit. One big mistake involved poor strategic decisions over its financials. Feeling undervalued, the retailer bought back $400 million in stock in 2010 when its net profit was $206 million. It did something similar in 2011 when its net profit had declined to $72 million and it did another buy back for $113 million. In the end, it spent more than $500 million trying to push up the stock price.

However, the company didn’t make enough money to finance the buy back and had to borrow money, which increased its debt-to- value ratio and left RadioShack vulnerable to a declining profits. Rather than buying back so much of the stock and taking on debt, it should have accepted the valuation, closed a few inefficient stores and avoided bankruptcy.

Another significant mistake was its decision to change its product market strategy. In prior years, RadioShack was known as the place to go for hard-to-find parts and components needed to build things. It also had knowledgeable staff who could help customers with high-level customer service. Customers were willing to pay higher prices because of this additional value. After all, there is a difference between getting helpful information in person and trying to explain an issue via the phone, an online chat, or a Google search.

When RadioShack changed its business model to sell finished products like laptops and phones, it lost that competitive edge. Customers could get those finished products from many other retailers and e-commerce sites at lower prices. And a higher-level of customer service wasn’t needed for those products.

While it’s too late for RadioShack, its demise offers some important takeaways for other bricks and mortar stores:

Find a competitive edge. If you offer a unique product, like RadioShack did with selling specialty components, don’t disregard that strength.

Be aware of price sensitivities. A big challenge for bricks and mortar stores is that they have to pay for overhead whereas online retailers don’t, allowing them to charge lower prices. Customers are more likely to price shop for larger and more expensive items, especially ones readily available online like phones and tablets. They tend to be less price sensitive about small and specific goods like the components previously sold by RadioShack.

Focus on customer experience. A big draw for RadioShack was its knowledgeable staff. When it moved to selling finished products, the need for that staff — and consumer willingness to pay higher prices — disappeared. Many people still value talking to a real person.

Looking ahead, it won’t be smooth sailing for traditional stores. But it won’t be all doom and gloom either if they can learn from the mistakes of retailers like RadioShack.

Andrey Malenko is the Jon D. Gruber Career Development Assistant Professor of Finance at the MIT Sloan School of Management.

Evidence of the Financial Accelerator using Corn Fields

The role of financial frictions in amplifying and propagating economic shocks has received significant attention in explaining fluctuations over the business cycle. Financial frictions introduce a wedge between the cost of external finance and the opportunity cost of internal funds. This implies that the strength of firms’ balance sheets will affect the manner in which their investment activity reacts to economic shocks. Current firm investment affects future balance sheet strength, creating a dynamic feedback loop that propagates economic shocks over time. Theoretical models of this so-called “financial accelerator” have played an important role in the literature (e.g. Bernanke and Gertler (1989); Kiyotaki and Moore (1997); Shleifer and Vishny (1992), Bernanke (2007)).

In spite of their importance, empirically testing financial accelerator models has proven to be difficult. While a vast literature exists examining the presence of financial frictions, these frictions serve only as a necessary ingredient for financial accelerator models. See, for example, Lamont (1997), Rauh (2006), Hennessy and Whited (2007) and Kaplan and Zingales (1997), Hubbard and Kashyap (1992), and Rajan and Ramcharan (2012). The essence of financial accelerator models—namely, the role that financial frictions play in propagating economic shocks—remains understudied empirically. There are at least three reasons why this is the case. First, it is difficult to measure exogenous shocks that affect firm productivity. Second, measuring firm productivity, in and of itself, is quite challenging. Indeed, standard productivity measures, such as TFP, are often residuals of regressions relating (mismeasured) outputs and inputs. Finally, it is difficult to obtain clean measures of collateral values, which often play an important role in financial accelerator models.

In a recent project we test the central predictions of financial accelerator models by focusing on a novel setting – the agricultural sector in Iowa. This sector provides a natural environment, rich in data, to examine how shocks to productivity are propagated, both during normal times as well as during crises. As a source of identification, we use exogenous shocks to productivity arising from variation in weather. To analyze productivity, and relate it to productivity shocks as well as measures of financial constraints, we exploit the rich data available on farm crop yields. Finally, focusing on the agricultural sector provides us with a measure of collateral values: land is a main source of collateral for farms and data on land prices are readily available.

We find that the effect of weather shocks is indeed persistent: past weather-driven shocks to productivity affect both current farm yields as well as current land values, up to two years following the shock. We also find these effects are weaker for counties with higher per-capita income. Consistent with the importance of financial frictions in accelerator models, our results show that the sensitivity of farm yields and land values to past weather shocks increases during the 1980s farm debt crisis. The effect is economically substantial, with the sensitivity of yields to past shocks increasing during the debt crisis by a factor of more than three. The result highlight how temporary shocks to productivity can have long lasting effects.

Rajkamal Iyer is an Associate Professor of Finance at MIT Sloan School of Management.

Contributors to this post include Nittai Bergman,  Associate Professor of  Finance at MIT Sloan and Richard Thakor, a doctoral student at MIT Sloan.

Unfunded State and Local Healthcare Benefits, the Elephant in the Room?

Last week Bob Pozen, a Visiting Senior Lecturer here at MIT Sloan with a distinguished background in government, business and education gave an eye-opening lunch talk. The topic was “Other Post-Employment Benefits” or OPEBs—which is accounting jargon for the liabilities governments incur for retiree healthcare.

Here’s what he found:

“The 30 largest American cities had over $100 BILLION in retiree healthcare deficits in 2013, as estimated by the Pew Charitable Trust. In that year, New York City showed the most serious retiree healthcare deficits at $22,857 per household. The retiree healthcare deficits of the States were even larger in 2013 — a total of $528 BILLION according to the credit rating agency Standard & Poor’s.”

How have such enormous liabilities gone largely under the radar? One reason appears to be lack of transparency in how they are reported. Governments are not required to fund those liabilities, and in most places they don’t appear on balance sheets. (That omission will be corrected if GASB, the government accounting standards setter, prevails.) OPEBs also lack the constitutional protections of pension promises. That means cities like Detroit that run into financial trouble may find a way out of those obligations. In most places though, taxpayers will foot the bill unless benefits are renegotiated.

You can read more about OPEBs and Bob’s suggestions for how to address them at:

Real Clear Markets – “Unfunded Retired Healthcare Benefits are the Elephant in the Room

Boston Globe – “Boston Must Rein Retiree Health Plans