Mostly Economics

Yueran Ma and Andrei Schleifer in this NBER paper trace history and invention of corporate governance:

RBI Bulletin (Apr 2025) has this interesting article by Avnish Kumar, Priyanka Sachdeva, and Indranil Bhattacharyya.

They analyse three years of RBI’s Standing Deposit Facility:

Marking three years since its introduction, the Standing Deposit Facility (SDF) has been an important component of the Reserve Bank of India’s liquidity management framework, replacing the fixed rate reverse repo as the floor of the LAF corridor. This article presents an assessment of the SDF in India in the overall context of standing facilities made available by central banks.

• • The institution of the SDF is generally in line with global best practices wherein deposit facilities are in the form of unsecured deposits.
  • The simultaneous occurrence of liquidity deficit conditions alongside substantial fund placements under the SDF suggests asymmetric distribution of liquidity within the banking system.
  • The increase in the share of SDF balances as a proportion of total absorption by the Reserve Bank reflects the increased liquidity preference of banks.
  • The empirical results bear testimony to the importance of liquidity conditions, liquidity uncertainty and market microstructure in determining the WACR and its spread.

Tim Sablik of Richmond Fed traces the history of data in macro policy:

Fed officials frequently describe their monetary policy decisions as data dependent. As the central bank has navigated the recovery from the COVID-19 pandemic, a common refrain in its policy statements is that the Federal Open Market Committee (FOMC) will “carefully assess incoming data, the evolving outlook, and the balance of risks” when considering further adjustments.

“We are looking at the data to guide us in what we should do,” Fed Chair Jerome Powell said at the press conference following the FOMC’s meeting at the end of January.

The demand for data in economics as a whole has only grown in recent decades. A 2017 article in the American Economic Review found that the profession has become increasingly empirical since 1980, relying more on data analysis over theoretical models. This “empirical turn,” as some economists have called it, has been facilitated by computerization, which has both increased the supply of data and aided in its analysis. At the same time, challenges around data quality and timeliness have emerged. How does the Fed ensure it’s getting the best information to guide monetary policy?

The importance of surveys has declined as people do not respond to the surveys:

In recent decades, however, researchers have faced mounting challenges to using surveys for data collection. One of the biggest is falling survey response rates. Early in the 20th century, most surveys were conducted face-to-face. From the 1960s to the 1990s, the proliferation of phones in households offered a new method for sampling large populations. While phones initially made it easier to reach survey participants, inventions like the answering machine and caller ID (which smartphones have made ubiquitous) made it easier for households and businesses to avoid such calls. The rise of phone and text scams may have also contributed to the growing unwillingness of individuals to respond to requests from unknown numbers. Finally, surveys may have become a victim of their own success. Between the 1980s and 2000s, the number and length of government and private surveys exploded. Some researchers suggest that this has led to survey fatigue among households, contributing to lower response rates.

Big data?

These challenges can increase the likelihood that preliminary economic indicators are subject to significant revisions later as new data become available. Last August, the BLS revised the number of jobs created from April 2023 to March 2024 down by more than 800,000. Such revisions pose a clear challenge for monetary policymakers trying to get a real-time picture of the economy to guide their decisions.

This has led Fed researchers to explore alternative data sources. In addition to helping survey-based research, the growing computerization of household and business activity has led to an explosion of new economic data. Often referred to as “big data,” these datasets offer the potential to give researchers a much more granular and timelier snapshot of economic activity.

During the initial weeks and months of the COVID-19 pandemic, researchers across the Federal Reserve System turned to a variety of such nontraditional data sources to get a better understanding of what was happening to the economy. According to a 2022 book chapter by Tomaz Cajner, Laura Feiveson, Christopher Kurz, and Stacey Tevlin of the Fed Board of Governors, Fed researchers looked at employment data from payroll processors, retail sales from Fiserv card swipe data, restaurant reservations from OpenTable, and airport departures from the Transportation Security Administration, among other nontraditional data sources.

Prof Olivia Mitchell of Wharton on financial literacy:

Twenty-five states now require high school students to take a personal finance course before graduating, yet financial literacy remains low in the United States. About half of American adults cannot correctly answer three multiple-choice questions designed to test basic money knowledge.

Wharton economist Olivia S. Mitchell, who came up with those questions with Dr. Annamaria Lusardi at Stanford, wants to change that. As a professor in the Department of Business Economics and Public Policy, Mitchell has spent her career studying spending and saving. She said a major reason why Americans aren’t saving nearly enough for a comfortable retirement is that they fail to budget. They spend too much money on overdraft fees, credit card interest, and luxury items.

Low financial literacy costs Americans an estimated $390 billion a year, Mitchell said, and people with poor budgeting skills are seven times more likely to spend over 20 hours a week dealing with personal finance issues.

“Moreover, if you don’t save when you’re young, you forego all the beauty of interest compounding that will make you a happier retiree,” she said in an interview with Wharton Business Daily.

She says what kids should know in each age-group:

Parents can play a critical role in teaching their children how to be smart with money, and there are lessons to be learned at every age. Mitchell offered this advice:

• • Ages 3 to 5: Start explaining the concepts of money. Show them bills and coins and how to make change, both physically and digitally.
  • Ages 6 to 9: Introduce the idea of doing tasks to earn extra money. Have a physical or virtual piggy bank or savings jar to reinforce the difference between spending and saving. Explain that time and money are finite resources, so choices have to be made.
  • Ages 10 to 13: Start teaching them how to comparison shop and save for bigger purchases, such as a bicycle or a gaming console. Open a savings account for them, which helps them understand the role of banks.
  • Ages 14 to 18: Teach them how to budget, track their expenses, and live within their means. If teens take a part-time job, explain paychecks and taxes. It’s also important at this stage to explain credit, the benefits of good credit, and the risks of debt.