The Bank for International Settlements (BIS) is an international financial institution owned by central banks that "fosters international monetary and financial cooperation and serves as a bank for central banks".The BIS carries out its work through its meetings, programmes and through the Basel Process – hosting international groups pursuing global financial stability and facilitating their interaction. It also provides banking services, but only to central banks and other international organizations. It is based in Basel, Switzerland, with representative offices in Hong Kong and Mexico City.
We document that firms in emerging markets borrow more in foreign currency when the local currency actually provides a better hedge in downturns. Motivated by this fact, we develop an international corporate finance model in which firms facing adverse selection choose the foreign currency share of their debt. In the unique separating equilibrium, good firms optimally expose themselves to currency risk to signal their type. Crucially, the nature of this equilibrium depends on the co-movement between cash flows and the exchange rate. We provide extensive empirical evidence for this signaling channel using a granular dataset including more than 4,800 firms in 19 emerging markets between 2005 and 2021. Our results have implications for evaluating and mitigating risks arising from currency mismatches in corporate balance sheets.
This paper studies the interaction between monetary policy and macroeconomic stability in a model with two distinguishing features. First, financing - cash flows - underpins all economic activity, with banks generating deposits by granting loans. Money is non-neutral as the policy interest rate anchors the real economy. Second, bank lending is subject to an endogenous boom-bust cycle due to externalities in the loan market. Together, these features imply that monetary policy may have long-lasting impact on the real economy through its in fluence on the financial cycle. In this 'finance-based' economy, there is no well-defined natural rate of interest to which the economy gravitates. The possibility of a 'low interest rate trap' emerges: monetary policy that leans insufficiently against the build-up of financial imbalances increases the vulnerability to financial busts over successive cycles. As a result, low rates can beget lower rates.
Several emerging markets (EMs) have graduated from the “original sin” and are able to borrow abroad in their local currency. Using a two-country model this paper shows that the shift from foreign currency to local currency debt can reduce the original sin concern, but does not eliminate it. Instead, it morphs the original sin into a "redux” (Carstens and Shin (2019)). As long as EMs rely on foreign intermediated funding, local currency borrowing shifts the currency mismatch problem to the balance sheet of financially constrained global lenders. As exchange rate movements significantly affect lenders’ capacity to lend, this still leaves EMs vulnerable to sharp capital flow reversals, as in the original sin case. We document empirical evidence in favor of this prediction of the model using data on currency composition of external debt of EMs. We also use the model to show that a large domestic investor base is key to eliminating the original sin, and foreign exchange intervention proves to be a useful addition to the policy toolkit of central banks.
Prevailing explanations of persistently low interest rates appeal to a secular decline in the natural interest rate, or r-star, due to factors outside monetary policy's control. We propose informational feedback via learning as an alternative explanation for persistently low rates, where monetary policy plays a crucial role. We extend the canonical New Keynesian model to an incomplete information setting where the central bank and the private sector learn about r-star and infer each other's information from observed macroeconomic outcomes. An informational feedback loop emerges when each side underestimates the effect of its own action on the other's inference, possibly leading to large and persistent changes in perceived r-star disconnected from fundamentals. Monetary policy, through its influence on the private sector's beliefs, endogenously determines r-star as a result. We simulate a calibrated model and show that this `hall-of-mirrors' effect can explain much of the decline in real interest rates since 2008.
This paper examines the impact of generative artificial intelligence (Gen AI) on labour productivity through a quasi-experiment involving software developers. In September 2023, Ant Group introduced CodeFuse, a large language model (LLM) designed to support coding tasks. While some programmer teams began using CodeFuse, others were not made aware of its release. Exploiting this natural variation in exposure, we identify comparable treatment and control groups of programmers to estimate the causal effect of Gen AI adoption on productivity. We find that the use of CodeFuse increases code output by over 50%. However, these productivity gains are statistically significant only among junior or entry-level staff; the impact on more senior developers is more limited. Importantly, most of the productivity gains stem not from the direct use of LLM-generated code, but from time savings that allow programmers to work more efficiently. Recognising that LLMs can inflate code volume, we assess robustness using alternative, task-based productivity measures and find a 22% increase in tasks completed. An ex-post survey of programmers supports these findings.
