11-20 of 362 Results

Article

The Empirics of Network Models  

Pedro CL Souza

The literature documenting a wide range of network or peer effects has blossomed in the past decade and have appeared in most major economics journals, using a variety of methods and identification strategies. Reviewing the empirics of those papers suggests a few broad classes of econometric models. The first and canonical model is the “linear-in-means,” which grows from Manski’s seminal work. More recently, it has shown that network asymmetry conditions (known as “peers-of-peers” instrument approach) can be used to instrument the endogeneity inherently present in the linear-in-means model. Moving to more recent empirical practice reveals novel and creative instrumentation strategies exploring particular empirical settings. The network identification strategies can also be combined with traditional differences-and-differences, event-study, and regression discontinuity designs. For example, under certain conditions, one can explore the variation that stems from the differential comparison of the evolution over time of well-connected versus less-well-connected individuals; or explore the variation in the network structure induced by a discontinuous change in the network. Randomized, controlled studies had substantial importance in revealing network effects in the past literature using standard methods; and, more recently, in understanding the extent to which networks can themselves be endogenous to the provision of the treatment itself. This, in turn, will present future challenges for the econometrics of networks and the identification or evaluation of treatment effects under a causal framework with endogenous interference.

Article

Foreign Exchange Intervention  

Helen Popper

The practice of central bank foreign exchange intervention for a time ran ahead of either compelling theoretical explanations of its use or persuasive empirical evidence of its effectiveness. Research accelerated when the emerging economy crises of the 1990s and the early 2000s brought fresh data in the form of urgent experimentation with foreign exchange intervention and related policies, and the financial crisis of 2008 propelled serious treatment of financial frictions into models of intervention. Current foreign exchange intervention models combine financial frictions with relevant externalities: with the aggregate demand and pecuniary externalities that inform macroeconomic models more broadly, and with the trade-related learning externalities that are particularly relevant for developing and emerging economies. These models characteristically allow for normative evaluation of the use of foreign exchange intervention, although most (but not all) do so from a single economy perspective. Empirical advances reflect the advantages of more variation in the use of foreign exchange intervention, better data, and novel econometric approaches to addressing endogeneity. Foreign exchange intervention is now widely viewed as influencing exchange rates at least to some extent, and sustained one-sided intervention; and its corresponding reserve accumulation appear to play a role in moderating exchange rate fluctuations and in reducing the likelihood of damaging consequences of financial crises. Key avenues for future research include sorting out which frictions and externalities matter most, and where foreign exchange intervention—and perhaps international cooperation—properly fits (if at all) into the blend of policies that might appropriately address the externalities.

Article

Econometric Methods for Business Cycle Dating  

Máximo Camacho Alonso and Lola Gadea

Over time, the reference cycle of an economy is determined by a sequence of non-observable business cycle turning points involving a partition of the time calendar into non-overlapping episodes of expansions and recessions. Dating these turning points helps develop economic analysis and is useful for economic agents, whether policymakers, investors, or academics. Aiming to be transparent and reproducible, determining the reference cycle with statistical frameworks that automatically date turning points from a set of coincident economic indicators has been the source of remarkable advances in this research context. These methods can be classified into different broad sets of categories. Depending on the assumptions made in the data-generating process, the dating methods are parametric and non-parametric. There are two main approaches to dealing with multivariate data sets: average then date and date then average. The former approach focuses on computing a reference series of the aggregate economy, usually by averaging the indicators across the cross-sectional dimension. Then, the global turning points are dated on the aggregate indicator using one of the business cycle dating models available in the literature. The latter approach consists of dating the peaks and troughs in a set of coincident business cycle indicators separately, assessing the reference cycle itself in those periods where the individual turning points cohere. In the early 21st century, literature has shown that future work on dating the reference cycle will require dealing with a set of challenges. First, new tools have become available, which, being increasingly sophisticated, may enlarge the existing academic–practitioner gap. Compiling the codes that implement the dating methods and facilitating their practical implementation may reduce this gap. Second, the pandemic shock hitting worldwide economies led most industrialized countries to record 2020’s most significant fall and the largest rebound in national economic indicators since records began. Under these influential observations, the outcomes of dating methods could misrepresent the actual reference cycle, especially in the case of parametric approaches. Exploring non-parametric approaches, big data sources, and the classification ability offered by machine learning methods could help improve dating analyses’ performance.

Article

Happiness and Productivity in the Workplace  

Mahnaz Nazneen and Daniel Sgroi

Happiness has become an important concept in economics as a target for government policy at the national level. This is mirrored in an increasing understanding of the microeconomic effects of increased happiness. While correlational studies have for many years documented a relationship between individual-level happiness and productivity, more recent work provides causal evidence that a positive shock to happiness can boost productivity significantly. These studies include three strands of research. The first provides a number of longitudinal surveys that have generated evidence linking happiness to productivity but run the risk of confounding happiness with other related variables that may be driving the relationship. The second includes laboratory experiments that simulate a workplace under tightly controlled conditions, and this strand has established a clear relationship between positive happiness shocks and rises in productivity. The third involves examining experimental field data, which sacrifices the control of laboratory experiments but offers greater realism. However, there is still work to be done generalizing these findings to more complex work environments, especially those that involve cooperative and team-based tasks where increases in happiness may have other consequences.

Article

Immigration and International Trade  

Katharina Erhardt and Andrea Lassmann

International trade involves the movement of goods across borders, while immigration pertains to the movement of people across national boundaries. These two phenomena are strongly correlated. To some extent, the similarity between barriers to trade and migration explains this correlation, with distance being a crucial factor in both trade and migration. Geographically closer countries tend to engage in both trade and migration because of closer cultural connections. Immigration is itself also an important determinant of trade flows. Migrant networks play a vital role in reducing trade barriers by improving information sharing and facilitating business connections. This, in turn, leads to an increase in both exports and imports between countries. As trade increasingly relies on efficient firm and supplier matching, particularly within global supply chains, the influence of migrant networks becomes more significant. Finally, immigration also drives demand for goods and services from migrants’ home countries. Migrants often maintain strong ties to their home countries and prefer consuming products and services from those regions. This preference fosters increased bilateral trade flows between the home country and the country of immigration. In summary, international trade and immigration are closely linked, and understanding the interplay between international trade and immigration is crucial for comprehending the dynamics of the global economy.

Article

Real-Time Transaction Data for Nowcasting and Short-Term Economic Forecasting  

John W. Galbraith

Transaction data from consumer purchases is used for monitoring, nowcasting, or short-term forecasting of important macroeconomic aggregates such as personal consumption expenditure and national income. Data on individual purchase transactions, recorded electronically at point of sale or online, offer the potential for accurate and rapid estimation of retail sales expenditure, itself an important component of personal consumption expenditure and therefore of national income. Such data may therefore allow policymakers to base actions on more up-to-date estimates of the state of the economy. However, while transaction data may be obtained from a number of sources, such as national payments systems, individual banks, or financial technology companies, data from each of these sources contain limitations. Data sets will differ in the forms of information contained in a record, the degree to which the samples are representative of the relevant population of consumers, and the different types of payments that are observed and captured in the record. As well, the commercial nature of the data may imply constraints on the researcher’s ability to make data sets available for replication. Regardless of the source, the data will generally require filtering and aggregation in order to provide a clear signal of changes in economic activity. The resulting series may be incorporated into any of a variety of model types, along with other data, for nowcasting and short-term forecasting.

Article

Time Consistent Policies and Quasi-Hyperbolic Discounting  

Łukasz Balbus, Kevin Reffett, and Łukasz Woźny

In dynamic choice models, dynamic inconsistency of preferences is a situation in which a decision-maker’s preferences change over time. Optimal plans under such preferences are time inconsistent if a decision-maker has no incentive to follow in the future the (previously chosen) optimal plan. A typical example of dynamic inconsistency is the case of present bias preferences, where there is a repeated preference toward smaller present rewards versus larger future rewards. The study of dynamic choice of decision-makers who possess dynamically inconsistent preferences has long been the focal point of much work in behavioral economics. Experimental and empirical literatures both point to the importance of various forms of present-bias. The canonical model of dynamically inconsistent preferences exhibiting present-bias is a model of quasi-hyperbolic discounting. A quasi-hyperbolic discounting model is a dynamic choice model, in which the standard exponential discounting is modified by adding an impatience parameter that additionally discounts the immediately succeeding period. A central problem with the analytical study of decision-makers who possess dynamically inconsistent preferences is how to model their choices in sequential decision problems. One general answer to this problem is to characterize and compute (if they exist) constrained optimal plans that are optimal among the set of time consistent sequential plans. Time consistent plans are those among the set of feasible plans that will actually be followed, or not reoptimized, by agents whose preferences change over time. These are called time consistent plans or policies (TCPs). Many results of the existence, uniqueness, and characterization of stationary, or time invariant, TCPs in a class of consumption-savings problems with quasi-hyperbolic discounting, as well as provide some discussion of how to compute TCPs in some extensions of the model are presented, and the role of the generalized Bellman equation operator approach is central. This approach provides sufficient conditions for the existence of time consistent solutions and facilitates their computation. Importantly, the generalized Bellman approach can also be related to a common first-order approach in the literature known as the generalized Euler equation approach. By constructing sufficient conditions for continuously differentiable TCPs on the primitives of the model, sufficient conditions under which a generalized Euler equation approach is valid can be provided. There are other important facets of TCP, including sufficient conditions for the existence of monotone comparative statics in interesting parameters of the decision environment, as well as generalizations of the generalized Bellman approach to allow for unbounded returns and general certainty equivalents. In addition, the case of multidimensional state space, as well as a general self generation method for characterizing nonstationary TCPs must be considered as well.

Article

What Drives HIV in Africa? Addressing Economic Gender Inequalities to Close the HIV Gender Gap  

Aurélia Lépine, Henry Cust, and Carole Treibich

Ending HIV as a public health threat by 2030 presents challenges significantly different to those of the past 40 years. Initially perceived as a disease affecting gay men, today, HIV disproportionately affects adolescents and young women in Africa. Current strategies to prevent HIV mostly rely on using biomedical interventions to reduce the risk of infection during risky sex and to address that biologically; women are more vulnerable to HIV infection than men. Ongoing policies and strategies to end the AIDS epidemic in Africa are likely to fail if implemented alone, given they fail to address why vulnerable young women engage in risky sexual behaviors. Evidence strongly suggests economic vulnerability, rather than income level, is a primary driver of women's decision to engage in commercial and transactional sex. By viewing HIV through the lens of structural gender inequality, poverty, and use of risky sexual behaviors to cope with economic shocks, a new explanation for the HIV gender gap emerges. New and promising approaches to reduce HIV acquisition and transmission by protecting women from economic shocks and increasing their ability to participate in the economy have proven effective. Such interventions are vital to break the pattern of unequal HIV transmission against women and if HIV is to be beaten.

Article

Central Bank Monetary Policy and Consumer Credit Markets  

Xudong An, Larry Cordell, Raluca A. Roman, and Calvin Zhang

Central banks around the world use monetary policy tools to promote economic growth and stability; for example, in the United States, the Federal Reserve (Fed) uses federal funds rate adjustments, quantitative easing (QE) or tightening, forward guidance, and other tools “to promote effectively the goals of maximum employment, stable prices, and moderate long-term interest rates.” Changes in monetary policy affect both businesses and consumers. For consumers, changes in monetary policy affect bank credit supply, refinancing activity, and home purchases, which in turn affect household consumption and thus economic growth and price stability. The U.S. Fed rate cuts and QE programs during COVID-19 led to historically low interest rates, which spurred a huge wave of refinancings. However, the pass-through of rate savings in the mortgage market declined during the pandemic. The weaker pass-through can be linked to the extraordinary growth of shadow bank mortgage lenders during the COVID-19 pandemic: Shadow bank mortgage lenders charged mortgage borrowers higher rates and fees; therefore, a higher market share of them means a smaller overall pass-through of rate savings to mortgage borrowers. It is important to note that these shadow banks did provide convenience to consumers, and they originated loans faster than banks. The convenience and speed could be valuable to borrowers and important in transmitting monetary policy in a timelier way, especially during a crisis.

Article

The Implications of Pupil Rank for Achievement  

Richard Murphy and Felix Weinhardt

The significance of social interaction has become an increasingly important part of economic thought and models through the work on peer effects, social norms, and networks. Within this literature, a novel focus of ranking within groups has emerged. The rank of an individual is usually defined as the ordinal position within a specific group. This could be the work environment or a classroom, and much of this literature focuses on rank effects in education settings. The literature studies rank effects for various age groups. There is evidence that a rank position even during early life phases, such as in elementary education, has lasting effects on education outcomes such as test scores or subject specializations, choices during college, and wages. A first-order challenge in the study of rank effects is to separate them from other highly correlated effects. For example, individuals with a high rank academic rank in a group will likely have high academic ability in absolute terms. Papers in this field directly account for measured ability, and so rely on the variation in rank that exists across groups for any given ability measure, that is, a score of 80 in one group would rank the student top, while near the bottom in another. The comparability of achievement measures across settings is key; one commonly employed solution is to account for level differences across settings. While the literature has now established the importance of rank, there are several—potentially non-competing—ideas for the precise behavioral mechanisms of why rank matters so much. Future work will most likely focus on integrating rank effects into the literature on social interactions to discuss implications for optimal group formation.