Cambridge Working Papers in Economics (CWPE)
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Cambridge Working Papers in Economics (CWPE) is a new series of papers from the Faculty of Economics and the Department of Applied Economics. It supersedes the DAE Working Paper series.
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Please note the Working Papers often represent early stages in the presentation of research findings, and should not be quoted without permission.
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Item Open Access Asymptotic Theory Under Network Stationarity(Faculty of Economics, University of Cambridge, 2024-07-04) Vainora, J.This paper develops an asymptotic theory for network data based on the concept of network stationarity, explicitly linking network topology with the dependence between network entities. Each pair of entities is assigned a class based on a bivariate graph statistic. Network stationarity assumes that conditional covariances depend only on the assigned class. The asymptotic theory, developed for a growing network, includes laws of large numbers, consistent autocovariance function estimation, and a central limit theorem. A significant portion of the assumptions concerns random graph regularity conditions, particularly those related to class sizes. Weak dependence assumptions use conditional α-mixing adapted to networks. The proposed framework is illustrated through an application to microfinance data from Indian villages.Item Open Access Sticky Prices or Sticky Wages? An Equivalence Result(2023-10-20) Bilbiie, F. O.; Trabandt, M.We show an equivalence result in the standard representative agent New Keynesian model after demand, wage markup and correlated price markup and TFP shocks: assuming sticky prices and flexible wages yields identical allocations for GDP, consumption, labor, inflation and interest rates to the opposite case- flexible prices and sticky wages. This equivalence result arises if the price and wage Phillips curves' slopes are identical and generalizes to any pair of price and wage Phillips curve slopes such that their sum and product are identical. Nevertheless, the cyclical implications for profits and wages are substantially different. We discuss how the equivalence breaks when these factor-distributional implications matter for aggregate allocations, e.g. in New Keynesian models with heterogeneous agents, endogenous firm entry, and non-constant returns to scale in production. Lastly, we point to an econometric identification problem raised by our equivalence result and discuss possible solutions thereof.Item Open Access Pulled-in and Crowded-out: Heterogeneous Outcomes of Merit-based School Choice(Faculty of Economics, University of Cambridge, 2023-03-20) Dalla-Zuanna, A.; Liu, K.; Salvanes, K.We analyze the effect of reforming the high school admission system from a residence based allocation to a merit-based allocation. The merit-based system generates oversubscribed schools, which favor high-GPA students at the expense of displacing low-GPA ones. We use the potential outcomes framework to analyze the effect of the reform, separating the effects for those gaining access to competitive schools from those losing access and identifying these parameters by using the reform as an instrument within subpopulations defined by admission cutoffs and GPA. The small and negative overall effect of the reform hides large negative effects for the crowded-out students.Item Open Access How to Detect Network Dependence in Latent Factor Models? A Bias-Corrected CD Testy(Faculty of Economics, University of Cambridge, 2021-07-31) Pesaran, M. H.; Xie, Y.In a recent paper Juodis and Reese (2022) (JR) show that the application of the CD test proposed by Pesaran (2004) to residuals from panels with latent factors results in over-rejection. They propose a randomized test statistic to correct for over-rejection, and add a screening component to achieve power. This paper considers the same problem but from a different perspective, and shows that the standard CD test remains valid if the latent factors are weak in the sense the strength is less than half. In the case where latent factors are strong, we propose a bias-corrected version, CD*, which is shown to be asymptotically standard normal under the null of error cross-sectional independence and have power against network type alternatives. This result is shown to hold for pure latent factor models as well as for panel regression models with latent factors. The case where the errors are serially correlated is also considered. Small sample properties of the CD* test are investigated by Monte Carlo experiments and are shown to have the correct size for strong and weak factors as well as for Gaussian and non-Gaussian errors. In contrast, it is found that JR's test tends to over-reject in the case of panels with non-Gaussian errors, and has low power against spatial network alternatives. In an empirical application, using the CD* test, it is shown that there remains spatial error dependence in a panel data model for real house price changes across 377 Metropolitan Statistical Areas in the U.S., even after the effects of latent factors are Filtered out.Item Open Access Markets and Markups: A New Empirical Framework and Evidence on Exporters from China(Faculty of Economics, University of Cambridge, 2018-02-13) Corsetti, G.; Han, L; Crowley, M.; Song, H.We develop an empirical framework that decomposes the export price elasticity to the exchange rate into contributions from markup and marginal cost elasticities. This framework embodies a new estimator of the markup elasticity that controls for marginal costs and endogenous market participation, and a new classification of products based on Chinese linguistics that helps refine the analysis of firms' market power. Using Chinese customs data, we document a two- to three-fold increase in markup elasticities across product and firm types after 2005, indicating exporters from China acquired substantial market power in foreign markets.Item Open Access The Network Origin of Slow Labor Reallocation(Faculty of Economics, University of Cambridge, 2024-10-28) Bocquet, L.How fast do labor markets adjust to technology shocks? This paper introduces a novel network-based framework to model skill frictions between occupations. Using expert data on skills, I construct a network of occupations and find it is sparse, divided in clusters of similar occupations with 'bridge occupations' linking distinct clusters. Leveraging French administrative data, I show that workers transitioning through these 'bridges' move to occupations with higher wages and lower unemployment. Next, I build a tractable model of job search with networked labor markets, and demonstrate that bridge occupations significantly affect reallocation speed, with slow reallocation creating large adjustment costs. I then augment the model with quantitative extensions, leveraging hat-algebra methods to solve counterfactuals without having to estimate large numbers of parameters. Calibrated to French data, the model predicts that robot adoption induces slow reallocation, around 40 quarters, and that this sluggish reallocation reduces welfare gains by approximately 40%- an order of magnitude higher than previous estimates. However, policies targeting bridge occupations can speed-up reallocation, and much more so than policies targeting tight occupations directly. These findings highlight the crucial role of the occupation network in shaping reallocation dynamics and provide new insights for the design of labor market policies.Item Open Access The Effectiveness of Teamwork for Student Academic Outcomes: Evidence from a Field Experiment(Faculty of Economics, University of Cambridge, 2024-10-10) Banerjee, R.; Blunch, N-H.; Cassese, D.; Gupta, N. D.; Pin, P.An enduring question in education is whether team-based peer learning methods help improve learning outcomes among students. We randomly assign around 10,000 middle school students in Karnataka, India, to alternative peer learning treatments in Math and English that vary the intensity of collaboration. Teamwork with co-coaching outperforms simple teamwork and incentive treatments by increasing the test scores by about 0.25 standard deviation, but only in Math. This is both statistically and economically significant for students at the bottom of the ability distribution. We develop theoretical conditions under which teamwork with co-coaching outperforms simple teamwork as a peer-learning method.Item Open Access Forecasting 2024 US Presidential Election by States Using County Level Data: Too Close to Call(Faculty of Economics, University of Cambridge, 2024-10-21) Pesaran, M. H.; Song, H.This document is a follow up to the paper by Ahmed and Pesaran (2020, AP) and reports state-level forecasts for the 2024 US presidential election. It updates the 3,107 county level data used by AP and uses the same machine learning techniques as before to select the variables used in forecasting voter turnout and the Republican vote shares by states for 2024. The models forecast the non-swing states correctly but give mixed results for the swing states (Nevada, Arizona, Wisconsin, Michigan, Pennsylvania, North Carolina, and Georgia). Our forecasts for the swing states do not make use of any polling data but confirm the very close nature of the 2024 election, much closer than AP' s predictions for 2020. The forecasts are too close to call.Item Open Access University admissions during a pandemic(Faculty of Economics, University of Cambridge, 2024-10-03) Ilie, S.; Maragkou, K.The global education system experienced unprecedented disruption during the Covid- 19 pandemic. This paper investigates the impact of the exam cancellation in England, and the subsequent shift to teacher-assigned grades, on university application outcomes submitted prior to the outbreak. Using a newly linked administrative dataset covering four cohorts of university applicants, with both teacher-assigned and exam-based grades across multiple A-level subjects, we identify significant grade inflation relative to anticipated exam results. Our findings indicate that students from schools with higher levels of grade inflation were more likely to gain admission to first-choice or selective universities, and had a lower likelihood of remaining unplaced. Notably, private school students disproportionately benefited from higher levels of grade inflation. However, within state schools, disadvantaged students also experienced some of the largest relative gains. These results raise important questions about the long-term consequences of the pandemic-induced modification in assessment for educational equity and social mobility.Item Open Access The Impacts of Armed Conflict on Human Development: A Review of the Literature(Faculty of Economics, University of Cambridge, 2024-10-08) Vesco, P.; Baliki, G.; Brück, T.; Döring, S.; Eriksson, A.; Fjelde, H.; Guha-Sapir, D.; Hall, J.; Knutsen, C. H.; Leis, M. R.; Mueller, H.; Rauh, C.; Rudolfsen, I.; Swain, A.; Timlick, A.; Vassiliou, P. T. B.; von Schreeb, J.; von Uexkull, N.; Hegre, H.The detrimental impacts of wars on human development are well documented across research domains, from public health to micro-economics. However, these impacts are studied in compartmentalized silos, which limits a comprehensive understanding of the consequences of conflicts, hampering our ability to effectively sustain human development. This article takes a first step in filling this gap by reviewing the literature on conflict impacts through the lens of an inter-disciplinary theoretical framework. We review the literature on the consequences of conflicts across 9 dimensions of human development: health, schooling, livelihood and income, growth and investments, political institutions, migration and displacement, socio-psychological wellbeing and capital, water access, and food security. The study focuses on both direct and indirect impacts of violence, reviews the existing evidence on how impacts on different dimensions of societal wellbeing and development may intertwine, and suggests plausible mechanisms to explain how these connections materialize. This exercise leads to the identification of critical research gaps and reveals that systemic empirical testing of how the impacts of war spread across sectors is severely lacking. By streamlining the literature on the impacts of war across multiple domains, this review represents a first step to build a common language that can overcome disciplinary silos and achieve a deeper understanding of how war reverberates across society. This multidisciplinary understanding of conflict impacts may eventually help reconcile divergent estimates and enable forward-looking policies that minimize the costs of war.Item Open Access Tracing the Origins of Gender Bias in Teacher Grades(Faculty of Economics, University of Cambridge, 2024-10-03) Leckie, G.; Maragkou, K.This paper exploits a unique institutional feature of the university admissions process in England, where applications are based on teacher-predicted grades, to compare differences between predictions and actual exam results by student gender. Using newly linked administrative data, we find that boys receive lower grade predictions relative to equally performing girls. A substantial portion of this gap can be explained by girls’ advantage in overall scholastic competence. Once we account for this skill differential, the gender gap in non-STEM fields diminishes significantly, whereas in STEM, it shifts in favour of boys. We interpret the remaining gaps as indicative of potential gender bias in schools.Item Open Access Latent Position-Based Modeling of Parameter Heterogeneity(Faculty of Economics, University of Cambridge, 2024-10-01) Vainora, J.This paper proposes to use the Generalized Random Dot Product Graph model and the underlying latent positions to model parameter heterogeneity. We discuss how the Stochastic Block Model can be directly applied to model individual parameter heterogeneity. We also develop a new procedure to model pairwise parameter heterogeneity requiring the number of distinct latent distances between unobserved communities to be low. It is proven that, asymptotically, the heterogeneity pattern can be completely recovered. Additionally, we provide three test statistics for the assumption on the number of distinct latent distances. The proposed methods are illustrated using data on a household microfinance program and the S&P 500 component stocks.Item Open Access Robust Market Interventions(Faculty of Economics, University of Cambridge, 2024-11-02) Galeotti, A.; Golub, B.; Goyal, S.; Talamas, E.; Tamuz, O.A large differentiated oligopoly yields inefficient market equilibria. An authority with imprecise information about the primitives of the market aims to design tax/subsidy interventions that increase efficiency robustly—i.e., with high probability. We identify a condition on demand that guarantees the existence of such interventions, and we show how to construct them using noisy estimates of demand complementarities and substitutabilities across products. The analysis works by deriving a novel description of the incidence of market interventions in terms of spectral statistics of Slutsky matrices. Our notion of recoverable structure ensures that parts of the spectrum that are useful for the design of interventions are statistically recoverable from noisy demand estimates.Item Open Access Network Structures and Heterogeneity in Policy Preferences at the FOMC(Faculty of Economics, University of Cambridge, 2024-12-09) Bhattacharjee, A.; Holly, S.; Wasseja, M.Transcripts from the US Federal Open Markets Committee provide, albeit with a lag, valuable information on the monetary policymaking process at the Federal Reserve Bank. We use the data compiled by Chappell et al. (2005b) on preferred interest rates (not votes) of individual FOMC members. Together with information on which monetary policy decisions are based, we use these preferred rates to understand decision making in the FOMC, focussing both on cross-member heterogeneity and interaction among the members of the committee. Our contribution is to provide a method of unearthing otherwise unobservable interactions between the members of the FOMC. We find substantial heterogeneity in the policy reaction function across members. Further, we identify significant interactions between individuals on the committee. The nature of these interdependencies tell us something about information sharing and strategic interactions within the FOMC and provide interesting comparisons with the Bank of England’s Monetary Policy Committee.Item Open Access Empirical Welfare Analysis with Hedonic Budget Constraints(Faculty of Economics, University of Cambridge, 2024-12-09) Bhattacharya, D.; Oparina, E.; Xu, Q.We analyze demand settings where heterogeneous consumers maximize utility for product attributes subject to a nonlinear budget constraint. We develop nonparametric methods for welfare-analysis of interventions that change the constraint. Two new findings are Roy’s identity for smooth, nonlinear budgets, which yields a Partial Differential Equation system, and a Slutsky-like symmetry condition for demand. Under scalar unobserved heterogeneity and single-crossing preferences, the coefficient functions in the PDEs are nonparametrically identified, and under symmetry, lead to path-independent, money-metric welfare. We illustrate our methods with welfare evaluation of a hypothetical change in relationship between property rent and neighborhood school-quality using British microdata.Item Open Access How Air Pollution Makes Firms Less Innovative: Human Capital and Adaptive Strategies(Faculty of Economics, University of Cambridge, 2024-10-31) Cavalcanti, T.; Mohaddes, K.; Nian, H.; Yin, H.This paper explores the long-term impact of air pollution on firm-level R&D human capital composition and innovation, as well as the strategies firms adopt to mitigate these effects. Using a spatial regression discontinuity design based on China’s Huai River heating policy and exploring a novel dataset with detailed information on firmlevel R&D sector, we show that prolonged exposure to air pollution significantly reduces the proportion of R&D workers with advanced degrees, such as PhDs and master’s degrees. To counteract these challenges, firms in polluted areas increase their reliance on external strategies, such as acquiring technology and collaborating with universities, and adopt internal measures, including enhanced welfare subsidies for R&D staff and greater investment in experimental instruments. Despite these efforts, firms in polluted areas still produce lower R&D value compared to those in cleaner regions. Our results highlight the key importance of internal human capital in complementing external technological investments.Item Open Access Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects(Faculty of Economics, University of Cambridge, 2024-11-21) Linton, O. B.; Rücker, M.; Vogt, M.; Walsh, C.We develop new econometric methods for estimation and inference in high-dimensional panel data models with interactive fixed effects. Our approach can be regarded as a non-trivial extension of the very popular common correlated effects (CCE) approach. Roughly speaking, we proceed as follows: We first construct a projection device to eliminate the unobserved factors from the model by applying a dimensionality reduction transform to the matrix of cross-sectionally averaged covariates. The unknown parameters are then estimated by applying lasso techniques to the projected model. For inference purposes, we derive a desparsified version of our lasso-type estimator. While the original CCE approach is restricted to the low-dimensional case where the number of regressors is small and fixed, our methods can deal with both lowand high-dimensional situations where the number of regressors is large and may even exceed the overall sample size. We derive theory for our estimation and inference methods both in the large-T-case, where the time series length T tends to infinity, and in the small-T-case, where T is a fixed natural number. Specifically, we derive the convergence rate of our estimator and show that its desparsified version is asymptotically normal under suitable regularity conditions. The theoretical analysis of the paper is complemented by a simulation study and an empirical application to characteristic based asset pricing.Item Open Access Estimating Factor-Based Spot Volatility Matrices with Noisy and Asynchronous High-Frequency Data(Faculty of Economics, University of Cambridge, 2024-09-19) Li, D.; Linton, O. B.; Zhang, H.We propose a new estimator of high-dimensional spot volatility matrices satisfying a low-rank plus sparse structure from noisy and asynchronous high-frequency data collected for an ultra-large number of assets. The noise processes are allowed to be temporally correlated, heteroskedastic, asymptotically vanishing and dependent on the efficient prices. We define a kernel-weighted pre-averaging method to jointly tackle the microstructure noise and asynchronicity issues, and we obtain uniformly consistent estimates for latent prices. We impose a continuous-time factor model with time-varying factor loadings on the price processes, and estimate the common factors and loadings via a local principal component analysis. Assuming a uniform sparsity condition on the idiosyncratic volatility structure, we combine the POET and kernel-smoothing techniques to estimate the spot volatility matrices for both the latent prices and idiosyncratic errors. Under some mild restrictions, the estimated spot volatility matrices are shown to be uniformly consistent under various matrix norms. We provide Monte-Carlo simulation and empirical studies to examine the numerical performance of the developed estimation methodology.Item Open Access Should We Augment Large Covariance Matrix Estimation with Auxiliary Network Information?(Faculty of Economics, University of Cambridge, 2024-05-20) Ge, S.; Li, S.; Linton, O. B.; Liu, W.; Su, W.In this paper, we propose two novel frameworks to incorporate auxiliary information about interconnections among entities (i.e., network information) into the estimation of large covariance matrices. The current literature either completely ignores this kind of network information (e.g., thresholding and shrinkage) or imposes some very restrictive network structure that limits the application (e.g., banding). In the era of big data, we have easy access to auxiliary network information about these interconnections. Depending on the features of the auxiliary network information at hand and the structure of the covariance matrix, we provide two different frameworks correspondingly —the Network Guided Thresholding and the Network Guided Banding. We show that both Network Guided estimators have optimal convergence rates over a larger class of sparse covariance matrices. Simulation studies indicate that these estimators generally outperform other purely statistical methods, particularly when the true covariance matrix is sparse and the auxiliary network provides reliable information. Empirically, we apply our methods to estimate the covariance matrix of asset returns using various forms of auxiliary network data to construct the global minimum variance (GMV) and Mean-Variance Optimal (MVO) portfolios.Item Open Access 3-Party Covenant Financing of ‘Semi-Regulated’ Pumped Hydro Assets(Faculty of Economics, University of Cambridge, 2024-05-21) Simshauser, P.; Gohde, N.All credible scenarios of a decarbonising Australian power system with high levels of renewables rely on a portfolio of flexible, dispatchable storage and firming assets. Given our current understanding of costs and prices, such portfolios are thought to include short-duration batteries, intermediate-duration pumped hydro, with gas turbines providing the last line of defence. The stochastic intermittency of wind, the synchronicity of rooftop and utility-scale solar PV, and stubbornly inelastic aggregate final demand – at least over the medium term – only serve to underscore this point. Wind and solar output need to be moved through space (networks) and time (storage). The storage asset class with by far the highest energy density, pumped hydro, appears to be facing structurally high capital costs with most recent estimates given via high profile projects under development (viz. Snowy 2.0, Borumba) being ~$5000-$6000/kW in real terms. Yet under-development of pumped hydro will result in sharply rising renewable curtailment rates and a greater reliance on gas turbines – with the latter likely to be intractable. In this article, we focus on material reductions in the carrying cost of capital-intensive, ultra-long-lived pumped hydro assets through a 3-Party Covenant (3PC) financing structure between governments, the consumer base and plant investors. Our modelling suggests this reduces the annual capital costs and imputed cost of storage during operations by more than 35%. Our 3PC model is orchestrated through a semi-regulated business model and issuance of 10-year Commonwealth Government Bonds with a zero ‘credit spread’ – all of which are critical to minimise costs to consumers.