Consumption in the shadow of unemployment

By how much do employed households reduce their consumption when the aggregate unemployment rate rises? In Spain during the Great Recession a 1 percentage point increase in the unemployment rate was related to a strong drop in household consumption of more than 0.7% per equivalent adult. This reduction is the response of forward-looking agents to downward revisions of their expectations on future income growth rates: the shadow of unemployment. Using consumption panel data that include information on physical quantities, we show that the drop in consumption expenditure was indeed a reduction in quantities, and not a switch to cheaper alternatives¿En cuánto se reduce el consumo de los hogares, aun de aquellos que continúan empleados, cuando aumenta la tasa de paro? En España, durante la Gran Recesión, un punto adicional de aumento en la tasa de paro estuvo asociado a una caída de 0,7 % de caída del consumo por adulto equivalente. Esta reducción del consumo refl eja el deterioro de las expectativas de ingreso de los hogares. Utilizando datos de panel que incluyen información sobre cantidades físicas, encontramos que la caída en el gasto de consumo se debe a una reducción en las cantidades, y no en los precio

Measurement error and imputation of consumption in survey data

We study how estimators used to impute consumption in survey data are inconsistent due to measurement error in consumption. Previous research suggests instrumenting consumption to overcome this problem. We show that, if additional regressors are present, then instrumenting consumption may still produce inconsistent estimators. This inconsistency arises from the correlation between additional regressors and measurement error. We propose an additional condition to be satisfied by the instrument that reduces measurement error bias. This condition is directly observable in the data. We apply our findings by revisiting recent research that imputes consumption data from the CEX to the PSID.Campos and Reggio gratefully acknowledge the financial support by the Spanish Ministerio de Ciencia y Tecnología (Grants ECO2009-13169 and ECO2009-11165) and Ministerio de Economía y Competitividad (grants ECO2012-38134 and ECO2012-31358)

Optimal Unemployment Insurance : Consumption versus Expenditure

We study the optimal provision of unemployment insurance (UI) in a framework that distinguishes between consumption and expenditure. We derive a "sufficient statistics" formula for optimal UI that is expressed terms of observable variables and can therefore be used in applied work. Recent research has shown that unemployed households pay less per unit of consumption than employed households. This finding has two counteracting effects on the optimal level of UI. On the one hand, consumption smoothing benefits identified from expenditure data overestimate the true marginal benefits of UI. On the other hand, UI benefits become more valuable because they buy more consumption when unemployed. In an optimal design, which effect dominates depends on the curvature of the utility function. We show that for relative risk aversion larger than one the first effect dominates, leading to lower levels of optimal UI.We gratefully acknowledge financial support by the Spanish Ministerio de Economía y Competitividad (Grants ECO2012-38134 and ECO2012-31358)

Preliminary findings from a survey on the MD state of the practice

In the context of an Italian research project, this paper reports on an on-line survey, performed with 155 software professionals, with the aim of investigating about their opinions and experiences in modeling during software development and Model-driven engineering usage. The survey focused also on used modeling languages, processes and tools. A preliminary analysis of the results confirmed that Model-driven engineering, and more in general software modeling, are very relevant phenomena. Approximately 68% of the sample use models during software development. Among then, 44% generate code starting from models and 16% execute them directly. The preferred language for modeling is UML but DSLs are used as wel

ge_gravity2: a command for solving universal gravity models

We describe an algorithm for computing counterfactual trade flows, prices, output, and welfare in a large class of general equilibrium trade models. We introduce a command called ge_gravity2 that allows users to perform these computations in Stata. This command extends the existing ge_gravity command by allowing users to compute the general equilibrium effects of changes in trade policy in positive supply elasticity models. It can be used to solve any model that falls into the class of universal gravity models as defined by Allen, Arkolakis, and Takahashi [Universal Gravity, Journal of Political Economy, 128(2), 2020, 393-433]

Thick borders in Franco’s Spain: the costs of a closed economy

Entre las décadas de 1940 y 1970, en España se emplearon varias políticas económicas que limitaban el comercio internacional. La combinación de aranceles, restricciones cuantitativas, barreras administrativas y regímenes cambiarios utilizados sufrió frecuentes cambios durante ese período, lo que hace particularmente difícil la cuantificación de su impacto sobre el comercio internacional. En este trabajo se utilizan flujos comerciales históricos y un modelo de gravedad estructural para cuantificar la evolución de una medida, llamada border thickness, que captura de forma resumida las barreras al comercio internacional de un país. Según esta medida, las trabas al comercio en el período 1948-1975 fueron más elevadas que las de cualquier otro país de Europa occidental, incluso una vez iniciado el período de liberalización comercial, que comenzó en 1959. Los elevados impedimentos al comercio tuvieron un efecto negativo considerable sobre el bienestar de los consumidores. Los resultados de este trabajo indican que las pérdidas acumuladas de bienestar en el período 1948-1975 exceden el valor del 20 % del consumo total anual de España.Between the 1940s and 1970s, Spain used a variety of economic policies that hindered international trade. Because the mix of tariffs, quotas, administrative barriers, and exchange rate regimes varied greatly over time, the quantification of the effect of the various trade policies on international trade in this period is particularly elusive. In this paper, we use historical bilateral trade flows and a structural gravity model to quantify the evolution of Spain’s border thickness, a summary measure of its barriers to international trade. We find that Spain’s borders in the period 1948-1975 were thicker than those of any other country in Western Europe, even after the liberalization of trade that started in 1959. These comparatively higher impediments to international trade implied substantial negative effects on consumer welfare. We estimate that accumulated welfare costs over the period 1948-1975 exceed 20% of a year’s total consumption

Micro versus macro consumption data: the cyclical properties of the consumer expenditure survey

The Consumer Expenditure Survey (CEX) offers the most comprehensive consumption data at the consumer level for the United States. Several previous studies have shown a large gap between per-capita consumption from the CEX and the aggregate Personal Consumption Expenditure (PCE) series. While previous research has focused on consumption levels, we focus on the cyclical properties of consumption. We find that the cyclical properties of consumption expenditure data from the two sources are quantitatively very different. This result calls for caution when using CEX data for business cycle researchCampos and Reggio gratefully acknowledge the financial support by the Spanish Ministerio de Ciencia y Tecnología (Grants ECO2009-13169 and ECO2009-11165

Micro vs. macro consumption data : the cyclical properties of the consumer expenditure survey

The Consumer Expenditure Survey (CEX) offers the most comprehensive consumption data at the consumer level for the United States. Several previous studies have shown a large gap between per-capita consumption from the CEX and the aggregate Personal Consumption Expenditure (PCE) series. While previous research has focused on consumption levels, we focus on the cyclical properties of consumption. We find that the cyclical properties of consumption expenditure data from the two sources are quantitatively very different. This result calls for caution when using CEX data for business cycle research.This paper has benefited from comments by Thijs van Rens and by the audiences at Universidad de Vigo and at the Econometric Society European Meetings in Oslo, 2011. Paloma Corrales Asensio provided research assistance. Campos and Reggio gratefully acknowledge the financial support by the Spanish Ministerio de Ciencia y Tecnología (Grants ECO2009-13169 and ECO2009-11165)

On the Deployment of IoT Systems: An Industrial Survey

Internet of Things (IoT) systems are complex and multifaceted, and the design of their architectures needs to consider many aspects at a time. Design decisions concern, for instance, the modeling of software components and their interconnections, as well as where to deploy the components within the available hardware infrastructure in the Edge-Cloud continuum. A relevant and challenging task, in this context, is to identify optimal deployment models due to all the different aspects involved, such as extra-functional requirements of the system, heterogeneity of the hardware resources concerning their processing and storage capabilities, and constraints like legal issues and operational cost limits. To gain insights about the deployment decisions concerning IoT systems in practice, and the factors that influence those decisions, we report about an industrial survey we conducted with 66 IoT architects from 18 countries across the world. Each participant filled in a questionnaire that comprises 15 questions. By analyzing the collected data, we have two main findings: (i) architects rely on the Cloud more than the Edge for deploying the software components of IoT systems, in the majority of the IoT application domains; and (ii) the main factors driving deployment decisions are four: reliability, performance, security, and cost