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Showing papers in "Research Papers in Economics in 2018"


BookDOI
TL;DR: In this paper, the authors studied water allocation under an irrigation bureaucracy subject to corruption and rent-seeking, and found that the decline in water availability and land values from channel head to tail is accentuated along canals having greater lobbying power at the head than at the tail.
Abstract: Surface irrigation is a common pool resource characterized by asymmetric appropriation opportunities across upstream and downstream water users. Large canal systems are also predominantly managed by the state. This paper studies water allocation under an irrigation bureaucracy subject to corruption and rent-seeking. Data on the landholdings and political influence of nearly quarter million irrigators in Pakistan’s vast Indus Basin watershed allow the construction of a novel index of lobbying power. Consistent with a model of misgovernance, the decline in water availability and land values from channel head to tail is accentuated along canals having greater lobbying power at the head than at the tail.

571 citations


ReportDOI
TL;DR: In this paper, a task-based framework for the implications of automation and AI on the demand for labor, wages, and employment is presented. But it does not consider the impact of AI on non-automated tasks, and the authors highlight the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation.
Abstract: We summarize a framework for the study of the implications of automation and AI on the demand for labor, wages, and employment. Our task-based framework emphasizes the displacement effect that automation creates as machines and AI replace labor in tasks that it used to perform. This displacement effect tends to reduce the demand for labor and wages. But it is counteracted by a productivity effect, resulting from the cost savings generated by automation, which increase the demand for labor in non-automated tasks. The productivity effect is complemented by additional capital accumulation and the deepening of automation (improvements of existing machinery), both of which further increase the demand for labor. These countervailing effects are incomplete. Even when they are strong, automation in- creases output per worker more than wages and reduce the share of labor in national income. The more powerful countervailing force against automation is the creation of new labor-intensive tasks, which reinstates labor in new activities and tends to in- crease the labor share to counterbalance the impact of automation. Our framework also highlights the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation and weaken the resulting produc- tivity gains from this transformation: a mismatch between the skill requirements of new technologies, and the possibility that automation is being introduced at an excessive rate, possibly at the expense of other productivity-enhancing technologies.

429 citations


Posted Content
TL;DR: In this article, a literature review paper discusses the proper use of qualitative research methodology to discuss several aspects of the research for the improvement of the skill of the readers, which can be used to explore several areas of human behavior for the development of organizations.
Abstract: This literature review paper discusses the proper use of qualitative research methodology to discuss several aspects of the research for the improvement of the skill of the readers. During the last few decades, the use of qualitative research has been increased in many institutions. It can be used to explore several areas of human behavior for the development of organizations. The purpose of this study is to provide inspirations to the new researchers for the development of their qualitative articles. The paper analyzes the design of qualitative research giving some methodological suggestions to make it explicable to the reader. In this paper an attempt has been taken to study the background of the qualitative research methodology in social sciences and some other related subjects, along with the importance, and main features of the study.

339 citations


Posted Content
TL;DR: The authors disentangles central bank announcements from monetary policy and the central bank's assessment of the economic outlook and studies their effect on the economy using a structural vector autoregression estimated on both US and euro area data.
Abstract: Central bank announcements simultaneously convey information about monetary policy and the central bank's assessment of the economic outlook. This paper disentangles these two components and studies their effect on the economy using a structural vector autoregression estimated on both US and euro area data. It relies on the information inherent in high-frequency comovement of interest rates and stock prices around policy announcements: a surprise policy tightening raises interest rates and reduces stock prices, while the complementary positive central bank information shock raises both. These two shocks have intuitive and very different effects on the economy. Ignoring the central bank information shocks biases the inference on monetary policy non-neutrality. We make this point formally and offer an interpretation of the central bank information shock using a New Keynesian macroeconomic model with financial frictions.

324 citations


Posted Content
TL;DR: An assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions, and some highlights from the emerging econometric literature combining machine learning and causal inference.
Abstract: This paper provides an assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions. It begins by briefly overviewing some themes from the literature on machine learning, and then draws some contrasts with traditional approaches to estimating the impact of counterfactual policies in economics. Next, we review some of the initial “off-the-shelf” applications of machine learning to economics, including applications in analyzing text and images. We then describe new types of questions that have been posed surrounding the application of machine learning to policy problems, including “prediction policy problems,” as well as considerations of fairness and manipulability. We present some highlights from the emerging econometric literature combining machine learning and causal inference. Finally, we overview a set of broader predictions about the future impact of machine learning on economics, including its impacts on the nature of collaboration, funding, research tools, and research questions.

283 citations


ReportDOI
TL;DR: In this paper, the authors present evidence that estimates based on this "shift-share" instrument conflate the short-and long-run responses to immigration shocks, and propose a "multiple instrumentation" procedure that isolates the spatial variation arising from changes in the country-of-origin composition at the national level and permits them to estimate separately the short and long run effects.
Abstract: A large literature exploits geographic variation in the concentration of immigrants to identify their impact on a variety of outcomes. To address the endogeneity of immigrants' location choices, the most commonly-used instrument interacts national inflows by country of origin with immigrants' past geographic distribution. We present evidence that estimates based on this "shift-share" instrument conflate the short- and long-run responses to immigration shocks. If the spatial distribution of immigrant inflows is stable over time, the instrument is likely to be correlated with ongoing responses to previous supply shocks. Estimates based on the conventional shift-share instrument are therefore unlikely to identify the short-run causal effect. We propose a "multiple instrumentation" procedure that isolates the spatial variation arising from changes in the country-of-origin composition at the national level and permits us to estimate separately the short- and long-run effects. Our results are a cautionary tale for a large body of empirical work, not just on immigration, that rely on shift-share instruments for causal inference.

246 citations


Report SeriesDOI
TL;DR: In this paper, the authors focused on the risk of automation and its interaction with training and the use of skills at work and investigated the same methodology using national data from Germany and United Kingdom.
Abstract: This study focuses on the risk of automation and its interaction with training and the use of skills at work. Building on the expert assessment carried out by Carl Frey and Michael Osborne in 2013, the paper estimates the risk of automation for individual jobs based on the Survey of Adult Skills (PIAAC). The analysis improves on other international estimates of the individual risk of automation by using a more disaggregated occupational classification and identifying the same automation bottlenecks emerging from the experts’ discussion. Hence, it more closely aligns to the initial assessment of the potential automation deriving from the development of Machine Learning. Furthermore, this study investigates the same methodology using national data from Germany and United Kingdom, providing insights into the robustness of the results. The risk of automation is estimated for the 32 OECD countries that have participated in the Survey of Adult Skills (PIAAC) so far. Beyond the share of jobs likely to be significantly disrupted by automation of production and services, the accent is put on characteristics of these jobs and the characteristics of the workers who hold them. The risk is also assessed against the use of ICT at work and the role of training in helping workers transit to new career opportunities.

237 citations


ReportDOI
TL;DR: This article found that respondents greatly overestimate the total number of immigrants, think immigrants are culturally and religiously more distant from them, and are economically weaker -- less educated, more unemployed, poorer, and more reliant on government transfers than is the case.
Abstract: We design and conduct large-scale surveys and experiments in six countries to investigate how natives' perceptions of immigrants influence their preferences for redistribution. We find strikingly large biases in natives' perceptions of the number and characteristics of immigrants: in all countries, respondents greatly overestimate the total number of immigrants, think immigrants are culturally and religiously more distant from them, and are economically weaker -- less educated, more unemployed, poorer, and more reliant on government transfers -- than is the case. While all respondents have misperceptions, those with the largest ones are systematically the right-wing, the non college-educated, and the low-educated working in immigration-intensive sectors. Support for redistribution is strongly correlated with the perceived composition of immigrants -- their origin and economic contribution -- rather than with the perceived share of immigrants per se. Given the very negative baseline views that respondents have of immigrants, simply making them think about immigration in a randomized manner makes them support less redistribution, including actual donations to charities. We also experimentally show respondents information about the true i) number, ii) origin, and iii) ``hard work'' of immigrants in their country. On its own, information on the ``hard work'' of immigrants generates more support for redistribution. However, if people are also prompted to think in detail about immigrants' characteristics, then none of these favorable information treatments manages to counteract their negative priors that generate lower support for redistribution.

232 citations


Posted Content
TL;DR: In this article, the authors analyze the relationship between economic growth, factor inputs, institutions, and entrepreneurship and investigate whether entrepreneurship and institutions, in combination in an ecosystem, can be viewed as a "missing link" in an aggregate production function analysis of cross-country differences in economic growth.
Abstract: We analyze conceptually and in an empirical counterpart the relationship between economic growth, factor inputs, institutions, and entrepreneurship. In particular, we investigate whether entrepreneurship and institutions, in combination in an ecosystem, can be viewed as a “missing link” in an aggregate production function analysis of cross-country differences in economic growth. To do this, we build on the concept of National Systems of Entrepreneurship (NSE) as resource allocation systems that combine institutions and human agency into an interdependent system of complementarities. We explore the empirical relevance of these ideas using data from a representative global survey and institutional sources for 46 countries over the period 2002–2011. We find support for the role of the entrepreneurial ecosystem in economic growth

224 citations


Posted Content
TL;DR: This lecture exposits the use of external instruments and provides conditions on instruments and control variables under which external instrument methods produce valid inference on dynamic causal effects, that is, structural impulse response functions.
Abstract: An exciting development in empirical macroeconometrics is the increasing use of external sources of as-if randomness to identify the dynamic causal effects of macroeconomic shocks. This approach – the use of external instruments – is the time series counterpart of the highly successful strategy in microeconometrics of using external as-if randomness to provide instruments that identify causal effects. This lecture exposits this approach and provides conditions on instruments and control variables under which external instrument methods produce valid inference on dynamic causal effects, that is, structural impulse response functions. These conditions can help guide the search for valid instruments in applications. We consider two methods, a one-step instrumental variables regression and a two-step method that entails estimation of a vector autoregression. Under a restrictive instrument validity condition, the onestep method is valid even if the vector autoregression is not invertible, so comparing the two estimates provides a test of invertibility. Under a less restrictive condition, where multiple lagged endogenous variables are needed as control variables in the one-step method, the conditions for validity of the two methods are the same.

215 citations


Posted Content
TL;DR: In this article, the authors developed and validated a new measure of corporate stakeholder responsibility (CStR), which refers to an organization's context-specific actions and policies designed to enhance the welfare of various stakeholder groups by accounting for the triple bottom line of economic, social, and environmental performance.
Abstract: Recent research on the microfoundations of corporate social responsibility (CSR) has highlighted the need for improved measures to evaluate how stakeholders perceive and subsequently react to CSR initiatives. Drawing on stakeholder theory and data from five samples of employees (N = 3,772), the authors develop and validate a new measure of corporate stakeholder responsibility (CStR), which refers to an organization's context-specific actions and policies designed to enhance the welfare of various stakeholder groups by accounting for the triple bottom line of economic, social, and environmental performance; it is conceptualized as a superordinate, multidimensional construct. Results from exploratory factor analyses, first- and second-order confirmatory factor analyses, and structural equation modeling provide strong evidence of the convergent, discriminant, incremental, and criterion-related validities of the proposed CStR scale. Two-wave longitudinal studies further extend prior theory by demonstrating that the higher-order CStR construct relates positively and directly to organizational pride and perceived organizational support, as well as positively and indirectly to organizational identification, job satisfaction, and affective commitment, beyond the contribution of overall organizational justice, ethical climate, and prior measures of perceived CSR.

ReportDOI
TL;DR: It is suggested that policies which encourage transparency and sharing of core datasets across both public and private actors may be critical tools for stimulating research productivity and innovation-oriented competition going forward.
Abstract: Artificial intelligence may greatly increase the efficiency of the existing economy. But it may have an even larger impact by serving as a new general-purpose “method of invention” that can reshape the nature of the innovation process and the organization of R&D. We distinguish between automation-oriented applications such as robotics and the potential for recent developments in “deep learning” to serve as a general-purpose method of invention, finding strong evidence of a “shift” in the importance of application-oriented learning research since 2009. We suggest that this is likely to lead to a significant substitution away from more routinized labor-intensive research towards research that takes advantage of the interplay between passively generated large datasets and enhanced prediction algorithms. At the same time, the potential commercial rewards from mastering this mode of research are likely to usher in a period of racing, driven by powerful incentives for individual companies to acquire and control critical large datasets and application-specific algorithms. We suggest that policies which encourage transparency and sharing of core datasets across both public and private actors may be critical tools for stimulating research productivity and innovation-oriented competition going forward.

Posted Content
TL;DR: In this article, the authors analyze how decentralization affects consensus effectiveness, and how the quintessential features of blockchain reshape industrial organization and the landscape of competition, and further discuss anti-trust policy implications targeted to blockchain applications, such as separating consensus record-keepers from users.
Abstract: Blockchain technology provides decentralized consensus and potentially enlarges the contracting space using smart contracts with tamper-proofness and algorithmic executions. Meanwhile, generating decentralized consensus entails distributing information which necessarily alters the informational environment. We analyze how decentralization affects consensus effectiveness, and how the quintessential features of blockchain reshape industrial organization and the landscape of competition. Smart contracts can mitigate informational asymmetry and improve welfare and consumer surplus through enhanced entry and competition, yet the irreducible distribution of information during consensus generation may encourage greater collusion. In general, blockchains can sustain market equilibria with a wider range of economic outcomes. We further discuss anti-trust policy implications targeted to blockchain applications, such as separating consensus record-keepers from users.

Book ChapterDOI
Stijn Baert1
TL;DR: This paper provided an exhaustive list of correspondence studies on hiring discrimination that were conducted between 2005 and 2016 (and could be found through a systematic search) and the direction of the estimated treatment effects is tabulated.
Abstract: This chapter aims to provide an exhaustive list of all (i.e. 90) correspondence studies on hiring discrimination that were conducted between 2005 and 2016 (and could be found through a systematic search). For all these studies, the direction of the estimated treatment effects is tabulated. In addition, a discussion of the findings by discrimination ground is provided.

Posted ContentDOI
TL;DR: This chapter reports on a competition run through the Santa Fe Institute in which participants from a range of relevant disciplines applied a variety of time series analysis tools to a small group of common data sets in order to help make meaningful comparisons among their approaches.
Abstract: Throughout scientific research, measured time series are the basis for characterizing an observed system and for predicting its future behavior. A number of new techniques (such as state-space reconstruction and neural networks) promise insights that traditional approaches to these very old problems cannot provide. In practice, however, the application of such new techniques has been hampered by the unreliability of their results and by the difficulty of relating their performance to those of mature algorithms. This chapter reports on a competition run through the Santa Fe Institute in which participants from a range of relevant disciplines applied a variety of time series analysis tools to a small group of common data sets in order to help make meaningful comparisons among their approaches. The design and the results of this competiton are described, and the historical and theoretical backgrounds necessary to understand the successful entries are reviewed.

Posted Content
TL;DR: In this article, the information content of the digital footprint was analyzed for predicting consumer default, and it was shown that even simple, easily accessible variables from the digital footprints equal or exceed the information contents of credit bureau scores.
Abstract: We analyze the information content of the digital footprint – information that people leave online simply by accessing or registering on a website – for predicting consumer default. Using more than 250,000 observations, we show that even simple, easily accessible variables from the digital footprint equal or exceed the information content of credit bureau scores. Furthermore, the discriminatory power for unscorable customers is very similar to that of scorable customers. Our results have potentially wide implications for financial intermediaries’ business models, for access to credit for the unbanked, and for the behavior of consumers, firms, and regulators in the digital sphere.

ReportDOI
TL;DR: The authors calibrates a New Keynesian model that embeds banking frictions, including the strictness of capital constraints, the degree of pass-through to deposit rates, and the initial capitalization of banks.
Abstract: The reversal interest rate is the rate at which accommodative monetary policy reverses and becomes contractionary for lending. Its determinants are 1) banks' fixed-income holdings, 2) the strictness of capital constraints, 3) the degree of pass-through to deposit rates, and 4) the initial capitalization of banks. Quantitative easing increases the reversal interest rate and should only be employed after interest rate cuts are exhausted. Over time the reversal interest rate creeps up since asset revaluation fades out as fixed-income holdings mature while net interest income stays low. We calibrate a New Keynesian model that embeds our banking frictions.

Posted Content
TL;DR: In this article, the authors apply the GMM regression estimation approach to a matched sample of French firms listed on Euronext Paris during the period 2001-2010 in order to investigate the relationship between female directors and earnings management by considering their specific (statutory and demographic) attributes.
Abstract: We apply the system GMM regression estimation approach to a matched sample of French firms listed on Euronext Paris during the period 2001–2010 in order to investigate the relationship between female directors and earnings management by considering their specific (statutory and demographic) attributes. We first find that the presence of female directors deters managers from managing earnings. However, this finding does not hold when the statutory and demographic attributes of female directors are taken into account, thus showing that the detection and the correction of earnings management require particular competencies and skills. Interestingly, we find that business expertise and audit committee membership are key attributes of female directors that promote the effective monitoring of earnings management. An important implication of our findings is that the decision to appoint women on corporate boards should be based more on their statutory and demographic attributes than on blind implementation of gender quotas. Finally, our supplementary analysis reveals that female CEOs and CFOs are strongly inclined to reduce earnings management.

BookDOI
TL;DR: A short history of the evolution of the Climate Smart Agriculture approach and its links to climate change and sustainable agriculture debates can be found in this article, where the authors discuss the role of information and insurance under climate change.
Abstract: Chapter 1: Introduction.- Chapter 2: A Short History of the Evolution of the Climate Smart Agriculture Approach and its Links to Climate Change and Sustainable Agriculture Debates.- Chapter 3:Economics of Climate-Smart Agriculture.- Chapter 4: Innovation in Response to Climate Change.- Chapter 5: Use of Satellite Information on Wetness and Temperature for Decision of Crop Yield Prediction, River Discharge and Planning.- Chapter 6: Early Warning Techniques for Local Climate Resilience: Smallholder Rice in Lao PDE.- Chapter 7 : Farmers' Perceptions of and Adaptations to Climate Change in Southeast Asia: The Case Study from Thailand and Vietnam.- Chapter 8: U.S. Maize Yield Growth and Countervailing Climate Change Impacts.- Chapter 9: Understanding Tradeoffs in the Context of Farm-Scale Impacts: An Application of Decision-Support Tools for Assessing Climate Smart Argiculture.- Chapter 10: Can Insurance Help Manage Climate Risk and Food Insecurity?: Evidence from the Pastoral Regions of East Africa.- Chapter 11: Can Cash Transfer Programs Promote Household Resilience?: Cross-Country Evidence from Sub-Saharan Africa.- Chapter 12: Input Subsidy Programs and Climate Smart Agriculture.- Chapter 13: Robust Decision Making for a Climate-Resilient Development of the Agricultural Sector in Nigeria.- Chapter 14: Using AgMIP Regional Integrated Assessment Methods to Evaluate Vulnerability, Resilience and Adaptive Capacity for Climate Smart Agricultural Systems.- Chapter 15: Climate Smart Food Supply Chains in Developing Countries in an Era of Rapid Dual Change in Agrifood Systems and the Climate.- Chapter 16: The Adoption of Climate Smart Agriculture: The Role of Information and Insurance under Climate Change.- Chapter 17: A Qualitative Evaluation of CSA Options in Mixed Crop-Livestock Systems in Developing Countries.- Chapter 18: Identifying Strategies to Enhance the Resilience of Smallholder Farming Systems: Evidence of Zambia.- Chapter 19: Climate Risk Management Through Sustainable Land and Water Management in Sub-Saharan Africa.- Chapter 20: Improving the Resilience of Central Asian Agriculture to Weather Viability and Climate Change.- Chapter 21: Managing Environmental Risk in the Presence of Climate Change: The Role of Adaption in the Mile Basin of Ethiopia.- Chapter 22: Diversification as Part of a CSA Strategy: The Cases of Zambia and Malawi.- Chapter 23: Economic Analysis of Improved Smallholder Paddy and Maize Production in Northern Vietnam and Implications for Climate-Smart Agriculture.- Chapter 24: Synthesis: Devising Effective Strategies and Policies for CSA.- Chapter 25: Conclusions and Policy Implications.

Posted Content
TL;DR: This article measured trends in the diffusion of misinformation on Facebook and Twitter between January 2015 and July 2018, focusing on stories from 570 sites that have been identified as producers of false stories and found that interactions with these sites on both Facebook, while they continued to rise on Twitter, with the ratio of Facebook engagements to Twitter shares falling by approximately 60 percent.
Abstract: We measure trends in the diffusion of misinformation on Facebook and Twitter between January 2015 and July 2018. We focus on stories from 570 sites that have been identified as producers of false stories. Interactions with these sites on both Facebook and Twitter rose steadily through the end of 2016. Interactions then fell sharply on Facebook while they continued to rise on Twitter, with the ratio of Facebook engagements to Twitter shares falling by approximately 60 percent. We see no similar pattern for other news, business, or culture sites, where interactions have been relatively stable over time and have followed similar trends on the two platforms both before and after the election.

ReportDOI
TL;DR: This article reviewed theory and evidence on this topic, with the goal of facilitating more systematic study of belief biases and their integration into economics, and drew general lessons for when people update too much or too little, reflecting on modeling challenges, and highlighting some possible directions for future work.
Abstract: Errors in probabilistic reasoning have been the focus of much psychology research and are among the original topics of modern behavioral economics. This chapter reviews theory and evidence on this topic, with the goal of facilitating more systematic study of belief biases and their integration into economics. The chapter discusses biases in beliefs about random processes, biases in belief updating, the representativeness heuristic as a possible unifying theory, and interactions between biased belief updating and other features of the updating situation. Throughout, I aim to convey how much evidence there is for (and against) each putative bias, and I highlight when and how different biases may be related to each other. The chapter ends by drawing general lessons for when people update too much or too little, reflecting on modeling challenges, pointing to areas of economics to which the biases are relevant, and highlighting some possible directions for future work.

Posted Content
TL;DR: The authors analyzed the effect of local-level labor market concentration on wages using plant-level U.S. Census data over the period 1977-2009, and found that locallevel employer concentration exhibits substantial cross-sectional and time-series variation and increases over time.
Abstract: We analyze the effect of local-level labor market concentration on wages. Using plant-level U.S. Census data over the period 1977–2009, we find that: (1) local-level employer concentration exhibits substantial cross-sectional and time-series variation and increases over time; (2) consistent with labor market monopsony power, there is a negative relation between local-level employer concentration and wages that is more pronounced at high levels of concentration and increases over time; (3) the negative relation between labor market concentration and wages is stronger when unionization rates are low; (4) the link between productivity growth and wage growth is stronger when labor markets are less concentrated; and (5) exposure to greater import competition from China (the “China Shock”) is associated with more concentrated labor markets. These five results emphasize the role of local-level labor market monopsonies in influencing firm wage-setting.

Posted Content
TL;DR: In this paper, the authors present a monthly indicator of geopolitical risk based on a tally of newspaper articles covering geopolitical tensions, and examine its evolution and effects since 1985, concluding that high geopolitical risk leads to a decline in real activity, lower stock returns, and movements in capital flows away from emerging economies and towards advanced economies.
Abstract: We present a monthly indicator of geopolitical risk based on a tally of newspaper articles covering geopolitical tensions, and examine its evolution and effects since 1985. The geopolitical risk (GPR) index spikes around the Gulf War, after 9/11, during the 2003 Iraq invasion, during the 2014 Russia-Ukraine crisis, and after the Paris terrorist attacks. High geopolitical risk leads to a decline in real activity, lower stock returns, and movements in capital flows away from emerging economies and towards advanced economies. When we decompose the index into threats and acts components, the adverse effects of geopolitical risk are mostly driven by the threat of adverse geopolitical events. Extending our index back to 1900, geopolitical risk rose dramatically during the World War I and World War II, was elevated in the early 1980s, and has drifted upward since the beginning of the 21st century.

Posted Content
TL;DR: In this paper, the authors empirically examined the interlinkages between energy consumption and economic growth in top ten energy-consuming countries i.e. China, the USA, Russia, India, Japan, Canada, Germany, Brazil, France and South Korea.
Abstract: This paper empirically examines the inter-linkages between energy consumption and economic growth in top ten energy-consuming countries i.e. China, the USA, Russia, India, Japan, Canada, Germany, Brazil, France and South Korea. We use the quantile-on-quantile (QQ) approach of Sim and Zhou (2015) to explore some nuanced features of the energy-growth nexus and to capture the relationship in its entirety. The results show a positive association between economic growth and energy consumption, with considerable variations across economic states in each country. A weak effect of economic growth on energy consumption is noted for the lower quantiles of economic growth in China, India, Germany and France, which suggests that energy as an input has less importance at low levels of economic growth. A weak effect of economic growth on energy consumption is also noted for the highest quantiles of income in the United States, Canada, Brazil and South Korea, which indicates that energy demand decreases with the increase in economic growth as these countries have become more energy efficient. The weakest effect of energy consumption on economic growth is observed at lower quantiles of energy consumption in China, Japan, Brazil and South Korea. The results of the present study can help in the design of energy development and conservation policies for sustainable and long-term economic development.

Posted Content
TL;DR: In this article, the authors investigated the effects of an exogenous lending cut by a large German bank on firms and counties, and found that the lending cut affected firms independently of their banking relationships, through lower aggregate demand and agglomeration spillovers in counties exposed to the bank's lending cut.
Abstract: Lending cuts by banks directly affect the firms borrowing from them, but also indirectly depress economic activity in the regions in which they operate. This paper moves beyond firm-level studies by estimating the effects of an exogenous lending cut by a large German bank on firms and counties. I construct an instrument for regional exposure to the lending cut based on a historic, postwar breakup of the bank. I present evidence that the lending cut affected firms independently of their banking relationships, through lower aggregate demand and agglomeration spillovers in counties exposed to the lending cut. Output and employment remained persistently low even after bank lending had normalized. Innovation and productivity fell, consistent with the persistent effects.

ReportDOI
TL;DR: In this paper, an examination of matched individual-level survey and administrative records shows that a large and growing fraction of those with self-employment activity in administrative data have no such activity recorded in household survey data.
Abstract: The rise of the “gig economy” has attracted wide attention from both scholars and the popular media. Much of this attention has been devoted to jobs mediated through various online platforms. While non-traditional work arrangements have been a perennial subject of debate and study, the perception that new technology is producing an accelerated pace of change in the organization of work has fueled a resurgence of interest in how such changes may be affecting both workers and firms. This paper provides a typology of work arrangements and reviews how different arrangements, and especially gig activity, are captured in existing data. A challenge for understanding recent trends is that household survey and administrative data paint a different picture, with the former showing little evidence of the growth in self-employment that would be implied by a surge in gig activity and the latter providing evidence of considerable recent growth. An examination of matched individual-level survey and administrative records shows that a large and growing fraction of those with self-employment activity in administrative data have no such activity recorded in household survey data. The share of those with self-employment activity in household survey data but not administrative data is smaller and has not grown. Promising avenues for improving the measurement of self-employment activity include the addition of more probing questions to household survey questionnaires and the development of integrated data sets that combine survey, administrative and, potentially, private data.

Posted Content
TL;DR: The authors decompose news conveyed by central banks into news about monetary policy, economic growth, and separately, shocks to risk premia, finding that non-monetary news accounts for a significant part of financial markets' reaction during the financial crisis and in the early recovery, while monetary shocks gain importance since 2013.
Abstract: We quantify the importance of non-monetary news in central bank communication. Using evidence from four major central banks and a comprehensive classification of events, we decompose news conveyed by central banks into news about monetary policy, economic growth, and separately, shocks to risk premia. Our approach exploits high-frequency comovement of stocks and interest rates combined with monotonicity restrictions across the yield curve. We find significant differences in news composition depending on the communication channel used by central banks. Non-monetary news prevails in about 40% of policy decision announcements by the Fed and the ECB, and this fraction is even higher for communications that provide context to policy decisions such as press conferences. We show that non-monetary news accounts for a significant part of financial markets' reaction during the financial crisis and in the early recovery, while monetary shocks gain importance since 2013.

Posted Content
TL;DR: In this article, the authors examined to what extent perceived corporate social responsibility (CSR) reduces employee cynicism, and whether trust plays a mediating role in the relationship between CSR and employee cynicism.
Abstract: This study examines to what extent perceived corporate social responsibility (CSR) reduces employee cynicism, and whether trust plays a mediating role in the relationship between CSR and employee cynicism. Three distinct contributions beyond the existing literature are offered. First, the relationship between perceived CSR and employee cynicism is explored in greater detail than has previously been the case. Second, trust in the company leaders is positioned as a mediator of the relationship between CSR and employee cynicism. Third, we disaggregate the measure of CSR and explore the links between this and with employee cynicism. Our findings illustrate that the four distinct dimensions of CSR of Carroll (economic, legal, ethical, and discretionary) are indirectly linked to employee cynicism via organizational trust. In general terms, our findings will help company leaders to understand employees’ counterproductive reactions to an organization, the importance of CSR for internal stakeholders, and the need to engage in trust recovery.

Posted Content
TL;DR: In this article, the enforcement effect of an increased availability of third-party information, and sheds light on how governments can harness this information despite collusion opportunities, is investigated, and the role of the value of rewards in improving enforcement is investigated.
Abstract: Access to third-party information trails is widely believed to be critical to the development of modern tax systems, but there is limited direct evidence of the effects of changes in information trails. This paper investigates the enforcement effect of an increased availability of third-party information, and sheds light on how governments can harness this information despite collusion opportunities. I exploit unique administrative data on firms and consumers from an anti-tax evasion program in Sao Paulo, Brazil (Nota Fiscal Paulista) that created monetary rewards for consumers to ensure that firms report final sales transactions, and establishes an online verification system that aids consumers in whistle-blowing firms. Using variation in intensity of exposure to the policy, I estimate that firms' reported revenue increased by at least 21% over four years. Heterogeneous effects across firms shed light on mechanisms: the results are consistent with fixed costs to conceal collusive deals and positive shifts in detection probability from whistle-blower threats. I also investigate the effect of whistle-blowers directly: firms report 7% more receipts and 3% more revenue after receiving the first consumer complaint. To study the role of the value of rewards in improving enforcement, I show evidence consistent with the possibility that lottery incentives amplify consumer responses due to behavioral biases, which would make it more costly for firms to try to match government incentives in a collusive deal. Finally, I find that although firms significantly adjusted reported expenses, there was an increase in tax revenue net of rewards of 9.3%.

Posted Content
TL;DR: The main ideas of the wild cluster bootstrap are reviewed, tips for use are offered, why it is particularly amenable to computational optimization is explained, and the syntax of boottest, artest, scoretest, and waldtest is state.
Abstract: The wild bootstrap was originally developed for regression models with heteroskedasticity of unknown form. Over the past thirty years, it has been extended to models estimated by instrumental variables and maximum likelihood, and to ones where the error terms are (perhaps multi-way) clustered. Like bootstrap methods in general, the wild bootstrap is especiallyuseful when conventional inference methods are unreliable because large-sample assumptions do not hold. For example, there may be few clusters, few treated clusters, or weak instruments. The Stata package boottest can perform a wide variety of wild bootstrap tests, often at remarkable speed. It can also invert these tests to construct confidence sets. As a postestimation command, boottest works after linear estimation commands including regress, cnsreg, ivregress, ivreg2, areg, and reghdfe, as well as many estimation commands based on maximum likelihood. Although it is designed to perform the wild cluster bootstrap, boottest can also perform the ordinary (non-clustered) version. Wrappers offer classical Wald, score/LM, and Anderson-Rubin tests, optionally with (multi-way) clustering. We review the main ideasof the wild cluster bootstrap, offer tips for use, explain why it is particularly amenable to computational optimization, state the syntax of boottest, artest, scoretest, and waldtest, and present several empirical examples for illustration.