Working papers

Alexey Chernikov, Klaus Ackermann, Caitlin Brown and Denni Tommasi

2025-02

Ensuring street food safety in developing countries is crucial due to the high prevalence of foodborne illnesses. Traditional methods of food safety assessments face challenges such as resource constraints, logistical issues, and subjective biases influenced by surveyors personal lived experiences, particularly when interacting with local communities. For instance, a local food safety inspector may inadvertently overrate the quality of infrastructure due to prior familiarity or past purchases, thereby compromising objective assessment. This subjectivity highlights the necessity for technologies that reduce human biases and enhance the accuracy of survey data across various domains.

This paper proposes a novel approach based on a combination of Computer Vision and a lightweight Visual Large Language Model (VLLM) to automate the detection and analysis of critical food safety infrastructure in street food vendor environments at a field experiment in Kolkata, India. The system utilises a three-stage object extraction pipeline from the video to identify, extract and select unique representations of critical elements such as hand-washing stations, dishwashing areas, garbage bins, and water tanks. These four infrastructure items are crucial for maintaining safe food practices, irrespective of the specific methods employed by the vendors. A VLLM then analyses the extracted representations to assess compliance with food safety standards. Notably, over half of the pipeline can be processed using a user’s smartphone, significantly reducing government server workload. By leveraging this decentralised approach, the proposed system decreases the analysis cost by many orders of magnitude compared to alternatives like ChatGPT or Claude 3.5. Additionally, processing data on local government servers provides better privacy and security than cloud platforms, addressing critical ethical considerations. This automated approach significantly improves efficiency, consistency, and scalability, providing a robust solution to enhance public health outcomes in developing regions.

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Simon D Angus

2024-07

Democratic resilience is as much about the narratives of our nation we affirm, as the institutions that enshrine our values and laws, a fact re-affirmed by scholarship across many branches of social science in recent decades. For policymakers and quantitative social scientists, analysing or tracking public discourse through the lens of narrative and framing has historically involved the annotation of texts by hand, placing severe limitations on the scale and modality of discourse under inquiry. In this study, we consider a variety of tools from the field of computational linguistics, which either automate the standard approach to textual annotation, or introduce entirely new ways of conceptualising `text as data', opening up new horizons for the tracking of public narratives of democratic resilience. In particular, we assess the regime-shift occurring in natural language processing and artificial intelligence brought about by the advent of the transformer architecture. These new tools offer, perhaps for the first time, the `holy grail' of the quantitative social scientist: the ability to identify, accurately, and efficiently, nuanced narratives in text at scale. We conclude by contributing data and research recommendations for public stakeholders who wish to see these opportunities realised.

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Chun Chee Kok & Gedeon J. Lim

2024-06

This paper studies the long-run effects of a colonial-era resettlement program of ethnic minorities, on contemporary economic outcomes and political preferences of ethnic majority individuals in receiving areas. In ethnic Malay-majority Malaysia, the colonial British relocated 500,000 rural ethnic Chinese minorities into fenced-up, isolated, mono-ethnic camps (1948 – 1960) all across rural Malaysia. This brought some pre-existing ethnic Malay majority areas into closer contact with ethnic Chinese minorities but not others. Site selection criteria were largely military in nature. Using a spatial randomization inference-type approach, we construct counterfactual village locations based on this criteria. Areas located immediately next to Chinese New Villages (0-2km) experienced better economic outcomes and, in turn, had lower vote shares for the ethno-nationalistic coalition, than polling districts located next to similarly suitable, counterfactual locations. We provide suggestive evidence that these lower vote shares were driven by all voters, not just the ethnic Chinese. Together, our results suggest that persistent differences in inter-ethnic proximity can have a lasting, negative impact on voter preferences for ethno-nationalistic politics, by improving economic outcomes and creating opportunities for sustained but transient inter-ethnic interactions.

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David Kreitmeir

2024-05

Over the past two decades, violence against land and environmental activists has been on the rise, besetting even stable democracies. Using a unique, fine-grained data set on social conflict events in Peru and exogenous variation in world mineral prices, I document a strong link between local mineral rents and violent staterepression of socioenvironmental protests in a democratic institutional setting. I show that the increase in the use of excessive force cannot be explained by changes in protester behavior. Empirical findings highlight the role of local authorities: the election of a pro-mining mayor is associated with a higher prevalence of state repression and corruption in the constituency. The legal and democratic accountability of local authorities is, however, found to be limited. The reported increase in corruption does not translate into more investigations against pro-mining mayors for corruption offenses nor are reelection results of incumbents found to be negatively affected by state violence against protesters. Finally, I show that violent state repression is successful in forestalling conflict resolution agreements that acknowledge protesters’ demands.

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Rhys Murrian, Paul A. Raschky and Klaus Ackermann

2024-03

This paper empirically investigates how an individual’s network influences their purchase and subsequent use of experience goods. Utilising data on the network and game-ownership of over 108 million users from the world’s largest video game platform, we analyse whether a user’s friendship network influences their decision to purchase single-player video games. Our identification strategy uses an instrumental variable (IV) approach that employs the temporal lag of purchasing decisions from second degree friends. We find strong peer effects in the individual game adoption in the contemporary week. The effect is stronger if the friend who purchased the game is an old friend compared to a key player in the friendship network. Comparing the results to adoption decisions for a major label game, we find peer effects of a similar size and duration. However, the time subsequently spent playing the games is higher for players who were neither influenced by a peer who is a key player nor an old friend. Considering the increasing importance of online networks on consumption decisions, our findings offer some first insights on the heterogeneity of peer effects between old and key player friends and also provide evidence in consumers’ biases in social learning.

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Simon D Angus and Lachlan O'Neill

2024-02

Detecting and quantifying issue framing in textual discourse - the slant or perspective one takes to a given topic (e.g. climate science vs. denialism, misogyny vs. gender equality) - is highly valuable to a range of end-users from social and political scientists to program evaluators and policy analysts. Being able to identify statistically significant shifts, reversals, or changes in issue framing in public discourse would enable the quantitative evaluation of interventions, actors and events that shape discourse. However, issue framing is notoriously challenging for automated natural language processing (NLP) methods since the words and phrases used by either `side' of an issue are often held in common, with only subtle stylistic flourishes separating their use. Here we develop and rigorously evaluate new detection methods for issue framing and narrative analysis within large text datasets. By introducing a novel application of next-token log probabilities derived from generative large language models (LLMs) we show that issue framing can be reliably and efficiently detected in large corpora with only a few examples of either perspective on a given issue, a method we call `paired completion'. Through 192 independent experiments over three novel, synthetic datasets, we evaluate paired completion against prompt-based LLM methods and labelled methods using traditional NLP and recent LLM contextual embeddings. We additionally conduct a cost-based analysis to mark out the feasible set of performant methods at production-level scales, and a model bias analysis. Together, our work demonstrates a feasible path to scalable, accurate and low-bias issue-framing in large corpora.

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Johannes S Kunz, Carol Propper and Trong-Anh Trinh

2024-01

Digital access may bring important health gains, particularly where physical infrastructure is limited. We examine the impact of internet access in Indonesia on health outcomes using the COVID19 pandemic as a health shock. We utilize sub-national data on mobile broadband, COVID-19 spread, and an instrumental variable approach using lightning strikes as an exogenous shock to connectivity. Access to 3G internet significantly reduced the transmission of COVID-19. Areas with internet access had approximately 45% fewer cases. Regions with higher literacy and capacity for telework benefited significantly more. These findings offer novel insights into how digital infrastructure affects public health outcomes

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David H. Kreitmeir and Paul A. Raschky

2023-01

We analyse the effects of the ban of ChatGPT, a generative pre-trained transformer chatbot, on individual productivity. We first compile data on the hourly coding output of over 8,000 professional GitHub users in Italy and other European countries to analyse the impact of the ban on individual productivity. Combining the high-frequency data with the sudden announcement of the ban in a difference-in-differences framework, we find that the output of Italian developers decreased by around 50% in the first two business days after the ban and recovered after that. Applying a synthetic control approach to daily Google search and Tor usage data shows that the ban led to a significant increase in the use of censorship bypassing tools. Our findings show that users swiftly implement strategies to bypass Internet restrictions but this adaptation activity creates short-term disruptions and hampers productivity.

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Ashani Amarasinghe and Paul A, Raschky

2022-02

This paper studies the effects of talk radio, specifically the Rush Limbaugh Show, on electoral outcomes and attitude polarization in the U.S. We propose a novel identification strategy that considers the radio space in each county as a market where multiple stations are competing for listeners' attention. Our measure of competition is a spatial Herfindahl-Hirschman Index (HHI) in radio frequencies. To address endogeneity concerns, we exploit the variation in competition based on accidental frequency overlaps in a county, conditional on the overall level of radio frequency competition. We find that counties with higher exposure to the Rush Limbaugh Show have a systematically higher vote share for Donald Trump in the 2016 and 2020 U.S. presidential elections. Combining our county-level Rush Limbaugh Show exposure measure with individual survey data reveals that self-identifying Republicans in counties with higher exposure to the Show express more conservative political views, while self-identifying Democrats in these same counties express more moderate political views. Taken together, these findings provide some of the first insights on the effects of contemporary talk radio on political outcomes, both at the aggregate and individual level.

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Michael Kitchner, Nandini Anantharama, Simon Angus, and  Paul A. Raschky

2021-01

This paper proposes a new method to predict individual political ideology from digital footprints on one of the world's largest online discussion forum. We compiled a unique data set from the online discussion forum reddit that contains information on the political ideology of around 91,000 users as well as records of their comment frequency and the comments' text corpus in over 190,000 different subforums of interest. Applying a set of statistical learning approaches, we show that information about activity in non-political discussion forums alone, can very accurately predict a user's political ideology. Depending on the model, we are able to predict the economic dimension of ideology with an accuracy of up to 90.63% and the social dimension with and accuracy of up to 82.02%. In comparison, using the textual features from actual comments does not improve predictive accuracy. Our paper highlights the importance of revealed digital behaviour to complement stated preferences from digital communication when analysing human preferences and behaviour using online data.

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Lachlan O'Neill, Nandini Anantharama, Wray Buntine and Simon D. Angus

2021-12

Empirical social science requires structured data. Traditionally, these data have arisen from statistical agencies, surveys, or other controlled settings. But what of language, political speech, and discourse more generally? Can text be data? Until very recently, the journey from text to data has relied on human coding, severely limiting study scope. Here, we introduce natural language processing (NLP), a field of artificial intelligence (AI), and its application to discourse analysis at scale. We introduce AI/NLP’s key terminology, concepts, and techniques, and demonstrate its application to the social sciences. In so doing, we emphasise a major shift in AI/NLP technological capability now underway, due largely to the development of transformer models. Our aim is to provide the quantitative social scientists with both a guide to state-of-the-art AI/NLP in general, and something of a road-map for the transformer revolution now sweeping through the landscape.

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Richard Bluhm, Christian Lessmann and Paul Schaudt

2021-11

We study the link between subnational capital cities and urban development using a global data set of hundreds of first-order administrative and capital city reforms from 1987 until 2018. We show that gaining subnational capital status has a sizable effect on city growth in the medium run. We provide new evidence that the effect of these reforms depends on locational fundamentals, such as market access, and that the effect is greater in countries where urbanization and industrialization occurred later. Consistent with both an influx of public investments and a private response of individuals and firms, we document that urban built-up, population, foreign aid, infrastructure, and foreign direct investment in several sectors increase once cities become subnational capitals.

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Nathan Lane

2021-10

I study the impact of industrial policy on industrial development by considering a canonical intervention. Following a political crisis, South Korea dramatically altered its development strategy with a sector-specific industrial policy: the Heavy and Chemical Industry (HCI) drive, 1973-1979. With newly assembled data, I use the sharp introduction and withdrawal of industrial policies to study the impacts of industrial policy—during and after the intervention period.

I show:

1. HCI promoted the expansion and dynamic comparative advantage of directly targeted industries
2. Using variation in exposure to policies through the input-output network, I show HCI indirectly benefited downstream users of targeted intermediates
3. I find direct and indirect benefits of HCI persisted even after the end of HCI, following the 1979 assassination of the president.

These effects include the eventual development of directly targeted exporters and their downstream counterparts. Together, my findings suggest that the temporary drive shifted Korean manufacturing into more advanced markets and created durable industrial change. These findings clarify lessons drawn from South Korea and the East Asian growth miracle.

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Lachlan O'Neill, Simon D Angus, Satya Borgohain, Nader Chmait, David L Dowe

2021-09

As the discipline has evolved, research in machine learning has been focused more and more on creating more powerful neural networks, without regard for the interpretability of these networks. Such ``black-box models'' yield state-of-the-art results, but we cannot understand why they make a particular decision or prediction. Sometimes this is acceptable, but often it is not.

We propose a novel architecture, Regression Networks, which combines the power of neural networks with the understandability of regression analysis. While some methods for combining these exist in the literature, our architecture generalizes these approaches by taking interactions into account, offering the power of a dense neural network without forsaking interpretability. We demonstrate that the models exceed the state-of-the-art performance of interpretable models on several benchmark datasets, matching the power of a dense neural network. Finally, we discuss how these techniques can be generalized to other neural architectures, such as convolutional and recurrent neural networks.

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Ashani Amarasinghe

2021-08

Do citizens hold their government accountable for the delivery of public goods? The literature has traditionally answered this question using temporally aggregated voting data. This paper proposes an alternative, fine-grained approach to explore the short term dynamics underlying public sentiments towards governments, for 132 countries over the period 2002-2016.

Focusing on terror attacks as a government accountability shock, and using high-frequency, text-based event data to quantify public sentiments, I find that the average level of Public Discontent increases by approximately 14% in the 11 months following a successful terror attack. This effect is not merely driven by fear, and is influenced by information on government competence and attack-specific features. Citizens are less reproachful if the government made a reasonable effort to keep the public safe, and for events that may be beyond the government's control. Interestingly, young leaders and new leaders demonstrate an ability to mobilize the masses to rally 'round the flag in the aftermath of terror attacks.

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Daniel Auer (U Mannheim & WZB), Johannes S. Kunz (Monash U)

2021-07

This paper investigates the intergenerational effect of communication barriers on child health at birth using a natural experiment in Switzerland. We leverage the fact that refugees arriving in Switzerland originate from places that have large shares of French (or Italian) speakers for historical reasons and upon arrival are by law randomly allocated across states that are dominated by different languages but subject to the same jurisdiction.

Our findings based on administrative records of all refugee arrivals and birth events between 2010 and 2017 show that children born to mothers who were exogenously allocated to an environment that matched their linguistic heritage are on average 72 gram heavier (or 2.2%) than those that were allocated to an unfamiliar language environment.

The differences are driven by growth rather than gestation and manifest in a 2.9 percentage point difference in low birth weight incidence. We find substantial dose-response relationships in terms of language exposure in both, the origin country and the destination region. Moreover, French (Italian) exposed refugees only benefit from French- (Italian-) speaking destinations, but not vice versa. Contrasting the language match with co-ethnic networks, we find that high-quality networks are acting as a substitute rather than a complement.

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Klaus Ackermann, Sefa Awaworyi Churchill and Russell Smyth

2021-06

We examine the effects of mobile phone coverage on violent conflicts in Africa using a new monthly panel dataset on mobile phone coverage at 55x55km grid cell levels for 32 African countries covering the period from 2008 to 2018. The base rate of a conflict event in a month across our data set is 0.0039 with a standard deviation of 0.0620. We find that access to mobile phone coverage increases the probability of a conflict occurring in the next month by 0.0028. This finding is robust to a suite of sensitivity checks including the use of various specifications and alternative datasets.

We examine heterogeneity on the impact of mobile phone coverage across state-based conflict, non-state-based conflict and one-sided conflict, and find that our results are being driven by non-state conflicts. We examine economic growth as a channel through which mobile phone coverage influences conflict. In doing so, we construct new satellite data for night-time light activity as a proxy for economic growth.

We find that economic activity is a channel through which mobile phone coverage influences conflicts, and that higher economic growth weakens the positive effect of mobile phone coverage on conflict.

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Johannes S. Kunz, Kevin E. Staub, And Rainer Winkelmann

2021-05

Many applied settings in empirical economics require estimation of a large number of individual effects, like teacher effects or location effects; in health economics, prominent examples include patient effects, doctor effects, or hospital effects. Increasingly, these effects are the object of interest of the estimation, and predicted effects are often used for further descriptive and regression analyses. To avoid imposing distributional assumptions on these effects, they are typically estimated via fixed effects methods. In short panels, the conventional maximum likelihood estimator for fixed effects binary response models provides poor estimates of these individual effects since the finite sample bias is typically substantial. We present a bias-reduced fixed effects estimator that provides better estimates of the individual effects in these models by removing the first-order asymptotic bias. An additional, practical advantage of the estimator is that it provides finite predictions for all individual effects in the sample, including those for which the corresponding dependent variable has identical outcomes in all time periods over time (either all zeros or ones); for these, the maximum likelihood prediction is infinite. We illustrate the approach in simulation experiments and in an application to health care utilization. The installation documents for the Stata command are found here.

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Sascha O. Becker, Francisco Pino, Jordi Vidal-Robert

2021-04

The Protestant Reformation in the early 16th century challenged the monopoly of the Catholic Church. The printing press helped the new movement spread its ideas well beyond the cradle of the Reformation in Luther’s city of Wittenberg. The Catholic Church reacted by issuing indexes of forbidden books which blacklisted not only Protestant authors but all authors whose ideas were considered to be in conflict with Catholic doctrine.

We use newly digitized data on the universe of books censored by the Catholic Church during the Counter-Reformation, containing information on titles, authors, printers and printing locations. We classify censored books by topic (religion, sciences, social sciences and arts) and language and record when and where books were indexed. Our results show that Catholic censorship did reduce printing of forbidden authors, as intended, but also negatively impacted on the diffusion of knowledge, and city growth.

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Ashani Amarasinghe

2021-03

When faced with intense domestic turmoil, governments may strategically engage in foreign interactions to divert the public’s attention away from pressing domestic issues. I test this hypothesis for a globally representative sample of 190 countries, at the monthly level, over the years 1997-2014. Using textual data on media–reported events retrieved from the GDELT database, I find robust evidence that governments resort to diversionary tactics in times of domestic turmoil and that such diversion takes the form of verbally aggressive foreign interactions, typically targeted at ‘weak’ countries and countries closely linked along religious, linguistic and geographic dimensions. Strategically important trade partners are unlikely to be victimized. These findings suggest that diversionary foreign policy is, in fact, systematically practised by governments as a strategic tool, and that such diversion is exercised in a manner that may not lead to large scale costs or risks of retaliation.

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Sascha O. Becker, Volker Lindenthal, Sharun Mukand, Fabian Waldinger

2021-02

We study the role of professional networks in facilitating the escape of persecuted academics from Nazi Germany. From 1933, the Nazi regime started to dismiss academics of Jewish origin from their positions. The timing of dismissals created individual-level exogenous variation in the timing of emigration from Nazi Germany, allowing us to estimate the causal effect of networks for emigration decisions. Academics with ties to more colleagues who had emigrated in 1933 or 1934 (early émigrés) were more likely to emigrate. The early émigrés functioned as “bridging nodes” that helped other academics cross over to their destination. Furthermore, we provide some of the first empirical evidence of decay in social ties over time. The strength of ties also decays across space, even within cities. Finally, for high-skilled migrants, professional networks are more important than community networks.

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Sascha O. Becker, Cheongyeon Won

2021-01

In the mid 19th century, pre-colonial Korea under the Joseon dynasty was increasingly isolated and lagging behind in its economic development. Joseon Korea was forced to sign unequal treaties with foreign powers as a result of which Christian missionaries entered the country and contributed to the establishment of private schools. We show that areas with a larger presence of Christians have higher literacy rates in 1930, during the Japanese colonial period. We also show that a higher number of Protestants is associated with higher female literacy, consistent with a stronger emphasis on female education in Protestant denominations.

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David Kreitmeir, Nathan Lane, Paul A. Raschky

2020-10

Civil society is essential to governance, especially where laws and authority are weak. We study how a core strategy of international civil society groups - informing and publicizing human rights abuses - impacts those tied to abuse. Our study focuses on a major trend at the center of on-going international media campaigns: the assassination of civil society activists involved in mining activity. Collecting and coding 20 years of data on assassination events, we use Event Study Methodology to study how publicity of these events impact the asset prices of firms associated with abuse. We show that publicizing abuses has a significant impact on multinationals. Firm's associated with an assassination have large, negative abnormal returns following the event. We calculate a median loss in market capitalisation of over 100 million USD, ten days following violence. We highlight the role of media publicity in our results. We show negative returns from assassinations are stronger during periods of low media pressure, versus when they coincide with competing newsworthy events. As well, we argue our results are driven by events where companies are explicitly named in media publicity, using a set of placebo events where no firms were identified by news coverage. Furthermore, we reject that our results are driven by other forms of unrest and conflict. Last, we show activist assassinations are positively related to the royalties paid by firms to domestic governments.

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Rossella Calvi, Jacob Penglase, Denni Tommasi, Alexander Wolf

2020-09

The structure of a family may have important consequences for the material well-being of its members. For example, in large families, an individual must share resources with many others, but she may benefit from joint consumption. In this paper, we study individual consumption in different types of households, with a focus on family structures that are common in developing countries. Based on a collective household model, we develop a new methodology to identify the intra-household allocation of resources and the extent of consumption sharing. We apply our method to Bangladeshi and Mexican households, and find that failing to account for intra-household inequality understates child poverty in both countries. Our results suggest that standard equivalence scales overstate scale economies (and hence understate poverty). We also show that consumption estimates that ignore scale economies may lead to an overestimation of poverty rates. The extent to which this is the case depends on the degree of joint consumption and how far households are from the poverty line.

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Richard Bluhm, Melanie Krause

2020-08

Tracking the development of cities in emerging economies is difficult with conventional data. Even the commonly-used satellite images of nighttime light intensity fail to capture the true brightness of larger cities. This paper shows that nighttime lights can be used as a reliable proxy for economic activity at the city level, provided they are first corrected for top-coding. We present a stylized model of urban luminosity and empirical evidence which both suggest that these ‘top lights’ can be characterized by a Pareto distribution. We then propose a correction procedure which recovers the full distribution of city lights. Our results show that the brightest cities account for nearly a third of global economic activity. Applying this approach to cities in Sub-Saharan Africa, we find that primate cities are outgrowing secondary cities but are changing from within. Poorer neighborhoods are developing and sub-centers are emerging, with the side effect that Africa’s cities are also becoming increasingly fragmented.

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Matthew Olckers, Joshua E. Blumenstock

2020-07

Mobile phone-based gambling has grown wildly popular in Africa. Commentators worry that low ability gamblers will not learn from experience, and may rely on debt to gamble. Using data on financial transactions for over 50 000 Kenyan smartphone users, we find that gamblers do learn from experience. Gamblers are less likely to bet following poor results and more likely to bet following good results. The reaction to positive and negative feedback is of equal magnitude, and is consistent with a model of Bayesian updating. Using an instrumental variables strategy, we find no evidence that increased gambling leads to increased debt.

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Richard Bluhm, Axel Dreher, Andreas Fuchs, Bradley C. Parks, Austin M.Strange, Michael J. Tierney

2020-06

This paper studies the causal effect of transport infrastructure on the spatial concentration of economic activity. Leveraging a new global dataset of geo-located Chinese government-financed projects over the period from 2000 to 2014 together with measures of spatial inequality based on remotely-sensed data, we analyze the effects of transport projects on the spatial distribution of economic activity within and between regions in a large number of developing countries. We find that Chinese-financed transportation projects reduce spatial concentration within but not between regions. In line with land use theory, we document a range of results which are consistent with a relocation of activity from city centers to their immediate periphery. Transport projects decentralize economic activity particularly strongly in regions that are more urbanized, located closer to the coast, and less developed.

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Caroline Le Pennec

2020-05

In representative democracy, individual candidates often run for parliamentary seats under a national party platform, which limits their ability to compete on policy issues at the local level.  I exploit a novel dataset of 30,000 candidate manifestos issued before the first and second rounds of nine French legislative elections to show that politicians strategically adjust their campaign communication to persuade voters who do not support their platform—not by moderating their policy positions but by advertising neutral non-policy issues instead. Doing so predicts better performance in office and may therefore provide voters with information that matters for representation.

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Klaus Ackermann, Simon D. Angus, Paul A. Raschky

2020-04

Alternative data is increasingly adapted to predict human and economic behaviour. This paper introduces a new type of alternative data by re-conceptualising the internet as a data-driven insights platform at global scale. Using data from a unique internet activity and location dataset drawn from over 1.5 trillion observations of end-user internet connections, we construct a functional dataset covering over 1,600 cities during a 7 year period with temporal resolution of just 15min. To predict ac- curate temporal patterns of sleep and work activity from this data-set, we develop a new technique, Segmented Functional Classification Analysis (SFCA), and compare its performance to a wide array of linear, functional, and classification methods. To confirm the wider applicability of SFCA, in a second application we predict sleep and work activity using SFCA from US city-wide electricity demand functional data. Across both problems, SFCA is shown to out-perform current methods.

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Simon D. Angus, Kadir Atalay, Jonathan Newton, David Ubilava

2020-03

Is the representation of editors at prestigious economics journals geographically diverse? Using data on the affiliations of academics working in an editorial capacity at such journals, we map the locations of editorial power within the economics profession. This allows us to rank institutions, countries and continents according to this measure of power. In addition, by considering the average distance of a journal’s editorial affiliations from a geographic mean, we rank journals by geographic diversity. The magnitudes of the geographic differences we find are striking. Over half the journals we consider have over two thirds of their editorial power located in the USA. A large majority of journals have a tiny editorial contribution from academics located outside of North America and Europe. Any one of the states of California, Massachusetts and Illinois has more power than the four continents of Asia, South America, Africa and Australasia combined.

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Klaus Ackermann, Alexey Chernikov, Nandini Anantharama, Miethy Zaman, Paul A. Raschky

2020-02

Reliable data about the stock of physical capital and infrastructure in developing countries is typically very scarce. This is a particular problem for data at the subnational level where existing data is often outdated, not consistently measured or coverage is incomplete. Traditional data collection methods are time and labor-intensive which often prohibits developing countries from collecting this type of data.

This paper proposes a novel method to extract infrastructure features from high-resolution satellite images. We collected high-resolution satellite images for 5 million 1km 1km grid cells covering 21 African countries. We contribute to the growing body of literature in this area by training our machine learning algorithm on ground-truth data.

We show that our approach strongly improves the predictive accuracy over existing models. Our methodology can build the foundation to then predict subnational indicators of economic development for areas where this data is either missing or unreliable.

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Roland Hodler, Michael Lechner, Paul A. Raschky

2020-01

We reassess the effects of natural resources on economic development and conflict, applying a causal forest estimator and data from 3,800 Sub-Saharan African districts. We find that, on average, mining activities and higher world market prices of locally mined minerals both increase economic development and conflict.

Consistent with the previous literature, mining activities have more positive effects on economic development and weaker effects on conflict in places with low ethnic diversity and high institutional quality. In contrast, the effects of changes in mineral prices vary little in ethnic diversity and institutional quality, but are non-linear and largest at relatively high prices.

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