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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.