Using all geo-located image tweets shared on Twitter in 2012–2013, I find that the volume of tweets is a valid proxy for estimating GDP at the country level, explaining 78 percent of cross-country ...variations. I also exploit the geographic granularity of social media posts to estimate and predict GDP at the sub-national level. I find that tweets alone can explain 52 percent of the variation in GDP across cities in the US. Estimates using Twitter data perform on par with the more common night-lights proxy. Furthermore, both indicators seem to capture different aspects of economic activity and thus complement each other.
•Article uses all geo-located image tweets shared in 2012–2013, and finds that the volume of tweets is a valid proxy for estimating current GDP in USD at the country level.•The goodness-of-fit of Twitter data as a proxy of economic activity is comparable to that of night-light data.•Given that these proxies capture different aspects of economic activity, they should be used together to increase predictive power.•I exploit the geographic granularity of tweets to estimate GDP at the sub-national level.•Twitter can be used to measure economic activity in a more timely and spatially disaggregate way than conventional data.
This paper shows that we can use social media data to improve the accuracy of GDP estimates at the country level for developing countries. I use all publicly available image tweets from 2012 and 2013 ...to estimate GDP at the country level for developing countries. First, I find that one can explain 76% of the cross-country variation in GDP with the volume of tweets sent from each country. I then show that the residuals on these Twitter-GDP estimates are significantly larger for countries with allegedly poor data quality. I then use Nigeria as a case study to show that this method delivers much more timely and accurate estimates than those presented by official statistic agencies.
Abstract
We use administrative data containing all business establishments in New York City to analyze how businesses reacted to flooding in the context of Hurricane Sandy (October 2012). We find ...that flooding led to reductions in employment (of about 4%) and average wages (of about 2%) among the affected businesses. The effects were substantially larger and more persistent in some parts of the city (Brooklyn and Queens) than others (Manhattan). Heterogeneity across boroughs reflects differences in the severity of flooding, building types and industry composition. The effects of flooding also vary by industry and businesses in sectors involved in rebuilding after the storm experienced employment growth. Flooding also led to establishment closings and relocation to other neighborhoods, which is a form of adaptation to increased flood risk.
Using a new composite climate-risk index, we show that population in high-risk counties has grown disproportionately over the last few decades, even relative to the corresponding commuting zone. We ...also find that the agglomeration is largely driven by increases in the (white) working-age population. In addition, we show that high-risk tracts have typically grown more than low-risk tracts
within
the same county, suggesting the presence of highly localized amenities. We also document heterogeneous population dynamics by degree of urbanization, region and type of natural hazard. Specifically, population has been retreating from high-risk, low-urbanization locations, but continues to grow in high-risk areas with high residential capital. Net migration flows have contributed to the higher growth of high-risk counties in the South and Northeast of the country, but the opposite has happened in the West and Midwest. Last, we provide evidence of
microretreat
in the case of
coastal flooding
: tracts with high levels of this risk have grown significantly
less
than other tracts in the same county, suggesting that residents are willing to relocate within short distances to avoid predictably risky locations.
In this article, we analyze the role of flood insurance on the housing markets of coastal areas. To do so, we assembled a parcel-level dataset of the universe of residential sales for two coastal ...urban areas in the United States—Miami-Dade County (2008–15) and Virginia Beach (2000–16)—matched with their Federal Emergency Management Agency (FEMA) flood maps, which characterize the flood-risk level for each property. First, we compare trends in housing values and sales activity among properties on the floodplain, as defined by the National Flood Insurance Program (NFIP), relative to properties located elsewhere within the same area. Despite the heightened flood risk in the past two decades, we do not find evidence of divergent trends. Second, we analyze the effects of the recent reforms to the NFIP. In 2012 and 2014, Congress passed legislation announcing important increases in insurance premiums and flood map updates. We find robust evidence of large price reductions for properties that were drawn into the flood zone of the new FEMA flood maps. We estimate that, as a result of the mandatory insurance requirement in the flood zone, NFIP insurance costs for such properties in Virginia Beach will increase by an average of about $3,500 per year and lead to a reduction in housing values of about $64,000.
Screening Economies Daniel Cuonz, Scott Loren, Jörg Metelmann / Daniel Cuonz, Scott Loren, Jörg Metelmann
2018, 2019, 201810, Volume:
183
eBook
The relationship between economy, finance and society has become opaque. Quantum leaps in complexity and scale have turned this deeply interdependent web of relations into an area of incomprehensible ...abstraction. And while the economization of life has come under widespread critique, inquiry into the political potential of representational praxis is more crucial than ever. This volume explores ethical, aesthetic and ideological dimensions of economic representation, redressing essential questions: What are the roles of mass and new media? How do the arts contribute to critical discourse on the global techno-economic complex? Collectively, the contributions bring theoretical debate and artistic intervention into a rich exchange that includes but also exceeds the conventions of academic scholarship.
This dissertation consists of two chapters that utilize distinct econometric methods and novel datasets. In the first chapter, “From Twitter to GDP: Estimating Economic Activity From Social Media”, I ...collect all geo-located image tweets shared on Twitter in 2012-2013 to study whether the volume of tweets is a valid proxy for estimating current GDP in USD at the country level. My preferred model explains 94 percent of the cross-country variation and the residuals from the model are negatively correlated to a data quality index, indicating that my estimates of GDP are more accurate for countries with more reliable GDP data. I then compare Twitter with the more commonly-used proxy of night-light data, and find that the variation in Twitter activity explains slightly more of the cross-country variance in GDP. Interestingly, I find that Twitter data is also valuable in estimating within country changes in economic activity from one period to the next. Furthermore, the results indicate that combining tweets and night-light luminosity can be used together to produce a more accurate estimate of annual variations in economic activity. I then study the underlying relationship between the volume of image tweets and economic activity and present a hypothesis that social media applications serve as a medium to showcase consumption of goods and services among the network of users. I divide tweets between those posted during working hours and non-working hours and regress them on personal consumption expenditure. The results support the idea that tweets are a byproduct of consumption. The last part of the chapter exploits the continuous time and geographic granularity of social media posts in order to estimate the local economic effects of natural resource extraction. Overall, my findings suggest that Twitter can be used to measure economic activity in a more timely and more spatially disaggregate way than conventional data and that governments’ statistical agencies could incorporate social media data to complement and further reduce measurement error in their official GDP estimates. In the second chapter, “What’s in a Tweet? Estimating Poverty Rates from Social Media Data”, I collect all geo-located image tweets posted from the US in 2012 to study whether they can estimate poverty rates in urban areas. In order to exploit the full potential of social media data, I not only use the volume of tweets from each location, but also extract several features from the content of tweets using natural language processing techniques. From the results presented in this chapter, it seems that data from tweets are not informative enough to replace survey data, as there is still substantial error in the estimates. But the chapter does present two different scenarios in which data from tweets can be valuable when estimating poverty rates. On one hand, social media data can be combined with alternative economic indicators to obtain reasonably accurate poverty rate estimates when such indicators are not officially available. On the other hand, social media data can be useful to study the relationship between poverty and an unobservable variable that can be proxied with social media data, such as human capital formation.
Sharing photos, videos and comments on social media may seem an idle pastime, but it is not without its uses where urban design is concerned. Analysing such posts can yield helpful indicators as to ...how people experience the built environment. Lev Manovich and Agustin Indaco, of the Software Studies Lab at the University of California, San Diego and the Graduate Center, City University of New York, here outline two of the Lab's recent research projects, which have involved examining extensive Instagram data from various cities around the globe.
Using all geo-located image tweets shared on Twitter in 2012-2013, I find that the volume of tweets is a valid proxy for estimating current GDP in USD at the country level. Residuals from my ...preferred model are negatively correlated to a data quality index, indicating that my estimates of GDP are more accurate for countries with more reliable GDP data. Comparing Twitter with more commonly-used proxy of night-light data, I find that variation in Twitter activity explains slightly more of the cross-country variance in GDP. I also exploit the continuous time and geographic granularity of social media posts to create monthly and weekly estimates of GDP for the US, as well as sub- national estimates, including those economic areas that span national borders. My findings suggest that Twitter can be used to measure economic activity in a more timely and more spatially disaggregate way than conventional data and that governments’ statistical agencies could incorporate social media data to complement and further reduce measurement error in their official GDP estimates.
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