Essays on the use of big data in development economics

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Essays on the use of big data in development economics

Ajayi Willa H. Friedman Adrienne M. Libecap Katherine D. Edwards Martin Fiszbein Gary D. Buera Joseph P. Bird Michael R. Carter Travis J. Lybbert Mary W. Walker Michael Kremer Sarah J. Kugler Mikko I. Kitchens Luke P. Dave Andrew I. Friedson Kyutaro Matsuzawa Joseph J. Katz Benjamin A. Rosenzweig Rafael J. Maue Marshall Burke Kyle J.

Levin Christopher M. Blanchflower Unhappiness and age w Daniel R. LaFave Evan D. Reid Eat Widely, Vote Wisely? Edmonds Caroline B. Evidence and Welfare Implications w M.This data explosion is the direct consequence of significant advances in technology. If the Big Data boosters are to be believed, the recent explosion of data will in fact drive significant advances in technology. According to this line of thinking, we can apply new approaches to old problems, approaches that are only possible now that data is so abundant.

An oft-cited example is speech recognition. With so many people talking to Siri, Apple engineers have the raw material they need to finally deliver speech recognition technology that actually, um, recognizes speech.

As with many claims about the wonders of technology, the promise of Big Data will likely take longer to realize than many of us would like. My claim is this: Big Data has the potential to accelerate economic development in parts of the world where development has been most elusive.

Without question, these data providers facilitated economic growth. Over the last few decades, companies have increasingly looked to do business across borders, helping to drive economic growth around the world see Japan, South Korea, Taiwan, Mainland China, India.

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Companies doing business across borders have largely done so without the benefit of data, because our data-providing institutions have been focused on the domestic market.

This has meant higher risk, but typically the rewards of expanding into big new markets or finding massive cost savings through low wage labor have more than compensated the risk-takers. Now, economic development in these emerging markets is leading to an increase in data, as increasingly sophisticated governments work to make more data available in order to further grease the wheels of commerce.

This seems to suggest that economic development comes first, then data, then more economic development.

Essays on Big Data

But what if data could come first? What if we could see more data coming out of emerging markets even before governments have the capacity to collect, and make available, large amounts of data? Might the transparency provided by data give more companies the confidence they need to do business in these markets — and in fact jump-start the process of economic development?

This brings us back to Big Data. Rather, technology has created a world where people are generating massive amounts of data simply by living their lives, and companies are generating massive amounts of data simply by going about their business.

Clearly, this is already the case in advanced economies. As the costs of key technologies continue to plummet, this will increasingly be the case in emerging economies as well. The result?Big Data.

Big data is a new frontier in innovation, characterized by vast data sets that require extensive computer processing to spot trends, and improve productivity. The ability to gather such data sets and effectively translate them in to strategy is seen as a new way of competing and innovating Manyika et al. My area of interest is fashion, an area where creativity remains more important than data processing. Yet, big data is having an impact on the mainstream fringes of the fashion business.

For example, mainstream retailers are seeking to acquire more data points from their customers about a variety of different variables, and use those data points to more accurately meet the market's needs. Each season, millions of dollars' worth of merchandise goes unsold, to be sent to discounters at a fraction of the expected cost. This creates waste in the economic system of both the producers and retailers.

Big Data Big Data. The article, published on the website Dataversity, notes that there is public concerns about data leaks at NSA. Consumers are becoming more aware about just how much of their information is available to the government. The author calls into question the dichotomy of private data and public data, in particular were corporate entities are gathering data, and then that data finds its way into the hands of government.

The author concludes by noting that big data is a growing field, but there are still going to be customers who are wary. The point Faris makes about customers being willing participants is valid in that most people are perfectly willing to divulge information. Only when they overtly know that this information is being used to market to them may they have a problem with it, but…… [Read More].

Big Data Analysis. Big Data Nowadays, enterprises are employing statisticians when carrying out sophisticated data analysis. This is caused by the increased affordability in data acquisition and data storage among large scale and small-scale enterprises. This is figured out as a shift from traditional enterprise data intelligence.

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The article presents its design philosophy as well as experience and techniques that portray MAD as one of the biggest advertising networks for the interactive media. Moreover, data parallel algorithms are presented for sophisticated techniques putting more emphasis on density methods. The article has included the knowledge from some prior study. For instance, the standard business practices applied in the large scale data analysis revolves around the…… [Read More].

Big Data Uses. Big Data ole in Obama e-Election The volumes, rapid velocity and variation of data otherwise referred to as Big Data has been used in the electioneering process especially in terms of directing the trends of voter psychology and the subsequent voting patterns, a specific example being the Obama reelection process that took more of the internet direction than the traditional door-to-door approach in the campaigns.

This paper will hence expound on the way Big Data has been and can be used in the monitoring and manipulation of the internet users especially the voting block, how this data can be mishandled and the possibility of infringements into the privacy of the individuals whose data is online. It will also evaluate the possibility of having checks on the handling, storage and use of personal data that is online. Literature review There have been varied uses of the Big data in the contemporary…… [Read More].

Big Data on Business Strategy.Haven't found the right essay? Get an expert to write your essay! Get your paper now. Professional writers and researchers. Sources and citation are provided. Essays on Big Data. Those who are into programming, data analysis, artificial intelligence might consider an essay on big data.

Big data stands for massive collections of data that can be analyzed computationally to extract useful information. Given the big amount of data, even subtle patterns or relationships could be revealed that could be missed by analyzing smaller datasets. In our computer age, data tends to accumulate faster and faster. In some fields, there is literally an explosion of data, such as that coming from genomic sequencing experiments in life sciences — this fully justifies the study of big data.

Review the essay examples below to learn about the trending topics in this field, how they are approached, how the essays are structured, etc. The goal of analytics is to help organizations make smarter decisions for better outcomes. The key to successfully using Big Data, is by gaining the right information by employing the most suited kind of analytics which delivers knowledge. The Big Data revolution has given birth Big Data Data Mining 1 Page.

Big data resembles to a data flood.

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The abundance of data extends day by day. Big data focus on the huge extent of data. The data may be in the form of structured, unstructured and semi structured. The structured data consist of text files that Big Data Data Collection 2 Pages. With the advance growing of technology, expansion of research areas, deployment of different commercial and open sources GIS systems has lead out a massive collection of data stored in different debases.What does Big Data mean for economic development?

Understand what you need to know about putting data to work for place marketing and business attraction. If you're like most people, you've heard a lot about the concept of Big Data over the last few years.

essays on the use of big data in development economics

And if you're like most people, your eyes glaze over when you hear the term, and you start wondering about important things like washing your cat, or what you might have for lunch. The GIS Planning team wants to put an end to all that. The buzzword part.

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Not the taco lunch. We love tacos. After all, our business is data. Specifically, slicing and dicing it to make it super useful.

essays on the use of big data in development economics

Putting it into intuitive, attractive and user-friendly formats you can share with the click of a mouse. Data is a lot like oil - much more valuable and useful the more it is purified and processed.

Which is why we offer you this handy primer on Big Data, so you can use the buzzword AND actually understand what it means and why it matters. Want to know more about Big Data and economic development? A Service from the Financial Times. Apply Geographic Information System data tools to your economic development website and generate more leads. Material on this website is protected by copyright and trademark laws.

All Rights Reserved. Terms and Conditions. Alissa Sklar Vice President of Marketing. Big Data makes sense for economic development if you understand that i t's the why behind the what.

That means that it's the vital information that helps a site selector or business choose your location over any other equivalent community in the world.

Essays on the Use of Big Data in Development Economics

Every business has a secret sauce of criteria they need to satisfy to find their best location. This is really important for place marketing, because you already know the kinds of "why" they are looking for: demographics, GIS maps, consumer spending, labor force information, business and industry data, infrastructure, education, talent pool data, etc. Which brings us to number 2. The goal is to give website users the critical data they need to make decisions.

Many economic development websites forget to make this data avaiable or easily accessible. If you aren't offering this information online, you risk losing leads without ever knowing you were being considered. Big Data is simply a reference to the way you harness, maintain and offer this information. Our newly redesigned US national selection portal, ZoomProspector. Big data is actually made up of lots and lots of little data.

It can be overwhelming, time-consuming and expensive for any economic development organization to track down, offer and maintain the huge amount of information they must offer to be competitive.Advisor: Pammolli, Prof.

In this thesis I approach problems within the literature of Development Economics. Using tools from policy evaluation, different quantitative methods and big data sources, I study the common problems that affect the development of the nations. I separate my thesis into three chapters. In Chapter 1, using policy evaluation techniques together with other quantitative methods, I study the effects of the policy integration for the academic sector within the European Union.

In the second chapter, I study one of the most important subjects presented in this thesis: inequality. Using the case of Colombian municipalities, I examine how international trade affects social conditions measured by the Multidimensional Poverty Index.

Finally, in the third chapter, I study the effect of the patent innovation using the complexity algorithm developed by Hidalgo and Haussmann.

essays on the use of big data in development economics

Here I do a comparative of the patent innovation using two aggregations: countries and cities. Next, I will explain in more detail the findings of each chapter. Chapter 1: It is generally accepted that the frequency of cross-border collaborations has been increasing in recent decades, which is principally regarded as a symptom of globalization.

In this way, we are able to interpret trends in cross-border activities according to more domain-specific trends. We focus our analysis on the impact of entry into the EU by new member states by quantifying the rate of cross-border collaboration before and after the enlargement of the European Union. In this sense, we build upon recent studies aimed at quantifying the impact of European Research Area integration policies on the activity of the European innovation system.

We combine descriptive complex networks techniques with panel regression Difference in Difference methods to reveal, counterintuitively, a decrease in cross-border activity by the new EU member states following their entrance. The results show that while the number of crossborder collaborations in academia is increasing in the old member countries, and despite that the number of cross-border publications in the new members is higher compared with past years, they would actually collaborate more being outside the European Union.

We use data for the inventor mobility network to show that these counterintuitive trends are none other than the negative externalities of unification associated with brain-drain. Chapter 2: We empirically measure the effects of international trade on inequality.

We define social inequality as the average shortfall of social conditions by municipality. Specifically we use the Colombian Multidimensional Poverty Index. As a result, we found empirical evidence for a strong neighborhood effect, which helps make the decision that would be used to improve social conditions in those municipalities without exporter firms.

Chapter 3: One of the most important questions in Developmental Economics is how technological innovation is able to shift development. Here, we use the Hidalgo and Hausmann complexity algorithm to estimate how the selection of the innovation field affects the leadership in innovation among countries by using the first patent of triadic families of the European, Japanese and United States patent office.

Our findings highlight the United States as a leading country in patent innovation during most of the years. In contrast, using the region aggregation level, we find that the Japanese regions are the leaders in patent innovation during every year in our data.

However, the most complex regions in the Unites States. On the patent side, we note that the fields related to chemistry, biotechnology and pharmaceuticals play a very important role in patent innovation. Finally, we compare our findings with similar works of other researchers, finding a strong relationship between academic research and patent innovation.

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In this thesis, I explore quantitative methods and Data Science techniques applied to social studies for different aggregations. In the second chapter, I explore how one single country and its municipalities relate to the world through trade, and how its relationship could modify the condition, while its minimal political level affects the social conditions through a mechanism accepted and studied by classical economics.

In Chapter 3, we study the behavior of nations, where every nation with innovative production is included. By alternating the level of aggregations from a nation to a city, we are able to determine the role that political regions play within the whole country. Recognizing the importance of the results and the methods that I use to explore these important development issues, such as international integration, inequality, international trade, innovation and relation country-cities, this thesis questions the classical economical methods and the relevance in accepting new methodologies based on Data Science to answer the question of classical problems that were answered by using theoretical methods in the past.

These new methodologies based on Big Data are evolving every day with importance, and, moreover, with information collected by governments, social networks, international organizations and private institutions.Pssst… we can write an original essay just for you.

Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes. Big data is a term applied to datasets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency.

And it has one or more of the following characteristics — high volume, high velocity, or high variety. Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable.

Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in better and faster decisions. Quora and Facebook use Big data tools to understand more about you and provide you with a feed that you in theory should find it interesting.

The fact that the feed is not interesting should show how hard the problem is.

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Credit card companies analyze millions of transactions to find patterns of fraud. Maybe if you bought pepsi on the card followed by a big ticket purchase, it could be a fraudster? Big data describes a holistic information management strategy that includes and integrates many new types of data and data management alongside traditional data.

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Big data has also been defined by the four Vs:Volume. The amount of data. While volume indicates more data, it is the granular nature of the data that is unique. Big data requires processing high volumes of low-density, unstructured Hadoop data—that is, data of unknown value, such as Twitter data feeds, click streams on a web page and a mobile app, network traffic, sensor-enabled equipment capturing data at the speed of light, and many more.

It is the task of big data to convert such Hadoop data into valuable information. For some organizations, this might be tens of terabytes, for others it may be hundreds of petabytes. The fast rate at which data is received and perhaps acted upon. The highest velocity data normally streams directly into memory versus being written to disk. Some Internet of Things IoT applications have health and safety ramifications that require real-time evaluation and action.

Other internet-enabled smart products operate in real time or near real time. For example, consumer eCommerce applications seek to combine mobile device location and personal preferences to make time-sensitive marketing offers. Operationally, mobile application experiences have large user populations, increased network traffic, and the expectation for immediate response. New unstructured data types. Unstructured and semi-structured data types, such as text, audio, and video require additional processing to both derive meaning and the supporting metadata.

Once understood, unstructured data has many of the same requirements as structured data, such as summarization, lineage, auditability, and privacy.

Further complexity arises when data from a known source changes without notice. Frequent or real-time schema changes are an enormous burden for both transaction and analytical environments. Data has intrinsic value—but it must be discovered. There are a range of quantitative and investigative techniques to derive value from data—from discovering a consumer preference or sentiment, to making a relevant offer by location, or for identifying a piece of equipment that is about to fail.

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