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MIT CDB Assistant Professor, Renee Gosline, Describes Digital Marketing's Newest Influencers

MIT CDB Video: Sinan Aral on Social Commerce


At the recent MIT CDB conference on Big Data, professor and social networking expert, Sinan Aral, discusses peer influence and how it can impact product marketing strategies and the online economy.




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Digital automation, and its impact on labor, society and the economy, has been studied from multiple perspectives and through many lenses.  In his new research and analysis, Daron Acemoglu, the Elizabeth and James Killian Professor of Economics at MIT, acknowledges inequalities created when automation displaces certain human skills. However, he also says it is possible for new technology to create more complex versions of existing tasks where labor has a comparative advantage, tipping the scales back toward a future with plentiful jobs.

http://economics.mit.edu/timages/7


Acemoglu completed his graduate work in mathematical economics and econometrics at the London School of Economics, where he also received his Ph.D. in economics. His recent research focuses on the political, economic and social causes of differences in economic development across societies; the factors affecting the institutional and political evolution of nations; and how technology impacts growth and distribution of resources. Acemoglu has published four books: Economic Origins of Dictatorship and Democracy (joint with James A. Robinson), Introduction to Modern Economic Growth, Why Nations Fail: The Origins of Power, Prosperity, and Poverty (joint with James A. Robinson), and Principles of Economics (joint with David Laibson and John List).


He recently spoke at an MIT IDE seminar on the topic of, "The Race between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment.” IDE Community manager Paula Klein followed up with four questions. Below are his responses.

 

 

Q.  The current rivalry between digital automation and humans seems focused on economics and labor issues— concerns that labor will be progressively marginalized and made redundant by new technologies. Is this focus premature or overstated?


A: It is certainly not premature. We have seen many different types of tasks produced and performed by labor, even fairly skilled labor, become automated over the last 30 years. We also know of new technologies that will automate some very major occupations (regulations permitting), including driving, airplane piloting, some aspects of surgery, certain types of diagnoses and even parts of the practice of law.

Yet there is an aspect of it that is overstated. These are still only some of the occupations that humans perform today. The more important overstatement comes as one turns from automation to the prospects for future employment creation. This rapid process of automation does not mean that the future economy will not create jobs. If you look at the last several decades, qualitative evidence suggests rapid automation has been going on for more than a century, and a lot of the new employment comes in new tasks and occupations. So, as machines take jobs previously performed by humans, the economy appears to create yet other tasks and jobs to employ the displaced workers.

 

Q: How does your task-based framework help explain the current economic situation and provide context? Can you briefly summarize the model and your research?


A: Our framework helps us understand the aforementioned patterns and why the fact that new employment will come from new tasks and activities. But more importantly, because it endogenizes the speeds at which existing tasks are automated and new tasks are created, it also highlights why a period of unusually rapid automation generally brings a subsequent period of rapid creation of new tasks. Put simply, rapid automation depresses the price of labor which has fewer tasks to work. This then makes it more profitable for new tasks, which employ new labor, to be created.

 

Q: How might these new tasks spur economic growth and innovation?


A: The growth implications of creating new tasks are essentially a corollary of what I have just described. Growth comes about both because of automation -- we can do things we have been doing more cheaply-- and because of the creation of new tasks; we have new goods and services using better technology. Anything that spurs innovation triggers faster economic growth. So, rapid automation is a double whammy: it benefits us directly and it spurs additional creation of further growth-enhancing new tasks.

 

Q: What guidance can you offer to employers, workers, students and policymakers to prepare and adjust for the Second Machine Age?


A: All of these scenarios are no consolation if you do not have the skills that new tasks and jobs will demand. Some economists are now questioning whether college is a good investment. There are certainly reasons for rethinking some of our long-cherished assumptions: college is expensive and college graduates have not done very well in the labor force over the last 15 years or so. Nevertheless, improving the skills of our workforce and improving our own skills still remain the only ways of ensuring that we adapt to the future of technology.

MIT Sloan Professor Scott Stern’s latest research draws a clear correlation between the elements present at the founding of entrepreneurial startups and their later success. In addition, he and MIT doctoral candidate Jorge Guzman, use other widely available data-- such as incorporation information, patents, trademarks, IPOs and venture capital funding-- to measure and identify the potential for future growth.

 

The findings of the study, “Nowcasting and Placecasting Growth Entrepreneurship,” were presented at an MIT IDE seminar in March by Stern, who is Professor of Management of Technology and Chair of the Technological Innovation, Entrepreneurship and Strategic Management Group at the MIT Sloan School of Management. He and Guzman were not only able to draw conclusions, but to observe data-documented entrepreneurial trends, using algorithms and estimation models. These can “help us understand the origins and dynamics of startups,” Stern said.

 

Shifting Growth Patterns

Placecasting can be used to “evaluate the role of regional ecosystems” in the growth—and decline -- of startups, and to identify clusters of “hyperinnovation.” “Our approach allows us to track the changing locational patterns of growth entrepreneurs over time,” and in real-time, he said, as opposed to traditional, static survey methods. For example, “in Massachusetts, we are able to document the transition from Route 128 growth entrepreneurship to clustering in Kendall Square in Cambridge and Boston.” Similarly, in California, he is tracking the move of entrepreneurship from Silicon Valley to San Francisco.

guzman chart.JPG


Using what he calls nowcasting, Stern expects to develop a predictive model of growth outcomes and assign a probability of growth based on current developments and past indicators. It will also be easier to spot and evaluate why some firms will not succeed. Going forward, Stern also understands that the pace of change and the “app economy” will require new criteria and there will be new shifts to track.

 

Stern works widely with both companies and governments in understanding the drivers and consequences of innovation and entrepreneurship, and has worked extensively in understanding the role of innovation and entrepreneurship in competitiveness and regional economic performance. For more about regional clusters, watch this video and for more on the research, contact Stern at sstern@mit.edu .

Two papers came out last year that examined important issues around jobs and wages. Both are in top journals. Both were written by first-rate researchers, none of whom specialize in studying the impact of technology. And both came to the same conclusion: that digital technologies were largely responsible for the phenomena they examined.

The first paper, by David Dorn and my MIT colleague David Autor,  is about how jobs and wages changed in America from 1980-2005. It was published last year in the American Economic Review and called “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market,” which is an admirably informative title.

Equally admirable are the graphs the authors draw to illustrate their main findings. Here’s the one for jobs (the one for wages has a pretty similar shape). It gives the changes in employment share — which you can think of as changes in the the ‘market share’ of jobs — between 1980 and 2005. And it shows vividly that low-skill and high-skill jobs gained market share over that period, which those in the middle of the skill range lost.

Autor

Autor and Dorn are clear on what accounts for this shift:

The adoption of computers substitutes for… workers performing routine tasks—such as bookkeeping, clerical work, and repetitive production and monitoring activities—which are readily computerized because they follow precise, well-defined procedures. Importantly, occupations intensive in these tasks are most commonplace in the middle of the occupational skill and wage distribution.

and what doesn’t:

We evaluate numerous alternative explanations for the pronounced differences in wage and employment polarization… including deindustrialization, offshoring, … and growing low-skill immigration. None of these alternatives appears central to our findings.

The second paper concentrates on wages, and tries to determine what’s caused the red line in the graph below to decline so fast in recent years

http://www.slideshare.net/amcafee/mc-afee-econ-data

profits and labor share

This line documents the labor share of GDP in the US over the post-war period — the percentage of GDP that gets paid out in compensation (wages and benefits) to workers. As the graph above shows, US labor share has  been heading down sharply just as corporate profits have reached hew heights.

Is this because of globalization? Nope, because it’s been happening around the globe. As Loukas Karabarbounis and Brent Neiman write in “The Global Decline of the Labor Share” (out in the current issue of the Quarterly Journal of Economics):

We document, however, that the global labor share has significantly declined since the early 1980s, with the decline occurring within the large majority of countries and industries. We show that the decrease in the relative price of investment goods, often attributed to advances in information technology and the computer age, induced firms to shift away from labor and toward capital.

The AER and QJE are the two top journals in the economics field, so this research is about as solid as it gets. In light of this and plenty of other work, it really is time to stop arguing about whether technology has been one of the tectonic forces reshaping work and the workforce in recent decades. The evidence is just too clear that it is, and that we see evidence of the second machine age everywhere, including in the statistics.

This post first appeared March 12 on my Business Impact of IT blog here.