How the Australian Relationship Between Unemployment and Inflation has Changed - Oliver Bolosky

            The relationship between inflation and unemployment has always been hard to pin down- it seems that establishing a trend that relates the two often doesn’t stand the test of time. This comes with a couple questions such as “why don’t the trends stay consistent” and “how have the models of this relationship changed over time.” This blog will focus on investigating these questions, specifically through looking at the evolution of these models in Australia. Starting with an earlier model dating back to the 50s, when William Phillips is credited as discovering the “Phillip’s curve” which presented the idea of an inverse relationship between inflation and unemployment. The major issue with this model is that it was predicated on the idea that it assumed that inflation expectations wouldn’t change. “When these findings were published, they suggested that policy makers faced a tradeoff between inflation and unemployment.” (Blanchard, 160). The result of which is that as inflation expectations changed, the model no longer became accurate, and inflation started accelerating in a way that wasn’t anticipated.

This increasing unemployment and inflation is relatively well known in the United States, with the stagflation of the 70s being recent and painful enough to entrench the memory into the American zeitgeist. Australia’s situation likely isn’t as well known to an American audience, so it’s best to start by looking at the Australian situation in the 70s. When the Phillips curve relationship broke down the Australians faced the same stagflation as America. “unemployment more than doubled in about 18 months to 5.3 percent in December 1975 when inflation had hit 14.4 percent.” (Yeates). The Phillips tradeoff had broken- both inflation and unemployment were rapidly increasing when they were supposed to be inversely correlated. Eventually the ill-advised continual expansion of the 70s came to an end, and a new consensus had to be formed. It was clear that the reaction in inflationary expectations had to be accounted for in order to accurately model the relationship between unemployment and inflation. This resulted in the creation of the expectation augmented Phillip’s curve “or the accelerationist Phillips curve (to indicate that a low unemployment rate leads to an increase in the inflation rate and thus an acceleration of the price level).” (Blanchard, 163). The major distinction here is that the rate of change in inflation is what’s predicted by the equation- not an absolute level of inflation. This more accurately predicts the kind of snowballing seen in the 70s, where inflation wasn’t just continuing to increase but was increasing at an increasing rate.

             Jumping forward now to 1999, estimations of the Phillips curve have become increasing complex. In a study attempting to model the relation in order to describe what causes inflation, what inflation will be, and how to approach it from a policy standpoint, it’s noted that “The framework of the 1990s has much in common with the one enunciated in Holmes’ paper written in 1971, although the modern version would contain some elements not present in the earlier version.” (Gruen, 253). Holmes was an advocate of the expectation adjusted Phillip’s curve, and Gruen’s paper advocates for something similar. Specifically, Holmes was a proponent at the time of using recent price changes as an indicator of inflation expectations and incorporating it into the relation. Almost three decades later more complex and accurate representatives of expectations are used incorporating both elements that predict based off of inflationary behavior farther back, as well extrapolating current trends. Additionally, a general formula for a relation between unemployment and inflation can’t be established between countries due to the multivariate nature of the relation being different because of the idiosyncrasies of differing economies. For example “The presence of import prices reflects the significant proportion of imported goods that are either consumed or enter the production chain in Australia.” (Gruen, 226) A model used for a different country might not accurately weigh the role of imports in the Australian economy, so a unique formula to Australia must be made to best fit the correlation. Overall, because the behavior of inflation and inflation expectations are the product of many different variables that relate to inflation in inconsistent ways between times and countries, it’s likely that Australia like many countries will have to continually re-evaluate the relation between unemployment and inflation.

 

 

 

 

 

Bibliogrpahy

Blanchard, Olivier. Macroeconomics Seventh Edition. Pearson, 2017.

David Gruen, Adrian Pagan, Christopher Thompson, The Phillips curve in Australia, Journal of                   Monetary Economics, Volume 44, Issue 2, 1999, Pages 223-258, ISSN 0304-3932,                              https://doi.org/10.1016/S0304-3932(99)00024-0.

Junankar, P.. (2002). Comment on ‘Fiscal Policy and the Job Guarantee’. Australian Journal of                     Labour Economics (AJLE). 5. 265-269.

Yeates, Clancy. “What's Stagflation, and What Would It Mean for You?” The Sydney Morning Herald, The Sydney Morning Herald, 24 Mar. 2022, https://www.smh.com.au/business/the-economy/what-s-stagflation-and-what-would-it-mean-for-you-20220315-p5a4q9.html.

Monetary Policy After the Global Financial Crisis and the Effective Lower Bound - Jessica Siler and Oliver Bolosky

  The global financial crisis had a profound impact on how monetary policy is conducted, and the tools that were used for the financial crisis are needed again because of the effects of the pandemic. The global financial crisis was extreme- the fed couldn’t manage the financial system effectively by setting the federal funds rate. The federal funds rate is constrained by an effective lower bound (the point at which lowering the interest rate isn’t effective enough in increasing output and to lower it further would be harmful). The in-class IS-LM model is useful for illustrating this problem, viewing the global financial crisis as a left shift in the IS curve. If a shift is far enough left that the output suffers from the flattening bottom of the IS curve (which yields diminishing returns in output with respect to lowering interest rates), the output can still fall short of the objective output at the federal fund rate’s lower bound (Blanchard, 93). This situation is shown in the figure (Blanchard, 122), and is the setup for using other tools for monetary policy.

    One of the main tools the fed has at its disposal when the federal funds rate hits its effective lower bound are balance sheet policies. There are quite a few balance sheet policies, although in the framework of the IS-LM model they have one goal, to shift the IS curve back to the right, where the ELB doesn’t limit output (and all its associated benefits). One example of balance sheet policies is quantitative easing, which is quite simple, the fed buys assets en masse injecting cash into the economy and expanding its balance sheet. Outside of balance sheet policies, the fed will engage in forward guidance, which is the fed communicating the state of the economy and likely future actions the fed will take.  The goal of forward guidance is to better inform the populace so that they won’t be surprised by monetary policy and will make better decisions. The onset of the global financial crisis caused the fed to use these tools- to varying effects depending on who’s asked. However, “The majority view is that balance sheet policies and forward guidance made up for some but not all of the shortfall [from the effective lower bound] (Caldara, 433). Regardless, the effective lower bound remains a barrier to effective monetary policy, as balance sheet policies and forward guidance don’t make up for the lost output. In fact, in a simulation of a financial shock between a relaxed effective lower bound and a realistic one, there’s a significant difference between the two in terms of GDP, this can be seen in the U.S. real GDP graph (Caldara, 444).

If the effective lower bound restricts effective monetary policy, and the alternative tools to combat that only partially made up for that restriction in 2008, then it’s reasonable to be concerned about monetary policy in a world facing a pandemic and even in the post-pandemic economy. In fact, some projections show that the effective lower bound will restrict monetary policy up to 40% of the time (Kiley, 8). With a number as daunting as that- the development of unconventional monetary tools makes sense.  The secretary of the treasury thinks that “the FOMC could consider a number of approaches.  Some involve the deployment of unconventional tools, such as longer-term asset purchases” (Yellen). These unconventional tools will likely become more effective than the policies used in 2008, especially if there’s an impetus from the effective lower bound becoming a barrier more.

Bibliography

Blanchard, Olivier. Macroeconomics Seventh Edition. Pearson, 2017. 

Caldara, Dario, et al. “Monetary Policy and Economic Performance since the Financial Crisis.” Federal Reserve Bank of St. Louis Review, vol. 103, no. 4, 4th Quarter 2021, pp. 425–60. EBSCOhost, https://search-ebscohost-com.ezproxy.plu.edu/login.aspx?direct=true&db=ecn&AN=1933158&site=ehost-live&scope=site.

Kiley, Michael T and John M. Roberts, “Monetary Policy in a Low Interest Rate World,” Brookings Papers on Economic Activity, Spring 2017.

Yellen, Janet L. “Comments on Monetary Policy at the Effective Lower Bound.” Brookings, Brookings, 14 Sept. 2018,  https://www.brookings.edu/blog/up-front/2018/09/14/comments-on-monetary-policy-at-the-effective-lower-bound/. 

Poverty, Microcredit, Investment, and Solow

 Prior to the onset of COVID-19 the World Bank, WHO, and UN had all been watching a concerning trend, the slowing of the rate at which people moved out of poverty. This trend was exacerbated during the onset on COVID-19 as economies slowed down and healthcare expenses for individual households skyrocketed worldwide. This created a new trend of negative poverty recovery, in other words, more people were pushed into poverty or deeper into extreme poverty. Estimates from the world bank show around half a billion people shoved into poverty, or deeper into poverty, in December 2021. The quote above comes from a 2020 estimate and discussion by the world bank, where they claimed 150 million are likely to be added into extreme poverty as a result of COVID-19. Current numbers are harder to pinpoint, but we can assume it is likely a very real issue globally.

First, we should define what tools are available to aid in the reduction of global poverty. These typically take the form of accelerated economic growth, agricultural growth, development of infrastructure, development of human resources, growth of employment, access to assets, and access to credit. While we don’t have time to unpack all of these, I would like to focus on what an increased access to credit does for individuals and economies facing poverty with a discussion using the Solow model to describe what we should expect to see on a economic level from an increased individual or group access to credit.

Microcredit is typically defined as the provision of small loans to underserved entrepreneurs and has been both celebrated and vilified as a development tool. It was the basis for the 2006 Nobel Peace Prize and has a wide following from policymakers, donors, and funders worldwide as an effective policy tool. However, there was a severe lack of evidence for some of microcredit’s biggest claim, that it could pull people out of poverty. Many theories of the impacts of microcredit ranged from poverty traps, general equilibrium effects, and credit market competition suggested that the expanding of credit to the impoverished need not be positive and may even be negative. Now for further discussion around microcredit using six randomized evaluations of microcredit complied by Abhijit Banerjee, Dean Karlan, and Jonathan Zinman to better understand the observed outcomes of microcredit.

Six randomized studies of microcredit from geographically different areas picking to serve different groups within each nation ranging from Bosnia, Ethiopia, India, Mexico, Mongolia, and Morocco. All except Bosnia have an annual household income of less than GDP per capita in the given year of each study. In each study loans were randomly offered to both a treatment group and control group, this could be an individual or a group, the key was how to have people take the access to credit, and this was from first screening the desire for credit. Some studies only offered loans to women within a certain age group while the study in Ethiopia had a random selection of households holding poverty status and a viable business plan, as these loans were mostly given to entrepreneurs with only some studies tracking how the funding was spent. Average loan sizes ranged from about $500 USD in Ethiopia to $1,800 USD in Bosnia. What is important is the proportion of the size of the loan compared to annual household income, this ranged from 118% in Ethiopia to 6% in Mexico. 

Now to move to challenges researchers faced when conducting these surveys. They found three main issues that influenced the rate at which loans were chosen and the credit market as a whole. First being there is a modest demand for microcredit. Second being both groups viewed are very similar and given economic conditions in each nation we should expect to see low take-up rates of loans, which was observed to range 17 to 31 percent. The last challenge seen focus on the relationship between microcredit and traditional credit in terms of substitute or complementary good which may affect either form of credit from increases in demand as consumption of microcredits increases, however evidence from the study suggests both goods seem as substitutes, so as microcredit use increases we see a reduced demand for credit from other Monetary Financial Institutions. 

We’ll briefly touch on all of the study’s findings then shift focus to one of them to analyze through the Solow model to give potential implications. Researchers grouped outcomes into groups, specifically: business activity (litmus test), Income, Consumption, and Social Indicators. The first finding focuses on micro entrepreneurial activity and we can think of this as starting a business or funding a current business. Since most surveys within this study focused on entrepreneurial activity as a prerequisite to loan consideration, we can use this outcome as a litmus test, or to show that the effect is measurable and exists regardless of noisy data. Here the study found modest effects on ownership, starts and closures so this suggests a partial passing of the litmus test. The second outcome group viewed was income, or more specifically household income and income composition. Here we see no change in household income but there is an interesting result of income composition, or the ratio of business income vs wage income. Due to increased investment into businesses, we see an offsetting of wage labor replaced by business income. A key observation within this group of observations is the growth of freedom of choice in the form of occupation and time spent rather than directly lifting people out of poverty. The third outcome group is consumption, specifically consumption expenditures which is a widely used proxy for standard of living. The results found no increase in total household expenditure and mixed effects in other metrics, but there was an interesting result in the composition of consumption. A robust finding is a decrease in the consumption of discretionary goods (temptation goods, recreation, or entertainment) it is unknown why this is but another observation found is no change in healthcare or education spending. The final outcome group of social indicators yields no change in the measured areas of child schooling and female empowerment. Each study measured these changes, yet no transformative effects on social indicators was observed. Now to transition to a widened discussion of the outcome group most relevant to my presentation, micro entrepreneurial activity. 

Most studies measured changes to business investment, size, and profits with increased access to credit through microcredit. Many of the studies found positive results around these areas, specifically that the average effects when pooled across studies is economically significant. The study concludes that with pooling many surveys we can see some evidence of expanded credit access yielding positive changes to business activity in the form of business investment. Let’s now view how this finding may affect an overall economy if access to credit were expanded further through analysis with the Solow model.

Here we can see a production function graphed as a function of capital per worker with two savings functions graphed below and a standard linear depreciation of capital. We should expect the increased investment from expanded access of credit by microcredits to push this savings function upward which pushes capital per worker outward to a new equilibrium point and may also show some increase to GDP per worker depending on where the original point of equilibrium is located. 

This increase to capital per worker and GDP per worker from increased access to credit from microcredit may yield growth in national level economies if the effects compound between many groups or individuals, we may see an increase to living conditions years into the future from repeated investment at a new higher equilibrium and a growing economy of a previously hurt or stagnant economy.

To Drive Technological Progress, We Need Worker Power: An Analysis

To Drive Technological Progress, We Need Worker Power: An Analysis

By Isaac Kim and Joy Mills

    In class, we learned of the Solow Growth Model which concisely describes the relationship between technological growth and GDP growth through worker capital and firm productivity. A reflection of this theoretical framework can elucidate one on how this might make intuitive sense, but as with any scientific/economic model, it must stand against real scrutiny from examples in history and the current era to be considered legitimate. It just so happens that recent events in Britain’s current post-COVID economic climate and historical precedent may just help with this aforementioned goal.

    A Jacobin article titled To Drive Technological Progress, We Need Worker Power written by Rae Hart outlines Great Britain’s current royal treasury policy to deal with the aftermath of the COVID-19 pandemic. This policy essentially cuts corporate tax rates as an ongoing effort to spur investment into spur Britain’s “lagging productivity”, which, according to the Bank of England, is expected to only grow by 1% a year towards 2024. This policy is just a continuance of the status quo, decade-long reduction in corporate taxes which have not translated well into overall GDP growth. It is obvious that tax cuts to these businesses have not translated to technological growth (which the Solow Growth Model stipulates is the driving factor for GDP growth). This suggests that perhaps tax cuts are not the incentive needed for companies to innovate.

    To preface a possible solution, ecologist Andreas Malm wrote the book Fossil Capital which outlines his analysis of the history of the Industrial Revolution in Britain. On the cusp of the Revolution in 1769, cotton spinners were the first to “unionize” to increase their collective bargaining power. The government was in line with the firm’s interests and made such action illegal and suppressed their voices to the wayside. However, as time went on, growing fears of a strike by the general populace forced the firms to increase worker wages. This was effective because despite the workers only making up 10% of the workforce, a strike could shut down the whole economy due to how crucial cotton spinners were to the supply chain. Firms were bleeding financially because of these concessions, and their only way out was to approach an engineer to create the first automatic machine: a “‘self-acting’ spinning mule” known as the “Iron Man”. This technology exploded investment into replacing the man-powered cotton spinners and installing Iron Men, which increased GDP growth tremendously. Not only that, but the development of the Iron Man was integral to the creation of the steam engine (which draws its power from Iron Men). 

The same principle derived from this historical account can be applied to the current situation. According to the Solow Growth Model, technological progress is required to increase productivity in the long run. Figure 1 below shows an economy in equilibrium. The equilibrium output per worker and capital per worker occur where the depreciation function meets the savings function. On the diagram, the equilibrium output and capital are labeled with the letters A and B respectively. The economy will remain in this equilibrium unless there is an increase in the savings rate or a decrease in the depreciation rate (which are both caused by technological growth). Our article talks about how the UK is stuck in a “productivity paradox,” where they are not experiencing significant economic growth. Thus, currently, the UK is stuck in equilibrium. According to this magazine article and the history of technological growth related to bargaining power from Malm’s book, bargaining power will increase technology and therefore economic growth. Currently, union membership in Great Britain is at historic lows in the post-WW2 era. Increasing bargaining power by increasing wages for workers as well as allowing for unions will begin to cost employers. They will see a decrease in productivity as more of their resources are going towards their employees. This will force employers to begin investing in their process. Innovation to increase productivity will occur, often in the form of technological innovation. This can be seen historically with the Industrial Revolution and the story of the Iron Man. This technological growth will result in a lowered depreciation rate, and this new depreciation function is seen in Figure 2 below. The new depreciation function sees an increase in the equilibrium output per worker as well as capital per worker. Economic growth is present, and we know that technological growth is required for long run economic growth, but in order to foster technological innovation, an increase in workers bargaining power is essential.

The article then goes on to make an even bolder claim. “[T]he technological innovations that our era demands — to tackle the climate crisis and build a more prosperous and just society — will be brought about only if the balance of power in the economy tips away from capital and toward working people.” Through this quote Hart is asserting that modern day innovation should be environmentally positive . While this may make sense to the environmentally conscious person, he supports this claim by pointing out that renewable energy is also increasing jobs. An increase in jobs is always a plus and will also assist in the increasing inflation we are seeing. Hart says, “Nationalization of key industries and the creation of jobs in the renewable energies sector is another way the government could help to alleviate some of the supply-side constraints causing the current elevated levels of inflation.” So the increasing bargaining power is not only good for economic growth and the wellbeing of employees, it can also lead to technological innovation that will increase jobs available resulting in a slowed inflation rate.

With benefits to the climate, economy, workers, and firms, this article stands as a compelling argument for the unionization of workers in the long run. Hopefully, Great Britain will use these facts to assess whether or not their policies will benefit individual firms through tax cuts.

   

Works Cited

Hart, Rae. “To Drive Technological Progress, We Need Worker Power.” Jacobin, 16 May 2022, https://www.jacobinmag.com/2022/05/technological-progress-worker-union-power-inflation-cost-of-living.

Malm, Andreas. Fossil Capital: The Rise of Steam Power and the Roots of Global Warming, Verso, London, 2016. 

Quinn, Kevin, and John Hoag. "What really happens in the Solow model: technological progress versus population growth?" American Economist, vol. 58, no. 2, fall 2013, pp. 149+. Gale Academic OneFile, link.gale.com/apps/doc/A349903273/AONE?u=taco36403&sid=bookmark-AONE&xid=6e2e76c9. Accessed 17 May 2022.

Connections Between Savings and Output in 2022

The Savings Boom is Over

As the world starts to return to normal the way Americans have saved since the start of the pandemic has changed drastically. At the start of the COVID pandemic savings rate soared to 34% as people stayed home and reduced their spending. As the government boosted the economy to avoid a full blown recession, people were still cautiously spending as many things remained uncertain. As of March, savings rates dropped to 6.2% which is the lowest rate since December 2013. The first three months of 2022 have all been below the pre-pandemic average of 7.5%. Initially this was praised as the economy returning to normal, but now savings rates are dropping at an alarming rate. As inflation rises to record levels, people are dipping into their savings. Incomes are not rising fast enough to keep up with inflation. The higher savings rates of the earlier stages of the pandemic were followed by an increase in output. Now as the savings boom ends the future of the American economy and economies everywhere are uncertain (Hardy, 2022).

In one article we looked at the first large growth periods of the U.S and Japan were looked at with respect to the savings and investment rates. In the United States a residual outward shift in the personal saving function accounted for almost two-thirds of the rise in the net investment rate while in Japan at least one-third of the rise in the ratio of net private investment to national product was due to an unexplained outward shift in the savings function (Suto and James, 1999). High savings rates mean investment is high and when investment is high then firms are growing and expanding leading to increased growth. In both cases these rise in rates were attributed to exogenous factors just as in 2020. The pandemic shifted the way people saved, so when stimulus checks came in and work picked back up people were still saving. Investment then due to this and the low interest rates the economy took off. These circumstances match very closely with those mentioned in the paper where there was record high growth. Unfortunately, the periods mentioned in the paper lead up to the Great Depression. 

As inflation rises and savings decreases at an alarming rate, investment will begin to dwindle. Rate hikes from the FED coupled with money not going as far as it used too could lead to serious trouble. We want the economy to cool off, but we do not want to see the savings rate drop this fast. Uncertainty lies ahead for the American economy and economies around the globe as a multitude of factors move in different directions. Historically we would expect to see drops in output, so this brings to question. Are we knocking on the door of a new recession or while the FED and Congress find a solution to tackle the inflation rates that are damaging savings rates?

Graph 1: We can see the first spike at the beginning of COVID. The savings rate dropped and then spiked again as more stimulus rolled in later in the pandemic. Now rates have returned beyond normal levels due to a rise in inflation. These higher savings rates were periods where output was growing, so this makes us wonder if a large drop in output is coming.

Solow Model

Looking at our Solow models we are seeing our savings function decrease. Now at the same levels of output savings is not keeping up with depreciation and our overall output per work and capital per worker decreases. Is this what is in store for the US economy? 

The Growth of Growth: Factors Which Enable Economic Expansion

 By Jessica and Reid

Economic growth is widely regarded as one of the most important goals for a country to aim towards. It creates jobs, increases wages, and creates more things to buy—so how do we go about making it happen, and what factors are most important in doing so? According to Blanchard, the sources of economic growth are capital accumulation and technological progress, with technological progress being the more key long-term factor (232). This is due to the nature of decreasing returns to capital. There comes a point where more capital simply does not have a significant effect. The first riding mower can make a large difference in the speed a golf course can be mowed, but the forty-first is unlikely to be much more important than the fortieth. Therefore, Blanchard continues by stating that “Sustained growth requires sustained technological progress…the economy’s rate of growth of output per person is eventually determined by its rate of technological progress” (232-233). More efficient tools allow workers to work more effectively, improving the rate at which things are accomplished without needing to buy a larger number of tools or hire a larger set of workers. There is no similar effect of diminishing returns as there is with capital since technological improvements improve tools for each worker rather than simply giving workers more tools that they might not need or want.

The idea that technology is key to economic growth is widely known, and the subject of much focus. An article for the Wall Street Journal exuberantly praises the transformative impact of technology, stating that “Not labor, not capital, but technology has been the single greatest driver of economic growth since the Industrial revolution…Nearly all wealth in the world has been created since the rise of industry” (Yu) This technological progress, however, is expected to come as a result of global turmoil and conflict. The article focuses heavily on the tech race—not mere incremental improvements, but direct efforts by countries to outpace each other in the field of technological progress, catapulting the world forward. Yu cites technological marvels such as putting a person on the moon and creating the internet as a result of the previous Cold War tech race, both of which had major positive impacts on the economy. She then wonders where the next tech race could take us. Despite Yu’s expectations for conflict-driven technological progress remaining in the future, for now, efforts to improve technology are certainly not stalled in the present. Automation provides an interesting field combining both capital and technological prowess by allowing the capital to work itself. In an article for the New York Times, Ben Cassleman notes that “The push towards automation goes far beyond the restaurant sector… 43 percent of businesses said they expected to reduce their workforces through new uses of technology.” This is an intense shift, both a swift motion of technological progress and heavy investment into new capital. Logically, economic growth will follow. However, it isn’t entirely good news for workers. Cassleman couches the bad news with a positive spin, stating that “Automation may harm specific workers, but if it makes the economy more productive, that could be good for workers as a whole.” Jobs that can be replaced by automation certainly result in increased efficiency, as the number of workers goes down but the product remains the same, but that is still increasing unemployment. 

The above diagram illustrates the impact of new technological developments on economic growth (Marmer).

There are factors outside of technology and capital that are relevant to economic growth. Stephen Knack, in his essays, describes a broad range of influences such as life expectancy, property rights, and terms-of-trade shifts. The two he focuses on most, however, are a democracy—where he finds that an intermediate amount of civil liberties and political rights are most beneficial to growth—and inflation, where he finds that at levels higher than 20% growth is impaired. Inflation logically causes uncertainty about the value of money in the future that deters savings and investment, which will naturally hinder the growth of the economy. His findings on democracy are interesting, as they suggest that the harshest dictatorships need to allow more civil liberties to grow, yet the freest democracies lose some efficiency as well. Additionally, in direct news about the United States’ economic growth, the Biden administration and the Conference Board both predict around 3% growth in the coming year. This is heavily influenced by what both identify as another major factor towards economic growth—stability. Economic stability allows general access to essential life resources, including financial resources, housing, food, and secure jobs. In the case of the two aforementioned sources, the more immediate effects of the Ukraine crisis are examined, identifying instability as a source of higher inflation and a strain on energy prices and food. The CNBC article additionally notes spikes in oil prices, which can be a notable hindrance to the economy and its growth.  As expressed by the aforementioned sources, some factors that determine the efficiency of economic growth include technological advancement, especially those that influence production, democracy in relation to civil and political rights, and macroeconomic stability. 

Works Cited

Blanchard, Olivier. Macroeconomics. 7th ed., Pearson, 2016. 

Casselman, Ben. “Pandemic Wave of Automation May Be Bad News for Workers.” The New York Times, The New York Times, 3 July 2021, https://www.nytimes.com/2021/07/03/business/economy/automation-workers-robots-pandemic.html.

Economic Forecast for the US Economy - The Conference Board. https://www.conference-board.org/research/us-forecast. 

Franck, Thomas. “White House Sees Strong GDP Growth in 2022 despite Inflation Risks.” CNBC, CNBC, 21 Apr. 2022, https://www.cnbc.com/2022/04/21/white-house-sees-strong-gdp-growth-in-2022-despite-additional-economic-risks.html. 

Knack, Stephen. "Determinants of Economic Growth." Southern Economic Journal, vol. 65, no. 1, July 1998, pp. 185+. Gale Academic OneFile, link.gale.com/apps/doc/A21034141/AONE?u=taco36403&sid=bookmark-AONE&xid=2dc86207. Accessed 27 Apr. 2022.

Marmer, Max. “A Look at How Technology Is Reshaping the Global Economy.” Medium, Medium, 13 Feb. 2018, https://maxmarmer.medium.com/a-look-at-how-technology-is-reshaping-the-global-economy-c716c4681e06. 

Yu, Shirley. “Opinion | The World Is in Crisis, and That's Good for the Economy.” The Wall Street Journal, Dow Jones & Company, 22 Apr. 2022, https://www.wsj.com./articles/world-in-crisis-and-good-for-economy-growth-gdp-recession-inflation-technology-innovation-semiconductors-china-taiwan-ukraine-starlink-musk-11650634091

Technology and Economic Development (and War)

By Yadira & Logan

Our team read the article The impact of information technology on postwar US economic growth. The paper highlighted the crucial role that IT played in economic growth for the United States between 1947 and 2010. The Bureau of Economic Analysis provided a significant amount of industry-level data, but they also made modifications building off Jorgenson, Ho, and Kevin Stiroh’s work to derive their industry-level production account produced input and output tables which included 86 industries – 6 IT producing, 41 IT using, 40 non IT using. They found that information technology producing (IT) industries accounted for only 1.7% of value-added in the US economy during the post-war period (1947-2010). However, they contributed 7.6% of postwar econ growth and 32.8% of postwar productivity growth. This shows that the IT industry boosted worker productivity far above the money that was invested into it. This is a great example of how technological development leads to economic growth. 

Current Views 

Recently, the popular discussion about technology and economic growth has shifted to that of a “new Cold War.” With Russian military aggression becoming harder for the West to ignore, and an increasing (perceived or real) threat from China to the Western, democratic world order it is widely believed that both sides of a Western-Russian/Chinese axis will drastically increase investments in technologies. Global economic growth may increase long-term thanks to this renewed commitment. This assumption is based on past performance. In 1900 global GDP was $3.4 trillion (adjusted to 2011 USD) in 2020 global GDP was $112.7 trillion. During that time the global population increased by about 5 fold by productivity increased by about 33 fold. Technology is the force behind this leap. The first Cold War produced the technology that kick-started the internet and the information age we live in today. The author of the recent Wall Street Journal article The World is in Crisis and that is Good for the Economy argues that without geopolitical tensions between autocratic states like China and Russia the US would not have focused on bringing chip production back within its borders and thus would have invested less than it has in the last few years. China’s growing independent technological research in areas such as Artificial Intelligence could also spur competition in the US and other Western Democracies.

How it applies to what we have learned in class

At a basic level, we understand that if the global population grows, the global economic output should grow as well because more people will be working and buying goods. However, we have seen that since the early 1900s global GDP has grown faster than the population. Technology explains this extra growth. Each worker can, in theory, become more productive and earn more (for themselves or others) with the same level of resources. This relationship between productivity and growth can be captured by the Output per worker- Capital per worker graph we studied in class. If we follow along one of the curves we see that the more capital you supply to a worker the higher their output which makes intuitive sense. But we also notice that at high levels of capital per worker we get diminishing returns to productivity. When we improve our technology, however, we shift this output function up resulting in a new function wherever output-capital pairing is higher. Thus, with the same resources as before we have grown the economy.

Citations

Blanchard, O. (2016). Macroeconomics (7th ed.). Pearson. 

Jorgenson, D. W., Ho, M. S., & Samuels, J. D. (2016). The impact of Information Technology on postwar US economic growth. Telecommunications Policy, 40(5), 398–411. https://doi.org/10.1016/j.telpol.2015.03.001 

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