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?