# macroeconomics

## Could knowledge about Central banks impact households’ expectations?

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Emma Rockall

Should central banks care if people understand them? Whereas once Alan Greenspan famously declared: “If I seem unduly clear to you, you must have misunderstood what I said”, central bankers now dedicate considerable time and thought to transparency and communications. While transparency initiatives have value in their own right in improving accountability, results from the Bank’s Inflation Attitudes Survey suggest that they could have potentially far-reaching effects on the economy through their impact on households’ expectations. If they improve households’ knowledge of central banks, they may produce inflation expectations that are more stable and closer to the inflation target in the medium term – that is, ‘better-anchored’ expectations.

How might institutional knowledge affect expectations?

‘Institutional knowledge’ captures households’ awareness and understanding of central banks, rather than the economy more generally. While a large body of work has examined the impact of central bank transparency and communications on the expectations of financial markets and professional forecasters (see, for example, Blinder et al. for a survey of the theory and empirical evidence), much less has been done to look at the impact on the expectations of households. From a theoretical perspective, the expectations of households are no less important than those of financial markets.  It is reasonable to suppose that the more someone knows about a central bank and how it conducts policy, the more confidence they will have that the central bank will act to bring inflation back to target.  This appears to be borne out by the IAS, which shows that higher knowledge scores are associated with a statistically significant improvement in households’ view of the Bank of England’s credibility, and their confidence that the MPC will return inflation to target. Greater understanding of a central bank may also help households understand how the central bank might adjust interest rates to meet the inflation target, thereby improving the accuracy of their interest rate expectations (with associated financial planning benefits).

There have also recently been some attempts to try and quantify the impact on households’ expectations empirically. Binder shows that following the Fed’s announcement of a 2% inflation target, households’ inflation expectations were better anchored, but that anchoring increased more for more well-informed households compared to less well-informed households. And Haldane and McMahon, using the institutional knowledge score discussed above, show that for the UK, higher knowledge corresponds to greater satisfaction with the Bank, and inflation expectations closer to 2% at all horizons.

…and why should central banks care?

If institutional knowledge does effect households’ expectations, this could have far-reaching implications for the economy more broadly. Woodford goes as far as to argue that, ‘Not only do expectations about policy matter, but, at least under current conditions, very little else matters.’ In theory, households’ inflation expectations feed through into a range of real economy variables, by affecting wage setting, the timing of consumption, and saving and borrowing (by changing perceptions of real rates). And empirically, a number of papers (e.g. Crump et al., D’Acunto et al. and Duca et al.) use household surveys to show that households with higher inflation expectations are more likely to move forward consumption or increase spending on consumer durables. And 16.5% of respondents to the IAS in 2018 said they would try and increase their income (e.g. by asking for a pay rise) as a result of their 1 year inflation expectations. Only 3.3% said they would take no action. How well-anchored households’ inflation expectations are can also have important implications for the persistence of inflation, and the trade-off between inflation and output that central banks face when trying to bring inflation back to target.

How can households institutional knowledge be measured?

I construct a measure of individual households’ knowledge about the Bank of England specifically (their ‘institutional knowledge score’) based on three questions from the Bank/TNS Inflation Attitudes Survey (IAS) (see the technical annex for details on how the score is constructed).

From this score, one can see how households’ institutional knowledge of the Bank has evolved (Figure 1). Peaking in 2005 at 4.3, the mean knowledge score (for all households) fell post-crisis to a low of 3.9 (although it has recovered slightly since). The aggregate picture marks a more dramatic fall for households with below GCSE level qualifications, whose average knowledge score fell from 4.2 in 2004 to a low of 3.4 in 2016. For some households the level of institutional knowledge is very low – in 2018, 24% of households answered none of the questions correctly.

Figure 1: Households’ mean knoweldge scores, by qualifications held

Evidence from the Inflation Attitudes Survey

Using, microdata from the IAS I explore the relationship between households’ knowledge scores and the differences between their inflation expectations and the MPC’s forecast for inflation one and two years ahead (with the 2% target taken as the MPC’s forecast 5 years ahead).  The survey also asks households what they expect Bank Rate to be over the same horizons. Although the MPC doesn’t publish a forecast for Bank Rate, I can compare households’ expectations with those of financial markets (captured in the yield curve at the relevant horizon, which the MPC conditions their forecasts on). The heatmap below shows the number of households in each ‘bucket’ (comprising the possible combinations of knowledge scores and deviations in expectations) relative to what you would expect if expectations and institutional knowledge were uncorrelated. The ‘hot’ diagonal (running from top right to bottom left) suggests that on average, higher knowledge scores correspond to smaller deviations in households’ inflation expectations from the MPC’s forecast (Figure 2); households’ Bank Rate expectations show a similar pattern.

Figure 2: Heatmap of households’ knowledge scores and inflation expectation deviations 5 years ahead

Regression analysis shows that these relationships are robust to the inclusion of household-specific controls. Regressions (1a) and (3a) (Table 1) show that even with controls, households with higher institutional knowledge scores tend to have inflation expectations closer to the Bank’s forecasts at both short and long horizons (see the technical annex for results at the 2 year horizon, which are similar to those at the 5 year horizon). And regressions (4a) and (6a) show that the same is true of households’ Bank Rate expectations.

Not only are all coefficients on institutional knowledge statistically significant at the 1% level, comparison to the coefficients on the controls would suggest they are also economically significant. For example, all else equal, moving from the lowest knowledge score to the highest would imply five year inflation expectations 0.56pp closer to the 2% target, and five year Bank Rate expectations 0.34pp closer to those of financial markets. This is larger than the impact of any of the controls (including age, often thought to be an important determinant of inflation expectations). See the technical annex for the full results of all estimations.

Table 1: Regressions of households’ expectations on their knowledge scores

However, these results do not necessarily imply that there is a causal relationship from institutional knowledge to expectations. While reverse causality isn’t a major concern (it’s unlikely that better anchored expectations produce a better understanding of the Bank), it’s possible that external factors, not captured in the controls, produce both higher institutional knowledge and better anchored expectations. One plausible candidate would be knowledge about the economy more generally. In this case, institutional knowledge would be proxying for economic knowledge in the regressions above, and the impact of institutional knowledge alone would be overstated.

From the perspective of motivating greater central bank transparency, this might not actually matter. Central bank communication efforts are likely to have a positive impact on both institutional knowledge and economic knowledge, rather than only one in isolation. In this case, even if the results above overstate the marginal impact of institutional knowledge, they may still provide support for the usefulness of greater transparency.

However, I can also use the IAS to try and tease out the impact of institutional knowledge specifically. From questions on the transmission mechanism of monetary policy, I  construct an ‘economic knowledge score’ to try and control for broader economic knowledge (see the technical annex for details on how it’s constructed). Table 2 shows the results of the previous regressions controlling for economic knowledge. These demonstrate a role for economic knowledge – a higher score has a statistically significant negative impact on deviations in inflation expectations from the MPC’s forecast. At the same time, the coefficients on institutional knowledge remain statically significant (and largely unchanged), suggesting the results above are robust to controlling (albeit imperfectly) for broader economic knowledge.

Table 2: Regressions of households’ expectations on their knowledge scores, controlling for economic knowledge

Conclusion

These results add weight to the need for and desirability of central bank transparency. Above and beyond the important role communication efforts play in bolstering democratic accountability, the IAS would suggest that, to the extent that they improve knowledge about central banks, they could help to better anchor expectations, thereby improving the efficacy of monetary policy. While there is still much to learn about how households form their expectations and how those expectations feed through into the real economy, this evidence provides support for recent initiatives undertaken by central banks (such as at the ECB, the BOI, and at the Bank of England – see here and here) to make their communications more accessible to more people.

View Technical Annex

Emma Rockall works in the Bank’s Structural Economics Division.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

## Low-Carbon Macro

### Tags

Carsten Jung, Theresa Löber, Anina Thiel and Thomas Viegas

Governments have pledged to meet the Paris Target of restricting global temperature rises to ‘well below’ 2˚C.  But reducing CO2 emissions and other greenhouse gases means reallocating resources away from high-carbon towards low-carbon activities. That reallocation could be considerable: fossil fuels account for more than 10% of world trade and around 10% of global investment.  In this post, we consider the macroeconomic effects of the transition to a low-carbon economy and how it might vary across countries. While much of the discussion has focussed on the hit to economic activity and the potential for job losses in higher-carbon sectors, we highlight that the transition also offers opportunities. And the overall impact depends crucially on when and how the transition takes place.

A transition to a low-carbon world could have significant implications for global trade flows…

Carbon-intensive energy is a widely traded good. Fossil fuels made up about a sixth of global trade in the last years (Chart 1).  The euro area, Japan and India are among the countries that have sizeable fossil fuel trade deficits, meaning they import more fossil fuels than they export.  By contrast, in the Middle East and North Africa fossil fuels make up nearly 70% of exports. And China is a key player in global trade of high-carbon goods, accounting for more than half of total global coal demand and just under half of steel demand.

Chart 1: Fossil fuels as share of total imports and exports (2011-2016 average)

* Middle East and North Africa. Sources: World Bank and authors’ own calculations.

That means the transition could result in a significant reallocation of resources, and a disruption of existing patterns of trade. Fossil fuel exporters would face a deterioration of their terms of trade – the relative prices of imports to exports – as demand for their exports falls. On the flip side, fossil fuel importers would benefit from lower fossil fuel prices, improving their external balances.  Countries could boost their exports by exploiting opportunities related to low-carbon products in areas ranging from the manufacturing of batteries to financial services that provide funding for the transition.  In addition, economies could reduce their foreign energy dependency by producing renewable energies domestically.

…and the transition could have big implications for investment

The energy sector accounted for 10% of global capital investment in 2016, with around two-fifths of that in oil and gas (Chart 2).  The bulk of these investments are made in emerging market economies (EMEs). Countries in the Middle East and Latin America invest heavily in fossil fuels, but China and India remain the two largest investors.

Chart 2: Annual energy investment by sector, 2016

Sources: International Energy Agency and Bank calculations.

For economies with large fossil fuel investments, the move to a low-carbon economy could lead to the risk of “stranded assets”. Such assets stop earning a return before the end of their expected economic lifetime when fuels they extract, or other products and services they provide, are no longer in demand. When assets are stranded, the invested capital is no longer productive.  New investments need to make up for the loss in capital stock, in addition to investment already needed to support the transition. These investments would boost GDP growth, but from a lower level, and so is not welfare enhancing. The later and more abrupt the transition to a low-carbon world is the greater the stranded asset problem will be. The IEA (2017) estimated that a late and sudden transition could mean about three times as many stranded assets as a smooth and early transition (Chart 3).

Chart 3: Estimates for stranded assets under different forward-looking scenarios, 2015-2050

Sources: International Energy Agency and Bank calculations.

While the risk of stranded assets is important, the flipside is often overlooked – the transition to a low-carbon economy provides investment opportunities for growth-enhancing investment, for example in renewables, end-use efficiency and infrastructure. Timely investment will be important to avoid technical bottlenecks to integrating renewable energy into total energy supply.  And upfront investment in electric vehicle infrastructure as well as investment to make buildings more sustainable will be needed to allow for a smooth transition.

In most countries the effects on employment of the transition may be small, as carbon-intensive sectors employ proportionately fewer people than low-carbon sectors

Perhaps the most discussed channel is the loss of jobs in carbon-intensive industries.  The worry is that if certain jobs are eliminated in energy-intensive and polluting industries and not replaced elsewhere, this could create potentially damaging dislocations and employment mismatch issues.

But the effects are likely to be small.  In many countries the shift of workers from high to low-carbon industries has been taking place for decades, for reasons unconnected to the low-carbon transition. In the UK, for example, 1 in 20 workers were employed in the coal industry in the 1920s. But in 2016, this had fallen to a low of 1 in every 40,000 workers.

And new jobs will be created as a result of the transition, in emerging green sectors such as renewable energies where the demand for goods and services is expanding.  There is evidence that this is already happening. In 2016, US employment in both the solar and wind energy sectors increased markedly, by 25% and more than 30% respectively.  This explosion in “clean” jobs in the US means that nearly as many workers are now employed in low-carbon generation technology jobs as in the coal, oil, and gas sectors.

Crucially, the overall magnitude of potential labour reallocation is smaller than often thought: the OECD estimates that job reallocation as a result of climate policies across sectors in advanced economies will amount to 1.5% of total employment by 2050.  This would be a relatively modest addition to the reallocation of labour between sectors, jobs, and tasks, which happens anyway. To put this into perspective, between 1995 and 2005, the amount of sectoral job reallocation in OECD countries amounted to 20% of employment.

A reduced reliance on fossil fuels could alter government finances, with differing consequences for fossil-fuel exporters and importers

In some economies, fossil fuels make up a significant proportion of government revenues as well as expenditures.  Between 2011 and 2014, the share of fossil fuel revenues in government revenue was 7% in G20 economies, 21% in the rest of the world and as high as 81% in OPEC countries.  For many governments the net effect on their finances from the transition could be positive, due to increased tax revenues from carbon pricing.   For governments of fossil-fuel exporting countries, the transition would be revenue-negative given the reduction in fossil-fuel-related revenues as global demand falls.  Consequently, several governments have already taken steps to reduce their support and dependence on fossil fuels such as Saudi Arabia’s Vision 2030 plan. And it’s easy to forget the other side of the balance sheet –  money that governments spend supporting the production of global fossils fuels would no longer be required – a saving of around $260bn. The speed and timing of the transition matters Policymakers sometimes talk about an “early and smooth” versus a “late and abrupt” transition. The former would allow adequate time for investment in alternative energy and infrastructure to support technological progress to keep energy costs at reasonable levels and help the economy adjust. But most governments’ current policies are not on track towards meeting their Paris Agreement commitments. Carbon prices would need to increase significantly globally and cover more sectors to meet the commitments. And the IEA estimates that to achieve a climate-friendly scenario, global oil demand would need to be more than 10% lower by 2025, and EMEs’ renewables capacity would have to increase twice as fast than otherwise (Chart 4). Chart 4: Difference in global energy demand between business as usual and 2°C scenarios in AEs and EMEs Sources: International Energy Agency and Bank calculations. If policy is delayed, a late and abrupt transition will lead to large and persistent negative macroeconomic effect. Alternative sources of energy could be low in supply and expensive, a quantity constraint on the use of high-carbon energy may need to be imposed, and costly technologies used to remove carbon from the atmosphere. But a late transition would also exacerbate the physical costs of climate change, as increased frequency and severity of climate- and weather-related events would damage physical assets and disrupt trade flows. And stranded assets – if not mitigated – could have significant implications for financial stability which would be transmitted to the real economy. The sooner climate policies are put in place, the more certainty will be provided to firms and households to realise opportunities and minimise the costs of the transition to a low-carbon world. Collectively, we think these add up to a strong economic case for an early and gradual transition. The net effects of the transition will vary a lot across countries What could the overall macroeconomic effects of the transition be? Even a smooth and early transition might not be cost-free, as it requires climate-friendly policies that incentivise substitution to cleaner but possibly more expensive technologies. For instance, in many countries, carbon-intensive flying can still be cheaper than rail transport. In addition, stranded assets and frictions associated even with an early and smooth transition would subtract from global activity. But most studies find the economic costs of this should be small. That needs to be compared to the much bigger cost of inaction: a recent study by the IPCC suggests that the risks to economic growth rise materially if global warming is not limited to 1.5° but 2°, with countries in the tropics and Southern Hemisphere subtropics projected to experience the largest impacts. And others, such as the OECD, find that policies associated with the transition could in fact be growth enhancing (Chart 5). They find that benefits to economic activity crucially rely on increases in both private and public investment to support the transition, as well as the effective implementation of structural reforms, such as carbon-pricing schemes with revenues invested into sustainable R&D. Chart 5: GDP impact by 2021 of climate policies in line with 50% likelihood of limiting warming to 2°C Source: OECD. Conclusions Whether the low-carbon transition is growth-enhancing or not depends crucially on when and how climate policies are implemented. As macroeconomists, we need to deepen our understanding of how the transition to a low-carbon economy will affect the global economy. Although much popular debate focuses on potential job and output losses in carbon-intensive industries, available research suggests these would be small, and will likely be more than offset by increases elsewhere. And we think there could be significant effects via other channels – particularly a re-wiring of global trade flows and potential fiscal impacts for economies specialised in fossil fuel production. It is important for economists and policymakers to incorporate the channels through which climate change could affect the macroeconomy into their mainstream thinking. Carsten Jung works in the Bank’s Fintech Hub Division, Theresa Löber works in the Bank’s International Surveillance Division, Anina Thiel works in the Bank’s Global Analysis Division and Thomas Viegas works in the Bank’s International Surveillance Division. If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below. Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees. ## On Modern Monetary Theory and Some Odd Twists and Turns in the Evolution of Macroeconomics Published by Anonymous (not verified) on Wed, 17/10/2018 - 2:41am in ### Tags Mainstream neoclassical economics is hooked on the idea of individual worker-savers as prime movers in capitalist market economies. As workers, individuals choose how much to work, determining the economy’s output; as savers, they determine how much of that output takes the shape of the economy’s capital investment. With banks as conduits channeling saving flows into investment, firms churn inputs into outputs that match worker-savers’ tastes. In this way, the neoclassical world gets shaped by what rational intertemporal utility-maximizing worker-savers wish it to be. In its most fanciful version – erected on supposedly sound micro foundations and known as “real business cycle theory” (RBC) – the neoclassical fantasy world of intertemporally optimizing worker-savers is subject to exogenous shocks to tastes and technology. Random technology shocks may be either positive or negative, and as Edward Prescott—acclaimed RBC founding father, together with Fynn Kydland—famously explained, negative technology shocks arise whenever there is a traffic jam on some bridge (see Romer 2016). That’s truly creative: Imagine a couple of dancers receiving the Nobel prize in medicine for wildly hopping around a coconut tree while peeing on a rotten banana and screaming voodoo until they are blue in the face. Unlikely to happen in medicine, you might say, but in economics voodoo routines and hallucinations of this kind can still earn you a pseudo-Nobel prize properly known as “The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel.” There also exists a “New Keynesian” variety of mainstream neoclassical economics that accepts the RBC framework as its core but adds some “frictions” to the modeled worker-saver paradise that hinder continuous and smooth full-employment equilibrium. Both camps share a common modeling technique (or speak the same language) known as “Dynamic Stochastic General Equilibrium” (DSGE) methodology. The only thing “Keynesian” about the New Keynesian variety is that it provides a rationale for government stabilization policies. Hardcore (“New Classical”) RBC proponents interpret the Great Depression as a worker-saver mass movement into the world of leisure. By contrast, New Keynesians offer an apology for why market economies might take their time in returning to full employment. Regaining full employment may then be accelerated by government intervention, preferably to be enacted by an independent central bank – with central bank independence being re-interpreted as “rules rather than discretion” in another extraordinarily muddled piece of obscurantism by said RBC-duo Kydland and Prescott (1977) (see Bibow 2001). Needless to say, and obvious to any serious economist, the worker-saver fantasy world depicted in DSGE models has little in common with capitalism as we know it on this planet. In fact, modern mainstream macroeconomics has completely unlearned the “Keynesian revolution” and essentially turned macroeconomics into an especially shoddy version of microeconomics. Keynes identified two key flaws in the mainstream neoclassical economics of his time. The first was a fallacy of composition regarding the working of the labor market: while the individual worker may price themselves into employment by accepting a lower wage, workers in the aggregate can only price the macroeconomy into debt deflation by going down that route. Keynes observed that the only reliable expansionary effect of a falling wage level arises through competitiveness gains and net exports. Writing at a time when the world was engaging in “beggar-thy-neighbor” competitive devaluations, that seemed hardly a promising strategy to rely on. The second flaw Keynes identified concerns the neoclassical capital market supposedly channeling worker-savers’ saving flows into investment, with banks collecting loanable funds as deposits which they then lend out to investing firms. Keynes exposes a fatal neoclassical confusion between money and saving (Bibow 2009). In capitalism nothing much happens without money, so it’s money first, then saving – if money can make James Meade’s (1975) investment dog smile and wag its tail. In Keynes’s vision of capitalism, entrepreneurial investors and their financiers emerge as the prime movers, while worker-savers are largely relegated to a more passive role. They, too, try to optimize – but under the macroeconomic constraint posed by the level of effective demand. Interestingly, Schumpeter’s vision of capitalism is quite similar to Keynes’s, with entrepreneurial-investors driving the never-ending process of “creative destruction” and banks acting as “ephor” (gatekeepers) of capitalist development. Schumpeter, too, understood the money-first principle and saw banks as money producers rather than loanable funds conduits. Minsky stood on both giants’ shoulders, elaborating on the central role of finance in capitalism and the endogenous emergence of financial fragility as the driving force behind boom-bust cycles. However, Minsky clearly leaned towards the Englishman rather than the Austrian regarding the role of government as a player in its own right potentially stabilizing the macro economy. It was Abba Lerner (1943, 1944), who pushed Keynes’s macroeconomic insights to its logical conclusion with regard to fiscal policy. Lerner’s “functional finance” approach proposes that the government, not facing the usual monetary constraints that can hold back private actors, should let its budgetary position passively adapt to whatever may be required to achieve macroeconomic equilibrium. Keynes responded to Lerner’s functional finance as a “splendid idea” but had reservations as far as putting it into practice was concerned: “functional finance is an idea and not a policy; part of one’s apparatus of thought but not, except highly diluted under considerable clothing of qualification, an apparatus of action. Economists have to try to be very careful, I think, to distinguish the two.” It is here that “Modern Monetary Theory” (MMT) comes into the picture. As a recent conference held in New York City made clear, MMT is a call for action. It is a program to alert policymakers and the public that decisions about, for instance, infrastructure, the environment, or progressive social programs are nothing but political choices within the fiscal powers of sovereign states. MMT’s theoretical roots reach back to Keynes, Lerner, and a less well-known German political economist with the name of Georg Friedrich Knapp (1905). The latter is known for his “state theory of money” (or: “chartalism”) emphasizing that money is a creature of the state rather than a convenient market invention to reduce transaction costs. MMT features the money-first principle: the state has to first issue its money, either by literally spending it into existence or by having its central bank purchase (“monetize”) assets, for taxpayers to then send it back to the treasury as taxes. Seen in this way, taxes do not “finance” government spending. Rather, they are a means to contain inflation depending on the economy’s real resource constraints (as made clear in Keynes’s [1940] “How to pay for the war”). Similarly, government bond issuance – supposedly collecting loanable funds from worker-savers – is not a means to “finance” government spending either, but an instrument to manage interest rates (as Keynes made clear in his reflections on monetary policy and debt management during WWII). These insights into modern money and state power are inconvenient from the perspective of those who favor a small state and unfettered finance (i.e., the powers of wealth). It is therefore somewhat ironic to see that the current U.S. government has embraced MMT with much enthusiasm. Recall that the Republicans in Congress opposed the “Obama stimulus” in 2009 when a second Great Depression was looming. Recall also that in 2011 a Republican Congress engineered a grossly premature turn to fiscal austerity that pummeled the still shaky recovery and forced the Federal Reserve into extended monetary overdrive. Officially, both acts of folly were made in the name of “fiscal responsibility.” But Republican Senate leader Mitch McConnell made it public that his primary ambition was to wreck the Obama presidency and limit it to one term. Attempting to sabotage his black president and unnecessarily putting the economy and the well-being of his American compatriots in jeopardy did not make him a traitor, as one would think, but a Republican hero masterminding plenty more dirty work on behalf of his subversive party rather than the nation. And here we are today. Imagine a populist takeover of the nation by a gang of ruthless kleptocrats. Confronting a society botched with income and wealth inequalities similar in degree to the time before the Great Depression, they go about filling their own pockets by squandering tax cuts on the super-rich without paying any attention to the budgetary consequences. Fiscal responsibility was yesterday. Today is self-indulgence without fiscal worries of any tomorrow. Ironically, certain conservative economists had remarkably clear foresight of modern developments under conservative government. James Buchanan’s vision of public policy was inspired by little else but fears of plundering kleptocrats. Milton Friedman favored fixed rules for public policy precisely because he feared discretion in the hands of incompetent and/or corrupt policymakers. It is difficult to deny today that they had a point. Today’s political realities probably also play a part in explaining why there is significant popular interest in MMT at the other end of the political spectrum. The speech in NYC by Stephanie Kelton titled “Mainstreaming MMT” highlighted that MMT has indeed made important inroads into public life, the media, and academia (excluding the neoclassical economics mainstream of course). Participants and activists present at the NYC conference were equally enthusiastic about conceiving an active role for the state for progressive causes – unhindered by “sound finance” myths. One can rest assured that conservatives will rediscover their love for fiscal responsibility as soon as they lose their reach to the public purse. Crying “socialism” whenever responsible fiscal action on behalf of society gets discussed, they will once again demand nothing but “sound finance.” It would be a shame if, for a third time in a row, a government inheriting Republican fiscal wreckage declared “sound finance” as their policy priority. Instead, the next government might be well advised to set out and prove Buchanan and Friedman wrong by showing that honest, responsible, and competent “government of the people, by the people, for the people” is actually possible. Sadly, kleptocrats’ imaginative powers never reach beyond their own pockets. Imagine a government that really cares about the environment, good infrastructure, and a healthy and well-educated society, a government that understands these political choices are possible here and now – if only we as a society went for it. Bibow, J. (2009). Keynes on Monetary Policy, Finance and Uncertainty: Liquidity Preference Theory and the Global Financial Crisis, Routledge. Bibow, J. (2001). Reflections on the Current Fashion for Central Bank Independence, Working Paper No. 334, Levy Economics Institute of Bard College. Updated here: (2004). Cambridge Journal of Economics, Vol. 28, No. 4, pp. 549-576 Keynes, J.M. (1940). How to Pay for the War: A Radical Plan for the Chancellor of the Exchequer, Macmillan. Knapp, G.F. (1905). Staatliche Theorie des Geldes, Munich and Leipzig, Duncker & Humblot. Lerner, A.P. (1943). Functional Finance and the Federal Debt, Social Research. Lerner, A.P. (1944). The Economics of Control, Macmillan. Meade, J.E. (1975). The Keynesian revolution, in M. Keynes ed. Essays on John Maynard Keynes, Cambridge, Cambridge University Press. Romer, P. (2016) The Trouble with Macroeconomics, Commons Memorial Lecture of the Omicron Delta Epsilon Society delivered on January 5, 2016, New York University, manuscript, September 14. Kydland, F.E. and Prescott, E.C. (1977). Rules Rather Than Discretion: The Inconsistency of Optimal Plans, Journal of Political Economy, vol. 85, issue 3, 473-91. ## On Modern Monetary Theory and Some Odd Twists and Turns in the Evolution of Macroeconomics Published by Anonymous (not verified) on Wed, 17/10/2018 - 2:41am in ### Tags Mainstream neoclassical economics is hooked on the idea of individual worker-savers as prime movers in capitalist market economies. As workers, individuals choose how much to work, determining the economy’s output; as savers, they determine how much of that output takes the shape of the economy’s capital investment. With banks as conduits channeling saving flows into investment, firms churn inputs into outputs that match worker-savers’ tastes. In this way, the neoclassical world gets shaped by what rational intertemporal utility-maximizing worker-savers wish it to be. In its most fanciful version – erected on supposedly sound micro foundations and known as “real business cycle theory” (RBC) – the neoclassical fantasy world of intertemporally optimizing worker-savers is subject to exogenous shocks to tastes and technology. Random technology shocks may be either positive or negative, and as Edward Prescott—acclaimed RBC founding father, together with Fynn Kydland—famously explained, negative technology shocks arise whenever there is a traffic jam on some bridge (see Romer 2016). That’s truly creative: Imagine a couple of dancers receiving the Nobel prize in medicine for wildly hopping around a coconut tree while peeing on a rotten banana and screaming voodoo until they are blue in the face. Unlikely to happen in medicine, you might say, but in economics voodoo routines and hallucinations of this kind can still earn you a pseudo-Nobel prize properly known as “The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel.” There also exists a “New Keynesian” variety of mainstream neoclassical economics that accepts the RBC framework as its core but adds some “frictions” to the modeled worker-saver paradise that hinder continuous and smooth full-employment equilibrium. Both camps share a common modeling technique (or speak the same language) known as “Dynamic Stochastic General Equilibrium” (DSGE) methodology. The only thing “Keynesian” about the New Keynesian variety is that it provides a rationale for government stabilization policies. Hardcore (“New Classical”) RBC proponents interpret the Great Depression as a worker-saver mass movement into the world of leisure. By contrast, New Keynesians offer an apology for why market economies might take their time in returning to full employment. Regaining full employment may then be accelerated by government intervention, preferably to be enacted by an independent central bank – with central bank independence being re-interpreted as “rules rather than discretion” in another extraordinarily muddled piece of obscurantism by said RBC-duo Kydland and Prescott (1977) (see Bibow 2001). Needless to say, and obvious to any serious economist, the worker-saver fantasy world depicted in DSGE models has little in common with capitalism as we know it on this planet. In fact, modern mainstream macroeconomics has completely unlearned the “Keynesian revolution” and essentially turned macroeconomics into an especially shoddy version of microeconomics. Keynes identified two key flaws in the mainstream neoclassical economics of his time. The first was a fallacy of composition regarding the working of the labor market: while the individual worker may price themselves into employment by accepting a lower wage, workers in the aggregate can only price the macroeconomy into debt deflation by going down that route. Keynes observed that the only reliable expansionary effect of a falling wage level arises through competitiveness gains and net exports. Writing at a time when the world was engaging in “beggar-thy-neighbor” competitive devaluations, that seemed hardly a promising strategy to rely on. The second flaw Keynes identified concerns the neoclassical capital market supposedly channeling worker-savers’ saving flows into investment, with banks collecting loanable funds as deposits which they then lend out to investing firms. Keynes exposes a fatal neoclassical confusion between money and saving (Bibow 2009). In capitalism nothing much happens without money, so it’s money first, then saving – if money can make James Meade’s (1975) investment dog smile and wag its tail. In Keynes’s vision of capitalism, entrepreneurial investors and their financiers emerge as the prime movers, while worker-savers are largely relegated to a more passive role. They, too, try to optimize – but under the macroeconomic constraint posed by the level of effective demand. Interestingly, Schumpeter’s vision of capitalism is quite similar to Keynes’s, with entrepreneurial-investors driving the never-ending process of “creative destruction” and banks acting as “ephor” (gatekeepers) of capitalist development. Schumpeter, too, understood the money-first principle and saw banks as money producers rather than loanable funds conduits. Minsky stood on both giants’ shoulders, elaborating on the central role of finance in capitalism and the endogenous emergence of financial fragility as the driving force behind boom-bust cycles. However, Minsky clearly leaned towards the Englishman rather than the Austrian regarding the role of government as a player in its own right potentially stabilizing the macro economy. It was Abba Lerner (1943, 1944), who pushed Keynes’s macroeconomic insights to its logical conclusion with regard to fiscal policy. Lerner’s “functional finance” approach proposes that the government, not facing the usual monetary constraints that can hold back private actors, should let its budgetary position passively adapt to whatever may be required to achieve macroeconomic equilibrium. Keynes responded to Lerner’s functional finance as a “splendid idea” but had reservations as far as putting it into practice was concerned: “functional finance is an idea and not a policy; part of one’s apparatus of thought but not, except highly diluted under considerable clothing of qualification, an apparatus of action. Economists have to try to be very careful, I think, to distinguish the two.” It is here that “Modern Monetary Theory” (MMT) comes into the picture. As a recent conference held in New York City made clear, MMT is a call for action. It is a program to alert policymakers and the public that decisions about, for instance, infrastructure, the environment, or progressive social programs are nothing but political choices within the fiscal powers of sovereign states. MMT’s theoretical roots reach back to Keynes, Lerner, and a less well-known German political economist with the name of Georg Friedrich Knapp (1905). The latter is known for his “state theory of money” (or: “chartalism”) emphasizing that money is a creature of the state rather than a convenient market invention to reduce transaction costs. MMT features the money-first principle: the state has to first issue its money, either by literally spending it into existence or by having its central bank purchase (“monetize”) assets, for taxpayers to then send it back to the treasury as taxes. Seen in this way, taxes do not “finance” government spending. Rather, they are a means to contain inflation depending on the economy’s real resource constraints (as made clear in Keynes’s [1940] “How to pay for the war”). Similarly, government bond issuance – supposedly collecting loanable funds from worker-savers – is not a means to “finance” government spending either, but an instrument to manage interest rates (as Keynes made clear in his reflections on monetary policy and debt management during WWII). These insights into modern money and state power are inconvenient from the perspective of those who favor a small state and unfettered finance (i.e., the powers of wealth). It is therefore somewhat ironic to see that the current U.S. government has embraced MMT with much enthusiasm. Recall that the Republicans in Congress opposed the “Obama stimulus” in 2009 when a second Great Depression was looming. Recall also that in 2011 a Republican Congress engineered a grossly premature turn to fiscal austerity that pummeled the still shaky recovery and forced the Federal Reserve into extended monetary overdrive. Officially, both acts of folly were made in the name of “fiscal responsibility.” But Republican Senate leader Mitch McConnell made it public that his primary ambition was to wreck the Obama presidency and limit it to one term. Attempting to sabotage his black president and unnecessarily putting the economy and the well-being of his American compatriots in jeopardy did not make him a traitor, as one would think, but a Republican hero masterminding plenty more dirty work on behalf of his subversive party rather than the nation. And here we are today. Imagine a populist takeover of the nation by a gang of ruthless kleptocrats. Confronting a society botched with income and wealth inequalities similar in degree to the time before the Great Depression, they go about filling their own pockets by squandering tax cuts on the super-rich without paying any attention to the budgetary consequences. Fiscal responsibility was yesterday. Today is self-indulgence without fiscal worries of any tomorrow. Ironically, certain conservative economists had remarkably clear foresight of modern developments under conservative government. James Buchanan’s vision of public policy was inspired by little else but fears of plundering kleptocrats. Milton Friedman favored fixed rules for public policy precisely because he feared discretion in the hands of incompetent and/or corrupt policymakers. It is difficult to deny today that they had a point. Today’s political realities probably also play a part in explaining why there is significant popular interest in MMT at the other end of the political spectrum. The speech in NYC by Stephanie Kelton titled “Mainstreaming MMT” highlighted that MMT has indeed made important inroads into public life, the media, and academia (excluding the neoclassical economics mainstream of course). Participants and activists present at the NYC conference were equally enthusiastic about conceiving an active role for the state for progressive causes – unhindered by “sound finance” myths. One can rest assured that conservatives will rediscover their love for fiscal responsibility as soon as they lose their reach to the public purse. Crying “socialism” whenever responsible fiscal action on behalf of society gets discussed, they will once again demand nothing but “sound finance.” It would be a shame if, for a third time in a row, a government inheriting Republican fiscal wreckage declared “sound finance” as their policy priority. Instead, the next government might be well advised to set out and prove Buchanan and Friedman wrong by showing that honest, responsible, and competent “government of the people, by the people, for the people” is actually possible. Sadly, kleptocrats’ imaginative powers never reach beyond their own pockets. Imagine a government that really cares about the environment, good infrastructure, and a healthy and well-educated society, a government that understands these political choices are possible here and now – if only we as a society went for it. Bibow, J. (2009). Keynes on Monetary Policy, Finance and Uncertainty: Liquidity Preference Theory and the Global Financial Crisis, Routledge. Bibow, J. (2001). Reflections on the Current Fashion for Central Bank Independence, Working Paper No. 334, Levy Economics Institute of Bard College. Updated here: (2004). Cambridge Journal of Economics, Vol. 28, No. 4, pp. 549-576 Keynes, J.M. (1940). How to Pay for the War: A Radical Plan for the Chancellor of the Exchequer, Macmillan. Knapp, G.F. (1905). Staatliche Theorie des Geldes, Munich and Leipzig, Duncker & Humblot. Lerner, A.P. (1943). Functional Finance and the Federal Debt, Social Research. Lerner, A.P. (1944). The Economics of Control, Macmillan. Meade, J.E. (1975). The Keynesian revolution, in M. Keynes ed. Essays on John Maynard Keynes, Cambridge, Cambridge University Press. Romer, P. (2016) The Trouble with Macroeconomics, Commons Memorial Lecture of the Omicron Delta Epsilon Society delivered on January 5, 2016, New York University, manuscript, September 14. Kydland, F.E. and Prescott, E.C. (1977). Rules Rather Than Discretion: The Inconsistency of Optimal Plans, Journal of Political Economy, vol. 85, issue 3, 473-91. ## Gross Domestic Problem On World Animal Day Published by Anonymous (not verified) on Thu, 04/10/2018 - 11:16pm in ### Tags Nervous now, future worse: pronghorn antelope at the edge of a growing economy. (Photo Credit: Michael Shealy) ##### ~Republished from The Daly News for World Animal Day 2018~ by Brian Czech If you like animals, your feelings may have been nurtured by “Hedgehogs Being Adorable,” “Baby Hippo Has Won Our Hearts,” and other such gems. The Huffington Post, The Animal Blog, and various animal-lover media take a heartfelt approach to the appreciation of animals—wild as well as domesticated—reminding us of the needs and vulnerabilities of our fellow creatures. It’s a refreshing approach compared to the stodgy science and economics of conservation. And it’s important. Mahatma Gandhi said, “The greatness of a nation and its moral progress can be measured by the way in which its animals are treated.” Abraham Lincoln said, “I care not much for a man’s religion whose dog and cat are not the better for it.” Animal welfare is a barometer of national “goodness” in a sense that resonates with our common sense. Yet if we are serious about animal welfare, we have to get beyond the mere adoration of hedgehogs and hippos. We have to face up to the big-picture, systematic erosion of wild animal welfare. It’s all around us and getting worse by the day, and our public policies precipitate it. The most prevalent source of animal suffering is habitat destruction. Habitat includes food, water, cover, and space. When any of these elements are destroyed or depleted, wild animals suffer and often die more miserable deaths than if killed by hunters or predators. Some animals survive an initial wave of habitat destruction only to be stranded in an unfamiliar, unforgiving environment. When a food or water source is destroyed, wild animals may starve, die of thirst, or suffer from malnutrition and the associated agonies. When thermal cover is lost, animals expend valuable time and energy trying to regulate body temperature. This lowers the time and energy available for feeding, playing, and mating. When hiding cover is lost, wild animals experience fear and stress, seeking cover from predators that may or may not be present. What kind of a life does that sound like? It would be like getting thrown out of your home, into a perilous world with no social net, no health system, no Salvation Army, and no street corner to beg from. Yet it’s the life we’ve been forcing animals into by the millions. How can we stop? We often hear of “human activity” being the cause of habitat loss. That’s a start, recognizing our basic role in the problem, but we have to dig deeper to detect precisely what type of human activity is problematic. After all, the habitat destruction caused by humans beings isn’t spiritual activity, or neighborhood activity, or political activity (at least not directly), but almost always economic activity. The macroeconomic nature of the problem is evident when we consider the causes of species endangerment. These causes are essentially the sectors and byproducts of the whole, interwoven economy, starting with agricultural and extractive sectors such as mining, logging, and livestock production. These activities directly remove or degrade the habitat components required by wild animals. Another major cause of endangerment is urbanization. Urbanization reflects the growth of the labor force and consumer population as well as a variety of light industrial and service sectors. Few types of habitat destruction are as complete as urbanization. While extractive activities can be a traumatic experience for the denizens of wildlands, logging, ranching, and even mining usually leaves some habitat components. But when an urban area expands, it does so with pavement, buildings, and infrastructure. These developments are devastating to most of the animals present. The economic system extends far into the countryside, too. Roads, reservoirs, pipelines, power lines, solar arrays, and wind farms are examples. It would be hard to conceive of a more prevalent danger to animals than roads. Roads and the cars upon them leave countless animals mangled and left, during their final hours, to be picked apart by wild and domestic scavengers. Power lines induce electrocution, a significant source of bird death and crippling. Power line collisions cause their share as well. Wind farms and solar arrays, thought to be the keys to “green growth,” are the latest hurdles for migratory birds. Pollution is an inevitable byproduct of economic production. Pollution is an insidious and omnipresent threat to wild animals. Whether it’s nerve damage from pesticides, bone loss from lead poisoning, or one of the many other horrible symptoms of physiology gone wrong, pollutants ensure some of the most excruciating diseases and slowest deaths in the animal kingdom. Climate change is another threat to species, although its mechanisms are less direct. Temperature is a key factor in the functioning of ecosystems and the welfare of the animals therein. Climate change is pushing polar bears and other polar species off the ends of the earth; at what point will this climate-controlled conveyor belt stop? Climate change, too, is a result of a growing (and fossil-fueled) economy. We should give thanks for the Humane Society, International Fund for Animal Welfare, and Society for the Prevention of Cruelty to Animals. These and related organizations do the good work that Gandhi and Lincoln would have endorsed. Yet when is the last time you’ve heard these organizations give a hoot about economic growth, the single biggest threat to animal welfare? And why does no one put in a word for our furry and feathered friends when Congress, the President, and the Fed pull out all the stops for GDP growth? Where are the advocates of humane treatment of animals, when the biggest decisions are made about the rate of habitat loss and therefore animal suffering? When a hundredth percentage point less in GDP growth could save hundreds of thousands of animals a year? Why don’t we have a mainstream media, which isn’t afraid to expose nastiness to horses and chickens, talking about the millions of animals suffering at the cumulative hand of economic growth? Has economic growth become the inconvenient truth for animal welfare? It’s definitely inconvenient—and that’s an understatement—for millions of animals. The post Gross Domestic Problem On World Animal Day appeared first on Center for the Advancement of the Steady State Economy. ## Financing private investment in China: the role of alternative finance and banking reforms Published by Anonymous (not verified) on Fri, 28/09/2018 - 6:00pm in ### Tags Noëmie Lisack Small, young private firms in China have long been struggling to obtain formal bank loans. To bypass financial constraints, these firms have resorted to alternative, less formal financing sources. In this context, Chinese authorities are aiming to develop a more formal, market-based, and better regulated credit sector. In a Staff Working Paper, I argue that carefully designed credit sector reforms are crucial to avoid throwing out the baby with the bath water. Despite the interest rate liberalisation progressively implemented by Chinese authorities, a general crackdown on alternative finance would remain detrimental to the dynamism of small enterprises. Selectively tightening the limits around informal financing could better balance financial stability on the one hand, and welfare and efficiency on the other. Constrained financing conditions… Since the start of the 20th century, business lending in China has been controlled by four state-owned banks and mostly directed to large, state-owned enterprises. While credit has been readily available for state-owned enterprises, private, small and medium enterprises (SMEs) have been struggling to obtain formal bank loans, thereby hindering these firms’ abilities to invest, grow and spur economic growth. Access to informal, alternative funding – mainly social networks and personal connections – is key for SMEs’ development. A well-known example is Wenzhou, a prosperous Chinese city, where a clan-like social organization and strong mercantile traditions facilitated the flow of credit to SMEs … alleviated by alternative finance Survey data from the World Bank show that in 2003, before the start of banking sector liberalisation, large, private enterprises were financing the majority of their investments through formal bank loans. Enterprises with more than 1,000 employees, for instance, financed 58% of their total investment through bank loans (See Chart 1). This reflects the incentives given to banks by Chinese authorities in favour of loans to larger enterprises. Conversely, smaller enterprises faced strict collateral requirements and were often unable to secure bank loans – problems that persist to this day. In addition, SMEs could not access capital by raising equity (listing shares) because Chinese capital markets were not well developed. To bypass these financial constraints, SMEs could turn to self-financing (reinvesting their profits). However, this is only possible for enterprises that are active long enough to accumulate sufficient profits. Instead, small, young firms rely on informal financing sources through social connections; obtaining funding through family, friends, non-listed equity or moneylenders (referred to as “alternative” in Chart 1 and for the remainder of this post) Chart 1: Average shares of funding sources for new investment, by enterprise size (number of employees) Note: Enterprise survey data, 2003, private firms only. Small firms: below 50 employees, medium firms: between 50 and 250, large firms: between 250 and 1000, very large firms: above 1000 employees. To better understand the importance and implications of alternative finance and credit sector reforms, I built a theoretical model where heterogeneous enterprises choose how much to invest and how to finance it between bank loans, retained earnings and alternative finance. This model is able to reproduce enterprise decisions observed in the data in a general equilibrium framework, and allows me to simulate the Chinese aggregate economy and individual firms’ behaviour under various scenarios. First, without introducing any credit sector reforms, and assuming alternative finance did not exist, I find that aggregate output would be 6% lower and aggregate capital 7% lower. The presence of alternative sources of funding has a strong positive impact on the main economic aggregates – increasing aggregate consumption by 6% and aggregate welfare by 4% – and decreasing resource misallocations by 13%. Indeed, alternative finance provides enterprises facing credit constraints with additional resources to finance investment and produce more efficiently. Credit sector reforms Keeping in mind the impact of alternative finance in a pre-reform scenario, let us now evaluate the impact of credit sector reforms in China on national aggregates. What are these reforms? They concern three main areas: 1. A progressive liberalisation of retail interest rates. Before 2004, the retail deposit and lending rates were fixed by the government. From then on, the interest rates have been allowed to fluctuate within progressively widening bands, until banks were authorised to freely set their rates in 2015. 2. Window guidance, which in the Chinese context corresponds to oral or written directives from the authorities to direct credit towards various sectors of the economy. The People’s Bank of China (PBoC, Chinese central bank) started monitoring credit to small and medium-sized enterprises in 2004 and incentivising banks to direct more funds towards these enterprises. 3. Financial regulation. From 2004 onwards, the PBoC has stated its intention to “curb usury” by tightening rules regarding banks’ off-balance sheet assets and their role as intermediaries between households and non-bank financial institutions. The impact of the reforms: banking sector liberalisation… Using the same theoretical model as above, I simulate the impact of the first two reforms mentioned above; allowing more credit to be directed to SMEs by relaxing the collateral constraint, and letting the interest rate vary accordingly. I consider two cases: (a) when alternative finance is not accounted for in the model both before and after the reforms; (b) when alternative finance is taken into account in the model, both before and after the reforms. While case (a) gives us an idea of how off the results would be if completely ignoring alternative finance, case (b) reflects the market’s reality. Two facts are striking. First, irrespective of whether alternative finance is accounted for, the impact of these liberalisation reforms is overly positive; all economic aggregates go up: GDP, capital and consumption increase by up to 10% (Chart 2). Resource allocation and welfare significantly improve too. Second, it is important to account for alternative finance when evaluating the impact of the reforms. At first glance, the increase in capital and production is similar in both cases. Yet, the reforms’ effect on consumption and welfare is about a quarter smaller when alternative finance is included in the model (Chart 2, blue bars). Following the liberalisation, welfare increases by 2.6% without alternative finance as opposed to 1.9% with, a non-negligible difference from both a household’s and from a policy-maker’s perspective. Chart 2: Impact of liberalisation reforms (1. and 2.), including or not alternative finance in the model … with a tighter regulation Despite its overall positive impact, liberalisation reforms increase the borrowing capacity of all firms, which pushes up the share of non-performing loans by 11%. A reasonable policy reaction to this change may be to tighten financial regulation. The last, and most realistic, reform scenario I examine is to combine the liberalisation (points 1 and 2 above) with a tighter regulation (point 3). Since alternative finance is a broad term, my model separates it into two types: (i) “cheap” alternative finance, corresponding to funds obtained from close social circles, requiring very low interest rates, and generally easier to obtain than bank loans; (ii) “expensive” alternative finance, for which cash-starved enterprises have to search beyond their close social circle, and which requires much higher interest rates – an extreme example being moneylenders, charging very high interest rates to enterprises that have no access to cheaper financing sources. In the simulations, tightening financial regulation means either shutting down all alternative finance, both cheap and expensive, or shutting down only the expensive kind. Despite being combined with the liberalisation of the banking sector, shutting down all alternative finance (Chart 3) has a strong detrimental impact on the economy. This reform scenario lowers both production and consumption, despite an aggregate capital increase. Producing less with more capital means that resources are used in a less efficient way, which is corroborated by the 10% increase in resource misallocation. Logically, aggregate welfare decreases by 0.7%. The easier access to formal credit and the interest rate liberalisation cannot compensate for the disappearance of all alternative funding sources. Chart 3: Impact of liberalisation and regulation reforms, when alternative finance is successively maintained, partially shut down or fully shut down The red bars in Chart 3 show the effects if only expensive alternative finance is shut down. Although efficiency gains are not as high as without regulatory tightening, both capital and production increases are more pronounced. As a result, consumption and welfare gains from liberalisation are almost entirely preserved. This combination of reforms – banking sector liberalisation and selective regulatory tightening – seems the most desirable answer to the trade-off between efficient resource allocation and financial stability. While limiting the power of moneylenders, it preserves the role of social networks in the provision of finance for SMEs. Conclusion Alternative finance allows Chinese firms to partially bypass credit constraints, thus facilitating a better resource allocation and a higher production level, and should be accounted for when examining ongoing liberalisation and regulatory reforms. Even if conducted in parallel to a reform of the Chinese banking system, tightening the regulation of alternative finance should be careful and selective, so as to avoid undermining the dynamism of smaller, younger enterprises unable to obtain formal loans. The PBOC has recently proposed a plan to facilitate bank loan access for micro- and small enterprises. While the crackdown on the informal financing sector has been ongoing for several years, this plan could partially compensate for the increased financing constraints faced by SMEs, and help strike a balance between liberalisation, regulation and financial stability. This post was written whilst Noëmie Lisack was working in the Bank’s Global Analysis Division. If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below. Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees. This post is published jointly on Bank Underground and Duke University’s FinReg Blog. ## ‘The world turned upside down’: How the global economy was hit by the crisis Published by Anonymous (not verified) on Thu, 20/09/2018 - 6:00pm in ### Tags David Young For the global economy, it was the best of times, and then it was the worst of times. Buoyed by very strong growth in emerging markets, the global economy boomed in the mid-2000s. On average, annualised world GDP growth exceeded 5% for the four years leading up to 2007 – a pace of growth that hadn’t been sustained since the early 1970s. But it wasn’t to last. In this post, I illustrate how the failure of Lehman Brothers in September 2008 coincided with the deepest, most synchronised global downturn since World War II. And I describe how after having seen the fallout of the Lehman collapse, macroeconomic forecasters were nevertheless surprised by the magnitude of the ensuing global recession. How severe was the Global Financial Crisis? The Global Financial Crisis (GFC) caused the worst peacetime contraction in world economic activity since the Great Depression. Using Maddison Historical Statistics, we can estimate annual world GDP growth rates over the entire 20th century (and even earlier), as shown in Chart 1. Chart 1: World GDP Sources: Maddison Historical Statistics, Thomson Reuters DataStream, IMF WEO and author calculations. Maddison data used to calculate growth rates from 1901 until 1982. The Great Depression and both World Wars caused steeper falls in global GDP – but the GFC was by far the deepest global downturn that has occurred in the post-war period. Indeed, so far, 2009 has been the only year since World War II in which world activity contracted relative to the previous year. Of course, there have been other downturns – the most severe occurred in the mid-1970s, the early 1980s, and the early 1990s. But annual global growth never fell below 1% in any year of the post-war period – until the GFC. A similar picture emerges when looking at other indicators of global activity, such as world trade. Chart 2 shows a long time series of annual rates of growth in world trade volumes, taken from Federico and Tena-Junguito (2016). Again, though less severe than during World War I and the Great Depression (the data don’t cover the World War II period), the fall in global trade volumes during the GFC was by far the deepest contraction that has occurred in the post-war period. Trade volumes fell by around 12% in 2009 – almost 5 percentage points more than during the next-worst contraction in 1975. Chart 2: Global trade volumes Sources: Federico and Tena-Junguito (2016) and author calculations. Moreover, the GFC wasn’t just the deepest downturn of the post-war period – it was also the most synchronised. This can be illustrated using four-quarter GDP growth rates from the OECD database. Starting in 1961, these data cover 25 major economies, rising to 44 economies by the time of the GFC. As shown in Chart 3, GDP contracted in 40% to 60% of countries during the global downturns in the 1970s, 1980s and 1990s – whereas the share of countries experiencing negative four-quarter GDP growth spiked up sharply to almost 90% in 2009. And the share of countries experiencing slowing GDP growth reached an unprecedented 100% in 2008. Chart 3: Share of economies in recession or experiencing a slowdown Sources: OECD database and author calculations. Economies are classed as experiencing slowing GDP growth in a given quarter if four-quarter GDP growth was lower than in the previous quarter. “How did things get so bad, so fast?” World activity deteriorated extraordinarily quickly. Global growth was exceptionally strong in the years running up to the GFC, supported by buoyant growth in emerging market economies (EMEs), especially in China. Annual average global growth reached its pinnacle of around 5½% in 2007 – but as shown in Chart 4, the pinnacle immediately preceded a precipice. Chart 4: Annualised quarterly GDP growth in advanced economies, emerging market economies, and the world Sources: OECD database, Thomson Reuters DataStream, IMF WEO and author calculations. Lehman Brothers filed for bankruptcy on 15 September 2008. By this point, in the face of tightening financial conditions, advanced economies (AEs) had already entered recession – annualised quarterly AE GDP growth was around -2½% in 2008Q3. But there was much worse to come: AE GDP growth plummeted to -8½% in 2009Q1 before recovering. Annualised world GDP growth dropped from above 6% in 2007Q4 to -5¼% in 2009Q1 – an 11 percentage point turnaround in just over a year. And it was a truly global recession, with aggregate EME GDP also contracting in 2008Q4 and 2009Q1. It was always going to be challenging to accurately forecast the deepest peacetime contraction in global activity for 80 years, especially given how rapidly the outlook deteriorated. Indeed, it could easily be argued that the GFC – or at least the magnitude of its repercussions – was inherently unpredictable. Nevertheless, it is interesting to examine the evolution of macroeconomic forecasts in the run up to and in the months following the failure of Lehman Brothers, when the GFC entered its most acute phase. All major macroeconomic forecasters substantially overpredicted world GDP growth during the GFC. This can be seen in Chart 5, which plots one year ahead forecast errors for world GDP growth from the IMF and Consensus Economics, and for total OECD GDP growth from the OECD. In all cases, GDP growth in 2009 was four to five percentage points lower than projected. And it’s worth emphasising that these forecasters were far from alone in producing – with hindsight – overly optimistic projections during the GFC. Chart 5: One year ahead GDP growth forecast errors Sources: OECD database, Consensus Economics, Thomson Reuters DataStream, IMF April WEOs since 1998, and author calculations. The IMF and Consensus errors are annual world GDP growth outturns minus IMF forecasts from April the previous year, or minus Consensus Economics forecasts from Q2 the previous year. The OECD errors are total OECD annual GDP growth outturns minus OECD forecasts from June the previous year. The evolution of IMF forecasts in the months before and after the failure of Lehman Brothers illustrates the dramatic deterioration of the global outlook. Chart 6 shows successive IMF forecasts of annual average world GDP growth for the year 2009. It can be seen that in the April 2008 World Economic Outlook (WEO), the IMF was projecting that annual world GDP growth in 2009 would be 3.8% – well above the outturn of around -½%. Of course, 3.8% growth would have been a material slowdown relative to 2007; the WEO’s opening line was “The global expansion is losing speed in the face of a major financial crisis.” With hindsight, it’s also interesting to read that “the IMF staff now sees a 25 percent chance that global growth will drop to 3 percent or less in 2008 and 2009 – equivalent to a global recession.” For world GDP growth in 2008, at least, 3% turned out to be right on the money. But 2009 would prompt the IMF to reconsider its definition of a global recession. Chart 6: Successive IMF forecasts for annual GDP growth in 2009 Sources: IMF WEOs from April 2008 until October 2009, Thomson Reuters DataStream and author calculations. By October 2008, 3% was the IMF’s central projection for world growth in 2009. Around the publication of the October 2008 WEO, Olivier Blanchard (then the IMF’s chief economist) said “it is not useful to use the word ‘recession’ when the world is growing at 3%.” In the end, of course, 2009 proved to be a recession in every sense of the word. The October WEO also included a fan chart around the IMF world GDP growth projections – this showed that the IMF thought world GDP growth in 2009 would be between 1% and 4%, with 90% probability. The failure of Lehman Brothers prompted a rapid deterioration in the economic outlook, leading the IMF to publish updated forecasts just one month after the October WEO (the October WEO was published after Lehman Brothers filed for bankruptcy, but before the ramifications could be incorporated in the IMF’s projections)World growth in 2009 was revised down again, to 2.2%. This was the first IMF forecast in which AE GDP was projected to contract in 2009. The April 2009 WEO was the first IMF forecast in which global GDP was projected to contract in 2009, and the full magnitude of the crisis was recognised. The first subsection of the WEO is simply entitled, “How Did Things Get So Bad, So Fast?” At this point, the IMF had revised down its growth forecasts by 5 percentage points after just one year – an unprecedented revision. Conclusion To sum up, the Global Financial Crisis was the deepest, most synchronised global downturn since World War II, and it happened incredibly quickly. After Lehman Brothers failed, macroeconomic forecasters underestimated the economy-wide impacts of an extraordinary financial shock that resulted in the failure of financial institutions, the evaporation of market liquidity, dramatic falls in assets prices, and a collapse in consumer and business confidence. It served as a sobering reminder that financial crises have sizeable effects on the real economy. David Young works in the Bank’s Global Analysis Division If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below. Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees ## Teaching macroeconomics as though Lehmans didn’t happen Published by Anonymous (not verified) on Mon, 17/09/2018 - 11:42pm in ### Tags September 15th marked the tenth anniversary of the fall of Lehman Brothers, destabilizing Western economies at levels not seen since the 1930s. It also marked the second week of fall classes, with many economics graduate students cranking through equations that define the discipline’s conventional macroeconomic models. With such names as New Classical, Real Business Cycle and New Keynesian, these models can all be traced to the rational expectations revolution of the 1970s, which sought to explain stagflation when the conventional Keynesian framework could not. The rational expectations approach attempted to provide more precise behavioral microfoundations than the Keynesian model by positing that economic actors can form expectations of future economic values, say inflation, such that on average, their predictions of future values tend to be correct. This assumes the actors share the same understanding of the structure of the economy and past economic data. This research program would come to dominate macroeconomic scholarship and strongly influence policy makers, culminating in the creation of the dynamic stochastic general equilibrium (DSGE) model, a popular forecasting and policy analysis tool used in central banks and finance departments. This approach to macroeconomic modeling came under scrutiny following the 2008 crisis, with Nobel laureate Paul Krugman asserting that most of the macroeconomics over the past 30 years was “spectacularly useless at best, and positively harmful at worst”. While this did spark some soul-searching within the discipline, the debate has been inconclusive. Several policy-making bodies are taking seriously the limitations of 1970s macroeconomics. In its recent Medium-term Research Plan, the Bank of Canada recognises that the crisis has challenged its reliance on New Keynesian DSGE models, encouraging the exploration of alternative modeling paradigms, such as agent-based and stock-flow consistent models. On Canadian campuses, however, where the next generation of macroeconomists are being trained, there is no clear signal that similar changes are being made in the curriculum of grad-level macroeconomics. A recent panel discussion among academic economists featured the admission that the 2008 crisis was the most embarrassing empirical failure of the profession since the Great Inflation of the 1970. Yet, in the same breath, that professor said he wouldn’t change a thing in his teaching. Indeed, a glance at the macroeconomics syllabuses of several top Canadian grad schools find little evidence of a shift away from teaching the rational expectations-grounded macro models that have come under criticism. Professors tend to teach what they are taught. With the sunk cost of prepping for PhD macroeconomic comprehensive exams, they have little incentive to develop a new course involving subject matter in which they are not trained. Further reinforcing the status quo is the tendency to teach what you research. Working in a climate of publish or perish, macroeconomic profs have good reason to not deviate from the dominant research agenda, which remains wedded to 1970s macro. In the absence of strong leadership for change or a mandate from either the dean or the premier to sit down with one another and re-design the curriculum, teaching macro in the post-crisis era will continue to be business as usual. Yet this is not in the public interest. Given the acute financial stress experienced ten years ago, we have a stake in knowing that the policy makers of tomorrow are well prepared to confront episodes of economic downturn and instability. Learning to use a larger modeling toolbox is part of such preparation. So, what are Canada’s economics students to do in the meantime as they are grind through the math describing a DSGE model? As befitting any college course where critical thinking is one of the learning outcomes, here are some questions students may ask about the models they are taught: 1. Who is in the model? The basic models tend to have a single agent representing all consumers who are assumed to be sufficiently alike as autonomous rational optimizers sharing common knowledge. Can the model accommodate multiple actors who may differ by age, preference, belief, resources and class? 2. Is there room for “black swans”? The 2008 crisis was precipitated by the collapse of the U.S. subprime mortgage market, an event deemed of low risk but of high impact. How does the model address this and other examples of fundamental uncertainty? 3. What kind of markets are modelled? Models with perfect competition behave very differently from more realistic models with imperfect competition, information asymmetries, price rigidities and institutional constraints. 4. Is there a financial sector? Perhaps the strongest criticism of the 1970s macro models was the reduction of complex financial plumbing to a single interest rate variable. Can these models feature lenders and borrowers? Are there banks? How does money fit in? 5. Does the model have to move to equilibrium? Following an economic shock, standard models tend to instantaneously jump to a new equilibrium path. However, observations of macroeconomic variables as they unfold over time suggest that such adjustment may be a much slower, sequential process. Understanding this path of adjustment may be of greater importance than the equilibrium destination. 6. How are these models empirically tested? A model’s usefulness should be judged by how it explains actual economic history. With these and other critical questions about the core macro teaching models, tomorrow’s dismal scientists should be better prepared to confront challenging economics times. ## Teaching macroeconomics as though Lehmans didn’t happen Published by Anonymous (not verified) on Mon, 17/09/2018 - 11:42pm in ### Tags September 15th marked the tenth anniversary of the fall of Lehman Brothers, destabilizing Western economies at levels not seen since the 1930s. It also marked the second week of fall classes, with many economics graduate students cranking through equations that define the discipline’s conventional macroeconomic models. With such names as New Classical, Real Business Cycle and New Keynesian, these models can all be traced to the rational expectations revolution of the 1970s, which sought to explain stagflation when the conventional Keynesian framework could not. The rational expectations approach attempted to provide more precise behavioral microfoundations than the Keynesian model by positing that economic actors can form expectations of future economic values, say inflation, such that on average, their predictions of future values tend to be correct. This assumes the actors share the same understanding of the structure of the economy and past economic data. This research program would come to dominate macroeconomic scholarship and strongly influence policy makers, culminating in the creation of the dynamic stochastic general equilibrium (DSGE) model, a popular forecasting and policy analysis tool used in central banks and finance departments. This approach to macroeconomic modeling came under scrutiny following the 2008 crisis, with Nobel laureate Paul Krugman asserting that most of the macroeconomics over the past 30 years was “spectacularly useless at best, and positively harmful at worst”. While this did spark some soul-searching within the discipline, the debate has been inconclusive. Several policy-making bodies are taking seriously the limitations of 1970s macroeconomics. In its recent Medium-term Research Plan, the Bank of Canada recognises that the crisis has challenged its reliance on New Keynesian DSGE models, encouraging the exploration of alternative modeling paradigms, such as agent-based and stock-flow consistent models. On Canadian campuses, however, where the next generation of macroeconomists are being trained, there is no clear signal that similar changes are being made in the curriculum of grad-level macroeconomics. A recent panel discussion among academic economists featured the admission that the 2008 crisis was the most embarrassing empirical failure of the profession since the Great Inflation of the 1970. Yet, in the same breath, that professor said he wouldn’t change a thing in his teaching. Indeed, a glance at the macroeconomics syllabuses of several top Canadian grad schools find little evidence of a shift away from teaching the rational expectations-grounded macro models that have come under criticism. Professors tend to teach what they are taught. With the sunk cost of prepping for PhD macroeconomic comprehensive exams, they have little incentive to develop a new course involving subject matter in which they are not trained. Further reinforcing the status quo is the tendency to teach what you research. Working in a climate of publish or perish, macroeconomic profs have good reason to not deviate from the dominant research agenda, which remains wedded to 1970s macro. In the absence of strong leadership for change or a mandate from either the dean or the premier to sit down with one another and re-design the curriculum, teaching macro in the post-crisis era will continue to be business as usual. Yet this is not in the public interest. Given the acute financial stress experienced ten years ago, we have a stake in knowing that the policy makers of tomorrow are well prepared to confront episodes of economic downturn and instability. Learning to use a larger modeling toolbox is part of such preparation. So, what are Canada’s economics students to do in the meantime as they are grind through the math describing a DSGE model? As befitting any college course where critical thinking is one of the learning outcomes, here are some questions students may ask about the models they are taught: 1. Who is in the model? The basic models tend to have a single agent representing all consumers who are assumed to be sufficiently alike as autonomous rational optimizers sharing common knowledge. Can the model accommodate multiple actors who may differ by age, preference, belief, resources and class? 2. Is there room for “black swans”? The 2008 crisis was precipitated by the collapse of the U.S. subprime mortgage market, an event deemed of low risk but of high impact. How does the model address this and other examples of fundamental uncertainty? 3. What kind of markets are modelled? Models with perfect competition behave very differently from more realistic models with imperfect competition, information asymmetries, price rigidities and institutional constraints. 4. Is there a financial sector? Perhaps the strongest criticism of the 1970s macro models was the reduction of complex financial plumbing to a single interest rate variable. Can these models feature lenders and borrowers? Are there banks? How does money fit in? 5. Does the model have to move to equilibrium? Following an economic shock, standard models tend to instantaneously jump to a new equilibrium path. However, observations of macroeconomic variables as they unfold over time suggest that such adjustment may be a much slower, sequential process. Understanding this path of adjustment may be of greater importance than the equilibrium destination. 6. How are these models empirically tested? A model’s usefulness should be judged by how it explains actual economic history. With these and other critical questions about the core macro teaching models, tomorrow’s dismal scientists should be better prepared to confront challenging economics times. ## Making big data work for economics Published by Anonymous (not verified) on Wed, 05/09/2018 - 6:00pm in ### Tags Arthur Turrell, Bradley Speigner, James Thurgood, Jyldyz Djumalieva, and David Copple ‘Big Data’ present big opportunities for understanding the economy. They can be cheaper and more detailed than traditional data sources, and on scales undreamt of by survey designers. But they can be challenging to use because they rarely adhere to the nice neat classifications used in surveys. We faced just this challenge when trying to understand the relationship between the efficiency with which job vacancies are filled and output and productivity growth in the UK. In this post, we describe how we analysed text from 15 million job adverts to glean insights into the UK labour market. First, a health warning: the research which underlies this blog post, and therefore the post itself, is quite technical, combining advanced statistical processing techniques with economic theory. Some technical details, including of the algorithm we developed for the text analysis, are included for those interested but are not needed to understand the article – they can be safely skipped. Our job vacancy data come from an online recruitment website, Reed.co.uk. These ads have been posted each day online, over a number of years. They contain rich information including descriptions of the job description, title, and sector. This information can help us understand the supply and demand for labour in different occupations in the UK. But in order to make the most of it, we needed to combine it with other labour market data. That meant that we needed to classify the jobs advertised using the Office for National Statistics (ONS’) standard occupational classification (SOC) numbers (also known as SOC codes). They classify every job in the UK economy into pre-determined sectors. Assigning SOC numbers to each advert proved to be much easier said than done. Putting SOCs on SOC numbers are just a shorthand way of describing a job. So our task was to devise an algorithm to read a job advert and classify it with a SOC number. The difficulty is that job descriptions also contain a lot of information which is not specific to the occupation being advertised, and algorithm needed to discard this information while retaining the salient parts of the job description. The need to take text data and match it to official categories is sure to arise frequently for economists and statisticians. We have therefore released this algorithm as the first repository on the Bank of England’s new Github account so that others can use and adapt it. You can find details of how to download and use the code at the end of the blog post. More technical details of the algorithm now follow – so feel free to skip ahead to the next section if these don’t interest you. Our algorithm relies on materials from the ONS which describe SOC codes in great detail. From these, we extracted all phrases up to three words long associated with each SOC code. To get these words into a quantitative form, we used term frequency – inverse document frequency which represents our phrases as a matrix where each SOC code $d$ is a row and each phrase a column. Its dimensions are therefore given by the number of unique terms, $T$, and the number of SOC codes, $D$. The neat part of this is that we can then use the same matrix to express job vacancies, $i$, as vectors $\mathbf{v}_i$ in the same vector space described by the columns of $D$. Because the job vacancy vectors are created from only the official titles and descriptions produced by the ONS, much of the extraneous information in the vacancies (the requirement to have a driver’s licence and the like) fell away in this stage. With vacancies expressed as vectors, the process of finding the top SOC code for each job vacancy $i$ was completed by solving: $\arg \max_{d}\left\{\mathbf{v}_i \cdot \mathbf{v}_d\right\}$ This finds the SOC code closest to the job ad vector in question. Because there may be several similar official jobs to the vacancy, we found the top five SOC codes using this method and then we chose between them based on which had the closest job title to the job vacancy title using fuzzy matching. Fuzzy matching, in this case, counts the number of changes it takes to go from one word to another. For instance, to get from ‘ekonomist’ to ‘economist’ is just one move. A deeper look at the labour market Classifying the job adverts, as described above, allows for a much richer view of developments in job vacancies. Understanding vacancies is crucial for understanding many aspects of the labour market and the economy as a whole. Some important open questions about the current state of the economy relate directly to it. A crucial outstanding question is why productivity growth has been so slow in the UK over the past decade. A factor which may shed light on this is how long it takes the unemployed to find new jobs, what kind of jobs they find, and whether the picture varies across different regions of the country. To help understand this we combined the data described above with other labour market information using a popular model of the labour market to help give structure to the analysis. Again, the details of the model matter but are fairly technical. Feel free to skip the next paragraph if you’re not interested in the model of the labour market we use. We use the Diamond-Mortensen-Pissarides (DMP) matching model of equilibrium unemployment, in which vacancies play a key role. The cornerstone of the DMP model is the idea that it takes time for a worker to find a job, and for employers to fill vacancies. This is represented by a ‘matching function’,$M(u,v)\$, which takes as its inputs the stock of job seekers (unemployed people) and job vacancies, and returns the number of newly created jobs in each time period.

Once estimated, the model we use can generate so-called ‘Beveridge curves’, which show the relationship between vacancies and unemployment. Usually, they show the aggregate relationship between vacancies and unemployment, as in Figure 1. The circles show quarterly unemployment-vacancy rate data, and the green line the relationship suggested by the model.  But the aggregate picture may conceal very different underlying situations.  Using the data produced by our algorithm, we are able to produce Beveridge curves at the level of occupations, shown in Figure 2.

Figure 1: Beveridge curve showing theoretical relationship between vacancies and unemployment alongside points representing quarterly data. Arrows indicate the flow of time. Source: ONS, Reed, author calculations.

Figure 2: Beveridge curves for different occupations. SOC numbers are shown in brackets. Source: ONS, Reed, author calculations.

There are significant differences between occupations which are hidden by the aggregate Beveridge curve, particularly in how ‘tight’ the markets for different types of labour are.  Tightness is just the ratio of vacancies to unemployment; a ‘tight’ labour market means that there are many vacancies relative to the number of unemployed so it is typically more difficult for firms to recruit workers.

We can use the data and model to investigate further.  Figure 3 shows that matching efficiency – the speed with which job vacancies are filled – differs substantially across occupations. The lowest matching efficiency occurs in the most productive occupation.  An important thing to bear in mind with this analysis is that the speed of match is not the only thing that matters.  Identifying the right person for a job is also important, and it may be that employers take longer to fill higher-productivity roles because they are more productive, rather than despite it.  Nonetheless, the longer a vacancy takes to be filled, the larger the amount of lost output.

Figure 3: Estimates of productivity (left-hand y-axis) and of the matching efficiency (right-hand y-axis). Standard errors are shown for the estimates of the matching efficiency. Source: ONS, Reed, author calculations.

The picture shown in Figure 3 has two important implications. First, because matching efficiencies are heterogeneous, the speed with which jobseekers find employment will depend on the composition of vacancies.

The second implication is that a shock to the demand for labour will have different effects on output depending on how it falls across occupations.  For example, all else equal, if the demand for managers and professionals increased the short-term output loss relative to potential while the vacancies were filled would be relatively large because of the combination of high productivity but slow matching efficiency.

In this blog post, we have barely scratched the surface of the insights such data sources can provide.  In the paper associated with this post, we also look at what our data can tell us about regional mismatch and its effects.  All this has only been possible because we have combined our ‘Big Data’ with the outputs of statistical agencies.  The full benefits of such ‘naturally occurring’ data come from using it as a complement to, rather than as a replacement for, existing survey data.

Using and applying the occupational coding algorithm

The Python package we created to apply SOC codes to job descriptions is called occupationcoder. There are instructions on how to install this package in the ‘README’ file on the Github repository. Once installed, occupationcoder can be used with the following Python code:

import pandas as pd
from occupationcoder.coder import coder
myCoder = coder.Coder()

To run the code on a single job, use the following syntax with the

codejobrow(job_title,job_description,job_sector)

method. For example

myCoder.codejobrow('Physicist',

'Make calculations about the universe, do research, perform experiments and understand the physical environment.',

'Professional scientific')

will return

job_title
job_description
job_sector
SOC_code

Physicist
Make calculations about the universe, do research, perform experiments and understand the physical environment.
Professional, scientific & technical activities
211

Arthur Turrell works in the Bank’s Advanced Analytics Division, Bradley Speigner works in the Bank’s Structural Economic Analysis Division, James Thurgood works in the Bank’s Technology Division, Jyldyz Djumalieva formerly worked in the Bank’s Technology Division and David Copple works in the Bank’s External Monetary Policy Committee Unit Division