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HomeEconomicsThe Grumpy Economist: Papers: Dew-Becker on Networks

The Grumpy Economist: Papers: Dew-Becker on Networks

I have been studying quite a lot of macro currently. Partly, I am simply catching up from just a few years of e-book writing. Partly,  I need to perceive inflation dynamics, the search set forth in “expectations and the neutrality of rates of interest,” and an  apparent subsequent step within the fiscal principle program. Maybe weblog readers may discover attention-grabbing some summaries of latest papers, when there’s a nice thought that may be summarized with out an enormous quantity of math. So, I begin a sequence on cool papers I am studying. 

Right this moment: “Tail threat in manufacturing networks” by Ian Dew-Becker, an attractive paper. A “manufacturing community” method acknowledges that every agency buys from others, and fashions this interconnection. It is a scorching subject for many causes, under.  I am as a result of costs cascading by way of manufacturing networks may induce a greater mannequin of inflation dynamics. 

(This submit makes use of Mathjax equations. For those who’re seeing rubbish like [alpha = beta] then come again to the supply  right here.) 

To Ian’s paper: Every agency makes use of different corporations’ outputs as inputs. Now, hit the economic system with a vector of productiveness shocks. Some corporations get extra productive, some get much less productive. The extra productive ones will broaden and decrease costs, however that modifications everybody’s enter costs too. The place does all of it calm down? That is the enjoyable query of community economics. 

Ian’s central thought: The issue simplifies loads for giant shocks. Often when issues are difficult we have a look at first or second order approximations, i.e. for small shocks, acquiring linear or quadratic (“easy”) approximations. 

On the x axis, take a vector of productiveness shocks for every agency, and scale it up or down. The x axis represents this total scale. The y axis is GDP. The precise hand graph is Ian’s level: for big shocks, log GDP turns into linear in log productiveness — actually easy. 

Why? As a result of for big sufficient shocks, all of the networky stuff disappears. Every agency’s output strikes up or down relying solely on one important enter. 

To see this, we’ve got to dig deeper to enhances vs. substitutes. Suppose the worth of an enter goes up 10%. The agency tries to make use of much less of this enter. If the most effective it could do is to chop use 5%, then the agency finally ends up paying 5% extra total for this enter, the “expenditure share” of this enter rises. That’s the case of “enhances.” But when the agency can lower use of the enter 15%, then it pays 5% much less total for the enter, regardless that the worth went up. That’s the case of “substitutes.” That is the important thing idea for the entire query: when an enter’s worth goes up, does its share of total expenditure go up (enhances) or down (substitutes)? 

Suppose inputs are enhances. Once more, this vector of expertise shocks hits the economic system. As the dimensions of the shock will get greater, the expenditure of every agency, and thus the worth it expenses for its output, turns into increasingly more dominated by the one enter whose worth grows probably the most. In that sense, all of the networkiness simplifies enormously. Every agency is simply “related” to at least one different agency. 

Flip the shock round. Every agency that was getting a productiveness enhance now will get a productiveness discount. Every worth that was going up now goes down. Once more, within the giant shock restrict, our agency’s worth turns into dominated by the worth of its costliest enter. However it’s a distinct enter.  So, naturally, the economic system’s response to this expertise shock is linear, however with a distinct slope in a single route vs. the opposite. 

Suppose as an alternative that inputs are substitutes. Now, as costs change, the agency expands increasingly more its use of the most cost effective enter, and its prices and worth change into dominated by that enter as an alternative. Once more, the community collapsed to at least one hyperlink.  

Ian: “damaging productiveness shocks propagate downstream by way of components of the manufacturing course of which can be complementary ((sigma_i < 1)), whereas constructive productiveness shocks propagate by way of components which can be substitutable ((sigma_i > 1)). …each sector’s habits finally ends up pushed by a single considered one of its inputs….there’s a tail community, which depends upon (theta) and by which every sector has only a single upstream hyperlink.”

Equations: Every agency’s manufacturing operate is (considerably simplifying Ian’s (1)) [Y_i = Z_i L_i^{1-alpha} left( sum_j A_{ij}^{1/sigma} X_{ij}^{(sigma-1)/sigma} right)^{alpha sigma/(sigma-1)}.]Right here (Y_i) is output, (Z_i) is productiveness, (L_i) is labor enter, (X_{ij}) is how a lot good j agency i  makes use of as an enter, and (A_{ij}) captures how necessary every enter is in manufacturing. (sigma>1) are substitutes, (sigma<1) are enhances. 

Companies are aggressive, so worth equals marginal price, and every agency’s worth is [ p_i = -z_i + frac{alpha}{1-sigma}logleft(sum_j A_{ij}e^{(1-sigma)p_j}right).; ; ; (1)]Small letters are logs of huge letters.  Every worth depends upon the costs of all of the inputs, plus the agency’s personal productiveness.  Log GDP, plotted within the above determine is [gdp = -beta’p] the place (p) is the vector of costs and (beta) is a vector of how necessary every good is to the buyer. 

Within the case (sigma=1) (1)  reduces to a linear method. We will simply clear up for costs after which gdp as a operate of the expertise shocks: [p_i = – z_i + sum_j A_{ij} p_j] and therefore [p=-(I-alpha A)^{-1}z,]the place the letters symbolize vectors and matrices throughout (i) and (j). This expression exhibits among the level of networks, that the sample of costs and output displays the entire community of manufacturing, not simply particular person agency productiveness. However with (sigma neq 1) (1) is nonlinear with no recognized closed kind answer. Therefore approximations. 

You possibly can see Ian’s central level instantly from (1). Take the (sigma<1) case, enhances. Parameterize the dimensions of the expertise shocks by a hard and fast vector (theta = [theta_1, theta_2, …theta_i,…]) instances a scalar (t>0), in order that (z_i=theta_i instances t). Then let (t) develop preserving the sample of shocks (theta) the identical. Now, because the ({p_i}) get bigger in absolute worth, the time period with the best (p_i) has the best worth of ( e^{(1-sigma)p_j} ). So, for big expertise shocks (z), solely that largest time period issues, the log and e cancel, and [p_i approx -z_i + alpha max_{j} p_j.] That is linear, so we are able to additionally write costs as a sample (phi) instances the dimensions (t), within the large-t restrict (p_i = phi_i t),  and  [phi_i =  -theta_i + alpha max_{j} phi_j.;;; (2)] With substitutes, (sigma<1), the agency’s prices, and so its worth, will likely be pushed by the smallest (most damaging) upstream worth, in the identical manner. [phi_i approx -theta_i + alpha min_{j} phi_j.] 

To specific gdp scaling with (t), write (gdp=lambda t), or while you need to emphasize the dependence on the vector of expertise shocks, (lambda(theta)). Then we discover gdp by (lambda =-beta’phi). 

On this massive worth restrict, the (A_{ij}) contribute a continuing time period, which additionally washes out. Thus the precise “community” coefficients cease mattering in any respect as long as they don’t seem to be zero — the max and min are taken over all non-zero inputs. Ian: 

…the boundaries for costs, don’t rely on the precise values of any (sigma_i) or (A_{i,j}.) All that issues is whether or not the elasticities are above or under 1 and whether or not the manufacturing weights are better than zero. Within the instance in Determine 2, altering the precise values of the manufacturing parameters (away from (sigma_i = 1) or (A_{i,j} = 0)) modifications…the degrees of the asymptotes, and it could change the curvature of GDP with respect to productiveness, however the slopes of the asymptotes are unaffected.

…when fascinated with the supply-chain dangers related to giant shocks, what’s necessary is just not how giant a given provider is on common, however quite what number of sectors it provides…

For a full answer, have a look at the (extra attention-grabbing) case of enhances, and suppose each agency makes use of somewhat bit of each different agency’s output, so all of the (A_{ij}>0). The most important enter  worth in (2) is similar for every agency (i), and you’ll shortly see then that the most important worth would be the smallest expertise shock. Now we are able to clear up the mannequin for costs and GDP as a operate of expertise shocks: [phi_i approx -theta_i – frac{alpha}{1-alpha} theta_{min},] [lambda approx  beta’theta + frac{alpha}{1-alpha}theta_{min}.] Now we have solved the large-shock approximation for costs and GDP as a operate of expertise shocks. (That is Ian’s instance 1.) 

The graph is concave when inputs are enhances, and convex when they’re substitutes. Let’s do enhances. We do the graph to the left of the kink by altering the signal of (theta).  If the id of (theta_{min}) didn’t change, (lambda(-theta)=-lambda(theta)) and the graph can be linear; it could go down on the left of the kink by the identical quantity it goes up on the appropriate of the kink. However now a completely different (j) has the biggest worth and the worst expertise shock. Since this have to be a worse expertise shock than the one driving the earlier case, GDP is decrease and the graph is concave.  [-lambda(-theta) = beta’theta + frac{alpha}{1-alpha}theta_{max} gebeta’theta + frac{alpha}{1-alpha}theta_{min} = lambda(theta).] Subsequently  (lambda(-theta)le-lambda(theta),) the left aspect falls by greater than the appropriate aspect rises. 

You possibly can intuit that fixed expenditure shares are necessary for this end result. If an trade has a damaging expertise shock, raises its costs, and others cannot cut back use of its inputs, then its share of expenditure will rise, and it’ll abruptly be necessary to GDP. Persevering with our instance, if one agency has a damaging expertise shock, then it’s the minimal expertise, and [(d gdp/dz_i = beta_i + frac{alpha}{1-alpha}.] For small corporations (industries) the latter time period is prone to be an important.  All of the A and (sigma) have disappeared, and principally the entire economic system is pushed by this one unfortunate trade and labor.   


…what determines tail threat is just not whether or not there may be granularity on common, however whether or not there can ever be granularity – whether or not a single sector can change into pivotal if shocks are giant sufficient.

For instance, take electrical energy and eating places. In regular instances, these sectors are of comparable measurement, which in a linear approximation would indicate that they’ve comparable results on GDP. However one lesson of Covid was that shutting down eating places is just not catastrophic for GDP, [Consumer spending on food services and accommodations fell by 40 percent, or $403 billion between 2019Q4 and 2020Q2. Spending at movie theaters fell by 99 percent.] whereas one may count on {that a} important discount in accessible electrical energy would have strongly damaging results – and that these results can be convex within the measurement of the decline in accessible energy. Electrical energy is systemically necessary not as a result of it is vital in good instances, however as a result of it could be necessary in unhealthy instances. 

Ben Moll turned out to be proper and Germany was in a position to substitute away from Russian Gasoline much more than individuals had thought, however even that proves the rule: if it’s arduous to substitute away from even a small enter, then giant shocks to that enter indicate bigger expenditure shares and bigger impacts on the economic system than its small output in regular instances would recommend.

There is a gigantic quantity extra within the paper and voluminous appendices, however that is sufficient for a weblog evaluation. 


Now, just a few limitations, or actually ideas on the place we go subsequent. (No extra on this paper, please, Ian!) Ian does a pleasant illustrative computation of the sensitivity to giant shocks:

Ian assumes (sigma>1), so the primary components are what number of downstream corporations use your merchandise and a bit their labor shares. No shock, vans, and vitality have massive tail impacts. However so do legal professionals and insurance coverage. Can we actually not do with out legal professionals? Right here I hope the subsequent step appears arduous at substitutes vs. enhances.

That raises a bunch of points. Substitutes vs. enhances certainly depends upon time horizon and measurement of shocks. It could be simple to make use of rather less water or electrical energy initially, however then actually arduous to scale back greater than, say, 80%. It is normally simpler to substitute in the long term than the quick run. 

The evaluation on this literature is “static,” that means it describes the economic system when every little thing has settled down.  The responses — you cost extra, I exploit much less, I cost extra, you utilize much less of my output, and so on. — all occur immediately, or equivalently the mannequin research a long term the place this has all settled down. However then we speak about responses to shocks, as within the pandemic.  Absolutely there’s a dynamic response right here, not simply together with capital accumulation (which Ian research). Certainly, my hope was to see costs spreading out by way of a manufacturing community over time, however this construction would have all worth changes immediately. Mixing manufacturing networks with sticky costs is an apparent thought, which among the papers under are engaged on. 

Within the principle and information dealing with, you see an enormous discontinuity. If a agency makes use of any inputs in any respect from one other agency,  if (A_{ij}>0), that enter can take over and drive every little thing. If it makes use of no inputs in any respect, then there isn’t a community hyperlink and the upstream agency cannot have any impact. There’s a massive discontinuity at (A_{ij}=0.) We would like a principle that doesn’t bounce from zero to every little thing when the agency buys one stick of chewing gum. Ian needed to drop small however nonzero parts of the input-output matrix to produces smart outcomes. Maybe we should always regard very small inputs as at all times substitutes? 

How necessary is the community stuff anyway? We have a tendency to make use of trade categorizations, as a result of we’ve got an trade input-output desk. However how a lot of the US trade input-output is just vertical: Loggers promote bushes to mills who promote wooden to lumberyards who promote lumber to Residence Depot who sells it to contractors who put up your own home? Vitality and instruments feed every stage, however do not use an entire lot of wooden to make these. I have never checked out an input-output matrix just lately, however simply how “vertical” is it? 


The literature on networks in macro is huge. One method is to select a latest paper like Ian’s and work again by way of the references. I began to summarize, however gave up within the deluge. Have enjoyable. 

A method to think about a department of economics isn’t just “what instruments does it use?” however “what questions is it asking?  Lengthy and Plosser “Actual Enterprise Cycles,” a traditional, went after concept that the central defining function of enterprise cycles (since Burns and Mitchell) is comovement. States and industries all go up and down collectively to a exceptional diploma. That pointed to “mixture demand” as a key driving drive. One would suppose that “expertise shocks” no matter they’re can be native or trade particular. Lengthy and Plosser confirmed that an enter output construction led idiosyncratic shocks to provide enterprise cycle widespread motion in output. Good. 
Macro went in one other manner, emphasizing time sequence — the concept recessions are outlined, say, by two quarters of mixture GDP decline, or by the better decline of funding and sturdy items than consumption — and within the mixture fashions of Kydland and Prescott, and the stochastic progress mannequin as pioneered by King, Plosser and Rebelo, pushed by a single economy-wide expertise shock.  A part of this shift is just technical: Lengthy and Plosser used analytical instruments, and have been thereby caught in a mannequin with out capital, plus they didn’t inaugurate matching to information. Kydland and Prescott introduced numerical mannequin answer and calibration to macro, which is what macro has carried out ever since.  Possibly it is time to add capital, clear up numerically, and calibrate Lengthy and Plosser (with updated frictions and client heterogeneity too, perhaps). 
Xavier Gabaix (2011)  had a distinct Huge Query in thoughts: Why are enterprise cycles so giant? Particular person corporations and industries have giant shocks, however (sigma/sqrt{N}) should dampen these on the mixture degree. Once more, this was a traditional argument for mixture “demand” versus “provide.”  Gabaix notices that the US has a fat-tailed agency distribution with just a few giant corporations, and people corporations have giant shocks. He amplifies his argument by way of the Hulten mechanism, a little bit of networkyiness, for the reason that influence of a agency on the economic system is gross sales / GDP,  not worth added / GDP. 

The large literature since then  has gone after quite a lot of questions. Dew-Becker’s paper is in regards to the impact of huge shocks, and clearly not that helpful for small shocks. Bear in mind which query you are after.

The “what is the query” query is doubly necessary for this department of macro that explicitly fashions heterogeneous brokers and heterogenous corporations. Why are we doing this? One can at all times symbolize the aggregates with a social welfare operate and an mixture manufacturing operate. You could be curious about how aggregates have an effect on people, however that does not change your mannequin of aggregates. Or, you could be curious about seeing what the mixture manufacturing or utility operate appears like — is it per what we learn about particular person corporations and folks? Does the dimensions of the mixture manufacturing operate shock make sense? However nonetheless, you find yourself with only a higher (hopefully) mixture manufacturing and utility operate. Or, you may want fashions that break the aggregation theorems in a big manner; fashions for which distributions matter for mixture dynamics, theoretically and (more durable) empirically. However do not forget you want a motive to construct disaggregated fashions. 

Expression (1) is just not simple to get to. I began studying Ian’s paper in my standard manner:  to be taught a literature begin with the newest paper and work backward. Alas, this literature has developed to the purpose that authors plop outcomes down that “all people is aware of” and can take you a day or so of head-scratching to breed. I complained to Ian, and he mentioned he had the identical downside when he was getting in to the literature! Sure, journals now demand such overstuffed papers that it is arduous to do, however it could be awfully good for everybody to begin together with floor up algebra for main ends in one of many infinite web appendices.  I finally discovered Jonathan Dingel’s notes on Dixit Stiglitz tips, which have been useful. 


Chase Abram’s College of Chicago Math Camp notes right here  are additionally a incredible useful resource. See Appendix B beginning p. 94 for  manufacturing community math. The remainder of the notes are additionally actually good. The primary half goes somewhat deeper into extra summary materials than is actually essential for the second half and utilized work, however it’s a fantastic and concise evaluation of that materials as properly. 


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