Flexibility and Frictions in Multisector Models

Miranda-Pinto, Jorge; Young, Eric

doi:10.2139/ssrn.3186354

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…Relaxing the assumption of unitary elasticity among materials will only have quantitative and not qualitative effects on the main results. 29 Miranda-Pinto and Young (2016) show that this model delivers occasionally binding borrowing constraints that fit the observed time-varying negative (positive) correlations between sectoral elasticities of substitution between materials and value added (among materials) and spreads on corporate bonds in the U.S. 30 There is no reason to think of different sectors having the same working capital constraints. It might well be the case that for some firms it is easier to pay workers at the end of the month, e.g.…”

Section: Firmsmentioning

confidence: 87%

“…As in Miranda-Pinto and Young (2016), the difference of this model with respect to Atalay (2014) is the value of the elasticity of substitution between inputs, while the difference from Bigio and La'O (2016) is that we allow sectors to be different regarding which inputs have financial constraints. As shown later, combining these models will have important implications in terms of delivering the observed facts in section 2.…”

Section: The Model Economymentioning

confidence: 99%

“…This modeling assumption is based on Bigio and La'O (2016) and Miranda-Pinto and Young (2016). 29 This assumption can be better explained with two constraints.…”

Section: Firmsmentioning

confidence: 99%

“…Miranda-Pinto and Young (2016) estimate for the U.S. an average elasticity for service sectors of 1.2 and for manufacturing sectors 0.8. different networks amplify shocks differently. The only source of fluctuations in the model is given by the existence of sectoral productivity shocks.…”

Section: Introductionmentioning

confidence: 98%

“…Firms do not hold cash, therefore need external funds to pay for inputs, which can be obtained by pledging a fraction of total sales as collateral for loans. As in Miranda-Pinto and Young (2016) we assume that sectors are heterogeneous in the fraction of their labor an intermediate input cost to be paid in advance as well as in the fraction of sales they can pledge as collateral. 3 We study whether the network density amplifies or mitigates the likelihood that sectoral constraints become binding.…”

Section: Introductionmentioning

confidence: 99%

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Production Networks and Aggregate Fluctuations: Evidence, Theory, and Policy

Miranda

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In this dissertation we study the sources of aggregate fluctuations with emphasis on the role of firms' linkages and non-linearities in production. In Chapters 1 and 2, we present a new set of empirical facts on i) the relationship between the structure of inter-sectoral linkages and aggregate fluctuations, and ii) the relationship between firms' elasticity of substitution in production and the spreads on corporate bonds during periods of high corporate debt and in recessions. We then construct a multisector model that is able to deliver the observed empirical patterns. In Chapter 3 we test the predictions of different dynamic multisector models regarding how well they predict the observed co-movement of sectoral output growth in the U.S. industrial sectors. Finally, in Chapter 4 we study the normative implications of combining the models in Chapters 1-3.In Chapter 1, we study what features of the countries' domestic structure of sectoral linkages can help account for the observed differences in cross-country volatility of growth and the size of macroeconomic downturns. The empirical cross-country evidence, crosssectional and panel data, suggests that unlike previous multisector models predict, the density of sectoral connections -number of sectoral connections in countries' input-output tables -is strongly correlated with aggregate volatility and the size of downturns. Therefore, we develop a multisector model that is able to deliver a role for the density of connections.The key implications of the model are that: i) the density of sectoral connections amplifies shocks -as observed for manufacturing oriented countries -if firms have low elasticity of substitution between labor and intermediates, while ii) density mitigates shocks -as observed for service oriented countries -if firms have high elasticity. In addition, iii) the density of connections amplifies the size of downturns -as observed for manufacturing oriented countries -if low elasticity firms face frictions in the use of intermediates, while iv) density mitigates downturns -as observed for service oriented countries -if high elasticity firms face frictions in the use of labor.In Chapter 2, we study what are the sectors of the economy that have more troubles in financing inputs during recessions in the U.S. Using firm level and sector level data, we find that during times of high corporate debt i) manufacturing sectors with lower estimated elasticities of substitution between labor and intermediates pay higher spread on corporate bonds, while service sectors with larger elasticities pay higher spreads on corporate bonds.We also find that during recessions firms with low elasticity between labor and interme- In Chapter 3, we contrast the predictions of different multisector DSGE models with the observed correlations of sectoral output growth in the U.S. industrial sectors. We study models that differ in the timing of the investment decision and in the technology used to build physical capital. To focus on the role of capital accumulation, we as...

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Section: Firmsmentioning

confidence: 87%

Section: The Model Economymentioning

confidence: 99%

“…This modeling assumption is based on Bigio and La'O (2016) and Miranda-Pinto and Young (2016). 29 This assumption can be better explained with two constraints.…”

Section: Firmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations