Border effects in Public Procurement
Transcript of Border effects in Public Procurement
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Border effects in Public ProcurementThe Aggregate Effects of Governments’ Home Bias
Manuel Garcıa-Santana Marta Santamarıa
UPF, CREi (visiting Princeton) University of Warwick
STEG - Plenary Workshop, Sep. 2021
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionGovernment Procurement
Government purchases represent a big fraction of World’s GDP
- 11-20% depending on country and year
- Governments are the largest buyers in many industries
- Potentially powerful industrial policy tool
Development/macroeconomic implications not well understood
Low import penetration rates
Firms Households Governments
France 17.02 13.09 2.24
Spain 17.22 13.48 2.90
Notes: Imports/total expenditure. Source: WIOD input-output tables.
Garcıa-Santana, Santamarıa Border effects in Public Procurement 1/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionGovernment Procurement
Government purchases represent a big fraction of World’s GDP
- 11-20% depending on country and year
- Governments are the largest buyers in many industries
- Potentially powerful industrial policy tool
Development/macroeconomic implications not well understood
Low import penetration rates
Firms Households Governments
France 17.02 13.09 2.24
Spain 17.22 13.48 2.90
Notes: Imports/total expenditure. Source: WIOD input-output tables.
Garcıa-Santana, Santamarıa Border effects in Public Procurement 1/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionGovernment Procurement
Government purchases represent a big fraction of World’s GDP
- 11-20% depending on country and year
- Governments are the largest buyers in many industries
- Potentially powerful industrial policy tool
Development/macroeconomic implications not well understood
Low import penetration rates
Firms Households Governments
France 17.02 13.09 2.24
Spain 17.22 13.48 2.90
Notes: Imports/total expenditure. Source: WIOD input-output tables.
Garcıa-Santana, Santamarıa Border effects in Public Procurement 1/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionLocal share of procurement across French and Spanish regions
Share of procurement value
awarded to plants located
within the same region
Around 53% on average
Compares to 32% for total trade
Not driven by composition
– Machinery and elec. equip.: 50% vs. 37%
– Transport equipment: 41% vs. 19%
– Food: 78% vs. 27%
– Furniture: 51% vs. 18%
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionLocal share of procurement across French and Spanish regions
Share of procurement value
awarded to plants located
within the same region
Around 53% on average
Compares to 32% for total trade
Not driven by composition
– Machinery and elec. equip.: 50% vs. 37%
– Transport equipment: 41% vs. 19%
– Food: 78% vs. 27%
– Furniture: 51% vs. 18%
Garcıa-Santana, Santamarıa Border effects in Public Procurement 2/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionRelevant questions
Are governments responsible in explaining these patterns?
* Challenge: potentially driven by two types of origin-destination factors:
1. Natural frictions: τod abruptly increases when o ≠ d
- ex: geography, information frictions, path dependence, etc.
2. Governments’ home-bias
- governments intentionally discriminate against non-local firms
- Joe Biden: “we will not purchase anything that is not made in America”, Sep. 2020
- increasing cross-border participation is one of the EU Commission’s main goals
What are the consequences for aggregate efficiency and welfare? (not today)
* It benefits local firms
* Inefficient provision of public goods
Garcıa-Santana, Santamarıa Border effects in Public Procurement 3/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionRelevant questions
Are governments responsible in explaining these patterns?
* Challenge: potentially driven by two types of origin-destination factors:
1. Natural frictions: τod abruptly increases when o ≠ d
- ex: geography, information frictions, path dependence, etc.
2. Governments’ home-bias
- governments intentionally discriminate against non-local firms
- Joe Biden: “we will not purchase anything that is not made in America”, Sep. 2020
- increasing cross-border participation is one of the EU Commission’s main goals
What are the consequences for aggregate efficiency and welfare? (not today)
* It benefits local firms
* Inefficient provision of public goods
Garcıa-Santana, Santamarıa Border effects in Public Procurement 3/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionRelevant questions
Are governments responsible in explaining these patterns?
* Challenge: potentially driven by two types of origin-destination factors:
1. Natural frictions: τod abruptly increases when o ≠ d
- ex: geography, information frictions, path dependence, etc.
2. Governments’ home-bias
- governments intentionally discriminate against non-local firms
- Joe Biden: “we will not purchase anything that is not made in America”, Sep. 2020
- increasing cross-border participation is one of the EU Commission’s main goals
What are the consequences for aggregate efficiency and welfare? (not today)
* It benefits local firms
* Inefficient provision of public goods
Garcıa-Santana, Santamarıa Border effects in Public Procurement 3/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionRelevant questions
Are governments responsible in explaining these patterns?
* Challenge: potentially driven by two types of origin-destination factors:
1. Natural frictions: τod abruptly increases when o ≠ d
- ex: geography, information frictions, path dependence, etc.
2. Governments’ home-bias
- governments intentionally discriminate against non-local firms
- Joe Biden: “we will not purchase anything that is not made in America”, Sep. 2020
- increasing cross-border participation is one of the EU Commission’s main goals
What are the consequences for aggregate efficiency and welfare? (not today)
* It benefits local firms
* Inefficient provision of public goods
Garcıa-Santana, Santamarıa Border effects in Public Procurement 3/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionRelevant questions
Are governments responsible in explaining these patterns?
* Challenge: potentially driven by two types of origin-destination factors:
1. Natural frictions: τod abruptly increases when o ≠ d
- ex: geography, information frictions, path dependence, etc.
2. Governments’ home-bias
- governments intentionally discriminate against non-local firms
- Joe Biden: “we will not purchase anything that is not made in America”, Sep. 2020
- increasing cross-border participation is one of the EU Commission’s main goals
What are the consequences for aggregate efficiency and welfare? (not today)
* It benefits local firms
* Inefficient provision of public goods
Garcıa-Santana, Santamarıa Border effects in Public Procurement 3/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionRelevant questions
Are governments responsible in explaining these patterns?
* Challenge: potentially driven by two types of origin-destination factors:
1. Natural frictions: τod abruptly increases when o ≠ d
- ex: geography, information frictions, path dependence, etc.
2. Governments’ home-bias
- governments intentionally discriminate against non-local firms
- Joe Biden: “we will not purchase anything that is not made in America”, Sep. 2020
- increasing cross-border participation is one of the EU Commission’s main goals
What are the consequences for aggregate efficiency and welfare? (not today)
* It benefits local firms
* Inefficient provision of public goods
Garcıa-Santana, Santamarıa Border effects in Public Procurement 3/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionRelevant questions
Are governments responsible in explaining these patterns?
* Challenge: potentially driven by two types of origin-destination factors:
1. Natural frictions: τod abruptly increases when o ≠ d
- ex: geography, information frictions, path dependence, etc.
2. Governments’ home-bias
- governments intentionally discriminate against non-local firms
- Joe Biden: “we will not purchase anything that is not made in America”, Sep. 2020
- increasing cross-border participation is one of the EU Commission’s main goals
What are the consequences for aggregate efficiency and welfare? (not today)
* It benefits local firms
* Inefficient provision of public goods
Garcıa-Santana, Santamarıa Border effects in Public Procurement 3/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionRelevant questions
Are governments responsible in explaining these patterns?
* Challenge: potentially driven by two types of origin-destination factors:
1. Natural frictions: τod abruptly increases when o ≠ d
- ex: geography, information frictions, path dependence, etc.
2. Governments’ home-bias
- governments intentionally discriminate against non-local firms
- Joe Biden: “we will not purchase anything that is not made in America”, Sep. 2020
- increasing cross-border participation is one of the EU Commission’s main goals
What are the consequences for aggregate efficiency and welfare? (not today)
* It benefits local firms
* Inefficient provision of public goods
Garcıa-Santana, Santamarıa Border effects in Public Procurement 3/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionRelevant questions
Are governments responsible in explaining these patterns?
* Challenge: potentially driven by two types of origin-destination factors:
1. Natural frictions: τod abruptly increases when o ≠ d
- ex: geography, information frictions, path dependence, etc.
2. Governments’ home-bias
- governments intentionally discriminate against non-local firms
- Joe Biden: “we will not purchase anything that is not made in America”, Sep. 2020
- increasing cross-border participation is one of the EU Commission’s main goals
What are the consequences for aggregate efficiency and welfare? (not today)
* It benefits local firms
* Inefficient provision of public goods
Garcıa-Santana, Santamarıa Border effects in Public Procurement 3/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionThis paper
1. Dataset of procurement contracts awarded in Spain and France
- more than 1 million contracts in 38 regions
- info about the contract: value, product, etc.
- info about the seller: location, firm, etc.
- info about the buyer: location, agency (around 10,000 only in Spain!), etc.
→ key: classify buyers into different gov. types
2. New plant-level evidence on inter-regional and international procurement flows
- participation increases systematically with market size
- sales distributions are similar across markets
- big “border effects” both at the region and country level
Garcıa-Santana, Santamarıa Border effects in Public Procurement 4/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionThis paper
1. Dataset of procurement contracts awarded in Spain and France
- more than 1 million contracts in 38 regions
- info about the contract: value, product, etc.
- info about the seller: location, firm, etc.
- info about the buyer: location, agency (around 10,000 only in Spain!), etc.
→ key: classify buyers into different gov. types
2. New plant-level evidence on inter-regional and international procurement flows
- participation increases systematically with market size
- sales distributions are similar across markets
- big “border effects” both at the region and country level
Garcıa-Santana, Santamarıa Border effects in Public Procurement 4/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionThis paper
1. Dataset of procurement contracts awarded in Spain and France
- more than 1 million contracts in 38 regions
- info about the contract: value, product, etc.
- info about the seller: location, firm, etc.
- info about the buyer: location, agency (around 10,000 only in Spain!), etc.
→ key: classify buyers into different gov. types
2. New plant-level evidence on inter-regional and international procurement flows
- participation increases systematically with market size
- sales distributions are similar across markets
- big “border effects” both at the region and country level
Garcıa-Santana, Santamarıa Border effects in Public Procurement 4/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionThis paper
3. Strategy to identify the role of governments in explaining these border effects
– Sub-national vs. national governments have offices in the same location
- Ex: both the Federal Gov. and the Gov. of California have offices in San Francisco
- Ex: both the central Spanish Gov. and the Catalan Gov. have offices in Barcelona
– Use variation within a plant-origin-destination across government-types
- Allows us to control for τod!
– Hypothesis: geographical scope of governments main driver of home bias
- Regional governments discriminate against firms from other regions
- National governments discriminate against firms from other countries
4. Quantitative exercise
– Multi-region trade model with heterogeneous firms applied to procurement
- Chaney (2008), Breinlich, Cunat (2014)
– Use the model to quantify the aggregate effects of governments’ home bias
Garcıa-Santana, Santamarıa Border effects in Public Procurement 5/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionThis paper
3. Strategy to identify the role of governments in explaining these border effects
– Sub-national vs. national governments have offices in the same location
- Ex: both the Federal Gov. and the Gov. of California have offices in San Francisco
- Ex: both the central Spanish Gov. and the Catalan Gov. have offices in Barcelona
– Use variation within a plant-origin-destination across government-types
- Allows us to control for τod!
– Hypothesis: geographical scope of governments main driver of home bias
- Regional governments discriminate against firms from other regions
- National governments discriminate against firms from other countries
4. Quantitative exercise
– Multi-region trade model with heterogeneous firms applied to procurement
- Chaney (2008), Breinlich, Cunat (2014)
– Use the model to quantify the aggregate effects of governments’ home bias
Garcıa-Santana, Santamarıa Border effects in Public Procurement 5/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionThis paper
3. Strategy to identify the role of governments in explaining these border effects
– Sub-national vs. national governments have offices in the same location
- Ex: both the Federal Gov. and the Gov. of California have offices in San Francisco
- Ex: both the central Spanish Gov. and the Catalan Gov. have offices in Barcelona
– Use variation within a plant-origin-destination across government-types
- Allows us to control for τod!
– Hypothesis: geographical scope of governments main driver of home bias
- Regional governments discriminate against firms from other regions
- National governments discriminate against firms from other countries
4. Quantitative exercise
– Multi-region trade model with heterogeneous firms applied to procurement
- Chaney (2008), Breinlich, Cunat (2014)
– Use the model to quantify the aggregate effects of governments’ home bias
Garcıa-Santana, Santamarıa Border effects in Public Procurement 5/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionThis paper
3. Strategy to identify the role of governments in explaining these border effects
– Sub-national vs. national governments have offices in the same location
- Ex: both the Federal Gov. and the Gov. of California have offices in San Francisco
- Ex: both the central Spanish Gov. and the Catalan Gov. have offices in Barcelona
– Use variation within a plant-origin-destination across government-types
- Allows us to control for τod!
– Hypothesis: geographical scope of governments main driver of home bias
- Regional governments discriminate against firms from other regions
- National governments discriminate against firms from other countries
4. Quantitative exercise
– Multi-region trade model with heterogeneous firms applied to procurement
- Chaney (2008), Breinlich, Cunat (2014)
– Use the model to quantify the aggregate effects of governments’ home bias
Garcıa-Santana, Santamarıa Border effects in Public Procurement 5/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
IntroductionThis paper
3. Strategy to identify the role of governments in explaining these border effects
– Sub-national vs. national governments have offices in the same location
- Ex: both the Federal Gov. and the Gov. of California have offices in San Francisco
- Ex: both the central Spanish Gov. and the Catalan Gov. have offices in Barcelona
– Use variation within a plant-origin-destination across government-types
- Allows us to control for τod!
– Hypothesis: geographical scope of governments main driver of home bias
- Regional governments discriminate against firms from other regions
- National governments discriminate against firms from other countries
4. Quantitative exercise
– Multi-region trade model with heterogeneous firms applied to procurement
- Chaney (2008), Breinlich, Cunat (2014)
– Use the model to quantify the aggregate effects of governments’ home bias
Garcıa-Santana, Santamarıa Border effects in Public Procurement 5/18
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Data
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Procurement DataOur sample (all EU countries potentially)
We worked with France and Spain so far
Classify “buyers” into different gov. types
1. LOCAL: 18.56 %2. PROVINCIAL: 16.13 %3. REGIONAL: 23.58 %4. NATIONAL: 41.71 %
Work with 2 government types: national and sub-national
Big overlap in purchases across governments
- ex, motor vehicles: sub-national spend e5M and national spend e2M
- ex, medical consumables: sub-national spend e14M and national spend e5M
Years: 2011-2019
Plants: 192,155
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The role of governments: a simple decomposition
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
The role of governmentsA simple decomposition
Share of procurement value in region r awarded to firms from r
πrr =XrrXr
=Xnatrr +Xsub
rr
Xnatr +Xsub
r
Simple manipulations allow to decompose it into 4 different components
0.53«πrr =
0.13³¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xnatr
Xnatr +Xsub
r
)
0.29³¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xnatrr
Xnatr)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶πnatrr
+
0.87³¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xsubr
Xsubr +Xsub
r
)
0.56³¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xsubrr
Xsubr
)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶πsubrr
Naive counterfactual: set πsubrr = πnatrr and re-compute πrr
Garcıa-Santana, Santamarıa Border effects in Public Procurement 7/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
The role of governmentsA simple decomposition
Share of procurement value in region r awarded to firms from r
πrr =XrrXr
=Xnatrr +Xsub
rr
Xnatr +Xsub
r
Simple manipulations allow to decompose it into 4 different components
0.53«πrr =
0.13³¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xnatr
Xnatr +Xsub
r
)
0.29³¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xnatrr
Xnatr)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶πnatrr
+
0.87³¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xsubr
Xsubr +Xsub
r
)
0.56³¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xsubrr
Xsubr
)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶πsubrr
Naive counterfactual: set πsubrr = πnatrr and re-compute πrr
Garcıa-Santana, Santamarıa Border effects in Public Procurement 7/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
The role of governmentsA simple decomposition
Share of procurement value in region r awarded to firms from r
πrr =XrrXr
=Xnatrr +Xsub
rr
Xnatr +Xsub
r
Simple manipulations allow to decompose it into 4 different components
0.53«πrr =
0.13³¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xnatr
Xnatr +Xsub
r
)
0.29³¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xnatrr
Xnatr)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶πnatrr
+
0.87³¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xsubr
Xsubr +Xsub
r
)
0.56³¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xsubrr
Xsubr
)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶πsubrr
Naive counterfactual: set πsubrr = πnatrr and re-compute πrr
Garcıa-Santana, Santamarıa Border effects in Public Procurement 7/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
The role of governmentsA simple decomposition
Share of procurement value in region r awarded to firms from r
πrr =XrrXr
=Xnatrr +Xsub
rr
Xnatr +Xsub
r
Simple manipulations allow to decompose it into 4 different components
0.53«πrr =
0.13³¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xnatr
Xnatr +Xsub
r
)
0.29³¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xnatrr
Xnatr)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶πnatrr
+
0.87³¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xsubr
Xsubr +Xsub
r
)
0.56³¹¹¹¹¹¹¹¹¹¹¹¹¹¹·¹¹¹¹¹¹¹¹¹¹¹¹¹¹µ
(Xsubrr
Xsubr
)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶πsubrr
Naive counterfactual: set πsubrr = πnatrr and re-compute πrr
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
The role of governmentsA simple decomposition
Data Counterfactual
XnatrXnr +Xsubr
πnatrrXsubr
Xsubr +Xsubrπsubrr πrr πrr
Madrid (ESP) 0.72 0.68 0.28 0.80 0.71 0.68 ↓ 5%
Aragon (ESP) 0.02 0.35 0.98 0.78 0.77 0.35 ↓ 55%
Ile de France (FR) 0.68 0.68 0.36 0.88 0.78 0.68 ↓ 13%
Pays de la Loire (FR) 0.05 0.31 0.95 0.65 0.63 0.31 ↓ 51%
mean 0.13 0.29 0.87 0.56 0.53 0.29 ↓ 45%
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Plant-level evidence
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Plant-level evidenceThree stylized facts about procurement
1 Firms’ participation in procurement increases with market size, BUT
1.a Firms participate disproportionally more in their own country
1.b Within their country, firms participate disproportionally more in their own region
1.c This last pattern particularly strong for sub-national governments
2 The firms’ sales distribution within an origin o is similar across destinations
2.a Well approximated by a Pareto except for the very low end of the distribution.
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Plant-level evidenceThree stylized facts about procurement
1 Firms’ participation in procurement increases with market size, BUT
1.a Firms participate disproportionally more in their own country
1.b Within their country, firms participate disproportionally more in their own region
1.c This last pattern particularly strong for sub-national governments
2 The firms’ sales distribution within an origin o is similar across destinations
2.a Well approximated by a Pareto except for the very low end of the distribution.
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Plant-level evidenceThree stylized facts about procurement
1 Firms’ participation in procurement increases with market size, BUT
1.a Firms participate disproportionally more in their own country
1.b Within their country, firms participate disproportionally more in their own region
1.c This last pattern particularly strong for sub-national governments
2 The firms’ sales distribution within an origin o is similar across destinations
2.a Well approximated by a Pareto except for the very low end of the distribution.
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Firm level evidenceParticipation in procurement by firms located in Catalonia
AND
ARA
CAN CYM
CYL
CAT
MAD
VAL
EXTGALIBA
RIONAV
PVA
ASTMUR
AUV
RHABOU
BRECVL
ALS
CHA
LOR
NPC
PIC
IDF
HAN
AQU
LIM
POC
LARMIP
PAL
PAC
110
100
1000
1000
0N
umbe
r of f
irms
1 10 100Market size (in awarded value, in billions of eur)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Plant-level evidenceThree stylized facts about procurement
1 Firms’ participation in procurement increases with market size, BUT
1.a Firms participate disproportionally more in their own country
1.b Within their country, firms participate disproportionally more in their own region
1.c This last pattern particularly strong for sub-national governments
2 The firms’ sales distribution within an origin o is similar across destinations
2.a Well approximated by a Pareto except for the very low end of the distribution.
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Plant-level evidenceThree stylized facts about procurement
1 Firms’ participation in procurement increases with market size, BUT
1.a Firms participate disproportionally more in their own country
1.b Within their country, firms participate disproportionally more in their own region
1.c This last pattern particularly strong for sub-national governments
2 The firms’ sales distribution within an origin o is similar across destinations
2.a Well approximated by a Pareto except for the very low end of the distribution.
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Firm level evidenceParticipation in procurement by firms located in Catalonia
ANDARA
CANCYM
CYL
CAT
MADVAL
EXTGALIBA
RIONAV
PVA
ASTMUR
AUVBREALSCHA
NPC
PIC
IDFHAN
AQULAR
110
100
1000
1000
0N
umbe
r of f
irms
1 10 100Market size (in awarded value, in billions of eur)
A) Sub-national governments
ANDARA
CANCYM
CYL
CAT
MAD
VAL
EXT GAL
IBA
NAV
PVA
AST
MUR
CVLLIM
MIP
110
100
1000
1000
0N
umbe
r of f
irms
1 10 100Market size (in awarded value, in billions of eur)
B) National governments
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Firm level evidenceParticipation in procurement by firms located in Catalonia
ANDARA
CANCYM
CYL
CAT
MADVAL
EXTGALIBA
RIONAV
PVA
ASTMUR
AUVBREALSCHA
NPC
PIC
IDFHAN
AQULAR
110
100
1000
1000
0N
umbe
r of f
irms
1 10 100Market size (in awarded value, in billions of eur)
A) Sub-national governments
ANDARA
CANCYM
CYL
CAT
MAD
VAL
EXT GAL
IBA
NAV
PVA
AST
MUR
CVLLIM
MIP
110
100
1000
1000
0N
umbe
r of f
irms
1 10 100Market size (in awarded value, in billions of eur)
B) National governments
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Plant-level evidenceThree stylized facts about procurement
1 Firms’ participation in procurement increases with market size, BUT
1.a Firms participate disproportionally more in their own country
1.b Within their country, firms participate disproportionally more in their own region
1.c This last pattern particularly strong for sub-national governments
2 The firms’ sales distribution within an origin o is similar across destinations
2.a Well approximated by a Pareto except for the very low end of the distribution.
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Plant-level evidenceThree stylized facts about procurement
1 Firms’ participation in procurement increases with market size, BUT
1.a Firms participate disproportionally more in their own country
1.b Within their country, firms participate disproportionally more in their own region
1.c This last pattern particularly strong for sub-national governments
2 The firms’ sales distribution within an origin o is similar across destinations
2.a Well approximated by a Pareto except for the very low end of the distribution.
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Plant-level evidenceThree stylized facts about procurement
1 Firms’ participation in procurement increases with market size, BUT
1.a Firms participate disproportionally more in their own country
1.b Within their country, firms participate disproportionally more in their own region
1.c This last pattern particularly strong for sub-national governments
2 The firms’ sales distribution within an origin o is similar across destinations
2.a Well approximated by a Pareto except for the very low end of the distribution.
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Model
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
A model of trade in procurementMain ingredients
Standard:
There are R regions indexed by o, r
Shipping goods across regions is costly (τor)
Each region is populated by Lr consumers who supply one unit of labor
Heterogeneous firms produce tradable differentiated intermediate goods
New:
In each region r, two gov-types combine ygor(j) to produce final public goods
Y gr = (R
∑o=1∫
Ωgor
(αgor)1σ ygor(j)
σ−1σ dj)
σσ−1
Governments’ home bias manifests in two forms:
– Intensive margin through a preference parameter (αgor)
– Extensive margin through entry costs (Egor)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
A model of trade in procurementMain ingredients
Standard:
There are R regions indexed by o, r
Shipping goods across regions is costly (τor)
Each region is populated by Lr consumers who supply one unit of labor
Heterogeneous firms produce tradable differentiated intermediate goods
New:
In each region r, two gov-types combine ygor(j) to produce final public goods
Y gr = (R
∑o=1∫
Ωgor
(αgor)1σ ygor(j)
σ−1σ dj)
σσ−1
Governments’ home bias manifests in two forms:
– Intensive margin through a preference parameter (αgor)
– Extensive margin through entry costs (Egor)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
A model of trade in procurementMain ingredients
Standard:
There are R regions indexed by o, r
Shipping goods across regions is costly (τor)
Each region is populated by Lr consumers who supply one unit of labor
Heterogeneous firms produce tradable differentiated intermediate goods
New:
In each region r, two gov-types combine ygor(j) to produce final public goods
Y gr = (R
∑o=1∫
Ωgor
(αgor)1σ ygor(j)
σ−1σ dj)
σσ−1
Governments’ home bias manifests in two forms:
– Intensive margin through a preference parameter (αgor)
– Extensive margin through entry costs (Egor)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Identification of αgor (similar intuitions apply for Egor)
Sales to gov-type g in region r by firm j (from region o) given by:
Xgor(j) =X
gr (
σ − 1
σ)σ−1
(P grτor
z(j)
wo)
σ−1
αgor
Assume:
αgor = 1 ∀g if o = r,
αgor = αg if o ≠ r
We use a dummy to identify the relative home-bias
ln Xgor(j) = β × 1(o ≠ r)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¶non-local
× 1(g = s)´¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¶
Sub-national gov
+µj + δgr + φor + εjrg
⇒αs
αn= exp(β)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Identification of αgor (similar intuitions apply for Egor)
Sales to gov-type g in region r by firm j (from region o) given by:
Xgor(j) =X
gr (
σ − 1
σ)σ−1
(P grτor
z(j)
wo)
σ−1
αgor
Assume:
αgor = 1 ∀g if o = r,
αgor = αg if o ≠ r
We use a dummy to identify the relative home-bias
ln Xgor(j) = β × 1(o ≠ r)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¶non-local
× 1(g = s)´¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¶
Sub-national gov
+µj + δgr + φor + εjrg
⇒αs
αn= exp(β)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Identification of αgor (similar intuitions apply for Egor)
Sales to gov-type g in region r by firm j (from region o) given by:
Xgor(j) =X
gr (
σ − 1
σ)σ−1
(P grτor
z(j)
wo)
σ−1
αgor
Assume:
αgor = 1 ∀g if o = r,
αgor = αg if o ≠ r
We use a dummy to identify the relative home-bias
ln Xgor(j) = β × 1(o ≠ r)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¶non-local
× 1(g = s)´¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¶
Sub-national gov
+µj + δgr + φor + εjrg
⇒αs
αn= exp(β)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Identification of αgor (similar intuitions apply for Egor)
Sales to gov-type g in region r by firm j (from region o) given by:
Xgor(j) =X
gr (
σ − 1
σ)σ−1
(P grτor
z(j)
wo)
σ−1
αgor
Assume:
αgor = 1 ∀g if o = r,
αgor = αg if o ≠ r
We use a dummy to identify the relative home-bias
ln Xgor(j) = β × 1(o ≠ r)
´¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¶non-local
× 1(g = s)´¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¶
Sub-national gov
+µj + δgr + φor + εjrg
⇒αs
αn= exp(β)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Main estimation results
when selling non-locally, plants sell 33% less to sub-national gov.
- αs
αn = exp(−0.38) = 0.67
similar identification strategy + data on firms’ participation implies that
- Es
En = 1.05
set some other important parameters to standard values
- σ = 5.00 (Elasticity of Substitution)
- θ = 8.00 (Pareto shape)
calibrate the elasticity of τor w.r.t distance and the level of E to match
- overall trade flows
- average participation rates
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Main estimation results
when selling non-locally, plants sell 33% less to sub-national gov.
- αs
αn = exp(−0.38) = 0.67
similar identification strategy + data on firms’ participation implies that
- Es
En = 1.05
set some other important parameters to standard values
- σ = 5.00 (Elasticity of Substitution)
- θ = 8.00 (Pareto shape)
calibrate the elasticity of τor w.r.t distance and the level of E to match
- overall trade flows
- average participation rates
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Main estimation results
when selling non-locally, plants sell 33% less to sub-national gov.
- αs
αn = exp(−0.38) = 0.67
similar identification strategy + data on firms’ participation implies that
- Es
En = 1.05
set some other important parameters to standard values
- σ = 5.00 (Elasticity of Substitution)
- θ = 8.00 (Pareto shape)
calibrate the elasticity of τor w.r.t distance and the level of E to match
- overall trade flows
- average participation rates
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Main estimation results
when selling non-locally, plants sell 33% less to sub-national gov.
- αs
αn = exp(−0.38) = 0.67
similar identification strategy + data on firms’ participation implies that
- Es
En = 1.05
set some other important parameters to standard values
- σ = 5.00 (Elasticity of Substitution)
- θ = 8.00 (Pareto shape)
calibrate the elasticity of τor w.r.t distance and the level of E to match
- overall trade flows
- average participation rates
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Quantification
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Results: come back to the original question
How much of the low penetration rates in procurement is explained bygovernments’ home bias?
(1) (2) (3)
Agg. local share (πrr) Nat. gov. local share (πnrr) Subnat. gov. local share (πsrr)
Data 0.53 0.29 0.56
Baseline 0.50 0.35 0.52
αs = αn 0.38 (24.39% ↓) 0.35 (0.00% ) 0.38 (26.29% ↓)
Es = En 0.46 (8.90% ↓) 0.35 (0.00% ) 0.47 (9.35% ↓)
both 0.35 (29.87% ↓) 0.35 (0.00% ) 0.35 (32.05% ↓)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Results: come back to the original question
How much of the low penetration rates in procurement is explained bygovernments’ home bias?
(1) (2) (3)
Agg. local share (πrr) Nat. gov. local share (πnrr) Subnat. gov. local share (πsrr)
Data 0.53 0.29 0.56
Baseline 0.50 0.35 0.52
αs = αn 0.38 (24.39% ↓) 0.35 (0.00% ) 0.38 (26.29% ↓)
Es = En 0.46 (8.90% ↓) 0.35 (0.00% ) 0.47 (9.35% ↓)
both 0.35 (29.87% ↓) 0.35 (0.00% ) 0.35 (32.05% ↓)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Results: come back to the original question
How much of the low penetration rates in procurement is explained bygovernments’ home bias?
(1) (2) (3)
Agg. local share (πrr) Nat. gov. local share (πnrr) Subnat. gov. local share (πsrr)
Data 0.53 0.29 0.56
Baseline 0.50 0.35 0.52
αs = αn 0.38 (24.39% ↓) 0.35 (0.00% ) 0.38 (26.29% ↓)
Es = En 0.46 (8.90% ↓) 0.35 (0.00% ) 0.47 (9.35% ↓)
both 0.35 (29.87% ↓) 0.35 (0.00% ) 0.35 (32.05% ↓)
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Results: come back to the original question
How much of the low penetration rates in procurement is explained bygovernments’ home bias?
(1) (2) (3)
Agg. local share (πrr) Nat. gov. local share (πnrr) Subnat. gov. local share (πsrr)
Data 0.53 0.29 0.56
Baseline 0.50 0.35 0.52
αs = αn 0.38 (24.39% ↓) 0.35 (0.00% ) 0.38 (26.29% ↓)
Es = En 0.46 (8.90% ↓) 0.35 (0.00% ) 0.47 (9.35% ↓)
both 0.35 (29.87% ↓) 0.35 (0.00% ) 0.35 (32.05% ↓)
Garcıa-Santana, Santamarıa Border effects in Public Procurement 17/18
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Results: come back to the original question
How much of the low penetration rates in procurement is explained bygovernments’ home bias?
(1) (2) (3)
Agg. local share (πrr) Nat. gov. local share (πnrr) Subnat. gov. local share (πsrr)
Data 0.53 0.29 0.56
Baseline 0.50 0.35 0.52
αs = αn 0.38 (24.39% ↓) 0.35 (0.00% ) 0.38 (26.29% ↓)
Es = En 0.46 (8.90% ↓) 0.35 (0.00% ) 0.47 (9.35% ↓)
both 0.35 (29.87% ↓) 0.35 (0.00% ) 0.35 (32.05% ↓)
Garcıa-Santana, Santamarıa Border effects in Public Procurement 17/18
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Conclusions
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Introduction Data The role of governments: a simple decomposition Plant-level evidence Model Quantification Conclusions
Conclusions and final discussion
Governments’ actions crucial to explain low penetration rates in procurement
What are the factors driving these actions?
– Consumers have preferences for local goods
- Morey, 2016
– Protectionism:
- Political economy determinants (Grossman, Helpman, 1994)
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