the persistent effects of peru's mining mita - by melissa dell, … · 2017-07-18 ·...

Introduction and Motivation Historical Background Data and Estimation Method Results Conclusion and Discussion

The Persistent Effects of Peru’s Mining Mitaby Melissa Dell, Econometrica (2010)

Microeconometrics, Summer Semester 2017

Eleonora Guarnieri

July 18, 2017

Microeconometrics, Summer Semester 2017 Eleonora Guarnieri

The Persistent Effects of Peru’s Mining Mita 1


Outline

Introduction and Motivation

Historical Background

Data and Estimation Method

Results

Conclusion and Discussion




Outline




Results





What “big picture” issues does the paper address?

I Massive divergence in economic prosperity within thedeveloping world since the mid-20th century

I How do we explain this divergence?I Historical institutions and governance organizations →

contemporary (under)development and differential growthpaths:

I Africa: organization of pre-colonial states (Michalopoulos &Papaioannu, 2013; Gennaioli & Rainer, 2007)

I Europe, South America, Asia: organization of historical states(Acemoglu et al., 2015; Boeckh et al., 2014, Dell et al., WP, ...)




Research question

This paper:I Examines the long-run impacts of the mining mita, a forced

labor system instituted by the Spanish government in Peru andBolivia (1573-1812)

I Implements a geographic (multidimensional) regressiondiscontinuity (RD) design across the mita boundary

I Identifies statistically significant impacts on:I Contemporary living standardsI Channels of persistence (land tenure and public goods

provision)




Contributions

1. MethodologicalI Multidimensional, semiparametric Regression Discontinuity

approach2. Literature on long-run development

I First paper focusing on channels of persistence and potentialmechanisms

I Starting point for modeling Latin America’s long-run growthtrajectory → role of large landowners in shielding individualsfrom an extractive state; extent to which the state can be usedto shape economic interactions




Outline




Results





“The mountain that eats men”

Source: The Guardian, “Story of cities #6: howsilver turned Potosí into the first city ofcapitalism”, 21 March 2016

I Potosí mines discovered in1545 → largest deposit ofsilver in the Spanish Empire

I Huancavelica minesI The mining mita: indigenous

villages within a contiguousregion were required to provideone-seventh of their adult malepopulation as mita laborers

I Subjected region: constantfrom 1578 onwards




The mita boundary




The Mita’s assignment

I The Spanish authorities required only a portion of districts intoday’s Peru to contribute to the mita Map

I Administrative and enforcement costs of coercing laborI Two criteria:

1. Distance to the mines at Potosí and Huancavelica →increasing administrative and enforcement costs in distance

2. Elevation → only highland people could survive intensivephysical labor




Outline




Results





Outcomes, channels of persistence and data

Mining Mita↓

I Land tenure and labor systemsI Public goodsI Proximate determinants of household consumption

↓Long run development:

I Household consumptionI Stunting in children





Mining Mita - Saignes (1984), Amat y Junient (1947)↓








Mining Mita↓

I Land tenure and labor systems - Parish reports, Cusco regionalgovernment, Peruvian Population Census

I Public goods - Population Census, 2001 Peruvian National HouseholdSurvey (ENAHO)

I Proximate determinants of household consumption 1993 PopulationCensus







Mining Mita↓



I Household consumption - 2001 Peruvian National Household SurveyI Stunting in children - Census from the Ministry of Education




Estimation Strategy

I How do we identify the mita effect on the aforementionedoutcomes?

I Can we simply compare mita to non-mita districts (in today’sPeru)?

→ Assignment to the mita based on (at least) two geographiccriteria→ Districts might differ in (observed/unobserved) predeterminedcharacteristics, in turn responsible for differential outcomestoday




Sharp Regression Discontinuity (Reminder)

Treatment D is a function of a known running variable X :

Di = 1{Xi ≥ c}

where c is the threshold. Therefore:

Di =

{1 if Xi ≥ c

0 if Xi < c




Multidimensional RDD

I In this context, the running (or assignment) variable X forthe Regression Discontinuity Design is “geography”

I Mita treatment is a deterministic and discontinuous functionof known covariates: longitude and latitude

I The border between mita and non-mita areas forms amultidimensional (geographic) discontinuity inlongitude-latitude space

I Idea: compare “mita” to “non-mita” households situated closeenough to the border




Estimation Strategy

Basic Regressioncidb = α + γmitad + f (geographic locationd) + X ′

idβ + Φb + εidb

where:

I cidb is the outcome variable of interest for observation i in district d alongsegment b of the boundary

I mitad is an indicator equal to 1 if the observation i belongs to a districtwhich was subject to mita

I f (geographic locationd) is the multidimensional RD polynomialI X ′

id is a vector of covariates (mean elevation/slope, demographicvariables)

I Φb is a vector of boundary segment fixed effects




Estimation Strategy

Basic Regressioncidb = α + γmitad + f (geographic locationd) + X ′

idβ + Φb + εidb

where:

I Bandwith: 100km, 75km, 50kmI RD polynomial:

I Cubic in latitude and longitude (preferred specification)I Cubic in distance to Potosí (single dimension)I Cubic in distance to the mita boundary (single dimension)

I Semiparametric vs nonparametric RDI GeoreferencingI Sample size




Estimation strategy - Stata implementationBasic Regressioncidb = α + γmitad + f (geographic locationd) + X ′

idβ + Φb + εidb

Example with:

I Cubic polynomial in latitude and longitude

I 100 km bandwidth




Identifying assumptions

1. Continuity: all relevant factors besides treatment varysmoothly at the mita boundary

2. No selective sorting across the treatment threshold




1. Continuity

Let c1 and c0 denote potential outcomes under treatment andcontrol respectively, let x denote longitude and y denote latitude.

Identification requires:E [c1 | x , y ] and E [c0 | x , y ] are continuous at the discontinuitythreshold

↓

Individuals located just outside the mita catchment are anappropriate counterfactual for those located just inside it




1. Continuity

I Not entirely testable, but balancing tests help assessing itsplausibility

I Test for difference in means for geographic and demographiccharacteristics: cgd = α + βmitad + εgd

I Identifying assumption: β = 0 for such outcomes




1. Continuity




1. Continuity Conley




1. Continuity




2. No Selective Sorting

I Violated if the mita effect directly provoked substantialout-migration of productive individuals, leading to a largerindirect effect

I Explore the possibility of migration as an interesting channel ofpersistence

I Low migration rates in the past 130 years → constantaggregate population distribution

I No statistically significant differences in rates of out-migrationbetween mita and non-mita districts (1993 census)

I Outmigration from mita districts during the period that themita was in force may have been substantial → evidencefrom 17th century population data




Outline




Results





Outcomes and Channels of Persistence

Mining Mita↓







Household consumption





Cubic polynomial in distance to Potosí





Mining Mita↓







Stunting in children




RD Plots




Specification tests and robustness

Results tend to be robust to...I ...14 different specification of the RD polynomial Robustness

I ...controls for ethnicityI ...the inclusion of metropolitan CuscoI ...the exclusion of districts falling along portions of the

boundary formed by riversI ...accounting for differential rates of migration today

Other Robustness




Baseline (pre-mita) covariates





Mining Mita↓







Land tenure and labor systems





Mining Mita↓







Public goods





Mining Mita↓







Proximate determinants of household consumption




Outline




Results





Conclusion

I This paper exploits exogenous variation in the assignment ofthe mita to identify channels through which it influencescontemporary economic development

I Its long-run effects lower household consumption by 25% andincrease stunting in children by around 6 percentage points

I Land tenure, public goods and market participation arechannels through which its impacts persist




Literature

I Governance and Long-Run DevelopmentAcemoglu, D.; C. Garcia-Jimeno & J. A. Robinson (2015): State capacity and economic development: Anetwork approach. The American Economic Review, 105, 2364-2409Becker, S. O.; K. Boeckh; C. Hainz & L. Woessmann (2016): The empire is dead, long live the empire!Long-run persistence of trust and corruption in the bureaucracy. The Economic Journal, 126, 40-74Gennaioli, N. & I. Rainer (2007): The modern impact of precolonial centralization in Africa. Journal ofEconomic Growth 12, 185-234Michalopoulos, S. & E. Papaioannou (2013), Pre-Colonial Ethnic Institutions and Contemporary AfricanDevelopment. Econometrica, 81, 113-152

I RD DesignHahn, J., P. Todd, & W. Van der Klaauw (2001): Identification and estimation of treatment effects witha regression-discontinuity design. Econometrica, 69, 201-209Imbens, G. W. & T. Lemieux (2008): Regression discontinuity designs: A guide to practice. Journal ofEconometrics, 142, 615-635Lee, D. S. & T. Lemieux (2010), Regression Discontinuity Designs in Economics. Journal of EconomicLiterature, 48, 281-355McCrary, J. (2008) Manipulation of the running variable in the regression discontinuity design: A densitytest. Journal of Econometrics, 142, 698-714




Literature

I Multidimensional RD DesignBayer, P; F. Ferreira, & R. McMillan (2007): A unified framework for measuring preferences for schoolsand neighborhoods. Journal of Political Economy, 115(4), 588-638Black, S. E. (1999) Do better schools matter? Parental valuation of elementary education. QuarterlyJournal of Economics, 114(2), 577-599Dell, M., N. Lane, P. Querubin (2017): The Historical State, Local Collective Action, and EconomicDevelopment in Vietnam. Working PaperKeele, L., S. Lorch, M. Passarella, D. Small & R. Titiunik (2016): An Overview of GeographicallyDiscontinuous Treatment Assignments with an Application to Children’s Health Insurance. Advances inEconometrics, 38Keele, L. & R. Titiunik (2016): Natural experiments based on geography. Political Science Research andMethods, 4, 65-95




Discussion

1. Density test (see McCrary, 2008) as an additional support forthe assumption of no selective sorting around the boundaryDensity plots




Discussion

2. → Continuity assumption: A portion of the mita’s bordercoincides with today’s border between the Aruepica and Cuscoregions → the first-level administrative unit in PeruPeru administrative divisions

Lee&Lemieux (2010): “What are all the things differingbetween the two regions other than the treatment ofinterest?”

I According to the Organic Law of Regional Governments, theresponsibilities of regional governments include planning regionaldevelopment, executing public investment projects, promoting economicactivities, and managing public property

→ The estimates could be capturing the effect of different local policies→ Placebo: test other regional border




You can access the paper’s replication files at:https://scholar.harvard.edu/dell/publications/persistent-effects-

perus-mining-mita




Questions/comments?




Appendix




Mita’s region and today’s borders Back




Conley standard errors Back

I Geographic data such as elevation, slope, terrain ruggedness(and weather) are spatially correlated → they are correlated atphysically nearby grids

I Standard errors are corrected for an unknown-form serialdependence based on location (see Conley, T. (1999): GMMestimation with cross sectional dependence, Journal ofEconometrics, 92, 1-45)

I Stata command: x_ols

x_ols coordlist cutlist dep regressors, coord() xreg()I download ADO file at:

http://economics.uwo.ca/people/conley_docs/code_to_download_gmm.html




Specification tests




Specification tests Back




Other robustness checks Back




Density Plots - consumption sample (left) and childrensample (right) Back

0.0

05.0

1.0

15.0

2D

ensi

ty

-100 -50 0 50 100Distance to border in km

0.0

1.0

2.0

3.0

4D

ensi

ty

-100 -50 0 50 100distance to border in km




Mita’s region and today’s borders Back



the persistent effects of peru's mining mita - by melissa dell, … · 2017-07-18 ·...

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