THE PERSISTENT EFFECTS OF PERU'S MINING MITA Melissa Dell, Econometrica (2010)

September 25, 2018, Institutions and Development

Tim Deisemann

Summary

• What long-run impacts did the mining mita, a forced labour system instituted by the Spanish government in Peru and Bolivia, have? (1573-1812)

• This paper examines said question by the use of a geographic (multidimensional) regression discontinuity (RD) design across the mitaboundary

§ It identifies statistically significant effects on:§ Contemporary living standards§ Channels of persistence (land tenure and public goods provision)

Outline

• Historical Background

• Data and Methodology

• Results

• Conclusion and Discussion

Outline

• Historical Background

• Data and Methodology

• Results

• Conclusion and Discussion

Historical background: The Mit’a

• Mandatory public service in the society of the Inca Empire• Unpaid, unfree, intermittent labour for public purposes (instead of

taxation)• Encomienda system: Modification and intensification by the Spanish

colonial government

Historical Background: The mita

• Potosí mines discovered in 1545 → largest deposits of silver in theSpanish Empire

• Huancavelica mines → provided mercury to refine thesilver ore

• The mining mita: indigenious villages nearby were required toprovide one-seventh of their adult male population as mita labourers• exploitation of the forced labour mit‘a of the prehispanic system of the mita

• Subjected region: constant from 1578 onwards

The mita boundary and assignment • The Spanish only

required some of thedistrics inside of Peru‘sborder to contribute tothe mita• Administrative and

enforcement costs ofcoercing labour• Two criteria for the

assignment:1. Distance to the mines2. Elevation

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

The mita boundary

Microeconometrics, Summer Semester 2017 Eleonora GuarnieriThe Persistent Effects of Peru’s Mining Mita 9

Outline

• Historical Background

• Data and Methodology

• Results

• Conclusion and Discussion

Channels of persistence and outcomes

• Mining mita:➤ Land tenure and labour systems ➤ Public goods

• Long-run development:➤ Household consumption➤ Stunting in children

Methodology

• How do we identify the mita effect on the aforementioned outcomes?• Method of choice: Regression Discontinuity Design• Can we simply compare mita to non-mita districts?

Multidimensional Regression Discontinuity

• In this context, the assignment variable X for the RDD is “geography”• Mita treatment is a function of known covariates: longitude and

latitude• The border between mita and non-mita areas forms a

multidimensional discontinuity in longitude-latitude space• Idea: compare “mita” to “non-mita” households situated close

enough to the border

Estimation Strategy for the Regression

• Range: • 100 km• 75 km• 50 km• 25 km

• RD polynomial: • Cubic in latitude and longitude (preferred speciation)• Cubic in distance to Potosí (single dimension)• Cubic in distance to the mita boundary (single dimension)

RDD: Identifying assumptions

1. Continuity: all relevant factors besides treatment vary smoothly at the mita boundary• Elevation• terrain ruggedness• soil fertility• Rainfall• Ethnicity• preexisting settlement patterns• local tax rates (1572), allocation of tribute revenues (1572) • …

RDD: No selective sorting

2. No selective sorting across the treatment threshold:• Did the mita directly lead to substantial migration of productive

individuals? (leading to a larger indirect effect)• Migration may be one channel of persistence

• Low migration rates in the past 130 years + constant aggregate population distribution• No statistically significant recent differences in rates of migration

between mita and non-mita districts (1993 census)• Migration out of mita districts during the mita period may have been

substantial

Outline

• Historical Background

• Data and Estimation Method

• Results

• Conclusion and Discussion

Channels of persistence and outcomes

• Mining mita:➤ Land tenure and labour systems ➤ Public goods

• Long-run development:➤ Household consumption➤ Stunting in children

RD Plots 1880 MELISSA DELL

Figure 2. - Plots of various outcomes against longitude and latitude. See the text for a de- tailed description.

The results can be seen graphically in Figure 2. Each subfigure shows a district-level scatter plot for one of the paper's main outcome variables. These plots are the three-dimensional analogues to standard two-dimensional RD plots, with each district capital's longitude on the x axis, its latitude on the y axis, and the data value for that district shown using an evenly spaced mono- chromatic color scale, as described in the legends. When the underlying data are at the microlevel, I take district-level averages, and the size of the dot in- dicates the number of observations in each district. Importantly, the scaling on these dots, which is specified in the legend, is nonlinear, as otherwise some would be microscopic and others too large to display. The background in each plot shows predicted values, for a finely spaced grid of longitude-latitude co-

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RD Plots

1880 MELISSA DELL

Figure 2. - Plots of various outcomes against longitude and latitude. See the text for a de- tailed description.

The results can be seen graphically in Figure 2. Each subfigure shows a district-level scatter plot for one of the paper's main outcome variables. These plots are the three-dimensional analogues to standard two-dimensional RD plots, with each district capital's longitude on the x axis, its latitude on the y axis, and the data value for that district shown using an evenly spaced mono- chromatic color scale, as described in the legends. When the underlying data are at the microlevel, I take district-level averages, and the size of the dot in- dicates the number of observations in each district. Importantly, the scaling on these dots, which is specified in the legend, is nonlinear, as otherwise some would be microscopic and others too large to display. The background in each plot shows predicted values, for a finely spaced grid of longitude-latitude co-

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RD Plots PERSISTENT EFFECTS OF MITA 1881

Figure 2.- Continued.

ordinates, from a regression of the outcome variable under consideration on a cubic polynomial in longitude-latitude and the mita dummy. In the typical RD context, the predicted value plot is a two-dimensional curve, whereas here it is a three-dimensional surface, with the third dimension indicated by the color gradient.21 The shades of the data points can be compared to the shades of the predicted values behind them to judge whether the RD has done an adequate job of averaging the data across space. The majority of the population in the region is clustered along the upper segment of the mita boundary, giving these

21 Three-dimensional surface plots of the predicted values are shown in Figure A2 in the Sup- plemental Material, and contour plots are available upon request.

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RD Plots

PERSISTENT EFFECTS OF MITA 1881

Figure 2.- Continued.

ordinates, from a regression of the outcome variable under consideration on a cubic polynomial in longitude-latitude and the mita dummy. In the typical RD context, the predicted value plot is a two-dimensional curve, whereas here it is a three-dimensional surface, with the third dimension indicated by the color gradient.21 The shades of the data points can be compared to the shades of the predicted values behind them to judge whether the RD has done an adequate job of averaging the data across space. The majority of the population in the region is clustered along the upper segment of the mita boundary, giving these

21 Three-dimensional surface plots of the predicted values are shown in Figure A2 in the Sup- plemental Material, and contour plots are available upon request.

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Robustness

• Results are robust to:• Different specifications of the RD polynomial • Controls for ethnicity• The inclusion of metropolitan Cusco• The exclusion of districs falling along portions of the boundary formed by

rivers• Accounting for differential rates of migration today

Outline

• Historical Background

• Data and Estimation Method

• Results

• Conclusion and Discussion

Conclusion

• Dell’s paper uses exogenous variation in the assignment of the mita to identify channels through which it influences today’s economic development• Its long-run effects lower household consumption by 25% and

increase stunting in children by around 6%• Land tenure and public goods are channels through which its impacts

persist

Questions & Comments?

Multidimensional RDD: Continuity (I) 1872 M

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Multidimensional RDD: Continuity (II) PERSIS

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Land tenure and labour systems PERSISTENT EFFECTS OF MITA 1889

TABLE VI

Land Tenure and Labor Systems3

Dependent Variable

Percent of

Haciendas per Rural Tributary Percent of Rural 1000 District Population in Population in

Haciendas per Residents Haciendas Haciendas Land Gini District in 1689 in 1689 in ca. 1845 in 1940 in 1994

Panel A. Cubic Polynomial in Latitude and Longitude Mita -12.683*** -6.453** -0.127* -0.066 0.078

(3.221) (2.490) (0.067) (0.086) (0.053) R2 0.538 0.582 0.410 0.421 0.245

Panel B. Cubic Polynomial in Distance to Potosi Mita -10.316*** -7.570*** -0.204** -0.143*** 0.107***

(2.057) (1.478) (0.082) (0.051) (0.036) R2 0.494 0.514 0.308 0.346 0.194

Panel C. Cubic Polynomial in Distance to Mita Boundary Mita -11.336*** -8.516*** -0.212*** -0.120*** 0.124***

(2.074) (1.665) (0.060) (0.045) (0.033) R2 0.494 0.497 0.316 0.336 0.226

Geo. controls yes yes yes yes yes Boundary F.E.s yes yes yes yes yes Meandep.var. 6.500 5.336 0.135 0.263 0.783 Observations 74 74 81 119 181

aThe unit of observation is the district. Robust standard errors are in parentheses. The dependent variable in col- umn 1 is haciendas per district in 1689 and in column 2 is haciendas per 1000 district residents in 1689 (Villanueva Urteaga (1982)). In column 3 it is the percentage of the district's tributary population residing in haciendas ca. 1845 (Peralta Ruiz (1991)), in column 4 it is the percentage of the district's rural population residing in haciendas in 1940 (Direction de Estadistica del Peru (1944)), and in column 5 it is the district land gini (INEI (1994)). Panel A includes a cubic polynomial in the latitude and longitude of the observation's district capital, panel B includes a cubic polyno- mial in Euclidean distance from the observation's district capital to Potosi, and panel C includes a cubic polynomial in Euclidean distance to the nearest point on the mita boundary. All regressions include geographic controls and bound- ary segment fixed effects. The samples include districts whose capitals are less than 50 km from the mita boundary. Column 3 is weighted by the square root of the district's rural tributary population and column 4 is weighted by the square root of the district's rural population. 58% of the observations are in mita districts in columns 1 and 2, 59% in column 3, 62% in column 4, and 66% in column 5. Coefficients that are significantly different from zero are denoted by the following system: *10%, **5%, and ***1%.

In Table VI, column 1 (number of haciendas per district) and column 2 (num- ber of haciendas per 1000 district residents) show a very large mita effect on the concentration of haciendas in the 17th century, of similar magnitude and highly significant across specifications.29 The median coefficient from column 1, con-

29Given the mita's role in provoking population collapse (Wightman (1990, p. 72)), the latter measure is likely endogenous, but nevertheless provides a useful robustness check.

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