Evidence from the Repeal of the Corn Laws

The Distributional Consequences of Trade: Evidence from the Repeal of the Corn Laws

Stephan Heblich, Stephen J. Redding and Yanos Zylberberg

1 / 46 • We provide new theory and evidence on these distributional consequences using the 1846 repeal of the Corn Laws in Britain – Opened British markets to the New World “grain invasion” in the second half of the 19th century (especially after the end of American Civil War) – Distributional conict between urban proletariat and rural aristocracy • Research on the Corn Laws has traditionally emphasized the distributional eects across factors or industries – Heckscher-Ohlin and Specic-Factors models • We provide evidence on distributional consequences across dierent geographical areas within England and Wales – New, spatially-disaggregated data on population, employment and land values for around 11,000 parishes from 1801-1911 – Exogenous trade exposure measure based on agroclimatic conditions – Quantitative spatial model to evaluate the impact of this trade shock on industrialization, urbanization and income distribution – Quantify role of labor mobility for the distributional eects of trade

Motivation • Resurgence of interest in the distributional consequences of trade – China shock: Autor, Dorn & Hanson (2013) and Pierce & Schott (2016)

2 / 46 • Research on the Corn Laws has traditionally emphasized the distributional eects across factors or industries – Heckscher-Ohlin and Specic-Factors models • We provide evidence on distributional consequences across dierent geographical areas within England and Wales – New, spatially-disaggregated data on population, employment and land values for around 11,000 parishes from 1801-1911 – Exogenous trade exposure measure based on agroclimatic conditions – Quantitative spatial model to evaluate the impact of this trade shock on industrialization, urbanization and income distribution – Quantify role of labor mobility for the distributional eects of trade

Motivation • Resurgence of interest in the distributional consequences of trade – China shock: Autor, Dorn & Hanson (2013) and Pierce & Schott (2016) • We provide new theory and evidence on these distributional consequences using the 1846 repeal of the Corn Laws in Britain – Opened British markets to the New World “grain invasion” in the second half of the 19th century (especially after the end of American Civil War) – Distributional conict between urban proletariat and rural aristocracy

2 / 46 • We provide evidence on distributional consequences across dierent geographical areas within England and Wales – New, spatially-disaggregated data on population, employment and land values for around 11,000 parishes from 1801-1911 – Exogenous trade exposure measure based on agroclimatic conditions – Quantitative spatial model to evaluate the impact of this trade shock on industrialization, urbanization and income distribution – Quantify role of labor mobility for the distributional eects of trade

2 / 46 Motivation • Resurgence of interest in the distributional consequences of trade – China shock: Autor, Dorn & Hanson (2013) and Pierce & Schott (2016) • We provide new theory and evidence on these distributional consequences using the 1846 repeal of the Corn Laws in Britain – Opened British markets to the New World “grain invasion” in the second half of the 19th century (especially after the end of American Civil War) – Distributional conict between urban proletariat and rural aristocracy • Research on the Corn Laws has traditionally emphasized the distributional eects across factors or industries – Heckscher-Ohlin and Specic-Factors models • We provide evidence on distributional consequences across dierent geographical areas within England and Wales – New, spatially-disaggregated data on population, employment and land values for around 11,000 parishes from 1801-1911 – Exogenous trade exposure measure based on agroclimatic conditions – Quantitative spatial model to evaluate the impact of this trade shock on industrialization, urbanization and income distribution – Quantify role of labor mobility for the distributional eects of trade

2 / 46 • Substantial estimated treatment eects of the grain invasion following the Repeal of the Corn Laws for high-wheat suitability regions – Population (around −20 percent) – Migration (track individuals over time) – Structural transformation – Urbanization – Rateable values (land and buildings)

• Consistent with historical narrative of “great agricultural depression” • Develop a spatial equilibrium model of trade and production to account for these empirical ndings qualitatively and quantitatively

3 / 46 • Consistent with historical narrative of “great agricultural depression” • Develop a spatial equilibrium model of trade and production to account for these empirical ndings qualitatively and quantitatively

Main Findings • Key advantage of empirical setting is the dierence in agroclimatic conditions between Western and Eastern parts of England and Wales – Warm ocean current of North Atlantic Drift and prevailing SW winds – Western areas have greater cloud cover, more precipitation and lower average temperatures, and also more mountainous – Western grazing (pastoral) and Eastern corn (arable) locations – Exogenous exposure measure based on agroclimatic suitability for wheat • Substantial estimated treatment eects of the grain invasion following the Repeal of the Corn Laws for high-wheat suitability regions – Population (around −20 percent) – Migration (track individuals over time) – Structural transformation – Urbanization – Rateable values (land and buildings)

3 / 46 • Develop a spatial equilibrium model of trade and production to account for these empirical ndings qualitatively and quantitatively

• Consistent with historical narrative of “great agricultural depression”

3 / 46 Main Findings • Key advantage of empirical setting is the dierence in agroclimatic conditions between Western and Eastern parts of England and Wales – Warm ocean current of North Atlantic Drift and prevailing SW winds – Western areas have greater cloud cover, more precipitation and lower average temperatures, and also more mountainous – Western grazing (pastoral) and Eastern corn (arable) locations – Exogenous exposure measure based on agroclimatic suitability for wheat • Substantial estimated treatment eects of the grain invasion following the Repeal of the Corn Laws for high-wheat suitability regions – Population (around −20 percent) – Migration (track individuals over time) – Structural transformation – Urbanization – Rateable values (land and buildings)

3 / 46 Related Literature • Local labor market eects of international trade shocks – Topalova (2010), Autor, Dorn & Hanson (2013, 2016), Autor, Dorn, Hanson & Song (2014), Kovak (2013), Kovak & Dix-Carneiro (2015), Choi et al. (2020) • Distributional consequences of international trade – Stolper & Samuelson (1941), Jones (1971), Mussa (1974) • Urbanization and structural transformation – Matsuyama (1992), Uy, Yi & Zhang (2013), Bustos, Caprettini & Ponticelli (2016, 2020), Fajgelbaum & Redding (2018), Eckert & Peters (2018) • Quantitative spatial models – Redding & Sturm (2008), Allen & Arkolakis (2014), Redding (2016), Ramondo, Rodríguez-Clare & Saborío-Rodríguez (2016), Redding & Rossi-Hansberg (2017), Desmet, Nagy & Rossi-Hansberg (2018), Caliendo, Parro, Rossi-Hansberg & Sarte (2018), Galle, Rodríguez-Clare & Yi (2018), Allen & Donaldson (2018), Monte, Redding & Rossi-Hansberg (2018), Fajgelbaum & Redding (2018), Caliendo, Parro & Dvorkin (2019), Fajgelbaum, Morales, Suárez Serrato & Zidar (2019) • Economic history of the corn laws, agricultural depression, industrial revolution, and decline of aristocracy in 19th-century Britain – Graham (1892) Nicholson (1904) Barnes (1930), Irwin (1989), Williamson (1990), O’Rourke (1997), Howe (1998), Schonhardt-Bailey (2006), Sharp (2009), Sharp & Weisdorf (2013), Cannadine (2019), Caprettini & Voth (2019) 4 / 46 Outline

• Historical Background

• Data

• Reduced-form Evidence

• Theoretical Model

• Quantitative Evidence

5 / 46 • Population and economic growth with industrial revolution after 1760 – Britain systematic net importer of wheat (mostly from Prussia/Russia) • Revolutionary and Napoleonic Wars from 1792-1815 – Loss of wheat imports from continental Europe (blockade) – Large rise domestic wheat price and wheat production • Corn Law of 1815 motivated by fears of agricultural crisis – Prohibited wheat imports when price were under 82.5 shillings per quarter (ports largely closed to wheat imports until 1825) – Political conict between wheat consumers (urban proletariat and Anti-Corn Law League) and producers (rural aristocracy) • Repeal of Corn Laws 1846 by Robert Peel to stem political discontent • Following American Civil War of 1861-65, new transport technologies of steamship and railroad led to new-world “grain invasion” – Repeal ensured that British markets remain open – “Great agricultural depression” after 1870

6 / 46 • Revolutionary and Napoleonic Wars from 1792-1815 – Loss of wheat imports from continental Europe (blockade) – Large rise domestic wheat price and wheat production • Corn Law of 1815 motivated by fears of agricultural crisis – Prohibited wheat imports when price were under 82.5 shillings per quarter (ports largely closed to wheat imports until 1825) – Political conict between wheat consumers (urban proletariat and Anti-Corn Law League) and producers (rural aristocracy) • Repeal of Corn Laws 1846 by Robert Peel to stem political discontent • Following American Civil War of 1861-65, new transport technologies of steamship and railroad led to new-world “grain invasion” – Repeal ensured that British markets remain open – “Great agricultural depression” after 1870

6 / 46 • Corn Law of 1815 motivated by fears of agricultural crisis – Prohibited wheat imports when price were under 82.5 shillings per quarter (ports largely closed to wheat imports until 1825) – Political conict between wheat consumers (urban proletariat and Anti-Corn Law League) and producers (rural aristocracy) • Repeal of Corn Laws 1846 by Robert Peel to stem political discontent • Following American Civil War of 1861-65, new transport technologies of steamship and railroad led to new-world “grain invasion” – Repeal ensured that British markets remain open – “Great agricultural depression” after 1870

6 / 46 • Repeal of Corn Laws 1846 by Robert Peel to stem political discontent • Following American Civil War of 1861-65, new transport technologies of steamship and railroad led to new-world “grain invasion” – Repeal ensured that British markets remain open – “Great agricultural depression” after 1870

Historical Background • Origins of the corn laws date back to laws of 1463 and 1670 – Sliding scale of import duties that part of regulations to stabilize the price of bread as the main source of sustenance – Initially, mostly self-sucient in wheat and suspended in scarcity • Population and economic growth with industrial revolution after 1760 – Britain systematic net importer of wheat (mostly from Prussia/Russia) • Revolutionary and Napoleonic Wars from 1792-1815 – Loss of wheat imports from continental Europe (blockade) – Large rise domestic wheat price and wheat production • Corn Law of 1815 motivated by fears of agricultural crisis – Prohibited wheat imports when price were under 82.5 shillings per quarter (ports largely closed to wheat imports until 1825) – Political conict between wheat consumers (urban proletariat and Anti-Corn Law League) and producers (rural aristocracy)

6 / 46 • Following American Civil War of 1861-65, new transport technologies of steamship and railroad led to new-world “grain invasion” – Repeal ensured that British markets remain open – “Great agricultural depression” after 1870

6 / 46 Historical Background • Origins of the corn laws date back to laws of 1463 and 1670 – Sliding scale of import duties that part of regulations to stabilize the price of bread as the main source of sustenance – Initially, mostly self-sucient in wheat and suspended in scarcity • Population and economic growth with industrial revolution after 1760 – Britain systematic net importer of wheat (mostly from Prussia/Russia) • Revolutionary and Napoleonic Wars from 1792-1815 – Loss of wheat imports from continental Europe (blockade) – Large rise domestic wheat price and wheat production • Corn Law of 1815 motivated by fears of agricultural crisis – Prohibited wheat imports when price were under 82.5 shillings per quarter (ports largely closed to wheat imports until 1825) – Political conict between wheat consumers (urban proletariat and Anti-Corn Law League) and producers (rural aristocracy) • Repeal of Corn Laws 1846 by Robert Peel to stem political discontent • Following American Civil War of 1861-65, new transport technologies of steamship and railroad led to new-world “grain invasion” – Repeal ensured that British markets remain open – “Great agricultural depression” after 1870

6 / 46 Outline

• Historical Background

• Data

• Reduced-form Evidence

• Theoretical Model

• Quantitative Evidence

7 / 46 Data

• Parish-level Population Census data for England and Wales – Around 11,000 parishes, aggregated into poor law unions and counties – Population by residence from 1801-1911 – Employment by occupation from 1851 onwards • Individual-level population census data – Name match individuals across population census waves (migration) – Data for 1851, 1861 and every census decade from 1881-1901 • Rateable value data – Rateable value data by parish from 1815-1896 – Market rental value of land and buildings after deducting expenses for repair and maintenance

• Domestic prices, import values and quantities of wheat • Global Agro-Ecological Zones (GAEZ) crop suitability, endowments of other natural resources (e.g. coal and iron), urban & rural status etc

8 / 46 Period Obs Mover Birth County 1851 17,563,681 0.40 1861 19,582,103 0.42 1881 25,954,290 0.41 1891 28,902,486 0.44 1901 31,909,682 0.45

Individual-Level Data • Follow closely the name matching algorithms used to construct linked population census data in US (Abramitzky et al. 2020) – Match on name, year of birth, and county of birth for men Period Obs Mover Mover Mover Matched Parish Reg District County 11,425 575 58 1851-1861 5,323,072 0.39 0.32 0.19 1861-1881 3,686,306 0.56 0.48 0.30 1881-1891 7,527,280 0.38 0.31 0.20 1891-1901 12,151,542 0.47 0.32 0.19 1861-1901 1,003,442 0.75 0.59 0.35

9 / 46 Individual-Level Data • Follow closely the name matching algorithms used to construct linked population census data in US (Abramitzky et al. 2020) – Match on name, year of birth, and county of birth for men Period Obs Mover Mover Mover Matched Parish Reg District County 11,425 575 58 1851-1861 5,323,072 0.39 0.32 0.19 1861-1881 3,686,306 0.56 0.48 0.30 1881-1891 7,527,280 0.38 0.31 0.20 1891-1901 12,151,542 0.47 0.32 0.19 1861-1901 1,003,442 0.75 0.59 0.35

Period Obs Mover Birth County 1851 17,563,681 0.40 1861 19,582,103 0.42 1881 25,954,290 0.41 1891 28,902,486 0.44 1901 31,909,682 0.45

9 / 46 Outline

• Historical Background

• Data

• Reduced-form Evidence

• Theoretical Model

• Quantitative Evidence

10 / 46 Corn Laws & Grain Invasion

11 / 46 Price of Wheat

Annual Price of Wheat (Pounds) 6 5 4 3 Price (Pounds) 2 1 1650 1700 1750 1800 1850 1900 Year

• Annual average price of wheat per quarter at Eton (Lord Ernle 1912) 12 / 46 1.3 Consumption and Production

The historical records suggests that the grain invasion was driven by an inux of cheap grain from the new world following the innovation of steamships and expanidng railway networks. Figure5 combines data on British wheat consumption, production and imports (in million quarters) over the period 1831–1911 to illustrate the timing and extent of the grain invasion. The graph shows a dip in domestic production following the grain invasion and it also shows that this was not driven by decreasing wheat consumption but rising imports. Before the invention of the steamship opened the British market to new world wheat, imports came predominantly from the rest of the world (ROW) which is mainly Prussia and Russia. After the Civil War, over the period between 1873 and 1882, the U.S. experienced “the most remarkable period that has been seen in American wheat growing” Veblen(1892). This rise was aided by the invention of railways and especially steam ships.2 During that time, the U.S. supplanted Russia as main supplier of wheat to Western Europe and became world leader. The U.S. remained in this position until poor harvests in 1904 and 1905 forced Britain to source other suppliers. Imports from Argentina, India and Russia lled the gap and subsequently, the U.S. lost their leadership in the market for wheat because steadily increasing domestic demand ate up the surplus wheat.

Figure 5: Wheat Consumption, Production and Imports in million quarters UK Consumption, Production and Imports of Wheat 30 30 25 25

ROW

ARG 20 20

US ROW

15 ROW AUS 15

ROW

US IND

10 US 10

ROW US Wheat Production (line) Wheat Consumption (bar)

ROW US CAN 5 5

US ROW RUS ROW ROW 0 0 1831 1841 1851 1861 1871 1881 1891 1901 1911 Year

13 / 46

Notes: The bars and the connecting black line show the overall wheat consumption in million quarters over the period 1831–1911. The corresponding scale is on the left. Shadings of the bars refer to import shares from Commonwealth countries (CW) which comprise Australia, Canada and India; the rest of the world (ROW) which is mostly Russia and Prussia; the United States (US) and Argentina (ARG). The height of the white shading of the bar corresponds to domestic production which is also depicted by the red line (with the corresponding scale on the right side).

2Transporting wheat was particularly cheap because grain was carried at very low cost since it served as ballast. With the adoption of water ballast tanks, this changed.

7 Wheat Suitability

14 / 46 Wheat Suitability

(a) Caird (1852) (b) Wheat Suitability (UN GAEZ)

15 / 46 Average Wheat Suitability 1 .45

Wheat .4 .9 .35 .8 .3 Normalized Grass Suitability Normalized Wheat Suitability .25

.7 Grass 100 200 300 400 500 600 700 Easting

• Eastings of British National Grid (London Guildhall : 532) • Vertical line: Avg. easting of Caird line separating grazing and corn counties

16 / 46 Structural Transformation

17 / 46 Laborers and Farmers 1851 & 1911 .5 .5 .4 .4 .3 .3 .2 .2 Share Farmer Share Laborer .1 .1

Lab 1851 Farm 1851 Lab 1911 Farm 1911 0 0 100 200 300 400 500 600 Easting (km)

18 / 46 Change in Employment Shares Share Agriculture Share Laborers .3 .3 .25 .25 .2 .2 .15 .15 .1 .1 .05 .05 0 0 Share Laborer, 1851-1911 ∆ Share Agriculture, 1851-1911 -.05 -.05 ∆ -.1 -.1 -.15 -.15 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 Normalized Wheat Suitability Normalized Wheat Suitability

Share Manufacturing Share Services .3 .3 .25 .25 .2 .2 .15 .15 .1 .1 .05 .05 0 0 Share Services, 1851-1911 ∆ -.05 -.05 Share Manufacturing, 1851-1911 ∆ -.1 -.1 -.15 -.15 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 Normalized Wheat Suitability Normalized Wheat Suitability

19 / 46 Arable Land Reallocation Away from Wheat • County data from the agricultural census from the 1870s onwards

Change in Wheat Share of Land Area 1878-1901 0 -.02 -.04 -.06 Change in Wheat Share 1878-1901 Wheat Share in Change -.08 -.1 0 .05 .1 .15 .2 .25 Wheat Share 1878 Note: Slope coefﬁcient: -0.3312; standard error: 0.0327; R-squared: 0.7679.

20 / 46 Arable Land Reallocation to Permanent Pasture • County data from the agricultural census from the 1870s onwards

Change in Permanent Pasture Share of Land Area 1878-1901 .15 .1 .05 0 -.05 Change in Permanent Pasture Share 1878-1901 Share Pasture Permanent in Change -.1 0 .05 .1 .15 .2 .25 Wheat Share 1878 Note: Slope coefﬁcient: 0.3208; standard error: 0.0922; R-squared: 0.1620.

21 / 46 Event-Study Specications

22 / 46 Reduced-Form Regressions • “Dierence-in-dierences” regression specication

τ=T ln Popit = ηi + ∑ βτ (Iτ × Wheati) + (Xi × δt ) + dt + uit τ=−T • Parishes i, calendar year t and treatment year τ • Iτ: Indicator for treatment year τ (1841 excluded) • Wheati: Indicator for parishes with above average wheat suitability • Xit: Controls for – Distance to nearest of 76 industrial centers × year – Distance to London × year – Distance to nearest coaleld × year – Urban indicator (based on 1801 population density) × year – Wales indicator × year • Standard errors clustered by poor law union – Robustness tests using Heteroscedasticity and Autocorrelation Consistent (HAC) standard errors following Conley (1999) and standard errors clustered by 100 bins wheat suitability and poor law union

23 / 46 High Wheat Suitability .1 0 -.1 -.2 Estimated Effect on Log Population -.3 1801 1811 1821 1831 1841 1851 1861 1871 1881 1891 1901 1911 Census Year

24 / 46 Wheat Suitability Terciles .1 .05 0 -.05 -.1 Estimated Effect on Log Population -.15

1801 1811 1821 1831 1841 1851 1861 1871 1881 1891 1901 1911 Census Year

25 / 46 Placebo: High Grass Suitability .1 0 -.1 Estimated Effect on Log Population -.2

1801 1811 1821 1831 1841 1851 1861 1871 1881 1891 1901 1911 Census Year

26 / 46 Rateable Values .1 0 -.1 -.2 Estimated Effect on Log Rateable Value -.3 1810 1820 1830 1840 1850 1860 1870 1880 Census Year

27 / 46 Migration Individual-Level Data

28 / 46 Migration by Distance 1 .5 Migration incidence (cumulative) 0 10 kms 50 kms 200 kms Distance (log)

29 / 46 Out Migration & Wheat Suitability

.6 1851-61 1861-81, 81-91, 91-01 .4 Migration probability .2

.7 .8 .9 1 Wheat suitability

30 / 46 Occupation Change & Wheat Suitability

.8 1851-61 1861-81, 81-91, 91-01 .6 .4 Probability of occupational change .2 .7 .8 .9 1 Wheat suitability

31 / 46 Industry Change & Wheat Suitability

.6 1851-61 1861-81, 81-91, 91-01 .4 Probability of industry change .2 .7 .8 .9 1 Wheat suitability

32 / 46 Outline

• Historical Background

• Data

• Reduced-form Evidence

• Theoretical Model

• Quantitative Evidence

33 / 46 Model Outline

• World economy consists of many locations indexed by i, n, m ∈ N – Subset of locations are in England & Wales: N E – Subset of locations are foreign countries: N R • Two types of agents: workers and landlords – Landlords immobile and earn income and consume where born – Workers mobile within countries but immobile across countries (can be extended to allow for mobility across countries) • Each worker can migrate from location i to n within a country by incurring a bilateral migration cost • Economic activity takes place in a number of sectors k, j, ` ∈ K, including agriculture, manufacturing and services • Locations can dier in amenities, sectoral productivities and bilateral trade and migration costs • We use the model to examine the impact of an international trade shock that is concentrated in a sector (grain invasion) across locations that dier in sectoral specialization

34 / 46 Preferences

• Preferences of worker ψ born in location i who chooses to work in sector k in location n

k k bn (ψ) wn j H uni (ψ) = ! , ∑ α + α = 1 αj j αH j∈K κni ∏ Pn Qn j∈K

k • with idiosyncratic amenity bn (ψ), wage wn, bilateral migration cost j κni, consumption goods price indexes Pn, and price of oor space Qn • Match-specic amenities drawn independently across individuals, locations and sectors from the following Fréchet distribution:

k −e k −(b/Bni) Fn (b) = e , e > 1

k • Bni determines average amenities for location n and sector k • Landlords same preferences but immobile

35 / 46 Production

• Goods are produced using labor and land under perfect competition • Goods can be traded between locations subject to iceberg variable k trade costs τni ≥ 1 • Cost to a consumer in location n of purchasing good ϑ in sector k from location i

βk 1−βk τk w Q k (ϑ) = ni i i , 0 < βk < 1, pni k ai (ϑ)

• where sectors dier in factor intensity (βk) k • Idiosyncratic productivities ai (ϑ) are drawn independently for each good ϑ, sector k and location i:

k −θ k −(a/Ai ) Gi (a) = e , θ > 1,

36 / 46 Choice of Sector and Location • The probability that a worker born in location i chooses sector k and location n exhibits a gravity equation: ! !−e αj k e j αH Bniwn κni ∏ Pn Qn j∈K k = λni ! !−e αj ` e j αH ∑ ∑ Bmiwm κmi ∏ Pm Qm `∈K m∈N E j∈K • Total employment in each location n k ¯ Ln = ∑ ∑ λniLi k∈K i∈N E • Expected utility for workers born in location i

1  ! !−e e e j ` j α αH u¯i = Ei [u] = δ  ∑ ∑ Bmiwm κmi ∏ Pm Qm  `∈K m∈N E j∈K

37 / 46 Market Clearing

• Land market clearing

H QnHn = 1 − α QnHn " # 1 − βk + 1 − αH (1 + ) k + k ∑ dn wnLn ∑ k wnLn k∈K k∈K β

• Goods market clearing

k k k k k Yi = ∑ πniα Xn + ∑ πniα Xn n∈N E n∈N R

38 / 46 Outline

• Historical Background

• Data

• Reduced-form Evidence

• Theoretical Model

• Quantitative Evidence

39 / 46 Wages : Step 1

k • Using data on employment (Lnt) and rateable values (Qnt), recover wages (wnt) from land market clearing

Qnt wnt = H − k 1−α (1 + ) k + 1 β k αH dnt ∑ Lnt ∑ αH βk Lnt k∈K k∈K • where payments for commercial and residential oor space are constant multiples of labor payments

40 / 46 Price of Floor Space : Step 2

• Following Saiz (2010), assume constant oor space supply elasticity (µ)

µ Hnt = hQntKn

• Recover price (Qnt) and supply (Hnt) of oor space from observed rateable values (Qnt) and geographical land area (Kn)

1 + Qnt 1 µ Qnt = hKn µ + Qnt 1 µ Hnt = hKn hKn

41 / 46 Productivity : Step 3

k • Recover productivity (Ai ) from goods market clearing " # w Lk 1 − αH 1 − βk i i = π∗ ζkαk (1 + d ) 1 + L + Lk w βk ∑ ni n n αH n ∑ αH βk n n n∈N E k∈K k k + ∑ ξniMn n∈N R

k k −θ k β 1−β k τniwi Qi /Ai π∗ = ni k k −θ k β 1−β k ∑ τnmwm Qm /Am m∈N E k • ζn share of domestic expenditure in total expenditure within sector k for location n within England and Wales; k • Mn is country n’s total imports from England and Wales in sector k k • ξni is the share of domestic location i in foreign country n’s total imports from England and Wales in sector k

42 / 46 Amenities : Step 4

k • Recover amenities (Bni) in sector k and location n for workers born in each location i using migration choice probabilities

! !−e αj k e j αH Bniwn κni ∏ Pn Qn j∈K k = ¯ Ln ∑ ! !−e Li ∈ E αj i N ` e j αH ∑ ∑ Bmiwm κmi ∏ Pm Qm `∈K m∈N E j∈K

k k −θ k β 1−β k τniwi Qi /Ai πk∗ = ni k k −θ k β 1−β k ∑ τnmwm Qm /Am m∈N E 1 k k k k k k∗ θ k β 1−β k Pn = γ ζnπnn τnnwn Qn /Am

43 / 46 Counterfactuals • Use the model to quantify distributional consequences of trade and role of labor mobility in shaping those distributional consequences " # w Lk 1 − αH 1 − βk i i = π∗ ζkαk (1 + d ) 1 + L + Lk w βk ∑ ni n n αH n ∑ αH βk n n n∈N E k∈K k k + ∑ ξniMn n∈N R " ! # 1 − αH 1 − βk = (1 + ) k + k . QnHn H ∑ dn Ln ∑ H k Ln wn α k∈K k∈K α β ! !−e αj k e j αH Bnwn κni ∏ Pn Qn j∈K k = ¯ , Lni ! !−e Li αj ` e j αH ∑ ∑ Bmwm κmi ∏ Pm Qm `∈K m∈N E j∈K

1 k k k k k k∗ θ k β 1−β k Pn = γ ζnπnn τnnwn Qn /Am 44 / 46 Conclusions

• Distributional consequences of trade is one of the central questions in international economics

• Examine one of the most inuential historical trade shocks following the 1846 repeal of the Corn Laws in 19th-century Britain

• Traditionally, research on the Corn Laws has emphasized the distributional eects across factors or industries – Heckscher-Ohlin and Specic-Factors models

• We provide new evidence on the distributional consequences of the repeal of the Corn Laws across local labor markets – New, spatially-disaggregated data on population, employment and land values for around 11,000 parishes in England & Wales from 1801-1911 – Exogenous trade exposure measure based on agroclimatic conditions – Quantitative spatial model to evaluate the impact of this trade shock on industrialization, urbanization and income distribution – Quantify role of labor mobility for the distributional eects of trade

45 / 46 Thank You

46 / 46