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The Effects of Immigration in a Developing Country: Brazil in the Age of Mass Migration

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The effects of immigration are reasonably well understood in developed countries, but they are far more poorly understood in developing ones despite the importance of these countries as immigrant destinations. David Escamilla-Guerrero discusses the effects of immigration to Brazil during the Age of Mass Migration on its agricultural sector in 1920.

Image courtesy of interviewee. May 1, 2024

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This video video discusses the gaps in immigration literature, particularly emphasizing that most studies focus on developed countries, especially the U.S., while a significant portion of immigration (about 40%) occurs in developing countries. The speaker's project aims to investigate the effects of immigration in Brazil as a case study in a developing economy, highlighting the lack of comprehensive understanding in this area.

The existing research primarily addresses the labor market impacts of immigration in developed nations, with notable works examining U.S. counties and their socioeconomic outcomes tied to historical immigration patterns. In contrast, the speaker points out that previous studies on Brazil tend to focus on specific regions and historical contexts rather than providing a nationwide analysis.

The project aims to evaluate immigration's impact on agricultural development in Brazil, utilizing digitized historical census data and employing various control variables related to geography and market proximity. An instrumental variable strategy will be used to address potential endogeneity in immigrant settlement choices, looking specifically at the connection of municipalities to historical rail networks during periods of high immigration.

Preliminary findings suggest that an increase in the share of European immigrants correlates with higher farm values per hectare, indicative of agricultural development. The underlying mechanism identified is an increase in land use intensity due to temporary immigrants’ higher labor efforts compared to natives.

Furthermore, the research explores whether agricultural development slows or accelerates economic transformation, with results indicating a positive link between increased farm values and improvements in literacy rates, women’s employment in manufacturing, and state capacity—countering the notion that agricultural growth hinders broader economic progress.

In conclusion, the text highlights the importance of addressing the effects of immigration in developing countries to inform effective immigration policies and developmental strategies, which is particularly relevant given that over 40% of global migrants settle in these regions.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.135 --> 00:03.485
There is a large body of literature studying the effects

00:03.485 --> 00:06.885
of immigration, but most of these literature

00:07.405 --> 00:11.005
actually focuses on the effects in developed countries,

00:11.345 --> 00:13.485
mostly in the United States.

00:14.225 --> 00:18.565
And this is, quite, important to highlight

00:18.595 --> 00:23.205
because actually about 40% of,

00:23.355 --> 00:26.845
immigration heads to developing countries.

00:27.305 --> 00:30.285
So we know relatively little about the effects

00:30.285 --> 00:32.845
of immigration in developing economies.

00:33.025 --> 00:36.925
So that was the motivation behind starting this project.

00:37.465 --> 00:39.925
So as I was mentioning,

00:40.145 --> 00:41.645
before the economic

00:42.245 --> 00:45.245
economics literature has focused on the labor market effects

00:45.245 --> 00:48.885
of immigration in rich countries, primarily, particularly

00:49.965 --> 00:52.725
focusing on the United States after the 1960s.

00:53.195 --> 00:54.965
That is after the transition

00:54.965 --> 00:56.885
to its current immigration regime.

00:57.355 --> 01:00.645
This literature is complimented by studies

01:01.165 --> 01:04.445
focusing on the effects of immigration in the United States

01:04.825 --> 01:08.725
during the age of mass migration, which perhaps,

01:08.745 --> 01:11.685
the most important work in this,

01:12.345 --> 01:15.485
in this topic is the work by cicada non

01:15.505 --> 01:18.605
and published in the, review

01:18.605 --> 01:20.445
of economic studies in 2020,

01:20.915 --> 01:23.845
perhaps is the most influential, work on

01:23.845 --> 01:24.965
that, on that regard.

01:25.185 --> 01:27.845
And what they find is that, US counties

01:28.155 --> 01:31.765
with more historical immigration have higher income,

01:31.955 --> 01:35.645
less poverty, less employment, higher rates of organization,

01:36.025 --> 01:37.765
and greater educational attainment.

01:37.855 --> 01:40.125
Right? However, as I was mentioning

01:40.125 --> 01:44.245
before, these papers focus again on the effects

01:44.265 --> 01:48.325
of immigration on developed countries, rich countries.

01:48.635 --> 01:53.085
Therefore, you know, there is a substantial both,

01:53.705 --> 01:58.265
in, in addressing, the impact of immigration

01:58.805 --> 02:02.145
and what, Brazil provides is

02:02.685 --> 02:07.265
an a specific setting, a natural experiment of history,

02:07.365 --> 02:11.385
if you will, that will allow us to study in more detail

02:11.735 --> 02:15.065
what are the effects of immigration in developing countries.

02:15.605 --> 02:19.765
So there are few studies, on Brazil,

02:20.385 --> 02:24.645
but most of them, focus on, the impact

02:24.645 --> 02:28.725
of immigrant colonies that were, created in the early,

02:29.185 --> 02:30.405
20th century,

02:30.985 --> 02:35.325
and the impact as well of schools created by immigrants.

02:35.865 --> 02:38.765
And, but the main limitation in our view

02:38.905 --> 02:40.605
of these studies is

02:40.605 --> 02:43.405
that they are focused on one specific region,

02:43.735 --> 02:47.205
which is the south pa state, living the role

02:47.205 --> 02:50.365
of immigration in the development of other regions

02:50.385 --> 02:52.965
and sectors less clearly understood.

02:53.465 --> 02:56.285
In particular, what remains, lacking,

02:56.405 --> 02:58.725
I would say is an understanding of

02:58.725 --> 03:01.525
The effects of immigration on contemporary outcomes

03:02.365 --> 03:03.605
covering all of Brazil

03:03.945 --> 03:06.365
and explicitly addressing, you know,

03:06.545 --> 03:09.885
the potential endogeneity present in the location

03:09.885 --> 03:11.085
choice of immigrants.

03:11.385 --> 03:13.525
And this is exactly what our paper does.

03:13.985 --> 03:18.525
So, the paper in particular will eczema the impact

03:18.525 --> 03:21.125
of immigration and agricultural development.

03:21.825 --> 03:23.765
And to do so, we digitized

03:24.375 --> 03:28.045
Nobel historical data from the 1920 population

03:28.185 --> 03:30.325
and agricultural census of Brazil.

03:30.815 --> 03:33.605
These census provides a very rich set of

03:34.125 --> 03:37.325
variables at the municipality level that capture

03:38.405 --> 03:40.805
economic population, human capital,

03:41.225 --> 03:42.925
and labor force characteristics.

03:43.505 --> 03:46.085
In particular, we eczema the impact

03:46.085 --> 03:48.845
of immigration on farm value per hectare,

03:49.095 --> 03:51.965
which overall will reflect agricultural

03:51.965 --> 03:54.085
productivity and development.

03:54.825 --> 03:57.885
In addition, we collect data on a number

03:57.885 --> 03:59.725
of municipality characteristics

04:00.235 --> 04:02.365
that may have affected farm value

04:02.545 --> 04:05.925
and immigrant settlement to include them basically

04:05.945 --> 04:08.245
as controlled variables in our analysis.

04:08.665 --> 04:12.045
For example, to capture proximity to international

04:12.145 --> 04:16.565
and domestic markets, we create three variables that,

04:16.915 --> 04:19.125
capture distance to the nearest port

04:19.305 --> 04:20.885
or frontier custom hubs.

04:21.385 --> 04:24.245
We also use the distance to the nearest principal city

04:24.505 --> 04:26.685
and distance to the nearest principal town.

04:27.145 --> 04:30.805
In addition, we create a battery of variables to control

04:30.865 --> 04:33.245
for differences in geographic conditions

04:33.315 --> 04:36.685
that actually would matter a lot for the destination choice.

04:37.185 --> 04:40.245
And these variables include, the extension

04:40.465 --> 04:45.005
of the destination rocket ness, the attitude, the latitude,

04:45.035 --> 04:49.005
longitude, and, other, geographic features.

04:49.625 --> 04:54.045
And finally, we also will be, in the paper we use,

04:54.045 --> 04:57.365
data from the Global Ecological Science Project,

04:58.095 --> 05:01.285
which allows us to control different variables

05:01.285 --> 05:04.245
that will capture, you know, variation in terms

05:04.245 --> 05:06.805
of suitability of land for agriculture

05:07.145 --> 05:09.765
and the adaptability of land for the production

05:09.765 --> 05:11.325
of different crops, right?

05:11.465 --> 05:16.085
So we will be using these, large data sets

05:16.995 --> 05:18.575
to be, combined

05:18.875 --> 05:23.375
and then we will implement an instrumental variable strategy

05:23.375 --> 05:27.975
because our main concern is that,

05:28.155 --> 05:30.735
the immigrant location choice will endo,

05:30.735 --> 05:31.775
will be endogenous.

05:32.405 --> 05:35.375
What does I mean by, by endogeneity?

05:35.645 --> 05:38.535
Well, you know, immigrants may have settled in places

05:39.045 --> 05:41.055
with better land quality and

05:41.055 --> 05:43.735
therefore, that could be confounded

05:43.965 --> 05:45.575
with the impact of immigration.

05:45.655 --> 05:47.775
Right? So to address this challenge,

05:48.315 --> 05:51.015
we implement an identification strategy

05:51.485 --> 05:53.695
that creates an instrumental variable

05:53.715 --> 05:56.415
for a municipalities immigrant share of population

05:57.055 --> 06:00.325
Based on the interaction of aggregate immigrant arrivals

06:00.745 --> 06:04.685
and the development of the rail network, which,

06:04.855 --> 06:08.645
historical literature argues that was an important method

06:08.745 --> 06:10.725
for immigrants to reach their destination.

06:11.035 --> 06:14.845
Okay. Intuitively, what our identification

06:15.405 --> 06:20.165
strategy does is to compare to municipalities, one

06:20.165 --> 06:24.365
of which was linked to the real growth in, the year

06:24.385 --> 06:26.045
of a large immigration in flow,

06:26.745 --> 06:30.085
and the other of which was not linked in that year.

06:30.545 --> 06:34.445
The former, by virtue of having access to the rail network

06:35.145 --> 06:37.725
in the year of a large inflow, is predictive

06:37.785 --> 06:39.045
to have a greater share

06:39.045 --> 06:41.405
of immigrants relative to population.

06:42.225 --> 06:45.645
And therefore these methods enable us to control for

06:46.155 --> 06:48.885
rail linkage directly addressing concerns

06:49.235 --> 06:53.365
that rail perhaps was built, targeting also specific areas

06:53.585 --> 06:55.765
and had direct effect on the local economy.

06:56.585 --> 07:01.005
So this will be the, specific specification strategy

07:01.395 --> 07:04.285
that will allow us to really disentangle the effect

07:04.285 --> 07:07.465
of immigration on agricultural development.

07:08.005 --> 07:12.665
So, our preferred specification indicates

07:12.735 --> 07:16.625
that the one standard deviation increase in a municipality

07:16.835 --> 07:21.455
share of European immigrants generated an,

07:21.605 --> 07:24.895
0.7 standard deviation increase in its foreign

07:24.895 --> 07:25.935
values per hectare.

07:26.235 --> 07:30.095
In other words, places that received a larger share

07:30.095 --> 07:34.175
of immigrants experience a larger increase

07:34.715 --> 07:36.415
in farm values per hectare.

07:36.475 --> 07:39.735
So they experience agricultural development.

07:40.885 --> 07:43.615
What can actually explain this effect?

07:43.875 --> 07:45.895
In other words, what is the underlying

07:45.965 --> 07:47.855
mechanism of this effect?

07:48.595 --> 07:51.975
So what we find to be the most substantial mechanism

07:52.755 --> 07:54.935
is changes in land use patterns.

07:55.715 --> 07:59.015
In particular, we find that a greater share

07:59.015 --> 08:02.655
of European immigrants left to an increase in the share

08:02.835 --> 08:04.375
of pharma cultivated,

08:04.635 --> 08:08.295
and that this increase in the intensity of cultivation

08:08.835 --> 08:11.255
was responsible for about one quarter

08:11.835 --> 08:13.295
of the effect of immigration.

08:14.515 --> 08:15.615
We argue that the,

08:15.765 --> 08:20.135
this mechanism reflects temporary immigrants exerting,

08:20.135 --> 08:24.415
greater labor effort than natives or permanent immigrants.

08:25.075 --> 08:29.095
And, we also, provide evidence

08:29.245 --> 08:32.935
that this mechanism is in line with a large body

08:32.935 --> 08:35.935
of literature showing both theoretically

08:36.155 --> 08:39.135
and empirically that temporary immigrants

08:39.775 --> 08:40.935
increase their work effort

08:41.395 --> 08:43.935
or, you know, labor supply

08:44.505 --> 08:46.095
while they are in the destination.

08:46.475 --> 08:47.495
That's a great question.

08:47.835 --> 08:51.295
So that is the, last part of, of the paper.

08:52.115 --> 08:54.855
So there is a ongoing

08:54.985 --> 08:59.445
Debate about whether, agricultural development

09:00.125 --> 09:03.925
actually can slow down the structural transformation

09:03.985 --> 09:08.605
of the economy or if it can help to speed it up, right?

09:08.985 --> 09:10.125
So what we do is

09:10.125 --> 09:14.125
to test whether this was the case for Brazil.

09:14.585 --> 09:17.925
And what we find is that actually those places

09:18.035 --> 09:20.565
that experience a,

09:21.315 --> 09:23.445
greater increase in farm values

09:24.035 --> 09:26.565
also have greater literacy rates.

09:27.195 --> 09:31.645
Also, women tend to be, employed in,

09:31.645 --> 09:35.925
manufacturing and that they have a higher,

09:35.935 --> 09:37.325
state capacity as well.

09:37.905 --> 09:42.245
So our results point towards the positive view

09:42.635 --> 09:46.205
that actually agricultural development did not

09:46.755 --> 09:50.645
slow down the performance of what would become

09:51.345 --> 09:54.125
the biggest economy in late in America

09:54.295 --> 09:55.685
throughout the 20th century.

09:56.385 --> 10:00.315
So, you know, the near exclusive focus

10:00.575 --> 10:02.035
of the economics literature

10:02.095 --> 10:04.955
and the effects of immigration in developed countries

10:04.955 --> 10:08.155
creates an important blind spot given the importance

10:08.155 --> 10:11.595
of developing countries as destination for immigrants.

10:12.255 --> 10:15.115
So the combination of the recognized benefit

10:15.705 --> 10:17.595
that historical perspective brings

10:17.735 --> 10:20.195
to advancing the economics of immigration

10:20.335 --> 10:23.875
and Brazil's unique position in the age of mass migration,

10:24.055 --> 10:27.635
the period that we studied, you know,

10:28.425 --> 10:30.875
there's a stronger resemblance to modern

10:31.715 --> 10:34.475
countries in early development stages than any other

10:34.505 --> 10:36.435
destination in that period,

10:36.935 --> 10:40.675
and actually enables us to enrich this literature

10:40.735 --> 10:42.315
by shedding new light on the effects

10:42.315 --> 10:45.275
of integration in developing rather than

10:45.425 --> 10:46.515
developed countries.

10:46.985 --> 10:50.275
This is particularly relevant because as I mentioned

10:50.275 --> 10:53.995
before, developing countries host over 40%

10:54.295 --> 10:56.835
of the world migrants, and

10:56.835 --> 11:01.235
therefore, this could be, you know,

11:01.765 --> 11:03.355
these insights can be used

11:03.415 --> 11:06.835
to develop more efficient immigration policies,

11:06.855 --> 11:11.315
but at the same time developed, development strategies

11:11.815 --> 11:16.555
for receiving, countries that are still in early stages

11:16.575 --> 11:17.315
of development.

00:00 - There is a large body of literature studying the effects of immigration, but most of these literature actually focuses on the effects in developed countries, mostly in the United States. And this is, quite, important to highlight because actually about 40% of,

00:23 - immigration heads to developing countries. So we know relatively little about the effects of immigration in developing economies. So that was the motivation behind starting this project. So as I was mentioning, before the economic economics literature has focused on the labor market effects of immigration in rich countries, primarily, particularly focusing on the United States after the 1960s. That is after the transition

00:54 - to its current immigration regime. This literature is complimented by studies focusing on the effects of immigration in the United States during the age of mass migration, which perhaps, the most important work in this, in this topic is the work by cicada non and published in the, review of economic studies in 2020, perhaps is the most influential, work on that, on that regard.

01:25 - And what they find is that, US counties with more historical immigration have higher income, less poverty, less employment, higher rates of organization, and greater educational attainment. Right? However, as I was mentioning before, these papers focus again on the effects of immigration on developed countries, rich countries. Therefore, you know, there is a substantial both, in, in addressing, the impact of immigration and what, Brazil provides is

02:02 - an a specific setting, a natural experiment of history, if you will, that will allow us to study in more detail what are the effects of immigration in developing countries. So there are few studies, on Brazil, but most of them, focus on, the impact of immigrant colonies that were, created in the early, 20th century, and the impact as well of schools created by immigrants. And, but the main limitation in our view of these studies is

02:40 - that they are focused on one specific region, which is the south pa state, living the role of immigration in the development of other regions and sectors less clearly understood. In particular, what remains, lacking, I would say is an understanding of The effects of immigration on contemporary outcomes covering all of Brazil and explicitly addressing, you know, the potential endogeneity present in the location

03:09 - choice of immigrants. And this is exactly what our paper does. So, the paper in particular will eczema the impact of immigration and agricultural development. And to do so, we digitized Nobel historical data from the 1920 population and agricultural census of Brazil. These census provides a very rich set of variables at the municipality level that capture economic population, human capital,

03:41 - and labor force characteristics. In particular, we eczema the impact of immigration on farm value per hectare, which overall will reflect agricultural productivity and development. In addition, we collect data on a number of municipality characteristics that may have affected farm value and immigrant settlement to include them basically as controlled variables in our analysis.

04:08 - For example, to capture proximity to international and domestic markets, we create three variables that, capture distance to the nearest port or frontier custom hubs. We also use the distance to the nearest principal city and distance to the nearest principal town. In addition, we create a battery of variables to control for differences in geographic conditions that actually would matter a lot for the destination choice. And these variables include, the extension

04:40 - of the destination rocket ness, the attitude, the latitude, longitude, and, other, geographic features. And finally, we also will be, in the paper we use, data from the Global Ecological Science Project, which allows us to control different variables that will capture, you know, variation in terms of suitability of land for agriculture and the adaptability of land for the production of different crops, right? So we will be using these, large data sets

05:16 - to be, combined and then we will implement an instrumental variable strategy because our main concern is that, the immigrant location choice will endo, will be endogenous. What does I mean by, by endogeneity? Well, you know, immigrants may have settled in places with better land quality and therefore, that could be confounded with the impact of immigration.

05:45 - Right? So to address this challenge, we implement an identification strategy that creates an instrumental variable for a municipalities immigrant share of population Based on the interaction of aggregate immigrant arrivals and the development of the rail network, which, historical literature argues that was an important method for immigrants to reach their destination. Okay. Intuitively, what our identification strategy does is to compare to municipalities, one

06:20 - of which was linked to the real growth in, the year of a large immigration in flow, and the other of which was not linked in that year. The former, by virtue of having access to the rail network in the year of a large inflow, is predictive to have a greater share of immigrants relative to population. And therefore these methods enable us to control for rail linkage directly addressing concerns that rail perhaps was built, targeting also specific areas

06:53 - and had direct effect on the local economy. So this will be the, specific specification strategy that will allow us to really disentangle the effect of immigration on agricultural development. So, our preferred specification indicates that the one standard deviation increase in a municipality share of European immigrants generated an, 0.7 standard deviation increase in its foreign values per hectare. In other words, places that received a larger share

07:30 - of immigrants experience a larger increase in farm values per hectare. So they experience agricultural development. What can actually explain this effect? In other words, what is the underlying mechanism of this effect? So what we find to be the most substantial mechanism is changes in land use patterns. In particular, we find that a greater share of European immigrants left to an increase in the share

08:02 - of pharma cultivated, and that this increase in the intensity of cultivation was responsible for about one quarter of the effect of immigration. We argue that the, this mechanism reflects temporary immigrants exerting, greater labor effort than natives or permanent immigrants. And, we also, provide evidence that this mechanism is in line with a large body of literature showing both theoretically

08:36 - and empirically that temporary immigrants increase their work effort or, you know, labor supply while they are in the destination. That's a great question. So that is the, last part of, of the paper. So there is a ongoing Debate about whether, agricultural development actually can slow down the structural transformation of the economy or if it can help to speed it up, right?

09:08 - So what we do is to test whether this was the case for Brazil. And what we find is that actually those places that experience a, greater increase in farm values also have greater literacy rates. Also, women tend to be, employed in, manufacturing and that they have a higher, state capacity as well. So our results point towards the positive view

09:42 - that actually agricultural development did not slow down the performance of what would become the biggest economy in late in America throughout the 20th century. So, you know, the near exclusive focus of the economics literature and the effects of immigration in developed countries creates an important blind spot given the importance of developing countries as destination for immigrants. So the combination of the recognized benefit

10:15 - that historical perspective brings to advancing the economics of immigration and Brazil's unique position in the age of mass migration, the period that we studied, you know, there's a stronger resemblance to modern countries in early development stages than any other destination in that period, and actually enables us to enrich this literature by shedding new light on the effects of integration in developing rather than

10:45 - developed countries. This is particularly relevant because as I mentioned before, developing countries host over 40% of the world migrants, and therefore, this could be, you know, these insights can be used to develop more efficient immigration policies, but at the same time developed, development strategies for receiving, countries that are still in early stages of development.

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