Tag Archives: economic development

Evaluating the Distinctive Economic Impact of Historical Female Migration in the United States

A Woman’s Touch? Female Migration and Economic Development in the United States

By Viola von Berlepsch (London School of Economics), Andrés Rodríguez-Pose (London School of Economics) and Neil Lee (London School of Economics),

Abstract: Does the economic effect of immigrant women differ from that of immigrants in general? This paper examines if gender has influenced the short- and long-term economic impact of mass migration to the US, using Census microdata from 1880 and 1910. By means of ordinary least squares and instrumental variable estimations, the analysis shows that a greater concentration of immigrant women is significantly associated with lower levels of economic development in US counties. However, immigrant women also shaped economic development positively, albeit indirectly via their children. Communities with more children born to foreign mothers and that successfully managed to integrate female immigrants experienced greater economic growth than those dominated by children of foreign-born fathers or American-born parents.

URL: https://econpapers.repec.org/paper/cprceprdp/12878.htm

Circulated by NEP-HIS on 2018-05-08

Review by Fernando Arteaga (George Mason University)


What is the economic impact of female migration? The authors seek to answer the inquiry by using the United States in the late 19th and early 20th century as their study case. The goal of the paper is to highlight the distinctiveness of women immigration (compared to that of men), both in the processes that led women to migrate, the characteristics they had, and the places where they finally settled. The main thesis of paper stresses the long-lasting effect women have had; through their family role, as mothers, they facilitated the formation and transmission of social capital, which had a pervasive positive effect on income.

Female migrants in early America tended to settle mainly on urbanized areas in the Northeastern coast – compared to that of male immigrants (who settled mainly in the South and West). The migration levels of women, in absolute terms, were lower, and their marriage rates higher. More importantly, their labor participation rates were low: women tended to stay and work in domestic chores rather than find occupations in the market. These characteristics make female migration distinct to that of men, and motivate the goal of the paper in trying to assess their particular relevance.




Figure 1: Immigrant Women in the United States, 1880. By the nature of the variable, counties with a larger share of immigrant women imply a lower share of immigrant men.


The text relies on standard econometric analyses, based on intuition and on the literature of migration and culture transmission. The main data sources are the historical censuses of 1880 and 1910, which capture the amount of, male and female foreign-born population residents in each US county (among other data).  The paper presents two base regressions that aim to assess the direct and indirect economic impact of female migration, both in the short and the long term. The first model regresses economic income (GDP per capita by county) on female migration (foreign-born women as a share of the total population in the county). They find that the variables are negatively correlated: lack of labor market participation hindered the female contribution to income. The authors also found that this has had a long term negative effect (income today is also negatively correlated with female migration in late 19th and early 20th century) [1]. To correct for potential biases and to establish a causality linkage that goes strictly from migration to income, and not the other way around, the authors use three different instruments: a) the percentage of married persons; b) the number of persons living in a household; c) the urbanization rate of the county being examined. The first instrument accounts for the fact that female migrants tended to be married in larger shares than the rest of the population. The second accounts for the idea that migrants, especially women, tended to stay with members of their families through their lifetime. The last one maintains that female migrants favored settlement in urbanized zones. The validity of an instrument (marriage percentage, household size and urbanization) hinges upon it being correlated with the dependent variable (income) only by it causing the highlighted mechanism (female migration). The authors do several post-hoc statistical tests to evaluate the instrument’s validity and conclude that it is indeed a valid and strong one. In any case, the instrument variable outcomes do not change the results of the baseline ordinary least squares scenario, they just allow a more robust interpretation of them: it can be said that female migration did have a negative impact on income.

The second model emphasizes the indirect impact of migrant women. Maybe women themselves did not positively contribute to the economic wellbeing of their communities, but they could have done so through other means. The authors refer to the literature that stresses how mothers influence their children behavior and thus have an important role as social capital transmitters (which could positively affect economic wellbeing).  They regress economic income today on the share of children (in 1880 and 1910) born from: 1) a migrant mother and an American-born father; 2) a migrant father and an American mother; 3) both American parents. The standard base of comparison is the share of children that had both parents as immigrants [2].  By definition, the model can only capture the long-term effect of female migrants. The authors find that US counties with an historical larger share of children with migrant mothers are correlated with larger incomes today – in comparison to the other explanatory variables; having American parents is negatively correlated with income today; having a migrant father, and American mother, has a non-significant and null effect on economic outcomes today. The argument, again, rests on the case of social capital transmission: women, as mothers, matter very much.  To corroborate their OLS results they also use an instrumental variable. The authors assume that American-born women that had migrant mothers followed the cultural transmission pattern established by their forebears. They call this the “supply-push” component, which they estimate and use as their instrument. Just as the first model, the instrumental variable inclusion does not modify the basic results, it only permits to talk about causality from migrants in the past to better economic outcomes today.

In conclusion, the paper finds that female immigration, while having a negative direct short-term impact on economic income, has a long-lasting positive effect through the “cultural carrier” channel.


The paper is a very interesting one, being one of the few studies that aims to disentangle the impact of women as migrants compared to that of men. The results the authors present make intuitive sense. I would like to make just small technical comments based on the variables they use and how they use them.

First, related to the semantics of the concept of “migration.” Migration is normally thought as a flow variable, but here it is used as a stock variable. Given the data they use (measuring migrants as people classified as foreign born in two censuses) the authors cannot measure the impact of migration as a flow, only the impact of it in broad terms. This is not a problem. I just would have liked to see a minor explanation on the paper that clarified the interpretations that we could get out of this. In fact, I think it could explain why they find a negative impact of migrant women in income (if the variables were flows, through migration rates and economic growth, the results may be different).

Second, on a more technical note, I’m skeptic of the instruments being used. Even though the authors argue that they are valid and strong, I remain unconvinced. The authors show that all four of them are correlated with the dependent variable and uncorrelated with the error terms, yet there is almost no explanation, backed up by a narrative, of how exactly these instruments impact on income only through female migration. For each one of the instruments used I could think of other alternate channels by which they could impact income. For example, the use of percentage of marriage by county could indeed be correlated with female migration, but is that the only potential channel? Could it not be that maybe poverty or religion could be impacting income as well?

Lastly, I wish the narrative part could be explained in larger detail. For example, how exactly female migrants in 1880 have a direct impact on income in 2010. Or how exactly children of foreign mothers in 1880 and 1910 could affect income today. It is one thing to say that culture matters, it is another different thing to point how exactly it does. In fact, even though they do mention the pervasiveness of cultural traits through time, they fail to mention that this pervasiveness does not imply ipso facto a good outcome is assured. Sometimes, social capital is also correlated with bad outcomes.

[1] The authors do not provide a concise explanation of why this could be happening: how could a century year old female migration pattern directly impact economic wellbeing today?

[2] All the interpretations of results are in comparison to that baseline.


The Elephant (-Shaped Curve) in the Room: Economic Development and Regional Inequality in South-West Europe

The Long-term Relationship Between Economic Development and Regional Inequality: South-West Europe, 1860-2010

by Alfonso Díez-Minguela (Universitat de València); Rafael González-Val (Universidad de Zaragoza, IEB); Julio Martinez-Galarraga (Universitat de València); María Teresa Sanchis (Universitat de València); and Daniel A. Tirado (Universitat de València).

Abstract: This paper analyses the long-term relationship between regional inequality and economic development. Our data set includes information on national and regional per-capita GDP for four countries: France, Italy, Portugal and Spain. Data are compiled on a decadal basis for the period 1860-2010, thus enabling the evolution of regional inequalities throughout the whole process of economic development to be examined. Using parametric and semiparametric regressions, our results confirm the rise and fall of regional inequalities over time, i.e. the existence of an inverted-U curve since the early stages of modern economic growth, as the Williamson hypothesis suggests. We also find evidence that, in recent decades, regional inequalities have been on the rise again. As a result, the long-term relationship between national economic development and spatial inequalities describes an elephant-shaped curve.

URL: https://EconPapers.repec.org/RePEc:hes:wpaper:0119

Distributed by NEP-HIS on 2018-02-26

Review by: Anna Missiaia

The relationship between economic development and inequality in a broad sense has been at the core of economic research for decades. In particular, the process of industrialization has been much investigated as a driver of inequality: Kuznets (1955) was the first to propose an inverted U-shaped pattern of income inequality driven by the initial forging ahead of the small high-wage industrial sector and a subsequent structural change, with more and more labour force moving out of agriculture into industry. The first to suggest that a similar pattern could take place in the spatial dimension was Williamson (1965), who showed that the process of industrialization could lead to an upswing of regional inequality because of the initial spatial concentration of the industrial sector, which eventually touches the less advanced regions. The paper by Díez-Minguela, González-Val, Martinez-Galarraga, Sanchis and Tirado circulated on NEP-HIS on 2018-02-26 deals with this latter inequality. The authors formally test what is the relationship between the coefficient of variation (in its Williamson formulation) of regional GDP per capita and a set of measures of economic development, most importantly the level of national GDP per capita. The authors use for the analysis four Southwestern European countries (France, Spain, Italy and Portugal).  The paper starts in 1860 and therefore takes a much appreciated multi-country and long-run perspective compared to the original work by Williamson, who was looking only at the 20th century United States.

The work by Díez-Minguela and co-authors also relies on the framework developed by Barrios and Strobl (2009), going from a merely descriptive interpretation of an inverted U-shape of regional inequality to a theoretically-founded one. In particular, Barrios and Strobl (2009) use a growth model that takes into account region-specific technological shocks and their later diffusion on the entire national territory; they also include measures of trade openness to test the hypothesis that more market integration leads to more regional inequality; they finally consider regional policies implemented by the State to even out regional disparities. The original paper by Barrios and Strobl (2009) was only considering a sample of countries from 1975 onwards, basically overlooking the whole post-WWII industrial boom in some more developed countries. In this respect, the contribution by Díez-Minguela and coauthors is fundamental, as it proposes a long-run regional analysis not only confined to one specific country as it is customary in the field, but on a group of countries. The paper also proposes a formal testing of the drivers of regional inequality, moving forward from a mere descriptive approach. In terms of methodology, the authors propose an approach that makes use of both parametric and semi-parametric estimations. This is to take into account that the relationship might be different for different levels of GDP.

Moving on to the results, the first thing to note is that three out of four countries in the sample present an inverted U-shaped pattern between GDP per capita and regional inequality (as can be seen in Figure 1).


Figure 1: Regional Income Dispersion and Per-Capita GDP in France, Italy, Spain and Portugal (1860-2010). Source: Díez Minguela et al. (2017)

As for France, the authors suggest that the lack of a U-shaped pattern could be due to its early industrialization that pre-dates the first benchmark year available (1860). The analysis could thus be still capturing the downward part of the U-shape. In terms of the econometric analysis, the OLS regression confirms the predicted pattern through the significance of GDP per capita both in their quadratic and cubic forms.

One interesting discussion is on the controls used in the model: here both openness to trade and public expenditure are not significant, in spite of both being strong candidates for explaining regional inequality in the economic geography literature (see Rodríguez-Pose, 2012 on trade and Rodriguez-Pose and Ezcurra, 2010 on public spending). For the first variable (openness of trade), the explanation could be that the detrimental effect of trade on regional inequality could well have been offset by the increased integration of the financial and labour markets during the First Globalization.

Regarding the second control variable, public intervention (measured as public spending as a share of GDP): the authors admit that having a large public sector does not necessarily imply implementing effective cohesion policies. The example of Fascist Italy on this point is very illustrative: the 1920s and 1930s witnessed rising inequality in Italy, in spite of a growing intervention by the State in the economy and an alleged intent to favor the most backward parts of the country. In general, the impression is that more than one mechanism that is well present in empirical studies after WWII, might not be so in earlier periods. Finally, the authors test for the role of structural change in shaping regional inequality, which was the original explanation by Williamson (1965). This is measured as the non-agricultural value added and it is positive and significant in explaining the coefficient of variation of overall GDP per capita.

Although the paper represents an important step forward for explaining historical regional divergence, several aspects could be addressed in the future by either the authors or by other scholars in the same field. For instance, the use of only four countries from a specific part of Europe does not yet allow drawing general conclusions on the relationship between economic growth and inequality in the long run. As mentioned in the paper, several case studies from other parts of Europe do not entirely fit in the same path: this is the case of Belgium (Buyst, 2011) or Sweden (Enflo and Missiaia, 2018). It is possible that including more advanced economies such as Britain or even some peripheral but Northern ones in the sample might lead to re-consider the increase of regional inequality during modern industrial growth as a golden rule.


Barrios, S., Strobl, E., 2009. “The Dynamics of Regional Inequalities.” Regional Science and Urban Economics 39 (5), 575-591

Buyst, E., 2011. “Continuity and Change in Regional Disparities in Belgium during the Twentieth Century.” Journal of Historical Geography 37 (3), 329-337

Díez Minguela, A., González-Val, R., Martínez-Galarraga, J., Sanchis, M. T., and Tirado, D. 2017. “The Long-term Relationship Between Economic Development and Regional Inequality: South-West Europe, 1860-2010.” EHES Working Papers in Economic History 119

Enflo, K. and Missiaia, A. 2017. “Between Malthus and the Industrial Take-off: Regional Inequality in Sweden, 1571-1850.” Lund Papers in Economic History

Kuznets, S., 1955. “Economic Growth and Income Inequality.” American Economic Review 45 (1), 1-28

Rodríguez-Pose, A., 2012. “Trade and Regional Inequality.” Economic Geography 88 (2), 109-136

Rodríguez-Pose, A., Ezcurra, R., 2010. “Does Decentralization Matter for Regional Disparities? A Cross-Country Analysis.” Journal of Economic Geography 10 (5), 619-644.

Williamson, J.G., 1965. “Regional Inequality and the Process of National Development: a Description of the Patterns.” Economic Development and Cultural Change 13 (4), 1-8