<figure><img src="image_1.png" /></figure>

HAL
open science

# The Grammatical Origins of Gender Roles

Victor Gay, Estefania Santacreu-Vasut, Amir Shoham

► To cite this version:

Victor Gay, Estefania Santacreu-Vasut, Amir Shoham. The Grammatical Origins of Gender Roles.
2013. hal-04046900

HAL Id: hal-04046900

https://hal.science/hal-04046900

Preprint submitted on 27 Mar 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
BEHL WORKING PAPER SERIES

WP2013-03

The Grammatical Origins of Gender Roles

Victor Gay

Estefania Santacreu-Vasut

Amir Shoham

22 April 2013

Berkeley Economic History Laboratory (BEHL)
University of California, Berkeley
530 Evans Hall / MC 3880
Berkeley, CA 94602
http://behl.berkeley.edu
BEHL Working Paper WP2013-03 | 22 April 2013

http://behl.berkeley.edu/wp/2013-03

# The Grammatical Origins of Gender Roles

Victor Gay

Estefania Santacreu-Vasut

Amir Shoham

## Abstract

We investigate the relation between gender marking in grammar and female participation in the labor market, the credit market, land ownership, and politics. Cross-country and individual-level analyses reveal that women speaking languages that more pervasively mark gender distinctions are less likely to participate in economic and political life and more likely to encounter barriers in their access to land and credit. These findings are robust to a large set of controls and robustness checks. We also found that the impact of a language's gender structure remains after controlling for culture, for historical agricultural use of the plough.

**Keywords:** culture, gender, grammar, labor, language, occupation, politics

JEL classification: J16, J82, 017, Z1.

**Victor Gay** Department of Economics, University of Chicago. victorgay@uchicago.edu

**Estefania Santacreu-Vasut** (corresponding author) Department of Economics at ESSEC Business School, Avenue Bernard Hirsch, 95021, Cergy-Pontoise, France; THEMA; and Visiting Professor at BEHL, University of California, Berkeley. +33 (0) 1 34 43 37 19 [santacreuvasut@essec.edu](mailto:santacreuvasut@essec.edu)

**Amir Shoham** FOX School of Business, Temple University, 1801 Liacouras Walk, Philadelphia, PA 19122-6083, USA; and College of Management Academic Studies, Israel, COMAS, 7 Yitzhak Rabin Blvd., Rishon LeZion, 75190, Israel. [amirls@colman.ac.il](mailto:amirls@colman.ac.il)
# The Grammatical Origins of Gender Roles

Victor Gay *

Estefania Santacreu-Vasut †

Amir Shoham ‡

## Abstract

We investigate the relation between gender marking in grammar and female participation in the labor market, the credit market, land ownership, and politics. Cross-country and individual-level analyses reveal that women speaking languages that more pervasively mark gender distinctions are less likely to participate in economic and political life and more likely to encounter barriers in their access to land and credit. These findings are robust to a large set of controls and robustness checks. We also find that the impact of a language's gender structure remains after controlling for culture, for the historical agricultural use of the plough and for linguistic families.

**Keywords:** culture, gender, grammar, labor, language, occupation, politics

We are grateful to participants of RES 2013, PAC-DEV 2013, NEUDC 2012, RES 2012 conferences, UC Berkeley, Ben Gurion University of the Negev, Paris School of Economics, College of Management Academic Studies Business School and THEMA seminars, and Temple University Brown Bag Seminar as well as to Pranab Bardhan, Greville Corbett, Barry Eichengreen, Ramon Ferrer-i-Cancho, Johanna Nichols, and Gerard Roland. We especially thank Alberto Alesina, Paola Giuliano, and Nathan Nunn for sharing their data on plough-based historical agriculture practices. We also thank Luc Aubry and Anatoli Colicev for excellent research assistance.

<footnote>* University of Chicago, victorgay@uchicago.edu</footnote>

<footnote>† Corresponding author. Department of Economics at ESSEC Business School, France. Avenue Bernard Hirsch, 95021, Cergy-Pontoise, France, and THEMA. +33 (0) 1 34 43 37 19, santacreuvasut@essec.edu</footnote>

<footnote>‡ FOX School of Business, Temple University, and College of Management Academic Studies, Israel. Temple University, 1801 Liacouras Walk, Philadelphia, PA 19122-6083, USA; COMAS, 7 Yitzhak Rabin Blvd., RishonLeZion, 75190, Israel. amir.shoham@temple.edu</footnote>
# 1 Introduction

Women's participation in markets and political institutions varies greatly across countries. As Sen (1999) argues, agency is a key determinant of their well-being. Furthermore, low levels of women's socioeconomic participation may hinder a country's economic development (World Bank Development Report 2012), decreasing the size of markets and leading to an inefficient allocation of resources. A growing body of literature (Fernández 2010; Alesina, Giuliano, and Nunn forthcoming) analyzes the role that attitudes and beliefs play in promoting or hampering women's agency: "attitudes of the family and of the society at large toward women's economic activities and the economic and social circumstances that encourage or resist change in these attitudes" (Sen 1999).

As North (1990, 1993) points out, language is an important component of those "social circumstances" and serves as a vehicle for cultural transmission. As early as 1935, Whorf claimed that language shapes thought: "We are inclined to think of language simply as a technique of expression, and not to realize that language first of all is a classification and arrangement of the stream of sensory experience which results in a certain world-order, a certain segment of the world that is easily expressible by the type of symbolic means that language employs."

For linguists—in particular, those of the *generativist* persuasion—language is part of human biology; from this perspective (see Chomsky 1980), all languages are fundamentally equal in structure and have only minor local differences. In contrast, Sampson, Gill, and Trudgill (2009) identify a new group of linguists who view languages as “institutions developed as part of a society’s cultural heritage and hence as differing and evolving in their levels of complexity, just as other cultural institutions do.” The current *coevolutionary* theories look to the interplay between genetic and cultural forces when accounting for language diversity and change.

Findings from cognitive psychology about the impact of language on cognition (Boroditsky and Gaby 2010) indicate that there may be a direct channel through which language structure influences socioeconomic choices and outcomes. Christiansen and Kirby (2003) review the research on the origins and evolution of human language; they report some authors theorizing that gram-
matical structure reflects biological adaptation while others posit that language is transmitted culturally through millennia from one generation to the next.

This paper analyzes one feature of a language's grammar: its gender system. As observed by linguist Greville G. Corbett: "In some languages gender is evident in almost every phrase, while in other languages it is absent" (quoted in Dryer and Haspelmath 2011). Is there a relation between the intensity of gender marking in languages and the economic outcomes of different genders?

In the paper's first part, we identify empirical regularities between a language's gender marking and female participation in politics and in the labor, financial, and land markets. In so doing we employ both cross-country and individual-level analysis.

We find that women's participation in the labor and credit markets and in politics is significantly lower in countries whose dominant (i.e., most spoken) language marks gender more intensively. In these countries, the occupational profile of women is biased toward services and against jobs traditionally occupied by men. In addition, women in such countries are more likely to face restrictions in their access to land ownership and to loans.

Similarly, individual-level data reveal that women who speak a language that marks gender more intensively are less likely to supply labor or to work in agricultural occupations. These results are robust to systematically controlling for geography, climate, history of colonization, religion, and continent.

In the second part we discuss our results and offer empirical analysis to help understand them. Toward this end, we first review the interdisciplinary literature on language origin, evolution, and cognitive psychology. Why does grammatical structure vary across languages? In particular, why is there such wide variation in the extent to which languages mark gender distinctions? Where and how did languages—and their grammars—originate? Does language influence cognition and/or reflect culture? These questions are much debated in the fields of linguistics and cognitive science, among others. From those debates have emerged two important findings. First, gender is one of language's most stable grammatical features and persists for thousands of years (Wichmann and Holman 2009). Second, gender marking in languages may reflect, inter alia, its speakers’
worldviews (Corbett 1999).

It is not our aim to provide definitive answers to these questions, but we do exploit three sets of evidence to investigate them. First, we incorporate studies of US immigrants’ labor supply behavior as a function of their native language; this allows us to compare individuals who share a common institutional environment but speak different languages. Second, we exploit information from the World Values Survey (WVS) which documents each country’s dominant language as well as the language(s) that its citizens speak at home. Further, we use data from Alesina, Giuliano, and Nunn (forthcoming) on plough-based historical agricultural practices—together with WVS data on attitudes toward gender roles—to analyze whether the impact of language’s gender marking can be transmitted via cultural capital or economic specialization dating to a distant past. Finally, we study whether the impact of languages grammar remains after we control for linguistic families.

Our first main result is that a grammar’s gender marking differentially influences the set of all migrants and its subset of women living in countries whose dominant language is low gendered. In particular, women speaking a high-gendered language at home but living in a low-gendered language environment (e.g., Spanish-speaking US immigrants) are more likely to be employed than are women generally. This finding suggests that the influence of language is determined by an interaction between the cognitive framework of speakers and their cultural environment. Gender marking in languages may affect the gender-related social constraints faced by women as much as (or more than) it affects their cognitive framework. Our second main result is that gender treatment in a dominant language affects the economic outcomes of women who speak that language—even after we account for historical (agricultural) practices, cultural values and linguistic families. This result suggests the existence of other channels (including, perhaps, cognition) through which the effects of language are manifested.

So far, there has been only limited use of linguistic variables in economic research. Licht, Goldschmidt, and Schwartz (2007) use the grammar of pronouns as an instrumental variable to study how countries that favor autonomy, egalitarianism, and mastery exhibit less corruption,
more democratic accountability, and a more effective rule of law. Mavisakalyan (2011) uses the gender of pronouns to investigate the impact of culture on women's share in the labor force. Chen (2013) investigates the impact of languages's markings for future time on such future-oriented decisions and outcomes as savings, debt, and health-related behavior. Tabellini (2008a and 2008b) uses the grammar of pronouns as an instrumental variable to assess the causal effect of cultural values on institutional outcomes.<fnref n="1" />

# 2 Data on Gender in Language

Corbett argues that gender “is the most puzzling of the grammatical categories” (Corbett 1991). In common parlance, gender is linked to biological sex and refers to the categories of male and female; in linguistics, however, gender is linked to a set of usage rules that depend on nouns of different types.<fnref n="2" /> These types are typically based on male–female distinctions but can also be based on other social constructs (including distinctions related to age, social status, human versus animal, etc.). In this section we describe all four gender-related grammatical features of language as classified by linguists in the World Atlas of Language Structure (WALS; Dryer and Haspelmath 2011).<fnref n="3" />

## 2.1 Individual Intensity Indices

A language's gender system may or may not be linked to biological sex. One example of a non-sex-based gender system is one based on the distinction between human and nonhuman (as in Danish) or between animate and inanimate. The Swedish language makes gender distinctions that are not sex based. These considerations lead us to building the Sex-Based Intensity Index (SBII), which is a dummy variable set equal to 1 for languages with a sex-based gender system

<footnote n="1">¹More generally, Guiso, Sapienza, and Zingales (2009) document that trust between the respective inhabitants of two countries is affected by their geographical proximity and the degree of commonality in their languages.</footnote>

<footnote n="2">²To avoid confusion, we shall use *gender* as in common parlance but use *grammatical* (or *language*) *gender* when referring to the broader concept of gender in linguistics.</footnote>

<footnote n="3">³See chapter 30 of the WALS (Corbett 2011a), chapter 31 (Corbett 2011b), chapter 32 (Corbett 2011c), and chapter 44 (Siewierska 2011). From these grammatical features we create four individual indices and three aggregate indices, as detailed in the text.</footnote>
(and to 0 otherwise).

The number of genders in a language tells us how many noun types may require different forms of agreement.<fnref n="4" /> Some languages, such as Nigerian Fula, feature twenty genders. When a language (e.g., French, Spanish) has two genders, they are usually “feminine” and “masculine.” Both English and German include “neuter” as a third gender, but other languages with multiple genders involve no sex-related distinctions. We therefore build our Number Gender Intensity Index (NGII), a dummy variable set equal to 1 for languages with exactly two genders and set equal to 0 otherwise (i.e., for languages with three or more genders or with no gender distinctions).

A gender assignment system provides a set of rules to help the speaker make appropriate agreements between nouns and the genders defined by the system. Assignment can depend on the meaning (semantic) or the form of the noun. For example, a semantic assignment system is used in Kannada (a language in southern India), where nouns denoting male (female) humans are masculine (feminine) and all remaining nouns are neuter. English also assigns gender on semantic grounds only. In contrast, some languages (including Russian and Spanish) have both a semantic and a formal assignment system. Thus, nouns that are neither masculine nor feminine need not be neuter; in Russian, for example, they can be assigned to the masculine or feminine gender as a function of their inflectional class (e.g., whether the noun takes the nominative or the accusative form). To capture these differences we build the Gender Assignment Intensity Index (GAII), a dummy variable set equal to 1 only for languages whose gender assignment system is both semantic and formal. This variable is otherwise set to 0 because gender is presumed to be more pervasive in the former case.

Finally, languages differ in the extent to which they distinguish gender in pronouns. For example, in English the pronominal sex-based gender system is determined by the use of she/he/it. Yet there are some languages that have a sex-based gender system but do *not* have sex-based pronouns. Hence we build the Gender Pronouns Intensity Index (GPII), a dummy variable set equal to 1 for languages with a gender distinction in third-person pronouns and also in the first and/or the second person; this variable is set equal to 0 otherwise—that is, if the language does

<footnote n="4">⁴Gender derives from the Latin *genus*, and it originally meant “kind” or “type”.</footnote>
not distinguish gender in pronouns (or does so only in the third-person pronoun).

The four maps in Figure I show the distribution of intensity indices for each country's dominant language. For each index, the countries shaded in black are those for which the dominant (most spoken) language features the more sex-based gender treatment; in other words, the black-shaded countries are those for which that panel's dummy variable was set to 1.

<figure><img src="image_3.png" /><figcaption>Figure I: Distribution of Individual Intensity Indices
Clockwise from upper left: NGII, SBII, GPII, and GII; the dominant language of countries shaded in black (resp. gray) is characterized by gender treatment that is more (resp. less) sex based for that index.</figcaption></figure>

As the figure indicates, languages vary in the degree to which they are gender intensive along the different indices; Table I shows the autocorrelation matrix of these four indices.<fnref n="5" /> Table II lists the percentage of countries for which the dominant language simultaneously exhibits gender-intensive features in all possible pairings of the individual indices.

In the Appendix we present the data alphabetically (by country) in Tables XXIX–XXXII

<footnote n="5">⁵All tables in this paper observe the convention of denoting probability values as follows: \*p < .10; \*\*p < .05; \*\*\*p < .01.</footnote>
<table><caption>Table I: Autocorrelation of Intensity Indices</caption><thead><tr><th></th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th></tr></thead><tbody><tr><th>NGII</th><td>1</td><td></td><td></td><td></td></tr><tr><th>SBII</th><td>0.548***</td><td>1</td><td></td><td></td></tr><tr><th>GAII</th><td>0.698***</td><td>0.595***</td><td>1</td><td></td></tr><tr><th>GPII</th><td>0.793***</td><td>0.480***</td><td>0.646***</td><td>1</td></tr></tbody></table>

<table><caption>Table II: Joint Occurrences (%) of Value-1 Intensity Indices for Countries' Dominant Languages</caption><thead><tr><th></th><th>NGII = 1</th><th>SBII = 1</th><th>GAII = 1</th><th>GPII = 1</th></tr></thead><tbody><tr><th>NGII = 1</th><td>1</td><td></td><td></td><td></td></tr><tr><th>SBII = 1</th><td>42</td><td>1</td><td></td><td></td></tr><tr><th>GAII = 1</th><td>50</td><td>59</td><td>1</td><td></td></tr><tr><th>GPII = 1</th><td>34</td><td>34</td><td>45</td><td>1</td></tr></tbody></table>

(where “n/a” denotes “not available.”)

It must be emphasized that linguistic structures vary widely across language families as well as within families. This fact is consistent with the evolution of language via three mechanisms: “One is that it is the result of contact between languages. The second possibility is that it reflects a genealogical relationship among at least some of the languages, involving a feature inherited from a common ancestor. The third possible explanation for shared features within a particular geographical area is that it is at least partly coincidence” (Dryer and Haspelmath 2011). Throughout this study we use the four individual indices just described—in addition to the three aggregate ones described in the next section—because their respective samples do not perfectly overlap.

## 2.2 Aggregate Intensity Indices

The four gender-related variables described in Section 2.1 reflect different features of grammatical gender and so capture different aspects of the use intensity of male-female distinctions
in a given language. Yet there are languages whose gender system is sex-based but that either
do not mark gender in pronouns or assign gender on semantic grounds only. To capture such
variations, we construct an aggregate index for each language as the sum of its individual indices.
Thus, our Gender Intensity Index (GII) is calculated as follows:

$$
\text{GII} = \text{NGII} + \text{SBII} + \text{GAII} + \text{GPII}, \quad \text{GII} \in \{0, 1, 2, 3, 4\}.
$$

For example, the GII for German is 1 + 1 + 0 + 0 = 2. It has a sex-based gender system (SBII = 1) and assigns gender on the basis of both semantic and formal rules (GAII = 1); however, German assigns gender to third-person pronouns only (GPII = 0) and does have a neuter gender (NGII = 0).

We construct two additional aggregate indices as

$$
\text{GIV1} = \text{NGII} + \text{SBII} + \text{GAII};
$$

$$
\text{GIV2} = \text{NGII} + \text{SBII} + \text{GPII}.
$$

Thus GIIV1 is an aggregate index for which all information regarding its component indices was
gathered by the same researcher (Corbett). The GIIV2 index excludes GAII from GII. Our motive
for this exclusion is to overcome sample-size limitations of our GII index owing to the relatively
high fraction of countries for which we lack information on the GAII grammatical variable.

Tables XXXIII–XXXV in the Appendix list our data sources for the socioeconomic, historical,
and geographic variables used in the paper.

3 Empirical Strategy

There is general consensus among linguists that the gender system is one of a language’s most
stable features and is capable of surviving thousands of years (Wichmann and Holman 2009).
In Section 5 we explore the linguistics literature that addresses the stability of grammatical
features and that describes the emergence (and disappearance) of gender in language. Overall,
this literature strongly suggests that gender is a predetermined feature of grammar; therefore, we should not be concerned with economic outcomes determining gender systems (rather than vice versa).

Yet establishing the direction of causality does not rule out the possibility of omitted variable bias. Hence we systematically perform robustness checks across our empirical analysis that control for the influence of geography and of historical contact between societies and their languages. With regard to geography, we control for continent dummies and for distance from the equator; according to Hall and Jones (1999), the latter factor may reflect “Western influence.” Following Bloom and Sachs (1998) and Gallup, Sachs, and Mellingeret (1998), who argue that climate may influence development, we also control for the country’s portion that is in the tropics, the number of frost days, coastline as percentage of the border, and a dummy for being landlocked. As for historical factors, we follow Acemoglu, Johnson, and Robinson (2001) in controlling for colonization and colonial origin (English and Spanish) and follow Weber (1904/05) in controlling for religion.

Later in the paper, when investigating the potential mechanisms behind our results, we make three additional robustness checks. First, we use WVS data to control for direct measures of cultural beliefs concerning gender roles. Second, we control for the historical use of the plough, a measure constructed by Alesina, Giuliano, and Nunn (forthcoming). Third, we control for linguistic family origin.

Finally, because we may not be able to completely rule out an omitted variable bias in the cross-country results, we also analyze individual-level data (when information is available concerning what languages are spoken by individuals at home).

## Specifications

For the country-level analysis we estimate the following equation(s) using OLS:

$$y_c = \beta(GenderIndex) + X_c\Gamma + \epsilon_c,$$
where $y_c$ is an outcome of interest, $c$ denotes country, *GenderIndex* is a placeholder for each of our intensity indices, $X_c$ is a vector of covariates at the country level (including a constant), $\beta$ is the coefficient of interest, and $\Gamma$ is a vector of regression coefficients.

For individual-level analysis using the World Values Survey, we estimate a logit model of the underlying probability of a binary dependent variable, $\pi_i$ as a linear function of the predictors:

$$ \operatorname{logit}(\pi_i) = \beta(\operatorname{GenderIndex}) + X_i\Gamma; $$

here $X_i$ is a vector of covariates at the individual level.

# 4 Results

## 4.1 Cross-Country Variations

This section investigates whether there is a statistically significant relation between variations in the intensity of gender marking in language and variations in the extent and nature of women's agency across countries where by *agency* we mean the ability of women to

> "... earn an independent income, to find employment outside the home, to have ownership rights and to have literacy and be educated participants in decision within and outside the family" (Sen 1999).

We shall present results of female participation in the labor market, the credit market, land ownership, and politics. Our goal here is to document the empirical findings in a systematic way. Their interpretation is deferred to Section 5, where we provide additional evidence to deepen the analysis.

### 4.1.1 Labor

This section presents the results related to female labor force participation and occupational choices relative to their male counterparts. All results reported are for the year 2000 to facilitate
comparison with Alesina, Giuliano, and Nunn (forthcoming). Results of similar magnitude and
significance were obtained for the years in the period 1980–2009 and are available upon request.

We control for the share of inhabitants for whom the dominant language is their mother tongue to reflect the possibility that a dominant language is less representative when there is greater linguistic diversity. Furthermore, as shown by research on ethno-linguistic fractionalization, linguistic diversity plays an important role in economic outcomes (Mauro 1995; Easterly and Levine 1997). We follow Goldin (1995) in controlling for economic development (measured by the logarithm of GDP per capita and its square term). In addition, we control for trade openness because single-industry studies show that the share of female employment is higher in firms that export (Baslevent and Onaran 2004); we also control for government size. As Cavalcanti and Tavares (2011) demonstrate, increases in government size and female labor force participation are related. Finally, we control for a communist past (Paxton, Hughes and Painter, 2010).

Table III presents cross-country OLS regression results regarding the female labor force par-
ticipation rate. This table shows that countries whose dominant language has two genders have a
female participation rate that is 16.6 percentage points (pp) lower than countries whose dominant
language has (no or) more than two genders. Similarly, column (2) shows that having a sex-based
gender system decreases the female labor force participation rate by 13 pp % relative to the base-
line value in countries with no gender system. This result, too, is significant both statistically
and economically. Columns (4) (resp., and (5)) shows that, when the dominant language’s having
gender assignment that is both semantic and formal, (resp.,or gender marking applies to a large
set of pronouns), female labor participation also decreases significantly.

Our results are virtually identical when using labor force's female share, a measure that
controls for overall size of the labor force.
<table><caption>Table III: Female Labor Force Participation Rate: OLS Regressions</caption><thead><tr><th rowspan="2"></th><th colspan="8">Gender Index</th></tr><tr><th>NGII<br>(1)</th><th>SBII<br>(2)</th><th>GII<br>(3)</th><th>GII<br>(4)</th><th>GPII<br>(5)</th><th>GII<br>(6)</th><th>GIIV1<br>(7)</th><th>GIIV2<br>(8)</th></tr></thead><tbody><tr><td>Coefficient</td><td>-0.166***<br>(0.0282)</td><td>-0.134***<br>(0.0309)</td><td>-0.141***<br>(0.035)</td><td>-0.150***<br>(0.0264)</td><td>-0.0513***<br>(0.0111)</td><td>-0.0771***<br>(0.016)</td><td>-0.0648***<br>(0.0118)</td><td></td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td></td></tr><tr><td>Observations</td><td>175</td><td>119</td><td>119</td><td>87</td><td>117</td><td>82</td><td>87</td><td>112</td></tr><tr><td>R<sup>2</sup></td><td>0.17</td><td>0.385</td><td>0.281</td><td>0.265</td><td>0.413</td><td>0.439</td><td>0.396</td><td>0.445</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.141</td><td>0.346</td><td>0.236</td><td>0.199</td><td>0.375</td><td>0.386</td><td>0.342</td><td>0.408</td></tr><tr><td>F-statistic</td><td>6.323</td><td>10.36</td><td>6.59</td><td>5.686</td><td>10.09</td><td>9.302</td><td>8.448</td><td>10.7</td></tr></tbody></table>

Notes: The OLS estimates are reported with robust standard errors given in parentheses; the unit of observation is a country. Female Labor Force Participation Rate is the percentage of women in the labor force in 2000, which ranges from 13% to 91% with a mean of 0.509 and a standard deviation of 0.156; these values correspond to the sample from the baseline regression column (1). The controls include log income and log income squared, trade openness, communist history, government size, and share of speakers of the dominant language measured (year 2000).
Table IV reports the set of robustness checks as detailed in the previous section for female labor force participation rate. Owing to space constraints, throughout the paper we report robustness checks only for GIIV2. We chose this gender marking index because it is the aggregate index with the largest sample size. Results for all of the other indices are similar; see the Appendix.<fnref n="6" />

<table><caption>Table IV: Female Labor Force Participation Rate: Robustness Checks</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>GIIV2</td><td>-0.0697***<br>(0.0123)</td><td>-0.0690***<br>(0.0117)</td><td>-0.0816***<br>(0.0124)</td><td>-0.0799***<br>(0.0107)</td><td>-0.0600***<br>(0.0121)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Geography</td><td>Yes</td><td></td><td></td><td></td><td></td></tr><tr><td>Climate</td><td></td><td>Yes</td><td></td><td></td><td></td></tr><tr><td>Colonization</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>Continent</td><td></td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Religion</td><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>112</td><td>112</td><td>112</td><td>112</td><td>112</td></tr><tr><td>R<sup>2</sup></td><td>0.46</td><td>0.565</td><td>0.532</td><td>0.597</td><td>0.574</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.418</td><td>0.517</td><td>0.491</td><td>0.548</td><td>0.522</td></tr><tr><td>F-statistic</td><td>10.1</td><td>13.02</td><td>11.57</td><td>12.88</td><td></td></tr></tbody></table>

Notes: Geography represents distance from the equator, measured as $\sqrt{\text{latitude}}/90$. Climate includes four variables: share of the population in tropical areas, average number of frost days per unit of population, a dummy for whether the country is landlocked, and the share of population within 100 km of the coast or an ocean-navigable river. Colonization includes two dummies (set equal to 1 only if the country ever was, respectively, a British or Spanish colony). Continent and Religion are a set of dummy variables indicating the country's continent and majoritarian religion (Protestantism, Catholicism, Islamic, Jewish, Hinduism, Buddhist).

The significance and magnitude of the coefficients for these gender indices are virtually unchanged when we control for our set of robustness checks. In other words, the results are very robust to accounting for a variety of geographical and historical forces that may influence both language and economic outcomes. Each of our seven intensity indices, (as reported in the Appendix), remains significant at the 1% level for our five robustness checks.

Table V reports cross-country OLS regression results for the ratio of female to male employment in the services sector. Table VI gives results of our robustness checks.

Countries with higher levels of pronoun gender marking exhibit, on average, a ratio of female to

<footnote n="6">⁶Available upon request</footnote>
<table><caption>Table V: Gender Ratio in Services Employment: OLS Regressions</caption><thead><tr><th></th><th></th><th colspan="8">Gender Index</th></tr><tr><th></th><th></th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th><th>GII</th><th>GIIV1</th><th>GIIV2</th></tr><tr><th></th><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><th>Coefficient</th><td></td><td>0.185***<br>(0.0648)</td><td>0.192***<br>(0.0718)</td><td>0.0988<br>(0.0908)</td><td>0.234***<br>(0.0806)</td><td>0.0659**<br>(0.0281)</td><td>0.0718*<br>(0.0372)</td><td>0.0996***<br>(0.0317)</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Observations</th><td>131</td><td>96</td><td>96</td><td>69</td><td>96</td><td>66</td><td>69</td><td>91</td></tr><tr><th>R<sup>2</sup></th><td>0.172</td><td>0.292</td><td>0.292</td><td>0.195</td><td>0.303</td><td>0.235</td><td>0.226</td><td>0.315</td></tr><tr><th>Adjusted R<sup>2</sup></th><td>0.132</td><td>0.235</td><td>0.236</td><td>0.102</td><td>0.247</td><td>0.143</td><td>0.137</td><td>0.258</td></tr><tr><th>F-statistic</th><td>3.836</td><td>7.354</td><td>8.732</td><td>3.826</td><td>5.75</td><td>4.614</td><td>5.059</td><td>7.28</td></tr></tbody></table>

Notes: The OLS estimates are reported with robust standard errors given in parentheses; the unit of observation is a country. Gender Ratio in Services Employment is the ratio of gender employment female to male employment in the services sector which ranges from 0.21 to 2.53 with a mean of 1.29 and a standard deviation of 0.363. For controls, see Notes to Table III.

<table><caption>Table VI: Gender Ratio in Services Employment: Robustness Checks</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><th>GIIV2</th><td>0.0860**<br>(0.033)</td><td>0.0917***<br>(0.0337)</td><td>0.0289<br>(0.0326)</td><td>0.0456<br>(0.0283)</td><td>0.0739**<br>(0.0357)</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Geography</th><td>Yes</td><td></td><td></td><td></td><td></td></tr><tr><th>Climate</th><td></td><td>Yes</td><td></td><td></td><td></td></tr><tr><th>Colonization</th><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><th>Continent</th><td></td><td></td><td></td><td>Yes</td><td></td></tr><tr><th>Religion</th><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><th>Observations</th><td>91</td><td>91</td><td>91</td><td>91</td><td>91</td></tr><tr><th>R<sup>2</sup></th><td>0.333</td><td>0.514</td><td>0.505</td><td>0.556</td><td>0.537</td></tr><tr><th>Adjusted R<sup>2</sup></th><td>0.268</td><td>0.446</td><td>0.45</td><td>0.488</td><td>0.466</td></tr><tr><th>F-statistic</th><td>6.013</td><td>9.654</td><td>9.781</td><td>9.745</td><td>.</td></tr></tbody></table>

Notes: For definitions of the variables, see Notes to Table IV.
male employment in the services sector that is nearly one standard deviation greater. In general, all but one of our indices have a significant and positive impact on female presence in services.

The GIIV2 variable remains highly significant and of a similar order of magnitude for our robustness checks—with the exceptions of Continent and Colonization. As for Colonization, it is noteworthy that the index SBII remains significant at the 5% level; as for Continent, the NGII remains significant at the 10% level. The other indices mirror the results for GIIV2.

Table VII reports cross-country OLS regression results for the ratio of female to male employment in the agricultural sector and Table VIII gives results of our reports robustness checks.

<table><caption>Table VII: Gender Ratio in Agricultural Employment: OLS Regressions
Gender Index</caption><thead><tr><th></th><th>(1)</th><th>NGII<br>(2)</th><th>SBII<br>(3)</th><th>GAII<br>(4)</th><th>GPII<br>(5)</th><th>GII<br>(6)</th><th>GIIV1<br>(7)</th><th>GIIV2<br>(8)</th></tr></thead><tbody><tr><td>Coefficient</td><td></td><td>-0.341***<br>(0.0946)</td><td>-0.252**<br>(0.107)</td><td>-0.104<br>(0.135)</td><td>-0.352***<br>(0.104)</td><td>-0.109**<br>(0.0439)</td><td>-0.121**<br>(0.0579)</td><td>-0.171***<br>(0.0445)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>138</td><td>94</td><td>94</td><td>67</td><td>96</td><td>65</td><td>67</td><td>90</td></tr><tr><td>R<sup>2</sup></td><td>0.261</td><td>0.279</td><td>0.234</td><td>0.185</td><td>0.301</td><td>0.252</td><td>0.235</td><td>0.314</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.227</td><td>0.22</td><td>0.171</td><td>0.0879</td><td>0.245</td><td>0.16</td><td>0.144</td><td>0.256</td></tr><tr><td>F-statistic</td><td>15.79</td><td>14.44</td><td>9.614</td><td>4.764</td><td>15.1</td><td>7.62</td><td>6.804</td><td>14.63</td></tr></tbody></table>

Notes: The OLS estimates are reported with robust standard errors given in parentheses; the unit of observation is a country. Gender Ratio in Agricultural Employment is the ratio of female to male employment in the agricultural sector, which ranges from 0 to 2.354 with a mean of 0.717 and a standard deviation of 0.459. For controls, see Notes to Table III.

All but one of our indices have a significant and negative impact on female presence in agricultural occupations. For example, countries with a sex-based gender system have a significantly lower ratio (exceeding half a standard deviation in magnitude) of female to male agricultural employment. Except for Continent and Colonization, GIIV2 remains highly significant and of a similar order of magnitude. Note that, for Continent, the NGII remains significant at the 5% level. The other indices mirror the results for GIIV2.

Overall these results suggest that gender marking in language is robustly correlated to female labor market outcomes. Indeed, women living in countries whose language marks female–male distinctions more pervasively are significantly less likely to participate in the labor market— even
<table><caption>Table VIII: Gender Ratio in Agricultural Employment: Robustness Checks</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>GIIV2</td><td>-0.146***<br>(0.052)</td><td>-0.165***<br>(0.047)</td><td>-0.0576<br>(0.0536)</td><td>-0.078<br>(0.0512)</td><td>-0.148**<br>(0.057)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Geography</td><td>Yes</td><td></td><td></td><td></td><td></td></tr><tr><td>Climate</td><td></td><td>Yes</td><td></td><td></td><td></td></tr><tr><td>Colonization</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>Continent</td><td></td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Religion</td><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>90</td><td>90</td><td>90</td><td>90</td><td>90</td></tr><tr><td>R<sup>2</sup></td><td>0.347</td><td>0.48</td><td>0.531</td><td>0.558</td><td>0.468</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.283</td><td>0.406</td><td>0.478</td><td>0.489</td><td>0.385</td></tr><tr><td>F-statistic</td><td>11.17</td><td>11.05</td><td>16.42</td><td>10.46</td><td>.</td></tr></tbody></table>

Notes: For definitions of the variables, see Notes to Table IV.

when we take into account historical, geographic, and religious factors. Furthermore, women are less (resp. more) likely, than men to be employed in agricultural (resp. service) occupations.<fnref n="7" />

### 4.1.2 Credit

In this section we study credit market supply- and demand-side behavior of women (relative to men) as well as the institutionally related features of female participation in financial markets. In particular, we study savings behavior, access to loans and the holding of accounts in formal financial institutions. We report results for the year 2011 because this is the only year for which the World Bank Gender Statistics provide data on credit market outcomes by gender. All the dependent variables in this section are taken from this data set.

In each of our regressions we control for “the strength of legal rights” index that ranges from 0 to 10— with higher scores indicative of laws that are better designed to expand credit access. We also control for legal origin and government size. As Hallward-Driemeier (2011) emphasizes, institutional factors play an important role in the gender gap evident in credit access. Moreover, La Porta et al. (1998) show that investor protection and ownership concentration is related to the

<footnote n="7">⁷We do not report the results for our Gender Ratio Employment Industry variable because they are not significant; however, these results are available upon request.</footnote>
origins of a country's legal system. We also control for income, given that Rajan and Zingales (1998) have demonstrated a strong link between economic and financial development, and also for Protestantism (following Weber [1904/05] and, more recently, Chen [2013]). Finally, we control for the share of inhabitants for whom the dominant language is their mother tongue in order to capture the representativeness of that language.

Duflo (2012) discusses the existing literature that addresses how intra intrahousehold bargaining power affects women's savings behavior and use of financial accounts. At the macroeconomic level we can capture that power with two related variables: the share of women who were first married by age 18 (as a percentage of women aged 20-24) and the share of female-headed households (the percentage of households with a female head)—both of which are available from the World Bank Gender database. To increase sample size, we build two variables by inputting data for a window of ±6 years around the year 2000 for married by age of 18 and similarly one of ±2 years for female headed households.<fnref n="8" /> Using this procedure yields 95 and 71 observations for the respective variables, which reduces to 53 and 46 observations when combined with our gender intensity indices. The small sample size prevents us from including these variables as controls in the baseline specification. Nonetheless, we present and discuss their correlation with our outcomes of interest and gender intensity indices following the main regression analysis. Table IX presents results for the gender ratio of “percentage who saved any money in the past year.”

<footnote n="8">⁸The year 2000 offers the largest sample size.</footnote>
<table><caption>Table IX: Gender Ratio for Savings: OLS Regressions</caption><thead><tr><th rowspan="2"></th><th colspan="8">Gender Index</th></tr><tr><th>NGII<br>(1)</th><th>SBII<br>(2)</th><th>GAII<br>(3)</th><th>GPII<br>(4)</th><th>GII<br>(5)</th><th>GIIV1<br>(6)</th><th>GIIV2<br>(7)</th><th>GIIV2<br>(8)</th></tr></thead><tbody><tr><td>Coefficient</td><td>-0.114***<br>(0.0327)</td><td>-0.106***<br>(0.0356)</td><td>-0.133***<br>(0.0368)</td><td>-0.0818**<br>(0.0371)</td><td>-0.0415***<br>(0.0123)</td><td>-0.0577***<br>(0.0157)</td><td>-0.0415***<br>(0.0139)</td><td></td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td></td></tr><tr><td>Observations</td><td>138</td><td>105</td><td>105</td><td>74</td><td>104</td><td>72</td><td>74</td><td>101</td></tr><tr><td>R<sup>2</sup></td><td>0.131</td><td>0.263</td><td>0.24</td><td>0.273</td><td>0.218</td><td>0.289</td><td>0.306</td><td>0.253</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.0767</td><td>0.194</td><td>0.168</td><td>0.17</td><td>0.143</td><td>0.186</td><td>0.209</td><td>0.179</td></tr><tr><td>F-statistic</td><td>14.39</td><td>5.699</td><td>12.55</td><td></td><td>12.32</td><td></td><td></td><td>11.44</td></tr></tbody></table>

Notes: The OLS estimates are reported with robust standard errors given in parentheses; the unit of observation is a country. Gender Ratio Savings is the female/male ratio of “percentage who saved any money in the past year” (age 15+), which ranges from 0.34 to 1.27 with a mean of 0.87 and a standard deviation of 0.15. The controls include log income, legal index, government size, legal origin, a dummy for Protestantism, and the share of speakers of the country’s dominant language.
Column (4) of Table IX shows that countries with a dominant language that assigns gender on both semantic and formal grounds have a lower Gender Ratio Savings (by nearly a whole standard deviation). Similarly, column (5) shows that gender marking in a large set of pronouns decreases this ratio by more than half a standard deviation. Indeed, all seven individual indices are negative and significant both statistically and economically. In short, women living in countries whose dominant language marks gender more intensively are less likely than men to save. This finding is consistent with the observation that these women are less likely to have an independent source of revenue, given their low participation in the labor market; it also could result from unequal bargaining power within the household. Table X reports the set of robustness checks results.

<table><caption>Table X: Gender Ratio for Savings: Robustness Checks</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>GIIV2</td><td>-0.0417***<br>(0.0143)</td><td>-0.0447***<br>(0.0156)</td><td>-0.0460***<br>(0.0163)</td><td>-0.0407**<br>(0.0163)</td><td>-0.0289**<br>(0.0144)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Geography</td><td>Yes</td><td></td><td></td><td></td><td></td></tr><tr><td>Climate</td><td></td><td>Yes</td><td></td><td></td><td></td></tr><tr><td>Colonization</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>Continent</td><td></td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Religion</td><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>101</td><td>101</td><td>101</td><td>101</td><td>101</td></tr><tr><td>R<sup>2</sup></td><td>0.253</td><td>0.268</td><td>0.26</td><td>0.272</td><td>0.348</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.17</td><td>0.158</td><td>0.168</td><td>0.153</td><td>0.25</td></tr><tr><td>F-statistic</td><td>10.51</td><td>8.952</td><td>9.243</td><td>10.63</td><td>.</td></tr></tbody></table>

Notes: For definitions of the variables, see Notes to Table IV.

The significance and magnitude of the coefficients for our gender indices coefficients are virtually unchanged when we control for this set of robustness checks. Except for SBII, which becomes marginally insignificant when we control for Continent, all other indices and robustness checks mirror the results for GIIV2—remaining strongly significant (at the 1% and 5% levels) and similar in order of magnitude.

Table XI reports the odds ratios from cross-country *logit* regressions of a variable that captures restrictions on women's access to loans from the World Bank Gender Statistics. In
particular, the dependent variable in this regression is a dummy set equal to 1 if women are either fully or partially restricted (and to 0 zero otherwise).

<table><caption>Table XI: Women's Restriction in Loan Access: Logit Regressions</caption><thead><tr><th></th><th></th><th colspan="7">Gender Index</th></tr><tr><th></th><th></th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th><th>GII</th><th>GIIV1</th><th>GIIV2</th></tr><tr><th></th><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><th>Coefficient</th><td></td><td>11.31**</td><td>5.689**</td><td>6.722*</td><td>8.077**</td><td>1.573</td><td>1.925*</td><td>2.074**</td></tr><tr><th></th><td></td><td>(11.05)</td><td>(4.981)</td><td>(6.566)</td><td>(8.51)</td><td>(0.456)</td><td>(0.758)</td><td>(0.75)</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Observations</th><td>95</td><td>66</td><td>66</td><td>54</td><td>66</td><td>52</td><td>54</td><td>63</td></tr><tr><th>Pseudo-R<sup>2</sup></th><td>0.389</td><td>0.36</td><td>0.323</td><td>0.343</td><td>0.349</td><td>0.301</td><td>0.335</td><td>0.323</td></tr><tr><th>χ<sup>2</sup></th><td>33.07</td><td>18.79</td><td>20.43</td><td>18.68</td><td>20.55</td><td>17.98</td><td>19.99</td><td>18.76</td></tr></tbody></table>

Notes: The odds ratios from logit regressions are reported with robust standard errors given in parentheses; the unit of observation is a country. Restriction in Access to Loans is an indicator variable set equal to 1 only if women are either partially or fully restricted in their access to loans. For controls see Notes to Table IX.

The odds ratios reported in Table XI all exceed 1 which means that women in countries whose dominant language marks gender more intensively are much more likely to face some restriction in their access to credit. These results are significant not only economically but also (except for the aggregate GII) statistically. Table XII presents results for the set of robustness checks.

<table><caption>Table XII: Women's Restriction in Loan Access: Robustness Checks</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><th>GIIV2</th><td>2.025*<br>(0.758)</td><td>2.644**<br>(1.106)</td><td>2.530**<br>(0.953)</td><td>2.542**<br>(1.128)</td><td>1.691<br>(0.604)</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Geography</th><td>Yes</td><td></td><td></td><td></td><td></td></tr><tr><th>Climate</th><td></td><td>Yes</td><td></td><td></td><td></td></tr><tr><th>Colonization</th><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><th>Continent</th><td></td><td></td><td></td><td>Yes</td><td></td></tr><tr><th>Religion</th><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><th>Observations</th><td>63</td><td>63</td><td>63</td><td>63</td><td>58</td></tr><tr><th>Pseudo-R<sup>2</sup></th><td>0.328</td><td>0.486</td><td>0.391</td><td>0.397</td><td>0.311</td></tr><tr><th>χ<sup>2</sup></th><td>17.88</td><td>22.98</td><td>24.85</td><td>314</td><td>19.3</td></tr></tbody></table>

Notes: For definitions of the variables, see Notes to Table IV.

As Table XII reports, the coefficient for GIIV2 remains greater than 1 one and significant
throughout (except when we control for religion). The rest of our indices are also robust to the inclusion of controls for climate, geography and continent but they lose significance (except for NGII) when we control for religion.

Next we analyze an institutional feature of female participation in the credit: the holding of an account at a financial institution, as reported in the World Bank Gender Statistics. Table XIII presents cross-country OLS regressions for the female/male ratio of “percentage holding an account at a formal financial institution”.

<table><caption>Table XIII: Gender Ratio for Financial Account: OLS Regressions</caption><thead><tr><th></th><th></th><th colspan="7">Gender Index</th></tr><tr><th></th><th></th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th><th>GII</th><th>GIIV1</th><th>GIIV2</th></tr><tr><th></th><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><th>Coefficient</th><td></td><td>-0.146***<br>(0.0501)</td><td>-0.112*<br>(0.0598)</td><td>-0.218**<br>(0.0839)</td><td>-0.164***<br>(0.052)</td><td>-0.0515**<br>(0.021)</td><td>-0.0718**<br>(0.0274)</td><td>-0.0587***<br>(0.0198)</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Observations</th><td>138</td><td>105</td><td>105</td><td>74</td><td>104</td><td>72</td><td>72</td><td>101</td></tr><tr><th>R<sup>2</sup></th><td>0.241</td><td>0.332</td><td>0.3</td><td>0.351</td><td>0.354</td><td>0.326</td><td>0.326</td><td>0.345</td></tr><tr><th>Adjusted R<sup>2</sup></th><td>0.194</td><td>0.269</td><td>0.234</td><td>0.26</td><td>0.292</td><td>0.228</td><td>0.228</td><td>0.28</td></tr><tr><th>F-statistic</th><td>13.06</td><td>7.208</td><td>11.15</td><td>8.925</td><td>.</td><td>.</td><td>.</td><td>8.797</td></tr></tbody></table>

Notes: The OLS estimates are reported with robust standard errors given in parentheses; the unit of observation is a country. The dependent variable is the female/male to male ratio of “percentage holding an account at a formal financial institution” which ranges from 0.169 to 0.177 with a mean of 0.83 and a standard deviation of 0.22. For controls see Notes to Table IX.

As column (4) of the table shows, the gender ratio for a formal financial account is one standard deviation smaller in magnitude for countries whose dominant language assigns gender on both semantic and formal grounds. Similarly, columns (6) and (7) show that our aggregate intensity indices have a large and very significant impact. Indeed, all seven individual indices are negative and are both statistically and in economically significant. Women living in countries whose language marks gender more intensively are less likely to hold accounts in the formal financial industry, a result that is consistent with these women being less likely to save and obtain loans.

Table XIV reports robustness check results with GIIV2 for the gender ratio of financial account holding. As the table shows, GIIV2 remains strongly significant at the 1% level for all robustness
<table><caption>Table XIV: Gender Ratio for Financial Account: Robustness Checks</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>GIIV2</td><td>-0.0568***<br>(0.0202)</td><td>-0.0480**<br>(0.0211)</td><td>-0.0790***<br>(0.0225)</td><td>-0.0678***<br>(0.0244)</td><td>-0.0601***<br>(0.0211)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Geography</td><td>Yes</td><td></td><td></td><td></td><td></td></tr><tr><td>Climate</td><td></td><td>Yes</td><td></td><td></td><td></td></tr><tr><td>Colonization</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>Continent</td><td></td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Religion</td><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>101</td><td>101</td><td>101</td><td>101</td><td>101</td></tr><tr><td>R<sup>2</sup></td><td>0.346</td><td>0.425</td><td>0.377</td><td>0.429</td><td>0.432</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.273</td><td>0.339</td><td>0.3</td><td>0.336</td><td>0.347</td></tr><tr><td>F-statistic</td><td>7.968</td><td>7.363</td><td>8.994</td><td>9.187</td><td></td></tr></tbody></table>

Notes: For definitions of the variables, see Notes to Table IV.

checks except for Climate (it is significant at the 5% level).

Overall, then, gender marking bears a strong relation to female participation in the credit market, as indicated by their savings rates, access to loans, and holding of financial accounts. Moreover, our results are robust to a set of geography, climate, religion, continent, and colonization controls.

Table XV reports the correlation matrix for the credit market outcomes studied in this section and two measures of household bargaining power: marriage by age 18 and female-headed households.

<table><caption>Table XV: Credit Market Outcomes and Household Bargaining Power: Pairwise Correlations</caption><thead><tr><th></th><th>Savings</th><th>Financial Account</th><th>Loan Restrictions</th><th>Married by 18</th><th>Female Head</th></tr></thead><tbody><tr><td>Savings</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Financial Account</td><td>0.5367***</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Loan restrictions</td><td>-0.1743*</td><td>-0.2681***</td><td>1</td><td></td><td></td></tr><tr><td>Married by 18</td><td>-0.0955</td><td>-0.1817</td><td>0.4660***</td><td>1</td><td></td></tr><tr><td>Female Head</td><td>0.2592**</td><td>0.3891***</td><td>-0.2300*</td><td>-0.3203***</td><td>1</td></tr></tbody></table>

As the literature has established, in countries with a higher proportion of women married by age of 18 women are less likely to participate in the credit market. In contrast, there is more women's participation in countries with a higher proportion of female-headed households.
<table><caption>Table XVI: Household Bargaining Power and Gender Intensity Indices: Pairwise Correlations</caption><thead><tr><th></th><th>Married by 18</th><th>Female Head</th></tr></thead><tbody><tr><td>Married by 18</td><td>1</td><td></td></tr><tr><td>Female Head</td><td>-0.3203***</td><td>1</td></tr><tr><td>NGII</td><td>0.1847</td><td>-0.2460*</td></tr><tr><td>SBII</td><td>0.0118</td><td>-0.0319</td></tr><tr><td>GAII</td><td>0.2748*</td><td>0.0315</td></tr><tr><td>GPII</td><td>0.0577</td><td>-0.1143</td></tr><tr><td>GII</td><td>0.1279</td><td>-0.1489</td></tr><tr><td>GIIV1</td><td>0.1562</td><td>-0.1522</td></tr><tr><td>GIIV2</td><td>0.1236</td><td>-0.1762</td></tr></tbody></table>

Table XVI reports the correlation between the household bargaining variables and our gender intensity indices. Gender intensity in language is positively correlated with marriage by age 18 and negatively correlated with the share of female-headed households. This result suggests that one channel through which gender marking may influence female participation in the credit market is gender roles within the household.

### 4.1.3 Land

One cause of women's relative lack of access to credit is their lack of collateral assets. Although this deficiency can in turn be explained by having no independent source of revenue, (e.g.,as labor income), women also face restrictions in terms of property ownership. In this section, we investigate the relation between male-female distinctions in language and a measure of the restrictions faced by women seeking to own land. Such restrictions—in the form of inheritance laws, for instance—are indeed a barrier to female agency. For example, Dyson and Moore (1983) argue that inheritance laws have a negative effect on female autonomy in northern India.

Table XVII reports the odds ratios from cross-country logit regressions in which the dependent variable is a dummy set equal to 1 if women are either fully or partially restricted with respect to land ownership (and to 0 otherwise), as measured in the World Bank Gender Statistics. That the odds ratios in this table are much greater than 1 one implies that women in gender-intensive linguistic environments are more likely to face restrictions in land ownership. All of our gender
<table><caption>Table XVII: Womens Restriction in Access to Land:-Country Logit Regressions</caption><thead><tr><th></th><th></th><th colspan="7">Gender Index</th></tr><tr><th></th><th></th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th><th>GII</th><th>GIIV1</th><th>GIIV2</th></tr><tr><th></th><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><th>Coefficient</th><td></td><td>10.23**</td><td>4.103</td><td>37.12***</td><td>8.090*</td><td>2.289**</td><td>2.874**</td><td>2.057*</td></tr><tr><th></th><td></td><td>(11.18)</td><td>(3.586)</td><td>(51.93)</td><td>(8.896)</td><td>(0.821)</td><td>(1.363)</td><td>(0.782)</td></tr><tr><th>Observations</th><td>101</td><td>73</td><td>73</td><td>54</td><td>73</td><td>52</td><td>54</td><td>70</td></tr><tr><th>Pseudo-R<sup>2</sup></th><td>0.389</td><td>0.37</td><td>0.326</td><td>0.373</td><td>0.361</td><td>0.326</td><td>0.338</td><td>0.345</td></tr><tr><th>χ<sup>2</sup></th><td>37.09</td><td>25.08</td><td>23.76</td><td>18.92</td><td>21.56</td><td>21.63</td><td>22.11</td><td>23.89</td></tr></tbody></table>

Notes: The odds ratios from logit regressions are reported with robust standard errors given in parentheses. Restriction in Access to Land is an indicator variable set equal to 1 only if women are either partially or fully restricted in their access to land. For controls, see Notes to Table IX.

indices, (excepting only SBII), are significant.

<table><caption>Table XVIII: Womens Restriction in Access to Land: Robustness Checks</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><th>GIIV2</th><td>2.089*<br>(0.786)</td><td>2.266**<br>(0.839)</td><td>2.835**<br>(1.254)</td><td>2.481**<br>(1.072)</td><td>2.381**<br>(0.983)</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Geography</th><td>Yes</td><td></td><td></td><td></td><td></td></tr><tr><th>Climate</th><td></td><td>Yes</td><td></td><td></td><td></td></tr><tr><th>Colonization</th><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><th>Continent</th><td></td><td></td><td></td><td>Yes</td><td></td></tr><tr><th>Religion</th><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><th>Observations</th><td>70</td><td>70</td><td>70</td><td>70</td><td>69</td></tr><tr><th>Pseudo-R<sup>2</sup></th><td>0.345</td><td>0.421</td><td>0.378</td><td>0.419</td><td>0.362</td></tr><tr><th>χ<sup>2</sup></th><td>24.08</td><td>27.79</td><td>29.96</td><td>29.58</td><td>30.44</td></tr></tbody></table>

Notes: For the definitions of variables, see Notes to Table IV.

Table XVIII reports on our set of robustness check regressions for the GIIV2. This index is robust—statistically and in order of magnitude—to the inclusion of geography, climate, colonization, religion, and continent controls. Coefficients for the other indices (see the Appendix), remain fundamentally unchanged in magnitude and significance from the baseline regressions when we control for geography. They become even more significant when we control for climate and for colonization, with SBII also becoming significant in both cases. Furthermore, all seven indices are significant when controlling for continent and religion.
### 4.1.4 Politics

In this section we study countries adoption of legislated gender political quotas, from 1971 to 2011. Gender quotas are one of the main determinants of increased female political participation today, as Jutting et al. (2006) points out. Indeed, when we look at the direct impact of our gender intensity variables on the share of political positions held by women in 2000 we do not find statistically significant results, similar to Alesina Giuliano, and Nunn (forthcoming).

We use survival analysis to investigate the time it takes for a country to adopt gender political quotas during the period as function of the intensity of gender marking of their dominant language. The dependent variable is an indicator variable equal to one if a country adopts legislated quotas at time t. Following Kenworthy and Malami (1999), Dahlerup (2003), Paxton et al. (2010), and Tripp and Kang (2008) we control for electoral family (3 dummies: majoritarian, combined, proportional), number of years since suffrage rights were granted to all women, communist past and income per capita. Table XIX presents odd ratios of a Cox's proportional Hazard Model for the period 1971 to 2011.

<table><caption>Table XIX: Gender Legislated Political Quotas: Cox Hazard Regressions</caption><thead><tr><th></th><th></th><th colspan="8">Gender Index</th></tr><tr><th></th><th></th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th><th>GII</th><th>GIIV1</th><th>GIIV2</th></tr><tr><th></th><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Coefficient</td><td></td><td>2.040*</td><td>1.615</td><td>3.073**</td><td>3.339***</td><td>1.767***</td><td>1.609**</td><td>1.631**</td></tr><tr><td></td><td></td><td>(0.796)</td><td>(0.666)</td><td>(1.705)</td><td>(1.308)</td><td>(0.344)</td><td>(0.360)</td><td>(0.312)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>3,490</td><td>2,399</td><td>2,399</td><td>1,676</td><td>2,365</td><td>1,576</td><td>1,676</td><td>2,244</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.0201</td><td>0.0437</td><td>0.0379</td><td>0.0672</td><td>0.0569</td><td>0.0960</td><td>0.0737</td><td>0.0539</td></tr><tr><td>χ<sup>2</sup></td><td>7.196</td><td>12.60</td><td>10.40</td><td>12.31</td><td>16.03</td><td>20.61</td><td>15.67</td><td>13.48</td></tr></tbody></table>

Notes: The odds ratios are reported with robust standard errors given in parentheses. For controls, see text.

Since they are significant and bigger than one, it means that countries who mark gender more intensively in their language are more likely to be an early adopter of quotas. To address the possibility that quotas are a not “window dressing” policy and to assess the effort of countries in implementing quotas we repeated the analysis using a dummy that equals 1 if the country
has legislated quotas with sanctions and 0 otherwise. The direction and significance of results is preserved when using this alternative variable, as table XX shows.

<table><caption>Table XX: Gender Legislated Political Quotas with Sanctions: Cox Hazard Regressions</caption><thead><tr><th></th><th></th><th colspan="8">Gender Index</th></tr><tr><th></th><th></th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th><th>GII</th><th>GIIV1</th><th>GIIV2</th></tr><tr><th></th><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Coefficient</td><td></td><td>3.751***<br>(1.903)</td><td>6.942**<br>(6.824)</td><td>4.106<br>(4.292)</td><td>2.395<br>(1.290)</td><td>2.165*<br>(0.931)</td><td>2.422<br>(1.737)</td><td>2.042***<br>(0.512)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>3,630</td><td>2,539</td><td>2,539</td><td>1,791</td><td>2,484</td><td>1,679</td><td>1,791</td><td>2,363</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.0530</td><td>0.0753</td><td>0.0779</td><td>0.0789</td><td>0.0584</td><td>0.120</td><td>0.105</td><td>0.0832</td></tr><tr><td>χ<sup>2</sup></td><td>7.597</td><td>17.42</td><td>9.640</td><td>9.330</td><td>10.49</td><td>13.43</td><td>10.99</td><td>16.50</td></tr></tbody></table>

Notes: See Notes to Table XIX.

The fact that countries where female political participation is lowest are the most likely and earlier adopters of quota policies is consistent with the increasing international demand from international organizations for female political empowerment. Indeed, international organizations have been pushing for quota adoption and the World Bank (2012) report recommends the use of quotas to increase women's access to political institutions, such as parliament. In countries where language gender intensity is high women are relatively deprived in their access to land, credit and labor markets, increasing the need of using regulatory instruments to promote female political participation. Table XXI reports robustness checks of our results.
<table><caption>Table XXI: Gender Legislated Political Quotas and Sanctions: Robustness Checks</caption><thead><tr><th rowspan="2"></th><th colspan="4">Quota</th><th colspan="4">Sanctions</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th></tr></thead><tbody><tr><td>GHIV2</td><td>1.630***<br>(0.308)</td><td>1.700***<br>(0.339)</td><td>1.598**<br>(0.323)</td><td>1.600*<br>(0.393)</td><td>1.250<br>(0.244)</td><td>1.979***<br>(0.492)</td><td>1.906***<br>(0.429)</td><td>1.848**<br>(0.508)</td><td>2.581***<br>(0.877)</td><td>1.516<br>(0.407)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Geography</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Climate</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Colonization</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Continent</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Religion</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>2,244</td><td>2,236</td><td>2,244</td><td>2,244</td><td>2,244</td><td>2,363</td><td>2,355</td><td>2,363</td><td>2,363</td><td>2,363</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.0539</td><td>0.0713</td><td>0.0580</td><td>0.0810</td><td>0.103</td><td>0.0840</td><td>0.106</td><td>0.120</td><td>0.181</td><td>0.145</td></tr><tr><td>χ<sup>2</sup></td><td>13.82</td><td>18.80</td><td>20.49</td><td>14.72</td><td>675.7</td><td>17.67</td><td>25.09</td><td>9207</td><td>948.7</td><td>17749</td></tr></tbody></table>

Notes: For the definitions of robustness check variables, see Notes to Table IV. For description of controls see the main text.
## 4.2 Individual-Level Data

In this section we use individual-level data from the last three waves of the World Value Survey, which span the period 1994–2007. These data allows us to exploit information on the language that female respondents speak at home together with their labor force participation choice and their occupation in the agricultural sector. We do not use earlier waves because they do not include information regarding the language the respondent speaks at home. We control for age, age squared, primary and secondary education (via dummies), number of children, marital status (single, married, divorced, widowed) and survey wave fixed effects. We cluster standard errors at the country level and perform three robustness checks for individual religion, migrant father, and migrant mother.

Table XXII presents the odds ratios from logit regressions of female labor force participation (indicator variable set equal to 0 when the respondent is neither employed nor seeking employment). The odds ratios reported in this table are all less than 1 (albeit not all significant). In other words, women who speak a language that marks gender more intensively are less likely to supply labor.

<table><caption>Table XXII: Female Labor Participation and Household Language:-Level Logit Regressions
Gender Index</caption><thead><tr><th></th><th>(1)</th><th>NGII<br>(2)</th><th>SBII<br>(3)</th><th>GAII<br>(4)</th><th>GPII<br>(5)</th><th>GII<br>(6)</th><th>GIIV1<br>(7)</th><th>GIIV2<br>(8)</th></tr></thead><tbody><tr><td>Coefficient</td><td></td><td>0.542***<br>(0.114)</td><td>0.855<br>(0.225)</td><td>0.776<br>(0.194)</td><td>0.522***<br>(0.115)</td><td>0.861*<br>(0.0766)</td><td>0.844<br>(0.0988)</td><td>0.797**<br>(0.0869)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>89,383</td><td>47,584</td><td>48,474</td><td>58,257</td><td>59,796</td><td>59,796</td><td>60,842</td><td>48,474</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.114</td><td>0.156</td><td>0.153</td><td>0.167</td><td>0.167</td><td>0.155</td><td>0.166</td><td>0.149</td></tr><tr><td>χ<sup>2</sup></td><td>542.7</td><td>417.5</td><td>433.7</td><td>525.8</td><td>556.4</td><td>558.4</td><td>526.7</td><td>444</td></tr></tbody></table>

Notes: The odds ratios from logit regression estimates are reported with robust standard errors (in parentheses) clustered at the country level. The dependent (indicator) variable is set equal to 1 when the respondent is employed full-time or part-time or is self-employed or unemployed (and to 0 otherwise). Indices for gendered grammar correspond to the language spoken at home. For controls, see the main text.

Table XXIII presents the odds ratios from logit regressions of female employment in agricultural occupations among working-age women in the labor force. As the table shows, women
speaking a gender-intensive language at home are less likely (conditional on being in the labor force) than other women to be employed in an agricultural occupation. That is, the odds ratios are significantly less than 1 for all seven gender-marking indices.

<table><caption>Table XXIII: Female Agricultural Employment and Household Language -Level Logit Regressions</caption><thead><tr><th></th><th></th><th colspan="8">Gender Index</th></tr><tr><th></th><th></th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th><th>GII</th><th>GIIV1</th><th>GIIV2</th></tr><tr><th></th><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Coefficient</td><td></td><td>0.279***<br>(0.0936)</td><td>0.270***<br>(0.102)</td><td>0.302***<br>(0.123)</td><td>0.232***<br>(0.0774)</td><td>0.641***<br>(0.0857)</td><td>0.563***<br>(0.0973)</td><td>0.549***<br>(0.0908)</td></tr><tr><td>Controls</td><td>Yes</td><td></td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>84,451</td><td>45,257</td><td>46,145</td><td>55,646</td><td>57,183</td><td>57,183</td><td>58,173</td><td>46,145</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.106</td><td>0.135</td><td>0.133</td><td>0.124</td><td>0.109</td><td>0.119</td><td>0.116</td><td>0.113</td></tr><tr><td>χ<sup>2</sup></td><td>450.4</td><td>242.7</td><td>239.4</td><td>336.8</td><td>343.8</td><td>348.4</td><td>359.6</td><td>239.5</td></tr></tbody></table>

Notes: The dependent (indicator) variable is set equals to 1 only for respondents employed in an agricultural occupation—conditional on being part of the labor force. See also Notes to Table XIX.

Table XXIV reports our robustness checks for the individual-level logit regressions of female labor force participation and female agricultural employment. Our first robustness check is region. In particular, we control for a set of dummy variables set equal to 1 if the individual self-identifies as either a Buddhist, Catholic, Hindu, Jew, Muslim, Orthodox, or Protestant (and to 0 otherwise). Our second robustness check captures the migrant status of the respondent's parents via a pair of dummy variables set equal to 1 if the father and mother are migrants (respectively) migrants. We have information on the migrant status of respondent families only for the last wave of the WVS.

The individual-level data results presented in this section closely track our previously given results at the country level. In sum: women speaking a highly gendered language at home are less likely, ceteris paribus, to be part of the labor force or (if they are in the labor force) to work in agricultural occupations.
<table><caption>Table XXIV: Individual-Level Data: Robustness Checks</caption><thead><tr><th></th><th colspan="2">Labor</th><th colspan="2">Agriculture</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>GIIV2</td><td>0.799***<br>(0.0695)</td><td>0.739<br>(0.138)</td><td>0.555***<br>(0.0766)</td><td>0.442***<br>(0.12)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Religion</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Migrant</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>48,474</td><td>16,091</td><td>55,646</td><td>16,091</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.185</td><td>0.156</td><td>0.149</td><td>0.188</td></tr><tr><td>&chi;<sup>2</sup></td><td>—</td><td>307.7</td><td>—</td><td>169.7</td></tr></tbody></table>

Notes: See the text for controls and definitions.

# 5 Discussion and Further Analysis

Two main questions emerge from these empirical findings. First, what exactly does the gender marking in a language's grammar reflect? Second, why do languages differ in their extent of gender marking? More specifically, we should like to know whether grammatical structure affects the speaker's cognitive framework and whether cultural values can be crystalized in grammar—as well as how languages originate and why male-female distinctions are pervasive in some languages yet entirely absent in others.

In this section we discuss interdisciplinary literature and provide additional empirical analysis related to these questions.

Wichmann and Holman (2009) construct a measure of stability for analyzing the linguistic features described in the World Atlas of Linguistic Structures, (Dryer and Haspelmath, 2011). They compare their findings with categorical statements in the literature regarding the stability of analyzed features and find a high degree of concordance. Wichmann and Holman define stability as “the probability that a given language remains unchanged with respect to the feature during 1000 years, that is, the feature undergoes neither internal change nor diffusion during the interval” (Wichmann and Holman, 2009). Gender is classified as an extremely stable feature of language. Indeed, “the term gender was first used in the 5th century BC by the Greek philosopher Protagoras, [who] ... divided Greek nouns into three classes: ‘feminine’, ‘masculine’ and ‘inanimate’ (nowadays
called ‘neuter’)” (Aikhenvald 2004).

From an economic perspective, the stability of grammatical features is not surprising and could be related to how network externalities affect technology adoption (Katz and Shapiro, 1986). Language can be viewed as a technology characterized by networks externalities because the value of mastering a language increases with the number of its speakers. Linguistic evolution can thus be seen as a type of technological adaption. According to Katz and Shapiro “in the absence of sponsors, the technology [that is] superior today has a strategic advantage and is likely to dominate the market”.<fnref n="9" /> This dynamic is likely to obtain in the case of languages, which are not owned or sponsored (in this context, an owner/sponsor is “an entity that has property rights to the technology and hence is willing to make investments to promote it”; (Katz and Shapiro, [1986]).

As for the origins of language and grammatical structure, Johansson (2005) reviews the interdisciplinary literature. From a purely evolutionary perspective, a feature of language will be selected (or not) on the basis of its utility to speakers. That being said, a feature's current function may differ from its original function. The advent of agriculture postdates language, which probably evolved among humans when they lived in modest-sized tribes of hunter-gatherers. (Daly and Wilson 1999; see also Nesse and Williams 1994 [cited in Johansson 2005]). This remark of Johansson (2005) is noteworthy: “the division of labor between hunting men and gathering women, with the organized cooperation and exchange of food that it entails, is invoked by Quiatt (2001) as an explanation for the emergence of language.” (2005).

Other forces driving language evolution might include its conferring an advantage for reproductive success (Deacon, 1997). However, the most widely accepted explanation is the sociopolitical hypothesis, which Aiello (1998) extends by “arguing that social complexity drove the need for language, but also that social intelligence provided the cognitive structure for language.” Factors proposed in the literature as exerting selective pressure for the emergence of language include hunting, tool making, sex, child care, teaching, and social relations.

<footnote n="9">⁹Tang and Koveos (2008) argue that changes in economic conditions are the source of cultural dynamics, whereas institutions (including language, religion, and the law) provide the foundation for cultural stability.</footnote>
Since grammatical features often force us to encode certain aspects of reality to the exclusion of other aspects, it follows that applying an evolutionary perspective to grammar formation could well reveal selective pressures to codify the most relevant or salient aspects of our ancestors' reality, including culture and past economic specialization.

Under this hypothesis, we are led to ask: Why should economic specialization and/or culture of the distant past influence today's female participation in society? One possible answer is that culture is an extremely slow-moving institution, (cf. Roland 2004) and that a language's grammar serves as a marker of this persistent effect. An alternative (though not necessarily exclusive), hypothesis is that, irrespective of why heterogeneity exists in male-female distinctions across languages, such distinctions shape the gender consciousness of speakers as well as the perceptions of individuals belonging to a speaker's community.

Cognitive scientists currently study cross-linguistic differences that are related to time, colors, objects, and events. For example, Boroditsky, Schmidt, and Phillipip*et al.* (2003) examine how grammatical gender influences the way speakers of different languages think about inanimate objects. As the authors point out such “questions touch on all the major controversies in the study of mind, with important implications for politics, law and religion. Yet very little empirical work had been done on these questions until recently.”

Hence we provide three pieces of additional evidence that address aspects of the hypotheses just described. First, we investigate whether male-female distinctions in language have similar effects when we control for culture (via the World Values Survey). Second, we use the plough measure constructed by Alesina, Giuliano, and Nunn (forthcoming) to investigate whether gendered in language still matters after we control for economic specialization in the distant past; note that the current configuration of languages' gender marking was established prior to the 17th century which is the earliest period for which plough data has been collected. Third, we analyze the labor market behavior of a sample of migrants (with heterogeneous linguistic backgrounds) who are living in the United States as well as the behavior of a subsample of the WVS respondents whose household language differs from their country's dominant one.
## 5.1 Grammar and Culture

Table XXV presents the odds ratios from logit regression estimates in which the dependent (indicator) variable is set equal to 1 if the respondent is in the labor force (whether employed or unemployed) and set to 0 otherwise, from the World Value Survey last three waves.

We control for the baseline set of controls and for two variables that capture the respondent beliefs concerning gender roles as measured in the World Value Survey: Menpriority, (set to 1 only for respondents who believe that men should have priority when jobs are scarce) and Children-priority, (set equal to 1 only for respondents who believe that a woman needs children to fulfill her natural function).

As the table shows, our variables for linguistic gender marking linguistic variables remain highly significant and fundamentally unchanged. This could suggest that gender marking in grammar reflects more than cultural values.

<table><caption>Table XXV: Female Labor Participation and Culture: Individual-Level Logit Regressions</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>GIIV2</td><td></td><td>0.797**<br>(0.0869)</td><td></td><td>0.801**<br>(0.0827)</td><td></td><td>0.797**<br>(0.0869)</td><td></td><td>0.819**<br>(0.0823)</td></tr><tr><td>Menpriority</td><td></td><td></td><td>0.600***<br>(0.064)</td><td>0.591***<br>(0.0603)</td><td></td><td></td><td></td><td></td></tr><tr><td>Childrenpriority</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.801**<br>(0.0757)</td><td>0.816**<br>(0.0812)</td></tr><tr><td>Controls</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>89,383</td><td>58,257</td><td>87,498</td><td>57,194</td><td>89,383</td><td>58,257</td><td>58,471</td><td>38,165</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.114</td><td>0.167</td><td>0.123</td><td>0.174</td><td>0.114</td><td>0.167</td><td>0.13</td><td>0.178</td></tr><tr><td>χ<sup>2</sup></td><td>542.7</td><td>525.8</td><td>548.8</td><td>556.8</td><td>542.7</td><td>525.8</td><td>460.9</td><td>620.2</td></tr></tbody></table>

Notes: See Notes to Table XIX.

## 5.2 Grammar and the Plough

Alesina, Giuliano, and Nunn (forthcoming) show that historical agricultural practices—in particular, the use of the plough—are negatively correlated with current cultural norms about female labor force participation and traditionally “female” occupations and female labour force participation today.
<table><caption>Table XXVI: Labor Market Outcomes and Historical Use of the Plough</caption><thead><tr><th></th><th colspan="3">Female Labor Force Participation Rate</th><th colspan="3">Gender Ratio Employment Agriculture</th><th colspan="3">Gender Ratio Employment Services</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th></tr></thead><tbody><tr><td>GIIV2</td><td>-0.0648***<br>(0.0118)</td><td></td><td>-0.0667***<br>(0.0112)</td><td></td><td>-0.171***<br>(0.0445)</td><td></td><td>-0.141***<br>(0.0442)</td><td></td><td>0.0996***<br>(0.0317)</td><td></td><td>0.0796***<br>(0.0272)</td><td></td></tr><tr><td>Plough</td><td></td><td>-0.118***<br>(0.0301)</td><td></td><td>-0.0924***<br>(0.0307)</td><td></td><td></td><td>0.355***<br>(0.0981)</td><td>0.485***<br>(0.132)</td><td></td><td>-0.312***<br>(0.0836)</td><td>-0.394***<br>(0.0904)</td><td></td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td></td></tr><tr><td>Observations</td><td>175</td><td>112</td><td>172</td><td>111</td><td>138</td><td>90</td><td>135</td><td>89</td><td>131</td><td>91</td><td>128</td><td>90</td></tr><tr><td>R<sup>2</sup></td><td>0.17</td><td>0.445</td><td>0.26</td><td>0.498</td><td>0.261</td><td>0.314</td><td>0.355</td><td>0.451</td><td>0.172</td><td>0.315</td><td>0.295</td><td>0.505</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.141</td><td>0.408</td><td>0.229</td><td>0.459</td><td>0.227</td><td>0.256</td><td>0.32</td><td>0.396</td><td>0.132</td><td>0.258</td><td>0.253</td><td>0.456</td></tr></tbody></table>

Notes: The OLS estimates are reported with robust standard errors given in parentheses; the unit of observation is a country. The controls include log income and log income squared, trade openness, communism, government size, and share of speakers of the dominant language measured (year 2000).
This finding supports the Boserup (1997) hypothesis that gender roles originated from an economically grounded division of labor across genders. Similarly, Bardhan (1974) suggests that the heterogeneous role of women (across regions in India) may be due to the type of cultivation (i.e., wheat-versus rice-growing regions).

Table XXVI presents OLS regressions of female labor market outcomes that control for use of the plough. We also present results of plough regressions in which our gender marking indices are excluded. As the table shows, the plough indeed has a strongly significant and negative impact on the rate of female labor force participation rate. In fact, these figures echo the results presented by Alesina, Giuliano, and Nunn (forthcoming). Even so controlling for the plough does not reduce the significance and magnitude of the coefficients of gender marking language variables.

<table><caption>Table XXVII: Credit Market Measures and Historical Use of the Plough</caption><thead><tr><th></th><th colspan="4">Savings</th><th colspan="4">Formal Account</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>GIIV2</td><td></td><td>-0.0415***<br>(0.0139)</td><td></td><td>-0.0388***<br>(0.0141)</td><td></td><td>-0.0587***<br>(0.0198)</td><td></td><td>-0.0629***<br>(0.0202)</td></tr><tr><td>Plough</td><td></td><td></td><td>0.0312<br>(0.0418)</td><td>0.0394<br>(0.0453)</td><td></td><td></td><td>-0.0307<br>(0.0559)</td><td>-0.0618<br>(0.0596)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>138</td><td>101</td><td>135</td><td>99</td><td>138</td><td>101</td><td>135</td><td>99</td></tr><tr><td>R<sup>2</sup>-squared</td><td>0.131</td><td>0.253</td><td>0.137</td><td>0.26</td><td>0.241</td><td>0.345</td><td>0.24</td><td>0.353</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.0767</td><td>0.179</td><td>0.0752</td><td>0.176</td><td>0.194</td><td>0.28</td><td>0.185</td><td>0.28</td></tr></tbody></table>

Notes: The OLS estimates are reported with robust standard errors given in parentheses; the unit of observation is a country. The controls include log income, legal index, government size, legal origin, the share of speakers of the dominant language, and a dummy for Protestantism.

Table XXVII presents cross-country OLS regressions for credit market-related variables when we control for use of the plough. As the table shows, in this case the significance and magnitude of the coefficients for our gender-marking variables are not reduced. Furthermore, it is interesting that the plough does not have a significant impact on women's credit market participation.

Table XXVIII presents cross-country OLS regressions for the variables capturing women's restrictions in loan and land access when we control for historical use of the plough. As the table shows, controlling for the use of the plough does not reduce the significance and magnitude of
<table><caption>Table XXVIII: Loan and Land Restrictions and Use of the Plough: Cross-Country OLS</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>GIIV2</td><td></td><td>2.074**<br>(0.75)</td><td></td><td>2.142**<br>(0.777)</td><td></td><td>2.057*<br>(0.782)</td><td></td><td>2.296**<br>(0.886)</td></tr><tr><td>Plough</td><td></td><td></td><td>0.596<br>(0.406)</td><td>1.726<br>(1.581)</td><td></td><td></td><td>1.254<br>(0.797)</td><td>2.745<br>(2.034)</td></tr><tr><td>Observations</td><td>95</td><td>63</td><td>93</td><td>62</td><td>101</td><td>70</td><td>99</td><td>69</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.389</td><td>0.323</td><td>0.382</td><td>0.321</td><td>0.389</td><td>0.345</td><td>0.376</td><td>0.354</td></tr></tbody></table>

Notes: The odds ratios from logit regressions are reported with robust standard errors in parentheses. For controls see Notes to Table XXIV.

the coefficients for our gender gender-marking language variables. Much as with its influence on credit market, the plough does not have a significantly affect restrictions on women's land ownership. These findings suggest that a fruitful direction for future research is to analyze the relation between historical agricultural practices and the gender systems of different languages' grammars.

## 5.3 US Immigrants

In this section we use the publicly available dataset version of the New Immigrant Survey (NIS) data set, a multi-cohort longitudinal study of new legal immigrants and their children to the United States based on nationally representative samples.<fnref n="10" /> This data set allows us to study, for working-age (i.e., 18–65) female migrants, the effect of their languages’ gender structure on their labor supply in the United States. We run OLS regressions with robust standard errors clustered at the country-of-origin level (using sample weights provided by the NIS). Our dependent variable is the number of hours worked annually in the United States in the respondent’s “self-designated main (current) occupation.” We control for age, age squared, years of schooling completed before migrating, schooling in the United States, a dummy for current US schooling, a dummy for married spouse present in the United States, hours worked annually before migrating, and number of children.

The results are given in Table XXIX. This table shows that, except for GAI1, all gender

<footnote n="10">¹⁰For more information, please see http://nis.princeton.edu/index.html.</footnote>
<table><caption>Table XXIX: US Working Hours of Female Migrants: OLS Regressions</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr><tr><th>Gender Index</th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th><th>GII</th><th>GIIV1</th><th>GIIV2</th><th></th></tr></thead><tbody><tr><th>Coefficient</th><td>183.8**<br>(84.34)</td><td>341.9***<br>(72.78)</td><td>109.1<br>(104.1)</td><td>169.7**<br>(82.46)</td><td>64.25**<br>(25.07)</td><td>102.8***<br>(33.80)</td><td>79.89**<br>(32.19)</td><td></td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Observations</th><td>2,990</td><td>2,823</td><td>2,823</td><td>2,536</td><td>2,829</td><td>2,536</td><td>2,820</td><td>2,536</td></tr><tr><th>R<sup>2</sup></th><td>0.063</td><td>0.072</td><td>0.081</td><td>0.073</td><td>0.072</td><td>0.078</td><td>0.077</td><td>0.077</td></tr><tr><th>Adjusted R<sup>2</sup></th><td>0.0605</td><td>0.0690</td><td>0.0784</td><td>0.0695</td><td>0.0686</td><td>0.0750</td><td>0.0742</td><td>0.0741</td></tr><tr><th>F-statistic</th><td>26.35</td><td>30.05</td><td>49.02</td><td>33.81</td><td>32.44</td><td>37.04</td><td>33.21</td><td>37.18</td></tr></tbody></table>

Notes: The OLS estimates are reported with robust standard errors (in parentheses) clustered at the country-of-origin of origin level. The unit of observation is an individual (working-age female migrants in the United States). For controls, see the main text.

intensity indices are positively and significantly correlated to the US labor supply of migrant women. In other words, female immigrants to the United States work more hours if the source country's language marks gender more frequently. Table XXVII presents robustness checks results for visa type, race/ethnicity, religion, and English mastery.

<table><caption>Table XXX: US Working Hours of Female Migrants: Robustness Checks</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><th>GIIV2</th><td>93.37***<br>(23.46)</td><td>102.5***<br>(36.55)</td><td>101.9***<br>(36.71)</td><td>106.5***<br>(25.68)</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Visa</th><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><th>Ethnicity</th><td>No</td><td>Yes</td><td>No</td><td>No</td></tr><tr><th>Religion</th><td>No</td><td>No</td><td>Yes</td><td>No</td></tr><tr><th>English</th><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><th>Observations</th><td>2,820</td><td>2,610</td><td>2,820</td><td>2,750</td></tr><tr><th>R<sup>2</sup></th><td>0.117</td><td>0.082</td><td>0.085</td><td>0.107</td></tr><tr><th>Adjusted R<sup>2</sup></th><td>0.112</td><td>0.0769</td><td>0.0796</td><td>0.103</td></tr><tr><th>F-statistic</th><td>85.05</td><td>27.58</td><td>23.72</td><td>62.67</td></tr></tbody></table>

Notes: Visa includes dummies for the visa type (family, employment, etc.) Ethnicity includes dummies for ethnicity (Hispanic, Indian, White, etc.) Religion: includes dummies for the country's most prevalent religion (Catholic, Protestant, Islamic, etc.) English includes dummies indicating (four) levels of English mastery.

Why does a language's greater extent of gender marking lead to *lower* labor force participation among women (at both the country and individual level) for the general population but to *higher*
labor force participation among migrants living in the United States?

To understand the potential mechanisms behind this finding, we use the WVS and compare the labor market participation of women living in countries with low-gendered languages (GII ≤2) who speak languages with different gender grammatical structure at home. We focus on this subset of individuals to capture the situation of migrants arriving to the United States, whose dominant language (English) is a low gendered language. We analyze the impact of the immigrants’ home language on their labor supply behavior and obtain results that are consistent with our previous findings.

In particular, Table XXX reports the odds ratios from logit regression estimates (with robust standard errors, in parentheses, clustered at the country level) for the subsample of female respondents living in countries whose dominant language is low gendered (GII ≤2) but who speak a language with different gender grammatical structure at home. The dependent (indicator) variables is set equal to 1 if the respondent is employed full-or part-time or is either self-employed or unemployed (and is set to 0 otherwise). The grammar language variables correspond to the language spoken at home.

<table><caption>Table XXXI: Women in Low-Gendered Dominant Language Countries: WVS</caption><thead><tr><th></th><th>(1)<br>GII</th><th>(2)<br>GIIV1</th><th>(3)<br>GIIV2</th></tr></thead><tbody><tr><td>Coefficient</td><td>1.281***<br>(0.0639)</td><td>1.096<br>(0.0830)</td><td>1.361***<br>(0.0792)</td></tr><tr><td>Observations</td><td>1,689</td><td>1,350</td><td>3,816</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.178</td><td>0.172</td><td>0.165</td></tr><tr><td>χ<sup>2</sup></td><td>306.5</td><td>226.1</td><td>692.3</td></tr></tbody></table>

Notes: See description and definitions in the main text.

The odds ratios here are greater than 1, which mirrors our previous results concerning migrants to the United States. That is, women who reside in a low-gendered language environment are more likely to be employed if their household language is a more gender-intensive one. It is not surprising to obtain different results for migrants given that they are a self-selected group and so will naturally differ from baseline groups. Furthermore, migrants may less attached to their
culture and even migrate to escape their country of origin cultural constraints.

## 5.4 Grammar and Linguistic Families

Table XXXII presents cross country OLS results for female labor force participation controlling for language families origin and grammatical gender marking indices. We also analyzed the regressions presented in the paper controlling for (1) Indo-European family origin, the most represented family in the sample, in addition to the baseline set of economic controls and gender grammatical indices, (2) clustering standard errors at the family level and (3) restricting the sample to countries with Indo-European languages. The later results are included in the appendix available upon request.

The results of these suggest that both language families origin and gender marking matter. Namely, both are very significant. Therefore, the impact of gender marking intensity on socio-economic outcomes comes both from across and within linguistic family variations in languages grammatical structure.
<table><caption>Table XXXII: Female Labor Force Participation and Linguistic Families</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Gender Index</td><td>NGII</td><td>SBII</td><td>GPII</td><td>GIIII</td><td>GII</td><td>GIIV1</td><td>GIIV2</td><td></td></tr><tr><td>Coefficient</td><td>-0.0759***<br>(0.0224)</td><td>-0.0384<br>(0.0453)</td><td>-0.0835***<br>(0.0313)</td><td>-0.0478<br>(0.0381)</td><td>-0.0310**<br>(0.0141)</td><td>-0.0443**<br>(0.0219)</td><td>-0.0404***<br>(0.013)</td><td></td></tr><tr><td>Linguistic Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td></td></tr><tr><td>Observations</td><td>162</td><td>119</td><td>119</td><td>118</td><td>87</td><td>83</td><td>87</td><td>113</td></tr><tr><td>R-squared</td><td>0.46</td><td>0.523</td><td>0.489</td><td>0.505</td><td>0.513</td><td>0.549</td><td>0.537</td><td>0.537</td></tr><tr><td>Adjusted R-squared</td><td>0.409</td><td>0.464</td><td>0.426</td><td>0.443</td><td>0.441</td><td>0.479</td><td>0.469</td><td>0.477</td></tr></tbody></table>

Notes: The OLS estimates are reported with robust standard errors (in parentheses). The unit of observation is a country. Linguistic controls include a set of dummy variables equal to 1 if language family is Afro-Asiatic, Niger-Congo, Altaic, Austronesian, Austro-Asiatic, Nilo-Saharan Sino-Tibetan, Uralic, Kartvelian, Macro-Ge, Japanese, Korean, Tai-Kadai or Indo-European. For the sample in Column (8), 63 of the countries have a Indo-European language, 22 Afro-Asiatic, 7 Altaic and 5 have a Niger-Congo language.
# 6 Conclusion

This paper presents novel empirical relations between language's grammatical gender markings and female economic and political participation. At both the country and the individual level, women who speak in a language—or who live in countries with a dominant language—that marks gender more intensively are less likely to participate in economic and political life and more likely to face formal and informal barriers when seeking to access credit and land. These findings are relevant to several long-debated questions about the origin of language, its evolution, and its effect on speakers.

To control for omitted variable bias, we perform a systematic and exhaustive set of robustness checks that aim to capture the influence of history, geography, religion, and colonization on women's economic outcomes. Our results are strongly robust to these controls.

So that we may further investigate and better understand the nature of our findings, we also analyze the impact of language grammar in a sample of migrants living in the United States, and therefore facing similar institutional environments. Their labor supply behavior suggests that the effect of an individual's own spoken language interacts with the language spoken by neighbors and other residents. We then show that language influences female labor force participation even after we control for current beliefs and values, for historical agricultural practices (i.e., use of the plough), as suggested by Alesina, Giuliano, and Nunn (forthcoming) and for linguistic families.

The direct and possibly cognitive influence of a language on its speakers and on economic life may have important policy implications. For instance, understanding this connection would facilitate the debate regarding the need to implement quotas or to opt for market forces to drive women economic participation up and thereby increase overall economic prosperity.
# 7 References

Aikhenvald, Alexandra Y. (2004) Gender and Noun Class. In Geert Booij, Christian Lehmann Joachim Mugdan (eds.), Morphology: a Handbook on Inflection and Word Formation volume 2, 1031-1045. Berlin: Mouton de Gruyter.

Acemoglu, D., Johnson, S., and Robinson J. A. (2001). ‘The colonial origins of comparative development: an empirical investigation’, *American Economic Review*, vol. 91(5), pp. 1369-401.

Aiello, L. C. (1998). ‘The foundations of human language’. In Jablonski, N. G. Aiello, L. C., (Eds), The origin and diversification of language. San Francisco: California Academy of Sciences

Aikhenvald, Alexandra Y. (2004) Gender and Noun Class. In Geert Booij, Christian Lehmann Joachim Mugdan (eds.), Morphology: a Handbook on Inflection and Word Formation volume 2, 1031-1045. Berlin: Mouton de Gruyter.

Alberto Alesina, A., Paola Giuliano, P., and Nathan Nunn, N. (forthcoming). "On the Origins of Gender Roles: Women and the Plough," Quarterly Journal of Economics, forthcoming.

Bardhan, Pranab. (1974) ‘On Life and Death Questions’ Economic and Political Weekly 9(32-34) pg 1293-1304.

Baslevent, C. and Onaran, O. (2004). ‘The effect of export-oriented growth on female labor market outcomes in Turkey’, *World Development*, vol. 32(8), pp. 1475-93.

Bloom, D. and Sachs, J. D. (1998). ‘Geography, demography, and economic growth in Africa’, *Brookings Papers on Economic Activity*, vol. 29(2), pp. 207-95.

Boroditsky, L. and Gaby, A. (2010). ‘Remembrances of times East: Absolute spatial representations of time in an Australian aboriginal community’, *Psychological Science*, vol. 21(11), pp. 1635-9.

Boroditsky, L., Schmidt, L., and Phillips, W. (2003). ‘Sex, syntax, and semantics’, in (D. Gertner and S. Goldin-Meadow (ed.), *Language in Mind: Advances in the Study of Language and Cognition*, MIT Press: Cambridge, MA.

Boserup, Ester. (1970) Womans Role in Economic Development, Lon- don: George Allen and
Unwin Ltd.

Cavalcanti T. V. De V. and Jos Tavares, 2011. 'Women Prefer Larger Governments: Growth, Structural Transformation, And Government Size,' Economic Inquiry, Western Economic Association International, vol. 49(1), pages 155-171, 01.

Chen, M. K. (2013). The Effect of Language on Economic Behavior: Evidence from Savings Rates, Health Behaviors, and Retirement Assets. Forthcoming: American Economic Review
Chomsky, N. (1980). *Rules and Representations*, New York: Columbia University Press.

Christiansen, M. H. and Kirby, S. (2003). 'Language evolution: The hardest problem in science?', in (M.H. Christiansen and S. Kirby ed.), *Language Evolution: The States of the Art*, Oxford: Oxford University Press.

Corbett, G. G. (1991) Gender (Cambridge Textbooks in Linguistics). Cambridge: Cambridge
University Press.

Corbett, G. G. (2011a). 'Number of genders', in (M. S. Dryer, and M. Haspelmath ed.), *The World Atlas of Language Structures Online*, Munich: Max Planck Digital Library, chapter 30.

Corbett, G. G. (2011b). 'Sex-based and non-sex-based gender systems ', in (M. S. Dryer, and M. Haspelmath ed.), *The World Atlas of Language Structures Online*, Munich: Max Planck Digital Library, chapter 31.

Corbett, G. G. (2011c). 'Systems of gender assignment ', in (M. S. Dryer, and M. Haspelmath ed.), *The World Atlas of Language Structures Online*, Munich: Max Planck Digital Library, chapter 32.

Corbett, Greville C. (1991) Gender (Cambridge Textbooks in Linguistics). Cambridge: Cambridge University Press.

Daly, M., Wilson, M. I. (1999). Human evolutionary psychology and animal behaviour. Animal
Behaviour, 57(3), 509-519.

Deacon, T.W. (1997). "What makes the human brain different?" Annual Review Anthropology
26: 337-57.

Doepke, Tertilt, & Voena 2011 *The Economics and Politics of Womens Rights* cited in T-XXXIII
Dryer, M. S. and Haspelmath, M. (ed.). (2011). *The World Atlas of Language Structures Online*, Munich: Max Planck Digital Library.

Duflo, E. (2012) ‘Womens Empowerment and Economic Development’, *Journal of Economic Literature*, Vol. 50, No. 4: 1051-79

Dyson, Tim and Mick Moore (1983) On Kinship Structure, Female Autonomy, and Demographic Behavior in India Population and Development Review Vol.9, No. 1, pp 35-60.

Easterly, W. and Levine, R. (1997). ‘Africa’s growth tragedy: Policies and ethnic Divisions’, *The Quarterly Journal of Economics*, vol. 112(4), pp. 1203-50.

Fernández, R. (2010). ‘Does Culture Matter?’, NBER Working Papers No. 16277.

Gallup, J. L., Sachs, J. D. and Mellinger, A. D. (1998). ‘Geography and economic development’, NBER Working Paper No. 6849.

Goldin, C. (1995). ‘The U-shaped female labour force function in economic development and economic history’, in (T. P. Shultz ed.), *Investment in Womens Human Capital and Economic Development*, pp. 61-90, Chicago: University of Chicago Press.

Guiso, L., Sapienza, P. and Zingales, L. (2009). ‘Moral and social constraints to strategic default on mortgages’, NBER Working Paper No. 15145.

Hall, R. E. and Jones, C. I. (1999). ‘Why do some countries produce so much more output per worker than others?’, *The Quarterly Journal of Economics*, vol. 114(1), pp. 83-116.

Hallward-Driemeier, Mary Rijkers, Bob Waxman, Andrew, (2011). ‘Ladies first ? firm-level evidence on the labor impacts of the East Asian crisis,’ *Policy Research Working Paper Series* 5789, The World Bank.

Johansson, Sverker (2005) ‘Origins of language : constraints on hypotheses’. Converging evidence in language and communication research vol. 5, Amsterdam : Benjamins.

Katz, M. L. and Shapiro, C. (1986). ‘Technology adoption in the presence of network externalities’, *Journal of Political Economy*, vol. 94(4), pp. 822-41.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A. and Vishny, R. W. (1998). ‘Law and finance’, *Journal of Political Economy*, vol. 106(6), pp. 1113-55.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A. and Vishny, R. W. (1999), ‘The quality of govern-
ment’, *Journal of Law, Economics and Organizations*, vol. 15(1), pp. 222-79.

Licht, A. N., Goldschmidt, C. and Schwartz, S. H. (2007). ‘Culture rules: The foundations of the rule of law and other norms of governance’, *Journal of Comparative Economics*, vol. 35(4), 659-88.

Mauro, P. (1995). ‘Corruption and growth’, *The Quarterly Journal of Economics*, vol. 110(3), pp. 681-712.

Mavisakalyan, A. (2011). ‘Gender in language and gender in employment ’, ANUCBE School of Economics Working Paper.

Nesse, R. M. Williams, G. C. (1994). Why we get sick. London: Vintage

North, D. C. (1990). *Institutions, Institutional Change, and Economic Performance*, New York: Cambridge University Press.

North, D. C. (1993). ‘Douglass C. North Prize Lecture’.

Paxton, Pamela, Melanie Hughes, and Matthew Painter. (2010). ‘Growth in Womens Politi-
cal Representation: A Longitudinal Exploration of Democracy, Electoral System, and Gender
Quotas.’ European Journal of Political Research 49:25-52.

Quiatt, D. (2001). Language and evolution in the Middle Stone Age. In Gyori, G., (Ed), Language Evolution. Biological, linguistic and philosophical perspectives. Frankfurt am Main: Peter Lang

Rajan, R., Zingales, L., 1998. Financial dependence and growth. American Economic Review 88, 559586.

Roland, Gerard. 2004 Understanding Institutional Change: Fast-Moving and Slow-Moving Insti-
tutions, Studies in Comparative International Development

Ross, M. (2008). ‘Oil, Islam, and women’, *American Political Science Review*, vol. 102(1), pp. 107-23.

Sampson, G., Gill, D. and Trudgill, P. (ed.) (2009). *Language complexity as an evolving variable*.
Oxford: Oxford University Press.

Sen, Amartya. 1999 Development as Freedom. Oxford: Oxford University Press.
Siewierska, A. (2011). ‘Gender distinctions in independent personal pronouns’, in (M. S. Dryer, and M. Haspelmath ed.), *The World Atlas of Language Structures Online*, Munich: Max Planck Digital Library, chapter 44.

Tabellini, Guido (2008a) The Scope of Cooperation: Values and Incentives, Quarterly Journal of Eocnomics 123(3): 905-950.

Tabellini, G. (2008b) ‘Institutions and Culture. Presidential Address European Economic Association. Journal of the European Economic Association 6: 255-294.

Tang, L. and Koveos, P. (2008). ‘A framework to update Hofstede’s cultural value indices: Economic dynamics and institutional stability,’ *Journal of International Business Studies*, vol. 39(6), 1045-63.

Weber, Max, Die protestantische Ethik und der Geist des Kapitalismus, Archiv fu r Sozialwissenschaften und Sozialpolitik, 1904/05, 20-21, 1784; 1110. English Translation: The Protestant Ethic and the Spirit of Capitalism, London: Allen Unwin, 1930.

Wichmann, S. and Holman, E. W. (2009). ‘Assessing temporal stability for linguistic typological features’, Mnchen: LINCOM Europa.

World Bank Development Report 2012 cited in 1 of 1

# 8 Appendix
<table><caption>Table XXXIII: Intensity Indices for the Dominant Language by Country (A–F)</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Language</th><th colspan="5">Speakers</th></tr><tr><th>(%)</th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th></tr></thead><tbody><tr><td>Afghanistan</td><td>Pashto</td><td>52</td><td>1</td><td>1</td><td>1</td><td>n/a</td></tr><tr><td>Albania</td><td>Albanian</td><td>98</td><td>1</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Algeria</td><td>Arabic</td><td>72</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Argentina</td><td>Spanish</td><td>97</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Armenia</td><td>Armenian</td><td>93</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Australia</td><td>English</td><td>81</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Austria</td><td>German</td><td>92</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Azerbaijan</td><td>Azerbaijani</td><td>89</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Bahrain</td><td>Arabic</td><td>68</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Bangladesh</td><td>Bengali</td><td>95</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Belarus</td><td>Belorussian</td><td>66</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Belgium</td><td>Dutch</td><td>59</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Belize</td><td>English</td><td>51</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Bermuda</td><td>English</td><td>100</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Bolivia</td><td>Spanish</td><td>88</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Bosnia and Herzegovina</td><td>Serbian-Croatian</td><td>92</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Brazil</td><td>Portuguese</td><td>98</td><td>1</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Brunei</td><td>Malay</td><td>80</td><td>0</td><td>0</td><td>n/a</td><td>n/a</td></tr><tr><td>Bulgaria</td><td>Bulgarian</td><td>83</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Cambodia</td><td>Khmer</td><td>89</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Canada</td><td>English</td><td>60</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Chile</td><td>Spanish</td><td>90</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>China</td><td>Mandarin</td><td>72</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Columbia</td><td>Spanish</td><td>99</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Congo, Rep.</td><td>Kongo</td><td>51</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>Costa Rica</td><td>Spanish</td><td>97</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Croatia</td><td>Serbian-Croatian</td><td>96</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Cuba</td><td>Spanish</td><td>100</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Cyprus</td><td>Greek</td><td>74</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Czech Rep.</td><td>Czech</td><td>94</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Denmark</td><td>Danish</td><td>95</td><td>1</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Dominican Rep.</td><td>Spanish</td><td>98</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Ecuador</td><td>Spanish</td><td>93</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Egypt</td><td>Arabic</td><td>99</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>El Salvador</td><td>Spanish</td><td>100</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Eritrea</td><td>Tigrinya</td><td>49</td><td>n/a</td><td>n/a</td><td>n/a</td><td>1</td></tr><tr><td>Estonia</td><td>Estonian</td><td>65</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Ethiopia</td><td>Oromo (Harar)</td><td>30</td><td>1</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Fiji</td><td>Hindi</td><td>81</td><td>1</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Finland</td><td>Finnish</td><td>92</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>France</td><td>French</td><td>94</td><td>1</td><td>1</td><td>1</td><td>0</td></tr></tbody></table>
<table><caption>Table XXXIV: Intensity Indices for the Dominant Language by Country (G–M)</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Language</th><th colspan="5">Speakers</th></tr><tr><th>(%)</th><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th></tr></thead><tbody><tr><td>Georgia</td><td>Georgian</td><td>71</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Germany</td><td>German</td><td>91</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Greece</td><td>Greek</td><td>98</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Guatemala</td><td>Spanish</td><td>65</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Guinea</td><td>Fula</td><td>39</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>Honduras</td><td>Spanish</td><td>97</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Hong Kong</td><td>Cantonese</td><td>89</td><td>0</td><td>0</td><td>0</td><td>n/a</td></tr><tr><td>Hungary</td><td>Hungarian</td><td>99</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Iceland</td><td>Icelandic</td><td>96</td><td>0</td><td>1</td><td>1</td><td>n/a</td></tr><tr><td>India</td><td>Hindi</td><td>40</td><td>1</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Indonesia</td><td>Javanese</td><td>39</td><td>0</td><td>0</td><td>n/a</td><td>n/a</td></tr><tr><td>Iran</td><td>Persian</td><td>46</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Iraq</td><td>Arabic</td><td>77</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Ireland</td><td>English</td><td>98</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Israel</td><td>Hebrew</td><td>63</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Italy</td><td>Italian</td><td>94</td><td>1</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Japan</td><td>Japanese</td><td>99</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Jordan</td><td>Arabic</td><td>98</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Kazakhstan</td><td>Kazakh</td><td>46</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Kiribati</td><td>Kiribati</td><td>99</td><td>n/a</td><td>n/a</td><td>n/a</td><td>0</td></tr><tr><td>Korea, South</td><td>Korean</td><td>100</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Kuwait</td><td>Arabic</td><td>78</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Kyrgystan</td><td>Kirghiz</td><td>60</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Laos</td><td>Lao</td><td>53</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Latvia</td><td>Latvian</td><td>56</td><td>1</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Lebanon</td><td>Arabic</td><td>91</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Libya</td><td>Arabic</td><td>96</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Lithuania</td><td>Lithuanian</td><td>84</td><td>1</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Macau</td><td>Cantonese</td><td>86</td><td>0</td><td>0</td><td>0</td><td>n/a</td></tr><tr><td>Macedonia</td><td>Macedonian</td><td>67</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Madagascar</td><td>Malagasy</td><td>99</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Malawi</td><td>Chichewa</td><td>59</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>Malaysia</td><td>Malay</td><td>58</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Mali</td><td>Bambara</td><td>32</td><td>n/a</td><td>n/a</td><td>n/a</td><td>0</td></tr><tr><td>Malta</td><td>Maltese</td><td>90</td><td>1</td><td>1</td><td>1</td><td>n/a</td></tr><tr><td>Mauritania</td><td>Arabic</td><td>82</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Mexico</td><td>Spanish</td><td>92</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Moldova</td><td>Romanian</td><td>62</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Mongolia</td><td>Khalkha</td><td>84</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Morocco</td><td>Arabic</td><td>65</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Myanmar (Burma)</td><td>Burmese</td><td>69</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>
<table><caption>Table XXXV: Intensity Indices for the Dominant Language by Country (N-S)</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Language</th><th>Speakers</th><th rowspan="2">NGII</th><th rowspan="2">SBII</th><th rowspan="2">GAII</th><th rowspan="2">GPII</th></tr><tr><th>(%)</th></tr></thead><tbody><tr><td>Namibia</td><td>Ndonga</td><td>60</td><td>n/a</td><td>n/a</td><td>n/a</td><td>0</td></tr><tr><td>Nepal</td><td>Nepali</td><td>50</td><td>n/a</td><td>n/a</td><td>n/a</td><td>0</td></tr><tr><td>Netherlands</td><td>Dutch</td><td>96</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>New Zealand</td><td>English</td><td>91</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Nicaragua</td><td>Spanish</td><td>98</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Niger</td><td>Hausa</td><td>53</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Nigeria</td><td>Hausa</td><td>21</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Norway</td><td>Norwegian</td><td>97</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Oman</td><td>Arabic</td><td>77</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Pakistan</td><td>Panjabi</td><td>48</td><td>1</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Palau</td><td>Palauan</td><td>83</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Panama</td><td>Spanish</td><td>77</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Papua New Guinea</td><td>Papuan Lang.</td><td>78</td><td>0</td><td>1</td><td>0</td><td>n/a</td></tr><tr><td>Paraguay</td><td>Spanish</td><td>55</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Peru</td><td>Spanish</td><td>80</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Philippines</td><td>Tagalog</td><td>29</td><td>1</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Poland</td><td>Polish</td><td>98</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Portugal</td><td>Portuguese</td><td>99</td><td>1</td><td>1</td><td>n/a</td><td>n/a</td></tr><tr><td>Puerto Rico</td><td>Spanish</td><td>85</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Qatar</td><td>Arabic</td><td>40</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Romania</td><td>Romanian</td><td>89</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Russia</td><td>Russian</td><td>81</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Samoa</td><td>Samoan</td><td>99</td><td>n/a</td><td>n/a</td><td>n/a</td><td>0</td></tr><tr><td>Saudi Arabia</td><td>Arabic</td><td>95</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Senegal</td><td>Wolof</td><td>48</td><td>n/a</td><td>n/a</td><td>n/a</td><td>0</td></tr><tr><td>Serbia</td><td>Serbian-Croatian</td><td>75</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Singapore</td><td>Mandarin</td><td>77</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Slovakia</td><td>Slovak</td><td>86</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Slovenia</td><td>Slovene</td><td>88</td><td>0</td><td>1</td><td>n/a</td><td>0</td></tr><tr><td>Somalia</td><td>Somali</td><td>98</td><td>n/a</td><td>n/a</td><td>n/a</td><td>n/a</td></tr><tr><td>South Africa</td><td>Zulu</td><td>24</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>Spain</td><td>Spanish</td><td>74</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Sudan</td><td>Arabic</td><td>49</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Sweden</td><td>Swedish</td><td>90</td><td>1</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Switzerland</td><td>German</td><td>64</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Syria</td><td>Arabic</td><td>90</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>
<table><caption>Table XXXVI: Intensity Indices for the Dominant Language by Country (T-Z)</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Language</th><th rowspan="2">Speakers (%)</th><th colspan="4">Speakers</th></tr><tr><th>NGII</th><th>SBII</th><th>GAII</th><th>GPII</th></tr></thead><tbody><tr><td>Taiwan</td><td>Xiamen</td><td>67</td><td>0</td><td>0</td><td>n/a</td><td>n/a</td></tr><tr><td>Tajikistan</td><td>Tajik</td><td>62</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Tanzania</td><td>Nyamwezi-Sukuma</td><td>21</td><td>0</td><td>0</td><td>1</td><td>n/a</td></tr><tr><td>Thailand</td><td>Thai</td><td>53</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Togo</td><td>Ewe</td><td>23</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Tunisia</td><td>Arabic</td><td>99</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Turkey</td><td>Turkish</td><td>88</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Turkmenistan</td><td>Turkmen</td><td>77</td><td>0</td><td>0</td><td>n/a</td><td>0</td></tr><tr><td>Ukraine</td><td>Ukrainian</td><td>65</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>United Arab Emirates</td><td>Arabic</td><td>42</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>United Kingdom</td><td>English</td><td>97</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>United States</td><td>English</td><td>82</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Uruguay</td><td>Spanish</td><td>97</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Uzbekistan</td><td>Uzbek</td><td>76</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Venezuela</td><td>Spanish</td><td>96</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Vietnam</td><td>Vietnamese</td><td>87</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Yemen</td><td>Arabic</td><td>100</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Zimbabwe</td><td>Shona</td><td>72</td><td>0</td><td>0</td><td>1</td><td>0</td></tr></tbody></table>
<table><caption>Table XXXVII: Definitions and Data Sources and definitions: Institutions</caption><thead><tr><th>Dominant Language</th><th>Source: Britannica World Data in Britannica Year Book 2010 (World Data)</th></tr></thead><tbody><tr><td>fem.loan 2009</td><td>Women's access to bank loans (from 0 = no restriction to 1 = full restriction)</td></tr><tr><td>Credit Doepke_Tertilt_Voena</td><td>ADD to RefsDoepke,Tertilt, and Voena (2011), The Economics and Politics of Womens Rights</td></tr><tr><td>fem.land 2009</td><td>Women's access to bank loans (0 = full restriction, 1 = no restriction)</td></tr><tr><td>Land Doepke_Tertilt_Voena</td><td>Women's access to land (from 0 = no restriction to 1 = full restriction)</td></tr><tr><td>fem.prop 2009</td><td>Women's access to land (0 = full restriction, 1 = no restriction)</td></tr><tr><td>Prop Doepke_Tertilt_Voena</td><td>Women's access to property other than land (from 0 = no restriction to 1 = full restriction)</td></tr><tr><td>sex.ratio 2009</td><td>Sex ratio at birth (females per 1,000 males)</td></tr><tr><td></td><td>World Bank Gender Statistics</td></tr><tr><td></td><td>World Bank Gender Statistics</td></tr><tr><td></td><td>World Bank Gender Statistics</td></tr><tr><td></td><td>World Bank Gender Statistics</td></tr><tr><td></td><td>World Bank Gender Statistics</td></tr><tr><td></td><td>World Bank Gender Statistics</td></tr></tbody></table>
<table><caption>Table XXXVIII: Definitions and Data Sources and definitions: Gender socio-Socioeconomics vVariables</caption><tr><td>GDP per capita (2009)</td><td>PPP converted GDP per capita, G-K method, at current prices, in obscureintertiol $</td><td>Penn World Table 7.0</td></tr><tr><td>GDP per capita (2005)</td><td>PPP converted GDP per capita, G-K method, at current prices, in intertiol $</td><td>Penn World Table 7.0</td></tr><tr><td>lab-part 2009</td><td>Female Llabor participation rate, female (% of female population ageds 15+)</td><td>World Bank Development Indicators</td></tr><tr><td>lab_for 2009</td><td>Female Llabor force, female (% of total labor force)</td><td>World Development Indicators</td></tr><tr><td>agri_fem 2009</td><td>Agricultural employment, ratio of female to male</td><td>World Development Indicators</td></tr><tr><td>agri_fem 2005</td><td>Agricultural employmentEmployees, agriculture, ratio of gender employment female to male</td><td>World Development Indicators</td></tr><tr><td>ind_fem 2009</td><td>Industrial employment, ratio of female to male</td><td>World Development Indicators</td></tr><tr><td>ind_fem 2005</td><td>Industrial employmentEmployees, industry ratio of gender employment female to male</td><td>World Development Indicators</td></tr><tr><td>serv_fem 2009</td><td>Services employment, ratio of female to male</td><td>World Development Indicators</td></tr><tr><td>serv_fem 2005</td><td>Services Eemploymentees, services, ratio of gender employment female to male</td><td>World Development Indicators</td></tr><tr><td>agri_fem 2009</td><td>Employees, agriculture, ratio of gender employment female to male</td><td>World Development Indicators</td></tr><tr><td>ind_fem 2009</td><td>Employees, industry ratio of gender employment female to male</td><td>World Development Indicators</td></tr><tr><td>serv_fem 2009</td><td>Employees, services, ratio of gender employment female to male</td><td>World Development Indicators</td></tr><tr><td>prim_enrol</td><td>Enrollment in primary OK?education, Ratio of female to male primary enrollment (%)</td><td>World Development Indicators</td></tr><tr><td>sec_enrol</td><td>Enrollment in secondary education, Ratio of female to male secondary enrollment (%)</td><td>World Development Indicators</td></tr><tr><td>tert_enrol</td><td>Enrollment in tertiary education, Ratio of female to male tertiary enrollment (%)</td><td>World Development Indicators</td></tr><tr><td>Global Gender Gap Index 2009</td><td>Expected years of schooling, ratio of female to male</td><td>World Economic Forum, The Global Gender Gap Report012</td></tr><tr><td>school_ratio</td><td>HoursTime spent performingin domestic work, ratio of female to male (hours)</td><td>World Bank Gender Statistics</td></tr><tr><td>dom_work</td><td></td><td>World Bank Gender Statistics</td></tr></table>
<table><caption>Table XXXIX: Definitions and Data sSources and definitions: Language Variables</caption><tr><td>NGII</td><td>Intensity index dummy: (1 if NG2 = 1 ; 0 otherwise)</td><td>Dryer and, Matthew S. &amp; Haspelmath, Martin (eds.). (2011). The World Atlas of Language Structures Online</td></tr><tr><td>SBII</td><td>Intensity index dummy: (1 if SBIOK?Y = 1 ; 0 otherwise)</td><td>Dryer, andMatthew S. &amp; Haspelmath, Martin (eds.). (2011). The World Atlas of Language Structures Online</td></tr><tr><td>GAII</td><td>Intensity index dummy: (1 if GAH = 1 ; 0 otherwise)</td><td>Dryer, andMatthew S. &amp; Haspelmath, Martin (eds.). (2011). The World Atlas of Language Structures Online</td></tr><tr><td>GPII</td><td>Intensity index dummy: GII = NGI + SBI + GAI + GPI</td><td>Dryer, andMatthew S. &amp; Haspelmath, Martin (eds.). (2011). The World Atlas of Language Structures Online</td></tr><tr><td>GII</td><td>Aggregate index: GIIV1 = NGI + SBI + GAI + GPI</td><td>Dryer, andMatthew S. &amp; Haspelmath, Martin (eds.). (2011). The World Atlas of Language Structures Online</td></tr><tr><td>GIIV1</td><td>Aggregate sub-subindex: GIIV2 = NGI + SBI + GPI</td><td>Dryer, andMatthew S. &amp; Haspelmath, Martin (eds.). (2011). The World Atlas of Language Structures Online</td></tr><tr><td>GIIV2</td><td>Aggregate sub-subindex: GIIV2 = NGI + SBI + GPI</td><td>Dryer, andMatthew S. &amp; Haspelmath, Martin (eds.). (2011). The World Atlas of Language Structures Online</td></tr><tr><td>dom.sh</td><td>Share of speakers of the dominant language in the population</td><td>Britannica World Data in Britannica Year Book 2010 (World Data)</td></tr></table>
<table><caption>Table XL: Control vVariables</caption><tr><td>comm</td><td>Dummy for if communist regime in the past</td><td>Portland</td></tr><tr><td>disticaqu</td><td>Distance from equator (√latitudeSQRT(Latitude)/90)</td><td>Portland</td></tr><tr><td>tropsh</td><td>Share of the population in tropical areas</td><td>Masters and McMillan (2001)ADD</td></tr><tr><td>frost_days</td><td>Average number of frost days per unit of population</td><td>Portland</td></tr><tr><td>coast_sh</td><td>Share of population within 100 km of the coast (or an ocean-navigable river)</td><td>CIA wWorld fFa</td></tr><tr><td>landlocked</td><td>Dummy forif the country beingis landlocked</td><td></td></tr><tr><td>colo</td><td>Dummy forif colonized country</td><td></td></tr><tr><td>eng_col</td><td>Dummy forif English colonization</td><td></td></tr><tr><td>fren_col</td><td>Dummy forif French colonization</td><td></td></tr><tr><td>spa_col</td><td>Dummy forif Spanish colonization</td><td></td></tr><tr><td>prot_du</td><td>Dummy forif pProtestantism asis the main religion</td><td>Barro,</td></tr><tr><td>cat_du</td><td>Dummy forif chCatholicism asis the main religion</td><td>Barro,</td></tr><tr><td>mus_du</td><td>Dummy forif iIslam asis the main religion</td><td>Barro,</td></tr><tr><td>jew_du</td><td>Dummy forif jJudaism asis the main religion</td><td>Barro,</td></tr><tr><td>hind_du</td><td>Dummy forif Hinduism asis the main religion</td><td>Barro,</td></tr><tr><td>budd_du</td><td>Dummy forif bBuddhism asis the main religion</td><td>Barro,</td></tr><tr><td>Main religion > 50% ?</td><td>Dummy for whetherif main religion is followed by more than half the50% of population</td><td>Barro,</td></tr></table>
<table><caption>Table XII: More Control vVariables</caption><tr><td>lit_rate</td><td>Literacy rate, (%adult female (% of females ageds 15+) and above)</td><td>World Bank Gender Statistics</td></tr><tr><td>log_inc</td><td>ILog of GDP per capita (PPP current $)</td><td>Penn World Table 7.0</td></tr><tr><td>log_inc_sq</td><td>sSquared log of GDP per capita (PPP current $)</td><td>Penn World Table 7.0</td></tr><tr><td>openness</td><td>Oppensity at current prices in % (Eexports plus imports, divided by GDP)</td><td>Penn World Table 7.0</td></tr><tr><td>gov_size</td><td>Government consumption share of PPP, converted to GDP per capita at current prices (%)</td><td>Penn World Table 7.0.0</td></tr><tr><td>oil_rents</td><td>Annual oil and natural gas production, minus country-specific extraction costs</td><td>Ross (2008)</td></tr><tr><td>demo</td><td>Democracy index.: FFrom 0 (least democratic) to 10 (most democratic)</td><td>Polity 4 pProject</td></tr><tr><td>log_pop</td><td>Log of population (in millions of people)</td><td>Penn World Table 7.0</td></tr><tr><td>urb_sh</td><td>uUPban population share</td><td>World Bank Gender Statistics</td></tr><tr><td>legor_uk</td><td>Dummy for Englishif origin of country'sthe legal system is English</td><td>La Porta et al., (1999)</td></tr><tr><td>legor_fr</td><td>Dummy for Frenchif origin of the country's legal system is French</td><td>La Porta et al., (1999)</td></tr><tr><td>legor_ge</td><td>Dummy for Germanif origin of the country's legal system is German</td><td>La Porta et al., (1999)</td></tr><tr><td>legor_sc</td><td>Dummy for Scandinavianif origin of the country's legal system is Scandinavian</td><td>La Porta et al., (1999)</td></tr><tr><td>legor.so</td><td>Dummy for socialistif origin of the country's legal system is socialist</td><td>La Porta et al., (1999)</td></tr><tr><td>please confirm or reviseNo entry here?</td><td>Female Llabor force with tertiary education, female (% of female labor force)</td><td>World Bank Gender Statistics</td></tr><tr><td>Ploughw</td><td>Ploughw index</td><td>Alesinao, Giuliano, and Nunn (forthcoming2011), Fertility and the plough</td></tr></table>
<table><caption>Table XLII: Financial Independencet vVariables: Female/Male Ratio (aged 15+)</caption><tr><td>Ratio of Account at a formal financial institution (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>Ratio of Account used for business purposes (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>Ratio of Account used to receive wages (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>Ratio of Loan from a financial institution in the past year (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>Ratio of Loan from a private lender in the past year (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>Ratio of Loan from an employer in the past year (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>ratio of Loan from family or friends in the past year (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>ratio of Outstanding loan for health or emergencies (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>ratio of Outstanding loan to purchase a home (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>ratio of Personally paid for health insurance (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>ratio of Saved any money in the past year (female to male, age 15+)</td><td>World Bank Gender Statistics</td></tr><tr><td>Strength of legal rights index (from 0 = weak to 10 = strong)</td><td>World Bank Gender Statistics</td></tr></table>