rafmacalaba/datause-encoder-data huggingface dataset:
for using any system of grammatical gender. We code a language as a gender language if it meets two criteria: first, the language must use a system of noun classes that includes masculine and feminine as two of the possible categories; second, the masculine and feminine categories must include some inanimate objects — i.e. assignment to the gender noun classes should not be based exclusively on the biological sex (or human gender) of the referents.<sup>14</sup> Second, whenever possible, we also code an indicator for dichotomous gender languages (e.g. Spanish) that assign all nouns to either the masculine or the feminine noun class.

We identify languages as gender or non-gender in several different ways. First, some languages are explicitly identified as gender or non-gender languages in linguistic or pedagogical materials. For example, the UCLA Language Materials Project characterizes Serbian by stating: “Three grammatical genders (masculine, feminine, and neuter) and two numbers (singular and plural) are also distinguished” (UCLA Language Materials Project 2014). Some sources are equally explicit about the absence of grammatical gender. For example, _A Reference Grammar of Maithili_ states: “Modern Maithili, however, has no grammatical gender. In other words, in modern Maithili distinctions of gender are determined solely by the sex of the animate noun” (Yadav 1996). In other cases, grammatical materials characterize the different noun classes present within a language (or the absence of a noun classification system), and provide examples of words that fall into each class. For each language, we record specific quotes characterizing the gender structure. Whenever possible, we use two independent sources to confirm the structure of each language.

We successfully classify 4,336 languages which together account for more than 99 percent of the world’s population. We classify all but four of the 383 languages with more than one million native speakers, and we are able to confirm the gender structure using two independent data sources for 324 of these large languages. We are able to account for

> 14As discussed above, linguistic sources do not always use the same implicit definition of grammatical gender. For example, the phrase “marks gender” can be used to indicate either grammatical gender or a more limited system of indicating the gender of a human referent. Since many linguistic sources explicitly distinguish between grammatical gender and lexical marking of human/animate gender, we only use sources that indicate whether inanimates are classed in terms of nominal gender.

15
established during the preceding import-substituting period - seem to have any influence on the probability that local firms are engaged in exporting.

# **5. Competitive and Anti-Competitive Effects**

It was argued earlier that MNCs may improve industrial efficiency and resource allocation in their hiost countries by entering into industries where high entry barriers reduce the degree of domestic competition. The entry of MNCs into these monopolistic industries is likely to raise the level of competition and force existing firms to become more efficient. Foreign entry may, of course, also lead to a fall in the number of firms in the industry if the least efficient local companies are forced out of business. This raises the fear that foreign MNCs may outcompete all local ifirms and establish monopolies that are even worse than the domestic oligopolies they replace: in addition to restricting competition, there is a risk that MNC monopolies may also repatriate profits and avoid taxation through transfer pricing.

However, it is likely that competition generally becomes more fierce, because the MNC affiliates' strategies typically stir up the established patterns of "gentlemanly competition". Hence, it has been said that "whatever the market structure that results from the influence of direct investment, it can be argued that entry by a foreign subsidiary is likely to produce more active rivalrous behavior and improvement in market performance than would a domestic entry at the same initial scale" (Caves, 1971, p. 15). Another point to note is that the increase in competition is often more effective in inducing technological change and productivity improvements than profit incentives, since "threats of deterioration or actual deterioration from some previous state are more powerful attention-focusing devices than are vague possibilities for improvements" (Rosenberg, 1976, p. 124).

Yet, the uncertainty regarding the generality of competition effects motivates an examination of some studies of FDI and industry structure in host countries. One central problem here is the question whether MNC entry and presence explain industry structure, or whether industry structure determines if MNCs will enter or not. This is an important question, since we have argued that one of the reasons to expect significant effects of foreign presence is the 29
visited primary health care facilities in the Canton of Middle Bosnia. The household survey is found to have 97.5 percent sensitivity and 75 percent specificity rates (Kapetanovic, 2004).<sup>4</sup> Table 1 presents the mean and standard deviation of the raw scores by country. Given the aforementioned concerns with cross-national comparability, this overview does not directly compare levels of psychological distress across the different national surveys, but rather explores the observed commonalities and differences in correlates of mental health. To do this, we standardize the mental health scores by subtracting the country mean and dividing by the country standard deviation. The standardized GSI then has a mean of zero and a standard deviation of one within each country.

# **3. Characteristics of Those with Poor Mental Health – Patterns and Divergences**

This section summarizes and extends the work in Das et al. (2007), examining the correlation of mental health status with individual, household, and community-level factors. We identify demographic and socioeconomic characteristics that could be consistently measured across the different datasets and that the existing literature has found to be associated with mental health. These include age, gender, marital status, physical health, and education. We add to this household measures of expenditure (or income in the case of Tonga), household size, and the average mental health of other household members. For the three large surveys, we additionally include community-level mental health scores, which are the average mental health of other households in the community.<sup>5</sup>

# <u>3.1 Mental health in the cross-section</u>

Table 2 reports the results of ordinary least squares regressions of the standardized mental health scores on these characteristics, where analysis is restricted to 15-80 year olds and standard errors are clustered at the household level. A possible concern is that much of the linear association between mental health scores and the covariates is driven by differences between individuals with very good compared to average mental health, thus masking important heterogeneity relevant to those with the highest mental health scores (presumably those in greatest need of treatment). In Table 3 we therefore report marginal effects from probit estimation of the probability of an individual being in the worst 10% or worst 5% of

> 4Although these results are promising for the relevance of household survey modules to assess mental health distress, validation exercises are made on a sample of individuals that might differ significantly from the larger population of interest. Field validations undertaken in future related studies may address this concern. 5 We have also experimented with community fixed effects to control for unobserved community factors. The main results do not change appreciably with the inclusion of fixed effects and so we do not report those results here.

- 6 -
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Table 1: Critical debt level _B_<sup>_∗_</sup> , % of GDP

|Country|AUS|AUT|BEL|CAN|DNK|FIN|FRA|DEU||
|---|---|---|---|---|---|---|---|---|---|
|_B_<sup>_∗_</sup>|78.9|58.9|56.1|75.9|76.7|54.1|59.5|58.1||
|Country|IRE|ITA|JPN|NLD|PRT|ESP|SWE|GBR|USA|
|_B_<sup>_∗_</sup>|56.4|54|79.1|57.1|58.3|57.4|77.2|78.8|79.2|

The calculation of these thresholds are based on prudential debt targets advocated by the OECD and meant as part of a simulation exercise. The numbers should not be interpreted as policy suggestions.

data with panel PVAR is performed before the main empirical analysis. Main empirical results are presented with two alternative definitions of high-debt regime, a model-based from the last section and a conventional one.

# **3.1 Overview of the narrative data**

Cross-country empirical studies of the effects of fiscal policy have suffered from a lack of data: quarterly narrative series exist for a number of advanced economies, such as Romer and Romer(2010) for the United States and Cloyne (2013) for the United Kingdom. Devries et al. (2010) constructed cross-country narrative series at annual frequency, but such data were not available at quarterly frequency until recently. In this section, I use a newly-constructed IMF quarterly panel dataset of narrative tax series to study expansionary austerity.

The narrative dataset is compiled by the fiscal affairs department at the IMF and it is described in Dabla-Norris and Lima(2018). The dataset contains detailed information on the expected revenue impact, motivation, announcement and implementation dates of nearly 2,500 tax measures of fiscal consolidation across 10 OECD countries between 1978 and 2014. The authors rely on contemporaneous primary sources including budget documents, reports from the Ministry of Finance and tax authorities, technical reports and notes produced during the legislative procedure, and discussions on tax reforms during parliamentary debates. When necessary, these are complemented by information from secondary sources such as IMF staff reports, OECD Economic Surveys, Stability and Growth Pact documents, and news articles 8
Appendix Table 6 continued: RDD estimates of gaps in teacher characteristics associated with teachers hired pre- and post-FPE

# <u>(bandwidths of 5, 7, and 10 years)</u>

||Bandwidth||Urban|Age|Male|Born in<br>district|“Perman<br>ent”<br>contract<br>status|Secondar<br>y or<br>more|Diploma<br>or more|Bachelor<br>s or more|Teaching<br>certificati<br>on|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Uganda|5|Coef|0.075|0.782|0.103|0.196|-0.163|0.028|0.028|0.027|0.006|
|||SE|(0.082)|(1.026)|(0.105)|(0.109)|(0.107)|(0.023)|(0.050)|(0.027)|(0.035)|
|||Pval|0.360|0.447|0.326|0.073|0.128|0.229|0.573|0.328|0.865|
||7|Coef|0.037|0.837|0.120|0.193|-0.143|0.021|0.031|0.017|0.021|
|||SE|(0.074)|(0.846)|(0.091)|(0.091)|(0.092)|(0.020)|(0.041)|(0.022)|(0.035)|
|||Pval|0.619|0.323|0.186|0.034|0.121|0.296|0.456|0.427|0.541|
||10|Coef|-0.010|0.908|0.092|0.217|-0.108|0.019|0.013|0.004|0.017|
|||SE|(0.065)|(0.689)|(0.076)|(0.079)|(0.076)|(0.014)|(0.034)|(0.018)|(0.028)|
|||Pval|0.874|0.189|0.227|0.006|0.155|0.179|0.717|0.812|0.539|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-13
factors that can be used by policymakers to target programs to youth with type I, II, or III risk.

# **A. Cluster Analysis**

Cluster analysis is a means to identify correlations across large data sets without imposing, a priori, a structure on the data. Observations are grouped based on minimizing a distance measure between each variable for each observation, i.e. the observations in a cluster share a set of common variables. By comparing the mean values of various variables across clusters, we can characterize each cluster.

Ward’s method (minimum-variance) of testing was selected since it provided the most distinct and interpretable clusters.<sup>11</sup> Ward’s method uses the error sum of squares criteria.<sup>12</sup> The variance is minimized by calculating the sum of squared errors from the mean of the cluster for each of the _m_ variables for each observation: _i_ = 1,...., _n_ observations, _j_ = 1,..., _m_ variables, and _k_ = 1,..., _l_ clusters Initially, each of the n observations forms its own cluster. The first merge is identified by calculating the sum of squares for each pair of cluster. The pairing with the smallest sum of squares is identified and those clusters are joined, leaving n-1 clusters. The second grouping calculates the sum of squared errors again and pairs the two clusters that have the smallest value, leaving n-2 clusters. The process is repeated until the optimal number of clusters has been reached.

Three tools were used to determine the optimal number of clusters. First, stop commands following two possible rules (Calinsky-Harabasz and Duda-Hart) were used to find criterion for each cluster possibility.<sup>13</sup> The Calinski and Harabasz method suggests the optimal number of clusters (g) that maximizes an index C(g) which uses the pooled withincluster covariance matrix (W) and the between-cluster covariance matrix (V), where The Duda and Hart method maximizes D(g), where

> 11 There are many different ways to perform cluster analysis, and no particular method is considered the best. Ward’s linkage cluster analysis is a commonly used agglomerative hierarchical method.

> 12 An attractive feature of the Ward’s method is that it performs well with groups that are of unequal size, which, as will be shown in the results, strongly characterizes the data.  See Everitt et al. (2001) for a theoretical discussion of Cluster Analysis and Ward’s criterion.  See Cunningham and Maloney (2001) for an application. 13 These two methods are implemented in STATA.  They were identified as the two best methods available (out of 30) by Milligan and Cooper (1985) and are discussed in Everitt et al. (2001).

7
cases, further validation either using physical patient counts or repeat measurements can be used to improve accuracy in the estimated variation in the underlying distributions.

# **7 Conclusion**

The World Health Organization argues that 90% of LMICs suffer from critical health worker shortages.<sup>32, 33</sup> Using insights from country-level estimates for the WHO African Region combined with nationally representative facility-level survey data in 10 countries, we submit that the consensus of a human resource crisis in low-income countries needs a fundamental reassessment. The problem appears not to be so much a general “shortage” of providers as it is a complex issue of unequal caseloads across unequal providers, which results in under-utilization of high-competence providers while low-competence providers manage large caseloads.

Combining novel data on these two dynamics, we estimate that, in half of the countries in this study, the productivity losses due to misallocation are quite large. We emphasize that these calculations are intended as a sufficient statistic to estimate productivity losses in primary health care quality due to systematic excess capacity among highcompetence providers, not as a policy prescription. A policy of provider reallocation is potentially realistic only to the extent that (public) health systems can post or transfer individual providers to different facilities. In practice, our results suggest that a wide variety of allocative strategies, such as triage and patient-transfer, may be worth further investigation at the local and subnational levels, in addition to centralized provider reassignment.

In the other half of the countries, the potential gains from reallocations are much lower. The inconsistent quality of LMIC medical education appears to be a significant constraint as there are so few high-competence providers that reallocating them does very little. In these countries, there is no easy way out of the quality crisis. Their problem is still not a general shortage of health care providers – as in other systems, there is plenty of excess capacity among the workforce. Instead, the inability of these countries to train and retain highly competent providers leads us to believe that a total overhaul of their health care systems is required.

# **Ethical Approvals**

This study used only de-identified secondary data provided by the SDI program and therefore was not classified as human subjects research. Each of the SDI surveys was carried out by the SDI program in collaboration with the Ministry of Health in each country. All survey participants consented to participate in the survey. Details regarding data collection ethical approvals are available in each country survey report. The authors therefore declare that no further ethical approval was necessary to conduct this study.

# **Disclaimer**

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its

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Inheritance Law Reform and Women’s Access to Capital: Evidence from India’s Hindu Succession Act Klaus Deininger, Aparajita Goyal, Hari Nagarajan<sup>_∗_</sup> JEL: D31, H31, J16, K36, O12 Keywords: Inheritance Law, Land Bequests, Intergenerational Transfers, Human capital, Gender, India

> _∗_ Thanks to Bina Agarwal, Kaushik Basu, Jishnu Das, Quy-Toan Do, Marcel Fafchamps, Leora Friedberg, Jed Friedman, Markus Goldstein, Lakshmi Iyer, Hanan Jacoby, Karen Macours, Ghazala Mansuri, Vijayendra Rao, Mark Rosenzweig, Alessandro Tarrozi, and various seminar participants for many useful comments. This is a joint research project between the National Council for Applied Economic Research (NCAER) and the World Bank’s research group. Funding from the World Bank’s Global Land Tools Network and the Gender Action Plan Trust Fund is gratefully acknowledged. Contact Email: agoyal3@worldbank.org

1
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