rafmacalaba/data-use-mentions-extended huggingface dataset:
among children (e.g. Thomas 1990 for Brazil; Allendorf 2007 for Nepal; Qian 2008 for China; Luke and Munshi 2011 for India; Menon et al 2014 for Vietnam).<sup>1</sup> Finally, lack of ownership of land and other productive assets by women can lead to inefficiencies. Goldstein and Udry (2008) show that Ghanaian women underinvest in soil fertility due to tenure insecurity, which causes a loss of output for the household as a whole. Dillon and Voena (2017) find that, in communities with weak inheritance rights for widows, concerns over eviction deter land investments even among currently married women. These microeconomic inefficiencies may be exacerbated if gender gaps in the ownership of land and other immovable property, due to their dual function as collateral, reinforce gender gaps in access to credit and other productive inputs (World Bank 2011).

# **2.2 Women’s pathways to property ownership**

This section discusses several pathways through which women obtain ownership of land and housing property, and the constraints they encounter relative to men. We focus on life events, particularly marriage and inheritance, which, in most developing countries, are the principal channels for both women and men to acquire property. For example, the Gender Asset Gap project shows that most agricultural parcels are inherited in Ecuador (53 percent), Ghana (59 percent) and Karnataka, India (86 percent). Similarly, between 34 percent (Karnataka) and 45 percent (Ghana) of all housing lots and between 8 percent (Ecuador) and 57 percent (Karnataka) of principal residences are received as inheritances. Besides life events, the section also discusses, although in – less detail, other channels through which individuals obtain property principally purchases. Within each pathway, gender gaps can emerge from an interaction between households, markets and social norms or institutions (World Bank 2011).

We begin with the first pathway, marriage, by reviewing how basic institutional rights to property ownership
fatalities per VKT.<sup>23</sup> Likewise, the percentage of population living in urban areas (URBAN) has an insignificant effect on the occupant fatality rate.<sup>24</sup>

**_Motorization._** Increases in the size of the motor vehicle fleet (VEH) increase

occupant fatalities per VKT, as the formal model suggests, but this result is significant only in some specifications. Moreover, the negative coefficient on the vehicle-time trend interaction (ln(VEH)*t) indicates that the effect diminishes over time. For example, in the Base Model the elasticity of occupant fatalities with respect to the size of the vehicle fleet decreases from 0.38 in the mid-1960s to approximately zero by the mid-1990s. This result is consistent with newer, safer vehicles providing additional protection to vehicle occupants.

The percentage of motorized two-wheelers in the vehicle stock (2WHEELERS) has no effect on the occupant fatality rate (Model 7). This is somewhat surprising given the increased vulnerability of motorcycle accident victims to bodily injury.

The results suggest that the occupant fatality rate increases with a country’s rate of motorization. In the Base Model in Table 7, the coefficient on the annual growth rate of the motor vehicle fleet (VEHGROWTH) is found to be 0.587 ( _0.278_ ). Since VEHGROWTH enters the model in linear form, the magnitude of this effect is not large. Considering that VEHGROWTH fell from approximately 0.06 to 0.02 in several

> 23 Few other studies have examined the effect of this age cohort on occupant and pedestrian fatalities separately.  Noland (2003a) did find an increase in the percentage of population aged 65 and over to be associated with an increase in total traffic deaths but the result loses significance as he adds other variables (medical treatment proxies) to the model. Using U.S. county level data for 1970 and 1980, Keeler (1994) also found a positive but insignificant effect of elderly on the
of a constant mean across the areas within the same post-stratum is hardly true, leading to typically large design bias in PS-SYN estimators. It is important to note that a reduction of design variance at the expense of a large design bias is not an acceptable estimation strategy.

Hansen, Hurwitz and Madow (1953), p. 483, were the first to use a model for synthetic estimation, in an application based on the 1945 Radio Listening Survey. The objective was to estimate the median number of radio stations heard during the day in family houses from _D_ = 500 US counties. They had estimates _xd_ , _d_ = 1 _, . . . , D_ , obtained from a mail survey conducted in the 500 counties, which were biased due to only 20% response rates and incomplete coverage. Additionally, they had estimates
and Serra, 2003; Buch, 2003, Buch and Lipponer, 2004; Wezel, 2004).<sup>5</sup> At the same time, a smaller number of papers have examined the determinants and behavior of cross-border claims (e.g., Dahl and Shrieves, 1999; Buch, 2000; Jeanneau and Micu, 2002; Kawai and Liu, 2002; and Buch and Lipponer, 2004).

This paper assembles a database on foreign claims, both cross-border and local, extended by Italian, Spanish, and U.S. banks to over 100 countries around the world during the period 1997-2002, to investigate the determinants of the share of local claims across countries and its implications for financial stability. Banks from Italy, Spain, and the United States are dominant players in the international banking market and they jointly account for approximately 30 percent of all outstanding foreign claims vis-à-vis the countries in our sample.

In studying the mix of cross-border and local claims extended by international banks across countries, we rely on the conceptual/theoretical framework used in the trade/multinational firm literature to study the choice between exporting goods (the equivalent to cross-border lending) and producing them abroad for foreign markets (FDI). In particular, studies such as Brainard (1997) and Helpman et al. (2003) discuss the tradeoff faced by multinational firms between paying the higher sunk costs of establishing affiliates overseas vis-à-vis confronting the transportation costs and trade barriers that arise from exporting their goods instead.

Following the trade literature, we investigate how the share of local claims to total foreign claims extended by international banks across countries is affected by the entry requirements and startup sunk costs of setting up operations overseas. These costs have been

> 5 A growing literature has examined the implications of foreign bank presence in developing countries. See Clarke et al. (2003) for a review of this literature. Among others, Claessens et al. (2000), Barajas et al. (2000), Denizer (2000)
The composite error term in parentheses contains the heterogeneity in weights. Now it is far from clear what standard subjective welfare regressions are telling us. Even if _Cov_ ( _X i_ ,  _i_ )  0 , a linear regression model for subjective welfare will not in general deliver the mean weights in the population in large samples. That requires that the interaction effects between the latent differences in weights and the values taken by the covariates are orthogonal to those covariates, i.e., that _Cov_ ( _X i_ ,  _i X i_ )  0. However, we can easily imagine that people with high weights on some welfare dimension tend to be better endowed with that dimension ( _Cov_ ( _X i_ ,  _i X i_ )  0); this may reflect parental decisions on schooling, say. Then (ignoring all other problems) the regression coefficient will tend to over-estimate the mean weight. Alternatively, suppose that people with low innate attributes (such as health) tend to put a higher value on those attributes; then one could find that _Cov_ ( _X i_ ,  _i X i_ )  0 . We will underestimate the mean weight.

Thus the likely heterogeneity in the weights attached to non-market goods clouds inferences from subjective-welfare regressions. Indeed, while one would presumably like to find weights appropriate to the trade-offs of poor people, it is far from clear that the weights implied by standard regressions are appropriate. One could try to model the heterogeneity in weights by adding interaction effects. This will presumably help, although latent heterogeneity in weights can never be ruled out.

A possible direction for research is to use surveys to try to focus directly on the subjective tradeoffs adopted by respondents. This would require more complicated and difficult survey questions, but a not dissimilar approach has been used with some success for inferring attributes of preferences, such as risk aversion or valuations of environmental factors.

# **6. Conclusions**

Subjective data offer to expand the information set traditionally used for assessing welfare and measuring poverty
are less likely to pay bribes and spend less manager time dealing with officials. In Bangladesh, growing firms report lower constraints from anti-competitive behavior by other firms and labor regulation.

There are also some interesting cross-country differences in reported constraints for the firm characteristics, which may be relevant for policy.

The most striking difference among the SAR countries concerns the **location** characteristic. Earlier we found that in contrast to the rest of the region, job-creation was uncorrelated with location in **India** once we controlled for other firm characteristics. Elsewhere, employment creation was more prevalent in big city locations. Turning to the constraints regression analysis, we find once again that India does not reflect the dualism pattern found elsewhere in low income countries. The typical pattern is that it is firms in urban (large city) locations that report higher costs of constraints than do firms in rural areas including small cities. Indeed for electricity, Indian firms in more rural locations report higher constraints than do firms in urban areas. In India, large-city firms report higher constraints only in relation to transport, courts, labor regulation, and customs administration. However, for the _majority of constraints_ , there is no significant large city – small city gap in the reported cost of constraints in India.

The standard dualism pattern for low-income countries (found in the large multiregion dataset) is characteristic of most SAR countries apart from India. It is especially pronounced in Bangladesh and Pakistan (red cells). This suggests that in terms of the reported constraints on growth, India is a more integrated economy between rural, small city and large city areas than are the other countries in this sample.

The positive **firm size** effects across a range of constraints are weaker in **India** than elsewhere. The difference between **services** and manufacturing firms is also different in India, where it is services firms that complain more about electricity. Unlike the case elsewhere, they are also more likely to have a generator
2 scale household survey in rural Tanzania designed to query households about their social connections and attitudes. Second, using this and data on incomes we show that a village's social capital has an effect on incomes of the households in that village, an effect that is empirically large, definitely social, and plausibly causal. Finally, we use the two data sets to examine a number of proximate channels through which social capital appears to operate.

# **Introduction**

Social capital, while not all things to all people, is many things to many people. A dramatic restriction of what one might mean must precede any attempt to estimate either "social capital' or its impact. What do we mean (and what do we not mean) by social capital and why do we think it might affect incomes?

By "social capital" we mean the quantity and quality of associational life and the related social norms. The basic survey instrument, the Social Capital and Poverty Survey (SCPS), asked individuals a variety of questions about three dimensions of social capital. First, individuals were queried about their membership in various voluntary associations or groups to investigate the raw magnitude. For each group in which an individual reported membership, questions were asked about that group's characteristics in several dimensions relevant to that group's contribution to social capital. For instance, if the group's membership is 'inclusive' we assumed any given individual's membership in that group contributed more to social capital than membership in a group in which membership is "exclusive' to a particular clan or ethnic group. With this data on the frequency of membership and the characteristics of groups we created an index of the village associational life, which we argue is a proxy for social capital.
49.24|
|_Secondary School (%)_|31.54|46.52|12.79|33.40|
|_High School (%)_|2.93|16.90|2.12|14.40|
|_College (%)_|0.00|0.00|0.93|9.58|
|Observations|4|09||2,268|

**_Panel II. Fishing Intensity: Monthly Boat Detection (VIIRS 2013-2015)_**

a. Near-shore ( _≤_ 20 _nm_ ):

|_Boat Detection Likelihood (%)_|59.13|49.16|39.78|48.94|
|---|---|---|---|---|
|_Number of Boats Detected_|4.62|8.20|3.33|7.84|
|Observations (grid-month)||25,560|8|4,960|
|b. Of-shore:|||||
|_Boat Detection Likelihood (%)_|72.84|44.48|54.62|49.79|
|_Number of Boats Detected_|5.09|7.10|3.00|5.38|
|Observations (grid-month)||51,624|20|7,324|

Note: this table presents the descriptive baseline statistics for the primary fishery characteristics – using data from the Labor Force Survey 2015 (Panel I), and fishing intensity – using monthly-interval satellite data from VIIRS Boat Detection module 2013-2015 (Panel II). “Formosa provinces” refers to observations belong to the four Formosa-affected provinces (Ha Tinh, Quang Binh, Quang Tri, and Hue). “Control provinces” refers to observations belong to all provinces south of Phu Yen, i.e. distant from the Formosa-affected region. 1 USD=22,550 VND (Vietnam
study compares the outcomes of Venezuelans with those of Brazilians.<sup>6</sup> All the integration measures indicate that in spite of little legal barriers, Venezuelans face challenges in accessing education, formal jobs and social protection in Brazil. Venezuelans are 0.47 times as likely to be in school, 0.3 times as likely to be employed in the formal sector, and 0.7 times as likely to be registered in single registry compared to their Brazilian counterparts. The findings suggest that only 42% Venezuelan children enroll in school and even when they do, they experience grade downgrading and capacity constraints, making it harder to attain productive human capital and make future generations self-reliant. In the formal labor market, their employment is of much lower quality than the Brazilian, characterized by high temporality and greater work hours, despite the higher schooling levels suggesting they also face occupation downgrading in Brazil. Controlling for selection into wage employment, there seems to be also a significant wage penalty for Venezuelans. Consequently, more educated Venezuelans and Venezuelans with higher number of children are more likely to register in the unified registry<sup>7</sup> than their Brazilian counterparts. Overall, integration seems to be higher where the population of Venezuelan migrants and refugees is lower, controlling for selection, especially in education, supporting previous literature by Lazaer (1999) and Carneiro et al. (2020). It should be noted here that theoretically a larger network of refugees and migrants can provide more information about job prospects, school requirements, social protection benefits and local customs and traditions, promoting integration (Gautier, 2020), but in the case of Brazil, concentration in certain localities like Roraima and most Venezuelan migrants and asylums being new to the situation, seem to be creating an overcrowding e↵ect constraining integration.

This paper is divided into six sections. Section 2 starts with a description of the migration trend in Brazil and key demographic characteristics of the Venezuelan refugees and migrants and then moves on to give
are likely to be randomly distributed and unexpected and therefore orthogonal to any possible confounders. The rich set of fixed effects in this specification isolates these localized and unexpected fluctuations in rainfall and temperature, facilitating causal inference. Standard errors are clustered at the Administrative 1 level (one level below country, i.e., a state in the United States) in order to account for spatial and serial correlation. Following Dell et al. _op_ cit among others, we weight observations based on grid cell population, to ensure that results are representative of the global economy. This implies that quasi empty cells in Siberia and say densely populated San Francisco receive different weights. Population is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).

Grid-cells data are then aggregated at the Administrative 1 level and at the country level to investigate how aggregation of weather data affects the results. Our identification strategy relies on the assumption that annual variations in precipitation and temperature are exogeneous with respect to cells’ economic activity.

# **3- Results**

## **3.A Causal Results**

Our main results are presented in Table 1. Column 1 displays the results of the baseline model (equation 1). Columns 2 and 3 aggregate data at the administrative 1 and country levels. Our results suggest a consistent increasing and concave relationship between rainfall and GDP growth -- a result that departs from findings in 5
industry (which received two-thirds of energy subsidies in 1994) have increased to world market levels, although gas prices still remain below border price levels (IFC, 2008).

The market-friendly reforms had a far-reaching impact on the transformation of the energy economies. When the sharp initial decline gave way to gradual economic recovery, energy consumption continued to decline and then stabilized at around 250 million tons of oil equivalent (mtoe) per year. Meanwhile, manufacturing output has increased by more than 50 percent during 2002- 2008, as shown in Figure 1. This has occurred because the average manufacturing energy intensity declined by 35 percent from 0.4 kilogram of oil equivalent (koe) per dollar (ppp) in 1998 to 0.26 koe per dollar (ppp) in 2008.<sup>5</sup> If energy intensity had remained at its 2002 level, energy demand would have been 776 mtoe higher, the equivalent of 10 years’ total energy consumption in Ukraine today.

While manufacturing energy intensity at the regional level had sharply declined, sub-regions show disparate behavior. Figure 2-a presents energy intensity trend for six sub-regions/countries. Central Asia is notably lagging behind in terms of the reduction in energy intensity. In fact, Kazakhstan, the Kyrgyz Republic, and Tajikistan had all experienced an increase in energy intensity since 2000 as shown in Figure 2-b. In the rest of the paper, we explore why countries were not doing equally well and identify the driving factors behind the trend of energy productivity.

# **III. Data**

Data used in this paper come from three sources. The energy consumption data are obtained from the International Energy Agency (IEA) World Energy Statistics and Balances database (2012). The database reports final energy consumption of manufacturing in 28 ECA countries. The manufacturing sector is further broken out by 11 subsectors at the two-digit level of the international standard industrial classification Revision 4 (ISIC Rev. 4).

The aggregate value added of manufacturing is reported in constant 2000 US dollars
payments**|0.6|0.1|0.1|0.2|
||**Foreign interest payments**|0.4|0.5|0.5|0.6|
||**Total**|39.6|36.6|37.0|37.4|

Total household incomes increase at a slightly lower rate than GDP, primarily because of a slowdown in remittance growth. However, given negative population growth, per-capita household income and real consumption both increase at a n average rate of 4.2 percent per year, permitting a significant decrease in the national poverty rate, which has fallen to 9.5 by the end of simulation period according to the MAMS poverty module (Figure 10).

Regarding inequality, MAMS produces a picture of slowly increasing Gini coefficient (see Figure 11). Changes in inequality are generated by the evolution of per-capita consumption growth across households; the fact that it is higher for the three households with the highest initial within-group inequality drives up aggregate inequality up in our base scenario. In addition, the growth in consumption by household group is not favoring farmers and entrepreneurs, who have rather low per-capita consumption levels in 2012 (see Figure 12).

20
the trade-off between the statistical precision of the poverty and inequality estimates and the level of disaggregation.

The census is assumed to enjoy complete coverage (of all rural households), such that sampling error may safely be ignored. The basic idea behind the small area estimation method is to replace a small number of exact observations of expenditure/income (using households from the survey) with a large number of estimates of expenditure/income (using households from the census) to obtain accurate estimates of aggregate poverty and inequality. This means that we will be replacing sampling error with approximation error. As approximation errors cancel out on average, the errors induced by approximation tend to be small when the number of households is large.

# _III.1 The ELL framework_

Let us provide a brief review of the ELL methodology. In the standard setup, we consider the following model: Where ln( _ych_ ) denotes the dependent variable (think of logarithmic per capita expenditure), _xch_ the vector of explanatory variables,  the vector of regression coefficients,  _c_ the cluster-specific random effect and  _ch_ the household-specific random effect. The subscript _ch_ refers to household _h_ living in cluster _c_ . The explanatory variables _xch_ must be available in both census and survey. The household specific errors are assumed to be independent from each other, and independent from the cluster error.

Once all the parameters of interest have been identified, the dependent variable is imputed into the census: where <sup>ˆ</sup> ,  ˆ and _c_  ˆ denote the estimates for _ch_  ,  _c_ and  _ch_ . Now suppose that we want to estimate poverty for a given district. As an illustrative example, let us consider the head-count index, which measures the percentage of poor households in the district: where<sup>1</sup> ( _ych_  _z_ )<sup>denotes the indicator function that equals 1 if</sup><sup>_y < z_and 0 otherwise, and</sup>