rafmacalaba/data-use-mentions-extended huggingface dataset:
Several researchers have argued that the effect of the legal system per se on property rights is limited, and that differences across countries in their enforcement depends on a broader range of cultural and social considerations. Thus, according to the **_Culture view_** , differences in culture, defined as a system of beliefs, can help explain the differences in investor protection. Stulz and Williamson (2003) and LLSV (1999) both use religion as a proxy for culture. We use four categories of classification for the religion variable: Catholic, Protestant, Muslim and Other Religions. The data on religious composition is taken from LLSV (1999) to create four corresponding dummy variables, each of which takes the value 1 if the religion it represents is the dominant religious group (highest percentage of practicing population) in the country and 0 otherwise<sup>5</sup> .

Easterly and Levine (1997) show that ethnic diversity is also an important determinant of rent seeking and social polarization, affecting property rights and social institutions.<sup>6</sup> We term this the **_Ethnic Diversity view._** Empirically, to capture these broader ethnic diversity effects we use a measure of ethnic fractionalization of the country compiled by Alesina et al (2003). Unlike measures used in the earlier literature, this measure captures important aspects of ethnicity like racial origin and skin color.<sup>7</sup> The explanatory power of the different variables may be affected due to nonlinearities arising from the way these variables are constructed. Unlike legal origin, the other theories posit determinants of property rights that cannot be directly observed. These determinants are accordingly proxied by variables that have been independently scaled for other purposes. Conventionally, these proxies are then entered linearly in an equation that predicts a variable of interest. However, there is no reason to believe that the scales are designed to detect material effects of the determinant on the variable of

> America’s agriculture is conducive to small farms
# **4.1.3 Degree of Norm Misperception across the World**

Figures 5 to 7 present maps of the degree of misperception across the globe with respect to the broad norm, the breadwinner norm, and the caregiver norm. These maps are color-coded to represent the varying degrees of misperception across different countries. Where darker shades indicate a greater disparity between personal beliefs and perceived community norms; and lighter shades suggest that norms are more closely aligned with actual beliefs.

In Figure 5, the map quantifies the misperception of the broad norm at the country level and presents the absolute value of the difference. We show a universal underestimation of support for gender equality. Figures 6 and 7 explore misperceptions around the gender role specific norms. Here, positive values signify a perception that society is more traditional or gender-stereotypical than personal beliefs, while negative values indicate the opposite. Darker shades of blue highlight countries where the breadwinner norm is believed to be more conservative or stereotypical in society than actual beliefs held by the Facebook sample. Light blue and yellow-green shades demonstrate a closer match between perceived norms and beliefs or a slight underestimation of the norm i.e. they think their community is more gender progressive or counter-stereotypical than actual beliefs.

Collectively, these figures reveal regional patterns where certain areas consistently show misperceptions across all norm constructs, while others display varying degrees of misperception depending on the norm in question. To discern whether misperceptions regarding gender norms are more prevalent among men or women, we delve into a comprehensive analysis in Appendix D. This examination leverages individual responses from our dataset to evaluate the discrepancies between perceived community norms and actual beliefs, dissected by gender and geographic region.

According to Table D1, the extent of misperception is substantial, with the frequency of under- and over-estimation eclipsing the instances of accurate assessments of community beliefs. It is important to note that when evaluating the level of misperception (i.e., whether an individual overestimates or underestimates a norm), we juxtapose an individual’s perception of
5 1 0 .5 1 0 .5 1 .5 1 .4 .6 .8 1 .4 .6 .8 1 0 .5 1 .6 .7 .8 .9<br>Dominican Republic: census(1970) Ecuador: census(1990) Mexico: census(1970)<br>Electricity Electricity Electricity<br>supplyWater .510 supplyWater .8.6.4.2 supplyWater<br>Sanitation .8.6.4.2 Sanitation .6.4.20 Sanitation<br>.51 enrollmentSchool .9.8.7.6 enrollmentSchool .51 enrollmentSchool<br>.50 completionPrimary .51 completionPrimary .6.4.20 completionPrimary<br>.5 1 0 .5 1 0 .5 1 .2 .4 .6 .8 .6 .7 .8 .9 .2 .4 .6 .8 .2 .4 .6 .8 0 .2 .4 .6 .5 1<br>Panama: census(1980) Peru: census(1993) Uruguay: census(1985)<br>Electricity Electricity Electricity<br>.8.6.41 supplyWater .510 supplyWater .8.61 supplyWater<br>.510 Sanitation .510 Sanitation .9.8.71 Sanitation<br>.8.61 enrollmentSchool .51 enrollmentSchool .95.85.9 enrollmentSchool<br>.8.6.4.2 completionPrimary .510 completionPrimary .6.8.7 completionPrimary<br>.4 .6 .8 1 0 .5 1 .6 .8 1 0 .5 1 0 .5 1 0 .5 1 .5 1 .6 .8 1 .6 .8 1 .7 .8 .9 1 .85 .9 .95<br><!-- End of picture text -->

**Source:** Own elaboration based on IPUMS data.

16
, so that they can extract personal benefits and channel resources to their supporters (Cruz and Keefer 2015). This implies that the potential for cabinet size to be associated with poor governance and corruption is especially likely in settings where programmatic political parties are weak or absent, as is the case in many Sub-Saharan African countries.

Clientelistic politics is not the only driver of cabinet size. Examining data on post-war cabinets in 17 Western European countries, Indridason and Bowler (2014) find that inter-party politics influences the size of the cabinet. As coalition parties require an agreement about the division of ministerial portfolios, increasing the size of the cabinet can at times smooth that process. Portfolios tend to be allocated roughly proportional to the government parties’ legislative strength. Achieving proportionality is difficult in small cabinets, but the problem can often be solved by adding a portfolio. Intra-party politics matters, too, when otherwise dissatisfied party members and/or factions are brought on board by altering the size of the cabinet. The need for coalition building through cabinet appointments is not limited to parliamentary democracies, as Cheibub (2007) shows for presidential systems in Latin America.

> 3 In the words of van de Walle (2012: 114): “Hiring a member of one’s ethnic group to a senior position in the customs office is an example of patronage. Allowing the customs officer to use the position for personal enrichment by manipulating import and export taxes is an example of a prebend.”

> 4 This problem is not limited to African countries. In the United Kingdom, there has been a significant increase in junior ministers without cabinet rank over the past decades. This has boosted the “payroll vote”, comprising Members of Parliament with a government job who are expected to vote as instructed or resign (House of Commons 2010: 8).

3
river network is considered to be as one of the most extensive in the world and its groundwater resources is considerably widespread.

The extractive sector accounts for about two-thirds of DRC’s GDP in 2014, while it represents about 97 percent of export earnings (Table 1). Besides its contribution to GDP, the mining sector provides provincial employment and business opportunities, although in some cases it played a role in fueling conflicts. Overall, mining exerts an ambiguous impact on development.

Table 1

|DRC: Contributionofthe Natural|Resources S|ectorto|DRC’sEconomy (2000-2014)|
|---|---|---|---|
|2000<br>2002|<br>2004<br>2006|<br>2008|2010<br>2011<br>2012<br>2013<br>2014|
|% of ExportReceipts<br>83.615.6|19.9 98.7|98.8|97.7 97.6 98.3 97.8 97.3|

Source: Author’s calculation based on Government, World Bank, and IMF Database.

DRC’s enjoyed remarkable economic performance in the 2000s, supported by sound policies, and structural reforms. Real GDP growth accelerating at an annual average rate of more than 7 percent over the past five years. In 2014, DRC posted a record high growth of 9 percent and is one of Africa’s most rapidly growing economies. DRC growth was more than four percentage above the average of the Sub-Saharan African countries and it was the second fastest African economy, with an average GDP growth of 7.6 percent in 2010-2015. Mineral production and related investments are the main drivers of this robust growth, although economic activity is strengthening in other sectors, such as agriculture. Between 2010 and 2014, copper production doubled to reach 1 million tons. Copper and cobalt production account for more than 80 percent of exports of goods.

5
filled the short-term survey. This should mitigate the concern that the results could be driven by over-exposure to the questionnaire and its interaction with the treatment (e.g., recall bias). To recover the average treatment effects of the two series in the short and medium terms, we estimated the following linear model via OLS: where _i_ is the individual, and _t_ stands for the survey wave, namely, baseline ( _t_ = 0), short term ( _t_ = 1) and medium term ( _t_ = 2). Treatment 1 is a dummy indicator equal to 1 if the individual was assigned to the humorous drama with implicit messaging and 0 otherwise, while Treatment 2 equals 1 if the individual was assigned to the docuseries with explicit messaging and 0 otherwise. Y indicates the outcomes of interest, specifically, the general and topic indexes described previously as well as variables measuring information-seeking and posting behaviors.

Most of our outcomes were only collected at follow-ups. Given the experimental design, the lack of baseline values should not affect the causal interpretation of the results as both the treatment and control groups are identical in expectations (Bruhn and McKenzie, 2009). As shown below, this is further confirmed by observed balance for the pre-treatment values of observable characteristics and attitudinal self-reported outcomes. As such, we show plain estimates from a parsimonious specification with no controls. Nevertheless, to improve the efficiency of the estimator, we also report the results controlling for a series of socio-economic indicators measured at baseline, represented by vector **X’** in (1).<sup>19</sup> Among them, we always include an index measuring the baseline stance of the individual towards gender norms and VAW. Specifically, we rely on those few knowledge and attitudinal variables that were – – measured at baseline and aggregate them into an index i.e., the Baseline Stance Index which captures the ex-ante progressiveness of the respondents’ view.<sup>20</sup> Finally, when the
2017) and Berg _et al._ (2018), we measure market access as MA� �∑��� P�τ���� where MA� is a district _i_ ’s level of market access, P� is the population of district _j_ , and τ�,� is the estimated road travel time between districts _i_ and _j_ . Population data are from the 2014 round of SUSENAS. To estimate road travel time, we construct a road data set by transferring the 2014 road categories on a paper map published by NELLES to a geo-referenced network-enabled road data set (DeLorme) and assume uniform travel time speeds by road category identical to those assumed by Jedwab and Storeygard (2015) and Berg _et al._ (2018): expressway 105km/h, principal highway 100km/h, highway 90km/h, secondary road 75km/h, track 50km/h, 6 km/h for the unknown category and 5 km/h in the absence of a road. For the elasticity of trade, _θ_ , we assume 3.8 following Donaldson (2018).

18
Table 1: Balance tests for baseline covariates

||||Me|an<br>by<br>treat|ment|||||
|---|---|---|---|---|---|---|---|---|---|
||||Control|Treated|P-value|2023<br>Mexi|co<br>ENOE<br>Survey|2014<br>CR|EA<br>Survey|
|Control Variable|Mean|SD|N = 695|N = 1513|(Joint = .5)|Mean|SD|Mean|SD|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Years of business operation|4.21|5.32|3.98|4.32|0.099|9.73|10.3|2.14|2.65|
|Is a family business|0.510|0.500|0.498|0.516|0.592|||||
|Age|40.1|10.1|39.9|40.2|0.418|46.0|14.2|42.3|11.3|
|Married|0.555|0.497|0.522|0.570|0.055|0.594|0.491|||
|In State of Mexico or Mexico City|0.276|0.447|0.269|0.280|0.707|0.210|0.407|0.427|0.495|
|In Guatemala|0.075|0.264|0.079|0
different groups of customers, use different technologies, and/or have different effects on competition (see DeYoung, Hunter, and Udell 2004 for a survey).

Turning to the dynamic performance effects associated with domestic M&As, the empirical evidence on U.S. bank M&As using data from the 1980s generally finds little or no cost efficiency improvement after consolidation on average, although some M&As yielded substantial gains and others yielded substantial losses (e.g., Berger and Humphrey 1992). The studies using U.S. data from the early 1990s are mixed, with some finding cost efficiency gains, and others finding little cost efficiency improvement (e.g., Rhoades 1998). Studies of European M&As also give mixed cost efficiency findings (e.g., Vander Vennet 1996).

Profit efficiency studies of U.S. bank M&As from the 1980s and early 1990s find that M&As improved profit efficiency, and that this improvement can be linked to portfolio shifts that generated higher revenues due to improved risk-expected return frontiers (e.g., Akhavein, Berger, and Humphrey 1997, Hughes, Lang, Mester, and Moon 1999). U.S. banks involved in M&As may also have improved the quality of their outputs in the 1990s in ways that increased costs, but still improved profit productivity by increasing revenues more than costs (Berger and Mester 2003). Other studies also find that large U.S. banks shift their portfolios away from SME lending after M&As, although other local banks tend to react by increasing their SME credit supplies (e.g., Berger, Saunders, Scalise, and Udell 1998).

# **2.2. Foreign ownership and acquisitions**

Studies of the effects of foreign governance typically include static efficiency differences between foreign-owned and domestically-owned private institutions. Foreign-owned banks are usually part of large banking organizations, and so generally face the same scale economies and diseconomies
terms while

> 13We do not have any other reliable data which could serve as a comparison point. We had coded up an alternative data source, the Agricultural Census. This Census is conducted every five years by the Agricultural Census Division of the Ministry of Agriculture since 1970 and collects information on agricultural operational holdings in India. However, data quality on land tenancy appeared to be poor, with often no land tenancy recorded in land-record states (where formal registration records are in place).

12
before 2021 (Perkins, 2020).

Our analysis of oil and gas asset data between 1960 and 2018 suggests that, on average across all projects worldwide, it took 7.5 years to get from discovery to production. In Sub-Saharan Africa, the 15 There was significant price volatility from 2007 to 2009, but prices on average were very high by historical standards.

16 The structure of the regulatory framework for the LNG project will depend on whether the blocks and the LNG plant are treated as separate entities. If, as expected, they are, PSAs will govern the blocks and a host government agreement will govern the LNG plant.

10
0.198|0.198|0.198|0.198|0.208|0.208|
|MeanLn(U<sup>f</sup>)|0.021|0.021|0.021|0.022|0.061|0.061|
|MeanLn(U<sup>m</sup>)|0.002|0.002|0.002|0.003|0.017|0.017|
|Year FE||Y|Y|Y|Y|Y|
|Country FE|||Y|Y|Y|Y|
|X controls||||Y|Y|Y|
|Religion controls||||||Y|

_Source:_ Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. All estimates are of equation (1). The dependent variable in each regression is a binary dummy variable taking the value one if the individual female respondent was a victim of violence, defined as physical (panel A), sexual (panel B) or either (panel C) in the last twelve months. _P-value F-test_ refers to the F-test that the coefficients on ln(U<sup>f</sup> ) and ln(U<sup>m</sup> ) are equal. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. The restricted sample is the sub-sample of surveys for which information on respondent religion is non-missing.

28
, valuation committees began to georeferenced these data and linked it to geographical information systems (GIS) (Kertscher 2004). The evolution of the management of price data enables Germany to explore methods to increase transparency and ensure that data can be analyzed in the most accurate way.

Germany’s LR process may also use redistribution by relative size.<sup>15</sup> Redistribution by relative sizes provides a useful instrument when the value of land is hard to determine. By defining the assignment of land after development based on initial size rather than value, the public agency avoids the need for a valuation exercise. This may prove to be of great use in countries where land markets are weak or information on property prices is not available. The challenge in this case may prove to be convincing landowners to accept redistribution by relative size as a reallocation criterion. This may be more useful for projects where plots have very similar characteristics and no dramatic differences in prices are expected across different plots.

While Germany’s LR process serves as a model for successful implementation of urban development processes, some argue that the German case presents challenges in its adoption. Since LR is local authority-led and considered mandatory, it is not seen as truly participatory. Landowners have a right to appeal compensation amounts as well as express their views, but do not play a role in the LR process itself. Larsson also argues that all landowners that fall within the project area must participate in readjustment since there is no way to exempt oneself from the process (Larsson 1997). Further, while it is true that consensus of the majority of landowners is not required in Germany for commencing a LR project, it may be an important

> 15 In this case, the area of each landowner’s property deducted by the municipality shall not exceed 30 percent, or 10 percent in the case of already developed land, and should the municipality need less the full 30 percent, the landowner must pay the municipality the difference in the post-development market value.
**_III)_** _Estimatesfrom Indonesian Data_

This section has three sub-sections. First, estimating the variability in income with some accounting for measurement error. Second, computing the average vulnerability and number of households that are either "in poverty" and "vulnerable to poverty." Third, calculating vulnerability and proportion vulnerable by various household characteristics.

# _A) Variability in Expenditures and Measurement Error_

Obviously in all of this, the key missing item is the variability of expenditures experienced by households over time. In a single cross section one can only estimate the variability of expenditures _across_ households. With a panel with only two observations per household one could have an estimate of the variability of expenditures household by household, but only with extremely large imprecision. However, one can use such a panel to estimate the variability of expenditures by groups of households<sup>3</sup> . For instance, landless households versus land owning households, or households with differing levels of education could face different variability of their incomes.

We are almost uniquely blessed in having not one, but two independent panel data sets to estimate this variability. We use the two different panel data sets, the "Mini-Susenas" and "100 Village Survey," to estimate standard deviations of the 3 An obvious extension is to estimate the variability as a function of a number of HH characteristics with a multivariate procedure and then use the HH's predicted variability in the vulnerability analysis.

9
approach, or by H3.

2. Using the estimates obtained in step 1 as true values of the model parameters, simulate a vector of census welfare _yc_<sup>_∗_= (</sup><sup>_yc_1</sup><sup>_, . . . , ycN_</sup> _c_<sup>)</sup><sup>_T_asfollows:</sup> ln( _ych_<sup>_∗_) =</sup><sup>_xchβ_ˆ0+</sup><sup>_η_</sup> _c_<sup>_∗_+</sup><sup>_e∗_</sup> _ch_<sup>_,h_= 1</sup><sup>_, . . . , Nc,_</sup> where the location effect is generated as In the homoscedastic case, household-specific errors are generated as and, in the case of heteroscedasticity, as 3. With the simulated census of welfare _yc_<sup>_∗_=(</sup><sup>_yc_1</sup><sup>_, . . . , ycN_</sup> _c_<sup>)</sup><sup>_T_foreacharea</sup><sup>_c_=1</sup><sup>_, . . . , C_,produce</sup> indicators of interest: _τc_<sup>_∗_=</sup><sup>_f_(</sup><sup>_y_</sup> _c_<sup>_∗_)</sup><sup>_._</sup> where _f_ ( _yc_<sup>_∗_)canbeanyindicatorfunction,suchasoneoftheFGTpovertyindicators.</sup> 4. Use the model parameter estimates obtained in step 1 to obtain new sample welfares, _ych,s_<sup>_∗_,for</sup> every
representative survey on fraud victimization. By utilizing nationally representative data on fraud victimization and household financial circumstances, we can better understand the nationwide patterns of fraud victimization and make more representative inferences about the impact of credit constraints on fraud victimization.

The patterns of fraud approaching are in Table 1. In CHFS-III, each household head is asked whether the family members were approached by fraudsters last year, and if so, the intermedium through which the fraudsters approached. If a household was indeed approached by fraudsters last year, the household is considered as a fraud-approached household. Perhaps alarmingly, 67.4 percent of urban Chinese households were approached by fraudsters through various intermedium.<sup>10</sup> In the past decades, urban areas in China have expanded vastly with massive population inflow, among which are many fraudsters seeking more “opportunities.” The denser neighborhoods and the lifestyle in urban areas also involve more social interactions and easier leakage of private information. Since private information protection and fraudster arresting are costly, local provision of public safety services against fraud approaching is far from sufficient.

We classify the intermedium through which urban households were approached into telephone, texting (i.e., SMS, mail, and e-mail), and face-to-face contacts. Telephone is the most frequently used intermedium: 65.1 percent of urban households have been contacted by fraudsters over the telephone. While both texting and face-to-face contacts are less popular, texting is used more frequently than face-to-face contacts (14.1 percent vs. 8 percent).

Table 2 reports the differences in the probability of fraud victimization after being approached, using the subsample of approached urban households. We split the approached households into being credit constrained and not being credit constrained. Credit constraints happen when there are frictions in the supply of capital and individuals are unable to obtain sufficient loans from banks or other institutions (Hurst and Lusardi, 2004; Farre-Mensa and Ljungqvist, 2016). Following previous studies (Jappelli et al., 1998; Lee and Sawada, 2010;