rafmacalaba/data-use-mentions-extended huggingface dataset:
known for violating human rights; law enforcement cannot be effective in addressing violence against women as long as the broader police force has a reputation for abuse.

# **Institutional reform of police, the judiciary, forensic medicine, and legal aid**

The example of Zambia highlights the need to go beyond legislative reform by strengthening key law enforcement institutions, including police, the judiciary, the forensic medical system, and legal aid. Even in countries with strong legal systems, law enforcement institutions have historically responded to intimate partner and sexual violence with bias, neglect and mistreatment. For example, in Britain, researchers noted that police and judges often minimized the criminal nature of gender-based violence and expressed bias against victims of sexual abuse until serious reform began in the 1970s (Morley and Mullender, 1994). In middle and low-income countries, law enforcement institutions often face a severe lack of resources for personnel, equipment, training, and transportation. Many legal systems are plagued by police corruption, expensive, slow and inaccessible courts, and an unaccountable judiciary (Human Rights Watch, 1997, 1999, 2000, 2001, 2003a, and 2003b).

A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned.

# *
- Student-teacher ratio – the total number of students in the school divided by the total number of teachers in the school (these numbers are based on headcounts, not full time equivalents).

> 24 We do exclude „ordinary‟ evening schools. For the regression analysis below, however, we do include all types of schools, controlling for differences through type-specific dummies. 25  We lose about 5 % of the schools by doing this, mainly rural schools.

> 26 We have this indicator for those schools that we were able to match to the MoE database.  For some schools, we clearly have unrealistic participation ratios. We keep them in the sample here but will exclude these schools in further analysis – see below for details.

15
gender. On legal identification, we see mixed patterns: in Nigeria, non-displaced persons are significantly overrepresented in MPI poor households with intrahousehold inequality, whereas this is true for the displaced in Somalia.

Gender does not reveal any significant differences in intrahousehold inequality for employment in the countries, except for Nigeria, where men are more likely than women to live in MPI poor households where they are employed, and another adult is unemployed and looking for work. As Nigeria’s results for differences by displacement were insignificant, we can rest assured that of the two traits, gender is the primary characteristic driving those differences. Moreover, in the legal identification indicator, the test for gender records robust significant differences in intrahousehold inequality only among the MPI poor of Somalia, where men more commonly have a form of legal identification when another household member does not.

# **6 Policy Implications and Conclusions**

As leaders and policy makers reckon with the backwards slide of progress in the wake of the global pandemic, they must consider the disparate and varied experiences of boys and girls, and men and women, within the household, as questions of building back better turn into policies and programs directed at leaving no one behind.

The decomposability afforded by the MPI’s individual-level indicators is a step towards informing better policies. The indicators analyzed in the individual and intrahousehold analysis – school attendance, primary school completion, unemployment, and legal identification – can be targeted by government policies and programs to improve the situation for those who are multidimensionally poor.

Our comparisons of the household and individual-level deprivations and intrahousehold analyses suggest that displacement status most affects household status, whereas gender drives differences within households. The individual deprivations reflect aspects of both displacement and gender. More broadly, the analysis underlines the importance of going behind household-level outcomes, especially in education.

Among the multidimensionally poor, children in displaced households, and especially girls, face greater barriers to educational equity than those of their host community peers.

26
the sense that the only taxes levied are those of the host country. The polar opposite case is that of ‘worldwide’ taxation, meaning—in its purest form—that the parent country will top up any taxes paid in the host country to the level applied by the country in which it resides, applying a credit for any taxes paid to the host country. The implication is that the tax paid to the host country is less relevant for inward FAI when the parent resides in a country applying worldwide taxation, because the final tax paid will be that of the residence country. In practice, for reasons touched on in a moment, the distinction between territorial and worldwide systems is not as sharp as this sounds. Nevertheless, a meaningful distinction of this kind can be made, and we do so by constructing a list of territorial corporate tax system using PWC World Tax Summaries.<sup>44</sup> Table 5 reports results allowing tax effects to differ according to the regime applied by the parent country.

In column (1), the coefficient of the rate differential is large and highly significant whether the parent is from a worldwide or a territorial country—but somewhat smaller (and less significant), as the considerations above would suggest, in the worldwide case. A similar story emerges in column (2), where the rate differential is split into its host and non-host components and the effects of each allowed to differ according to the regime applied by the residence country. There the effect of the host rate is both smaller and less significant when the parent is from a worldwide country. That it is not zero is consistent with two respects on which worldwide taxation in practice differs from its textbook form. One is that application of residence country taxation can often be avoided, not least by deferring repatriation of profits to the parent: this has been common practice, for instance, among US multinationals. The other is that host country tax remains final if it is above the tax due under
determine the sunkenness of capital are also important determinants of ...rm size dispersion, as measured by the variance of the market shares of the largest 50 ...rms in each industry. We ...nd again that the lower is the sunkenness of the capital that is employed in an industry, the smaller is the dispersion and hence, the tighter is the distribution of ...rms around the optimum. The parametric restrictions that are implied by the model are con...rmed by these data. Indeed, the maintained hypothesis H02 : c1 = c2 is not rejected at the 5 percent level in all three temporal cross-sections.<sup>12</sup> Table 2 presents estimates of (15) and (18) using the pooled data from the 1982, 1987, and 1992 censuses. Both OLS and the asymptotically more e¢ cient

> 12 For the 1982 cross-section, F = :07 < F :195;396<sup>=3:87,andtheprobabalitylevelatwhicha</sup> standard F-test rejects the restriction is .79; for 1987, F = :03 < F1<sup>:95</sup> ;426<sup>= 3:86;andtheprobabality</sup> level at which a standard F-test rejects the restriction is .87; for 1992, F = :25 < F1<sup>:95</sup> ;425<sup>=3:86,</sup> and the probabality level at which a standard F-test rejects the restriction is .62.

14
000 tons in 2009 (of which 8 percent was wood, see Table 5). At the same time, the volume of official imports from the DRC to the RC, and data on transit flows through the DRC only add up to 101,000 tons, compared to the 343,000 tons of incoming traffic from Kinshasa reported by Brazzaville port. More than half of trade handled by the port of Brazzaville is in fuel and cement. The volume of shipments into Brazzaville exceeds that of shipments out of Brazzaville by a factor of about ten. Outbound shipments may suffer from particular
**Figure 6.  The change in non‐food deprivations**

<!-- Start of picture text --> Share of Respondents Who Face Deprivations<br>0.85<br>0.75<br>0.65<br>0.55<br>0.45<br>0.35<br>0.25<br>0.15<br>Water Primitive Energy No Municipal Employ Any Face Three or<br>Insufficient Source Waste Collection Food Coping Four<br>Strategy Deprivations at<br>Once<br>Before Blockade After Blockade<br><!-- End of picture text --> Source: WFP Mobile Phone Survey, November 2017.

**Table 3.  Changes in Food Coping Strategies Following the Blockade**

||Reduce<br>Number of<br>Meals<br>Dependent|Restrict<br>Consump<br>tion<br>Variable:|Borrow<br>to Pay for<br>Food|<br>Relied on<br>Less<br>Expensive<br>Food|Limit<br>Portion<br>Size|Water<br>Insufficient|Primitive<br>Energy<br>Source|No<br>Municipal<br>Waste<br>Service|Relied on<br>a Food<br>Coping<br>Strategy<br>for at<br>Least One<br>Day|<br> <br>Experienced<br>Deprivations<br>in at Least 3<br>Dimensions|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression relying on panel data estimation. The analysis is restricted to working age individuals in the years 2012 and 2018. In columns (1) to (4), the analysis is restricted to the sample of males. In columns (5) to (8), the analysis is restricted to the sample of females. The dependent variable is a dummy variable indicator equal to one if the individual is employed in the corresponding year 2012 and 2018 (according to the extended definition of labor force participation). The main independent variable of interest is a dummy variable indicator equals to one if the individual reports that finding a job is one of the purposes of Internet use in the corresponding year 2012 and 2018. The regressions also include time-varying controls: age, a dummy variable for being married, three dummies for the individual’s educational attainment (primary or preparatory education, secondary education either general or vocational and above secondary education either post-secondary institute or university and above, and the reference category is no educational degree either illiterate individuals or literate individuals without any diploma), a dummy variable for rural areas as well as dummies for the various geographical regions included in the last column (Greater Cairo, Alexandria, and Suez cities, Urban Lower Egypt, Urban Upper Egypt, Rural Lower Egypt, and Rural Upper Egypt). The regressions also include individual and year fixed effects. The dependent variable mean is reported in the last row.

39
behind this pattern lies in the higher energy needs for domestic heating services prevalent in colder regions, thus leading to increased domestic energy usage. Conversely, individuals residing in warmer regions exhibit lower heating energy needs, resulting in decreased domestic energy usage. Consequently, applying a uniform national threshold indicates a higher likelihood of surpassing the threshold

> 24 For example, the Energy Poverty Barometer developed in the case of Belgium relates energy expenditure to disposable income after deduction of housing costs (King Baudouin Foundation, 2018).

13
ISA) project.<sup>20</sup> The IHS3 data were collected within a two-stage cluster sampling design, and are representative at the national, urban/rural, regional, and district levels, covering 12,271 households in 768 enumeration areas (EAs). The IHS3 instruments included Household, Agriculture, Fishery, and Community Questionnaires.

All sample households were administered the multi-topic Household Questionnaire that collected individual-disaggregated information on demographics, education, health, wage employment, nonfarm enterprises, anthropometrics, and control of income from non-farm income sources, as well as data on housing, food consumption, food and non-food expenditures, food security, and durable and agricultural asset ownership, among other topics. The sample households that were involved in agricultural activities (through ownership and/or cultivation of land, and/or ownership of livestock) were administered the Agriculture Questionnaire. The Agriculture Questionnaire solicited information on land areas, physical characteristics, labor and non-labor input use, and crop cultivation and production at the plot level, separately for the reference rainy and dry seasons.<sup>21</sup> The data allow for agricultural production estimates at the plot level and for the identification of the manager of the plot<sup>22</sup> , as well as household members that owned<sup>23</sup> and/or worked on each plot.<sup>24</sup> Handheld global positioning system (GPS)-based locations and land areas of the plots were recorded, permitting us to link household- and plotlevel data to outside geographic information system (GIS) databases.

The descriptive statistics and the results from the tests of mean differences by the gender of the plot manager are presented in Table 1. The full sample consists of 16,372 plots, 26% of them managed by females.<sup>25</sup> Table 1 clearly demonstrates the (unadjusted) gender gap: the 20 The lead author was the point person for the World
and Bradford Jensen, 1999), we find evidence of “self-selection” on innovation activities; innovative firms pay higher wages already in the three previous years to become innovative. In addition, we also find evidence of increases in the wage premium after becoming innovative, an effect that persists three years later.

The paper is structured as follows. The next section briefly describes the literature on wage premiums and the links to the innovation premium. The third section describes the data and methodology used. Section 4 shows the results for the estimated wage premium and its decomposition by type of firm and worker. Section 5 focuses on the causality between innovation and wages. The last section concludes.

# **2 Innovation Wage Premium**

The availability in the last two decades of microdata linking firms and workers characteristics has spurred a significant number of empirical studies looking at different wage premiums associated to key workers or firms characteristics. In a perfectly competitive labor market, sector and firms’ differences are expected to result in a different composition of workers, but should not influence wages. However, in imperfectly competitive labor markets, firms and workers have some ability to influence wages and capture some of the rents associated with improved performance. Evidence that firms and workers have some ability for wage-setting (Mortensen, 2003) has triggered a large literature trying to estimate what types of workers and firms have larger wage premiums, since these premiums can account for a significant share of wage inequality. For example, Alvarez et al. (2018) document a large decrease in earnings inequality in Brazil from 1992 to 2012 and find that firm premiums account for 40 percent of the total decrease in inequality. This section summarizes some of the most relevant studies that focus on premiums associated with firm size and age, international activity - exporters and FDI - and technology and innovation.

One of the first analyses of wage premium goes back to Moore (1911), who finds that the mean rate of wages in the textile industries increases with the establishments’ size. Over
Central Statistical Organization (CSO), Ministry of Statistics & Programme Implementation, Government of India.(2007)

- Greenstone, Michael, Richard Hornbeck and Enrico Moretti, “Identifying Agglomeration Spillovers: Evidence from Winners and Losers of Large Plant Openings”, Journal of Political Economy 118 (2010), 536-598.

- Greve, Arent, and Janet Salaff, “Social Networks and Entrepreneurship”, Entrepreneurship Theory and Practice28 (2003), 1-22.

- Hasan, Rana, and Karl Jandoc, “The Distribution of Firm Size in India: What Can Survey Data Tell Us?”, ADB Economics Working Paper 213 (2010).

Helsley, Robert, and William Strange, “Coagglomeration”, Working Paper (2010).

Helsley, Robert, and William Strange, “Matching and Agglomeration Economics in a System of Cities”, Regional Science and Urban Economics 20 (1990), 189-212.

Henderson, J. Vernon, “Marshall's Scale Economies”, Journal of Urban Economics 53 (2003), 1-28.

24
the guerrillacontrolled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table D.25. Effects of Guerrilla Control on Homicide and Victimization Rates

||Homicides|Victim of Any Crime|Victim of Gang Extortion|
|---|---|---|---|
||(2017)|(2004-2016)|(2004-2016)|
||(1)|(2)|(3)|
|Guerrilla control|-0.0110|-0.210***|-0.193***|
||(0.0562)|(0.0552)|(0.0637)|
|Observations|3,652|94|94|
|Bandwidth (Km)|2.266|2.266|2.266|
|Dependent mean|0.314|0.688|0.0420|

_Note:_ The table presents the results of equation 1 for our outcomes related to current crime. Column 1 shows the number of homicides reported to police for each census tract in 2017. Column 2 shows the share of people within a census tract who reported being a victim of any type of crime in the LAPOP survey. Column 3 shows the share of people within a census tract who reported being a victim of extortion in the LAPOP survey. The unit of observation in all Columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. We use the algorithm of Calonico, Cattaneo and Titiunik (
The population totals for 2018 and geographic aggregations are mapped in Figure A2.

# **4.1.2 Gallup World Polls**

Our primary outcome, attitudes towards immigrants, comes from the Gallup World Polls (GWP). We use the GWP because it offers significantly more coverage across locations and time than other opinion polls. The GWP aims to conduct annual, nationally representative surveys of approximately 1,000 individuals in each country on a wide range of topics.<sup>16</sup> We use data from 2005 to 2018. This covers 168 countries with at least one survey, 1,732 survey-years, and 2,017,774 observations in total to select sub-samples for analysis.

Since refugees are often geographically concentrated within a host country, we use the lowest sub-national level that is reported in the GWP data. There are multiple considerations when using the data at this level. First, sub-national locations are not reported for all country-years and the geographic divisions vary by country. For instance, respondents’ locations in Kenya are reported at the province level, which divides the country into 47 sub-national units. Locations in Uganda, in contrast, are reported as one of four sub-national regions.<sup>17</sup> Second, the data is not representative of specific sub-national regions, since the multi-stage stratification at the country level may select only a few sampling units within the region. However, analyzing the aggregatate of enough regions (according to their refugee presence and policy) mitigates most concerns about representativeness. Third, some subnational regions were not included in the GWP. Some were excluded randomly during sampling, while others’ exclusion was an intentional decision by Gallup due to security or sparse populations.<sup>18</sup>

> 16We exclude those who were not born in the country of interview from the sample.

> 17The number of reported geographic divisions could affect the statistical power, precision, and accuracy of our estimates. Countries with fewer geographic units likely capture respondents who
EDI). This survey was designed and fielded to study the implications of the alternative survey designs for consumption expenditure measures and labor market indicators (see Bardasi et al., 2010 for a description of the labor survey experiment). Here we focus on the component that applies to consumption expenditure measures.

The field work was conducted from September 2007 to August 2008 in villages and urban areas from seven districts across Tanzania: one district in the regions of Dodoma, Pwani, Dar es Salaam, Manyara, and Shinyanga and two districts in the Kagera Region. The districts were purposively selected to capture variations between urban and rural areas as well as across other socio-economic dimensions to inform survey design related to labor statistics and consumption expenditure for low-income settings. The sample was constructed to be representative at the district level, but not at the national level. Data from the 2002 Census were used to enumerate all villages in the district. In the first stage of the sampling process, a probability-proportional-to-size (PPS) sample of 24 villages was selected per district. In a second stage, a random sub-village (or
small STI probabilities (see e.g. Kahneman and Tversky, 1979). Thus they may behave as if the expected loss is substantially higher than it truly is.

# **_High Discounting_**

In a society with a generalized AIDS epidemic (Tanzania has an estimated 6% adult prevalence rate), the expected cost of an AIDS diagnosis might be considered far larger than the modest cash rewards offered. For individuals who are present-focused and heavily discount the future though, the prospect of an AIDS diagnosis many years in the future may not be considered a high cost. But if the price of risky sex would be incurred within months instead (the RESPECT study tested and offered cash rewards every four months), then high discounters may perceive an increased (discounted) price of risky sex, and thus behaviorally respond to this shortening of the time horizon. This of course depends on the extent of high discounting; studies in this context (including measures in the RESPECT study) have found extremely high rates of discounting, implying that even a timeframe of months may result in substantial discounting of the potential reward value. And a reward in the timeframe of months may have little impact on those risky sexual behaviors that may be driven by strong hyperbolic discounting (similar to the concept of compulsive immediate gratification used in developmental psychology) as discussed in the behavioral economics literature (O‘Donoghue and Rabin, 2001).

# **_Other “Nudges”_**

A variety of other behavioral economics and psychological hypotheses have been proposed regarding the operation of incentives, now sometimes referred to as ―nudges‖ following the popularization of the term by Thaler and Sunstein (2008). For example, some argue that introducing explicit monetary incentives into the sexual decision-making process may alter the frame within which people assess costs and benefits, resulting in unpredictable deviations from neoclassical theory. Others suggest that the incentives provide individuals with an excuse for deviating from social norms in order to act on underlying preferences for less risky behavior. Several such theories