rafmacalaba/data-use-mentions-extended huggingface dataset:
89)|
|Ave. # of Regular Clients Seen||16.90<br>|||20.20<br>||
|||(33.40)|||(30.11)||
|Ave. # of Casual Clients Seen||-4.42|||-5.24||
|||(16.07)|||(12.08)||
|Activity Controls|No|No|Yes|No|No|Yes|
|<br>Observations|1532|1532|1532|1320|1320|1320|
|Number of Women|190|190|190|190|190|190|
|_p_-value for Prop. Days death this week +|_-_|_-_|_-_|0.97|0.94|0.89|
|Prop. Days death last week = 0|||||||

Notes: All variables are means aggregated over a week. Regressions are fixed effects regressions with month fixed effects. Standard errors are clustered at the individual level. Sickness is an indicator variable equal to 1 if respondent reports a cough, fever, malaria, typhoid, diarrhea, cuts, burns, or other illnesses. Sexual activity controls include vaginal sex, anal sex, oral sex, kissing, massage, stripping, whether the women gave a client company, whether the woman had unprotected sex, and whether she engaged in any other activities. All sexual activity variables measure the number of clients with whom the woman engaged in the given activity at least once, since we did not record the number of repetitions for all activities.

Sexual activities were missing for some women. To avoid dropping them from the regressions, we code the activity as 0 and generate dummies for whether the activity is missing. See text for more details.

1Mean of Dependent variable is mean when all shocks equal 0 over the week. Exchange rate was roughly 70 Kenyan shillings to
other areas; for example, it was not able to change teachers’ negative attitudes towards girls who became pregnant or who contracted an STI.

# **Community mobilization**

# **_Mobilizing parents and community members to monitor school safety_**

In theory, parent organizations and other community groups could pressure schools to ensure students’ safety and enforce sexual harassment policies. Leach et al. (2003) investigated sexual abuse and harassment in schools in Zimbabwe, Ghana, and Malawi. They followed up their research with pilot interventions such as seminars and workshops with students, parents and teachers (in all three countries); workshops with government officials and NGOs (in Zimbabwe and Malawi); and community theatre in the context of a _Durbar--_ a traditional meeting of community stakeholders that aims to raise awareness of a problem and find solutions (in Ghana). Researchers evaluated the results of these efforts using informal, qualitative methods and concluded that community workshops and theatre raised awareness of abuse, increased willingness of parents to report abuse, and allowed the community to confront the problem of abuse without putting individual girls at risk of retaliation. In one site, Leach and colleagues report that the initiative resulted in sanctions against the head of a school who had repeatedly sexually abused students.

# **Individual behavior change strategies**

# **_School-based sexual abuse prevention programs_**

Beginning in the 1970s, schools and universities throughout the United States launched programs designed to prevent child sexual abuse, “dating violence,” or other forms of sexual coercion among students. Typically these programs were based on the theory that children and young women could be taught to protect themselves by recognizing abuse, reporting threatening situations, and adopting safety precautions. Few low and middle-income countries have adopted this model, but the number of programs and the breadth of research on these initiatives make them worthy of mention. By 1992, these programs were so widespread in the United States that a national survey of young people (aged 10-16, n=
Factors on the Pathogenesis of Cardiovascular Disease and Implications for Therapy.” _Journal of the American Heart Association._ Daniel J Brotman, Sherita H Golden, Ilan S Wittstein. 2007. The Cardiovascular Toll of Stress. _The Lance_ t: Vol. 370: 1089–100.

SSCCSE (2009). Southern Sudan - National Baseline Household Survey. Southern Sudan Centre for Census, Statistics and Evaluation, Juba.

WFP (2011). Annual Needs and Livelihoods Analysis (ANLA) – Southern Sudan. ANLA Technical Working Group, World Food Program (WFP) Juba, January 2011.

World Bank Briefing Note (2015). The Fiscal Impact of Declining Oil Prices on South Sudan. World Bank (January 2015).

World Bank (2016). HFS Market Surveys in South Sudan (2012 to 2016) World Bank. 2017. South Sudan Economic Update: Taming the tides of high inflation. Policy options for South Sudan. https://openknowledge.worldbank.org/handle/10986/28560.

World Bank Macro Poverty Outlook (2018). South Sudan Macro Poverty Outlook. World Bank (Spring 2018).

16
.<br>|.|
|Ln Value in 2004|-9.27***|-40.22***|4.62|-22.77***|-18.70***|
||[-2.77]|[-4.99]|[0.55]|[-5.54]|[-5.75]|
|Constant|138.08***|300.64***|-1.66|250.57***|225.37***|
||[5.71]|[6.13]|[-0.03]|[7.27]|[7.46]|
|R-squared|0.11|0.30|0.01|0.39|0.32|

Notes: t-statistics based on robust standard errors in parenthesis *** p<0.01, ** p<0.05, * p<0.1. All regressions use data on all 81 provinces.

19
as NAF beneficiaries are referred to the Ministry of Health to obtain an insurance card. In addition, poor uninsured households can apply to the Ministry of Social Development for referral; we simulate the conditions of eligibility<sup>44</sup> and randomly allocate among those the number that were benefitting from CIP through MoSD (15 thousand households). Insurance information is assigned at the household level based on the insurance status of the head or, if the head is older than 60 years old, based on the insurance status of the eldest household member (under 60 years old). According to this definition of insurance, 72 percent of Jordanians have some health insurance coverage. The Ministry of Health’s Civil Insurance Program (CIP) covers 33 percent of individuals, 3 percent of which are subsidised, the Royal Medical Services and the University Hospital cover another 28 percent or the population, 11 percent are privately insured or insured under other insurance providers.<sup>45</sup>

> 44 Household eligibility criteria used are: Jordanian; not benefiting from another health insurance program; total household income below JOP 300 per month; the household does not own more than one private use car, which should not be 7 or more years old; the household does not own livestock or animals with annual revenue of JOD 300; the household does not receive income from real state rental or shares and profits from firms. In addition, the household must have at least one eligible individual: children between 6-18 years old; students up to 25 years old in colleges/university; single non-working daughters; male sons above 18 years old who have a disability.

> 45 Using the individual report of insurance, 32 percent are uninsured, 32 percent are in the CIP and 27 percent in the Royal Medical Services or in the University Hospital, 10 percent are in private or other type of insurance.
and firm size. We take a number of steps to address this concern. We start by exploiting the fact that BvD computes ultimate owners themselves using the network structure of the raw ownership data it acquires. So if Mr. Jones owns 100% of company A which owns 50% of company B, he will appear with a 50% ownership share of company B in our data. However, it is unlikely that BvD is able to catch the totality of indirect ownership. Therefore, we additionally match names of any company shareholders with those of individual shareholders to identify those set up in the same name as their owners (which is relatively common) and assign the ownership shares of those companies to the matched individuals. We also include two alternative specifications in our robustness checks. The first classifies firms as FOFM if using our baseline method but only computing ownership shares of the total firm ownership by individuals (i.e. we drop company shareholders). The second drops all firms with any company owners. The stability of our results across these alternative specifications reassures us that the observed relationship between firm size and family management is not mechanically produced by our measure. Moreover, our main specification examines the likelihood that a firm of a given size is family-owned as trust changes, which abstracts from these size-dependent measurement concerns.

> 20Our full dataset covers more than 265,000 firms, but not all countries have regions available that we were able to concord across all our datasets. Where regional variation is not exploited for identification, such as in the cross-country regressions, this full dataset is used. As shown in Table I, the 11 countries used in our primary dataset are Bulgaria, Bosnia and Herzogovina, Estonia, Finland, Germany, Italy, Netherlands, Poland, Spain, Switzerland and the Ukraine. 21The 20% equity definition is used in previous work classifying firms as family-owned, such as La Porta, Lopez-de-Silanes and Shleifer (1999). Importantly, a key difference between our measures and theirs is that we identify whether
to small and medium firms (Rozkru, 2015). This counterintuitive result may be explained by the fact that micro firms may be more likely than larger firms to report improvements as innovation.

In the following sections we attempt to contribute to the literature on the impact of survey methodology on innovation measurement by shedding some light on the importance of problems typical of subjective data when measuring innovation in developing countries.

# **3. Data**

This paper draws on Innovation Enterprise Surveys data collected between 2012 and 2014 on a representative sample of the formal (registered) non‐agricultural, non‐mining private sector in Sub‐ Saharan Africa and South Asia. Fifteen countries are covered by the surveys: 11 in Sub‐Saharan Africa (DRC, Ghana, Kenya, Namibia, Nigeria, Sudan, South Sudan, Tanzania, Uganda, Malawi and Zambia) and four in South Asia (Bangladesh, India, Pakistan, and Nepal) (see Table A2 in the Appendix for more details). The data were collected by the Enterprise Analysis Unit of the World Bank Group as a part of a DFID‐ funded project, which aimed to increase the coverage of innovation surveys in developing countries while assessing at the same time the robustness of the Oslo Manual framework.

The project followed a two stage approach to data collection. In the first stage, a short innovation section (short questionnaire) consisting of 9 questions based on the CIS‐type of survey was incorporated into the Enterprise Surveys (ES). The ES is a multi‐topic firm‐level survey that collects data on firms’ characteristics, balance‐sheet, and firms’ experience of the business environment.<sup>3</sup> In the second stage, an in‐depth innovation module (long questionnaire) was administered to randomly selected ES respondents. The questionnaire used in the innovation module is also based on the Oslo manual but expanded to capture more details on the innovation introduced and to integrate other important questions on organization and management practices
group had to decide whether to give to people in need, without expecting anything in return. Participants in the treatment group were given the opportunity to verify whether their transfers actually reached poor individuals.

Our experimental design simulates at the micro level the choice faced by middle-class citizens on whether to support a shift of public resources toward targeted interventions that benefit the poor, under different designs of those interventions and different degrees of certainty about the delivery of the benefits to the intended recipients. The experiment thus allows us to provide rigorous evidence on: (1) the propensity for redistribution and relative support for different redistributive methods; and (2) the underlying impact of transparency and trust on redistributive preferences. In doing so, this paper bridges the gap between evidence obtained from traditional opinion surveys and the behavioral literature.

We have two main sets of results. First, although the effect of transparency on altruism is not statistically significant for the whole sample, the transparency-enhancing treatment caused significant increases in the support for redistribution among two groups of participants: “low-trust” individuals and “youth.” The first group consists of individuals who needed a credible signal of trustworthiness in order to exhibit altruism– people who were ex ante suspicious about the implementation of social safety nets. In the control group, such low-trust individuals were significantly less likely to give to the poor than individuals who trusted that social safety nets reach the intended beneficiaries. In the treatment group, on the other hand, the giving rate of low-trust individuals matched that of high-trust participants, suggesting that the transparency-enhancing treatment mitigated the effect of their initial mistrust on altruistic behavior. The second group– youth, defined with different age thresholds– represents those least likely to give to the poor in the absence of the material guarantees of delivery. With increased transparency, this group experienced the highest increase in the rate of giving.

Second, we provide evidence that the transparency-enhancing treatment particularly increased redistribution to the poor through unconditional cash transfers as opposed to in-kind or conditional cash transfers. In the control group
.3|
|Misiones|28.6|65.8|12.5|71.6|5.3|
|Prv Soc Men|49.7|49.7|34.2|43.4|1.0|
|Rio Negro|24.6|23.7|47.3|28.4|1.2|
|Salta|73.3|58.7|75.9|7.5|4.5|
|San Juan|34.2|76.4|23.1|68.8|1.5|
|San Luis|31.6|87.1|12.9|80.9|35.5|
|Sant del Est|*81.1|*84.4|***71.8|***13.8|3.7|
|Tucuman|66.7|67.0|53.4|43.8|4.6|

Sources: See Table 2 with regard to % of pre-privatization assets and liabilities in the residual entity. The % nonperforming and the % normal loans data comes from B.C.R.A. balance sheets. Data on physical assets comes from Fondo Fiduciario balance sheets for residual entities at the time of privatization. * Calculations described in Table 2.

** 1993 data were used. By 1994, the effects of privatization were evident in Chaco's portfolio quality data. *** 1993 data were used. No data were available for 1994.

A simple regression shows that the worse the public provincial bank's portfolio, the larger the residual entity.<sup>23</sup>

- 23 The observation for San Luis was dropped from the estimation because the physical assets reported in its residual entity balance sheet implied an implausibly high percentage of those assets in its portfolio (Table 4). Although not reported here, the qualitative result
Africa (RIA) in 2017-18.<sup>6</sup> Formal firms are defined as firms that have legally registered either at the national registrar general or country-level revenue authority, at the local authority or municipality, or both. This definition aligns with what Ulyssea (2020) refers to as the dominant approach in the literature to define informality, namely a legalistic approach: he defines informal firms as those that do not register and pay entry fees to achieve a formal status.<sup>7</sup> Informality of the enterprise is referred to as the extensive margin

> 5 As mentioned above, the recent empirical literature has emphasized that unnecessary regulatory costs, in particular unduly high registration costs do not seem to play a major role in firms’ decisions and their elimination does not seem to generate widespread formalization. In particular, Benhassine et. al. (2018) find that an effort in Benin to induce firms to formalize (while at the same time decreasing costs of formalization) resulted in a significant increase in formalization but this formalization did not bring firms higher sales or profits. De Giorgi and Rahman (2013) find that an information campaign in Bangladesh made firms more aware but had no impact on registration. More generally, in their review of the literature, Bruhn and McKenzie (2014) conclude that “evidence on the effects of entry reforms and related policy actions to promote firm formalization ... result in only a modest increase in the number of formal firms, if there is any increase at all. Most informal firms appear to not benefit on net from formalizing”. See also World Bank Group (2021).

> 6 Because RIA did not have independent information on the distribution of microenterprises, sampling for the business survey was done in parallel to the sampling for the RIA household survey examined in Atiyas and Doğanoğlu (2020). Specifically, the national census sample frames were split into urban and rural Enumerator Areas (EAs). Then EAs were sampled for each stratum using probability
|**973**|1,644|996|1,678|1,002|1,698|
|**Indorwsoa**|203|350|212|363|211|356|
|**Brazil**|201|345|178|292|180|306|
|**Nigeria**|153|425|163|529|163|529|

These revisions have affected the projected stationary populations dramatically. The eventual stationary world population now appears almost 10 percent greater than it did in 1980. For Africa in particular, the projected stationary population is now 21 percent larger than in the 1980 projections.

Aggregate changes since last year's projections, on the other hand, are minor. Projected world population in the year 2000 is up half of 1 percent this year as compared with last year. The largest revision for a geographic region is for Asia, but is barely above half of 1 percent.

a Projections for practically all countries have changed, but in the majority of cases only minimally, and about as often upward as downward. The larger changes have been due more often to revisions in current demographic indicators rather than to revisions in assumed future trends. Current indicators may have changed, for instance, because of new data or new analysis of census or survey data. Revised current population estimates account for new projections for countries like Swaziland, Costa Rica, Jamaica, Peru, and Papua New Guinea. Revised fertility estimates, often from Demographic and Health Surveys, account for new projections for countries like Nepal, Pakistan, Bolivia, Guatemala, and Nicaragua.

# Comparisons among World Bank regions

The four World Bank regions, comprising all the borrower countries
producers or input sharing and 3/ knowledge spillovers (Rosenthal and Strange 2004)<sup>2</sup> . In this paper we are primarily concerned with agglomeration economies that arise from labor-market interactions or matching, sharing Bertaud’s (2014) view that cities can primarily be seen as labor markets.

The analysis presented in this paper shows that in Nairobi County, Kenya, employment accessibility tends to be limited for a large share of the population that cannot afford to purchase cars. This situation is problematic both for productivity and for livability. On the productive side, it limits the effective size of the labor markets, thereby preventing workforce/employer sorting which hampers the

> 1 The authors are grateful to Sarah Elizabeth Antos for performing the network analysis which provided the automobile transport times matrix and to Annie Bidgood for GIS assistance. Joel Hamann ensured the processing of the matatu GTFS data through a Dijkstra algorithm that he also coded. Dean Cira and Sheila Kamunyori’s guidance as well as efforts to put us in contact with counterparts in Nairobi and locate datasets are highly appreciated. The authors would also like to thank Nancy Lozano Gracia, Adrien Vogt-Schilb, Gunnar Eskeland, Vernon Henderson, Roger Gorham, Tony Venables, Paul Collier, Daniel Rodriguez, Marianne Fay and Stephane Hallegatte for comments on this work and crucial suggestions.

> 2 A more comprehensive list differentiating between up to 12 types of economies of scale can be found in the 2009 World Development Report (World Bank 2009, p 128).

2
# _IV.4 Inequality and poverty_

We also examine the spatial pattern of expenditure inequality (the Gini coefficient) in Vietnam. The provincial estimates of the Gini coefficients can be found in Table A.10 in the Appendix. Inequality varies across provinces and districts albeit with small differences. Average inequality (based on expenditure) is rather low at 0.27 for provinces and 0.25 for districts. The province with the lowest Gini (0.23) is Thai Binh, while the province with the highest Gini (0.35) is Lam Dong. At the district level, the Gini coefficient varies from 0.17 to 0.47. Meo Vac disitrict of Ha Giang province has the lowest Gini (0.17), while Da Lat city of Lam Dong province has the highest Gini (0.47).

Interestingly, low levels of inequality are found in both the poorest provinces and the richest provinces. Figure 3 plots the relationship between poverty and inequality. The quadratic relationship is highly significant both at the province and district level. Inequality tends to be lower in areas with relative low poverty and areas with relatively high poverty rates, although the differences are not enormous. This finding is consistent with the Kuznets hypothesis that inequality first increases as the economy develops, and then decreases once a high level of economic development is reached.

**Figure 3 Inequality (Gini index) and poverty (P0)**

<!-- Start of picture text --> 0 20 40 60 80 0 20 40 60 80 100<br>The poverty incidence in 2006 The poverty incidence in 2006<br>Source: Authors’ estimation from VHLSS 2006 and ARFC 2006<br>.35 .5<br>.4<br>.3<br>.3<br>Gini coefficient Gini coefficient<br>.25<br>.2<br>.2 .1<br><!-- End of picture text -->
correct for the noise and heteroskedasticity, we will weight all descriptive statistics and regressions **by** 3-digit industry-year total employment. That is, we will give more weight to larger, presumably less noisy industries.<sup>2</sup> As an alternative to employment weights, we will also use **FGLS** techniques to weight each observation **by** the inverse of its variance relative to industry and year means. That is, we will downweight observations which are very far from industry and year means. We discuss the technique in detail below. In order to measure changes in industrial policy, we use the detailed dataset of industrial policy in India constructed **by** Sharma (2006a,b) and used in Chamarbagwalla and Sharma (2011). This dataset identifies which 4-digit industries underwent reform in terms of freedom from licensing requirements in each year from **1970** to **1990.** Table 1 shows the quantum of de-

> 20Note that the **ASI** data is a series of repeated cross-sections of plants, not a panel of plants, thus it does **not** allow **one** to identify entry and exit.

> 21See Appendix Figures B.1 and B.2.

> 22Appendix Table B.1 shows the weighted averages of the key variables used in our analysis.

**17**