rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3. Share of connected firms in Ukrainian economy, percent**

<!-- Start of picture text --> Approach 1 Approach 2<br>35<br>25<br>30<br>20<br>25<br>20 15<br>15<br>10<br>10<br>5<br>5<br>0 0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>Number of firms Turnover Assets<br>Politically connected firms have captured an increasingly large share of assets over the last five<br>years (Fig. 3). Based on strict ownership and control criteria under the first approach, politically<br>connected firms representing less than 1 percent of the total number of firms have accessed over<br>one-fifth of debt financing for all firms and account for over one-quarter of all assets (Fig. 4).<br>Figure 4. Share of assets and debt of connected firms, Approach 1<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>assets debt<br>28.88 20.65 20.73 21.48<br>24.7725.00 24.7724.27 26.0123.83 26.9323.72 26.67 26.72 26.1923.86 25.12 26.39 17.28 17.39 17.3017.72 19.1718.74 19.1119.01 18.79 19.64 19.57<br>22.60 22.53 16.44<br>19.66 19.94 20.56 15.1814.88 15.46 15.83 15.11<br>2.08 2.03 2.00 2.01 2.07 2.06 2.09 2.10 2.15 2.23<br>0.32 0.33 0.33 0.34 0.35 0.33 0.33 0.32 0.34 0.34<br>29%<br>26%<br>25% 22% 24% 21% 24% 19% 24% 19% 23% 19% 22% 19% 24% 20% 25% 19% 21% 22%<br><!-- End of picture text --> Politically connected firms tend to operate in capital intensive industries. Politically connected firms as defined under the first approach are most prominent in the mining, energy and transport sectors where they account for over 40 percent of turnover and over half of all assets. Politically 5
<!-- Start of picture text --> 180 I<br>ay- OS $—t—t oi fmt — tt jbo = rt =<br>eS — ——— —— ——s — —— ia <= =<br>110<br>60%—<br>D 100 | a oat | a ae|<br>uw a<br>< z C1]<br>4om%- 70 |<br>Cf a: —— : — a a —— ——+ ——<br>we 2 —— 7 oS —_ ——<br>«of EE __ aim eo<br>me ae Z====za==<br>i= SI|| SS=: Se—— ——:<br>on =<br>SS Fe erento =========<br>cam | 2005 2004 2005 2006 2007 2008 2008 2010 2017 anz<br>TIME ——*<br><!-- End of picture text -->
- Donovan, J. L. (1995). Patient decision making: The missing ingredient in compliance research. _International Journal of Technology Assessment in Health Care 11_ (3), 443–455.

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17
In June 2007, trainers met for a second two day ‘training of trainers’ workshop and discussed business needs identified during the training sessions. With ECI staff, they identified the right resources and training to support their CO clients. A second set of 2- day sessions were conducted in July 2007.<sup>16</sup> While recent evaluations of business training programs differ significantly in their method of delivery, intensity, location and implementing organization, in part reflecting a different target population, it is nonetheless informative to compare the business training implemented in our experiment to others reviewed in McKenzie and Woodruff (2014). While most studies target exclusively urban entrepreneurs, this experiment covers a sample of rural microfinance clients engaged in diverse occupations. All training courses reviewed were classroom-based, delivered to groups of individuals with sessions of 15-25 participants per trainer. Ours was more hands-on, included a visit to a market and required participants to set up a business for a day. In addition, our training is relatively more intensive, containing a total of 46 hours of training compared to an average of 28.6 hours, and is taught by the staff of the microfinance institution in contrast to most programs that are taught by professional trainers.

From October 2007 to January 2008 one-on-one follow-up sessions called “HandHolding” sessions were organized for all participants in half of the COs that were offered training, selected at random. NRSP trainers would visit the member at their home or place of business once or twice a month and discuss the topics learned, answer questions and suggest solutions to potential problems.<sup>17</sup> While the business training sessions were being conducted, NRSP identified all the study members that were eligible for the loan lottery. Eligible members had to be borrowers of NRSP in good standing, that is, they were required to have successfully repaid at least one loan on time and to have no overdue loans. Roughly 55 percent of CO members in our sample were eligible (58 percent among male members and 52 percent among women). All eligible members, including those in COs not offered the business

> 16 The contents of the second training session included identification of technical/skill training needs, product design and marketing, and choice of input and output markets and distribution systems. 17 To the extent possible, men were visited by male trainers while women where visited by female trainers, but given that there were 15 male trainers and only 3 female trainers, some women had to be visited by male trainers. Another form of mentoring delivered by volunteer business owners is studied in Valdivia (2013).

10
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47
forms such as leasing or factoring have been promoted as conducive to easing financing constraints of SMEs, as they are based on the underlying assets and cash flows rather than borrowers’ financial history (Berger and Udell, 2006). On the other hand, banks, particularly large banks, have also shown increased interest in SME financing, exploiting scale economies and technology (Beck, Demirguc-Kunt and Martinez Peria, 2011a). The question on the size of financial institutions – often intertwined with the ownership question – is directly related to entry barriers and minimum capital requirements imposed by policy makers in developing countries to foster a specific market structure (Beck et al., 2011b; Beck et al., 2011c and World Bank, 2011).

This paper uses a unique and confidential dataset to shed light on the relationship between the structure of the financial system and the size of its institutions, on the one hand, and access to financial services by enterprises, on the other hand. Specifically, using data from the World Bank and IMF’s Financial Sector Assessment Program (FSAP), we are able to compute both the relative importance of different segments of the financial system that cater to low-end customers, such as small and medium-size enterprises, as well as the average size of institutions within this segment. We then match these country-level indicators to firm-level indicators from the World Bank’s Enterprise Surveys on financing obstacles and actual use of deposit and loan services by enterprises in developing and emerging countries. In addition, we examine the relationship between financial structure and firms’ access to finance across countries at different levels of GDP per capita, across firms of different sizes, and across industries with different needs of external finance, to thus take explicitly into account the potential cross-county, crossfirm and cross-industry heterogeneity in the effect of financial structure on firms’ access to finance.

Our research speaks to several literatures. First, the financial structure literature has discussed the implications of bank- vs. market-based financial systems for firm, industry and GDP per capita growth<sup>2</sup> , but has not considered the importance of other segments of the financial system, including specialized lenders such as leasing, finance or factoring companies or low-end financial institutions such as cooperatives, credit unions and microfinance institutions. This paper is the first, to our knowledge, that explores the relationship between the importance of

> 2 For the relationship between the degree to which a country is bank- or market-based and firm, industry and GDP per capita growth, see Demirguc-Kunt and Maksimovic (2002), Beck and Levine (2002) and Levine (2002), respectively.

3
# 3. <u>Data</u>

In this study, we employ the Russia Longitudinal Monitoring Survey (RLMS). The RLMS is a series of nationally representative surveys designed to monitor the effects of reforms on the health and economic welfare of households and individuals in the Russian Federation. These effects are measured by a variety of means: (i) monitoring individuals' health status and dietary intake, (ii) measuring household-level expenditures and service utilization, and (iii) collecting relevant community-level data, including region-specific prices and community infrastructure data. Data have been collected 19 times since 1992. The RLMS consists of household and individual level data.

For the purpose of this exercise, we construct a longitudinal dataset composed of 2,191 families who are followed from 2007 through 2010.<sup>5</sup> There are a few ways of defining households that were affected by an income shock. Dasgupta and Ajwad (2011) for example, use questions from a specially designed Crisis Response Survey to determine which families were more affected by income shocks than others. Similar approaches are applied in additional work in the field. For example, Andow and Koppe (2011) use a questionnaire revolving around job losses and prospective job losses, to estimate the households that were affected by the 2009 crisis in England and Scotland. EBRD (2011) argues that the wage reduction is the main channel through which crisis-affected households could be identified.

We define crisis-affected households as those households whose real incomes fell between 2008 and 2009 (as GDP contracted by 7.9 percent in 2009) by more than a given threshold. Given the size of the sample, as well as the distribution of the total household income, our preferred threshold is 30 percent of monthly household income (between 2008 and 2009). Therefore, any family that experienced a monthly income reduction of more than 30 percent between 2008 and 2009 is designated as crisis-affected. Conversely, the remaining families are classified as ‘notaffected’. By ascribing large negative changes in household incomes to the impact of the crisis, we may be including some households that are affected by idiosyncratic shocks (families losing income due to voluntary job losses unrelated to the crisis, death of a member in the family etc.). However, this attribution is unavoidable and past work has been unable to separate crisisaffected households from households affected by shocks unrelated to the crisis (Cunningham

> 5 We construct our dataset using guidance provided in the RLMS website.

7
elements of both vertical (e.g. perception of local authorities) and horizontal (community relations) social cohesion, as well as trauma are important factors to consider in creating opportunities for participation. As a result, our findings suggest that international humanitarian actors seeking to encourage engagement among IDPs should focus especially on innovating new programs that lessen the psychological harm of camps (Pham, Fozouni, Al-Saiedi, Coughlin & Vinck 2021), for example by increasing the provision of mental health care at the site of camps. Our results suggest that even among IDPs who experienced similar levels of pre-displacement violence, IDPs in camps are less likely to participate, suggesting that the experience of camp based displacement may do additional harm to residents sense of empowerment. Based on these insights, international actors should prioritize engagement with camp-based IDPs to better understand the salient constraints they face and work with actors such as UNHCR to make camp based displacement more empowering in the short term.

Additionally, beyond the substantive findings, our studies demonstrate that it is possible and valuable to collect data on social and political engagement at a local level in emergencies, and that doing so over time is important. In Uganda, we show that relying on a single snapshot may miss changes in perceptions that might be important for humanitarian and development responses to displacement. As we describe in our research design section, systematically capturing the perceptions of IDPs is often difficult as they can be hard to find or live in insecure areas. Our research shows that it is possible to conduct safe, ethical, and methodologically rigorous surveys of such groups. International humanitarian actors should seek out such data at regular intervals to monitor and better understand the populations they serve.

# **References**

- Adriano, Fermin & Thomas Parks. 2013. _The Contested Corners of Asia: Subnational Conflict and International Development Assistance: The Case of Mindanao, Philippines_ . Asia Foudation.

- Al-Saiedi, Abdulrazzaq, Kevin Coughlin, Muslih Irwani, Waad Ibrahim Khalil, Phuong N Pham & Patrick Vinck. 2020. _Never forget: views on peace and justice within conflict-affected communities in Northern Iraq_ . Harvard Humanitarian Initiative.

- Alrababa’h, Ala’, Andrea Dillon, Scott Williamson, Jens Hainmueller, Dominik Hangartner & Jeremy Weinstein. 2021. “Attitudes toward migrants in a highly impacted economy: Evidence from the Syrian refugee crisis in Jordan.” _Comparative Political Studies_ 54(1):33–76.

- Bateson, Regina. 2012. “Crime victimization and political participation.” _American Political Science Review_ pp. 570–587.

26
<!-- Start of picture text --> The effect of donor & non−state actor service<br> provision on deferring to the Tax Department<br>−0.1 −0.05 0 0.05 0.1 0.15 0.2<br>UN: helps a lot G<br>China: helps a lot G<br>Int'l donors/NGOs: help a lot G<br>US: helps a lot G<br>Int'l donors/NGOs:<br>G<br> too much influence<br>Domestic orgs./NGOs: G<br> too much influence<br>Int'l businesses:<br>G<br> too much influence<br>Int'l donors/NGOs:<br>G<br> right amount of influence<br>Traditional authorities<br>G<br> responsible for taxes<br>−0.1 −0.05 0 0.05 0.1 0.15 0.2<br>Probability<br><!-- End of picture text --> Figure 7: This figure illustrates the effect of donor and non-state actor service provision on deferring to the tax department. I model the effect of each of these indicators of donor and non-state actor service provision on deference to the tax department in separate models. Service provision by the UN followed by China has the largest relative effects.

35
6. Cl, 1 JJLAI - [ALi I 1 RAI t * BY MEASLES VACCINATION STATUS AND AGE AT VACCINATION: MATLAB INTERVENTION AREA, 1982-85 <!-- Start of picture text --> 100 r<br>rL] UNVACCINATED VACCINATED<br>80-<br>D<br>e<br>a<br>t<br>h 60 -<br>s<br>p<br>e<br>r 40-<br>I<br>0<br>0<br>0<br>20<br>0 S. 2 MO.4<br>AGE AT VACCINATION<br><!-- End of picture text -->

# CUMULATIVE MORTALITY RATE **36** MONTHS AFTER VACCINATION

*Reflects rates to 24 montlhs after vaccination

> SOTJRCF: KOENIG, ET AL. 1990
The facility survey collected information on staffing, expenditures, medical equipment, drug availability from the facility administrator, and provider knowledge about the appropriate clinical procedures for quality prenatal care. As part of survey, enumerators also conducted exit interviews with approximately 10 women who visited the facility to collect information on the actual clinical services (quality) provided during their prenatal care visit. The sample of facilities was well balanced at baseline. There were no differences in means for the 17 baseline characteristics presented in Table 3.

The household survey consists of a random sample of 13 households living in each facility’s catchment area, for a total sample of 2,158 households. To build the sample, we first sampled 13 census zones (each containing approximately 15 to 20 households) from each facility’s official list of zones in their catchment area. We then physically listed all households in the sampled zones and randomly selected one household with at least one child under 5 years old from each zone. The surveys were conducted by trained enumerators hired by external firms specialized in data collection. Interviewers did not inform respondents that the survey was associated with the P4P program, but rather that the survey was being conducted to assess health conditions.

Response rates were high as only 2 percent of sampled households refused to participate in the interview. In the follow-up survey, 88 percent of the baseline households were reinterviewed. The rate of attrition from the baseline sample was not statistically different between the treatment and comparison groups (12 percent each). Households that could not be found or interviewed were replaced with randomly selected households from the same zones. In addition, the household data were well balanced between treatment and comparison groups at baseline. Only 2 of the 30 characteristics reported in Table 4 were significantly different at the 5% level between treatment and control groups at baseline.

# **b. Provider Resources**

Health clinics in rural Rwanda are staffed with about 6 nurses and 4 medical technicians or midwives (Table 3 Panel A). There are almost no physicians practicing in these clinics. The availability of equipment and drugs needed to provide quality care is reasonable for rural Africa (Table 3 Panel B). The structural quality indices are the share of drugs and equipment available at the facility among those that the Ministry of Health guidelines define as necessary in order to 15
partial reallocation failure, considering that about 38 percent of displaced workers remain unemployed.

We also find that these climate-induced impacts are unequally distributed across educational levels and gender, with workers with no education, females and informal workers the most affected. In addition, the sensitivity analysis, based on an increasing treatment intensity, shows a sharp increase in the magnitude and significance of the impacts when drought shocks become severe, corresponding to SPEI values lower than -1.8 s.d. in all three labor market outcomes. Overall, our results are robust to various specifications and falsification tests.

The remainder of the paper proceeds as follows. Section 2 presents the data, our measures of drought shocks and some descriptive analysis. Section 3 outlines our empirical strategy, Section 4 presents the results, and Section 5 presents a set of robustness checks. Section 6 offers a discussion of the findings and some policy implications, and Section 7 concludes. Additional information on the data employed and empirical analysis can be found in the Appendix.

# **2 Data**

We employed two data sources. First, we used the Enquête Nationale sur l’Emploi (ENE), which provides nationally representative socioeconomic information at the individual level from 2000 to 2009. The ENE is conducted by the Haut Commissariat au Plan (HCP) and consists of demographics and labor market data. The sampling follows a two-phase stratification approach with an urban-rural strata and a regional strata. Our analysis is restricted to the period from 2000 to 2009 since the provincial identifier was not provided in the survey rounds from 2010 to 2019.<sup>3</sup> Despite the richness of the available information, the survey consists of repeated cross-sections, which does not allow for tracking individuals over time. We started with an initial sample of nearly 2.7 million observations across 54 provinces.

> 3 While from 2000 to 2005 around 40,000 households and 230,000 individuals were sampled, from 2006 these numbers increased to around 60,000 and 270,000, respectively.

7