rafmacalaba/data-use-mentions-extended huggingface dataset:
_priori_ . Finally, we include dummy variables indicating what the main income source of the individual is. Specifically, _Employed, Self-employed_ and _Agriculture_ are dummy variables indicating the employment status and sector, with the omitted category being dependent on pension or family. We also control for the ownership of a mobile phone, which might indicate stronger commercial needs and therefore demand for financial services. It might also indicate, however, openness to new technologies and therefore bank delivery channels.

We control for the personal circumstances of the individual by including dummy variables for being married **,** whether the survey respondent is head of household and – where available – whether the respondent is the main earner and decision taker. All four factors might increase the probability of using financial services, be they formal or informal, as being married and/or being head of household imply stronger economic responsibilities. Where available, we also include a variable indicating risk aversion, which is a positive response to the question: “do you disagree that to get ahead on life one need to take some risks?”. We also include an indicator of numeracy, which measures the extent to which the respondent can solve simple calculus problems. We again expect respondents that score higher on numeracy are more likely to use financial services.

The results in Table 9 show that - on average – women are not significantly more or less likely to use formal financial services. We report first the pooled regression with survey dummies and then 11 survey-specific regressions. The pooled regression includes only variables that are available for all surveys. The regression in column 1 of Table 9 shows an insignificant coefficient on the female dummy. This is confirmed by considering the countrylevel regressions. Only in the 2008 survey for South Africa does the female dummy enter significantly and negatively, suggesting that females have a 12.2 percent lower probability of using formal financial services. Unlike in the univariate comparisons of Figure 2, we 22
Robison and others 2007; Valentine and Practer, 2011).<sup>1</sup> These factors may make school principals reluctant to admit difficulties with fulfilling their managerial role effectively.

Additionally, it is important to note that the questionnaires used by both TIMSS and PIRLS to measure school safety do not measure leadership capabilities or style. However, results from the TIMSS and PIRLS assessments demonstrate that high occurrences of disciplinary issues (vandalism, violence and so on) beyond momentary class disturbance, as measured by principals, have adverse effects on students and teachers.<sup>2</sup> The Program for International Student Assessment (PISA) questionnaire includes a disciplinary climate index. The index describes what is happening in the classroom, and during lessons, according to students. It is not meant to reflect what happens outside the classroom. Its main purpose is to serve as a proxy for time on task, which is a key determinant of school quality. The underlying assumption is that the less disruption the classroom witnesses, the more students can focus on learning.

Classroom disciplinary climate is measured by five simple questions in the PISA survey, which have been asked quite consistently since the first round in 2000. These questions are meant to gauge the extent to which teachers lose learning time because of disciplinary issues. These five items are used to derive a disciplinary index, using either a simple factor decomposition or a likelihood-maximizing estimator. In both approaches, the reliability of the measure is very high, as witnessed by Cronbach’s alpha of 0.88 and KMO tests above 0.85 for the five variables. The disciplinary climate is also quite consistent across time for a given country as the items have remained identical in all rounds (see Appendix A for further details).

The focus comes both from the perspective of supporting future well-being and skills for children and youth to thrive in an ever-changing world, and from the perspective of lost years of schooling and the need for teachers to have the tools to manage classrooms
household head and refugee hosting regions. The total survey sample was 5,317 households with 3,627 refugee households (837 South Sudanese, 871 Somalis, 893 Eritreans, and 1,016 Sudanese) and 1,690 host community households.

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Refugee Host South Sudanese Somali Eritrean Sudanese<br>Community Male-headed HH Female-headed HH<br><!-- End of picture text --> Source: Author’s calculation using Skills Profile Survey (2017), Ethiopia

# Figure 2: Percentage of refugee and host community households: by gender of head and refugee groups

The SPS 2017 contains detailed information on household characteristics, food and non-food consumption, assets, livestock, education and economic conditions, access to services, and perceptions. It also includes individual-level information on employment, barriers to labor force participation, livelihood structures of refugees before displacement, movement and return intentions. The data combines detailed household questionnaire information with displacement-specific information including drivers of displacement, access to resettlement mechanisms, and return intentions.

# 3.1 Livelihood Activity Variables

For the empirical analysis, we use four types of livelihood variables, defined as follows:

- _Wage employment_ refers to work where the employee receive payment regularly in the form of cash or in kind. This includes paid work for an NGO, private company, paid farm work, or paid domestic work.

- _Self-employment_ refers to people who work on their own account including shop owners, family non-farm business, street traders, or people who own non-farm business.

- _Family business_ refers to any non-farm business owned by a family member. This includes helping to prepare food or drinks to sale, helping to sell at the shop.

- _Farm activities_ include working on a farm owned by the household
the husband could provide to his wife’s business.

Among married women, seeing an opportunity in the market, to make more money and to be more independent are positively associated with a female crossing over into a male-dominated sector. These reasons are particularly pertinent in the developing economies<sup>21</sup> and these finding are consistent with the results of the Global Entrepreneurship Monitor where the likelihood of necessity being cited as the reason for starting a business declines with higher economic development (GEM, 2016).

Additionally, we explore the role of the husband in the business. There is some literature which examined a husband’s role in women’s careers, concluding that husband’s impact women’s choices and career aspirations (Still and Timms, 1998; O’Neil and Bilimoria, 2005; Vanhala, 2005). In Ethiopia, women entrepreneurs fare better with support from their husband. Eighty-three percent of married women said their husband helped in the business in some way (mainly by contributing start-up money) and women were more likely to enter into more profitable male-dominated sectors when their husband was an entrepreneur himself (Alibhai et al. 2017). Wolf (2018) classifies the husbands of Ethiopian business women into three

> 21 Appendix D shows these results separately for developed and developing countries.

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the sum of _PRODY_ s using as weights the share of the particular service in the country‘s total service export basket. _EXPY_ s are constructed for each country and for each year with available data.

The trade data come from the IMF Balance of Payments statistics, which has data available for over 190 countries from 1990 to 2007. Due to data being reported inconsistently on service exports, we end up with a sample of approximately 100 countries 1990 to 2007. GDP per capita data is taken from the World Bank Indicators database. Before describing the _service EXPY_ we would like to discuss two of its characteristics that differ from preexisting goods index – the need for _dynamic PRODYs_ and the high level of aggregation.

First, the _Goods EXPY_ was constructed using _static PRODY_ s (the _PRODY_ for each good is held constant at the average value 1999-2001). This means that any increase in _Goods EXPY_

> _xij_ 5 _X i_ is hence the value-added share of commodity j in the country‘s overall export basket.

> 6 The rational for using revealed comparative advantages as weights is to control for country size when ranking the services.

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Policy Research Working Paper 9985

# **Abstract**

Exclusionary policies, such as limits on refugees’ movement and the right to work, are often justified as reasons to minimize economic and social tensions with host communities. While these policies have a negative effect on refugees’ economic outcomes, their ability to mitigate frictions with host communities is unknown. Inclusionary policies, on the other hand, could foster mutual gains and positive relations. This paper builds an extensive dataset of attitudes and economic outcomes, refugee populations, and policies at the sub-national level covering 14 years (2005-2018) and most low- and middle-income countries. Using event study and difference-in-differences methodologies, it assesses the effects of the arrival of large waves of refugees and finds little evidence that large refugee arrivals have a negative effect on average attitudes or economic outcomes in the short-term. There are also no significant differences between places with restrictive and inclusive policies, including de jure access to the labor market and opening camps.

This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aksoyc@ebrd.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the
data. First, weights fluctuate substantially over time. Second, there are significant reallocations across regions, especially at times of stress. For example, the figures for equity funds show that, even though the epicenter of the crisis was in the U.S., managers started liquidating their exposure to emerging economies after the collapse of Bears Stearns while they increased their exposure to North America. This is consistent with a relatively smaller collapse in some asset prices in the U.S. than, for instance, in emerging Asia. Only in early 2009 managers started reversing that trend. Among global emerging funds, managers sold their positions in emerging Europe and Latin America and moved to emerging Asia. For example, between June 2008 and July 2009 the mutual fund exposure in Asia increased from 45% to 55%, while it decreased from 14% to 9% in emerging Europe (after having dropped to 7%) and from 24% to 21% in Latin America. Among bond funds, the large substitution took place between developed Europe and North America in

> 18 This time split is important due to the relevance of global equity funds. EPFR Global starts reporting information for global equity funds in June 2001. The introduction of this type of funds adds nearly 90,000 million U.S. dollars to the total assets of all equity funds.

9
conducted from September to December 2013. Post-harvest activities were conducted from January to early March 2014. Crop-cutting was conducted at any point during this period when the maize was deemed ready for harvest by the respondent. Area measurement was conducted in the post-planting visit. The data collection for LASER was conducted in 3 zones of the Oromia region in Ethiopia. In total, 85 enumeration areas (EAs) were randomly selected using the Central Statistical Agency of Ethiopia’s Agricultural Sample Survey (AgSS) as the sampling frame. Within each EA, 12 households were randomly selected from the AgSS household listing completed September 2013. Partners in the study include the Central Statistical Agency of Ethiopia, the World Agroforestry Centre (ICRAF), and the World Bank. This study also used Garmin eTrex 30 units to collect GPS land area data.

The last batch of data used in this analysis comes from the Nigeria Area Measurement Validation Study. The primary focus of this study was validation of area measurements. Extraordinary differences between GPS and farmer self-reported areas were observed in the first wave of the Nigeria General Household Survey, invoking the need to validate the methodologies. This study was conducted on a subsample of the General Household Survey panel households. After the measurement conducted in the second wave of the national survey a special team was deployed to re-measure a subsample of plots using three area measurement methods. Fieldwork for the area measurement validation study ran from March to May 2013. Four states were selected for inclusion in the study based on safety, location, and previous performance of farmer self-reported area and GPS area. The plot selection was stratified on plot size to ensure a complete range of plot sizes included. In total, 211 households were selected, including 518 plots. The study was implemented by the National Bureau of Statistics, Nigeria, and the World Bank. The GPS Unit utilized for this study was the Garmin GPS Maps 62.

In each of the three studies, agricultural plots were measured first by farmer self-reported estimate, then by compass and rope, and
exercise suggests is that, again, selection plays an important role in explaining the differences in productivity, sales and exports. However, in contrast to what one would have expected on the basis of the comparison of firms that have at least one formal employment contract and those that have none (Table 3 above), selection seems to play a smaller role in the case of log employment. This suggests that the difference in employment levels between informal and formal firms may be more closely related to the status of informality itself rather than selection. Put differently, if the evidence were taken at face value, one would conclude that that informality does not cause low productivity, it causes lower sales to some extent, and it seriously constrains jobs. This suggests that there may be factors in the business environment associated with informality that discourage or prevent informal firms from employing more people relative to formal firms; it suggests that informal firms may prefer to keep a low profile and stay below the enforcement radar, to prevent the hiring of additional workers possibly jeopardizing the informality status of these firms. It might also indicate that firms may face more pressure to formalize if they hire full-time employees: with weighted data, the share of formal firms with self-employed owners and no full-time employees is only 33 percent, whereas it rises to 38 and 48 percent for firms with 1-2 fulltime employees and 3 or more employees, respectively. It could also suggest high costs of formality, especially associated with labor regulations (e.g., restrictions on firing). As indicated above, these insights, especially the suggestion that selection may be playing a limited role in the case of employment, should be treated as tentative conclusions that need to be further explored with better data.<sup>17</sup>

# **_4.2 Correlates of (in)formality_**

What types of firms choose to be formal? With a single cross section, it is only possible to carry out an exploratory analysis to identify variables that are corelated with
for Venezuelans. According to the Ecuador’s official entry records, the majority of Venezuelans entered Ecuador by land from Colombia, through the Rumichaca bridge in the Tulcan canton (82%). Other land points of entry were San Miguel (5%) and Huaquillas (7%). Almost all other Venezuelans arrived by air through the airports of Quito and Guayaquil (5%). In our analysis, we focus on 3 gateways: the Rumichaca bridge and the airports of Quito and Guayaquil.<sup>20</sup> The second set of factors we consider measure economic conditions at the canton level.

> 17Ecuador is divided into 24 provinces, in turn subdivided into 221 _cantons_ .

> 18The highest concentrations of Venezuelans, relative to the canton’s population, are found in a few sparsely populated cantons. The ENEMDU survey cannot be representative of those populations. Accordingly, we omit them from our regression analysis.

> 19This exercise is in the spirit of gravity models of trade and migration flows, but considering a single origin and multiple destinations.

> 20More specifically, we compute the Euclidian distance between each of the points of entry and each canton’s centroid.

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the financial history of more than 1,500 individual bonds at a monthly level, combining historical data on bond prices, coupon payments, and haircuts due to default. The result is the most ambitious dataset of sovereign debt to date, taking further the work of Lindert and Morton (1989), Homer and Sylla (2005), and Obstfeld and Taylor (2005), among others.

We are the first to quantify the returns on external sovereign bonds with long-run pricing data, despite the fact that this is one of the largest and oldest asset classes worldwide.<sup>9</sup> The likely explanation is data limitations. Studies on long-run asset returns typically use annual data of representative benchmark bonds or aggregate indices (e.g., Dimson, Marsh, and Staunten 2001; Jordà et al. 2017).<sup>10</sup> This standard

> 6 Eaton and Gersovitz (1981), among others, assume permanent exclusion after a sovereign default, which is at odds with the data (see also Aguiar and Gopinath 2006; Panizza, Sturzenegger, and Zettelmeyer 2009; and Cruces and Trebesch 2013).

> 7 See Financial Times, June 22, 2017, “The rush for Argentina’s 100-year bond points to an investment bubble.”

> 8 For surveys, see Panizza, Sturzenegger, and Zettelmeyer (2009), Aguiar and Amador (2014) and Mitchener and Trebesch (2021).

> 9 Historically, bonds issued by foreign governments accounted for about 10% of all financial assets trading in London (Michie 2001). Today, foreign-currency sovereign bonds continue to be a dominant asset class, especially for emerging markets. For 2017, the BIS reports a stock of US$1.9 trillion of external sovereign bonds (BIS 2018), about the same as total German government debt or about 10% of total gross US government debt.

> 10 Dimson, Marsh, and Staunten (2001) gather annual data on equities, bonds, and
percent, and poorest 35 percent nationally. The PPMT cut-off World Bank (2019) for more. Hence, it is possible that, in addition to the smaller set, pseudo-PMT may be employing inferior proxies.

> 10 Refer to Box A 1, which presents a simple example for a quick understanding of how PPMT model and its referral probabilities can be calculated.

> 11 The continuous security issues prevented data collection from 14 qhadas (districts) and the final SWIFT sample covers only 106 of the 120 districts in the country. See <mark>Sharma & Wai-Poi (2019).</mark>

> 12 Currently there is no MoLSA’s CT program in Kurdistan region. However, a full PMT model was devised just like in the other three regions using the SWIFT 2017/18 data and same methodology for an emergency CT pilot that is currently being prepared by the Kurdistan Regional Government (KRG) MoLSA and the World Bank.

11
.5|0.6|3.1|5.4|6.5|2.2|1.1|
|Albania|0.6|0.7|1.2|4.3|5.5|9.4|1.2|3.9|

Source: BIS Locational Statistics and IMF WEO Database October 2010; authors’ calculations.

A caveat is in order. On the one hand, the strength of the results may be specific to the region, which indeed experienced some of the largest external imbalances (and, eventually, external adjustments) that emerging markets have ever had to tackle. Moreover, it must be acknowledged that some of the global events that characterized the increased process of integration might not repeat themselves in the near future; for example, the excessive global liquidity of 2003-07. On the other hand, however, some regularities are evident from the data and, thus, provide policy prescriptions going forward: a better understanding of the determinants of different growth-vulnerability combinations seems warranted and this paper attempts to provide some preliminary answers.

# **1. Methodology and Stylized Facts**

The goal of this paper is to empirically investigate the relative importance of economic policies and other factors in defining the balance between vulnerability (risk) and growth (reward). As mentioned earlier, a multifaceted approach to vulnerability is taken. Specifically, several indicators are 2
we can construct the growth rate in the share of output that is exported for the three sectors. For agriculture and mining the trend is not positive. The share expanded from 1995 to 2005, but contracted from 2005 to 2015. For manufacturing there is an increase from 2005 to 2015, but the share only goes from 1.4% to 2.0% of output which is barely higher than the share already attained in 1995 which was 1.8%. Of course, the aggregates in EORA includes the output of smaller firms and probably also an estimate for the informal sector where exports are likely to be negligible.

It is difficult to compare our findings for Rwanda with other countries because administrative data that covers all firms is rarely available. Ethiopia is one country with a high quality manufacturing firm census that several researchers have used. Recently, Siba and Gebreeyesus (2017) report that in 2008-2009 (the last year in their sample) only 4.0% of the Ethiopian manufacturing firms show positive exports.<sup>8</sup> While the share of exporters did not 8 Note that their sample only includes medium and large firms with at least ten employees.

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, follow unit root processes (i.e. the A = I case above).

Hakkio and Rush apply their test to U.S. data for the period 1950:11 to 1988:IV as well as the sub-samples: 1964:I-1988:IV and 1976:1II-1988:IV. For the later two periods, they conclude there is no cointegration between T, and G, + rB,-,. Like Trehan and Walsh and Kremers, therefore, they reach the conclusion that recent US fiscal policy is not sustainable once the evidence from the 1980s is included in the dataset. The similarity in the 26
Mymensingh, Rajshahi, Rangpur, and Sylhet. Each division is subsequently divided into 64 districts, or _zilas._ Each district is further subdivided into smaller geographic areas with clear rural and urban designations. In addition, urban areas in the main divisions of Chittagong, Dhaka, Khulna and Rajshahi are classified into City Corporations (CCs), or Metropolitan Areas and Other Urban areas. Table 2 shows the distribution of the population by division and across urban, rural, and CCs areas.

**Table 2 Distribution of population by division (%)**

|**Division**|**Number of**<br>**districts**|**Rural**|**Urban**|**City**<br>**Corporation**|**Total**<br>**(%)**|
|---|---|---|---|---|---|
|Barisal|6|5.3|1.1|-|6.3|
|Chittagong|11|14.3|1.6|3.2|19.1|
|Dhaka|13|13.6|1.7|10.6|25.9|
|Mymensingh|4|6.1|0.8|-|6.8|
|Khulna|10|9.2|1.4|1.3|11.9|
|Rajshahi|8|10.3|1.6|0.6|12.5|
|Rangpur|8|9.8|1.5|-|11.3|
|Sylhet|4|5.2|1.0|-|6.2|
|Total|64|73.7|10.6|15.7|100.0|

Source: Household Income and Expenditure Survey (