rafmacalaba/datause-encoder-data huggingface dataset:
Half of Somalia’s estimated 12 million people live in rural areas, and many are pastoralists, highly vulnerable to climate shocks. The agriculture sector remains the backbone of the economy and accounts for about 75 percent of GDP, among the highest in the world (World Bank, 2019). The 2011 East Africa drought resulted in the deaths of a quarter million people, half of them children under the age of five years, as well as the displacement of nearly 1 million people. In 2016/17, the country experienced another large-scale drought, leaving an estimated 6.7 million people in urgent need of humanitarian assistance.

As of 2020 there were over 2,000 recorded IDP settlement sites in Somali ~~a~~<sup><u>[7]</u></sup> but information on the exact location of sites, living conditions, access to services and exact numbers of IDPs in each location is sparse (UN Office for the Coordination of Humanitarian Affairs [OCHA] & REACH 2019). UNHCR (2021) estimates that there were 3 million people (about 17 percent of the population) internally displaced at the end of 2020. Many IDPs have been displaced during different phases and types of armed conflict, others by clan conflict, drought or famine or by other disasters triggered by natural hazards, such as recurrent floods. Still others had to move due to forced evictions (Government of Somalia, 2017). It is estimated that the 2017 drought, followed by extreme floods in 2018, displaced more than 926,000 people (World Bank, 2019).

The majority of IDPs settle in informal and unplanned settlements characterized by overcrowding, poor shelter, unsanitary conditions, and limited access to basic services (UNDP, 2018). Access to humanitarian assistance and services provided by private contractors are often disrupted. One barrier to services delivery is that most settlements are controlled and dominated by gatekeepers who are often community leaders, members of militias, or landowners (Drumtra, 2014; Government of Somalia, 2017; Yarnell, 2019). IDPs outside settlements who have been disconnected from their networks and IDPs from minority groups tend to be excluded from access to jobs and opportunities and may have been denied permission to live in clan-controlled settlements (Drumtra, 2014). Also, high insecurity can restrict the delivery of services and monitor programs (Refugees International, 2019). In response to these challenges, the federal government 7
term contracts to avoid paying the dismissal fine (see Barros et al., 1999; and Gonzaga, 2003). This can happen if the rates of return on FGTS savings are consistently below market, if the mandate for precautionary savings is too high and/or there are credit constraints that impede dissavings. In our analysis, however, the focus will be on the effect of FGTS on contribution densities and retirement ages.

Figure 2: Mandate of the Income Protection System and Redistribution <!-- Start of picture text --> 120% 9 0.5 times average wage<br>8<br>100%<br>7 Average wage<br>80% 6<br>5<br>60% 2 times average wage<br>4<br>40% 3<br>2<br>20%<br>1 Accumulations related to FGTS<br>0% 0<br>0.4 1 1.6 6 11 16 21 26 31 36<br>Level of Earnings (proprotion average wage) Number of months of contributions<br>25% 2.5<br>20% 2<br>15% 1.5<br>10%<br>1<br>5%<br>0.5<br>0%<br>< 25% 25%-50% 50%-75% 75%-100% 100%- 150%- >200% 0<br>150% 200% 25%-50% 50%-75% 75%-100% 100%-150% 150%-200% >200%<br>Income (percent of average earnings) Income (percentage average earnings)<br>Replacement rate (% earnings) Number of months of salaries<br>Take-up Rate<br>Average UI transfer<br>(proportion average earnings)<br><!-- End of picture text --> _Source:_ Authors’ calculations based on legislation and PME survey (Panel Survey of Workers in Metropolitan Areas).

In terms of general labor market dynamics, there is evidence of considerable labor mobility in Brazil and the existence of a labor market that is not fully segmented. The average duration of formal sector jobs is around 4.5 years, while the duration of self-employment and informal sectors jobs is respectively 2.3 years and a little less than one year (see Bosh and Maloney, 2007). Unemployment risks are significant, particularly among low income and informal sector workers (see **Figure 3** ). There is also evidence of considerable mobility between formal and informal sectors, with flows being often symmetric after controlling for the likelihood of separation (see Bosh and Maloney, 2007).<sup>6</sup> For instance, in a given year, 42.8 percent of informal sector workers who separate from their jobs will transit to a formal sector job while 31 percent of formal sector workers will transit to an informal sector job.

> 6 In Brazil informal sector jobs are mainly referred to jobs without social security coverage.  Workers in the informal sector are thus workers _Sem Carteira,_ a card that is issued by the Ministry of Labor.

8
# **Figure 3 – Bank Lending and Interest Rates to Local Governments and Firms**

Panel A plots the share of bank lending channeled to the government (out of total bank lending to all government entities and private firms) relative to March 2016, the month prior to the implementation of the Law. Panel B plots the average interest rates on bank loans—net of cost of funds—charged to state governments and private firms. The vertical lines in both panels mark the <u>introduction of the FD Law.</u> <!-- Start of picture text --> Share of Bank Lending to Government Interest Rate Spread<br>2014m1 2015m1 2016m1 2017m1 2018m1<br>Time<br>2014m1 2015m1 2016m1 2017m1 2018m1 Government Firms<br>time<br>.28 5<br>.26<br>4<br>.24 Percent<br>.22 3<br>.2<br>Share of Lending to Government<br>2<br>.18<br><!-- End of picture text --> 40
**Table 5:** Impact of Curfew Revision after June 6th on Crashes and Crash Rate

||Cr|ashes|Cra|sh Rate|
|---|---|---|---|---|
||Curfew Hours<br>(22:00-4:00)<br>(1)|Pre-Curfew Hours<br>(19:00-22:00)<br>(2)|Curfew Hours<br>(22:00-4:00)<br>(3)|Pre-Curfew Hours<br>(19:00-22:00)<br>(4)|
|Curfew Period (Post)|0.764<br>(0.492)|1.176<br>(0.767)|1.647<br>(2.060)|0.994<br>(0.775)|
|_I_2020 (2020)|0.241<br>(0.177)|0.413**<br>(0.170)|-0.047<br>(0.344)|0.410<br>(0.207)|
|Curfew Period (Post) x 2020|-0.657***<br>(0.208)|-0.058<br>(0.264)|-0.071<br>(0.543)|0.185<br>(0.356)|
|Constant|-24.674<br>(22.705)|2.403<br>(17.486)|11.028<br>(67.791)|8.483<br>(21.619)|
|Observations<br>|259|259|258|258|
|R<sup>2</sup>|0.229|0.178|0.273|0.216|

_Notes_ . Robust standard errors are reported in parentheses for columns (1) and (2) and bootstrap standard errors are reported in parentheses for columns (4) and (5). *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include fixed effects for week and day of the week, and controls for weather (temperature, precipitation and two measures of wind).

# **4.4 Robustness Checks**

We conduct a number of different robustness checks (Table 6). First, there is the possibility that the likelihood of a crash being recorded by the police may have been affected by the curfew policy if it changed the likelihood of reporting a crash to the police or if the curfew led to increased police presence that increased reporting. In order to check if this may be driving the results, we use a different source of data on crashes coming from Flare to analyze the impacts of the curfew on crashes. We see very similar results for the curfew hours and pre-curfew hours as with the police data (Panel A of Table 6). The coefficient for the decrease in crashes during the curfew hours is smaller; therefore, in the triple difference, we do not see a significant drop in crashes during the curfew period hours in comparison to the daytime hours. We see a very large and significant increase during the pre-curfew hours.

Since we have data on road traffic crashes from the police, we go further back in time to 2017 and use data from 2017, 2018 and 2019 as a comparison to 2020 and comparing the pre-COVID-19 policies period of Jan 1-March 15 with the post-curfew implementation period of March 27-June 6 for each year. When adding in the historical data, the results remain consistent and significant (Table 6 Panel B).

For the main analysis, the crash data is aggregated daily for each time period, but we also explore running the analysis at the individual hourly level and also aggregating further at the weekly level. In both cases, we find the results to remain consistent and significant (Table 6 Panel C and Panel D).

Given some extreme outliers in the number of crashes in a given daily time interval, we also test removing the top 1% of time intervals with the most crashes (out of all time intervals with at least one crash). This means we remove all time intervals that have 5 crashes or more, and we see that the results remain stable and are not driven by these extreme outliers (Table 6 Panel E).

16
proportion reticent, it is notable that four states have estimates of more than 50% and more than half of the states have estimates of over 25%.

# **4. Do Reticent Respondents Answer Sensitive Questions Differently?**

The main purpose of identifying reticent respondents is to determine in what way and by how much respondent reticence distorts responses to other questions in the survey. In Romania, AM found that reticent respondents typically report lower incidences of corruption and other sensitive acts compared to possibly candid respondents. Our analysis for Nigeria confirms this finding, but with one caveat: the extent that reticence matters appears to depend importantly on the wording of the sensitive questions. In particular, reticent respondents admit to sensitive acts at lower rates primarily when asked questions that are personal in nature or refer to the respondent’s own business, when the answers to questions are most likely to imply personal misbehavior on the part of respondents themselves.<sup>10</sup> We therefore structure the rest of this analysis according to how personally the sensitive questions in the Nigerian survey are worded. We organize a number of potentially sensitive questions from the survey into four groups, contained in Tables 4-7, and also examine some placebo questions in Table 8. In each table, we regress responses to the questions on the same set of control variables included in Table 2, and then a dummy taking the value one if the respondent is reticent, and zero otherwise, using OLS. For reasons of space we do not report the coefficients of the controls in the tables. Sensitive questions used in the analysis below have all been oriented the same way, so that a higher value of the dependent variable indicates admission to acts of a sensitive nature. To the extent that reticence effects are important, we should therefore expect

> 10 One possible reason for the difference between the Romanian and Nigerian results might be that the Romanian survey was specifically focused on corruption whereas the Nigerian survey had only a small proportion of questions on corruption.  Romanian respondents might have had heightened sensitivities because of the nature of the survey in general.

9
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|---|---|---|---|---|---|---|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|31,090|31,090|2,927|2,927|10,355|10,355|
||**Pa**<br>|**nel B: Margi**<br>|**nal effect**<br>||||
|Informal Competition (CY|0.260***|0.166***|0.307***|0.433***|0.369***|0.287***|
|avg.)|(0.069)|(0.062)|(0.096)|(0.004)|(0.079)|(0.089)|
|Controls(as above)|Yes|Yes|Yes|Yes|Yes|Yes|

Standard errors clustered on country-year in brackets. Significance is denoted by *** (1%), ** (5%), * (10%) 48
46 Vandaele, and H.I. Weisberg. Statistical Methods for Comparative<sup>Studies:</sup> Technigues for Bias Reduction (New York: Wiley, 1980).

36 R.J. Light and D.B. Pillemer. Summing Up. (Cambridge:<sup>Harvard University</sup> Press, 1984).

_2IJ_ While age is normally positively related to learning,<sup>overage students</sup> frequently achieve at lower levels, reflecting both later<sup>age at entry and</sup> grade repetition. We have no information on previous<sup>schooling, however.</sup> 28/ A similar pattern was seen by Lee & Bryk for American<sup>Catholic high</sup> schools.

__9 The sample size is sufficiently large to -ule out the probability of<sup>high</sup> Type II error rates, which would spuriously lead to<sup>no-effect findings.</sup> 0i9 As this is a secondary analysis, we had no control<sup>over the original</sup> questionnaire items in the IEA survey. However, we believe<sup>that there might</sup> be some natural bias against females at the outset in the items constituting this factor ("Boys have more natural ability in math than<sup>girls"; "Boys need</sup> more mathematics than girls"; "Men make better scientists<sup>and engineers than</sup> women"). It does not seem surprisina that female students would<sup>strongly</sup> disagree with these statements.

41 See Jimenez and Lockheed for the Thai study; Lee and Bryk<sup>for the Catholic</sup> school study.

A-2 We are aware of the fact that the hierarchical nature<sup>of these data (i.e.</sup> students nested in classrooms/schools suggests that an appropriate<sup>method of</sup> estimating the effects of schools on individuals is to use a statistical method which takes this jstructure into effect. However,<sup>the small size of</sup> the school sample (n-40), and the particularly small number<sup>of girls' schools</sup> (n-4) made the use of HLM problematic in this instance.

A-J In this paper, we are concerned with those school<sup>characteristics that</sup> differentially effect the achievement of girls and boys.<sup>Textbooks and other</sup> school quality inputs are assumed to have equivalent effects on students<sup>of</sup> both sexes, and are therefore irrelevant to this discussion.

_44/ J._ Armstrong. "Achievement and Participation of Women in Mathemathics: Results of Two National Surveys." Journal for Research<sup>in Mathematics</sup> Education, 12(5) (1981): 356-372; C.P. Benbow and J.C. Stanley.<sup>"Sex</sup> Differences in Mathematical Ability: Fact or Artifact?" Science,<sup>210</sup> (1980):1252-1264; Educational Testing Service. National College-Bound<sup>Seniors.</sup> (Princeton, NJ: Author, 1984); E. Fennema and J. Sherman.<sup>"Sex-'elated</sup> Differences in Mathematics Achievement, Spatial Visualization,<sup>ind</sup> Affective Factors." American Educational Research Journal,<sup>14</sup> 7;,:,1-71; A.M. Pallas and K.L. Alexander. "Sex Differences in Quantitative<sup>SAT</sup> Performance: New Evidence on the Differential Coursework Hypothesis." American Educational Research Journal, 20(2) (1983):165-182; Lockheed,
Policy Research Working Paper 6589

# **Abstract**

Livestock are an important component of rural livelihoods in developing countries, but data about this source of income and wealth are difficult to collect because of the nomadic and semi-nomadic nature of many pastoralist populations. Most household surveys exclude those without permanent dwellings, leading to undercoverage. This study explores the use of a random geographic cluster sample as an alternative to the household-based sample. In this design, points are randomly selected and all eligible respondents found inside circles drawn around the selected points are interviewed. This approach should eliminate undercoverage of mobile populations. The results of a random geographic cluster sample survey are presented with a total sample size of 784 households to measure livestock ownership in the Afar region of Ethiopia in 2012. The paper explores the data quality of the random geographic cluster sample relative to a recent household survey and discusses the implementation challenges.

This paper is a product of the Poverty and Inequality and Computational Tools Teams, Development Research Group. 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://econ.worldbank. org. The authors may be contacted at khimelein@worldbank.org.

_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 Research Support Team
# **5 Technology and Income Taxes**

Income taxes paid by individuals, companies as well as employees are an important revenue source for African economies, representing about 33 percent of total revenues (UNU-WIDER Government Revenue Dataset, 2021). Unlike Pay-As-You-Earn (PAYE) taxes that are deducted at source on wages and salary, corporate income and other personal income taxes are typically self-declared, providing more opportunity for evasion (Kleven et al., 2011). This makes them particularly vulnerable to broader structural issues like high informality and low tax morale that to an extent also affect other tax categories.

One key obstacle to optimizing income taxes is informality: many individuals and companies operate completely outside the purview of the tax authority. The existence of an informal economy generates horizontal inequities and economic distortions that potentially undermine tax perceptions and the moral fiber of a society (Joshi, Prichard and Heady, 2012). Within the informal sector, most harm to revenue is produced not by micro-enterprises such as street vendors, but by hard-to-tax professionals such doctors, lawyers, architects, etc. (Ogembo, 2020). Even among those formally registered, tax minimizing schemes by professionals and high net worth individuals determine large revenue losses and damage to the fairness of the tax system (Keen, 2012; Kangave et al., 2016; Santoro and Waiswa, 2022).

A second major challenge for income taxes is that even among taxpayers that are formally registered, there is a high level of non-compliance: the vast majority of registered taxpayers in Africa either do not file their returns or zero-file (Santoro, 2021; Mascagni, Dom and Santoro, 2022; Almunia et al., 2022). In addition, African taxpayers’ morale and willingness to pay are remarkably low. Different factors associated with low willingness to pay include: distrust of the state (Bratton and Gyimah-Boadi, 2016; Isbell, 2017), dissatisfaction with public services (Blimpo et al., 2018), perceived unfairness of the tax system (D’Arcy, 2011; Okunogbe and Edjigu, 2022) and the complexity of navigating obscure tax regulations (Aiko and Logan, 2014; Mascagni and Santoro, 2018).

A third constraint is that African tax administrations experience important limitations in 19
||||**RIF esti**|**Table**<br>**mates South(e**|**11**<br>**xcluding the**|**west)**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||_mean_|_10th_<br>_Percentile_|_20th_<br>_Percentile_|_30th_<br>_Percentile_|_40th_<br>_Percentile_|_50th_<br>_Percentile_|_60th_<br>_Percentile_|_70th_<br>_Percentile_|_80th_<br>_Percentile_|_90th_<br>_Percentile_|
|_A. Gender Differential_|||||||||||
|Male Managedplot|12.53***|10.23***|11.05***|11.68***|12.28***|12.73***|13.08***|13.46***|13.89***|14.75***|
||(0.0802)|(0.236)|(0.122)|(0.166)|(0.0971)|(0.0682)|(0.0672)|(0.0601)|(0.112)|(0.172)|
|Female managedplot|12.29***|9.842***|10.85***|11.52***|11.99***|12.54***|12.94***|13.30***|13.77***|14.59***|
||(0.0963)|(0.211)|(0.193)|(0.108)|(0.0993)|(0.117)|(0.0918)|(0.0628)|(0.0932)|(0.121)|
|Gender Differential|0.239*|0.384|0.199|0.159|0.286**|0.193|0.133|0.160*|0.124|0.167|
||(0.125)|(0.316)|(0.228)|(0.198)|(0.139)|(0.136)|(0.114)|(0.0869)|(0.146)|(0.210)|
|_B. Aggregate Decomposition_|||||||||||
|Endowment Effect|0.287***|0.921***|0.751***|0.914***|0.417***|0.278**|0.199**|0.0953|0.131|0.0814|
||(0.111)|(0.295)|(0.186)|(0.181)|(0.121)|(0.109)|(0.0932)|(0.0724)|(0.127)|(0.173)|
|Share of the|||||||||||
|Gender Differential|120%|240%|377%|575%|146%|144%|150%|59%|106%|49%|
|Male Structural Advantage|-0|-0|-0|-0|0|-0|-0|0|-0|-0|
||(0.0144)|(0.0407)|(0.0327)|(0.0340)|(0.0193)|(0.0244)|(0.0171)|(0.0108)|(0.0214)|(0.0270)|
|Share of the|||||||||||
|Gender Differential|0%|0%|0%|0%|0%|0%|0%|0%|0%|0%|
|Female Structural Advantage|-0.0484|-0.537|-0.552**|-0.756***|-0.131|-0.0850|-0.0655|0.0648|-0.00706|0.0860|
||(0.130)|(0.411)|(0.253)|(0.224)|(0.149)|(0.145)|(0.121)|(0.0927)|(0.158)|(0.238)|
|Share of the|||||||||||
|Gender Differential|-20%|-140%|-277%|-475%|-46%|-44%|-50%|41%|-6%|51%|
|**Observations**<br>Robust standard errors inparenthe|ses. ***p<0.|01,**p<0.05,|*p<0.1. State|fixed effects n|ot displayed.†|**945**<br>indicates dum|myvariable||||

43
<mark>Long, J. and Ferrie, J., 2007. The path to convergence: intergenerational occupational mobility in Britain and the US in three eras.</mark> _<mark>The Economic Journal</mark>_ <mark>,</mark> _<mark>117</mark>_ <mark>(519), pp.C61-C71.</mark> <mark>López-Calva, L.F. and Rodríguez-Castelán, C., 2016. Pro-growth equity: a policy framework for the twin goals. World Bank Policy Research Working Paper No. 7897.</mark> Lopez-Calva, Luis-Felipe; Ortiz-Juarez, Eduardo. 2014. A Vulnerability Approach to the Definition of the Middle Class. Journal of Economic Inequality 12:23–47.

<mark>Mazumder, B., 2005. Fortunate sons: New estimates of intergenerational mobility in the United States using social security earnings data.</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>,</mark> _<mark>87</mark>_ <mark>(2), pp.235-255.</mark> <mark>Rama, M., Béteille, T., Li, Y., Mitra, P.K. and Newman, J.L., 2014.</mark> _<mark>Addressing Inequality in South Asia</mark>_ <mark>. World Bank Publications.</mark> <mark>Ravallion, M., 2010. The developing world’s bulging (but vulnerable) middle class.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>38</mark>_ <mark>(4), pp.445-454.</mark> <mark>Roemer, J.E., 1998.</mark> _<mark>Theories of distributive justice</mark>_ <mark>. Harvard University Press.</mark> Vakis, Renos; Rigolini, Jamele; Lucchetti, Leonardo. 2016. Left Behind: Chronic Poverty in Latin America and the Caribbean. World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/21552 License: CC BY 3.0 IGO.

World Bank. 2015. Armenia Poverty Note. Washington, DC: World Bank. Mimeo.

World Bank. 2015a. Azerbaijan Poverty Note. Washington, DC: World Bank. Mimeo. World Bank. 2015b. Georgia Poverty Note. Washington, DC: World Bank. Mimeo.

42