rafmacalaba/data-use-mentions-extended huggingface dataset:
The objective of the ratemaking process is to derive the premium rate that is used in the above formula. Fundamentally, a premium rate is an estimate of the expected value of future costs. One of its largest components is typically the provision for losses. This component is derived from the expected loss component, which is represented by the loss cost ratio (LCR), i.e., indemnity divided by liability. The LCR is a measure of loss per unit of exposure. Because loss costs vary between crops and locations, the MPCI procedure establishes rates from each crop and each county. This empirical approach based on observed losses typically precludes from the use of individual data because it is rare that a single insured is sufficiently large to allow the insurer to derive the LCR solely from the insured’s loss history. Consequently, the aggregate experience of a group of similar risks is considered in developing premium rates. The main five steps of the MPCI procedure are detailed below (Josephson et al. 2000) _Adjusting loss and exposure to a common coverage level_ MPCI is offered at various coverage levels, generally from 50% to 85%. In order to use extensively the historical data, the first step in the ratemaking process is to adjust the data to a common coverage level (CCL) set at 65%.

The adjustment of the liability simply requires one to multiply the current liability by the ratio of the common coverage level to the CCL. For example, to adjust the liability from 75% to 65%, one multiplies the current liability by the ratio 0.65/0.75.

The adjustment of the indemnity is less simple and two cases need to be considered. For coverage levels higher than the CCL, the adjusted indemnity is equal to the unadjusted indemnity minus the reduction in liability, for all actual yields (in percentage of APH yield) lower than the CCL. The adjusted indemnity is zero for other actual yields. For coverage levels lower than the CCL, the adjustment procedure is more
warehousing sector confront not only the usual licensing and other policy-induced start-up difficulties facing any new business in Russia, especially entry barriers posed by neighboring local administrations, but also must deal with organized crime, which is quite active in this sector.

_Corruption._ Surveys indicate that corruption as a barrier to entry in Russia is pervasive; virtually all firms pay bribes to tax inspectors, customs officers and a host of local bureaucrats.<sup>9</sup> Indeed crosscountry evidence suggests that Russian SMEs operate in a business environment that may be worse than most transition economies. On average it takes four times as long to establish a small enterprise in Moscow than it does in Warsaw, that Russian SMEs are subjected to twice as many yearly inspections than their Polish counterparts, and that the number of regulatory agencies involved in SME development in Russia is half again as many as there are in Poland. A recent OECD survey reveals that SME managers in Russia are subject to manipulation by local authorities of 40 to 50 tax rates and deductions. But official taxes are not the only levies new Russian businesses have to pay. Many small enterprises have to pay the mafia to survive. The local press is replete with stories of entrepreneurs paying for a "roof' _(krysha)_ for protection. One story reported that mafia levies commonly start from 5% of profits but are often higher, and are usually collected as a flat monthly fee. There is also the threat of violent crime against entrepreneurs: in the WB-RAS survey, 16% of the General Directors indicated they are "very worried" about becoming a victim of violent crime.

_Capital Barriers to Entry._ In almost any country it is difficult to persuade banks or other financial institutions to back a start-up business; in transition economies, where capital market imperfections are pronounced and institutions that internffediate savings into investment capital are typically nascent, the problem is particularly acute. Like many other countries in the region, in Russia bank loans for new businesses-if they are available at all-are
17 the value of education and policy efforts to increase quality can influence parental assessment of the importance of sending children to school and investing in education.

Community radio stations have low signal strength and any one station is accessible to only a few villages in our sample. Yet, because of the large number of community broadcasters, at least one community signal is accessible to 93 percent of our sampled villages. In contrast, while private commercial radio has greater signal strength, these are established in the southern communes of Benin, outside our study area; only a few villages in our sample (23 percent) are able to access their signals. Government-owned public radio has several relay transmitters allowing it to cover most of the villages in our sample (95 percent) with uniform programming. We were not able to gather reliable data on the nature of programming on religious radio, but our household data suggests that almost no one listens to it.

Most households that listen to radio report listening either to the national public radio or to a local community radio, or both. Of the 3828 households that listen to some radio, 64 percent report listening to at least one community radio station and 45 percent report listening to national public radio.<sup>7</sup> Only 13 households report listening to religious radio and only 176 to private commercial radio.<sup>8</sup> These listening preferences reflect what broadcast media experts have contended: poor households in rural Africa prefer to listen to local radio stations because their 7 ―Listenership‖ data was gathered by simply asking households to name the station they listen to and then having our investigating team post-code the response for type of radio. We did not directly prompt the household to categorize what _type_ of radio they like listening to.

8 Even among these 176 that report listening to some private commercial radio, only 67 report listening _only_ to commercial radio; the rest listen as well to at least one other public or community radio station.
in startups in Africa and their importance in startups’ growth in Africa. Fintech is the fastest growing startup industry in Africa. According to Disrupt Africa (2022), Fintech is the power behind the region’s startup revolution. Only in 2022, it raised 43% of the total funding. The second sector in terms of funding was E-commerce and retail tech, with 17% of the region’s total. When we analyze the total number of companies in the African Entrepreneurial Ecosystem, almost 20% of the companies are in Fintech, while 13% are E-commerce firms (Briter Bridges, 2022). The rationale for selecting the IT sector has to do with its characteristics, as it includes technologies like software, IT infrastructure and services, and data storage and management, which are associated with core activities of the digital sector.

For the purpose of this paper, we define startups as digital businesses belonging to these sectors (Fintech, E-Commerce, or IT), younger than ten years of age. The age definition is arbitrary, but follows the convention observed in many regulatory acts aiming to promote innovative and high-growth startups. For example, Tunisia’s Start-Up Act, approved by the parliament in 2018, defines up to eight years as the threshold. This law has been used as a reference by many other African countries (e.g., Senegal). This threshold is used as a reference when comparing the level of disruptiveness between digital startups and older firms.<sup>5</sup> One limitation of the Crunchbase data is that it has only a cross-section. Although it includes very detailed information about the companies, such as the year when the firm was founded, similar firms, the amounts and different rounds of funding, and characteristics of the founders and investors, among others; it is a cross-section and takes the most recent values or attributes of the firm. This does not allow us to analyze the evolution of these firms over time. Therefore, any reference to the
Vandercasteelen, 2019). Poverty density, on the other hand, is seen to drop dramatically with distance from the urban center, consistent with the concentration of the rural population in the peri-urban areas reported in the URCA global database. Overlaying information from the Demographic and Health Surveys, the Afrobarometer, and pollution data with population density data from the Gridded Population of the World Version 4 (GPWv4) for Sub-Saharan Africa, Gollin and co-authors (2021) further show the existence of rising gradients by population density in a series of poverty correlates such as private wealth and consumption, housing quality, access to public goods and amenities, and child health. They do not find any decline in air quality nor a noticeable increase in crime.

Yet, the spatial information base underpinning these findings is often crude or limited to a few case countries<sup>7</sup> and the importance of different economic forces (agglomeration, skill sorting) affecting these outcomes, the strength of which differs along the urban hierarchy, and by extension, the surrounding hinterlands, remains poorly understood. Just like all urban areas are not the same, neither are all rural places made equal: not all rural societies are constituted by dispersed and relatively isolated villages, with little access to services and living off agriculture only and other primary activities. In fact, less than 1 percent of the global population lives in the rural hinterland (Cattaneo, Nelson, & McMenomy, 2021). On the other hand, in Sub-Saharan Africa, only 12 percent lives within 1 hour from a large city, while 41 percent of the rural population lives within 1 hour from a small city or town. As a result, it may be that strengthening bonds between smaller cities and surrounding rural areas has a greater potential for economic growth and poverty reduction than a focus on large cities, which are further away from where the poor live. For their case region Kagera, in Tanzania, De Weerdt and coauthors (2021) thus find that the deterring effect of distance
Students in grades 4, 8, and 12 returned for Term 3 from January-March 2021 while other students returned for Term 2; their Term 3 was shifted to May-July 2021. Grade 8 and 12 students sat national exams in March-April. 2021 Term 1 for all students began in late July 2021. Terms and breaks for the 2021-2023 academic calendars were shortened to allow a gradual return to the standard pre-pandemic term schedule (running from January-October) in time for the 2024 academic year.

We focus on the impacts of the partial school reopening for several reasons. First, unlike initial school closures, the partial reopening did not coincide with other pandemic-related policies, allowing for cleaner identification. Second, we can exploit discontinuities in the timing that children enrolled in different grades were eligible to return to school to isolate the effect of the childcare shock. Further, because households vary in whether the students eligible for the partial reopening are net suppliers or demanders of childcare—depending on the presence of younger siblings—this shock sheds light on the importance of sibling-provided childcare.

# **2.2 Data**

Data come from the Kenya COVID-19 Rapid Response Phone Survey (RRPS) panel, collected by the World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California at Berkeley (Pape 2021).<sup>4</sup> The main sample ( _∼_ 80%) is drawn from the nationallyrepresentative Kenya Integrated Household Budget Survey conducted in 2015-2016, and this sample is supplemented by random digit dialing. The sample is intended to be representative of the population of Kenya using cell phones—80% of households nationally report owning a mobile phone, and these have better socioeconomic conditions on average than households that do not (Pape et al. 2021). We use data from the first four survey rounds, covering May 2020-March 2021. In addition, we construct measures for February 2020, before the first COVID-19 cases in Kenya, using recall questions from the first round.

The outcomes of interest are
as a measure of absolute mobility of these disadvantaged children.<sup>31</sup> We report evidence consistent with the parallel trends assumption for the linear mobility model in equation (2). Event study graphs for the two parameters of interest _β_ 4 (intercept eect) and _β_ 7 (slope eect) are in Figure 1 (sons) and Figure 2 (daughters). The event study graph for sons show that the placebo eect of a ctitious school construction on the probability of primary completion of children at various years before the actual construction of schools is zero for both parameters of interest. In contrast, there is an appreciable shift in the estimated eects for the post school construction years for the fully exposed birth cohorts (1968-1972). The evidence is similar for girls.<sup>32</sup> In the online appendix, we report the event study graphs including the partially exposed cohorts (see Figures A.1 (sons) and A.2 (daughters)).

# (4.2) Estimating Equation: Quadratic Model

Many existing studies on intergenerational educational mobility, both in economics and sociology, rely on the linear model discussed above (see, for example, Hertz et al. (2008), Azam and Bhatt (2015), Emran and Shilpi (2015)). For a survey, see Torche (2019)). However, recent theoretical and empirical analysis suggest that the linear model may be inadequate for understanding intergenerational educational mobility (Becker et al. (2015), Emran et al. (2021)). For recent contributions where the mobility curve is nonlinear, and relative mobility and the eects of policy interventions vary across the distribution of father's education, see Card et al. (2022), Asher et al. (2023), Emran et al. (2021), and Ahsan et al. (2021).

Many authors use the estimate of relative mobility
# **B. Impact of Fiscal Policy on Inequality and Poverty**

As shown in Table 3, income inequality measured by the Gini index declines across the different income concepts considered. It goes from 0.461 to 0.445 when moving from Pre-Fiscal Income to Net Market Income, that is, after the intervention of direct taxes and contributions to health system. The decline is similar after direct transfers (going from Net Market Income to Disposable Income), as it goes from 0.445 to 0.429. Finally, the highest reduction of the Gini coefficient is due to in-kind transfers: the Gini coefficient of the Adjusted Income is 0.383, equivalent to 0.046 percentage points lower than Disposable income. The total reduction in the Gini coefficient amounts to 0.078 percentage points when comparing Adjusted income to income before fiscal interventions.<sup>19</sup> The redistributive effect of fiscal interventions may be measured as the change in the Gini coefficient. In turn, this change can be decomposed in the reranking effect (RE) and the vertical equity (VE) measure. The latter is concerned with the principle that people with different income should be treated differently. Therefore, under vertical equity, fiscal interventions would lead to income units (households) to be closer than before the interventions. Nevertheless, these interventions might also lead to changes in positions (for example, A is richer than B before fiscal policy instruments are put in place, but B is richer than A after they are). These changes are called reranking effect.

**Table 3.** Evolution of inequality and poverty through different income concepts. Uruguay, 2017

||Gini I|ndex|Headcount p<br>Officialpov|overty<br>ertyline|
|---|---|---|---|---|
||Index|Diff.|Index.|Diff.|
|Pre-fiscal income|0,461||
**11I.2 The Determinants of Capital Flows:** Some **Econometric** Evidence

To capture the main determinants of net capital inflows, we follow the literature and regress capital flows on a constant, the uncovered interest differential between Turkish three month T-bills and the TL equivalent of three-month LIBOR rate, and the growth rate of real GDP or the industiial production index. Estimation was done for total, portfolio and short term capital flows, using monthly data for 1990-1997 as well as foreign direct investment using quarterly data for the period 1990-19973. The results are presented in Table 8.

2 No objective behind the move to a (managed) float from a real exchange rate targeting rule was ever officially announced. With increasing capital inflows, the feasibility of real exchange raLte depreciation had decreased substantially and the Central Bank seemed not to have another option but allow for some real appreciation.

3 Since portfolio flows are mainly composed of debt instruments placed by Turkish residents in foreign markets, foreign interest rates are likely to be most relevant measure of opportunity costs of these flows. Therefore we broke down the uncovered differential into the continued
# Figure 15.  Commodity exporters with and

<!-- Start of picture text --> without fiscal rules<br>1.0<br>Without Fiscal Rules With Fiscal Rules<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Without Fiscal Rules = 0.48***<br>-0.2 With Fiscal Rules = 0.44***<br>-0.4<br>-0.6<br>-0.8<br>Azerbaijan<br>Ecuador Uruguay Liberia Argentina Côte d'Ivoire<br>Rwanda Congo, Rep. Timor-Leste<br>Kenya<br>Brazil Namibia Equatorial Guinea Niger<br>Chad Colombia Tanzania Central African Republic Gabon Iceland Mongolia Senegal Mali Burkina Faso<br>Paraguay Burundi Benin Turkmenistan<br>Iran, Rep. of<br>Togo<br>Cabo Verde Uganda Cameroon Indonesia<br>Russian Federation<br>Nigeria Peru South Sudan<br>Armenia<br>Botswana<br>Kazakhstan Guinea-Bissau Costa Rica<br>Chile<br>New Zealand<br>Norway<br>Australia<br>Correlation between cyclical components of real government spending and real GDP 1980-2020<br>Canada<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with and without fiscal rules. “With” fiscal rules indicates that at least one fiscal rule is being enforced; “without” means no fiscal rule is being enforced. The difference between the two averages is not significant. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Sources: Authors’ calculations based on Davoodi et _al._ (2022) fiscal database and WEO (
**Table D.3:** Health Outcomes

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Health outcomes index**(SUR)||0.021<br>(0.023)<br>[0.322]|0.055<br>(0.025)<sup>_∗∗_</sup><br>[0.094]<sup>_∗_</sup>|_−_0.010<br>(0.027)<br>[0.463]|5053|
|(1) U5 Child death per HH|0.039|_−_0.009|_−_0.015|_−_0.004|5053|
||(0.193)|(0.005)<sup>_∗_</sup>|(0.005)<sup>_∗∗∗_</sup>|(0.006)||
|||[0.544]|[0.039]<sup>_∗∗_</sup>|[1.000]||
|(2) Maternal death per HH|0.001|0.000|0.001|_−_0.001|5053|
||(0.035)|(0.001)|(0.001)|(0.001)||
|||[1.000]|[0.462]|[1.000]||
|(3) Illness/injury in HH|
curity) were less likely to return, as were respondents who identified as white/mestizo (as opposed to other racial categories, such as Afro-Colombian or indigenous). That non-white IDPs aremorelikelytoreturnisinmanywayssurprising: multipleinterviewswithstateofficialshighlightadearthofspecialistsinethnicissuesanda“needforgreaterdialogue”withminoritycommunities affected by the conflict.<sup>29</sup> On the other hand, if victims are returning largely without the help of the state it may be that these communities have a higher capacity for collective action that facilitates return (Arjona, 2016).

With respect to restitution, men also tend to be more likely than women to formally seek out restitution of lost property. This gender gap is also reflected in an earlier national survey by CODHES (2015), who find that although women-headed households constitute 45% of the displaced population, they comprise only 30% of restitution cases (pg. 130). In addition, respondents who have attended local meetings for victims of the conflict also appear more likely to seek out restitution, while the probability that a victim seeks out restitution decreases in how rural their origin municipality is. Interestingly, despite the legalistic nature of restitution processes, income and education seem to have little predictive power in who seeks restitution.

> 29Author interview with Director of Access to Justice Program (March 9th, 2017) and interview with Director of _DefensoriaPublica_ and head of _GroupfortheJudicialRepresentationofVictims_ (March 7th, 2017).

20
for _CM_ . Because we have controlled for municipal and year fixed effects, the natural choice for the IV would be a measure of the level of program rollout in the municipality in each year. Yet, in section 5, we explain at great length that the available administrative data do not provide a valid instrument. The problem is that, according to the official data, the program was rolled out in similar increments in almost all municipalities each year so that almost all the variation in the data is accounted for by the two sets of fixed effects.

We do, however, have serious concerns about the quality of the official data because there appear to be only two types of municipalities: those that have the same increments as almost all others, and those that exhibit unreasonable jumps. While the latter are clearly errors in the data, the former seem 21
; *** p<0.001

Another way of estimating the impact of school closures is to compare the departure from the learning achievement trend between countries with shorter and longer school closures, according to UNESCO country-level data. These results are presented in columns (4) and (5) using the number of weeks of full school closures and the sum of weeks of partial and full school closures. In both cases, for the EU, countries with no closures achieved 2021 results as predicted based on their previous achievement (with an insignificant overall decline by -0.48 points and -2.25 points, depending on the specification). However, the results decline by 1.14 points every week of full closures in EU countries. Thus, ten weeks of closure resulted in 11.9 points decline (or 16% of within-countries SD), while 25 weeks of closure resulted in 29 points decline or 39% of within-countries SD (the most prolonged full closures were in Poland and lasted for 26 weeks). When considering the total number of full or partial closures weeks, the score decline is 0.41 points per week, but in this case, the closures are longer, ranging from 12 weeks (France) to 49 weeks (Latvia). Taking all ECA+ countries, estimates are not significant for the weeks of full closures with the significant average decline of 15.7 points. For the total number of full and partial closures the results show a decline in achievement by 0.32 for every week with an overall decline by 7.9 points. Thus, for 25 weeks of full or partial closures, the estimated decline is 15.9 points, and 23.9 points for 50 weeks.

7