rafmacalaba/data-use-mentions-extended huggingface dataset:
and further promote the use of public transit, the revenue raised can be used to improve access to and the quality of public transit.

Singapore adopted the first congestion pricing scheme in 1975. Some European cities have adopted area-based congestion pricing (London in 2003, Stockholm in 2006, Milan in 2008, Gothenburg in 2013). In the United States, several area-based schemes were proposed but failed to be implemented over the years. New York state’s 2019 budget proposes congestion pricing on vehicles that enter Manhattan below 60th Street. If adopted, New York City would become the first US city to use congestion pricing.<sup>15</sup> Real-time congestion pricing is now technically feasible. Singapore is slated to become the first city to use a GPS-based system in 2020 (DoT 2014). The more flexible congestion charges raise privacy concerns, however, as they rely on collecting commuters’ travel information (Parry et al. 2007).<sup>16</sup> Several studies examine the effectiveness of congestion pricing designs. They include Olszewski & Xie (2005) (Singapore), Beevers & Carslaw (2005) (London), Simeonova et al. (2018) (Stockholm), and Gibson & Carnovale (2015) (Milan). These studies find that the schemes reduce congestion by about 10–30 percent and provide significant environmental benefits for the priced area.<sup>17</sup> (For a review of studies on the impacts of existing congestion pricing schemes, see Anas & Lindsey 2011.<sup>18</sup> ) Congestion pricing has been rare in developing country. Two recent studies attempt to study the potential benefits of adopting such a policy in developing countries. Using real-time, fine-scale traffic data from Beijing, Yang et al. (forthcoming) analyze the relationship between traffic density and speed. They estimate the optimal time-varying and location-specific congestion charges to be between CNY 0.05 and CNY 0.39 per kilometer; they conclude that
that does occur takes place in large firms which are more than four times as likely to innovate as small or medium size enterprises. Moreover, although they comprise only 3% of the total sample, large enterprises accounted for 52% of the total spending on innovation.

**Table 5 Innovative enterprises by enterprise size group, 1999-2001**

||Number of<br>employees|Number of<br>enterprises|Number of innovative<br>enterprises|Share of innovative<br>enterprises (%)|
|---|---|---|---|---|
|Small enterrises|10-19|2113|268|12.7|
|p|20-49|1338|231|17.3|
|Medium enterrises|50-99|495|140|28.3|
|p|100-249|297|118|39.7|
|Large entreprises|≥250|155|90|58.1|
|Total||**4398**|**847**|**19.3**|

_Source_ : Latvia Survey of Innovation Activities, 2002.

> 30  According to the innovation survey (Chapter 3, page 20), an innovation has taken place if a new or significantly improved product (good or service) has been introduced into the market or if a new or significantly improved process has been introduced into the enterprise. The innovation is based on the results of new technological developments, new combinations of the existing technology or other combinations, or utilization of other knowledge acquired by a specified enterprise. The innovation can be new to the specified enterprise but it needs not necessarily be new to the market. Changes of solely aesthetic nature are not considered as innovation.

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# **1. Introduction**

If, in the next decades, Africa catches up with the rest of the world, then that will almost certainly coincide with intergenerational mobility out of rural into urban areas and out of agriculture into non-agricultural activities (Lewis 1954; Harris and Todaro 1970). Historically, in both rich developed countries and fast-growing developing countries, this type of migration has moved in lockstep with development and poverty reduction (Collier and Dercon 2009). Recently, China’s urban population officially surpassed its rural one: of China’s 1.35 billion people, 51 percent lived in urban areas at the end of 2011, rising from less than 20 percent in 1980 (UN, 2012). Furthermore, UNDP (2009) reports that of the one billion migrants worldwide, three-quarters are internal migrants. With international migration open to only very few Africans, we should expect massive internal migration to form a core part of the development process.

The scale of this demographic process is captured in the data that form the basis of this paper, further motivating our focus on internal migration. These data are part of an exceptional panel data set from the Kagera region in Tanzania, spanning nearly two decades of migration and development. The 2010 follow-up survey attempted to trace all 6,353 individuals listed on the baseline 1991/94 household rosters and reinterview them irrespective of their location. Once we exclude the 1,275 individuals who had died by 2010, we are left with 4,996 baseline individuals whose 2010 locations are known.<sup>1</sup> Of those, 45 percent were found residing in the baseline village, 53 percent had migrated within the country, 2 percent to another East African country (primarily Uganda) and 0.3 percent had moved outside of East Africa. This region – not atypical of remote rural Africa – is clearly on the move, with internal migration dwarfing international migration.

We attempt to understand how this powerful current of internal migration, which is
<mark>individual and country-level factors that drive account ownership and use of accounts such as for savings. The Global Findex team reconsidered that analysis u</mark> sing data from the 2021 edition of the Global Findex database, specifically to assess consumer protection regulations as potential determinants of account ownership at financial institutions. To do so, we combine Global Findex 2021 data with data from the World Bank's 2017 Financial Inclusion and Consumer Protection (FICP) Survey, which tracks the supervisory activities and enforcement powers of the consumer protections agencies in a country (World Bank Group 2017). The final merged dataset consists of more than 91,000 observations covering 89 economies. Data on GDP per capita are from the World Bank’s World Development Indicators Database.

We construct three indices based on FICP indicators on the following themes: (1) if there are simplifications or exemptions to the documentation (KYC) requirements for certain types of applicants or products; (2) if the country has strong and enforced consumer protection provisions, such as collection of data on consumer complaints, and market monitoring of provider’s advertisements and websites, etc.; and (3) if tax incentives for saving schemes have been implemented to promote financial inclusion.<sup>7</sup> A higher score indicates a more extensive set of monitoring activities undertaken by consumer protection authorities and enforcement powers held by them.

The results of the country-level analysis show a positive correlation between pro-inclusion regulations—such as tax incentivized savings schemes—and increases in account ownership. In all, an increase in the quality of regulations is associated with a 4.3 percent increase in the share of adults in the country who have an account. When we in turn examine the individual observations, however, consumer protection regulations appear to be the only ones associated with increases in financial account ownership. These results highlight the importance of transparent terms and conditions and clear recourse to increase trust in financial institutions enough to encourage take-up of financial products.

> 7
<br>0 20 40 60<br>Analysis time<br><!-- End of picture text -->

# **D. Country income classification**

<!-- Start of picture text --> Average number of years of delay<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Low Income Middle Income High Income<br><!-- End of picture text --> Source: World Bank, MINEX Consulting, World Bank staff estimates A, B. Copper = 1 indicates copper mines, copper = 0 indicators gold mines. C. Low inflation is below 10% while low government debt is below 40% of GDP. Credit boom is defined as periods when percent of private credit out of GDP is at least 0.5 times standard deviation above mean. * Indicates statistical significant of the log-rank test of the difference at the 5 percent level.

- D. Country income classification are defined by the World Bank, based on GDP per capita

cumulative hazard rate, for instance, for gold discoveries increases at an accelerating rate and then begins to taper off.

Next, we examine the Kaplan-Meier survivor functions based on policy, institutional or governance variables. From (1), using the sample with or without the characteristic of interest, we calculate the expected duration of non-development, captured in the Kaplan-Meier survivor 13
# **3 Data and Empirical Strategy**

# **3.1 DATA**

In this paper, the sample of women-owned enterprises were drawn from the registration database of the Women Entrepreneurship Development Project (WEDP). WEDP is the World Bank's International Development Association (IDA) funded program that provides loans and entrepreneurship training to growth-oriented female entrepreneurs in Ethiopia. The WEDP clients register their business at their nearest One Stop Shop, a local government branch created to support small businesses. The study entrepreneurs are a relatively homogeneous group of urban, growth-oriented women entrepreneurs. The average age of women business owners in our sample was mid-30s with 60% married and 70% having completed secondary school education or higher in Mekelle and 85% in Addis Ababa. The average age of the business was 6 years and retail was the main sector of business operation with approximately 50% of the sample in retail, 20% in café and restaurants and 7% in beauty salons.

# **3.1.1** **_DOT study_**

For the DOT training experiment, the impact evaluation team drew upon the WEDP registration database of clients in Mekelle only and randomly assigned 800 women entrepreneurs to either a treatment group (399) who were offered the DOT training immediately or a control group (400) who had to wait before being offered the training.<sup>6</sup> The baseline data collection was conducted in October and November 2014 and the questionnaire contained a set of questions on household demographic characteristics, socioeconomic status, business sales, profits, costs, employees, entrepreneurial profile (e.g., age, place of birth, education level), and questions designed to elicit an entrepreneur's business knowledge and level of financial literacy. The DOT training was offered

> 6 The final sample size in the DOT study was 799 rather than 800 since one firm was incorrectly duplicated in the sampled list of firms.
designated as controls, which contaminated the original evaluation design. Nonetheless, a followup survey was carried out in late 2010 and early 2011 in the six districts (Bushenyi, Ibanda, Isingiro, Kamwenge, Kiruhura, and Mbarara) where the SD voucher service had first been launched.

Both baseline and follow-up surveys used a two-stage cluster sample design. First, geographic data obtained from the Uganda Bureau of Statistics (UBOS) were used to identify parishes within six to ten kilometers of 13 facilities that had been contracted to provide services to voucher clients and within three kilometers of a major road. Parishes were then randomly selected from among those within the stipulated distance to the facilities. In the second stage, villages were randomly selected from the sampled parishes. At baseline, 58 parishes were randomly selected for inclusion in the study. There were 231 villages within these parishes with populations ranging from 75 to 1,803 inhabitants, giving a total of 102,260 persons, according to the 2002 census. A total of 94 villages were randomly selected for inclusion in the baseline survey study.

In the follow-up survey, 75 parishes were randomly sampled, and 133 villages from these parishes were selected for inclusion in the study. Of the sampled villages, 68 had been included in the 2008 survey, and 65 were sampled from within five kilometers of the contracted facilities to maximize the possibility of contacting respondents likely to have used the vouchers. This approach was adopted because the 2010 voucher claims data showed a very high concentration of clients in parishes within five kilometers of contracted facilities. Of the 94 baseline and 133 endline villages, none of the baseline and 26 of the endline villages were within five kilometers, and all 94 of the baseline and 68 of the endline villages were within six to ten kilometers.

Besides the population surveys, cross-sectional health facility assessment (HFA) data were collected in 2010 through a number of techniques, including observations of client-provider interactions (CPIs) during consultations, client exit interviews, interviews with SPs, record reviews, facility
Source: Bloomberg; Institute of Shipping Economics and Logistics; International Energy Agency; New York Metropolitan Transportation Authority; Ministry of Transport of China; World Bank.

A. “NYC subway ridership” is the sum of entries into each station in New York’s Metropolitan Transportation Authority network, which serves a population of 15.3 million people across a 5,000-square-mile travel area surrounding New York City, including Long Island, southeastern New York State, and Connecticut. “China passenger journeys” include all daily passenger journeys in China.

B. Year-on-year growth. Last observation is March 2020.

- C. Percent of global oil consumption.

D. Shaded area shows IEA estimates for year-on-year demand growth in 2020Q2 and 2020Q3.

E. Based on a Bayesian vector autoregressive estimation. Cumulative response to a 1-percentage-point decline on oil prices on impact or after four quarters. Orange whiskers reflect the 16th-84th percentile confidence bands. The model includes U.S. growth, Euro Area growth, 10-year U.S. government bond interest rate, VIX volatility index, China’s growth, oil price, and commodity-importing or commodity-exporting EMDE growth over 2000Q1 to 2019Q2. The model has four lags. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.

F. Chart shows the contribution to explained six-month log changes (in percent) in oil prices. Decomposition based on structural vector autoregression estimation. For each of the seven episodes, only the month with the deepest sixmonth oil price plunge is shown (consecutive months are not shown). The gap between the total price decline and the contributions of demand and supply represents speculative demand factor.

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# <u>Actuarial justification</u>

If it has been judged that the yield data should be de-trended it is appropriate and consistent to use de-trended data for calculation of both probable yields and premium rates. If, for example, there is judged to have been a large positive trend in yields over the last ten years but this trend is not removed for the probable yield calculations, the resulting threshold yields will be unreasonably low. Farmers would be offered products with low commercial premium rates that offered very little protection. Calculating probable yields based on de-trended threshold yields would result in products that offered a more typical degree of protection for a commensurately higher commercial premium rate.

The removal of (up to two) official calamity years in calculating the probable yield acts to increase the threshold yield and associated commercial premium rate and government subsidy rate for those insurance units in which droughts have been officially reported. The effect is therefore to target subsidies to districts in which droughts have been reported in the last seven years.

Appropriate smoothing of probable yields within districts decreases the standard error of average yield estimates, increasing equity and decreasing the potential threat from adverse selection. It is understood that probable yields are to be based on the most recent seven year average of nondrought year yields. Using seven years of data is likely to increase the stability of probable yields, relative to using three or five years of data as was required under the NAIS. The probable yield for a given insurance unit could be calculated as a weighted average of historical yields for that insurance unit only, as done for NAIS. However, if this was the case, a large part of the resulting variation in threshold yields within a district could be caused by natural statistical variation, rather than a variation in underlying agronomic characteristics; seven data points is not enough to obtain an accurate estimate of the mean yield unless the risk faced is very low. If premium rates were then set at the crop/district level farmers would be able to adversely select against the insurance
Clusters|144|144|144|144|144|144|
|**_Panel B: Spillovers o_**|**_n compound_**|**_s not eligible _**|**_for discon_**|**_nection_**|||
|Coefcient|-0.015|-0.009|-0.340|0.303|-0.002|0.006|
||(0.023)|(0.029)|(0.723)|(3.617)|(0.025)|(0.026)|
||r0.507s|r0.752s|r0.639s|r0.933s|r0.924s|r0.815s|
|Control Mean|0.409|0.734|8.544|51.156|0.722|0.655|
|Observations|1930|1930|1930|1930|1930|1930|
|Number of Clusters|143|143|143|143|143|143|

_Notes_ . Clustered standard errors based on the level of randomization (street) in parentheses and associated p-value in brackets. *** p<0.01, ** p<0.05, * p<0.1. Panel A includes all compounds that were eligible for the enforcement intervention. The comparison is between compounds assigned to the control group in treatment clusters with all disconnection-eligible compounds residing in control clusters. Panel B includes all compounds that were not eligible for the disconnection intervention. The comparison is between disconnection-ineligible compounds in treatment clusters with all disconnection-ineligible compounds residing in control clusters. Time periods are benchmarked on the end date of the intervention (November 2018) and use data downloaded from the Nairobi Water billing data for December 2018 and September 2019 to estimate impacts 1 and 9 months after the intervention respectively. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise
_GLOBAL FOOD INSECURITY OUTLOOK_ _17_ In both projections, the poverty rates are carried forward using an income group-specific poverty - GDP per capita elasticity. Simple GDP-based extrapolations have been shown to work well, even when compared to more sophisticated methods (Mahler et al., 2021). Early work on monitoring pandemic-related impacts on poverty by Lakner et al. (2020) estimated that in 2020, global poverty rates were hit approximately 2.5 times harder than GDP per capita (ppp), and up to 3 times in downside estimates. As a conservative estimate, the poverty rates in 2020 are increased by 2 times the country-specific GDP per capita shock. The percentage of imports, land cover, and agricultural GDP shares are kept fixed at their last known value. Outlooks are typically not available for these variables. The percentage of agricultural land and GDP typically evolve slowly over time and fixing it may thus be reasonable. In section III.D, the analysis will use numerical optimization and counterfactual techniques to investigate optimal agricultural GDP values to simulate how a reorganization of the domestic food production system may help turn the tide on rising food insecurity.

<!-- Start of picture text --> Severe Food Insecurity in IDA + IBRD (Prevalence, Rate) 2005−01−01 / 2027−01−01 Severe Food Insecurity in IDA + IBRD (Headcount, millions) 2005−01−01 / 2027−01−01<br>15 15<br>1000 1000<br>14 14<br> 900 900<br>13 13<br> 800 800<br>12 12<br> 700 700<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan<br>2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027<br><!-- End of picture text --> Figure 1. Predictions
loans.<sup>25</sup> _TTC_ The role of the term _PDi_<sup>_TTC_</sup> / _PD_ in (8) is to weight the influence from the macroeconomic variables on the PDs by the relative size of the asset’s probability of default. This weighting scheme therefore ensures that an asset class that is, in relative terms, more likely to default than another class when no macroeconomic shocks are considered is also more likely to default when macroeconomic shocks are accounted for. For instance, if an asset class is two times more likely to default than the average of the considered asset classes in the TTC scenario, it is also two times more likely to default than the average of the considered asset classes in a Stress scenario. Hence, the proportionality across the _PDi_<sup>_TTC_</sup> values before and after macroeconomic shocks is preserved. The last term in (8) adds the TTC PDs specific to each asset class to the effect coming from the macroeconomy.

# **4.6. Incorporating Bank Specific Characteristics: The Idiosyncratic Component of Credit Risk**

We use data on credit (EAD) growth for each bank and each asset class before the peak of the last credit cycle, or the most recent positive credit growth if the cycle is in an upturn, to get a measure of the relative aggressiveness of bank lending. The intention here is to approximate the quality of the underwriting standards of the individual banks in each asset class relative to the average of the banking system. This is under the assumption that relatively more aggressive lending is associated with laxer underwriting standards.

To describe the construction of bank specific PDs, let _PDi_<sup>S</sup> , _j_<sup>denotethePDforbank</sup><sup>_j_</sup> holding asset class _i_ under scenario S, where S is now re-defined as S = _{TTC_ , _PIT_ , _Stress}_

> 25Note that we work here with the NPL
in meters at the 3 arc-seconds spatial resolution (90 _mts_<sup>2</sup> ). The digital elevation model (DEM) was created based on the images of the Shuttle Radar Topography Mission (SRTM) of NASA. In this study, we calculated the average elevation for each census tract.

- **Slope:** this study uses the terrain() function in R to compute the slope from the elevation data accordingly with Ritter (1987).<sup>39</sup> The algorithm uses four neighboring pixels to compute each pixel’s slope in degrees. Thus, higher values represent steeper terrain. Our study uses the average of the slope at the census tract level.

> 38 2 The inverse hyperbolic sine transformation is defined as _log_ ( _yi_ +( _yi_<sup>+1)1</sup><sup>_/_2) and can be interpreted as a logarithmic</sup> dependent variable (Pence, 2006).

> 39Documentation of the R tool can be found at https://www.rdocumentation.org/packages/raster/ versions/3.4-10/topics/terrain

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