rafmacalaba/datause-encoder-data huggingface dataset:
16 experience. This part of the loan file describes the company's markets, customers, suppliers, competitors, and depth of management. The purpose of collecting this information is to establish whether the borrower can use the loan proceeds productively. The database includes the firm's past income statements and tax returns. It also should contain the firm's formal projections of cash flow, earnings, interest, and taxes.

Capital measures the value of resources from which a borrower might still repay the loan even if the project being financed works out poorly. A borrower's capital is measured in the same way as a bank's own capital. Data for calculating capital come from business balance sheets and supplementary data on value of relevant intangible assets.

Collateral represents supplementary ways to enhance a borrower's credit so as to qualify the loan for a more favorable interest rate. Lenders look for outside guarantors and for assets whose title can be pledged to the lender to curtail the banks' , xposure to loss if the borrower should fail to deliver the stream of repayments promised. EXoert Sstems for **Credit AnaIl** The object of credit analysis is to determine the odds that a particular loan will go bad. The information-processing activity of seasoned credit professionals or "experts" can be likened to an artificial intelligence system (Singleton and Surkan, 1991).

Expert systems for credit analysis seek to objectify and automate routine parts of the process of assigning and aggregating bond-issuer or customer scores for the five Cs of creditworthiness. The goal is to approximate the probability-formation process that experts go through in trying to assess the financial condition and stability of potential borrowers. This means positing n proxy variables that can be interpreted as theoretically relevant credit attibutes (Aj, j=1, ..., n) and incorporating these variables into a statistical model of the probability of default for a given loan to the kth borrower, Pk. The various attributcs that characterize a given borrower (say, the kth customer) come from reported facts and estimated economic exposures.

The specific value the jth attribute takes on for bank borrower k can be denoted by Ajk. The Aj include economic, financial and personal variables: loan terms; borrower characterstics; applicable economic conditions; and legal constraints the bank may face (e.g.. on their ability to foreclose against loan collateral).
<!-- Start of picture text --> Following the discussion in the introduction ofthis part, we recommend basing inference<br><!-- End of picture text --> structing confidence intervals. We accep the null hypothesis if it falls entirely within the observations. To construct a confidence interval Horowitz (2001) recom ends the percentile method: observations at random with replacement from the data. You now have a Estimate hetreatme effect of interest on the bootstrap sample and sto _ it. times. You now have a sample of estimates of size mp the quanti s 0.025 and 0.975 of the sample of estimators, . ou now h e bootstrap critical values. Your confidence _ terval is: For diff ent nificance levels, adjust step 4. Alternative resampling procedures in step 1 are possible. For example, we can sample clusters instead of individual observations to acstatistics. For example, compute the standard deviation of the sample of estimators in step 4 to estimate standard rrors. How many boots ap repetitions are necessary? Computation is often trivial, so that a conservatively large Cameron and Trivedi 2005) suggest at least 348 repetitions for confidence intervals at level thousand repetitions and often as many as ten thousand.

tion between different sources of estimation noise. Consider, for example, the IPW estimator of treatment effects (see Section 3.3). We first compute weights for each observation, before taking the difference between weighted quantiles (or averages, variances, etc.). One should not overlook estimation error from the first step, which depends on the weight estimator. In the case of parametric models, it is sufficient to re-estimate the weights at each iteration in step 2 to adjust percentile intervals. Nonparametric or semiparametric estimators are the kernel bandwidth). Most data-dependent procedures pick optimal tuning parameters, in the sense that they minimize mean squared error. However, optimal convergence rates often generate asymptotic bias, which invalidates inference (whether it draws on asymptotic theorameter. Moreover, these procedures themselves introduce additional estimation noise. It is unclear, however, whether one should correct for it. In a study of kernel density estimators, for example, Hall and Kang (2001) argue against re-estimating the bandwidth at each bootThe usual caveats about cluster-robust estimation apply. We refer the reader to the excellent survey of Cameron and Miller (2015) for details, in particular Subsection VI.C.3 (“Bootstrap with caution”). For most estimators, analytical corrections for asymptotic confidence intervals are possible. See Newey and McFadden (2001) for a general discussion of inference for two-step estimators.
questions.<sup>8</sup> If certain conditions are met, the sum of the eight dummy variables that comprise the FIES module responses can be used to classify the food access of individuals or households as moderately or severely food insecure (e.g., FAO 2016).<sup>9</sup><sup>_,_10</sup> These individual- or household-level classifications can then be used to estimate the prevalence of food insecurity for the entire country, or regions within that country. However, countrylevel estimates for the prevalence of severe or moderate food insecurity according to the FIES can also be calculated by applying an Item Response Theory model (the Rasch Model), which assesses the suitability of each of the component questions and tries to make the scale more comparable across countries and contexts (e.g., Cafiero et al. 2018). As discussed in the introduction, the FIES has not been validated as fully as other more established food access metrics despite its rapid adoption globally in recent years, and the rest of the paper is devoted to better validating the FIES in Nigeria.<sup>11</sup>

# **Section 3. A Simple Model of Consumption**

Despite the importance of validating food access measures using welfare indicators aside from estimates of food consumption and diet quality, previous work has not fully characterized how poor food access and monetary poverty might overlap given a fixed level of income. In standard consumer theory, preferences over food and non-food goods dictate the share of their income that households spend on food, and there are no firm predictions about how monetary poverty and food access might be related. In this section, however, we illustrate the conditions that result in many key dimensions of poor food access and monetary poverty to overlap.

Specifically, this section adopts the simple framework presented in Jensen and Miller

> 8The FIES is based on a nearly identical measure developed by the United States Department of Agriculture and expanded globally in the Gallup World Poll in 2014 (e.g., Coleman-Jensen et al. 2021). 9Specifically, using the raw score in this way is only possible if the data satisfy the underlying assumptions of the Rasch model, which underpins the FIES approach. Specifically, the infit statistics should range between 0.7 and 1.3 (FAO 2016).

> 10The FIES module and the individual questions on food access can be found at ”https://www.fao.org/in-action/voices-of-the-hungry/fies/en/” (accessed July 2022).

> 11As mentioned above, the initial surveys in which the measure was captured did not include other food access metrics and did not include a variety of other household information that would help to more fully validate the measure. There have been validations of the measure in the United States (e.g., National Research Council 2006; etc.), and some similar measures have been more extensively validated in specific contexts (e.g., Coates et al. 2006; etc.).

9
# **5. Conclusion**

Time poverty has long been recognized as a constraint to development in Sub-Saharan Africa, with women working especially long hours due in part to a lack of access to basic infrastructure services such as water and electricity, but also due to the rising demands from the ‘care economy’. Yet despite a growing number of studies on time use in Africa and elsewhere, time poverty has remained loosely defined. In this paper, we have argued that the techniques used for the measurement and analysis of the determinants of consumption poverty can readily be applied to the issue of time poverty. The very concept of time poverty and the evidence on high workloads for women could be of use for policy makers. But when combined with other dimensions of welfare, such as consumption or income poverty, the analysis of time poverty can be even more revealing. We find for example that when we use a definition of time poverty that relies in part on the fact that an individual belongs to a household that is poor, time poverty affects women even more, and is especially prevalent in rural areas where infrastructure needs are highest.

One difficult issue in the measurement of time poverty is whether one should consider as time poor comparatively wealthy individuals or households whose members work longer hours in order to achieve higher levels of income or satisfaction at work. It could be argued that time poverty should apply to such individuals, because long working hours will indeed reduce the time available for leisure, rest, or friends and family. Moreover, in many cases, time can only be used in ‘chunks’, that is, it cannot be freely decreased (or increased) in a regular fashion. This means that (rich) individuals who work long hours may not have any other option but to work long hours. However, our approach does not rank these individuals who are not time and consumption poor as worse off than other individuals– simply, long hours of work would then be one of the many dimensions that may affect an individual’s level of welfare and satisfaction with life, but not time poverty per se in the absence of consumption poverty.

In this paper, we have taken the view that for development policy within the context, for example, of the Poverty reduction Strategies being implemented by many sub-Saharan countries, it is probably better to define as time poor only individuals working long hours without much choice not do so because they are already poor or would become poor if they worked less than the time poverty line. It also seems that this is a definition of time poverty that applies well to developing countries and is closer in spirit to the International Labor Office’s definition of 21
# **9. References**

Acharya, A., Blackwell, M., Sen, M., 2016. Explaining causal findings without bias: detecting and assessing direct effects. Am. Polit. Sci. Rev. 110 (3), 512–529.

Agnolucci, P., Fischer, C., Heine, D., Oca Leon, M. Pryor, J. Patroni, K. Hallegatte, S. (2023) “Measuring Total Carbon Pricing” World Bank Policy Research paper 10486.

Black, S., Liu, A. Parry, I and Vernon, N,. (2023). “IMF Fossil Fuel Subsidies Data: 2023 Update.” IMF Working paper WP/23/169.

<mark>Favennec, JP. (2022). “Economics of Oil Refining”. In: Hafner, M., Luciani, G. (eds) The Palgrave Handbook of International Energy Economics. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-86884-0_3</mark> Frankel, J. A. (2010). The natural resource curse: a survey (No. w15836). National Bureau of Economic Research.

Fuchs, Alan. (2023). Poverty and Distributional Consequences of Air Pollution in Tbilisi. World Bank. Washington D.C.

Heine, Dirk and Simon Black _Benefits beyond Climate: Environmental Tax Reform._ Fiscal Policies for Development and Climate Action. December 2018, 1-6. https://doi.org/10.1596/978-1-4648-1358-0_ch1 International Energy Agency 2023. World Energy Outlook.

International Monetary Fund (2019) Policy Papers “Fiscal Policies for Paris Climate Strategies—from Principle to Practice (May 1).

International Monetary Fund (2019) Fiscal Monitor “Fiscal Monitor: How to Mitigate Climate Change” (October).

Kojuma, M. and Koplow, D. (2017) “Fossil Fuel Subsidies: Approaches and Valuation” World Bank Policy Research paper 7220.

Lienard, Clémentine (2022) “Mitigating climate change in the MENA: shifting to a new paradigm” Brussels International Center, March.

Linsenmeier, M., Mohommad, A., and Schwerho_, G. (2022). Policy sequencing towards carbon pricing - empirical evidence from G20 economies and other major emitters. IMF Working Paper No. 2022/066.

Parry Ian, Dirk Heine, Elisa Liz and Shanjun Li (2014). Getting Energy Prices Right: From Principle to Practice. International Monetary Fund. DOI: https://doi.org/10.5089/9781484388570.071 <mark>Meckling, J., Sterner, T. & Wagner, G. (2017) Policy sequencing toward decarbonization.</mark> _<mark>Nat Energy</mark>_ **<mark>2</mark>** <mark>, 918–</mark> 36
Rosenthal S. and W. Strange. 2001. The determinants of agglomeration. Journal of Urban Economics, 50: 191–229 Rosenthal S. and W. Strange. 2003. Geography, industrial organization, and agglomeration. The Review of Economics and Statistics 85(2):377–393 Rosenthal S. and W. Strange. 2004. Evidence on the nature and sources of agglomeration economies. In: Henderson J, Thisse J‐F (eds) Handbook of regional and urban economics, vol 4. North Holland, New York, pp 2119–2171 Rossi‐Hansberg, E. 2004. Optimal urban land use and zoning. Review of Economic Dynamics, 7: 69–106.

Rossi‐Hansberg, E., P. Sarte, and R. Owens. 2009. Firm Fragmentation and Urban Patterns. International Economic Review, 50(1): 143‐186 Rothenberg, A., 2013. Transport Infrastructure and Firm Location Choice in Equilibrium: Evidence from Indonesia’s Highways, manuscript.

Small, K. and S. Song. 1994. Population and employment densities—Structure and change, Journal of Urban Economics, 36: 292–313.

Sakashita, N., 1995. An economic theory of urban growth control. Regional Science and Urban Economics 25: 427–434.

Santos, G. 2005. Urban Congestion Charging: A Comparison between London and Singapore. Transport Reviews 25 (5): 511–34.

Santos, G., and B. Shaffer. 2004. Preliminary Results of the London Congestion Charging Scheme. Public Works, Management and Policy, 9: 164–181.

Schafer, A. 2000. Regularities in travel demand: An international perspective. Journal of Transportation and Statistics, 3:1–31.

Schweizer, U., P. Varaiya and J. Hartwick. 1976. General equilibrium and location theory. Journal of Urban Economics, Elsevier, 3: 285‐303 Simon, C. 2004. Industrial reallocation across US cities, 1977‐1997. Journal of Urban Economics, 56:119‐ 143.

Staley, S., J. Edgens and G. Mildner. 1999. A line in the land: Urban‐growth boundaries, smart growth, and housing affordability. Reason Public Policy Institute 38
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 4. Spending on road maintenance and rehabilitation falls well short of needs**

<!-- Start of picture text --> maintenance rehabilitation<br>* Analysis of adequacy of road maintenance spending can only be performed for primary network under Federal jurisdiction.<br>Source: Gwilliam and others, AICD Background Paper No. 14, 2009<br>spending as % of requirements<br><!-- End of picture text --> To meet its spending needs for road maintenance, Liberia may need to consider a combination of a fuel levy and increased budgetary resources. Most African countries have tried road user charges to secure adequate resources for road maintenance. These are typically channeled through a ring-fenced road fund. Liberia plans to introduce a Road Fund but has not yet implemented road user charges. Based on the estimated road network preservation requirements, it is possible to estimate the level of the fuel levy that would be needed to fully fund maintenance. In the case of Liberia, the estimated optimal fuel levy is $0.25 per liter of fuel, which is rather high. This reflects the fact that Liberia—like other countries such as Congo and Niger—has a large road network relative to the amount of traffic that currently circulates. As a point of reference, the highest fuel levies in Africa to date were around $0.15 per liter in Ethiopia and Tanzania (figure 5). While Liberia definitely needs to introduce road user charging, it may not be possible to levy charges at the level that would be economically needed. The funding of road maintenance may therefore also require the use of budgetary resources. Another interesting aspect of road maintenance is its potential to create employment, particularly when labor intensive methods are utilized Transport spending needs can be estimated based on the assumption that key economic nodes should be connected _._ It is estimated that Liberia needs 800 kilometers of road to meet regional connectivity standards (linking Monrovia to the international frontiers), 1,500 kilometers to meet national connectivity standards (linking all provincial capitals to the regional network), and a further 5,700 kilometers to meet rural connectivity standards (linking land responsible for 80 percent of agricultural production value to the national network). In addition, the urban connectivity standard assumes an extension of the paved road network to within 500 meters of the population. Two scenarios are considered. In the base scenario, all infrastructure is maintained in good condition and higher end surfacing options are used (asphalt for all regional, national, and urban roads and single surface treatment for rural roads). In the pragmatic scenario, half the infrastructure is maintained in good condition and half in fair condition, and lower cost 9
# The Early Career Dynamics of Informality and Underemployment:

Evidence from the Arab Rebuplic of Egypt<sup>*</sup> Samia Ferhat<sup>†</sup> Cl ́ement Joubert<sup>‡</sup>

## **JEL Classification: F51, E24, E26, Z12**

**Keywords:** Informality, scarring effects

> *We are grateful to the Economic Research Forum for providing access to the data, and Kathleen Beegle, John Giles, Nishta Sinha, Paolo Verme, Nelly Ellmallakh, Daniel Lederman, and S Anukriti for helpful discussions and comments. The authors gratefully acknowledge funding from the MENA Chief Economist unit at the World Bank and the World Bank’s research group. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work

> †DARES. E-mail: samia.ferhat@cyu.fr

> ‡World Bank. E-mail: cjoubert@worldbank.org (corresponding author).
**Figure** 2:

<!-- Start of picture text --> General Gowrnment Expenditures (3)<br>in Transition Countries<br>1993-94<br>* Actual M Predicted<br>70 .. . . . . .<br>60 -6 - : -- .. . . . -:-- -. . . - -: ---.. . -- -- --- ,. . -- ....... --: -- ..,-- . - --<br>50<br>CD<br>u30<br>8t~~~~~~~~~~~~~ l. . ... ... .<br>2-<br>22.2 E 2 .2.~~ 2;';! ~~. a a44.<br>'I* _ _ o~~~~~ o ,S c O 4, O 4 ' X so= ae<br><!-- End of picture text -->

# **Regression Results: Patterns of Spending**

Expenditure/GDP ratios predicted on the basis of the preceding empirical analysis are quite revealing. First, several countries (Armenia, Tajikistan and Uzbekistan), particularly from the first group (large spenders with little success in reform), are heavily "overspending". Whether secondary education enrollment, infant mortality and old age dependency ratio, or per capita income, or secondary education enrollment jointly with public debt ratio and old age dependency ratio, are used as explanatory variables, and whether or not these are complemented by the regional dummies, Armenia, Tajikistan, and Uzbekistan, persistently show up as excessive spenders. All combinations of our proxies, for the level of social 16
approximately 60% of privatisation occurred created a one-off gain equal to 2.5% of the GDP while producers gain around two-thirds of the benefits (Mota, 2003) . Consumers could have benefited more from privatization since the start the presence of tougher regulation. The economic welfare impacts of partial privatization and restructuring of the Peruvian electricity market also proved worthwhile as the gains amounted to 542 million US dollars in 2007 prices (Anaya, 2010). The distributional gains suggested that government and producers benefited the most from welfare gains while consumers benefited the least.

Toba (2007) studied the welfare impacts of introduction of private sector participation into the Philippine electricity generation sector, through liberalization of the market for IPPs during the power crisis of 1990-1993. The introduction of IPPs presented significant gains contributing to resolving the crisis and promoting economic and social development while consumers and investors were net gainers. However, only about one-quarter of the total private investors’ gain is transferred to the domestic investors, as most of the investors are assumed to be foreigners. The largest portion of the net benefit equivalent to a net present value of 10.4 billion US dollars (in 1999 prices) was distributed to consumers while both domestic and foreign investors also gained. The government was the loser. In Israel, Tisher et al. (2006) undertook a cost-benefit analysis summarizing the government's reform plan using an unregulated regime as the counterfactual. The results suggested that the government's reform plan would only yield a small net benefit even when carried out flawlessly relative to the regulated regime. The reforms will also lead to large increases in electricity producer profit and government tax receipts at the expense of the electricity consumers. As such, a less-than-perfect transition to competition could easily preclude the potential gain of the government plan.

**Table 7: Reforms and Economic Welfare**

|**_Study_**|**_Approach_**|**_Region_**|**_Data_**|**_Method_**|**_Policy impacts and_**<br>**_relevance_**|
|---|---|---|---|---|---|
|Galal et al.<br>(1994)|multi-<br>country case<br>studies|global|public<br>enterprises<br>divestitures in<br>UK, Chile,<br>Malaysia and<br>Mexico; analyses<br>the privatisation<br>of the power<br>sector in Chile|social cost-<br>benefit<br>analysis|privatisation when<br>combined with<br>proper regulatory<br>framework can be<br>welfare enhancing,<br>private ownership<br>improves efficiency<br>of generation,<br>promote profit|

32