rafmacalaba/datause-encoder-data huggingface dataset:
2 transport are a growing source of carbon dioxide emissions. Motor vehicles produced 22 percent of global anthropogenic carbon dioxide emissions in 1990 (their _share_ of emissions is also growing), and roughly 70 percent of motor vehicle carbon dioxide emissions are produced in high-income countries.<sup>5</sup> Most motor vehicles are privately owned in market economies, and most of the roads they use are publicly owned. Relatively little is known about how road provision varies across countries or with vehicle ownership, and few analysts have examined the provision of roads at the national or urban level. In urbanized areas road construction has high economic costs and is politically contentious. The resettlement of households residing in the right of way of planned urban roads is a concern in both high income and developing countries.

As reported in many studies, this paper confirms that income is a strong determinant of vehicle ownership at both the country and city level, and that both national and urban motor vehicle ownership increase at about the same rate as income. More surprisingly, this paper also finds that income is a major determinant of the length of roads at the national level: national paved road length has also been increasing at about the same rate as income, and total road length less rapidly than income. Because national paved road networks are expanding at about the same rate as national motor vehicle fleets, congestion on them is unlikely to be worsening. But at the urban level, road length is growing much more slowly than income-and much more slowly than the number of motor vehicles. Urban congestion is therefore rising with income over time, and the increase in urban congestion is stimulating decentralized urban growth. Breaking the link between income growth, rising congestion, and decentralization at the urban level will be very difficult. Restraining auto ownership in urban areas requires very high tax rates, increasing the supply of roads in urban areas is very costly, and increasing the supply of transit alternatives is costly and has little effect on congestion in the few cases where it has been studied.

The paper outlines some basic economic hypotheses about motorization and road provision and then evaluates these hypotheses with some simple summary statistics compiled at the national and city level in high income and developing countries. Next, it surveys more sophisticated empirical work on motorization and on road provision and summarizes this knowledge as stylized facts. It then examines the production of motor vehicle transport services-passenger and freight transport that uses vehicles and roads as inputs-and ends with a prognosis and issues that need to be addressed.

# **2. Economic hypotheses concerning motorization and road provision**

Economic reasoning produces a number of hypotheses about the relationships among motor vehicle ownership, road provision, and the production of motor vehicle World Resource Institute, _World Resources 1996-97_ (New York: Oxford University Press, 1996).
-6-

in the case of Indonesia, while the 1991 factory registration file at the Ministry of Industry was used for Thailand. In both countries, the field survey took place in the latter half of 1992. The sample composition by location, employment size and the type of industry (two-digit SIC) are shown for all three countries in Appendix Tables Al through A9.

# B. Incidence of the Deficiencies by Firm Size and Region

For the purpose of comparisons, the Nigeria results are reported here with those of Indonesia and Thailand. The data from the three countries reveal that there are large variations in the availability and quality of public infrastructure services and in the firms' private provision responses across firm sizes and regions within each country as well as among the three countries. Such observations imply a need for a fresh look at the government strategy for improving delivery of services in infrastructure subsectors.

Table 2.1 shows that although only 7.8 percent of the sample firms in Nigeria did not have generators for the production of their own electricity, the proportion was 34.5 percent in Indonesia and 94 percent in Thailand. This implies that the lack of reliable power supply is much more serious in Nigeria than in Indonesia and more so in Indonesia than in Thailand. Although the availability of public electric power is greater in Thailand than in Nigeria and Indonesia, it does not mean that the quality is trouble free. Table _2.5_ indicates that 42 percent of the sample firms in Thailand reported production hours lost which amounted to 5.8 percent of the total production hours lost during the year, comparable to 6.9 percent in Indonesia. Many Thai firms indicated experiencing power interruptions in the form of 'blinking' which often serously disrupt power sensitive production operations.
production work. In the rest of the countries, on the other hand, the number of staff in the back office is almost double in other offices. One reason could be that the higher income countries have better IT support, but also a simpler overall portfolio to handle. Another interesting observation is that the total number of people required to perform all functions does not differ notably across all systems. We note, however, that among all, the in-house solutions have on average the highest number of people for each function and irrespective of the income level. This could be an indication on the need to retain a larger team to maintain such as system.

**Figure 7: Satisfaction with the Debt Management System: Overall**

<!-- Start of picture text --> Satisfaction With the DMS<br>1.0<br>In‐house<br>0.8<br>CS‐DRMS /<br>0.6<br>DMFAS<br>0.4 Commercial<br>0.2<br>0.0<br>DMIS System Technical Support Hardware<br><!-- End of picture text --> Source: World Bank Survey on Debt Management Systems The survey also drilled deeper into the satisfaction with the system regarding various attributes. These can be summarized as user-friendliness, security of data, reporting features, speed, connection with other platforms (e.g. Treasury, Excel, Bloomberg), integration among modules (front, middle and back offices), STP (e.g., settlement, repayment), customization of the system (such as the ability to create new instruments or transactions), maintenance and upgrade costs, and access to historical information.

On average, there was no clear distinction between the systems and the feedback received did not lead to clear conclusions regarding the differences. Nevertheless, the next graph shows that the respondents generally considered the commercial and public institution systems more user-friendly compared to the in-house built systems. Reporting capabilities and speed of these systems also received better feedback. The in-house systems scored better when it came to integration across offices and STP as well as customization of the system (i.e. creating new instruments).

While it is intuitive for the commercial and public institution systems to have better or more advanced reporting capabilities, it is somewhat surprising that the in-house systems have scored better on STP. One possible explanation could be that the commercial and public institution systems were purchased for the needs of one function only (i.e. back office) and were not integrated accordingly with the other offices. Other reasons could be that these countries have not yet decided to integrate it or did not have the resources and/or capacity to do so. This may especially be true for countries using a public DMS. This could also partially explain why commercial and public institution systems scored better on technical support compared to in-house supported systems.

The survey could not draw a clear distinction between satisfaction level versus income level.

26
_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 made with final model, aggregated across all IDA, Blend and IBRD countries (144).

_Note:_ The total food insecure population is based on the predicted prevalence rates, combined with the country population totals taken from the WDI database, and the population growth rates projected by the WEO. The black solid line corresponds to the period with observed data (2015-2019), the historical part of the black dashed line are predictions generated based on historical covariate values from the World Bank WDI database and the future values are generated using the IMF’s WEO outlook of April 2022. The red dash line is a downside projection that considers the slowed growth rates and higher inflation rates identified in the WEO’s downside analysis. _Source:_ Figure prepared by the author for this paper.

plistic scenario in which the GDP rate reductions are applied equally to all countries as point percentage reductions and the food price inflation rates are applied as relative multipliers (times 2 in 2022, times 1.5 in 2023) to the default WEO CPI inflation rates.
would buttress the argument that funds should be set aside by current populations to cover benefits received in the future.

- Some revenue sources are of a non-recurring and capital nature (asset disposals, auctioning of licenses, resource royalties and death duties).  One could argue that receipts should be included in a capital budget and be used to reduce the need for debt finance (Boothe 1993).

The application of “capital” to budgets of governments is therefore not a simple manner and judgement is needed to classify expenditures as public capital. Given that this exercise could involve value judgements, a concern could arise that far too many public expenditures might be classified by governments as capital in order to justify debt financing as later discussed in the next section on fiscal rules. In part, this is related to motives for debt financing of capital as discussed above.

# **<u>4. Survey of current practice</u>**

# **4.1 Fiscal rules**

Fiscal rules and capital budgeting has impacted on a number of practices – European fiscal targeting, golden rules for debt accumulation, public infrastructure agencies and public-private partnerships. Below, a brief review of the issues related to these practices is provided.

_Europe and the SGP._ The Maastricht Treaty contains a provision requiring member states to avoid running “excessive” deficits, whether or not they have adopted the euro. A protocol to the treaty specifies in turn that members’ fiscal stance is to be judged by two criteria: whether the budget deficit is less than 3 percent of GDP, and whether total government debt exceeds 60 percent of GDP. If the Council determines a deficit is excessive, there is a procedure to encourage its elimination. The Council may issue warnings and impose deposit requirements and, eventually, fines.

The SGP corresponds to the provisions of the Maastricht excessive deficits procedure, but it clarifies the terms, introduces monitoring procedures, and it gives the Council greater teeth in the event of violations. Under the SGP, deficits may exceed the 3 percent level if the excess is "exceptional, temporary, and limited in size". Some discretion is accorded to the Council in determining whether this provision should apply.

The Pact also requires members have medium term budgets that are "close to balance or in surplus". Again, the definition of fiscal balance treats government investment expenditures on a cash basis. Importantly, the ECOFIN Council adopted a revised Code of Conduct in 2001, requiring that member states use common assumptions in their 16
_Table 8: Welfare effects (in percentage change) of child-relevant policy simulations in Ethiopia_

|**_Simulation_**|**_Monetary_**<br>**_poverty_**<br>**_headcount_**<br>**_(%)_**|**_Inequality_**<br>**_(Theil's_**<br>**_index)_**|**_Multi-_**<br>**_dimensional_**<br>**_poverty_**<br>**_headcount_**<br>**_(%)_**|**_Fiscal costs_**|**_Assumptions_**|
|---|---|---|---|---|---|
|**_Initial welfare i_**|**_ndex values_**|||||
|All children|25.8|0.267|53.7|||
|Boys|24.3|0.262|54.0|||
|Girls|27.3|0.273|53.5|||
|Rural|29.6|0.228|65.6|||
|Urban|12.0|0.270|10.7|||
|**_Simulation 1:_**|**_Universal educa_**|**_tion_**_(effects in_|_% change)_||No additional<br>|
|All children|-3.2|-0.9|-2.2|ETB 4.2 billion|administrative costs<br>|
|Boys|-2.7|-1.3|-2.6||for enrolling currently<br>lld|
|Girls|-3.7|-0.8|-2.1||unenroe<br>tdt N|
|Rural|-2.9|-1.4|-2.1||suens. o<br>behavioral chanes|
|Urban|-5.3|-1.1|-5.6||g.|
|**_Simulation 2:_**|**_PSNP transfer_**|**_s are doubled_**|_(effects in %_||No behavioral changes|
|_change)_||||ETB 3.1 billion|and no administrative|
|All children|-1.9|-0.9|||costs of doubling the|
|Boys|-1.6|-0.9|||PSNP transfer.|
|Girls|-2.1|-1.1||||
|Rural|-1.9|-1.0||||
|Urban|-1.6|-0.5||||
|**_Simulation 3:_**<br>_change)_|**_PSNP transfers_**|**_are retargete_**|**_d_**_(effects in %_|No fiscal cost: the<br>ETB 2.5 billion|No behavioral changes<br>and no administrative|
|All children|-6.9|-2.5||saved from the|costs of redistributing|
|Boys|-7.1|-2.5||non-poor are fully|the PSNP transfer.|
|<br>Girls|-6.8|-2.7||redistributed to||
|Rural|-6.8|-3.8||poor children.||
|Urban|-8.1|-0.8||||
|**_Simulation 4:_**|**_PSNP transfers_**|**_are doubled a_**|**_nd retargeted_**||No behavioral changes|
|_(effects in % ch_|_ange)_|||ETB 3.1 billion|and no administrative|
|All children|-8.8|-3.1|||costs of increasing the|
|Boys|-8.5|-3.0|||amount and|
|<br>Girls|-9.1|-3.4|||redistribution of the|
|Rural|-8.7|-4.5|||PSNP transfer.|
|Urban|-9.4|-1.0||||

**_Notes:_** _All simulation results are reported in percentage change. Monetary poverty and inequality indices are computed at the final income. Effects in % change of multidimensional poverty headcount are estimated only in Simulation 1 as education indicators directly enter the multidimensional child poverty index. However, this cannot be done for Simulations 2-4 since they are not directly linked to the index._

Doubling the amount of PSNP transfers to existing beneficiaries ( _Simulation 2_ ) reduces monetary poverty by 1.9% and inequality by 0.9%, with higher effects for girls than boys. Given that PSNP is currently rural-oriented, the effects of doubling its transfers are also more pronounced among children in rural areas than in urban areas. The overall cost of this fiscal action is estimated to be 22
**<u>Table 7: Changes in urbanization rates in Ethiopian Regions and Chartered Cities</u>**

|**% Urban**<br>**1994**|**% Urban**<br>**2007**|
|---|---|
|**_Early Implementing Geographic Areas_**||
|Addis Ababa<br>98.7|100|
|Amhara<br>9.1|12.6|
|Dire Dawa<br>68.8|67.5|
|Oromiya<br>10.5|12.2|
|Tigray<br>14.9|19.5|
|**_Later Implementing Geographic Areas_**<br> <br>||
|Afar<br>8.1|13.4|
|Benishangul Gumuz<br>7.8|13.5|
|Gambella<br>15.1|25.2|
|Harari<br>58.2|50.5|
|Somali<br>15.4|13.9|
|SNNP<br>6.8|10|

Source: Population and Housing Census (1994 & 2007): Ethiopian Central Statistical Agency Another important fact about the land reforms is that only the first stage of the land certification reforms had been implemented by 2005. The first stage of the reforms involved registration, and not the actual issuing of the certificates of ownership. The only exception in this regard is Amhara region, where some progress had been made in handing out certificates by 2005.

In Table 8, we re-estimate equation 1 and exclude Amhara given its relative progress in implementing the land certification reforms. Like most of our preceding results, the family law has a significant effect in increasing women’s share in occupations with more non-home-based employment. The reform also has a significant interactive effect with paid work. In the urban areas outside Amhara, the law seems to have a more limited effect. However, this reduced effect is partly driven by loss of statistical power from the smaller degrees of freedom.

31
00 Table 15 Net R_efit By Type of Trainee

|_<br>_<br>_<br>_<br>_<br>_ _<br>_<br>_<br>|Diret Tg<br>Csts<br>Searh Cost <sup>2</sup><br>_<br>_<br>_<br>(1)<br>~~~~~~|Oe<br>mi-a<br>(3)(4|_Oe_2muh<br>~~(2)|
|---|---|---|---|
|Mao||||
|AU|350.4<br>196.0|456.0|1,824.0|
|Wih prior Wk Exp.<br>No Pdior Wk Exp|350.4<br>-66.52<br>350.4<br>1,734.6|474.0<br>384.0|1,896.0<br>1,536.0|
|_Ase_<=25<br>**Ag**> 25<br>|350.4<br>631.5<br>350.4<br>_-575.22_|564.0<br>426.0|2,256.0<br>1,704.0|
|Meeing Education<br>Selection Crteds'|350.4<br>322.0|516.0|2,064.0|
|Fansaaes||||
|AU|350.4<br>435.0|0|0|
|_ah PA"ior_<br>Wk Exp.<br>No Pil Wk E|350.4<br>-513.71<br>3504<br>|0<br>|0<br>|
|o xp|.<br>2,088.2|0|0|
|Age <=25<br>Ag > 25|350.4<br>767.6<br>350.4<br>-348.32|0<br>0|0<br>0|
|Meeting Educadon<br>Selection Criterial|350.4<br>422.0|0|0|

E4uala th additional time trainees take to find a job (because of time in training) times the opoity cost of that time (which equals the wage of the controls -ic. the wage the trainees would hae received wihout the training).

2 Taine with work experience nd thow aged 25 and over find a job sooner than controls even when you take into account training period. Tbis estinate equals the extra time worked by aione relative to contrl * tine wage.
context, the provider assessment falls within the framework set out by Miller in 1990, that a holistic assessment of health worker competence should assess their clinical knowledge, if they know how to apply that knowledge, if they can show how to apply that knowledge, and if they actually apply that knowledge in practice.<sup>7</sup> Our vignette data capture a combination of clinical knowledge and their knowledge of how to apply it. Other, complementary methods (specifically, standardized patient studies) demonstrate whether health workers can show how to apply their knowledge and whether they actually apply that knowledge in practice.<sup>8-10</sup> Nationally representative standardized patient data are unavailable in the settings and for the wide array of conditions we study, but limitations revealed in the first aspects of the health worker assessment (what they know and if they know how to apply it) and to one aspect of practice (absenteeism) potentially imply even further limitations on the latter part (whether they can and do actually apply knowledge in practice).

# **Methods**

# **_Data_**

We draw on data from the Service Delivery Indicators (SDI) program—an ongoing initiative to collect informative and standardized measures of what providers know, what they do, and what they have to work with. The SDI program—piloted in 2010-2011—grew out of concern about quality of services in health and education, especially in Sub-Saharan Africa, most clearly (and perhaps most damagingly) manifested at the facility level, in fast-expanding systems of primary health provision.<sup>11,12</sup> To date, the SDI program has collected data from 10 countries: Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Senegal (2010), Sierra Leone (2018), Tanzania (2016), Togo (2014), and Uganda (2013), and has since expanded to countries outside the Africa region.<sup>13-17</sup> While every context is different, these 10 countries serve as a reasonable proxy for much of Sub-Saharan Africa, 4
regulations will allow foreign banks to open 100% capitalized wholly-owned subsidiaries in India. Foreign banks have typically focused their operations in the top 25 cities in the country, likely due in part to restrictions on branch expansion.<sup>15</sup> The foreign banks generally use more modern equipment, pay higher salaries, and attract better-trained employees (IndiaMart 2004).

Private banks are primarily de novo entrants that were granted banking licenses during the financial liberalization in the early 1990s. A total of 25 de novo private banks began operations between 1994 and 2000. There are also a small number of incumbent private banks that existed before 1990 and some state-owned institutions that have been successfully privatized. An example of the latter is ICICI, which was formed in 1955 as a state-owned institution at the initiative of the Government of India and the World Bank to create a development financial institution for providing medium- and long-term project financing to Indian businesses. During the 1990s, ICICI was privatized and evolved into a private, fullservice bank and is now India’s second largest bank offering a wide range of services to retail and corporate customers.

As shown in Table 1, the foreign banks are most numerous, but they have relatively few branches and accounts and also have fewer deposits and assets than the other types. Nationalized banks are the largest type as measured by number of branches, accounts, deposits, and assets. The state-owned banks combined – SBI plus the nationalized banks – dominate the banking sector with about 80% of deposits and assets.

# **4. Data and Summary Statistics**

We match data from nonfinancial firms, the banks with which they have relationships, and information on their local market. The firm data are from _Prowess_ , an electronic database produced by the Centre for Monitoring Indian Economy (CMIE). This database includes firms that are required to file annual accounting reports and have submitted reports for at least three years. This comprises: (i) firms that are listed on a national exchange (National Stock Exchange or Bombay Stock Exchange), and (ii) all unlisted public limited companies that have sales or assets more than Rs. 200,000,000 or about U.S. $4.23 million. Prowess includes balance sheet, income statement, and other financial information for over

> 15 Foreign banks currently operate only on a branch license basis under which they are required to keep locally $25 million in capital for the first three branches.  Further expansion does not require additional capital, but requires RBI approval, which is often difficult to receive.

12