rafmacalaba/datause-encoder-data huggingface dataset:
Table 8 presents the results. Column (1) uses the conservative classification of differentiated products of Rauch (1999), while Column (2) employs the liberal classification. The negative and significant coefficients in both columns indicate that the positive effect of improved CI on firm DVAR is less for firms importing more differentiated products. In other words, firms that import less differentiated imports will benefit more as HSR expands which allow them to find compatible domestic materials.

Table 6: The Impact of HSR on Firms’ DVAR: By Firm Ownership Type and Trade Regime

|Subsample|(1)<br>SOE|(2)<br>FIE|(3)<br>DPE|(4)<br>Processing|
|---|---|---|---|---|
|Dependent Variable|DVAR|DVAR|DVAR|DVAR|
|CI|0.003|0.032***|0.008|0.027**|
||(0.031)|(0.008)|(0.011)|(0.011)|
|Control|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|City-Year FE|Yes|Yes|Yes|Yes|
|Observations|5,981|155,451|67,232|85,005|
|Adjusted R<sup>2</sup>|0.554|0.729|0.697|0.682|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

Table 7: The Im act of HSR on Firms’ DVAR: B Industr <u>p y y</u>

|Industries|(1)<br>Chemicals,<br>Machinery<br>and Transportation|(2)<br>Electrical,<br>Metals<br>and Optical Equipment|(3)<br>Furniture<br>and<br>Wood Products|(4)<br>Miscellaneous|(5)<br>Plastic,<br>Rubber<br>and Toys|(6)<br>Textiles<br>and<br>Apparels|
|---|---|---|---|---|---|---|
|Dependent Variable|DVAR|DVAR|DVAR|DVAR|DVAR|DVAR|
|CI|0.007|0.031***|-0.020|0.056***|0.004|0.021|
||(0.012)|(0.012)|(0.016)|(0.015)|(0.020)|(0.023)|
|Control|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|City-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|60,055|68,081|15,492|27,384|21,584|33,152|
|Adjusted R<sup>2</sup>|0.663|0.712|0.704|0.745|0.769|0.663|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

22
above the minimum transactions balances and as such depositors will tend to be alert to warning signs. There is a deposit insurance scheme, but its coverage (about USD 3300) and assets (about 1.4% of total banking assets) are relatively small, and payout in the past has been slow. More to the point, the degree to which implicit deposit insurance applies on a large scale is limited by the consideration that the government is by far the largest creditor of the banking system. Yet, with a population of just 4 million Lebanon is a small country with a small elite business community and, despite their impressive performance, its banks too are small, limiting the resources which rating agencies can justify expending when researching their ratings.

# **_B.  Preconditions – By flavor of discipline and income level_**

We can attempt to quantify some of the preconditions for market monitoring, using in particular data from the second (2003) wave of the World Bank’s regulatory survey (of which the first wave results are discussed in Barth et al, 2001, 2003) and combining this with other sources of information. We begin with updated information on the overall “private sector monitoring” index proposed by Barth, Caprio and Levine and summarizing the extent to which the private sector is empowered to exercise market discipline. This private sector monitoring index captures elements of the preconditions for each of the flavors of market discipline we have discussed.<sup>15</sup> <!-- Start of picture text --> (a) Private monitoring index (b) Private monitoring index<br>by predominant ownership type by per capita income level<br>5.60 5.70 5.80 5.90 6.00 6.10 6.20 5.40 5.60 5.80 6.00 6.20 6.40 6.60 6.80 7.00 7.20<br>Mainly Govt 0-2500<br>Mainly Fgn 2500-5000 `<br>Fgn+Govt 5000-8000<br>Equally shared 8000+<br>Mainly local pvt High income<br><!-- End of picture text --> Figure 2: _Private Monitoring index and (a) predominant type of ownership; (b) per capita income level (PPP)_ (Source: World Bank Regulation and Supervision Survey; for definition of predominant type of ownership see note to Table 2)

> 15 The methodology for computing the summary index of private monitoring is as described in Barth, Caprio and Levine, 2001.  As such it covers bank accounting rules, auditing and disclosure requirements, rating of banks, deposit insurance and subordinated debt.

20
Motivated by the widespread use of FAR restrictions in India, the present paper analyzes the effects of such restrictions and attempts to appraise their costs. This task is carried out first on an abstract level, with the standard monocentric-city model used to show how FAR restrictions affect land use. The analysis, presented in section 2, reaches the natural conclusion that, by limiting population densities near the center, an FAR restriction causes the city to expand spatially. This spatial enlargement affects consumers both by raising their average commuting distance and by pushing up the housing prices they must pay. However, the analysis shows that the resulting per capita welfare loss can be measured simply by the increase in commuting cost for an individual living at the edge of the city, who is now more distant from the central workplace.

It should be noted that this welfare-cost measure abstracts from the potential effects of the FAR restriction on infrastructure costs. While the restriction may reduce costs in the center, as explained above, spatial expansion of the city also leads to higher costs by requiring a wider infrastructure network. Because the net change in costs is difficult to estimate, the analysis focuses purely on consumer losses from the FAR restriction, abstracting from infrastructure issues.

Armed with results of the theoretical analysis, the paper turns in section 3 to a case study of a particular Indian city, namely, Bangalore. Using detailed land-use data for this city, the analysis carries out a counterfactual exercise, asking what Bangalore would look like today had it developed in the absence of the FAR restriction. The theory shows how FAR values and dwelling sizes would change throughout the city following a hypothetical removal of the restriction, and these predicted changes can be used to spatially reallocate Bangalore's population, raising central densities and generating a shrinkage in the city's spatial size. The final step is to compute the resulting commuting-cost saving for an individual living at Bangalore's now-closer edge. This saving 3
policy change produced by moving resources between activities affected by the policy change. In Australia, for example, indirect effects are not included in costings because of the uncertainty associated with estimating the scale and timing of the effects and, because they are also likely to occur over a longer timeframe, they often may not occur within the forward estimates period.

A final practice of interest in estimates construction is what is referred to as the Contingency Reserve (CR). This is an aggregate provision within the forward estimates to reflect anticipated events that cannot be assigned to individual programs at the time budget estimates are prepared. It is not a general policy reserve, as the allowances are not appropriated. For our purposes, the most important component of the CR is the conservative bias allowance (CBA) – set as a percentage of total central general government sector expenses – which recognizes the tendency for estimates of existing programs to be revised upwards over time. The CBA is particularly important for demand driven programs where accurate cost estimates can be problematic. The CBA is reduced for earlier forward estimate years as program estimates are progressively updated, thereby decreasing the bias. These adjustments do not realize actual budgetary savings, nor offset spending measures: in other words, the CBA is a device to improve the accuracy of the forward estimates.

Economic and fiscal parameter forecasting obviously play a central role in framing revenue and expenditure projections for budget formulation within an MTEF. The most important of these are assumptions and projections relating to average economic growth rates, rates of employment and unemployment, rates of general and specific price inflation. Each of the surveyed jurisdictions has distinctive institutional arrangements for economic forecasting that help to characterize the role of economic forecasting inputs to costing exercises and hence estimates construction.

In Australia and Canada, economic ministries other than the finance ministry have lead responsibility for economic forecasting: the Treasury in the former, and the Department of Finance in the latter. However, both institutions have adopted different approaches to obtaining and assessing data inputs and assumptions. In Australia, data is sourced from the Australian Bureau of Statistics (ABS), and only supplemented by data from business and consumer survey data. The Treasury uses in-house econometric models to construct forecasts which are subject to review by the Joint Economic Forecasting Group, comprising representatives from key economic and policy departments within the Australian government. By comparison, the Canadian Department of Finance forecasts by surveying a group of private sector forecasters 34
|Line of business|A<br>Expected gross<br>premium 2012|B.<br>Proportional RI<br>cession|C=A*B<br>Ceded Premium <br>2012 (prop. RI)|D=A-C<br>| Retained Premium<br>2012 (prop. RI)|
|---|---|---|---|---|
|Motorvehicle liability insurance|20,000|50%|10,000|10,000|
|Other motor insurance|5,000|50%|2,500|2,500|
|Marine, aviation and transport insurance|5,000|50%|2,500|2,500|
|Fire and other damage to property insurance|15,000|50%|7,500|7,500|
|Generalliabilityinsurance|5,000|50%|2,500|2,500|

<!-- Start of picture text --> E. F=E *(1-B) G. H. 1=D*H<br>Line of business PML gross PML net retained | Non-prop XL RI Non-prop XL RI XL RI premium<br>after prop. RI 2012 Rate on GNPI 2012<br>Motor vehicle liability insurance 10,000 5,000 4,500 xs 500 1.0% 102<br>Other motor insurance 10,000 5,000 4,500 xs 500 2.5% 62<br>Marine, aviation and transport insurance 50,000 25,000 24,500 xs 500 9.6% 240<br>Fire and other damage to property insurance 0<br>Fire non natural catastrophe per risk 30,000 15,000 14,500 xs 500 11.4% 852<br>Fire natural catastrophe per event (EQ) 100,000 50,000 49,500 xs 500 5.9% 439<br>General liability insurance 5,000 2,500 2,000 xs 500 2.1% 52<br>tot<br><!-- End of picture text -->

|Line of business|E.<br>PML gross|F=E *(1-B)<br>PML net retained | <br>after prop. RI|G.<br> Non-prop XL RI<br>2012|H.<br>Non-prop XL RI<br>Rate on GNPI|1=D*H<br>XL RI premium<br>2012|
|---|---|---|---|---|---|
|Motorvehicle liability insurance|10,000|5,000|4,500 xs 500|1.0%|102|
|Othermotor insurance|10,000|5,000|4,500 xs 500|2.5%|62|
|Marine, aviation and transport insurance|50,000|25,000|24,500 xs 500|9.6%|240|
|Fire and other damage to property insurance|||||0|
|Fire non natural catastrophe per risk|30,000|15,000|14,500 xs 500|11.4%|852|
|Fire natural catastrophe per event (EQ)<br>|100,000<br>|50,000<br>|49,500 xs 500<br>|5.9%<br>|439<br>|
|General liability insurance<br>|5,000<br>|2,500<br>|2,000 xs 500<br>|2.1%<br><br>|52<br><br>|
|t|||||ts|
propose and implement tests of error correlation based on explicit identifying assumptions.

A third criticism of expert assessments is that they are subject to various biases. One argument is that many of these sources are biased towards the views of the business community, which may have very different views of what constitutes good governance than other types of respondents. In short, goes the critique, businesspeople like low taxes and less regulation, while the public good demands reasonable taxation and appropriate regulation. We do not think this critique is particularly compelling. If this is true, then the responses of commercial risk rating agencies who serve mostly business clients, or the views of firms themselves, to questions about governance should not be very correlated with ratings provided respondents who are more likely to sympathize with the common good, such as individuals, NGOs, or public sector organizations. Yet in most cases these correlations are in fact quite respectable. In Kaufmann, Kraay, and Mastruzzi (2007, Table 1) we document a strong correspondence between business-oriented sources of data on government effectiveness and other types of data sources. And in this paper, a glance at Table 2 suggests that crosscountry surveys of firms and cross-country surveys of individuals, such as the World Economic Forum's Executive Opinion Survey and the Gallup World Poll result in similar rankings of countries according to views of corruption, with the two surveys correlated at 0.7 across countries.

Another potential source of bias in expert assessments, particularly those produced by NGOs, is that they are colored by the ideological orientation of the organization providing the ratings. In Kaufmann, Kraay, and Mastruzzi (2004) we devised a simple test for such political biases. We examined whether the difference between the assessments of think-tanks and firm surveys was systematically correlated with the political orientation of the government in power in the countries being rated. We found that this was generally not the case, casting doubt on this possible source of bias. Potentially a greater problem of bias is at the country respondent level. For example, in a particular country, the views of a pro-government and an anti-government "expert" might be very different, and this could affect both levels and trends over time in the scores for that country. This risk is perhaps greatest for sources that rely on locallyrecruited experts, such as the Global Integrity Index. This is also much more difficult to 19
We also highlight resource-dependent countries and resource-rich countries in our sample (note that the two lists are not mutually exclusive), as they are highlighted to be particularly procyclical in the recent literature (Arezki and Bruckner, 2012; Konuki and Villafuerte, 2016):

1. Resource-dependent countries are countries for which commodity revenues (GGRC in WEO data set) account for at least 10 percent of total revenues minus grants (GGR and GGRG series in WEO data set) at least 50 percent of the time over the considered period. This definition is derived from Konuki and Villafuerte (2016) and enables us to capture countries with a resourcedependent revenue structure. For more details, see Table E.1 in Appendix E.

2. Resource-rich countries are defined by the IMF (2012), here is direct access to the report <u>(https://www.imf.org/external/np/pp/eng/2012/082412.pdf). According to this definition, we</u> capture countries where production on natural resources reached (or expected to reach) significant levels. For more details, see Table E.2 in Appendix E.

# Treatment of missing data

# We expand equation 1 as follows:

Cycl. rggpx�� �α�βCycl. rgdp�� �ρxX�� �γ� �δ� �ε�� (A-1) The vector of covariates X�� includes the standard deviation of the terms of trade (TT series in WEO database) measured with five years rolling window, two binary dummy (BD) variables equaling one if the original real primary expenditures and the cyclical components of real GDP were missing, zero otherwise. Last but not least, we include two additional BD variables equaling one if the cyclical components of expenditures and GDP were missing the previous year. This is the Set 1 of covariates. The Set 2 of covariates differs with the inclusion of two BD variables equaling one if the cyclical components of expenditures and GDP were missing from one to five years ago, zero otherwise. With this, we aim to capture inaccurate cycles due to the linear interpolation method.

28
Assessing the cost effectiveness of the _Jefes_ program requires comparing the program costs to the value associated with the income redistribution under the program.

_PROBECAT program in Mexico_ : Revenga, Riboud, Tan (1994) evaluate the effects of shortterm vocational training in Mexico provided by the PROBECAT program, which was offered to more than 250,000 unemployed people. Program participants were selected according to an eligibility index that gave weight to factors such as the number of economic dependents, whether the individual attained a basic level of education, whether the individual was unemployed for less than 3 months, and prior work experience. Also, to be eligible, individuals had to be within the age range of 20 and 55 and be registered at the unemployment office. The impact analysis is based on longitudinal data on PROBECAT trainees combined with a separate dataset on a control group of unemployed people who did not join PROBECAT, drawn from national labor force surveys, which survey individuals for 5 quarters. Women were 49% of the trainee group but only 33.8% of the comparison group. The average female trainee was 29 years old and 46% were married. Women were less likely than men to have completed secondary education (grades 10-12).

The key outcomes analyzed in the study are employment, monthly salary, and number of hours worked. Impacts of the program on the length of unemployment spells are assessed using a Cox proportional hazards model, estimated on the treatment group and on a subgroup of controls predicted to have a high probability of participating in the program based on their characteristics. Potential drawbacks of the analysis are that participants and controls were given different surveys, so that measurements across surveys may not be comparable, and the control sample was relatively small. On average, program trainees found jobs more quickly (program participation reduced unemployment spell by 1.9 months for females and 2.5 months for males). Subgroup analyses reveal that the positive program impacts in terms of shortening unemployment spells were concentrated on trainees older than 25 and those with work experience. Female trainees with work experience were more likely to be employed at 3, 6 and 12 months after the training than were similar controls. Training increased the number of hours worked for both women and men, but only increased monthly earnings for men. Revenga, Riboud and Tan (1994) conclude that the program was cost effective, in the sense of covering its costs, for women over age 25 but not for younger women.

16
The low level of interest of officials in EAP in school feeding is consistent with these same officials not seeing malnutrition and poverty as a major barrier to learning in the EAP region. The available data on malnutrition in young children the region (UNICEF, 2021), and evidence from other contexts show that nutritious school meals have positive benefits for both learning (e.g., Chakraborty and Jayaraman, 2019; Aurino et al., 2023), enrollment, as well as nutrition (Adelman et al., 2019; UNESCO, 2023). However, the lack of prioritizing of school feeding by officials aligns with the low levels of investment in school feeding programs by both EAP governments and the World Bank; there is currently only one school feeding program supported by World Bank investments in EAP (Cambodia).

_Figure 5: Officials in EAP prefer structured reading and in-service training relative to other interventions._ <!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Building Providing Providing Hiring extra Removing In-service Giving<br>hospitals laptops to free lunches specialist fees for teacher teachers<br>schools in primary teachers for secondary training structured<br>school students school early grade<br>with<br>disabilities<br>project<br>Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP<br>Share of respondants selecting the<br><!-- End of picture text --> Source: Authors’ calculations from the Discrete Choice Experiment as part of the CGD–World Bank survey 2022. See probit model results in the annex. The probabilities have been computed for a similar “average” respondent in all countries. About 50 percent of respondents select school feeding programs outside of the EAP region, which lies outside of the uncertainty range of the selection probability in the EAP region.

# Education Technology, Gender and Inclusion

## 4.2 Officials overestimate the effectiveness of EdTech and underestimate inequality.

Most EAP respondents agree with the statement that education technology was effective in supporting student learning during COVID-19-related school closures (83 percent, Figure 6). However, the data for EAP and much of the world show that EdTech was usually _not_ an effective support for most children during COVID-19-related school closures, or at least much less effective on average than in-person instruction, particularly for low-income students (Patrinos et al., 2022). Respondents’ views on the impact of EdTech during COVID-19-related school closures are 8 percent more positive in EAP than in non-EAP countries (Figure 6).

9
- _Teacher Index_ : The Pupil-Teacher Ratio (PTR) at the primary and upper primary levels together has been used for creating an index of teachers.

- **(ii)** **_Equity Index_** : This index includes:

- Girls’ age-specific enrolment ratio (as  a percentage of the girls in the 6-14 year age group enrolled in school  on the basis of the Census 2001 data).

- The female literacy rate of Census 2001 as a comparison to gauge the current level of girls’ enrollment vis-à-vis the historical backlog.

- **(iii) Outcome Index** : This index includes the following indicators relating to enrollment and completion rates:

- Percentage of children enrolled (6-14 years) – Household survey

- Enrollment in grade V/ Enrollment in grade I (Crude primary completion rate)

- Enrollment in grade VIII/ Enrollment in grade I (Crude elementary completion rate)

The EDI construction is structurally presented in Figure 1. The data used for this analysis were taken mostly from District Information System for Education (DISE), while some of the information was derived from Household Child Survey (SSA), Census 2001 etc.

||**Figure 1: Structural Rep**<br>|**resentation of the**<br>|**EDI**<br>**Sub-EDI**||
|---|---|---|---|---|
|**Dimension**|**Indicator**|**Dimension Indices**|<br>**Indicators**|**DEX**|
|Access|Primary School Coverage|Access Index||**IN**|
||Primary: UP ratio|||**NT**|
||Classroom availability|Infrastructure|INPUT INDEX|**ME**|
|Infrastructure|Toilets availability|<br>Index||**OP**|
||Drinking water availability|||**VEL**|
|Teacher|PTR|Teacher Index||**DE**|
|Enrolment|% of 6-14 years in school|Enrolment Index|OUTPUT/|**ON**|
|Completion|Primary school completion|Completion Index|OUTCOME|**ATI**|
||UP completion||INDEX|**UC**|
|Equity|Girls’ enrolment|Equity Index|EQUITY|**ED**|
||Female literacy||INDEX||

# **_Analysis of EDI status of states_**

Of the 19 major states considered for this analysis, Himachal Pradesh (HP) has the best input-related indices (especially the access- and teacher-related ones)<sup>10</sup> , while Kerala has the better equity and outcome-related index compared to all other states. Overall, HP, Kerala and Uttarakhand top the EDI rankings. As expected, Bihar, Jharkhand and Uttar Pradesh (UP) figured at the bottom of the EDI rankings. However, within each state, various districts vary widely in terms of EDIs, rendering _an overall state level EDI quite meaningless_ . For example in HP, the EDI of the best district is 0.77 while that of the worst district is 0.54. Similarly, within Bihar, the EDIs of the best and worst districts are 0.17 and 0.32, respectively. District level variations in EDI are the least in Kerala 9