rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

Acemoglu, D., 2009. In: Introduction to Modern Economic Growth. Princeton University Press, Princeton.

Aghion, P., Bloom, N., Blundell, R., Griffith, R., Howitt, P. (2005). Competition and innovation: An inverted U-relationship. Quarterly Journal of Economics 120, 701-728.

<mark>Aghion, Philippe, Philippe Askenazy, Nicolas Berman, Gilbert Cette, and Laurent Eymard. 2012. “Credit Constraints and the Cyclicality of R&D Investment: Evidence from France.” Journal of the European Economic Association 10, no. 5: 1001–1024.</mark> Aghion, P.; Blundell, R.; Griffith, R.; Howitt, P. and Prantl, S., 2009, ‘The Effects of Entry on Incumbent Innovation and Productivity,’ _Review of Economics and Statistics_ , 91, pp. 20–32.

Alam, Ashraful Moshfique Uddin, and Hassan Yazdifar (2019). “Institutional determinants of R&D investment: Evidence from emerging markets.” Technological Forecasting & Social Change 138: 34-44.

Amin, M. and Y. C. Soh (2020). Does Greater Regulatory Burden Lead to Higher Corruption: Evidence Using Firm-level Survey Data for Developing Countries. Forthcoming _World Bank Economic Review_ . <u><mark>https://doi.org/10.1093/wber/lhaa007</mark></u> <mark>Aterido, R., Hallward-Driemeier, M., & Pages, C. (2011). Big Constraints to Small Firms' Growth? Business Environment and Employment Growth Across Firms.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>, 59 (3): 609 – 647.</mark> Ayyagari, Meghana, Asli Demirguc-Kunt, and Vojislav Maksimovic. (2011). Small vs. Young Firms Across the World. Policy Research Working Paper 5631, World Bank, Washington, DC.

Amiti, M. and Khandelwal, A., 2013, ‘Import Competition and Quality Upgrading,’ _Review of Economics and Statistics_ , 95, pp. 476–490.

Arrow, K. (1962). Economic welfare and the allocation of resources for invention. In Nelson, R.R. (Ed.). The rate and direction of inventive activity. Princeton University Press. Princeton, NJ.

Audretsch DB (1995) Innovation, Growth and Survival. Int J Ind Organ 13(4):441–457. https://doi. org/10.1016/0167-7187(95)00499-8 <mark>Barge-Gil, Andrés and López, Alberto, (2014), R&D determinants: Accounting for the differences between research and development,</mark> _<mark>Research Policy</mark>_ <mark>, 43, issue 9, p. 1634-1648.</mark> Baron J.N, Hannan M.T, Burton M.D (2002) Labor Pains: Change in Organizational Models and Employee Turnover in Young, High-Tech Firms. Am J Sociol 106(4):960–1012. <u>https://doi.org/10.1086/320296</u> 35
transmission. Generation tends to be competitive at the national level while distribution is not. We select a set of the main international actors according to the numbers of countries where these firms have activity but also with respect to the numbers of own MW installed for generation and customers covered for distribution. This choice is partially based on the information available from the Annual reports of the International Energy Agency and on information provided by credit rating agencies in their regular reports and information most commonly available on the web site of the main actors of the sector. In most of the cases, the database source for each firm is its own webpage.

Note that in generation we rely on installed capacity instead of on production to compute the HHI for two reasons. First, it captures the potential effect of concentration since a firm can produce up to capacity.<sup>10</sup> Second, many firms do not publish production in their public reports and the sample size would have been much smaller.

# a. **Electricity Generation**

For generation, we consider a set of 16 firms present in 57countries (19 OECD and 38 non OECD) and credited with 16% of the global market. The set of firms is composed of: American Electric Power (AEP), AES Corporation, CLP Group, Duke Energy, E.ON, Electricité de France (EDF), Electricidade de Portugal (EDP), ENDESA, ENTERGY, Union Fenosa (UF), Hong Kong Electric Group (HEG), Intergen, Public Service Enterprise Group (PSEG), Reliant Energy Inc., Southern Energy Company and Tractabel Power.<sup>11</sup> At the global level, Table 2 generally confirms the expectations, although with a couple of surprises. Indeed, Table 2 shows that, as expected, this is not an overly concentrated market at least globally. In other words, generation is a competitive sector in general-although EDF and E.ON have a significantly larger market share than the others, their lead is not strong enough to distort competition in this market at the global level.

> 10 Note however that capacity can be considered as a different product in energy market

> 11 E.on has subsidiaries LG&E and Powergen. Intergen is a group formed from equal participation of Shell and Bechtel. Tractabel Power belongs to the Suez Group.

10
Table 14: P75/P25 ratio and Gini Index for the quality of jobs

||(1)<br>P75/P25|(2)<br>Gini|
|---|---|---|
|2007|3.42|0.38|
|2008|3.33|0.39|
|2009|3.22|0.37|
|2011|3.21|0.36|
|2012|3.25|0.36|
|2013|3.23|0.37|
|2014|3.27|0.38|
|2015|3.16|0.36|
|2016|3.27|0.37|
|2017|3.38|0.38|
|2018|3.28|0.38|
|2019|3.47|0.38|
|2020|3.54|0.38|
|2021|3.40|0.37|

_Notes:_ Based on data from EH 2007-2021.

Another inequality indicator that we consider is the Gini Index, a commonly used measure to analyze income inequality across the population. This indicator has remained relatively stable since 2007, consistently hovering around 0.38. Even during the pandemic, the inequality in job quality, as measured by the Gini Index, remained at levels similar to those reported since 2017. However, in 2021, the Gini index decreased by 0.01 point, indicating a slight improvement in reducing job quality inequality (evidenced as well by the P75/P25 ratio). By analyzing these two inequality indicators, it becomes evident that not all jobs offer the same labor conditions, and they are far from all workers. Despite the expectation that inequality would be greater given the average values reported over the years, the Gini Index did not indicate a high level of inequality. Nonetheless, it is clear that labor conditions still fall short of guaranteeing good quality jobs for all workers.

34
**<u>Table 6: Output and input prices and cash flow</u>**

||<br>**Total **|<br>**Center **|<br>**North **|**South **|**East**|**West**|**<50**|**50-120**|**120-500**|**>500**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Panel A: 2021**|||||||||||
|Output value (US$/ha)|872.0|903.7|815.1|1,051.6|661.3|962.4|704.1|866.3|907.2|1,084.6|
|Input cost (US$/ha)|154.03|153.04|153.80|200.92|124.44|166.98|110.96|140.36|189.67|261.76|
|Net revenue (US$/ha)|670.4|706.3|656.5|790.0|506.2|735.1|563.7|710.9|692.5|778.2|
|Sold anything in market|0.94|0.94|0.97|0.95|0.96|0.90|0.84|0.96|0.98|1.00|
|... if yes, % of value|0.83|0.84|0.85|0.81|0.83|0.80|0.83|0.85|0.85|0.77|
|Cash flow (US$/ha)|432.2|487.2|376.1|532.9|331.7|276.1|401.5|431.3|442.6|481.6|
|% w. negative cash flow|0.10|0.09|0.09|0.15|0.09|0.17|0.12|0.08|0.11|0.13|
|**Panel B: 2022**|||||||||||
|Output value (US$/ha)|571.3|595.7|498.1|680.2|384.5|738.6|471.4|565.8|591.3|704.4|
|... at 2021 price ($/ha)|704.8|735.1|688.9|864.8|468.2|855.9|576.2|698.9|732.3|870.0|
|Input cost (US$/ha)|183.61|187.02|171.19|226.47|153.76|187.36|149.05|169.51|222.21|260.91|
|... real input value ($/ha)|94.88|94.04|81.83|124.50|82.42|97.36|77.08|87.12|115.63|134.75|
|Net revenue (US$/ha)|345.9|363.5|311.5|417.8|216.6|496.0|294.2|379.6|345.3|368.0|
|Sold anything in market|0.77|0.78|0.76|0.75|0.80|0.71|0.58|0.80|0.85|0.89|
|... if yes, % of value|0.57|0.57|0.57|0.58|0.59|0.50|0.69|0.55|0.57|0.51|
|Cash flow (US$/ha)|-25.6|-30.3|-18.9|-36.2|-6.6|-38.3|89.1|8.1|-99.5|-188.6|
|% w. negative cash flow|0.46|0.43|0.46|0.51|0.47|0.51|0.40|0.43|0.50|0.56|
|No. ofobs. (farms)|1,714|852|213|367|89|192|413|632|419|250|

_Source:_ Own computation from MAPF/WB/EU small producer survey _Note:_ Cash flow is defined as the value of output sold minus cost of purchased inputs 25
8 Our research question is, “Are there design elements and other factors of policy-based loans which are tightly linked with success in achieving the intended development outcomes?” It should be emphasized that we do not seek to investigate the long-term or ultimate impact of development policy lending. Our focus is narrower. On the maintained assumption that the government, together with the World Bank’s teams, has identified the key areas for reform and the key results, we examine the factors that are associated with success in achieving these intended results.

# **_3.1 Data_**

The data sources are: (i) records maintained by the Operation Policy and Country Services unit of the World Bank and recently expanded for the purpose of this paper on all development policy lending (i.e., policy-based loans after the 2004 reform); (ii) the individual program documents of all development policy loans; (iii) the ICRs of each loan; (iv) the IEG validation of ICR ratings; (v) data extracted from the World Bank electronic record system; (vi) data on Country Policy and Institutional Assessment (CPIA) of the World Bank; and (vii) the World Bank database for the macroeconomic variables. There are 312 operations in the sample, spread across 92 countries and disbursing US$57.9 billion in total.

# **_3.2 Dependent variable_**

As in most of the related research, we use the World Bank’s Independent Evaluation Group (IEG) validation of the ICR rating as the outcome or dependent variable. IEG produces one rating for each loan, and has since 2010 produced one for each programmatic series. We construct a Likert scale from the IEG rating (highly unsatisfactory = 0, unsatisfactory = 1, moderately unsatisfactory = 2, moderately satisfactory = 3, satisfactory = 4, and highly satisfactory = 5)<sup>7</sup> . Of the entire 312-strong sample, 8 were highly satisfactory, 132 satisfactory, 111 moderately satisfactory, 42 moderately unsatisfactory, and 19 unsatisfactory.

The distribution of outcome ratings by region is presented in Figure 1 (Annex 1). SubSaharan Africa is by far the most active region, with 106 operations, followed by Latin America and the Caribbean with 71. The majority of ratings – 78 percent – fall in the range “moderately

> 7 No operations were rated highly unsatisfactory.  Hence there is a five-point scale.
**<u>Table 2: Value of the poverty thresholds</u>**

||Nutrition poverty line|Basic needs poverty line|
|---|---|---|
|Dakar|396|835|
|Other towns|369|647|
|Rural areas|356|558|

Source: “Pauvreté et Structure Familiale” (PSF) survey, authors' calculations. Note: Poverty lines per urbanization strata in Senegal. Values are given in CFA francs. Numbers correspond to daily expenditures per adult equivalent. Equivalent scale: 0.5: children 0 to 14 years old; 1: adults.

38
|•<br>•<br>•|**Increases psychosocial welfare.**<br>**Increases take-up of subsidies and social**<br>**programs.**<br>**Increases the number and percentage of**<br>**workers**<br>**in**<br>**the**<br>**household,**<br>**and**<br>**the**<br>**employment of household head.**|2003-2006|Hoces de la Guardia,<br>Hojman and Larrañaga<br>(2011)|
|---|---|---|---|
|•|**Reduces poverty and indigence in rural**<br>**areas.**|||
|•<br>•|**Increases use of health care system and**<br>**participation in programs of training and**<br>**intermediation for labor.**<br>**Positive effect along beneficiaries’ self-**<br>**esteem and self-efficacy dimensions.**|2003-2006|Galasso and Carneiro<br>(2008)|
|•<br>•<br>•|**Largest impacts for families that were**<br>**previously not accessing services.**<br>**Larger impact when the expansion of the**<br>**supply of social services is effectively**<br>**implemented.**<br>**Impact on employment outcomes only when**<br>**met by an increase in the supply of training**<br>**and employmentprograms.**|Cohorts participating<br>between 2002-2006;<br>impact measured up to<br>2009|Carneiro, Galasso and<br>Ginja (2014)|

Larrañaga, Contreras and Ruiz (2009) study short term effects using administrative data. They also find positive but small effects in terms of employment and housing after 2 years of accompaniment by the intervention. Nevertheless, they also measure a small reduction in net income.

Regarding the medium-term evaluations, Galasso and Carneiro (2008) look at the impacts of the intervention for the years 2003-2006. They find a reduction in poverty in rural areas, which is consistent with Perticara’s findings. In urban areas _Chile Solidario_ has positive effects over access to services and participation in job training programs. They also find positive results on psychosocial dimensions.

Under a five year period to evaluate impacts, Carneiro, Galasso and Ginja (2014) find that the intervention successfully increases participation in social programs. Impacts are however mainly concentrated on families who were not accessing services before _Chile Solidario_ . The intervention tackled therefore demand side constraints (information, transaction costs, or emotional costs) for households previously disconnected from the

> 7 Autonomous income is defined as all the payments received by the household as a result of the possession of productive factors. It includes wages, earnings from independent labor, self-provision of goods produced by the household, rents, interests and pensions.

36
- 15 -

digitized and converted to a uniform scale using a microcomputer.!1 Based on the corrected perimeter dimensions, a computer program then calculated the groundfloor area of each building. The total area of multi-story buildings was calculated by multiplying the ground floor area by the number of floors, as determined during the field survey.

3.14 Data from the field survey and floor area calculations were then merged and compared with the property records currently in use by the assessor. Tnis revealed two categories of discrepancies: (i) unrecorded expansions--or changes in use--of buildings already on the assessment rolls, and (ii) newly constructed buildings absent from the assessment rolls. In the former case, the revised data was used to calculate a new assessment without further field - investigation. IPLAN assumed that all other information on the property recordthe construction characteristics used for valuation, the ownership data used for billing--i.e., was correct.

3.15 In the case of newly constructed buildings, the property record information had to be acquired.!' IPLAN initially attempted to obtain this information through the mail: letters requesting data on ownership and building characteristics were mailed to the 60,000 new buildings discovered by the program. Only 12,000 responses were received, of which only 8,400 were usable. IPLAN now intends to follow up with a more stridently worded mailing and will provide property owners with assistance in filling out data sheets through the branch offices of the municipal governments. Where this fails, IPLAN will be forced to send a staff person to collect the required data.

# B. Valuation Reforms

3.16 IPLAN began work on a new calculation of unit costs in 1986. Uniquely among Brazilian municipalities, Rio does not value land and buildings separately when valuing built-upon property. Instead, a single factor--value per square meter of built area--or "Vu" is calculated for each neighborhood. Individual valuations are derived by multiplying the floor area of a building by the Vu for its neighborhood and applying three adjustment 'actors to reflect the building's age, its position on its lot and the relative diesirability of its location within the neighborhood. 3.17 To calculate new Vu's, IPIAN surveyed a sample of 1,200 properties currently on the market. (As in the rest of Brazil, official data on the price of actual transactions was assumed to be unreliable.) In middle- and higherincome areas, price information was taken from newspaper advertisements. Follow_fl/ The scale of aerial photographs will vary as the altitude of the plane and the elevation of the terrain being photographed change. Standard micro-computer software was used to transform the digitized data to a homogeneous scale. This process also requires the input of control points at known distances from each other. In the Rio revaluation, control points were derived from orthographically rectified maps prepared during the 1975 general revaluation._ _2/ The construction of a new building on formerly vacant land was assumed to imply a change in ownership._
Table 6. Aggregate Increase in Expenditures on Gas and Energy Products

|Scena|rio 1<sup>1</sup>|Scena|rio 2<sup>1</sup>||
|---|---|---|---|---|
|Natural Gas Price<br>$110|/tcm|$160|/tcm||
|Quintile<br>1000s lei|% of GDP|1000s lei|% of GDP|Distribution|
|**_All connected households _**<sup>**_2_**</sup>|||||
|Poorest<br>88,637|0.3|236,366|0.7|10.2|
|Second<br>118,697|0.4|316,525|1.0|13.6|
|Third<br>153,824|0.5|410,199|1.3|17.6|
|Fourth<br>211,584|0.7|564,224|1.8|24.2|
|Richest<br>297,627|0.9|793,671|2.5|34.1|
|Total<br>872,949|2.7|2,327,863|7.3|100.0|
|**_B. Connected households with positive_**|**_expenditures _**<sup>**_3_**</sup>||||
|Poorest<br>30,535|0.1|81,426|0.3|6.9|
|Second<br>52,836|0.2|140,896|0.4|11.9|
|Third<br>76,053|0.2|202,808|0.6|17.1|
|Fourth<br>114,484|0.4|305,290|1.0|25.8|
|Richest<br>170,434|0.5|454,490|1.4|38.4|
|Total<br>444,336|1.4|1,184,896|3.7|100.0|

Source: HBS and Bank staff calculations.

1See Table 4 and main text for a description of the two scenarios.

2These simulations assume that connected households with missing expenditure data have energy expenditure levels equal to the _average_ expenditure levels of households with non-missing observations.

3These simulations exclude connected households with missing expenditure data.

household quintiles, to keep them as well off as they were prior to the energy price increases.<sup>16</sup> 16 Table 5 presents two sets of estimates: The first assumes that connected households with missing expenditure data are spending the average amount of expenditures on energy. The second excludes households with missing expenditure data. Clearly these are two extreme assumptions. The first set of estimates is arguably upward biased because connected households are not necessarily consuming positive amounts of energy or may not be paying for the energy they consume. On the other hand, the second set of estimates could be downward biased because missing expenditure data may be due to any number of factors, including infrequency of payments, inability to identify energy product-specific expenditures, or the simple failure to report such expenditures in the HBS. The high collection rates across energy products 14
For each of the 14 skills, our baseline survey included one self-report scale and one behavioral measure: either a situational judgment test (SJT, for nine skills) or a taskbased measure (for five skills). The SES measures were rigorously developed over several years and validated in three Sub-Saharan African countries (Das et al., 2024; Clerkin et al., 2024). Among the self-report scales, five skills are measured using original items which were developed by the measurement team based on theory, and nine are adapted from existing scales with the addition of selected original items.<sup>4</sup> For each skill, the self-report scale includes six to 12 items and utilizes a five-point Likert response scale.

Turning to the behavioral measures, the measures of negotiation and perseverance are based on existing measures, and the remaining measures are original, designed by the measurement team based on theory. The SJTs, used for nine skills, each involve two to three scenarios. Each scenario is followed by a list of several “good” actions, associated with the successful use of a skill, and a few “poor” actions, associated with the poor use of a skill. Individuals are then asked their likelihood of taking each action. The scenarios were designed to examine SES in the context of economic empowerment, and to constrain the context in which individuals reported their likely behaviors in order to improve the comparability of responses from individuals with differing socioeconomic and demographic backgrounds.<sup>5</sup> For example, one SJT examining _problem solving and decision-making_ presented a scenario in which the individual owns a convenient store and profits have gone down because the price of lotion has increased. The individual is given some possible solutions and then asked how likely they are to “Calculate the effect of each solution on your profits” or “Decide which of these solutions to do, based on your gut feeling”. The former is considered a “good” action associated with the use of _problem solving and decision-making_ skills, while the latter

> 4The sources for these items can be found in Das et al. (2024), but include influential papers such as Schutte et al. (1998), Schwarzer and Jerusalem (1995), Duckworth and Quinn (2009), and Frese et al. (1997). 5Scenarios and responses were developed using critical incident sourcing and surveys of possible responses as recommended by Cabrera and Nguyen (2001). The list of actions following each scenario mirrors the content of the self-reported scales, such that the included definitions are as similar as possible. Measures utilize an adapted format that allows them to be administered verbally while minimizing cognitive load for respondents.

7
- d) The clarity of the writing style of the report – ie how well the report conveyed its message;  and

- e) The amount of information in the report.

- An index averaging the five variables a) – e) was statistically significant at the 95% level with a t-value of 2.46.  The coefficient estimate implied an increase in the combined quality rating from 5 to 7.5 is associated with a reduction in interest-rate surprises of 13 basis points and an increase from 7.5 to 10 (the maximum possible) of an additional 7 basis points<sup>63</sup> .

- The quality indices a) - e) were highly correlated with one another so that it was not possible to include more than one characteristic simultaneously in the equation.  However, they could be included individually.  All were correctly signed and significant at the 90% level or better.

- Very interestingly, the indicator with the highest t-value (5.1) was the _style_ variable – ie how clearly the report conveyed its message.  The _expertise_ variable had a t-value of 2.3 and the _convincingness_ variable a t-value of 2.1.

- Some highly ranked ICBs on the quality of their reports were relatively poorly ranked in terms of inflation surprises.  This was particularly noticeable for Chile and Thailand.  This suggests that it may take longer for developing countries to establish the credibility of their ICBs than for major developed countries<sup>64</sup> .

# **6.3.2 The Impact of ICB Transparency on Macro-economic Outcomes**

As yet, there are relatively few studies of this. However, Geraats op cit finds examples of several which suggest that greater transparency in information improves macro-economic performance<sup>65</sup> . Findings to date include

- (a) For an 87 country sample, increased information transparency is associated with lower average inflation rates (at least for countries without an exchange rate peg – where the issue is much less relevant. This result is cet par on a number of macro-economic and institutional variables.);  and

- (b) For a 63 country sample, increased information transparency is associated with lower variability of an inflation-output index.

Both the studies cited use the Fry et al index to measure information transparency.

> 63

> See Fracasso, Genberg and Wyplosz (2003), p.33 and Table 4.2.

> 64 See discussion by Fracasso, Genberg and Wyplosz op cit, p36.

> 65

> See Geraats op cit, p. F559 and her discussion of the studies (a) by Chortareas et al (2001) and

> (b) by Cecchetti and Krause.

50
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