rafmacalaba/datause-encoder-data huggingface dataset:
most of this information is provided voluntarily. This is true for information about the terms of the product (column 5) and fees and commissions (column 7).

# [Table 5A here]

Columns 1 and 2 of Table 5A report the total yield after one year that would accrue in the savings account under the assumption of “no usage” (column 1) and “usage” (column 2). “No usage” refers to an activity level with neither deposits nor withdrawals made for one year. In this case, without inactivity fees then the formula to calculate total yield coincides with the GAT. In column 2 “usage” refers to the activity level where average balances in the account are below the minimum allowed for 2 months. Columns 1 and 2 can be used to test Hypothesis 4 in Section 2, namely that all costumers will be offered similar products. Indeed, control auditors are offered accounts with a small negative total yield and experienced auditors are given roughly the same accounts. As expected, auditors with a preference for investment accounts are offered higher yields, so auditors are offered different products based on their preference but not based on their financial literacy. When usage fees are taken into account, it appears that experienced auditors (especially those that quote a low interest rate) are offered higher yielding accounts.

Columns 3 and 4 report the difference in total yields between the cheapest savings product that meets the needs of the customer that the institution could have offered and the product that was actually offered. We see that auditors are offered more expensive checking accounts by 2.8 percent or 3.2 percent depending on usage. Indeed, staff offered the basic account in only 2 of the 54 visits where a checking account was requested.<sup>24</sup>

# [TABLE 5B HERE]

Table 5B echoes again the findings of Table 5A. All auditors are offered similar products and yet there are large differences between the cheapest product available and that being offered to them. Without usage costs the difference in the total cost of credit is 32.6 percent and 43 percent once usage costs are included.

# [TABLE 6 HERE]

The last section of the questionnaire included a set of subjective questions about the auditor’s perceptions of the service provided by the staff. Table 6 reports these perceptions for the non-professional credit auditors.<sup>25</sup> In about 30 percent of the visits the control auditors thought that the staff was kind and that the information provided was clear. Interestingly, none of the control auditors would acquire the product. Experienced shoppers provide higher ratings of the staff performance and would acquire the credit

> 24 In a very different context Mowl and Boudot (2014) implement a similar audit study to ours in India where auditors were trained to actively ask for a mandated savings account. Even with a more proactive auditor profile, most banks refused to market the mandated accounts to eligible customers.

> 25 Results for the professional savings auditors are available but less relevant since the number of auditors is small and the fact that they are professional suggests that they may be less influenced by the staff.

19
-7-

Table 2 Selected bankina. system indicators (1974-83)

|1974|1975|1976|1977|1978|1979|1980|1981|1982|<br>1983|
|---|---|---|---|---|---|---|---|---|---|
|Lending interest rates(%)a|15.9|64.2|57.1|42.3|16.9|12.2|38.8|35.1|<br>15.9|
|Spreads<br>(%)b<br>|37.6|46.1|30.1|13.6|11.4|6.8|7.9|10.2|<br>11.2|
|Return on equity(%)||N.A.|N.A.|14.6|13.2|14.9|9.1|-2.7|<br>2.8|
|Foreign liabilities/total||||||||||
|liabilities<br>(%)||N.A.|N.A.|14.4|17.1|20.4|28.5|35.8|<br>25.6|
|Total liabilities/||||||||||
|capital & reserves||N.A.|N.A.|8.1|9.3|11.4|11.9|14.2|<br>16.9|
|Growth in total loans &||||||||||
|investments (%)C||N.A.|N.A.|N.A.|33.8|51.7|16.5|25.2|<br>13.1|
|Past due loans/total||||||||||
|loans(%)||N.A.|N.A.|0.0|1.6|1.2|2.3|4.1|<br>8.4|
|Risky loans sold to the<br>||||||||||
|Central Bank/total loans(%)||-|-|-|-|-|-|4.1|<br>10.0|
|Risky loans sold+past due||||||||||
|loans/capital & reserve(%)||-|-|0.0|10.8|10.5|22.4|78.8|158.1|

Source: Central Bank Bulletin and Superintendency of Banks (Informacion Financie'a).

a. Annual real interest rate charged for short-term loans (c-ompounded monthly). b. Gross spread: Difference between short-term lending and deposit rates. c. In real terms adjusted by CPI.
# **Figure 3 Number of Export and Import Procedures**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>East Asia & Pacific Europe & Central Latin America & Middle East & South Asia Sub-Saharan Africa OECD<br>Asia Caribbean North Africa<br>Documents for export (number) Documents for import (number)<br><!-- End of picture text --> _Source_ : Doing Business.

The World Bank (2008) Doing Business dataset reports procedural requirements for exporting and importing a standardized cargo of goods by ocean transport. Figure 3 shows the average number of export and import procedures across regions. South Asia has the highest number of export and import procedures, closely followed by SubSaharan Africa.

# **_Product Standards and Technical Regulations_**

Product standards and technical regulations can have a dual impact on trade costs. Meeting product standards can involve additional variable or fixed costs on exporters that need to alter production processes to adapt products to regulations in the importing country. Moreover, product certification necessary to demonstrate compliance with standards can involve additional costs for the exporters in multiple markets. However, product standards and technical regulations in the importing country can reduce exporter’s information costs if they convey valuable information as to consumer tastes or industry needs in the importing country. In the absence of standards, such information would be costly for the exporting firm to collect. Accordingly, standardization in sectors where information costs are important could help reduce trade costs and promote trade.

The net impact of product standards on trade depends on the relative magnitude of the effects. The empirical evidence on these issues is limited, primarily due to the 11
Political Institutions and Human Development _Does Democracy Fulfill its ’Constructive’ and ’Instrumental’ Role?_ Sebastian Vollmer<sup>_∗_</sup> Maria Ziegler<sup>_†_</sup> This draft: January 2009

## **Abstract**

Institutions are a major field of interest in the study of development processes. We contribute to this discussion concentrating our research on political institutions and their effect on the non-income dimensions of human development. First, we elaborate a theoretical argument why and under what conditions democracies compared to autocratic political systems might perform better with regards to the provision of public goods. Due to higher redistributive concerns matched to the needs of the population democracies should show a higher level of human development. In the following we analyze whether our theoretical expectations are supported by empirical facts. We perform a static panel analysis over the period of 1970 to 2003. The model confirms that living in a democratic system positively affects human development measured by life expectancy and literacy rates even controlling for GDP. By analyzing interaction effects we find that the performance of democracy is rather independent of the circumstances. However, democracy leads to more redistribution in favor of health provision in more unequal societies.

# **JEL classification:** I10, I20, H11

**Keywords:** human development, democracy, political institutions, life expectancy, literacy, panel analysis.

**Acknowledgements** The authors would like to thank participants of the American Economic Association’s annual conference 2009 in San Francisco, Stephan Klasen, Mark Dincecco, Dierk Herzer, Tatyana Krivobokova, Inmaculada Mart ́ınez-Zarzoso, Stefan Sperlich, Walter Zucchini, and participants of the International Economic Association’s conference 2008 in Istanbul for their helpful comments. Maria Ziegler acknowledges financial support from the German National Merit Foundation.

> _∗_ University of G ̈ottingen, Platz der G ̈ottinger Sieben 3, 37073 G ̈ottingen. Phone: +49551398171, Fax: +49551397302, Email: svollme@uni-goettingen.de.

> _†_ University of G ̈ottingen, Platz der G ̈ottinger Sieben 3, 37073 G ̈ottingen. Phone: +49551397242, Fax: +49551397302, Email: mziegle@uni-goettingen.de.

1
# Table 7: Effects of School Inputs on Educational Level: Regression Results for Adults

|Birth cohort:<br>Variable|1925-1939<br>Estimated<br>coefficient|1940-1944<br>Estimated<br>coefficient|1945-1949<br>Estimated<br>coefficient|1950-1954<br>Estimated<br>coefficient|1955-1959<br>Estimated<br>coefficient|1960-1966<br>Estimated<br> <br>coefficient|
|---|---|---|---|---|---|---|
|A. FEHALES1/|||||||
|HAD READING AND/OR MATH BOOK(S)|0.636**|0.692*|0.891**|-0.065|1.145**|0.725**|
|NUMBER OF GRADES IN SCHOOL|0.466**|0.282|0.523*|0.493|0.838**|1.138**|
|SCHOOL HAD FURNITURE|0.410|0.704|0.912*|-0.329|-0.484|0.223|
|SCHOOL SERVED FREE FOOD|-0.456|0.029|-0.330|0.723**|-0.012|-0.054|
|NUMBER OF TEACHERS IN SCHOOL|||||||
|I- 3 TEACHERS|0.290*|0.058|0.162|0.510*k|0.217|-0.035|
|4 - 6|0.011|0.584|0.118|0.088|0.117|0.039|
|6 +|0.031|0.149|0.034|0.106*|0.076|0.055|
|LAMBDA|-0.180|-0.192|-1.855**|-2.115**|-E.217**|-6.806|
|ADJUSTED R2|0.482|0.533|0.502|0.580|0.506|0.523|
|B. MALES|||||||
|HAD READING AND/OR MATH BOOK(S)|0.542**|0.249|0.307|0.583**|0.918**|0.31C|
|NUMBER OF GRADES IV SCHOOL|0.565**|0.848**|0.955**|1.198**|3.214**|1.342**|
|SCHOOL HAD FURNITURE|0.425|0.740|0.342|0.627|0.611|-0.139|
|SCHOOL SERVED FREE FOOD|-0.191|0.523|0.076|0.188|-0.262|-0.034|
|NUMBER OF TEACHERS IN SCHOOL|||||||
|1-<br>3 TEACHERS|0.212|0.036|0.273|0.172|0.480**|0.411**|
|4 - 6|0.113|0.134|0.089|0.109|-0.039*|-0.010**|
|6+|0.104|0.062|j.048|0.062|0.018**|0.025*|
|LAMBDA|0.296|-1.307|-3.002**|-3.206*|0.533|-5.688**|
|ADJUSTED R2|0.513|0.521|0.472|0.486|0.460|0.465|

j/ Other included variables are the same as in tables 5 and 6.

Ni
# Transition to Clean Capital, Irreversible Investment and Stranded Assets

Julie Rozenberg<sup>1, 2,</sup><sup>_∗_</sup> , Adrien Vogt-Schilb<sup>1, 2</sup> , Stephane Hallegatte<sup>2</sup>

> _1CIRED, 45bis avenue de la Belle Gabrielle, 94736 Nogent-sur-Marne, France._

> _2The World Bank, Sustainable Development Network, Washington D.C., USA_

_Keywords:_ intergenerational equity, energy efficiency standards, mothballing, social and political acceptability, climate mitigation policy _JEL classification:_ L50, O44, Q52, Q54, Q58 For the past centuries, economic growth has involved the accumulation of fossil-fueled capital that releases greenhouse gases (GHG) in the atmosphere (e.g. coal power plants, gasoline-fueled cars). From a global welfare perspective, this accumulation of polluting capital is sub-optimal because it does not internalize the future economic damages caused by climate change. Stabilizing climate change requires near zero emissions in the long-run, and therefore implies stopping the accumulation of fossil-fueled capital. Future economic growth thus has to rely on clean capital.

In theory, the optimal policy to trigger such a large-scale transition from polluting to clean capital is – in the absence of any other market failure – a Pigouvian price on GHG emissions, for instance a carbon tax (Nordhaus, 1991). However, governments have been timid about the carbon price<sup>1</sup> and have relied instead on instruments that redirect investment towards clean capital, such as energy efficiency standards on new capital (such as the corporate average fuel economy (CAFE) standards in the automobile industry, efficiency standards for new buildings, or feebate programs that tax energy-inefficient equipment and subsidize energy-efficient equipment (IEA, 2014).

In this paper, we investigate how the transition to clean capital is modified when using such _investment-based_ instruments instead of a carbon price. We model the accumulation of productive clean capital to replace polluting capital, as suggested by Ploeg and Withagen (1991), and we focus on the effect of the irreversibility of past investments on the transition. The transition to clean capital has been studied before, mostly through the lens directed technical change, focusing on the interaction between pollution and knowledge spillovers externalities (e.g., Gerlagh et al., 2009; Grimaud et al., 2011; Acemoglu et al., 2012).

> _∗_ Corresponding author _Email addresses:_ `rozenberg@centre-cired.fr` (Julie Rozenberg ), `vogt@centre-cired.fr` (Adrien Vogt-Schilb), `shallegatte@worldbank.org` (Stephane Hallegatte)

> 1Countries that price emissions at a national level are currently limited to the members of the European Union, Switzerland, Kazakhstan, Australia and New-Zealand. Combined with regional or sub-national initiatives, total priced emissions represent less than 10% of global emissions.
Policy Research Working Paper 10765

# **Abstract**

By 2023, more than 108 million people were forcibly displaced worldwide, with children under 18 constituting 45 percent. This study examines the human development gaps between forcibly displaced migrant and host children and adolescents, focusing on cognitive and socioemotional skills and physical and mental health. The study also explores how access to services and regularization programs are correlated with these disparities using a unique and comprehensive longitudinal data set of around 2,500 Venezuelan migrant and Colombian host children and adolescents, ages 5 to 17 and living in Medellín, Colombia. The findings reveal significant developmental delays among migrant children in physical and cognitive development, but interestingly, no significant differences in socioemotional and mental health outcomes. The research underscores how the availability of public services and engagement in regularization programs are crucial for mitigating these developmental gaps.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sandrarozo@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
revenues, shared taxes (including land and building and personal income tax), and (partial) natural resource revenues SDA.

# **_Lumpsum Allocations_**

8. The sum of _lumpsum allocations_ were 6.2 trillion for local government and 1.2 trillion for provinces. Local governments were to receive 18.51 billion per local government (or 31,875 per head). Provinces received 46.1 billion per province or 10,063 Rps per capita. The introduction of a lump sum figure creates incentives for the formation of new regions, especially in the case of smaller provinces (Fitrani, et al. 2005).

- (2). LSi = (LS-weight * Total DAU) / N

# **_Proportional Wage Bill Allocation_**

9. For the _proportional wage allocation,_ 31.1 trillion of the local government and 2.1 trillion of the provincial DAU were allocated by the relative percentage share of each local government or province in the total actual wage bill. The DAU 2002 base data suggests that the total annualized wage bill in 2001 was 40.5 trillion for 336 local governments and 6.6 trillion for 30 provinces.

- (3). PWBAi = (Wage Billi/Total Wage Bill) * (Total DAU PWBA)

# **_Fiscal Gap Formula Allocation_**

10. The _fiscal gap formula amount_ incorporates measures for fiscal capacity and expenditure needs to arrive at an allocation based on fiscal gap (FGi).

- (4). FGi = ENi - FCi

11. Depending on the relationship between the expenditure needs and fiscal capacity indicators, fiscal capacity can be negative. The Indonesian formulation imposed the condition that no region show a negative FG (and hence have STX and SDA transfers reduced).

12. _Fiscal capacity_ includes estimated own revenues (PADi*), realized tax shares (SXTi includes PBB, BPHTB, PPh shares for each region), and 75 % of realized natural resource revenue shares (SDAi). Own revenues were estimated from a regression of own revenues against region income for manufacturing and services.<sup>28</sup> This formulation seeks to avoid penalizing regions own tax effort that collect more than some average milestone of own revenues (e.g., through varying rates or tax collection effort).

-

- (5). PADi = 2.357 + 0.00957*RGDPSIi

> 28 The local government regression was run for annualized estimated FY 2000 PAD receipts per region. Provincial own-revenue estimates (PAD) were calculated as 0.87 percent of provincial manufacturing and services GDP.  The constant of the provincial regression was dropped, as it was negative.

30
|Winter prec*temp|(2.79)***<br>15.93<br>(2.44)**|(8.18)***<br>-0.00023<br>(0.51)|||
|---|---|---|---|---|
|Share of land areas with clay<br>soil|5,477<br>(8.07)***|0.556<br>(12.01)***|5,345<br>(8.55)***|0.423<br>(9.88)***|
|Share of land areas with silt<br>soil|3,412<br>(6.02)***|0.300<br>(7.76)***|3,259<br>(5.87)***|0.311<br>(8.18)***|
|Plain (1=Yes; 0=No)|727<br>(2.06)**|0.171<br>(7.11)***|975<br>(2.83)***|0.196<br>(8.30)***|
|Road (1=Yes; 0=No)|2,771<br>(3.86)***|0.108<br>(2.21)**|2,584<br>(3.64)***|0.103<br>(2.11)**|
|Distance to township<br>government|-32.02<br>(1.07)|-0.001<br>(0.69)|-30.89<br>(1.04)|0.002<br>(1.11)|
|Share of irrigated areas in<br>village|17.21<br>(4.01)***|0.00362<br>(12.36)***|15.68<br>(3.68)***|0.003<br>(11.82)***|
|If participate production<br>association<br>(1=Yes; 0=No)|2,747<br>(3.86)***|<br>0.168<br>(3.45)***|2,601<br>(3.67)***|0.137<br>(2.82)***|
|Share of labor without<br>receiving education|0.364<br>(0.05)|-0.00073<br>(1.48)|0.517<br>(0.07)|-0.001<br>(1.88)*|
|Cultivated land area per<br>household|-1,992<br>(11.66)***|-0.310<br>(26.55)***|-1,925<br>(11.35)***|-0.303<br>(26.09)***|
|Constant|41,700<br>(4.05)***|10.41<br>(14.81)***|22,465<br>(2.97)***|11.12<br>(21.44)***|
|Observations|8405|8405|8405|8405|
|Adjusted R-squared|0.15|0.39|0.15|0.39|
|F-test|51.21|189.32|58.47|213.53|

Absolute value of t statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

37
_35_ What was the impact of diesel prce increase on demand for diesel in tho industrl sactorl This would depend on both the size ofoutput loss, if any, and the ease of substitution fom diesel to other enorgy inputs In th present context substitution possibilities ar primarily between diesel and electricity. Diesel is used in powr genaon equipment and demand for diesel for back-up power-generation by industrial consumes in developiug couries ofte arises due to the low reliability of electric power supplies. Erratic power supples, due to nsufficient capacity during system peak hours, impose substantial outage costs in ten of lss ofproduction, dm to dectic motors and loss of working hours for labor. Bwak-u **p** ower geeraion is ofte used to improve the quality of electricity supplies for production. Producers substitute diesel with elcticity during worldng shifts such that electricity-using production facilities ao operated during off-peak hours. Also, substtution between diesel and electricity in the short- and medium-tenn arises due to the ease of replacing smaR diesel engines with electric motors. In other words, one w^".;d expelt greater substitution of electricity for diesel in response to an increase in diesel prices.

Closer observation of the data reveals that prior to the diesel price change in 1983. the share of disel ir indhtria laery demand was over _65_ percent and it feli to under 50 percent by the end of 1986. In absolute Wrms, diesel consumption by the industrid sector between 1983 and 1986 fell by nearly 25 pecent as a result of a 28 percent increse in prices, implying an arc elasticity of -0.9. The relatively high asicdty of demand is quite reasonable given the substtution possibilities between diesel and eleciricity. During 1983-85, inerfel subsitution data in the industrial sector indicates that conmption and relative shares of **ectiity,** natr gas and coadcoke have incrased, while consumption and the share of diesel decreased. Fge 2.2 ilustates te dhanges in the structure of energy consumption in the industrial sector during te 1980s Th oer importat policy issue reated to increased diesel prices is the subsidy effect Figure 2.3 shows the difference betweem domestic and border prices (i- a percentage of domestic price) for diesel.

_KerosenePriceIncreases J984-85: TheHousehold Sector_ Oil and electricity are the main sources of commercad energy in the household sector. Ofthe oil products, kerosene is wed most often, while more recentdy fi'! use of electricity and LPG has increased. In the prest context, the inc in kerosene prices is directy relevant The two most important policy issues related to the kerosene price icease in 1984-85 are welfare consequences for households of changes in kerosene prices, and the subsidy effect of tiese price changes.
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