rafmacalaba/datause-encoder-data huggingface dataset:
Finally, Table 6 shows the same set of total survey implementation and technical assistance cost scenarios depicted in Table 5 but for 102, as opposed to 78, countries, which includes all 78 countries of interest plus 24 countries that are either data deprived or vulnerable to data deprivation according to Serajuddin et al. (2015). Raising the number of target countries by 30 percent, using our preferred implementation cost assignments, and assuming a uniform technical assistance provision cost of USD 540,000 per survey with an annual growth rate of 2.5 percent, the total survey implementation and technical assistance costs rise to USD 890 million and USD 334 million, respectively. These estimates are 29 percent higher than the preferred total survey implementation cost estimate of USD 692 million, and 32 percent higher than the preferred total technical assistance cost estimate of USD 253 million.

# **V. Conclusion**

While the evidence base for monitoring poverty and shared prosperity has been improving over time, challenges remain. To address these challenges, the World Bank is committed to support 78 IDA and IDA/IBRD countries as well as India to collect the multi-topic household survey data needed to estimate poverty and shared prosperity every three years between 2016 and 2030. This translates to conducting 390 surveys over a 15-year period. This paper estimates the cost of filling these gaps in household survey data to monitor the World Bank’s poverty and shared prosperity goals. Once collected based on international best practices, the resulting multi-topic data would capture not only the progress towards the Twin Goals but also the profiles of the poor, the multidimensionality of household and individual welfare, linkages between poverty and other socioeconomic outcomes and policies, and a host of SDG indicators.

Our analysis 1) uses unique country-level information on actual survey implementation and technical assistance costs, 2) is anchored in the recommendations of the World Bank Household Survey Strategy for Household Surveys regarding optimal questionnaire and sampling design, and 3) is anchored in the World Bank’s experience in supporting the NSOs in the production of multitopic household survey data through the Living Standards Measurement Study program and the Poverty and Equity Global Practice. The budget envelope for delivering on the ambitious agenda laid out by WBG President Jim Yong Kim is estimated at **USD 945 million** , of which USD 692 million is projected to cover the costs of household survey implementation across 78 countries, and USD 253 million is projected to cover the costs of direct technical assistance provided to NSOs. These estimates imply an annual allocation of 46.1 million for survey implementation and 16.9 million for technical assistance.

This is an estimate for overall resource requirements and is not indicative of costs for each specific survey since the cost environment for surveys can differ significantly across countries. Moreover, as mentioned earlier, these cost estimates may be subject to change as the database of countries 15
or final product costs, are excluded. For instance, in Figure 1, the cost estimation in this study is associated with only mechanization and baking technology.

<!-- Start of picture text --> Mechanization<br>Coal, lignite, sawdust,<br>Animal, diesel, Electricity firewood, ricke husk,<br>electricity<br>bagasse, briquette, LPG<br>Raw material 1<br>Raw material 2 Clay mining/Preparation Molding/Drying Baking/Firing Final product<br>Raw material 3 Clay Water Baking Technology<br>Preparation<br><!-- End of picture text -->

**Figure 1: The brick-making processes**

Economic analyses are conducted from multiple perspectives. First, the current production costs are compared by technologies and by provinces from a private (financial) perspective. This is followed by the comparison of costs from a social perspective. From the private perspective, the production costs include capital expenditures (e.g., costs of a kiln, land rental, and regulatory and compliance equipment costs), energy costs (e.g., coal, biomass, and electricity), labor costs, and the cost of materials (e.g., clay, water, and additives). The social production costs include environmental and health damage costs associated with brick production in addition to the private costs. The study also includes the economics of the substitution of coal with advanced biomass fuels for commonly used brick production technology (ZZK) and the production of alternative bricks. All these economic analyses use the information from the techno-economic assessment and the rapid survey conducted.

First, we estimated the private cost of producing clay-fired bricks in Nepal. The private cost of brick production ( ) for each province ( _i_ ) and each technology type ( _j_ ) in Nepal PP includes operational expenditures (ii,jj _OpEx_ ) and capital expenditures ( _CapEx_ ) for a particular year CC (tt) ( _t_ ) ( _Equation 1_ ).

7
**Figure 4: Comparative regional trends in residential electricity tariffs 1994/02**

<!-- Start of picture text --> 0.16<br>0.14<br>EAP<br>0.12<br>ECA<br>0.10<br>LAC<br>0.08<br>SA<br>0.06<br>0.04 OECD<br>0.02<br>0.00<br>1994 1996 1998 2000 2002<br>Sources: OECD 2004, ERRA 2004, EIA 2004, OLADE 2004, UN-ESCAP 2004.<br>Notes: Data drawn from 84 countries worldwide, broken down as follows OECD<br>(23), LAC (19), ECA(18), EAP (8), SAS (3)<br>Average residential electricity tariff (US$/kWh)<br><!-- End of picture text --> In order to provide a broader evaluation of cost recovery, the analysis will refer to a number of indicative ranges within which tariffs would need to fall in order to have a high probability of recovering either operation and maintenance costs or full capital costs. These ranges are based on discussion with international experts on electricity tariffs summarized in Table 4 below. Different thresholds are provided for residential and industrial customers to reflect the lower cost of service provision to the latter group.

**<u>Table 4: Thresholds used for evaluating cost recovery of electricity tariffs</u>**

||Residentialcustomers|Industrialcustomers|
|---|---|---|
|<US$0.04/kWh|Tariff<br>_insufficient_to cover basic<br>operating and maintenance costs|Tariff<br>_insufficient_<br>to<br>cover<br>basic<br>operating and maintenance costs.|
|>US$0.05/kWh||Tariffs likely to be making a significant<br>contribution towards capital costs, in<br>most types ofsystems.|
|>US$0.08/kWh|Tariffs likely to be making a significant<br>contribution towards capital costs, in<br>most types ofsystems.||

Source: Own elaboration Based on this framework, it is possible to assess the evolution in the absolute value of the average residential electricity tariff expressed in dollar terms. The average residential electricity tariff in the 19 Latin American countries considered rose from US$0.07 to US$0.10 per kilowatt-hour between 1990/96, falling back down to US$0.09 per kilowatthour by 2002. Correspondingly, the percentage of countries recovering some degree of capital costs rose from 32% to 68% between 1990/96, falling back down to 63% by 2002.

11
Changes in methods, as well as in content, can reduce over-time comparability of indicators. Admirably, WEF has tried to increase the response rate of its Executive Opinion Survey, to enhance accuracy by making the sample more representative.<sup>12</sup> However, progress on this front can affect apparent trends. Suppose executives with the strongest opinions are the most likely to respond, and that strong opinions tend to be unfavorable. An increase in the response rate from one year to the next would then reduce the negative bias, but the year-on-year _change_ would be biased toward showing improvement.

# **3. Composite Corruption Indexes**

There are at least three possible justifications for constructing a single corruption index from multiple, distinct sources of corruption indicators. The first motive emphasizes substantive content: individual indicators, or even several indicators from one source such as the BEEPS, may be defined too narrowly for certain purposes. For example, no matter how many corruption indicators one aggregates from the BEEPS, the resulting index still reflects only corrupt interactions between firms and public officials.

The second motive is to reduce measurement error. Given the obvious difficulties in measuring corruption, any one source may be highly inaccurate. However, if errors in measurement are largely independent across sources, the errors will tend to cancel out when data are aggregated from multiple sources.

The third motive is to cover a larger number of countries. No one source covers all countries. Some sources do not overlap at all in country coverage, for example the UNECA’s African Governance Indicators and Nations in Transit.

> 12 The mean number of responses per country increased from 84 in 2004 to 94 in 2005.

15
# **Table 7a: Selected Multilevel Model Estimation Results from Cambodia**

||||<br>**R**|**esults from**|**Models Wi**|**th Covariate**|**s**|||
|---|---|---|---|---|---|---|---|---|---|
||||**Variance**||||||**% of**|
||**Sum of**<br>**Variance**|**Rank**|<br>**of**<br>**Residual**|**ICC Total**|**Rank**|**ICC-EA**|**ICC-I**|**Rank**|<br>**ICC-I in**<br>**ICC Total**|
|**Individual Qx Module (Equation 2)**||||||||||
|land|3.958|1|3.184|0.196|6|0.132|0.064|9|32.60|
|labor|0.411|2|0.309|0.247|4|0.146|0.101|5|40.84|
|migration|0.396|3|0.337|0.149|9|0.085|0.064|8|42.99|
|time use|0.334|4|0.165|0.506|1|0.236|0.270|1|53.39|
|livestock|0.102|5|0.074|0.274|2|0.162|0.112|3|40.96|
|health|0.074|6|0.061|0.173|7|0.059|0.113|2|65.59|
|mobile phones|0.045|7|0.033|0.266|3|0.214|0.052|10|19.54|
|education|0.035|8|0.030|0.132|10|0.061|0.071|6|53.83|
|financial assets|0.030|9|0.026|0.151|8|0.081|0.069|7|46.01|
|durables roster|0.028|10|0.022|0.198|5|0.096|0.101|4|51.20|
|**Household Qx Module (Equation 3)**||||||||||
|non-farm enterprise|1.517|1|1.379|0.091|10|0.040|0.051|10|55.88|
|children elsewhere|1.097|2|1.008|0.081|11|0.034|0.048|11|58.73|
|food cons.|0.890|3|0.383|0.570|1|0.129|0.442|1|77.46|
|household roster|0.537|4|0.369|0.312|4|0.203|0.110|5|35.07|
|non-food cons.|0.253|5|0.133|0.474|2|0.105|0.369|2|77.82|
|land roster|0.157|6|0.112|0.287|5|0.161|0.126|4|43.99|
|housing|0.076|7|0.042|0.444|3|0.170|0.274|3|61.65|
|cover|0.024|8|0.020|0.157|7|0.053|0.104|6|66.27|
|livestock roster|0.014|9|0.011|0.195|6|0.119|0.077|9|39.23|
|consumer durables|0.009|10|0.008|0.139|8|0.058|0.080|7|57.91|
|apartment roster|0.002|11|0.002|0.124|9|0.045|0.079|8|63.77|
|**# of EAs**|**252**|||||||||
|**# of Interviewers**|**42**|||||||||

23
# **Table 2: Contagion, Distance and Social Media—News based Protests**

|Zero-Inflated Poisson||Ne|ws-basedprote|sts||
|---|---|---|---|---|---|
||Distanc|e Only|Distance*|Common Soci|al Media|
||||Social Med|ia Penetration|Threshold|
||||**> 10%**|**> 20%**|**> 30%**|
||[1]|[2]|[3]|[4]|[5]|
|News-based protests(t-1)|0.238***|0.160***|0.234***|0.374***|0.372***|
||(0.014)|(0.012)|(0.014)|(0.021)|(0.022)|
|Neighbor's simple news-based protests*(t-1)|0.098***<br>(0.034)|||||
|Inverse distance weighted news-based<br>protests*(t-1)||0.128<br>(0.152)||||
|Inverse-Distance-Common Social Media<br>weighted News-based protests*(t-1)|||0.889***<br>(0.137)|0.872***<br>(0.155)|0.912***<br>(0.156)|
|Constant|-1.461***|-1.80***|-2.079***|-2.152***|-2.14***|
||(0.177)|(0.321)|(0.277)|(0.255)|(0.258)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|
|Month fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|36,806|49,469|31,348|26,218|24,318|
|Number of countries|154|207|165|138|128|
|N_zero|8045|16469|9050|7273|6495|
|Chi-square|26983|23141|29855|17864|17386|
|Log likelihood|-16695|-22067|-12989|-10529|-9772|

Note: Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1). _News-based protests_ captures the number of articles mentioning street protests in a country as percentage of total number of articles covering the country. * denotes foreign _news-based protests._ In columns [3],[4],[5], the threshold for a country to be defined to have large. Facebook penetration is 10%, 20% and 30% respectively. Since Facebook was launched in 2004, data in these 3 columns are restricted from January 2004 to December 2019.

We examine the role of _Polity_ characteristics, _Corruption_ level and _Language_ in protest spillovers. They are found to play an insignificant role in the spillover of actual protests (Columns [1] to [3] of Table 3), except for the spillover of news-based protests where they have some importance (columns [4] to [6]).

11
# **3. Tanzanian Consumption Survey Experiment and Consumption Aggregate**

# _Survey Experiment_

Our survey experiment entailed fielding eight alternative consumption questionnaires randomly assigned to 4,000 households in Tanzania. The eight designs vary by method of data capture, level of respondent, length of reference period, number of items in the recall list, and nature of the cognitive task required of the respondent. They are summarized in Table 1. Modules 1-5 are recall designs and modules 6-8 are diaries. These eight designs were strategically selected to reflect the most common methods utilized. The alternative designs focus on variation in the measurement of food consumption and expenditure on select frequently purchased non-food items, and not on general non-food expenditure where, due to the infrequency of purchase, there is a much greater degree of design harmonization in practice. Food consumption includes the quantity consumed from three sources (purchases, home production, and gifts/payments) and, for purchases only, the corresponding value of the quantity (in Tanzania shillings). Modules 1 and 2 seek the consumption values for a long list of 58 commodities. In module 3, the subset list consists of the 17 most important food items that constitute, on average, 77 percent of food consumption expenditure in Tanzania based on the previous Household Budget Survey. When comparing consumption expenditure in module 3 with other modules, we scale up food expenditures for that module as is commonly done in practice. Module 4 is a collapsed list where the 58 food items are aggregated into 11 comprehensive categories.<sup>10</sup> Among the recall modules, module 5 deviates from a reporting of actual expenditure over a specified time period. Instead it asks for “usual” consumption, following a recommendation in Deaton and Grosh (2000), where households report the number of months in which the food item is usually consumed by the household, the quantity usually consumed, and usual expenditure in those months. These questions aim to measure permanent rather than transitory living standards, without interviewing the same households repeatedly throughout the year. Hence, module 5 introduces two key differences from the other recall modules: a longer time frame and a different cognitive task required of respondents. The longer time frame should allow averaging over short-term fluctuations in living standards, which are caused not just by seasonality but by any short-term idiosyncratic shocks occurring in the survey reference period. Therefore, in comparison with a series of short-period surveys, staggered evenly over

> 10 Since respondents often use local units for reporting (Capéau and Dercon, 2006), the survey teams also established conversion factors for these local units and surveyed local prices so that quantity and value information in diaries could be checked for outliers.
middle-income and lower middle income), regions, and infrastructure sector (i.e transport, telecom, electricity, and water and sanitation).

As expected, the investment requirements as a share of GDP are the highest in low-income countries (14.1%). This is followed by lower-middle income (3.4%), and upper-middle income (2.6%) countries. The sectors accounting for the greatest share of infrastructure investment requirements is electricity (39%). Transport is the second largest (31%), followed by telecommunications (23%). The Water and sanitation sector only account for 7% of the share.

**Table 4: Expected Annual Investment Requirements, 2014–20** (at 2011 prices)

||Ca<br>|pital<br>|Maint<br>|enance<br>|Tot<br>|al<br>|
|---|---|---|---|---|---|---|
||US$Mn|%GDP|US$Mn|%GDP|US$Mn|%GDP|
|_By income group_|||||||
|Low Income|176,217|7.1%|172,089|6.9%|348,306|14.1%|
|Lower Middle Income|35,821|1.1%|72,063|2.3%|107,884|3.4%|
|Upper Middle Income|160,251|2.0%|203,047|2.6%|363,298|2.6%|
|_Developing countries by region_|||||||
|SAR|159,074|7.8%|144,427|7.1%|303,501|14.9%|
|SSA|28,946|3.2%|27,108|3.0%|56,054|6.2%|
|EAP|115,897|2.0%|96,244|1.7%|212,140|3.7%|
|MENA|14,826|1.1%|32,244|2.5%|47,070|3.7%|
|LAC|65,828|1.7%|74,450|1.9%|140,277|3.6%|
|ECA|24,178|0.8%|36,267|1.1%|60,445|1.9%|
|_Developing countries by sector_|||||||
|Electricity|152,577|1.1%|167,754|1.2%|320,331|2.4%|
|Transport|76,201|0.6%|178,818|1.3%|255,019|1.9%|
|Telecoms|123,351|0.9%|63,755|0.5%|187,105|1.4%|
|W&S+WW|20,160|0.1%|36,873|0.3%|57,033|0.4%|
||||**_All developi_**|**_ng countries_**|819,488|6.0%|
||||**_High Incom_**|**_e countries_**|285,045|0.8%|
||||**_World_**||1,104,532|2.2%|

Source: Authors’ estimation.

Note: GDP percentage is calculated based on the projected GDP2015 by income level and by region. For sectors, we used average projected GDP2015 for developing countries.

Income level, regions and sectors are defined according to the World Bank’s World Development Indicators Report.

10
Voucher Funds in the Transition Economies 19 4.22 The Czech and Slovak model may be, an improvement over the German model. In Germany, the Supervisoy Board may not be able to adequately supervise and control the Mlanagement Board in ali cases for at least two reasns. First, because workers are represented on the Supervisory Board, managers and rpentivsof the large investors may be reluctant to discuss critical issues or to disclose key information to this board. Second, the Supervisory Board only meets a few times each year and thus may not give adequate attention to the detailed problems of the company. The weaknesses o)f the Supervisory Board in Gennany are illustrate by the recent finacial difficulties of Germany's 14th largest firm, MetaIlgeseflshaft. In this case, the Supervisory Board admitted that it was not aware of the financial problem of this company until it was almost bankrupL'<sup>2</sup> 4.23 Voucher funds in the Czech and Slovak have indicated a desire to play an active role in the maaeetof the company by choosing to be represented on the Management Board along with the managers of the company. In particular, given the need for major restructuring of enterprises in these countries, this active role by the voucher funmds should be encouraged. Thoughi mixing representatives of owners and managers on the Management Board may create some unforeseen difficulties, this type of board has long excisted in the United Sbtats where the Board of Diretor consists both of managers and representatives of shareholders.

# Experience to Date

4.24 How successful have the voucher funds been in governing the enterprises in the Czech and Slovak Republics? In other words, have the new owners brought about needed improvements in efficiecy and profitability of enterprise? Unfortunately, the answer **to** this important question is that it is probably too early to tell with any degree of certainty. The voucher flmds became the owners of the enterprises legally only after Apnil 1993 when the shares of the enteprpnses were distributed to the voucher holders who successfully bid for the shares. The firs annual meetings of shareholders of the companies typically occuured shortly afte the distribution of shares. It was at this first annual meeting tha the voucher funds could elect their .representatives to the governace boards of the enterprises and officially exercise their rights of ownership.'13

## **12M** "Crort Governance Survey" (1994), p. 14.

13Some funds did begin to) exercise their ownership rights in the fail of 1992 under the sponsorship of the National Property Fund (NPF). Until the shares were distributed to the funds and other new shareholders, the National Property Fund exercised the ownership rightL of the State. During this time, the NPF appointed board members for those enterprises included in the voucher privatization. The NPW chose to take a passive role and ap.pointed rpentivsof the voucher funds who had purchased shares in the enterprises in the voucher auctions to sit on the boards of enterprises as early as the fallofl1992, even though the voucher funds were not yet technically the owners.
# Should Wall-Street be Occupied? An Overlooked Price Externality of Financial Intermediation

Maya Eden<sup>_∗_</sup> World Bank October 3, 2012

**Abstract**

This paper highlights a price externality that causes excessive resources to be spent on financing. Intermediated funds increase the demand for capital inputs, which raises their price (in both real and nominal terms). This implies that producers can buy less inputs with their internal funds, and must rely more heavily on financial intermediation. As intermediation is costly, this feedback is inefficient. I illustrate this mechanism in a simplified real model, and extend it to a dynamic model with money.

## **JEL Classification:** E44, G20

**Keywords:** Costs of the financial sector, financial intermediation

> _∗_ Please send comments to meden@worldbank.org. Earlier versions of this paper circulated under the titles “The Inefficiency of Financial Intermediation in General Equilibrium”, “The Wasteful ‘Money Creation’ Aspect of Financial Intermediation” and “Excessive Financial Intermediation in a Model with Endogenous Liquidity”. This work is based on a chapter of my thesis at MIT. I thank Fernando Broner, Ricardo Caballero, Ben Eden, Anton Korinek, Guido Lorenzoni, Luis Serven and Ivan Werning for helpful comments. I also benefited from comments made by various seminar and conference participants. This paper does not necessarily reflect the views of the World Bank, its Executive Board or the countries it represents. All errors are mine.

<mark>1</mark>