rafmacalaba/datause-encoder-data huggingface dataset:
|P118197|2013|2015|12/18/2014|4 Active|Uzbekistan|
|---|---|---|---|---|---|
|P118197|2013|2015|6/30/2015|5 Active|Uzbekistan|
|P118197|2013|2016|12/24/2015|6 Active|Uzbekistan|
|P118197|2013|2016|6/15/2016|7 Active|Uzbekistan|
|P118197|2013|2017|12/13/2016|8 Active|Uzbekistan|
|P118197|2013|2017|5/23/2017|9 Active|Uzbekistan|
|P118197|2013|2017|6/19/2017|10 Active|Uzbekistan|
|P118197|2013|2018|1/12/2018|11 Active|Uzbekistan|
|P118197|2013|2019|7/9/2018|12 Active|Uzbekistan|
|P118197|2013|2019|12/21/2018|13 Active|Uzbekistan|
|P118197|2013|2019|6/25/2019|14 Active|Uzbekistan|
|P118197|2013|2020|12/19/2019|15 Active|Uzbekistan|
|P118197|2013|2020|6/19/2020|16 Active|Uzbekistan|
|P118211|2010|2010||0 Closed|Yemen,Republic of|
|P118211|2010|2011||0 Closed|Yemen,Republic of|
|P118211|2010|2012|11/23/2011|1 Closed|Yemen,Republic of|
|P118211|2010|2012|6/30/2012|2 Closed|Yemen,Republic of|
|P118211|2010|2013|1/5/2013|3 Closed|Yemen,Republic of|
|P118211|2010|2013|5/16/2013|4 Closed|Yemen,Republic of|
|P118211|2010|2014|8/31/2013|5 Closed|Yemen,Republic of|
|P118213|2014|2014|12/24/2013|1 Active|Eastern Africa|
|P118213|2014|2014|6/17/2014|2 Active|Eastern Africa|
|P118213|2014|2015|11/15/2014|3 Active|Eastern Africa|
|P118213|2014|2015|6/29/2015|4 Active|Eastern Africa|
|P118213|2014|2016|2/1/2016|5 Active|Eastern Africa|
|P118213|2014|2017|10/17/2016|6 Active|Eastern Africa|
|P118213|2014|2017|2/23/2017|7 Active|Eastern Africa|
|P118213|2014|2018|7/25/2017|8 Active|Eastern Africa|
|P118213|2014|2018|1/27/2018|9 Active|Eastern Africa|
|P118213|2014|2018|2/17/2018|10 Active|Eastern Africa|
|P118213|2014|2018|6/8/2018|11 Active|Eastern Africa|
|P118213|2014|2019|11/14/2018|12 Active|Eastern Africa|
|P118213|2014|2019|5/9/2019|13 Active|Eastern Africa|
|P118213|2014|2020|12/8/2019|14 Active|Eastern Africa|
|P118213|2014|2020|6/19/2020|15 Active|Eastern Africa|
|P118226|2010|2011|1/26/2011|19 Closed|Sierra Leone|
|P118239|2011|2011|2/8/2011|1 Closed|Haiti|
|P118239|2011|2011|4/17/2011|2 Closed|Haiti|
|P118239|2011|2012|7/29/2011|3 Closed|Haiti|
|P118239|2011|2012|11/2/2011|4 Closed|Haiti|
|P118308|2010|2010||0 Closed|Turkey|
|P118308|2010|2011|1/4/2011|1 Closed|Turkey|
|P118308|2010|2012|10/5/2011|2 Closed|Turkey|
|P118308|2010|2012|5/1/2012|3 Closed|Turkey|
|P118308|2010|2013|1/2/2013|4 Closed|Turkey|
|P118308|2010|2014|10/28/2013|5 Closed|Turkey|
|P118308|2010|2015|7/5/2014|6 Closed|Turkey|
<!-- Start of picture text --> Appendix Readmission Rate<br>7.0%<br>6.0%<br>5.0%<br>4.0%<br>——PPTP<br>3.0% a= DTS<br>2.0%<br>1.0%<br>0.0%<br>Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><!-- End of picture text --> <!-- Start of picture text --> Hip Replacement Readmission Rate<br>25.0%<br>20.0%<br>15.0%<br>——PppPpTP<br>10.0% =——=DTS<br>5.0%<br>0.0%<br>Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><!-- End of picture text -->
**Chart 9. Public support of redistribution is correlated with perceived level of economic injustice**

<!-- Start of picture text --> 34<br>32<br>30<br>28<br>26<br>24<br>22<br>20<br>70 Lazy / lack of willpower Injustice in society<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Slovakia Estonia Czech Poland Lithuania Slovenia Hungary<br>Republic<br> Benefits (share of disposable income)<br>Slovakia Estonia Czech Republic Poland Lithuania Slovenia Hungary<br>Percentage of respondents<br><!-- End of picture text --> _Source:_ 2006 EBRD-World Bank Life in Transition Survey.

29. The LiTS data also show that, overall, about 44 percent of people in EU8 countries feel that people are in need because of “injustice in society”, with the ratio varying from 28 percent in Estonia to 61 percent in Hungary (Table 5). By contrast, 33 percent of respondents in Estonia felt that people were needy because they were “lazy / lacked willpower”, almost twice as high as the share of such responses in Hungary.

**Table 5: Perceptions regarding why people are in need**

||**Main re**<br>|**ason why peo**<br>|**ple are in n**|**eed inyour coun**<br>|**try**<br>|
|---|---|---|---|---|---|
|**Country**|**Injustice in**<br>**society**|**Inevitable**<br>**part of life**|**Unlucky**|**Lazy/lack of**<br>**willpower**|**Overall**|
|Hungary|61|16|7|17|100|
|Poland|48|22|14|16|100|
|Slovenia|38|32|11|19|100|
|Czech Republic|27|30|18|25|100|
|Latvia|48|20|7|25|100|
|Slovakia|32|20|17|31|100|
|Estonia|28|28|10|33|100|
|Lithuania|38|14|5|42|100|
|**EU8 countries**|**44**|**22**|**13**|**21**|**100**|

_Source:_ World Bank staff calculations based on 2006 LiTS
- 67 -

As a result, a series of policy challenges, from procurement design to contract management and project quality assurance, are interrelated to each other and need to be addressed in concert. New data were collected from 155 rural road upgrading contracts in Nepal, on which about 820 bids were submitted. Various important findings were discussed. However, in order to achieve the cost efficiency in public road procurement, competition is found the single most important factor. In addition, competition will prevent alleged collusion and corruption as well as foster local businesses.

To increase competition, it is essential to remove various entry barriers―both explicit and implicit ones. Public tender notices can be advertized more widely. Bidding documents can be distributed free of charge. E-procurement can be a good way to take these measures. It can also offer a one-stop service to publish all the information on business opportunities related to public works. In addition, e-procurement will be conducive to promoting accountability and transparency in public procurement systems. The capacity of local businesses also is also crucial to increase competition, because security issues are hampering the firms’ participation in the market. Moreover, firms are found less willing to undertake public works far from their locations. Hence, local businesses are essential.

Enlarging contract packages may help to attract more potential bidders. But the government should be aware of the risk of increasing the bid evaluation costs. It takes longer to evaluate larger contracts. In addition, more firms are expected to apply. The efficiency in contract awarding seems to vary significantly across regions. To make the evaluation process short, capacity building may be necessary at the local level. This will also help to reduce the risk of collusion and corruption caused by the lengthy bid evaluation and contract award process.

To mitigate cost overruns and project delays, security issues need to be addressed. Weather conditions are also one determinant. Particularly where it rains a lot, careful project and procurement planning is necessary. Otherwise, the efficiency of the budget execution will be undermined. Finally, roads deteriorate over time anyway. Traffic and precipitation will affect road conditions. However, security issues seem to be the most critical to implement timely
WPS4267

# **Financial Intermediation in the Pre-Consolidated Banking Sector in Nigeria**

## **Heiko Hesse** *

## World Bank

**Abstract:** This paper uses unique bank-by-bank balance sheet and income statement information to investigate the intermediation efficiency in the Nigerian pre-consolidated banking sector during 2000-2005. The author analyzes whether the Central Bank of Nigeria’s policy of recent banking consolidation can be justified and rationalized by looking at the determinants of spreads. Indeed, a spread decomposition and panel estimations show that the reform of the banking sector could be the first step to raise the intermediation efficiency of the Nigerian banking sector. The author finds that larger banks have enjoyed lower overhead costs, increased concentration in the banking sector has not been detrimental to the spreads, both increased holdings of liquidity and capital might have led to lower spreads in 2005, and a stable macroeconomic environment is conducive to a more efficient channeling of savings to productive investments.

JEL Classifications: G21, G30, O16 Keywords: Bank Spreads; Bank Efficiency; Financial Intermediation; Nigeria

### World Bank Policy Research Working Paper 4267, June 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org.</u>_

* World Bank: hhesse@worldbank.org.

I would like to thank Robert Cull and Michael Fuchs for numerous discussions and comments, the Central Bank of Nigeria for sharing the data and comments as well as Alexander Al-Haschimi, Thorsten Beck, Steve Bond, Martin Cihak, Patrick Honohan, Astrid Manroth, Charles Mordi, John Muellbauer, Thomas Muller, and Richard Podpiera.

1
# **3 Methodology**

## **3.1 Survey design and information treatments**

### **3.1.1 Measuring the characteristics, knowledge, and beliefs of respondents**

The survey has three sections, with the main information treatments provided between the second and third. The first section (Q1–Q12) collected respondents’ background characteristics, including standard demographic information (age, sex, education level, location, household size, and income) and information about household energy consumption. The questions about energy consumption are based on what was asked by Dechezleprêtre et al. (2022) in their recent cross-country survey experiment about attitudes toward climate change. They indicate whether respondents relied heavily on electricity/gas for cooking (and heating where applicable) or fuel for transport. Respondents were also asked to state where they perceived their household to be in the national income distribution; this is similar to questions in existing studies such as Hoy and Mager (2021). In addition, respondents were asked their views about the government, including their beliefs about government’s capacity to help households deal with the cost of living and their level of trust in the government. The former is a modified version of a question about the government’s capacity that has been used multiple times by Stantcheva and co-authors (e.g., see Stantcheva [2021]), and the latter is identical to what was asked by Dechezleprêtre et al. (2022).

The second section of the survey (Q13–Q18) introduced respondents to the concept of an energy subsidy and collected information about their knowledge of and beliefs about energy subsidies. Initially, all respondents were provided with a basic summary of energy subsidies, like that in Vieites et al. (2023), which indicated that subsidies reduce the cost of electricity, fuel, or gas. Respondents were then asked about whether they believe energy subsidies exist in their country; this question is similar to questions by Stantcheva and coauthors (e.g., see Stantcheva [2021]) that aim to capture people’s knowledge about policies. Respondents were then informed that energy subsidies do exist in their country. They were 16
**Figure 3:  Share of GVC Exports in Total Exports by World Regions, 1988‐2014**

<!-- Start of picture text --> 60%<br>East Asia & Pacific (all income levels)<br>50%<br>Europe & Central Asia (all income levels)<br>40%<br>Latin America & Caribbean (all income<br>levels)<br>30%<br>Middle East & North Africa (all income<br>levels)<br>20%<br>North America<br>10%<br>South Asia<br>0%<br>Sub‐Saharan Africa (all income levels)<br><!-- End of picture text --> <u>Notes: Values in percentage shares from original variables expressed in thousand current US$ and calculated on the total number</u> of 256 countries/geographies with available data on GVC Exports in at least some years of the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits, mirror data). All trade data is drawn from Comtrade on WITS, and countries' GDP data comes from WB WDI.

<u>Sources: Trade: World Bank MC‐GVC database</u> (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator). World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators).

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significant ATEs which are necessary for causal mediation analysis. Indeed, it is difficult to think of causal mediation effects when the treatment itself does not impact outcomes.

# **_4.2. Causal Mediation Analysis_**

The principal aim of this paper is to go beyond the ATE and to quantify the effect of the treatment that operates through a particular channel. That is, we focus on the causal mechanism by which the treatment _T_ causally affects outcomes _Y_ through a mediator variable _M._ In our analysis, we consider three mediator variables corresponding to three different dimensions of financial knowledge, namely, numeracy, awareness, and attitudes. Our goal then is to decompose the ATE into an indirect effect (which represents a particular mechanism) and a direct effect (which represents all other channels).

Decomposing the ATE to study the effect of mechanisms is not trivial. As Green, Ha, and Bullock (2010) point out, conventional regression approaches to isolate causal mechanisms that have been used in different fields — such as political science, psychology, and public health — all rely on strong and often implausible identification assumptions. Moreover, in the presence of confounding variables that are influenced by treatment and affect both the mediator and the outcome, conditioning on the mediator can lead to spurious effects (Rosenbaum, 1984). As a result, such an analysis suffers from bias and loses causal inference, even in experimental studies.

Recent developments in causal mediation analysis take these intermediate variable bias concerns seriously, and several papers have presented new estimation methods to isolate the influence of a mediating variable (e.g., Imai et al., 2011; Acharya, Blackwell, and Sen, 2016). The common thread among these methods is that they focus on issues of estimation as well as clarifying the identification assumptions that underlie mediation analysis. For our study, we apply two leading approaches to causal mediation, which we describe below.

# **_4.2.1. Estimating Average Causal Mediation Effects_**

While the experimental design allows us to obtain the ATE, it does not by itself offer possibilities for capturing the mechanisms underlying the change in financial outcomes. To this end, our first approach adopts the causal mediation analysis proposed in Imai et al. (2011) and Imai, Keele, and Yamamoto (2010). We start by defining the _indirect effect_ or the _causal mediation effect_ as: where Mi(1) and Mi(0) represent the potential value of the mediator when individual _i_ is assigned to the treatment and control group, respectively. As shown in equation (2), δi(t) amounts to the change in Yi due to the change in Mi from the control to the treatment group, holding the individual’s treatment status constant at _t_ . This quantity captures the effect of the treatment on the outcome via the mediating variable: since we fix the treatment and change only the mediator, δi(t) 12
of software investment. Since we include computer services in our ICT investment measure, we assume that the remaining software investment is equal to WITSA software spending.

It is perhaps worth noting that the distinction between software leases and software investment is somewhat blurred. The software spending category consists of the total value of purchased or leased packaged software. While purchasing software is investment from the firm’s perspective, from a macro perspective this is perhaps more appropriately viewed as a rental fee. The creation of new software is similar to investment in research and development (R&D). The returns to writing new software are the dividends from selling or leasing the rights to use that software. From a timing perspective, the value of the initial investment is the costs of programmers and associated capital costs for producing new software. The returns to the investment are the sales of software licenses, either permanent (purchases) or temporary (leases). From a macroeconomic perspective, software investment should be counted as the costs associated with developing new software (similar to R&D investment). However, given that this data is not available, we stick with the commonly adopted micro perspective and assume that software investment is software purchases and leases.<sup>6</sup> The fourth WITSA category, communication technology (CT), is defined as the total value of voice and data communication services and equipment. Conceptually, communication services (such as internet subscriptions or payments for phone usage) represent rental fees for communication infrastructure, rather than investment. Since we are interested in a pure investment measure, we substitute this category with a direct measure of CT investment from ITU.

Taken together, our final measure of nominal (current USD) ICT investment is the sum of TC investment (ITU), computer services spending (WITSA), adjusted computer hardware spending (WITSA), and computer software spending (WITSA). To construct ICT capital stocks we deflate each of these nominal investment series with the ICT price deflator estimated by Eden and Gaggl (2014) based on the BEA’s fixed asset accounts and employ the perpetual inventory method (PIM)

> 6Note that the high depreciation rate of software implies that there is no big difference between permanent purchases and temporary leases. Generally, most attempts to construct capital stocks take this perspective. The BEA’s computations for the NIPA tables are one example. See the official NIPA documentation for details: http://www.bea.gov/national/pdf/chapter6.pdf.

6
We estimate the above models (i.e., with and without scale effects) both as a pooled regression and including firm fixed effects, using the balanced data set consisting of firm-level data over the period 1999-2004. As discussed above, we lose a significant number of observations when we move from the unbalanced to the balanced data set since many firms do not report in each year.<sup>4</sup> However, as discussed in Fisher-Vanden et al. (2009), although there are significantly fewer firms in the balanced data set, these firms consume approximately 40% of total industrial energy consumption.

As a robustness test, however, we run the regressions on an unbalanced data set obtained by dropping firms with only one year of observations over the period 1997-2004. (See section VII). As shown in Table 4, most firms with only one year of observations are only reporting for the year 2004 since this is a census year. These firms are also smaller in size as shown in Table 5— the mean of gross value industrial output of firms in the balanced data set is five times higher than in the unbalanced data set. The discontinuity of the unbalanced data set across years also implies that we are unable to construct technology development stocks based on continual annual flows of technology development expenditures. Instead, we must use technology development flows rather than stocks in our robustness tests using the unbalanced data set which introduces potential timing and endogeneity issues.

# **VI. Results and interpretation**

> 4 The unbalanced data set contains 7934 observations, while the balanced data set only contains 1548 observations.

19
Gupta Sanjeev, Catherine Pattillo and Smita Wagh (2007). “Impact of Remittances on Poverty and Financial Development in Sub-Saharan Africa”, IMF Working Paper Paper WP/07/38, The International Monetary Fund, Washington DC.

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Ndikumana Leonce and James Boyce (2003). “Public Debt and Private Assets: Explaining Capital Flight from Sub-Saharan African Countries”, World Development, Vol. 31, No. 1, pp. 107-130.

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