rafmacalaba/datause-encoder-data huggingface dataset:
thanks to China only (since Concept 2 inequality without China was still increasing), after 2000, the decline takes place even _without_ China. As mentioned, this is due to the high growth rate of India. But it also shows that the world now has two “engines” of downward pressures on international (and ultimately global) inequality: high growth rates of China and India.

We now move to Concept 3 inequality. Table 2 shows the recalculated global Ginis and Theil indexes from household surveys covering approximately 120 countries and between 87 and 94 percent of the world population, and 95 and 98 percent of the world GDP. The new retrospective PPP exchange rates are simply applied to the existing survey data expressed in local curriencies from the benchmark years 1998, 1993, 1998 and 2002, and all inequality indicators recalculated. Obviously, an increase in China’s price level will reduce the level of incomes in China, measured in PPPs, the same way that it reduces the GDP numbers for China. Table 2 shows a significant increase in the level of inequality, compared to the “old” PPP data, ranging between 4.4 and 6.1 Gini points. For the most recent year (2002), the new Gini is 5.1 points higher than the Gini calculated using the “old” PPP exchange rates. The pattern of change however remains the same. After an increase in global inequality between 1988 and 1993, there was a modest decline, and then another increase. Note however that the increase between 1988 and 1993 is smaller with the new PPP data (1.5 Gini points) than with the “old” data (+3.2 Gini points). This can be also seen from Figure 2. There, the two dashed lines show the confidence interval for the new-PPP based Ginis. The new Gini is statistically significantly above the Gini calculated on the basis of old PPPs.

Theil coefficient with the new PPP values increases even more than the Gini, a reflection of Theil’s greater sensitivity to the changes at the extremes of income distribution. Since China, India, Philippines and Indonesia all had their PPP-equivalent incomes reduced significantly, the decline has made many poor people from these countries seem even poorer<sup>15</sup> Moreover, the Theil now shows an uninterrupted increase

> 15 This is not merely an “accounting” decline but a real one: if poor people really face much higher price levels in those countries than was previously thought then they are really much poorer.

11
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# **Table 8. Share of Market Capitalization of the Top 10 Largest Domestic Companies**

|||Full Sample|||Full Sample||Low- and|Middle-Incom|e Countries|Low- and|Middle-Incom|e Countries|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Stock Market Capitalization<br>(% of GDP)|0.083|||0.147|||0.097|||0.176|||
||(0.09)|||(0.151)|||(0.1)|||(0.159)|||
|Stock Market Total Value||0.002|||0.002|||-0.045|||-0.048||
|Traded (% of GDP)||(0.057)|||(0.058)|||(0.049)|||(0.053)||
|Stock Market Turnover<br>Ratio|||-0.010|||-0.016|||-0.0420*|||-0.0561*|
||||(0.022)|||(0.032)|||(0.025)|||(0.031)|
|Foreign Direct Investment,|0.00130*|0.001|0.000|0.001|0.001|0.000|0.022|0.011|0.005|0.024|0.011|0.005|
|net inflows (% of GDP)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.017)|(0.009)|(0.0073)|(0.017)|(0.009)|(0.007)|
|Political Stability and<br>Absence of Violence|0.068|0.030|0.045||||0.094|0.042|0.079||||
||(0.075)|(0.043)|(0.048)||||(0.097)|(0.048)|(0.062)||||
|Investment Profile||||0.035|-0.013|0.012||||0.100|0.009|0.054|
|||||(0.041)|(0.014)|(0.025)||||(0.074)|(0.025)|(0.048)|
|Constant|3.587***|3.985***|4.083***|3.018***|4.098***|3.971***|3.552***|4.105***|4.252***|2.400**|4.007***|3.790***|
||(0.372)|(0.151)|(0.0812)|(0.888)|(0.156)|(0.2)|(0.436)|(0.136)|(0.116)|(1.159)|(0.128)|(0.306)|
|Country Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|402|380|441|383|375|429|248|191|245|234|191|238|
|R-squared|0.083|0.04|0.034|0.135|0.042|0.035|0.112|0.114|0.048|0.217|0.113|0.072|

Note: The table reports fixed effects panel regressions using 5 years of data. The outcome variable is share of market capitalization of the top 10 largest domestic companies. FDI net flows as percentage of GDP, political stability, and absence of violence/terrorism, as well as investment profile are used as controls. This analysis includes both country and year fixed effects. One-year lags (t-1) are applied to all the explanatory variables and controls. Markings *, ** and *** denote significance at 10%, 5% and 1%, respectively. Robust standard errors are presented in parentheses. All variables are described in Table A-7 and Table A-9 in the annex.

21
Yet, when considered together, the studies in the four groups provide a series of _working hypotheses_ that are worth exploring more thoroughly. First, the overall quality of World Bank knowledge work is high, but it is heterogeneous both between and within groups of products. Second, the volume of resources available helps, but only to a point, and the growing availability of TFs may in fact reduce overall quality. Third, timeliness is a weak aspect of World Bank analytical work, without indications that knowledge products that take longer to prepare are of better quality. Fourth, the competence of the teams in charge of producing knowledge work matters for its quality, but it can be undermined by management processes that attend to other objectives. Fifth, within the World Bank, DEC is stronger on technical rigor, but possibly weaker on communication clarity and policy relevance. And sixth, the overall quality of World Bank knowledge work is higher when it is conducted in greater proximity to its intended users, hence with a better understanding of their needs and context.

# 3. A database of knowledge products

An empirical assessment of the six working hypotheses drawn from previous studies requires a database of knowledge products that is comprehensive both in its coverage and in the information set available for each entry. Specifically, data is needed on all the analytical work produced by the World Bank during a specific period, on the quality and reach of each piece, on the competence of the team in charge, on the budget resources devoted to it, on the time it took to complete it, on whether DEC was involved, and on the proximity between the preparation process and the ultimate client.

A comprehensive database along those lines was assembled for this paper by matching information available in three independent digital platforms:

- The _Image Bank_ (IB) is a document filing system where all entries are classified based on their sensitivity, which determines who inside or outside the World Bank can retrieve them. The criteria to decide which documents are filed have some fluidity; only a fraction of them qualify as knowledge products.

- The _Business Intelligence Warehouse_ (WH) is a digital platform organized around budget codes that is accessible only internally. A budget code supports the production of multiple documents, but there is typically one document summarizing its output – for example, a project appraisal document or a technical assistance report.  Analytical work is supposed to be funded through ASA budget codes.

12
# **The Market for Retirement Products in Australia**

**Gregory Gordon Brunner and Craig Thorburn**<sup>**1**</sup>

The views expressed in this paper are entirely those of the authors. They do not reflect the views of the World Bank, their Executive Directors, or the countries they represent.

> 1 FPDSN and FPDFS, World Bank.  The authors are grateful to Roberto Rocha and Dimitri Vittas for comments and suggestions on earlier drafts.
Table A1: Summary statistics by country (cont.)

||LSO|MRT|MWI|NER|SEN|TCD|TZA|UGA|
|---|---|---|---|---|---|---|---|---|
|Number of households|4,294|9,341|12,439|6,024|6,843|7,493|9,463|15,572|
|Survey years<br>|2017_/_18|2014|2016_/_17|2018_/_19|2018_/_19|2018_/_19|2017_−_1|9 2016_/_17|
|Lowest geographic unit avail-|HH|HH|HH|PSU|PSU|PSU|HH|Parish|
|able<br>|||||||||
|**DOU urban classifcation:**|||||||||
|Urban center|0.16|0.29|0.12|0.12|0.41|0.12|0.21|0.15|
|Urban cluster|0.20|0.17|0.17|0.12|0.18|0.16|0.23|0.40|
|Rural|0.64|0.54|0.72|0.76|0.41|0.73|0.56|0.45|
|**DB urban classifcation:**|||||||||
|Core|0.33|0.43|0.15|0.40|0.53|0.50|0.36|0.27|
|Suburb|0.20|0.04|0.24|0.15|0.08|0.13|0.24|0.29|
|Town|0.20|0.28|0.06|0.30|0.23|0.14|0.17|0.09|
|Other rural|0.47|0.53|0.62|0.45|0.40|0.37|0.40|0.44|
|**Consumption**<br>**(per**<br>**day**|||||||||
|**USD in 2017 PPP terms):**|||||||||
|Real per capita consumption|4.64|5.71|2.39|2.65|5.56|3.73|3.39|3.52|
|expenditures|||||||||
|Nominal per capita consump-<br>|4.62|5.74|2.44|2.79|5.82|3.86|3.50|3.54|
|tion expenditures|||||||||
|**Demographic:**|||||||||
|Household size|5.27|7.47|5.19|7.58|12.69|6.88|6.18|5.89|
|Age of household head|51.07|49.92|43.12|46.01|53.44|43.96|47.38|43.43|
|Household head is male|0.63|0.74|0.75|0.87|0.76|0.81|0.76|0.74|
|**Marital status (of house-**|||||||||
|**hold head):**|||||||||
|Married|0.58|0.83|0.78|0.93|0.86|0.86|0.75|0.79|
|Never married|0.06|0.01|0.01|0.00|0.02|0.01|0.03|0.03|
|Living together|0.00|0.00|0.00|0.00|0.00|0.00|0.04|0.00|
|Divorced/Separated|0.05|0.06|0.11|0.02|0.02|0.05|0.07|0.07|
|Widowed|0.31|0.09|0.10|0.05|0.11|0.08|0.11|0.10|
|**Education (of household**|||||||||
|**head):**|||||||||
|No education|0.00|0.75|0.16|0.68|0.55|0.52|0.20|0.13|
|Primary incomplete|0.48|0.09|0.55|0.17|0.17|0.21|0.14|0.40|
|Primary complete|0.16|0.02|0.06|0.01|0.05|0.03|0.52|0.18|
|Secondary incomplete|0.19|0.08|0.12|0.10|0.15|0.18|0.10|0.19|
|Secondary complete|0.08|0.02|0.07|0.00|0.01|0.01|0.00|0.02|
|Post secondary but not uni-|0.03|0.00|0.02|0.01|0.01|0.01|0.02|0.05|
|versity|||||||||
|University incomplete and<br>complete|0.05|0.04|0.01|0.02|0.06|0.05|0.02|0.03|
|Can read and write|0.79|0.61|0.72|0.36|0.47|0.32|0.76|0.72|
|**Employment (of house-**|||||||||
|**hold head):**|||||||||
|Not in labor force|0.26|0.20|0.48|0.02|0.17|0.02|0.29|0.08|
|Unemployed|0.04|0.17|0.06|0.00|0.00|0.00|0.09|0.01|
|Employed in Agriculture|0.24|0.19|0.06|0.66|0.31|0.69|0.36|0.54|
|Employed in Industry|0.20|0.07|0.07|0.07|0.12|0.06|0.05|0.09|
|Employed in Services|0.26|0.37|0.32|0.25|0.40|0.23|0.21|0.29|
|**Access to basic services**|||||||||
|Improved water|0.86|0.61|0.87|0.62|0.85|0.66|0.71|0.77|
|Improved sanitation facility|0.45|0.51|0.52|0.15|0.63|0.13|0.28|0.22|
|Access to electricity|0.41|0.46|0.93|0.21|0.74|0.10|0.56|0.39|

Source: International Urban Poverty Database. Note: Real per capita consumption expenditures are deflated using a spatial deflator calculated based on the WorldPop 250m dataset and the DOU method.

41
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE maximum revenue from their restricted usage. This is a serious problem in the region, one that results in too many vehicles chasing modest overall freight volumes.

**Figure 6. Time and cost of importing through alternative gateways involving Cameroon**

<!-- Start of picture text --> a. Cost b. Time<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Douala-Ndjamena Douala-Bangui Pointe Noire to Douala-Ndjamena Douala-Bangui Pointe Noire to<br>Brazzaville to Brazzaville to<br>Bangui Bangui<br>Border Administrative Port Transport Administrative Border Transport Port<br>Share (%) Share (%)<br><!-- End of picture text --> _Source_ : Teravaninthorn and Raballand 2009. _Note_ : The Pointe Noire-Brazzaville-Bangui corridor is included in the analysis because it runs through Cameroon.

Lengthy travel times in the Douala-Bangui and Douala-Ndjamena corridors are mainly associated with delays in the port of Douala, which is operating at the limit of its capacity. In 2010, transporting an 18-tonne, 40-foot container between Douala and N’Djamena took between four weeks and two months (World Bank 2011c). Port procedures are responsible for half of the time required to import to the Central African Republic and Chad. Productivity measures in the port of Douala are at the level of other regional ports, but behind the Sub-Saharan average (see section on ports). Time-consuming regulatory processes related to customs clearance and technical controls account for about one-third of the total time required for clearance (figure 5b). New port investments are on going on Kribi and Limbé but a comprehensive logistics strategy is still missing to address the growing demand for transport services (which may further accelerate in the mid-term if some major mining projects materialize).

Other nonphysical barriers and logistical inefficiencies play a major role in travel times. 2009 surveys of corridor operations reported between 70 and 150 checkpoints (legal and illegal) between Douala and N’Djamena and 45 between Douala and Bangui (World Bank 2011c). On a return trip between Douala and N’Djamena, transporters pay on average the equivalent of $580 in legal charges and illegal bribes. Another major obstacle to trade facilitation in the CEMAC region is institutional weakness at the regional level and to some extent at national level (World Bank 2011c).

Due to the physical and non-physical barriers, trade in the subregion is the lowest in Sub-Saharan Africa—but it is slowly increasing. With interregional trade accounting for just 0.5 to 1 percent of the total trade of its member states, the CEMAC subregion is the least integrated in Sub-Saharan Africa, far behind the Common Market for Eastern and Southern Africa (COMESA, 5 percent), the Southern African Development Community (SADC, 10 percent), the Economic Community of West African States (ECOWAS, 10 percent), and the West African Economic and Monetary Union (UEMOA, 15 percent).Between 2005 and 2008, annual flows of freight between Cameroon, Chad, and the Central 11
# **1. Introduction**

Can agent banking bring large numbers of poor, unbanked individuals and households into the formal financial system? Agents are local retailers that double as lower-cost alternatives to bank branches and enable customers to more conveniently make deposits, withdrawals, money transfers, and payments of loans (Lyman et al., 2006; Siedek, 2008; Mas and Kumar, 2008; Flaming et al., 2011). In Senegal and the world over, agents are small shops, supermarkets, petrol stations or agents of other existing money transfer service providers. Because of the convenience associated with their closer physical and social proximity to underserved market segments, agents may find it easier to reach poorer customers living farther from formal bank branches, and thus may deepen financial inclusion more cost effectively than the traditional banking model.

According to the Global Findex Database (Demirguc-Kunt et al. 2015), 38 percent of adults worldwide are unbanked. Among these individuals without a bank account, 22 percent cited costs, 21 percent reported that the bank is too far away, and 12 percent reported lack of trust in banks as a reason for being unbanked. Similarly, Beck et al. (2008) use data from over 200 banks in 62 countries to show that affordability of deposit services and physical distance deter financial access.

The goal of this paper is to estimate the effects of access to agent banking on opening and usage of a savings account. Since the decision to bank with an agent or a branch is typically endogenous, we use a randomized controlled trial (RCT) to identify the causal effects of agent banking in urban and peri-urban areas in Senegal. Using a 2x2 encouragement design, a randomly selected group of individuals was given information about a savings account. Half of the individuals were encouraged to open the account at the nearest agent, while the other half were encouraged to do so at the closest branch. In addition, half of the individuals in each of these two groups were also given a monetary incentive to open the account. Individuals thus fall into one of five groups: (i) offered information about the account (Info), given monetary incentives to open it (Incentives), and told to open it at the branch (Branch); ii) Info, Incentives and told to open it with an agent (Agent), (iii) Info, No Incentives and Branch (iv) Info, No Incentives and Agent and (v) No Info.

As documented in the literature, banking with an agent may be different from banking at a branch along several dimensions, including transactions costs such as transport costs, account opening fees and fees per transaction; trust; restrictions on the use of the account; 2
# I. Introduction

Tax administrations around the world are losing valuable tax revenues due to noncompliance by members of the shadow economy. According to some estimates, on average, one‐third of the world economy is informal (see, for example, Schneider, Buehn, and Montenegro 2010). Many consumers are all too familiar with the seductive temptation of buying goods without a bill or a receipt and saving on the value‐added tax (or sales tax) that would otherwise be paid. Whenever a consumer uses the services of a plumber or a carpenter and forks over the fees in cash, it may not have even crossed his or her mind that a VAT on the service rendered should actually have been added to the billed amount.

Cash has always been a facilitator of the shadow economy, since transactions made in cash do not leave an audit trail. Cash transactions between businesses and individual consumers are a typical risk area. Individuals are consumers of business goods and services, and if they purchase these goods and services in cash, they provide businesses with the opportunity not to report or to underreport those sales. However, consumers do not have any incentive to receive invoices or receipts from businesses, since they ordinarily do not claim input tax credit for VAT or report it as an expense for their income tax. Hence if business sellers offer customers prices lower than their competitors’ on the condition of cash payment, customers are very likely to accept that offer. In this case, it is very difficult for the authorities to detect and curb cash transactions, since sellers and buyers have no conflicting interests and provide no cross‐checks.

In this context, measures to encourage businesses and consumers to pay their bills using banking channels or electronic means of payment can and should be designed and implemented to secure audit trails. There appears to be a strong negative relationship between use of electronic or formal payments and the size of the shadow economy. A study of the European economy by A. T. Kearney and Schneider confirms this association (2009).

Figure 1 Shadow Economy and Electronic Transactions <!-- Start of picture text --> 50<br>40<br>30<br>20<br>R2 = 0.453<br>10<br>0<br>0 50 100 150 200 250 300<br>Average number of electronic transactions per person per year<br>percentage of GDP<br>Shadow economy as a<br><!-- End of picture text --> _Source:_ A. T. Kearney and Schneider (2009).

1
forest cover in each village.

The VCF measure is a prediction of the percentage of a pixel that is covered by forest, generated from a machine learning model based on a combination of images from global Landsat images and samples from higher resolution satellites. The measure employs not only the visible bandwidth but also other bandwidths. For example, VCF uses thermal signatures because forested areas tend to be cooler than non-forested plantation areas, allowing VCF to (partially) distinguish between forest cover and plantations. To the extent that thermal signatures and other correlates can distinguish forests from non-forest plantations, VCF substantially improves upon the Normalized Differenced Vegetation Index (NDVI) that has been widely used in understanding the causes of deforestation (for example, Foster and Rosenzweig (2003)). For all analyses, we restrict the sample of villages to those that had non-zero forest cover in 2000, a year predating the construction of all roads considered in this research. This is also the earliest year that these forest cover datasets are available.<sup>6</sup> Some earlier studies have used the Global Forest Cover (GFC) dataset, which describes baseline forest cover in the year 2000, and a binary indicator for the year of deforestation for each pixel, at a 30m resolution, where a pixel is considered deforested if over 90% of 2000 forest was lost by a given year, or reforested if a pixel goes from zero forest in 2000 to positive forest cover by 2012 (Hansen et al., 2013). While GFC and VCF are both based on the same underlying Landsat images, GFC is less useful for the study of forest cover in India, because forest change in India is not well summarized by a binary deforestation indicator. The VCF measures suggest that forest cover rose 15% over the sample period, an estimate consistent with official and international sources. Because most of these gains are in areas that had some pre-existing forest, they are not recorded by GFC, nor are partial forest losses. We can replicate GFC estimates using losses only in the VCF data, but they miss a significant share of forest change in the sample period. Because 92% of villages are larger than the VCF cell size, the resolution advantage of GFC would be minimal.

> 6Fewer than 10% of villages have zero forest cover in 2000; 95% of these villages have less than 1% forest cover in 2014; the mean of forest cover for pixels with non-zero forest is 12.76% in 2000.

9
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