rafmacalaba/datause-encoder-data huggingface dataset:
may capture aspects of well-being that are not captured by standard survey-based measures of poverty such as wealth and consumption.

To test for systematic misclassification of certain types of households, Table 4 displays the overlap in errors of exclusion and inclusion between methods. Our results suggest that the three classifiers misidentify the same households at a rate only slightly above random.<sup>8</sup>

# **3.3 Combining Targeting Methods**

Since the different targeting methods identify different populations as ultra-poor, there may be complementarities between asset, consumption, and CDR data. As shown in Panel A of Table 2, we find that a _combined method_ , which takes as input the wealth index, total consumption, and the output of the CDR-based method, performs better (AUC = 0.78) than methods using any one data source (AUC = 0.68 - 0.73). As shown in Table S5, the full method also outperforms methods based on any two data sources (AUC = 0.75 - 0.76). The method that combines CDR and asset data (AUC = 0.76) may, however, be more practical than the combined method, since consumption data is difficult to collect for large populations.

# **3.4 Targeting Households without Phones**

An important limitation of CDR-based targeting is that households without phones do not generate CDR. Here, we show how targeting performance is impacted when households without phones are included in the analysis. This analysis uses two additional samples of TUP households to evaluate targeting performance: (i) the _balanced sample_ , which adds all of the 472 households without phones to the sample of 535 for whom we have matched CDR; the balanced sample is intended to illustrate the performance of CDR-based targeting if — CDR were available from all operators in Afghanistan though it relies on the assumption that phone-owners observed on our mobile network are representative of all phone owners in Afghanistan (an assumption that is not fully satisfied, as shown in Table 1); and (ii) the _full sample_ , which includes all 2,814 households surveyed in the TUP baseline with complete asset and consumption data; this sample includes an additional 1,807 households who report

> 8The rates of overlap should be interpreted relative to the expected overlap in errors for random classifiers. Based on our selection of thresholds such that 27% of the sample is identified as ultra-poor, our three classifiers misidentify 15%-27% of the non-ultra-poor and 51%-65% of the ultra-poor. If these classifiers were random, we would expect approximately 20% overlap in inclusion errors and 55% overlap in exclusion errors.

13
the top tail account for and how this may vary across countries or time is not yet clear. The research discussed in this paper provides evidence from a number of countries that better measurement of the top tail increases the estimated shares of the top of the distribution and thus can potentially explain much of the difference between survey and national accounts estimates of total income or consumption.

# Seminal papers

In the absence of representative survey data on income, Kuznets and Jenks’s (1953) pathbreaking study used aggregated data from tax statistics in the US to measure incomes at the top of the distribution, as well as population and total national income from national accounts as controls. Kuznets (1963) compiled similar information from a variety of sources for 18 countries, including 11 developing countries.<sup>3</sup> His conclusion was that the share of the top 5 percent was much larger in developing countries than in developed countries, although the share of the bottom 60% was similar in developed and developing countries. Since some of the data came from tax statistics, shares for the middle and bottom of the distribution were sometimes imputed using parametric methods- for example by estimating the Pareto coefficient for the top of the income distribution using tax data and using this to impute the shares for different parts of the rest of the distribution (Kuznets, 1963:15).

# Recent Literature

Piketty et al. (2022) note that after Kuznets’ work in the 1950s and 1960s, household surveys and the microdata produced from them became much more common. This meant that the entire income distribution could be examined, rather than the top (or top and middle) only, encouraging research on poverty as well as inequality in many more countries that undertook representative household surveys. This meant that the focus on the top end of the income distribution was mostly neglected for several decades, in favor of index measures of inequality that did not bring into focus the very top end of the distribution, unlike Kuznets’ work (Milanovic, 2014).

The neglect of the top end of the income distribution ended with Piketty’s publication of long-run estimates of top income and wealth shares for France for the 20<sup>th</sup> century (Piketty, 2001, 2003), which reinvigorated research on the top end of the income and wealth distributions. Piketty (2003) used data and methods when analysing inequality in France for most of the 20<sup>th</sup> century that were similar to those used by Kuznets (1953)- national accounts data, published tax tables with aggregated data

> 3 Alvaredo and Atkinson (2022) note that similar research had already been undertaken for South Africa by Frankel and Herzfeld (1943) - a decade before Kuznets and Jenks' work for the United States, although this was limited to whites only.

3
**Financial markets and monetary policy.** There are two assets in this economy. One is a bond denominated in domestic currency that is issued by the home government and pays gross nominal interest rate . The other is a bond issued by the foreign government denominated in foreign currency with nominal tt RR interest rate ∗, which is the sum of the constant “world” interest rate, , plus a debt-elastic country spread, tt . The central bank sets the gross domestic nominal WW RR TT interest rate, , according to an inflation-targeting regime (floating ER) or a peg ψψ to the foreign currency (fixed ER). Uncovered interest rate parity (UIP) holds so tt RR that either bond provides the same expected return when converted to the same currency.<sup>15</sup>

**Constant natural resource production and precautionary savings.** An assumption in our NK model worth highlighting is that the production of the natural resource good is constant at s , which allow us to abstract from concerns about intergenerational equity. On the surface, this simplification may seem unrealistic— QQ almost by definition—for producers of _non-renewable_ natural resources.<sup>16</sup> But historical data suggest it is actually a good approximation for countries with large reserves relative to current production, and active mineral exploration. Indeed, since 2000, the largest 10–20 producers of key commodities have shown _increased_ reserves and production, likely due to technological progress and new discoveries (see Appendix Figures 3 and 4).<sup>17</sup> For example, BP argues that “over the last 35

15 We define the Consumer Price Index (CPI) as the price index of a unit of consumption in the home country (basket of the home and foreign goods) and the Producer Price Index (PPI) as the price index of a unit of the home good (basket of all good varieties). The real ER (RER) is defined as the price of the foreign good (converted to the home currency) over the CPI. The terms of trade is defined as the price of the foreign good (converted to the home currency) over the PPI. As in Galí and Monacelli (2005), the law of one price holds at the variety level: the price of a given variety in different countries is identical once expressed in the same currency. In the presence of home bias, the law of one price carries over to the PPI level but not to the CPI level. Note that the UIP implies that, in the linearized model, the return of the domestic bond is equal to the world interest rate plus the debt-elastic spread plus the expected devaluation of the nominal ER. 16 Naturally, agricultural commodities are renewable, and so a constant s is less controversial. 17 Appendix Figure 3 shows an index of the evolution of the reserves of the main natural resource QQ produced in 13 large commodity exporters from 2000 to 2017. The index is equal to 100 in 2000 and is built with data from the BP-Energy dataset and the US Geological Survey. One can see that reserves increased from 2000 to 2017 in 10 of 13 commodity exporters. For some countries, such as Peru, Venezuela, and Brazil, the increase is quite remarkable, with reserves tripling over 2000– 11
Policy Research Working Paper 8971

# **Abstract**

This paper presents an analysis of transit accessibility to employment for 11 African cities. The use of identical methodologies and similar data sets allows for the creation of the first benchmark to compare accessibility across urban areas in Africa through different metrics and visuals. The study shows how the spatial pattern of land use and transport systems perform in connecting people to employment opportunities in these various settings. This first comparable benchmark is achieved by overcoming two significant data hurdles that are common in many developing country cities and in Africa in particular: (i) the scarcity of information on the distribution of employment and (ii) the lack of information on transit routes and travel times. These data gaps are filled through a novel methodology to estimate the distribution of employment in urban areas (Employment Opportunity Areas) as well as a comprehensive mapping of informal transit networks. The analysis developed here can be replicated in different cities in the future. The computation of these baseline accessibility studies also opens up the possibility to assess the impacts of future transport investments and/or land use changes, through the use of counterfactual scenarios, which could assist decision makers in these cities. Finally, this analysis can serve as a demonstration that the computation of accessibility metrics is achievable, including in data scarce environments, and should be considered as a progress indicator for Sustainable Development Goal 11.2, which focuses on safe and affordable transport for all, including public transport.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice, the Transport and Digital Development Global Practice, and the Global Facility for Disaster Reduction and Recovery and the Global Solutions Group for Territorial Development. 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 tperaltaquiros@worldbank.org and pavner@worldbank.org.

_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
# Figure 5 anti-dumping Impacts on Household Income

# A) Total Household Income

<!-- Start of picture text --> 0 .1 .2 .3 0 .1 .2 .3<br>catfish share in 2002 catfish share in 2002<br>B) Per Capita Household Income<br>0 .1 .2 .3 0 .1 .2 .3<br>catfish share in 2002 catfish share in 2002<br>C) Total Net Household Income<br>0 .1 .2 .3 0 .1 .2 .3<br>catfish share in 2002 catfish share in 2002<br>.1 .1<br>0 0<br>-.1 -.1<br>-.2 -.2<br>growth in total income growth in total income<br>-.3 -.3<br>-.4 -.4<br>.1 .1<br>0 0<br>-.1 -.1<br>-.2 -.2<br>growth in per capita income growth in per capita income<br>-.3 -.3<br>-.4 -.4<br>.1<br>0<br>0<br>-.1<br>-.1<br>-.2<br>-.2<br>-.3<br>growth in net total income growth in net total income<br>-.3<br>-.4<br>-.4<br>-.5<br><!-- End of picture text --> Note: Own calculations based on the panel of aquaculture households from the VHLSS (2002 and 2004). The estimates represent the relationship between the growth rate in total household income (panel A), per capita household income (panel B) and total net/disposable income (panel C) and the exposure to the U.S. anti-dumping shock (measured by the share of income derived from catfish) relative to a household with marginal exposure. The graphs on the left are estimated using the M4 sample (which includes the Mekong provinces of An Giang, Can Tho, Dong Thap and Vinh Long); the graph on the right uses instead the M6 sample (which adds Soc Trang and Tien Giang).

24
# **3.1 Main Types of Provider Risks**

3.2 The main types of risks faced by providers of retirement products, e.g., investment, inflation and longevity risks, are similar but inverse to those faced by pensioners. Other risks faced by insurance companies and pension funds include underwriting, credit or counterparty, liquidity and operational risks.<sup>25</sup> 3.3 Discussing them in reverse order, operational risk is the risk of losses resulting from administrative failure or fraud caused by inadequate internal controls. Life insurance companies and pension funds are subject to operational risk like any other type of financial institution. Their vulnerability may be greater because of the long-term nature of some of the products they offer. Failure to maintain effective internal controls may lead to improper portfolio decisions, or transactions involving conflicts of interest, and may ultimately result in low returns or losses as well as fines or other impositions by the supervisor. Providers may also suffer from larger costs than necessary due to the use of outdated technology and greater exposure to losses arising from fraud committed by clients as well as staff.

3.4 Liquidity risk is the risk of losses resulting from insufficient liquid assets for the cashflow requirements associated with underwritten policies. Providers of retirement products are generally less exposed to liquidity risk. Their cash outflows are easily predictable since they are not exposed to a sudden large increase in claims or to early and voluntary termination of contracts. Nevertheless, they still require accurate forecasts of future cash outlays and need to build an asset portfolio capable of generating the necessary liquidity.

3.5 Credit or counterparty risk is the risk of losses arising from the deterioration of the credit quality of issuers of instruments and counterparties, especially the risk of default. This risk is most visible in the case of instruments, such as corporate bonds, that may suffer a downgrading of credit rating. But it is also present in reinsurance arrangements and derivative agreements. It is of major importance in the case of longterm interest rate swaps.<sup>26</sup> 3.6 Providers of retirement products may also face credit risk in their arrangements with agents and brokers with regard to premiums receivable. Other types of counterparty risks include settlement risk (arising from time lags between the trading and settlement dates of securities transactions), documentation and custody risk (arising from failures in the legal documentation or custody of instruments in the portfolio), and concentration risk (arising from excessive concentration of investments in an individual entity, a sector or a geographical area).

> 25 This section draws on Chapters 3 and 4 of Rocha and Thorburn (2007).

> 26 Long-term interest rate swaps are playing an increasing part in hedging the long-term liabilities of insurance companies and pension funds.  These tend to be over-the-counter customized instruments. Management of counterparty risk is a crucial aspect of the successful use of these instruments.

22
does environmental news impact the environmental performance of firms irrespective of the impact it may have on firms’ market value? Hamilton (1995) found that firms with most adverse stock market reaction do experience improvements in environmental performance. On the other hand, Doonan et al. (2006) found that managers in the Canadian pulp and paper industry do not perceive capital markets to be an important source of pressure for environmental enhancement. The study showed instead government pressure (through its monitoring and enforcement activities) to be the key predictor of firms’ environmental performance.

It is against this background that the present paper seeks to examine the impact of environmental news in the printed press on the environmental performance of publicly traded (stock market) firms. We do so in the context of a developing country. To our knowledge, no study in the context of a developing country has thus far attempted to assess whether or not changes in stock market values then induce an improvement in a firm’s environmental performance. For this purpose, a survey was conducted in the Republic of Korea in 2005. The survey included only publicly traded firms with adverse environmental news printed in Korean newspapers. The study shows that the publication of environmental news in printed media (newspapers) _along with_ the firm’s awareness of this news publication is the key predictor of environmental performance, irrespective of the reaction of stock markets. Without neglecting the potential role of other determining factors, this finding reemphasizes the key role of information and of the media as conveyor of that information to enhance firms’ environmental performance.

4
(although not by much) in Zimbabwe.<sup>7</sup> In all the countries, networks of ethnic minority firms produce a large share of value added in manufacturing and control a large share of the upstream supplier industries.

**Table 1: Ethnic Distribution of SMEs (percent of firms)**

||**Kenya**|**Tanzania**|**Zambia**|**Zimbabwe**|
|---|---|---|---|---|
|**_African_**|39|73|61|40|
|**_Asian_**|60|25|26|16|
|**_European_**|0.5|0.0|11|41|
|**_Other_**|0.5|2|2|3|
|**_No. of firms_**|184|158|159|132|

_Source: Enterprise Surveys, 1990s. Regional Program on Enterprise Development, World Bank_ Tables 2 and 3 present the financial characteristics of SMEs in our sample. We see that a large percentage of SMEs receive supplier credit in Zimbabwe compared with the other countries. This is explained by the deeper financial system and more highly developed manufacturing sector in Zimbabwe in the 1990s. SMEs in Kenya have relatively more access to bank finance than in the other countries – 25 percent of SMEs received at least some bank credit to start their firms, compared with 8 to 11 percent elsewhere. However, as table 3 shows, these characteristics differ significantly across ethnic groups. In all countries, minorities have much greater access to finance and longer relationship with their suppliers compared to indigenous African entrepreneurs; Tanzania is the only country where these differences are not significant, except for title to property.

**Table 2: Finance Characteristics of SMEs**

||**Kenya**|**Tanzania**|**Zambia**|**Zimbabwe**|
|---|---|---|---|---|
|**_Pct. receiving_**|30.3|11.8|19.2|66.4|
|<br>**_Supplier Credit_**|||||
|<br>**_Avg. years of_**|8.5|7.9|8.6|12.0|
|<br>**_supplier relation_**|||||
|<br>**_Pct with title to_**|37.4|37.1|47.9|43.2|
|**_property_**|||||
|<br>**_Pct. rec. any bank_**|24.6|8.2|11.4|11.2|
|<br>**_loan at startup_**|||||

_Source: Enterprise Surveys 1990s, Regional Program on Enterprise Development, World Bank_ 7 It should be noted that the RPED sample was drawn on the basis of employment rather than on the basis of the number of firms.

13
TA 2-specific findings) indicates that Models 1, 2 and 8 yield estimates of 21.6–22.5 – all within one standard error of the true poverty rate. Model 9 yields estimates of 23.3–23.4, within the 95 percent CI of the true poverty rate. Notably, Model 3.1’s estimates (28.4 percent and 28.7 percent) using the food expenditures data collected with the lighter questionnaire under TA 2 are outside of the 95 percent CI of the true rate. Likewise, Table 4 (for TA 3-specific findings) indicates that Models 1 and 2 estimate poverty rates at 21.6–22.6 percent, within one standard error of the true poverty rate. The estimates for Models 8 and 9, specifically 23.4-23.8, are still within the 95 percent CI of the true poverty rate. The estimates for Models 3.1 and 4.1, specifically 38.5–48.9 percent, are significantly higher than and outside of the 95 percent CI for the true rate.

However, when we standardize the distribution of the variables associated with Model 3 in TA 2 and Models 3 and 4 in TA 3 by those in NPS 2020/21, more promising results emerge. The means of the standardized variables in TA 2 and TA 3 are not statistically different from those in NPS 2020/21 (Appendix A, Table A.3). When we predict the poverty rate using standardized variables, the estimation results demonstrate statistically significant improvements for Model 3.2 in TA 2 (Table 3), as well as for Models 3.2 and 4.2 in TA 3 (Table 4), with all estimates falling within one standard error of the true poverty rate. The improvement after standardizing variables is consistent with earlier standardization evidence using household surveys and labor force surveys for Jordan (Dang _et al._ , 2017). Figure 2 provides a visualization of the key results from Tables 2– 4.

# _Within-year imputation with lighter questionnaires_

Tables 5 and 6 utilize the experimental TA 1 as the base round for imputing poverty rate in TA 2 and TA 3, respectively. Both tables yield the analogous results that Models 1–2 and 8–9 produce poverty estimates that are within one standard error of the expected value. By contrast, Models 3.1 19
<!-- Start of picture text --> 2000<br>1800 poe<br>1600 —s -* - ~ ~<br>a “ ae<br>15 1400 *¢ ~<br>S<br>“4 1200 —_ ee’ ~<br>= i oo =<br>ian<br>BR 1000 =e<br>5 800 oy<br>600 ///<br>400 “a<br>v<br>200 ;<br>fe)<br>2010 2011 2012 2013 2014 2015 2016 2017 2018<br>- = = IMF 2009 projection (estimate based on % non-oil GDP reported)<br>- — - World Bank 2009 projection (middle $75/barrel price scenario)<br>—— Actual revenue<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>50 >So SS SSssste<br>o o-<br>o-<br>o& 40 2 e =<br>as 2<br>Q 30 7*<br>5 ---"<br>20<br>10<br>fe)<br>2011 2012 2013 2014 2015 2016 2017 2018<br>- - - WB-2009 projection (middle $75/barrel price scenario)<br>—— Actual revenue per barrel<br><!-- End of picture text -->