rafmacalaba/datause-encoder-data huggingface dataset:
outcomes using binary indicators for ease of interpretation. Results are similarly robust whether using binary or continuous outcomes. The gains shown in Table 2 translate to 0.152 standard deviation increase for pro-climate beliefs, a 0.184 increase for behaviors, a 0.033 increase for policies, and a 0.130 for green party voting. Moreover, IV causal estimates are substantially larger than OLS estimates for beliefs, behaviors, and voting. One important potential explanation for these larger causal estimates is downward bias in the OLS estimates due to income effects. More educated individuals are often richer, and richer individuals are often more conservative – a standard assumption in political economy models (Meltzer and Richard, 1981) – and thus might be less proclimate. Indeed in Table A1 in the Online Appendix we see correlations exactly along these lines. The substantial increase in causal IV estimates relative to OLS estimates – more than a tripling in magnitude – highlights the importance of credible causal identification of the effects of education on pro-climate outcomes.

Table 2: **The effect of education on** **<u>pro-climate</u> outcomes** .

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Pro-climate<br>beliefs|Pro-climate<br>behaviors|Pro-climate<br>policy<br>preferences|Green<br>voting|
|_Panel A: indicators _|_for above-me_|_dian climate st_|_ance_||
|Years of education|0.040|0.058|0.010|0.036|
||(0.007)|(0.009)|(0.009)|(0.013)|
||[0.000]|[0.000]|[0.225]|[0.005]|
|Mean|0.640|0.682|0.627|0.103|
|Percent change|6.3 %|8.5 %|1.7 %|35.0 %|
|_Panel B: continuous _|_pro-climate _|_variables_|||
|Years of education|0.018|0.035|0.005|0.036|
||(0.004)|(0.007)|(0.006)|(0.013)|
||[0.000]|[0.000]|[0.408]|[0.005]|
|Mean|0.635|0.633|0.601|0.103|
|Percent change|2.9 %|5.6 %|0.8 %|35.0 %|
|Observations|17353|17349|16950|52493|
|Clusters|36|36|36|39|

_Notes:_ This table shows the causal effect of a year of education on each pro-climate outcome index, as in Equation (2). The outcome in Panel A denotes effects on being pro-climate defined in binary terms (relative to the median). Panel B shows averages of the continuous outcomes, where 1 is the most pro-climate response to each question and 0 is the least. Standard errors clustered by country _×_ CSL in parentheses. Standard errors are in parentheses and p-values are in brackets.

9
|Temperature|0.0945|Temperature|0.0566|
|---|---|---|---|
||(0.03)||(0.02)|
|Rain|0.0647|Rain|-0.046|
||(0.03)<br>||(0.09)|

to equation (8) for the functional form used to estimate EE = oo (ss, AA, εε) Notes: Standard errors clustered on households in parentheses. Total number of observations in the sample is 7451. Refer

**Table 9:  Instrumental Variables Estimates of Parameters of Translog Demand System**

|α||
|---|---|
|Heating and cooling|-0.0412<br>(0.022)|
|Lighting|0.0164<br>(0.018)|
|Domestic end-use|-0.0734<br>(0.010)|
|Business end-use|0.0167<br>(0.010)|
|**β**||
|Heating and cooling * Heating and cooling|-0.0343<br>(0.0051)|
|Heating and cooling * Lighting|0.0083<br>(0.0021)|
|Heating and cooling * Domestic end-use|-0.00369<br>(0.0041)|
|Heating and cooling * Business end-use|0.00194<br>(0.0065)|
|Heating and cooling * Composite goods|0.0257<br>(0.0047)|
|Lighting * Lighting|0.00367<br>(0.0028)|
|Lighting * Domestic end-use|-0.0138<br>0.00012|
|Lighting * Business end-use|0.00357<br>(0.0037)|
|Lighting * Composite goods|-0.00318<br>(0.0044)|
|Domestic end-use * Domestic end-use|0.0244<br>(0.0015)|
|Domestic end-use * Business end-use|-0.00602<br>(0.0034)|

49
Financial flows across countries have increased dramatically over the past decades (Bank of International Settlements 2010). Nevertheless, the global financial market is still far from being a fully integrated market due to pervasive frictions that hamper the flow of finance (Lane and Milesi-Ferretti 2008). The literature assigns a key role to informational frictions. In particular, gravity variables that affect the extent of informational frictions (e.g., lower distance, common language, common legal origin, etc.) have consistently been shown to increase bilateral financial flows and to mitigate home bias (Coeurdacier and Rey 2013). Portes and Rey (2005) and Lane and MilesiFerretti (2008) show this for cross-border equity flows. Aviat and Coeurdacier (2007) extend the analysis to bank loans, equity flows and bond holdings.<sup>1</sup> This paper posits that the cross-border movement of people reduces informational frictions across countries and stimulates bilateral financial flows. The reason is that migration from country _i_ to country _j_ has the potential to reveal information on country _i_ which is valuable for investors in country _j_ that seek to invest in country _i_ (e.g., information on the characteristics of the home country’s financial and political institutions). In addition, migrants create or integrate into international business and financial networks, thereby enhancing financial transactions between their home and host countries.

Anecdotal evidence suggests that this channel is indeed operative. For instance, the Bank of China owns a subsidiary in the US. The core business of the US subsidiary is lending to Chinese firms. About 90% of the employees working at the New York branch are locals and most of them are ethnically Chinese (Wall Street Journal 2016). Similarly, the Turkish bank Akbank owns a subsidiary in Germany, which hosts a large community of migrants from Turkey. The CEO of the German subsidiary in Frankfurt was born in Turkey in 1963, and she moved to Germany as a young child (Frankfurter Rundschau 2015). The German subsidiary, named Akbank AG, lends mainly to “multinationals based in Turkey” (Akbank annual report 2015: 62). Akbank AG receives finance from international banking institutions (Global Trade Review 2015), which indicates that the bank funnels funds from a wide range of global investors into Turkey. This evidence is consistent with the view that migrants working in financial institutions at their host countries help to facilitate the transfer of financial flows to their home countries.

> 1. Historical perspectives are provided by Chitu, Eichengreen, and Mehl (2013) and Flandreau (2006).

2
**Figure 1. Evolution of producer price, 2010-2013 (XOF/kg)**

<!-- Start of picture text --> 600<br>Producer price Official price<br>500<br>400<br>300<br>200<br>100<br>0<br>2010 2011 2012 2013 (proj.)<br>Source: World Bank.<br><!-- End of picture text -->

**Figure 2. Evolution of production, in ‘000 tons, and producer price, in XOF / kg, 2008-2013**

<!-- Start of picture text --> 205 500<br>450<br>200<br>400<br>195 350<br>300<br>190<br>250<br>185<br>200<br>180 150<br>100<br>175<br>50<br>170 0<br>2010 2011 2012 2013 (proj.)<br>Production, in '000 tons (solid line) - left axis<br>Farmer price, in XOF/kg (dotted line) - right axis<br>'000 tons XOF/kg<br><!-- End of picture text --> Source: World Bank and IMF.

It should be pointed out that the Agencia Nacional de Cajú - ANCA (2014) expressed concerns about the age of the average plantation (a significant fraction of the cashew trees being over 25 years old) and the presence of pests in some plantations (in Quidamel, Prabis and in the South).

4
workers with the principal component of key plant level observables described in Section 2.5. The results are presented in Table A2 in the Appendix and suggest that both the performance results (columns 1-2) and those on GVC participation (columns 4-5) are largely robust to the alternative specification. On the other hand, the impact on revenues is not significant. More importantly, the results on labor impact are not in line with Table 5. The employment effect is not distinguishable from zero (column 6) while the wage impact becomes negative and significant (column 7).

In a second set of robustness test, we go back to the baseline specification, but we cluster errors at the plant level instead of the 2-digit industry-year level. This might be important because our baseline specification has only 91 clusters. The results are presented in Table A3 in the Appendix. Similarly to the previous table, the results on both performance (columns 1-2) and GVC integration (columns 3-5) are robust to this test. However the effects on employment and wage are not significant (columns 6-7).

Taken together these tests provide confidence on the robustness of the positive impact of automation on plants’ performance and on their integration with international markets. At the same time they cast doubts on the negative employment effects estimated in table 5, reinforcing the case made through the results in Table 7 that the true impact of automation on labor demand is in fact not negative at the plant level.

# **4 Aggregate Impact of Automation**

Next we extend the plant level analysis to the aggregate level, both at the industry and local labor market level.

In the plant level analysis, the inclusion of industry-year fixed effects in (3) mitigates the concern that industry-level adoption might be correlated with other industry trends affecting the outcome variables. However, in both the industry-level and local labor market-level analyses presented in this section, including industry-year fixed effects is not possible.<sup>26</sup> Hence, to address the endogeneity concerns, we implement an instrumental

> 26In the industry-level analysis, including industry-year fixed effects would absorb all the variation of the explanatory variables. In the local labor market analysis, we aggregate industries at the local labor market level, which makes it impossible to control for industry-year fixed effects.

25
# **T AUBL-Et O F C ONNalrTFIlS**

|**I**|**.**<br>**DNrRODUCTION**<br>**.....................**|2|
|---|---|---|
|II.|**TERMS OFRKyERENCE .....................**|2|
||A.<br>Purpose ...........|1,|
||B.<br>Assumptions.|3|
||C.<br>Methodology.|4|
||D.<br>Definitions and Criteria for||
||Safe Motherhood Activities .......<br>...............|..........<br>5|
||E.<br>Official Multilateral and Bilateral Development Data ...|..... 8|
||F.<br>Developing Country Data .........<br>...................|.........<br>8|
|III.|**ENTERVIEW NOTES: SUMMARY........**<br>**..................**|**.........**<br>10|
||A.<br>Summary of Donor Financial Trends .10||
||B.<br>Safe Motherhood Financing By Developing Countries<br>.<br>.Countr|ie<br>11|
|**IV.**|**CONCLUSIONS............................**|13|
||A.<br>Statistical Methods ............................|13|
||B.<br>Financial Trends ................... ...................|.....13|
||C.<br>Policies .....................|14|
||D.<br>Endnotes .....................|15|
|**V.**|**REFERENCES.16**||
Formula

|10Y US Treasury yield|-|2Y US Treasury yield|
|---|---|---|
|USD CPI|avg|50d|
|USD CPI|avg|100d|
|USD CPI|avg|200d|
|3M USD Zero rate|avg|50d|
|6M USD Zero rate|avg|50d|
|1Y USD Zero rate|avg|50d|
|2Y USD Zero rate|avg|50d|
|3Y USD Zero rate|avg|50d|
|5Y USD Zero rate|avg|50d|
|7Y USD Zero rate|avg|50d|
|10Y USD Zero rate|avg|50d|
|30Y USD Zero rate|avg|50d|

Table 4: Calculated cross-sectional data The rationale behind pre-computing certain features is to achieve a better economy in model estimation.

Another data source provided to some versions of the models is the empirical (historical) portfolio distribution. It is calculated using the returns matrix associated to each observation to compute a vector of portfolio returns. The histogram of these returns is then used (the portfolio returns having equal weights).

For T observations of a returns vector with members _rt_ and a vector L [ _l_ 1 _. . . ln_ ] of bucket delimiters, the empirical distribution is defined as this vector: The buckets are delimited according to this rule: where R is the vector of one-day portfolio returns (defined as: _rt ∗ positions_ where position is the vector in (5)).

# 3.3 Target data

The model will try to forecast the distribution of the actual (forward-looking) 10-day returns of the portfolio.

11
# **Table A9: Real expenditure on consumption (goods), % yoy**

|||**Food**||**Other c**|**onsumable**|**goods**|**Du**|**rablegoo**|**ds**|
|---|---|---|---|---|---|---|---|---|---|
||**2015**|**2016**|**2017**|**2015**|**2016**|**2017**|**2015**|**2016**|**2017**|
|Average|‐1.7|‐2.5|1.0|‐0.4|‐0.6|2.2|‐10.6|‐5.5|9.8|
|||Poor and|vulnerable|populatio|n|||||
|Vulnerable|‐1.0|‐1.2|2.2|5.6|1.7|8.9|‐14.1|‐0.4|14.7|
|Official poverty|‐5.2|0.8|4.3|10.9|1.8|5.5|1.6|12.3|18.1|
|Moderatepoverty|2.1|3.4|3.4|8.4|11.2|2.9|‐2.8|10.3|17.7|
|||Pl|ace of resid|ence||||||
|Minsk|‐0.1|‐1.9|1.0|3.2|‐2.0|‐0.4|‐9.2|‐7.7|12.1|
|Large cities|‐0.7|‐4.0|0.4|‐3.3|0.3|4.0|‐6.3|2.0|0.1|
|Small cities|‐1.7|‐1.5|1.7|‐2.7|4.4|0.0|‐11.9|‐17.9|15.8|
|Rural area|‐6.2|‐1.3|1.5|0.7|‐3.8|3.6|‐17.6|‐6.2|21.7|
||||Age|||||||
|Child|‐0.6|‐4.7|1.7|1.6|‐0.4|5.7|‐12.8|‐3.7|7.1|
|Working age|‐2.7|‐3.4|0.4|‐1.7|‐0.6|3.5|‐13.3|‐5.2|11.7|
|Retiree|‐1.4|‐0.9|0.5|2.1|0.4|‐1.5|‐2.1|‐6.9|8.5|
||Emplo<br>|yment stat<br>|us for wor<br>|kingagepo<br>|pulation<br>|||||
|Employed|‐2.6|‐3.1|0.3|‐1.2|‐1.2|3.0|‐11.8|‐7.0|12.2|
|Partly employed|‐1.4|1.7|‐5.9|‐8.0|2.4|‐0.3|13.5|‐39.8|34.1|
|Non‐employed|‐1.7|‐3.6|2.9|1.6|‐4.1|6.6|‐17.9|5.3|8.3|
||<br>Edu|<br>cation leve|<br>l for workin|<br> gagepop|<br>ulation|||||
|High education|‐3.4|‐0.3|‐0.6|‐4.4|1.8|5.1|‐10.5|‐10.7|7.2|
|Secondary specialized|‐1.2|‐5.0|0.7|‐1.4|‐3.6|0.9|‐16.2|‐3.2|11.8|
|Other|‐4.5|‐4.4|0.5|‐1.9|‐0.7|2.5|‐15.7|‐1.9|15.4|
|||Type|of househo|ld(HH)||||||
|HH without children|‐2.9|‐1.0|1.0|‐1.3|‐1.2|‐0.1|‐8.2|‐9.4|16.4|
|Retiree HH|‐2.0|‐0.4|1.0|0.3|3.1|‐4.2|‐5.2|‐4.3|10.8|
|HH with children|‐0.2|‐5.1|1.8|0.7|0.1|5.0|‐13.5|‐0.6|2.7|
|1 child|0.0|‐5.6|1.1|‐1.8|‐0.1|3.5|‐16.1|0.4|0.8|
|2 children|‐4.6|‐3.2|2.6|0.3|‐0.2|7.9|‐9.5|‐1.5|4.3|
|3 and more children|‐2.7|‐7.1|4.4|2.4|3.6|7.5|‐17.5|‐6.3|15.2|
|singleparent|‐1.9|‐2.4|2.8|‐0.3|‐3.3|6.2|‐4.1|‐18.4|24.9|

Source: Authors’ estimates based on HSS data.

36
VPL and the proportion of households who are vulnerable are as presented in table

# 2.9

Table 2: Estimates of Vulnerability and Proportions of Vulnerable HH's When Headcount Poverty is 20 Percent of the Population (May 1997 Prices)

||Mini S<br>(December 199|usenas<br>8 - August 1999)|100 Villa<br>(May 1997 -|ge Survey<br>August 1998)|
|---|---|---|---|---|
||Ignoring<br>measurement<br>error|Net of<br>measurement<br>error (300%)|Ignoring<br>measurement<br>error|Net of<br>measurement<br>error (50(10)|
|Mean of log percapita expenditures<br>in the initial period|<br>10.901|<br>10.901|<br>10.730|<br>10.730|
|Inverse of mean of log percapita<br>expenditures (Rp/month)|54,251|<br>54,251|<br>45,687|<br>45,687|
|Standard deviation of changes in log<br>expenditures durmig the period|<br>0.412|<br>0.288|<br>0.462|<br>0.231|
|Yearly standard deviation of<br>changes in log expenditures|0.617|<br>0.432|<br>0.370|<br>0.185|
|Yearly coefficient of variability|_0.057_|<br>0.040|<br>0.035|<br>0.017|
|Yearly probability of falling to<br>below poverty line|0.238|<br>0.154|<br>0.137|<br>0.015|
|Average vulnerability for three<br>annual shocks|0.577|<br>0.395|<br>0.358|<br>0.043|
|Average vulnerability three years<br>ahead|0.406|<br>0.367|<br>0.358|<br>0.233|
|Vulnerability poverty line<br>[VPL(0.5,3,PL)]|10.967|<br>10.815|<br>10.629|<br>10.477|
|Headcount vulnerable rate|58.91%|<br>47.49%|<br>42.10%|<br>30.18%/0|
|Ratio of vulnerable to poor|2.95|2.37|<br>2.11-|<br>1.51|

The results with the usual standard deviations, which would ignore measurement error, show very high levels of vulnerability, 59 percent in MiniSusenas and 42 percent in 1 00 Village Survey. In MiniPSusenas, the estimated yearly standard deviation of changes in log expenditures is 0.617, while the mean of 9 All empirical vulnerability calculations using both Mini-Susenas and 100 Village Survey data sets, with the results presented in tables 2 and 5, are based on May 1997 prices.

14
# **1. Introduction**

The informal sector is a source of livelihood to millions of people around the world, by some estimates accounting for about a quarter of the GDP (Schneider, Buehn, and Montenegro, 2010) and around 70% of employment in the developing world (Loayza, 2018). Despite the size and importance of this sector, cross-regional comparable business-level data covering a wide range of topics around their operations are lacking. Since informal sector operations are generally not captured by official records, collecting data on the informal sector businesses, especially from representative samples, is challenging (ILO, 2013). The data on informal economic activity can be collected through two primary actors—the worker and the place of employment (i.e., business or firm). The former is often accomplished through household surveys, commonly the Labor Force Surveys (LFS); this is particularly suited to understand the 'intensive margin' of informality—i.e., unreported employment in otherwise formal sector businesses. The latter is often preferred for a detailed understanding of the workings of informal businesses, their production processes and experiences, the ‘extensive margin’ of informality.

To collect data directly from informal businesses, various survey methods have been implemented, with the corresponding data enabling insightful research into how informal businesses operate and generating development policies around them (see, e.g., Benjamin and Mbaye, 2012; LaPorta and Shleifer, 2014; Amin and Islam, 2015; Islam, 2019).<sup>2</sup> Aga et al. (2022), which this paper accompanies, examines the innovative application of the Adaptive Cluster Sampling (ACS) method (Thompson, 1990) to collecting data from informal businesses, rendering samples that are representative of the underlying population of informal businesses at the geographical area being covered.

This paper introduces the data collected across twenty-four cities in seven countries through the survey method examined in the companion paper (Aga et al., 2022) and provides a look into some of the descriptive findings from this combined and standardized data. The data from the Informal Sector Enterprise Surveys are made publicly available by the World Bank's Enterprise Analysis unit.<sup>3</sup> Section 2 introduces the data, briefly outlining the survey method (i.e.,

> 2 These methods include creating a random sample using the economic censuses that cover the informal sector (which is extremely rare), snowballing, a 1-2 method, and various simple area-based sampling methods.

> 3 Data can be downloaded from the Enterprise Surveys data portal: https://login.enterprisesurveys.org/ and the citylevel Informal Sector Enterprise Surveys indicators can be viewed or downloaded from the Informal Sector

2
Policy ReseaRch WoRking PaPeR 4924

# **Abstract**

Employing a view of culture as a communicative phenomenon involving discursive engagement, which is deeply influenced by social and economic inequalities, the authors argue that the struggle to break free of poverty is as much a cultural process as it is political and economic. In this paper, they analyze important examples of discursive spaces—public meetings in Indian village democracies _(gram sabhas)_ , where villagers make important decisions about budgetary allocations for village development and the selection of beneficiaries for anti-poverty programs. They examine 290 transcripts of _gram sabhas_ from South India to demonstrate how they create a culture of civic/political engagement among poor people, and how definitions of poverty and beneficiaryselection criteria are understood and interrogated within them. Through this examination, they highlight the process by which _gram sabhas_ facilitate the acquisition of crucial cultural capabilities such as discursive skills and civic agency by poor and disadvantaged groups. They illustrate how the poor and socially marginalized deploy these discursive skills in a resource-scarce and socially stratified environment in making material and non-material demands in their search for dignity. The intersection of poverty, culture, and deliberative democracy is a topic of broad relevance because it sheds light on cultural processes that can be influenced by public action in a manner that helps improve the voice and agency of the poor.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to understand local government and citizen-based engagement in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at vrao@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
Detailed results comparing all revenue, tariff revenue, cash collected, opex, and capex per kWh billed are shown in Table 6. It includes the full data for Figure 6 and Figure 7.

**Table 6: Revenues and costs, constant 2014 US$ per kWh billed**

|**Country**|**Revenue**|**Tariff**|**Bill collection**|**Cash**|**Opex**|**Capex**|**Total**|
|---|---|---|---|---|---|---|---|
||||**rate**|**collected**|||**cost**|
|Benin|$0.24|$0.22|95%|$0.23|$0.21|$0.05|$0.26|
|Botswana<sup>a</sup>|$0.08|$0.08|99%|$0.08|$0.13|$0.08|$0.22|
|Burkina Faso|$0.23|$0.23|98%|$0.23|$0.23|$0.11|$0.34|
|Burundi|$0.09|$0.09|74%|$0.07|$0.10|$0.11|$0.21|
|Cameroon|$0.12|$0.12|95%|$0.11|$0.11|$0.05|$0.16|
|Cape Verde|$0.42|$0.42|88%|$0.37|$0.39|$0.12|$0.51|
|Central African Republic|$0.14|$0.12|78%|$0.11|$0.10|$0.11|$0.20|
|Comoros|$0.21|$0.21|58%|$0.12|$0.48|$0.13|$0.61|
|Congo, Rep.|$0.12|$0.12|80%|$0.09|$0.06|$0.11|$0.17|
|Côte d’Ivoire|$0.14|$0.13|82%|$0.11|$0.16|$0.05|$0.21|
|Ethiopia<sup>a</sup>|$0.04|$0.04|87%|$0.04|$0.02|$0.15|$0.17|
|Gabon<sup>a</sup>|$0.21|$0.21|99%|$0.21|$0.18|$0.06|$0.25|
|Gambia, The|$0.23|$0.22|73%|$0.17|$0.33|$0.11|$0.44|
|Ghana|$0.11|$0.11|95%|$0.11|$0.10|$0.04|$0.14|
|Guinea|$0.11|$0.11|70%|$0.08|$0.22|$0.13|$0.35|
|Kenya|$0.16|$0.15|99%|$0.15|$0.12|$0.09|$0.22|
|Lesotho<sup>a</sup>|$0.07|$0.06|87%|$0.06|$0.02|$0.07|$0.09|
|Liberia|$0.52|$0.50|94%|$0.49|$0.54|$0.12|$0.66|
|Madagascar|$0.16|$0.16|60%|$0.09|$0.23|$0.09|$0.32|
|Malawi|$0.10|$0.09|93%|$0.09|$0.07|$0.09|$0.16|
|Mali|$0.21|$0.21|99%|$0.20|$0.27|$0.06|$0.33|
|Mauritania|$0.22|$0.22|81%|$0.18|$0.26|$0.08|$0.34|
|Mauritius<sup>a</sup>|$0.20|$0.19|99%|$0.19|$0.16|$0.06|$0.21|
|Mozambique|$0.09|$0.08|92%|$0.08|$0.06|$0.06|$0.12|
|Niger|$0.17|$0.17|86%|$0.14|$0.15|$0.04|$0.19|
|Nigeria|$0.09|$0.09|66%|$0.06|$0.13|$0.09|$0.21|
|Rwanda|$0.24|$0.21|95%|$0.23|$0.31|$0.12|$0.43|
|São Tomé and Príncipe|$0.23|$0.23|59%|$0.14|$0.43|$0.11|$0.54|
|Senegal|$0.23|$0.23|93%|$0.22|$0.29|$0.05|$0.35|
|Seychelles<sup>a</sup>|$0.33|$0.32|99%|$0.33|$0.26|$0.06|$0.32|
|Sierra Leone|$0.33|$0.33|79%|$0.26|$0.34|$0.21|$0.55|
|South Africa|$0.06|$0.06|98%|$0.06|$0.06|$0.06|$0.11|
|Sudan|$0.05|$0.04|100%|$0.05|$0.06|$0.09|$0.15|
|Swaziland<sup>a</sup>|$0.13|$0.11|99%|$0.12|$0.12|$0.06|$0.17|
|Tanzania|$0.15|$0.15|92%|$0.14|$0.12|$0.05|$0.17|
|Togo|$0.28|$0.28|88%|$0.25|$0.29|$0.05|$0.33|
|Uganda|$0.17|$0.17|99%|$0.17|$0.13|$0.03|$0.16|
|Zambia|$0.06|$0.06|96%|$0.05|$0.05|$0.03|$0.08|
|Zimbabwe|$0.10|$0.09|87%|$0.08|$0.08|$0.08|$0.16|
|Maximum|$0.52|$0.50|100%|$0.49|$0.54|$0.21|$0.66|
|Minimum|$0.04|$0.04|58%|$0.04|$0.02|$0.03|$0.08|
|Median|$0.16|$0.15|93%|$0.12|$0.15|$0.08|$0.21|

_Source_ : World Bank staff calculations.

a. Bill collection rates are assumed for these countries, as explained in section A1.3 of annex 1.

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