rafmacalaba/datause-encoder-data huggingface dataset:
25 The variable diversion takes on values between zero and one. A value of zero implies that all investments are directly financed from places where the real funding potential is located. In contrast, a value of one implies that all income flows are rerouted to exploit a preferential tax treatment in locations other than where the real funding potential lies.

Treaty-shopping opportunities reduce government revenue and, in turn, increase FDI. To quantify investment gains and revenue losses resulting from treaty-shopping we use equation (11) and compute Table 6 summarizes the results. We first assess effects of treaty-shopping with the unobservable cost parameter being constant across all SSA economies (see column 1). Using the structural parameters of Model 3 in Table 5, the simulation suggests that some 36 percent of all income flows are rerouted to reduce tax payments, resulting in tax revenue losses of around 5 percent of CIT revenue and increased investments of around 1 percent of FDI across SSA. Next, we allow treaty-shopping costs to differ depending on whether countries have concluded a DTT with an investment hub in columns 2 and 3. Using the parameter estimates of Model 5 in Table 5, we find considerably more income being rerouted in the former group (around 90 percent) than in the latter (30 percent). Because of treaty shopping, countries with an investment hub treaty risk losing around 15 percent of CIT revenue, while the reduced cost of capital may attract around 3 percent more FDI. Note that revenue losses due to treaty-shopping are around 10 percent larger among countries that have concluded a treaty with an investment hub. Contrasting this effect with the estimates presented in Section 5.A suggests that rerouted income accounts for around half of total revenue losses linked to DTTs with investment hubs.<sup>37</sup> These estimated magnitudes appear large compared to the existing literature on international tax avoidance. For instance, the OECD (2015) assesses revenue losses linked to base erosion and profit shifting between 4-10 percent globally. However, country-specific results can be much larger,<sup>38</sup> and empirical work has thus far primarily focused on arbitrage opportunities between countries’ statutory CIT rates. In contrast, we examine revenue losses related to treaty-shopping. Our findings are in line with recent evidence on the importance of offshore centers globally (see Damgaard and others 2018), indicating that more than half of all outward FDI flows pass through foreign shell companies. For SSA, we show that DTTs critically impact the extent of these diversions.”

> 37 The DD estimates indicate that total revenue losses due to investment hub treaties are around 20 percent of CIT revenue.

> 38 Beer and others (2018) estimate that MNEs’ tax avoidance might reduce CIT revenue in the US by 17 percent; Clausing (2016) puts this number between 19 and 30 percent.
**Figure 1. Population and Per Capita Income in Latvia**

<!-- Start of picture text --> 2.5 3000<br>2500<br>2.4<br>2000<br>2.3<br>1500<br>2.2<br>1000<br>2.1<br>500<br>2.0 0<br>1997 1998 1999 2000 2001 2002 2003<br>Population (mln) GDP per capita, LVL<br>Population, mln.<br>GDP per capita, LVL (in constant prices of 2000)<br><!-- End of picture text --> The official old age dependency ratio has been brought down to 346 pensioners per 1000 working age population with the help of pension reforms. This level is below pre-transition value of 368 (see Figure 4). The forecast developed by the Center of Demography of the University of Latvia predicts further falls of this ratio until 2009, followed by an eventual rise to 400 pensioners per 1000 individuals of working age by 2025 (see Appendix Figure 1).

For Latvia, a more useful statistic on old age dependency would be one which takes into account the low labor force participation rates of teenagers, as well as projected rises in both retirement ages and life expectancy. In 2001, life expectancy was 65.2 years for males and 76.6 years for females. A more adequate (and internationally comparable) measure would be the ratio of persons aged 20 to 64 to those aged 65 or more. This ratio has been forecast for Latvia by the ILO and the UN, and is presented in Appendix Figure 2. Note that the projected value for 2002 is 3.95, very close to the realized 3.87. This statistic suggests an almost unchanged dependency ratio until 2015, but a strong rise in old age dependency thereafter. As shown in Table 1, the cohort currently aged 45 to 64 is smaller than that between 15 and 44. This is clearly what drives the labor-supply-friendly demographic projection until 2015.

**Table 1.  Age Distribution of the Latvian Population, January 2003**

|Age|0-14|15-24|25-34|35-44|45-54|55-64|65+|Total|
|---|---|---|---|---|---|---|---|---|
|%pop.|16.3|14.9|13.8|14.6|13.2|11.6|15.7|100.0|

_Source_ : Demographic yearbook 2003.

5
- ii -

# **PAYING FOR URBAN SERVICES**

# A STUDY OF **WATER** VENDING _AND_ WILLINGNESS TO PAY **FOR** WATER IN ONITSHA, **NIGERIA**

# Table of Contents

||Page No.<br>EXECUTIVE SUMMARY ..............................**.....**<br>so<br>......................|
|---|---|
|Is|INTRODUCTION<br>..................<br>1|
||Background ........**.**<br>e .....e.e................e......................<br>2|
|II*|WATERVENDING IN ONITSHA<br>...........................................<br>.<br>4|
||A. The Study Area...........................................<br>4<br>B. Description of Water-Vending Practices in Onitsha<br>........ 4|
|III.|FIELD PROCEDURES<br>........*******************<br>****************<br>*******<br>000.....7|
|IV.|MONEY AND WATER TRANSACTIONS IN THE WATER VENDING<br>SYSTEMIN ONITSUA...................<br>o........<br>10|
|V.|RESULTS OF THE HOUSEHOT.D INTERVIEWS ..................... 14|
|VI.|CONCLUDING REMARKS AND POLICY IMPLICATIONS<br>.....<br>o..............<br>23|
||APPENDIX: FINANCIAL ASPECTS OF THE WATER VENDING BUSINESS<br>FOR DIFFERENT TYPES OF WATER VENDORS....<br>o........ 26|
||Boreholes: Revenues and Prices...................<br>26<br>Tanker Trucks: Revenues,Costs,<br>and Profits...... 26<br>Small Retail Water Vendors: Revenues, Costs,<br>and Profits .....<br>30<br>Distributing<br>Vendors: Revenues, Costs,<br>and Pricese................................<br>**......**<br>30|
||REFERENCES ...........................................<br>33|
income. From this income, gross profit is derived by deducting the cost of goods sold, and net profit is calculated by further deducting allowable depreciation expenses, interest, and other expenses. Moreover, businesses have the option to deduct losses carried forward or backward from their net income to determine their taxable income. Subsequently, tax rates are applied to this taxable income to ascertain their tax liabilities. It is important to note that even if a business generates substantial revenue, its reported tax liabilities may appear lower if it reports higher costs of goods and services.As an illustrative example, Cronin et al. (2023) demonstrates that deductible expenses can vary based on demographic factors such as race. Given the potential for variation in reported deductible expenses by gender, this comprehensive dataset enables us to not only investigate the presence of gender disparities in reported expenses but also to explore gender differences in reported income and costs. Additionally, the data employed for this study covers a span of two years (2011 and 2012).

It is worth noting that this administrative data source does not explicitly indicate the gender of business owners. Instead, gender assignments were made by merging this administrative data with survey data collected through a field experiment, which collected information on the business owner by Shimeles et al. (2017). The study targeted 5,400 randomly selected taxpayers from approximately 86,000 taxpayers in Addis Ababa for the fiscal year 2013/2014.<sup>3</sup> The sampling approach employed a stratified random sampling method based on geographic location, known as sub-city, and economic sectors. The study included an equal number of taxpayers from three major sectors: wholesalers, agro-processing and manufacturing, and other services. These taxpayers were drawn from five of the ten sub-cities in Addis Ababa, where the majority of taxpayers operate. Specifically, the study encompassed 1,350 taxpayers from each of the following sub-cities: Addis Ketema, Bole,

> 3 th th The Ethiopian fiscal year starts on July 8 and ends on July 7 .

10
# Pollution and Expenditures in a Penalized Vector Spatial

# Autoregressive Time Series Model with Data-Driven Networks

Bo Pieter Johannes Andr ́ee<sup>1a,1b,2,3,*</sup> , Phoebe Spencer<sup>1a</sup> , Andres Chamorro<sup>1a</sup> , Dieter Wang<sup>1a,2,4,5</sup> , Sardar Feredun Azari<sup>1b</sup> , and Harun Dogo<sup>1a</sup>

> 1a _Worldbank Group, Environment and Natural Resources Global Practice_

> 1b _Worldbank Group, Geo-Operations Support Team_

> 1c _Worldbank Group, Information and Technology Solutions_

> 2 _Tinbergen Institute_

> 3 _Department of Spatial Economics/SPINlab, VU University Amsterdam, Netherlands_

> 4 _Department of Finance, VU University Amsterdam_

> 5 _Department of Econometrics, VU University Amsterdam_

> * _Corresponding Author: email b.p.j.andree@vu.nl or bandree@worldbank.org_

**Keywords:** Poverty, Pollution, Penalized Inference, Spatial Models, Impulse Response.
program and hence a precautionary attitude may have been to wait until the mayor announced PYP as a permanent policy and explore other adaptation mechanisms before increasing their car stock. For example, in this phase there was some evidence of intertemporal substitution between restricted and unrestricted hours, indicating that some households reallocated trips. Regarding enforcement, although it has been considered satisfactory, there are indications of weak monitoring in the moderate phase. According to Acevedo (1998), there were too few police officers involved in the PYP enforcement (1,050 in total, of whom only 150 were motor officers), which may have affected compliance.<sup>28</sup> Also, drivers may have found routes where the probability of being caught in violation was very low. A newspaper reported that drivers were choosing secondary roads to avoid police controls and that some police officers had to focus on tasks unrelated to PYP enforcement, reducing the coverage in several areas (Nullvalue 1998).

Additionally, the following model is used to evaluate the effect of PYP on new sales: where _S_ t is monthly new regular car sales in logs at time _t_ , ���� is a set of lags of the dependent variable, _R_ t is GDP growth rate in logs at time _t_ , �� is the error term, and other variables are as above. � is the effect of PYP on new regular car sales in the drastic phase. The data spans from July 2000 to September 2011, and, due to the lack of pre-2000 sales data, the moderate phase cannot be assessed. The GDP growth rate is

> 28. Enforcement was mainly in the hands of the motor officers ( _caza – infractores_ ), who patrolled the major city corridors. The other 900 police officers standing in the roads could only marginally support the motor officers’ work as their main job was to focus on bottlenecks in traffic. When a driver was caught in violation by any of these officers, the driver was stopped and directed to a secondary road to receive the fine. According to the Metropolitan Police’s organizational structure, the police functions are separated; hence police officers not tasked with improving transit were not involved in PYP enforcement.

21
# **Transport Policies and Development**<sup>**1**</sup>

## **Claudia N. Berg, Uwe Deichmann, Yishen Liu, and Harris Selod**<sup>**2**</sup>

### **Keywords:  Transport, growth, poverty, sustainability**

### **JEL: O1, O2, R4, Q5**

> 1 The authors are grateful to Nathaniel Baum-Snow, Gilles Duranton, and staff from the World Bank’s Transport and ICT Global Practice for early discussions.  Funding from DFID under the World Bank’s Strategic Research Program “Transport Policies of Sustainable and Inclusive Growth” (P151104) and under the World Bank’s Knowledge for Change Program “Impacts of Transport Infrastructure” (P133411) is gratefully acknowledged.  Corresponding author: Harris Selod (email: <u>hselod@worldbank.org, address:</u> 1818 H Street, NW, Washington, D.C. 20433).

> 2 Berg, Deichmann, Selod: Development Research Group, The World Bank. Liu: The George Washington University.
# **Table S9**

**Analysis stratified by categories of** **_P.falciparum_ prevalence at baseline.** Effect of the IST intervention at 12 and 24 months follow-up on the prevalence of anemia, by baseline prevalence category of _P.falciparum_ (control school prevalence estimated using 12 month follow-up data) with adjustment for age, sex and stratification effects.

# **Table S10**

**Analysis stratified by number of AL treatments received.** Effect of the IST intervention at 12 and 24 months follow-up within the IST intervention group by number of positive results and subsequent treatments received at the individual level.

# **Checklist S1**

# **CONSORT checklist**

# **Protocol S1 Study Protocol**

30
# **Introduction**

How has public attention to poverty changed over time? Has less poverty in the world as a whole deflected that attention or enhanced it? How has it reflected economic and social thought more broadly? Were some periods more enlightened about poverty than others? Prevailing historical narratives that might throw light on such questions have relied heavily on the careful human reading of texts. While this is understandable, it seems that historians might also exploit the digital age.<sup>2</sup> The technology of digitizing texts has opened up new options for systematically searching words and phrases, as a complement to more traditional historical research methods.

A new window has recently been opened on the prominence of ideas in the writings of their time. The _<u>Google Books Ngram Viewer</u>_ (the _Viewer_ hereafter) was introduced by JeanBaptiste Michel et al. (2010). In this context, an “n-gram” is just a phrase, made up of words (“1grams”). Michel et al. created a corpus of n-grams (for n=1,5) from 5.2 million books (excluding periodicals). The Michel et al. corpus holds over 500 billion words (360 billion in English). By conventional human reading, it would take the working lives of 347 people to read the entire corpus at 300 words per minute.<sup>3</sup> The _Viewer_ is fast; word searches and plots over hundreds of years take only a small fraction of a second. Naturally the total number of words in a given year has increased over time. Michel et al. estimate that there were about one million words in English in 2000, but barely half that number in 1900. (And most of that growth was in the latter half of the 20th century.) The _Viewer_ ‟s counts are normalized by the total number of n-grams in the corpus for that year. I will refer to the normalized frequency as the “incidence.” This paper uses the _Viewer_ to study the evolution of attention to poverty and related topics in the literature. The vast majority of _Google Books_ are not, of course, specifically about these subjects. What we learn from reading them is more about the public‟s written attention to a topic, which need not correspond well with influence in more specific circles, such as academics or policy makers. (Nor does awareness, as reflected in language, imply action.) This is not a limitation for the present purpose, since _Google Books_ is being used here as a linguistic window on public awareness. If certain time periods were critical in the history of popular thought on

> 2 Maxwell (2010) comments that historians seem to have been slow to take up the scope for analysis using digitized texts.

> 3 I assume that they would work eight hours a day for 250 days per year for 40 years.

2
weighted poverty lines, which is evidence of the existing heterogeneity in terms of the size of the subnational population and the number of poverty lines by country.

The last two rows show the mean and median values of the currently-used $2.5 and $4 per person per day in 2005 PPP poverty lines –reported in Gasparini, Cicowiez, and Escudero (2013) – updated to 2011 PPPs based on CPI data of each country. The extreme poverty lines are approximately $3.3 per person per day on average, while the moderate poverty lines are approximately $6.4 per person per day on average. These numbers are different in some cases from those reported in the first four rows of the table. However, there are several issues regarding these currently-used poverty lines converted to 2011 PPPs. First, Jolliffe and Prydz (2015) show that poverty lines updated using CPI information are sensitive to the inflation data used to update the lines. For instance, the authors find that the global extreme poverty line is $1.70 per person per day when updated to 2011 values using official CPIs from the World Bank’s WDI database, whereas it increases to $1.82 when updated using inflation data from PovcalNet. Second, the $2.5 and $4 per person per day poverty lines are based on official poverty lines from main cities and, therefore, are estimated from a smaller sample of official poverty lines which can significantly affect poverty line estimates (Jolliffe and Prydz 2015 and Ferreira et al. 2015). In addition, since these poverty lines are based on information from major cities, they are likely to be higher than the poverty lines based on a larger sample of subnational poverty lines that include smaller cities. Third, Brazil, Haiti, and the Dominican Republic are excluded from the estimates, which is also likely to affect the estimates as Brazil is the largest economy in LAC and Haiti is the poorest country. Finally, the limited available information on the collection of the underlying official poverty lines, and the method used to estimate the currently-used $2.5 and $4 per person per day poverty lines in 2005 PPPs, makes it difficult, if not practically impossible, to replicate them.

One approach to validate the poverty lines shown in Table 3 is to estimate a _normative_ Engel coefficient, which is defined as the ratio between the extreme and the moderate regional poverty lines. The _positive_ Engel coefficient is generally understood as the proportion of total expenditure spent on food, which is expected to decrease as the level of income or development increases (Engel 1857; Clements and Chen 2010; Anker 2011). A _normative_ Engel coefficient, in contrast, can be understood as the ratio between the minimum expenditure on food required to not be considered as extremely poor and the minimum expenditure on non-food items required to not be considered as moderately poor. By analogy with the _positive_ Engel coefficient, the _normative_ 13
Table 13. Estimates of equation (10) testing balance in attrition rates and testing balance for the non‐attrition sample

||||non‐attritio|n sample||
|---|---|---|---|---|---|
|dependent variable:<br>_1 year of school readiness interve_|attrition<br>_ntion 1st gr_|baseline<br>community<br>TEHCI<br>score (SD)<br>_ade sample_|home<br>activity<br>index|female|CLRW|
|probability of being from a|‐0.05**|‐0.06|‐0.02|0|0.08|
|treatment community|(0.02)|(0.08)|(0.01)|(0.01)|(0.09)|
|constant|0.14***|‐0.01|0.78***|0.41***|0.58***|
||(0.01)|(0.04)|(0.01)|(0.01)|(0.05)|
|observations|1214|1049|1049|1049|1049|
|r‐square|0.00|0.00|0.01|0.00|0.00|
|<br>_1 year of school readiness interve_|_ntion 1st &_|_2nd grade sam_|_ple_|||
|probability of being from a|‐0.02|‐0.04|‐0.02|‐0.01|0.08|
|treatment community|(0.03)|(0.07)|(0.01)|(0.02)|(0.09)|
|constant|0.25***|‐0.01|0.78***|0.41***|0.58***|
||(0.02)|(0.04)|(0.01)|(0.01)|(0.05)|
|observations|1214|925|925|925|925|
|<sup>r‐square</sup>|000|000|001|000|000|
|<br>_2 years of school readiness interv_|.<br>_ention 1st g_|.<br>_rade sample_|.|.|.|
|probability of being from a|‐0.02|‐0.09|‐0.02|0.01|0.11|
|treatment community|(0.02)|(0.07)|(0.01)|(0.02)|(0.09)|
|constant|0.12***|0|0.78***|0.47***|0.47***|
||(0.01)|(0.03)|(0.01)|(0.01)|(0.05)|
|observations|1290|1140|1140|1140|1140|
|<sup>r‐square</sup>|0.00|0.01|0.01|0.00|0.01|

Table presents estimates of equation (10) for attrition at baseline and for the listed dependent variables at baseline for the non‐attrition sample. Non‐zero coefficients of the probability of being from a treatment community imply differential attrition rates or, for the non‐attrition sample, imbalance between students from treatment and non‐treatment communities as a result of the community‐school mapping. Standard errors included in parentheses. Statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively.

38
# **4. Cohort and age statistics**

Table A4: Share of men and women who believe that a woman has to have children to be fulfilled in India

|Age<br>|Statist<br>|||||Coh<br>|ort<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|group|ics|1940-<br>44|1945-<br>49|1950-<br>54|1955-<br>59|1960-<br>64|1965-<br>69|1970-<br>74|1975-<br>79|1980-<br>84|1985-<br>89|_Wgt_<br>_Ave_|
|**15-19**|_Share_|||||||84%|84%|82%||84%|
||_N_|||||||177|63|11|||
|**20-24**|_Share_||||||87%|83%|81%|78%||84%|
||_N_||||||332|289|201|60|||
|**25-29**|_Share_|||||89%|80%|80%|91%|||84%|
||_N_|||||265|294|245|162||||
|**30-34**|_Share_||||87%|85%|85%|86%||||86%|
||_N_||||215|299|276|118|||||
|**35-39**|_Share_|||89%|86%|89%|85%|||||88%|
||_N_|||276|287|225|142||||||
|**40-44**|_Share_||91%|88%|87%|82%||||||87%|
||_N_||143|197|249|111|||||||
|**45-49**|_Share_|93%|93%|84%|91%|||||||90%|
||_N_|90|176|162|93||||||||
|_Weight_<br>_average _|_ed_<br>|93%|92%|88%|87%|87%|84%|83%|85%|79%|||

Source: WVS for India. Note: The number, N, of each cell counts the number of women in each cell who responded to the question, excluding counting missing responses. This variable has high share of missing values. For Bangladesh, there is about 28 percent missing values, for India, 46 percent missing values and for Pakistan, 72 percent missing values. A closer look at the data shows that this question was not asked in more recent survey rounds. For Bangladesh, the question was not asked in 2018, for India, the question was not asked in 2006, and for Pakistan, it was not asked in 2012 and 2018.

54