rafmacalaba/datause-encoder-data huggingface dataset:
Analysis of the 2001 census data in Bangladesh reveals only 2.23% of rural households with $470 per capita annual income<sup>33</sup> or more could afford a “ _pucka_ ” house, a brick house with a concrete roof in 2001. However, Bangladesh is currently experiencing and expecting 6%‐8% GDP growth per year for the coming decades. It is expected that approximately 98% of the households will be living in brick houses by 2050, even after accounting for inflation. This in turn implies a significant reduction in housing damages and a substantial increase in household asset damages during cyclones over time.

Projection of population in coastal regions by 2050 without and with climate change indicates that an additional 7.08 million inhabitants (i.e. an additional 1.45 million houses ‐ under the assumption of an average family size of 4.89) will be exposed to significant damages from storm surges in a changing climate. Accounting for the larger areal extent of a cyclone under climate change an additional 1.6 million houses are projected to be damaged from a 10 year return period cyclone due to climate change.<sup>34</sup> The size of a standard house in Bangladesh is assumed to be 400 sq feet with 2,000 sq feet of brick wall surface and approximate value of household assets is $2,143 (Tk 150,000). Under the assumption that on average 50% of the walls and 50% of the household assets will be damaged during inundation, **re‐plastering the houses will cost $229 million (at Tk 10/ sq. ft) and an estimate of household content damage is expected to be approximately $1.718 million** .

# _<u>Potential damage in education infrastructure</u>_ <u>:</u>

The population projection indicates that with an additional 7.08 million inhabitants in coastal Bangladesh exposed to storm surges in a changing climate by 2050; the expected exposure of primary school students will be 456,690 (in 2,283 primary schools) and that of secondary school students will be 312,957 (in 2,086 secondary schools).<sup>35</sup> Accounting for the larger

> 32 In Jhupris, ceiling, which is less than four feet, is made of cheap construction materials straw, bamboo, grass, leaves, polythene, gunny bags, etc.

> 33 Monthly income Tk 2,750 per capita.

> 34 It has been assumed that inundation depth of 1m or less will have negligible impact.

> 35 These estimates are based on the following assumptions: (a) ratio of children of primary school age to total population will be 6.45% and the ratio of secondary school children to total population will be 4.42%  by 2050, (b) Bangladesh will attain 100% enrollment in primary schools and 70% enrollment in secondary schools  by 2050, which is the current rate of school enrollment of Brazil, Lebanon, Malaysia, Uruguay –countries with per capita income similar to projected income for Bangladesh in 2050, (c)  the standard capacity of a primary school is 200 students and a similar size school can accommodate up to 150 secondary students in Bangladesh.
**-4-**

- int of view of their social impact. Moreover, beyond broadly identifying impacts on the urban and rural poor, the President's Reports generally give little attention to examining the impacts of spec:^ic policy mixes and the resulting structural changes on major rural and urban socio-economic subgroups in poverty who may be affected through changes in real consumption (of public and private goods), income, or employment.

7. To some extent, the relatively weak analysis of the social impact is due to inadequate data, the absence of an established methodology, and time constraints. However, even a modest analysis of the likely social impact could help identify the optimal policy-mix for long and short run poverty reduction. It would also allow designing appropriate sh(:t run compensatory targeted projects. S"ch analysis could be done with little additional research and would not require sophisticated databases. This note illustrates major elements of such an analysis and where possible, the costs -- in terms of time and money -- that it might entail. The Social Dimensions of Adjustment (SDA) project and the Living Standards Measurement Surveys (LSMS) which are currently underway or are planned in a number of countries, are expected to yield useful household data and help improve such analyses.

# How have SALs Addressed the Social ImDact?

8. Most SAL President's Reports contain some measures intended to address the long or short run social impact (see list of measures in Annex 1).5 6/ **As specified In the** Operational **Guidelines,** tho **financing** of **theoe** intervontions **is** not **necessarily** supported by SALs **since** other donors **and cofinanciers and sometimes** other **Bank projects, may** provide **the** financing.
# Table IV: Non-Filing

Panel A: Non-ling among all tax-liable rms

||(1)<br>% Non-lers|(2)<br>Of which<br>registered<br>non-lers|(3)<br>Non-lers'<br>TPI sales as %<br>of declared<br>TPI sales|(4)<br>Undeclared<br>sales as % of<br>declared sales|(5)<br>Unreported<br>liability as %<br>of declared<br>liability|
|---|---|---|---|---|---|
|Income Tax 2010|0.384|0.185|0.155|0.211|0.071|
|Income Tax 2011|0.485|0.125|0.195|0.230|0.069|
|Income Tax 2012|0.528|0.116|0.115|0.168|0.071|
|Income Tax 2013|0.556|0.121|0.114|0.181|0.099|
|Sales Tax 2011|0.196|0.894|.|.|.|
|Sales Tax 2012|0.185|0.884|.|.|.|
|Sales Tax 2013|0.198|0.891|.|.|.|

Panel B: Non-ling among registered rms

||All|Self-Employed|Self-Employed<br>with TPI|Corporations|Corporations<br>with TPI|
|---|---|---|---|---|---|
|Income Tax 2010|0.104|0.095|0.082|0.081|0.045|
|Income Tax 2011|0.105|0.090|0.078|0.061|0.033|
|Income Tax 2012|0.115|0.080|0.070|0.065|0.028|
|Income Tax 2013|0.131|0.128|0.094|0.137|0.043|
|Sales Tax 2011|0.180|0.263|0.122|0.094|0.060|
|Sales Tax 2012|0.168|0.247|0.103|0.085|0.045|
|Sales Tax 2013|0.181|0.269|0.079|0.085|0.034|

Panel C: Non-ling among rms covered by information reporting

||All|Reported by<br>rms|Reported by<br>state|Reported by<br>card<br>companies|
|---|---|---|---|---|
|Income Tax 2010|0.574|0.579|0.235|0.293<br>.|
|Income Tax 2011|0.653|0.659|0.202|0.297<br>.|
|Income Tax 2012|0.673|0.679|0.186|0.308<br>.|
|Income Tax 2013|0.677|0.682|0.177|0.319<br>.|

Notes: These three panels show the share of non-lers for the income tax (rows 1-4) and the sales tax (rows 5-7). Non-lers are tax liabile rms which have not submitted their own tax declaration for the relevant scal period. The algorithm used to construct the share of non-lers is explained in appendix section 8.1. The share of non-lers for the sales tax is an average across months in a scal year. Panel A shows the share of non-lers among all tax liable rms (column 1), the share of non-lers that a registered (2), non-lers' third-party reported sales as share of lers' reported sales (3), non-lers' estimates sales as share of declared sales (4), and non-lers' estimated tax liability as share of declared tax liability (5). TPI stands for thirdparty information, and TPI sales is the sum of all third-party reports except cost reports. A non-ling rm's estimated sales in in period _t_ are the maximum of its third-party reported sales in _t_ and its self-reported sales in the most recent prior reporting period. A non-lers' tax liability is estimated using its estimated sales, applying the average prot rate of lers in the corresponding decile of the sales distribution of lers, and the applying the tax schedule. Panel B reports the share of non-lers among all registered rms (1), and among subsamples and registered rms as indicated by the column headings (2-5). Panel C reports the share of non-lers among all rms covered by information reporting (1), and among subsamples of rms reported by dierent informing agents, as indicated by the column headings (2-4).

34
characteristics of these households are significantly correlated with worse poverty indicators than those of other non-poor households, and they bear a strong resemblance to poor households not belonging to Set A (see Appendix). These results suggest that the consumption data systematically overestimate poverty in the South and in urban areas, with a corresponding underestimation of poverty in the Central and Northern regions and in rural areas. This pattern appears to be mostly due to price differentials, which are magnified by the fact that the share of consumption from household production is higher in the Central and Northern provinces and in rural areas than it is in the rest of the country.

For households in Set B, auto-consumption<sup>13</sup> prices for key items (maize [corn], rice, other cereals, cassava, groundnuts [peanuts], chestnuts, bananas and tomatoes) in the rural Northern and Central regions exceed both the purchase prices for these commodities and the auto-consumption prices defined for farmers in other areas; In sum, both of these surveys present significant evidence of measurement error: in 2002/03 and 2008/09, respectively, 27 percent and 21 percent of observations record a poverty status that is inconsistent with all other household characteristics. Among these, households in Set A are likely affected by underreporting of consumption, while the results for Set B appear to be skewed by the overestimation of prices for household production. In Section 4 we re-estimate these observations by using sample estimates based on the 1996/97 survey.

# **2.3 Using out-of-sample estimates to re-estimate unreliable observations**

The fact that the 1996/97 survey seems to be less affected by consumption underestimation and more consistent with other welfare indicators suggests that consumption-prediction models based upon it can produce a less biased fitted measure. Thus, for each survey (1996/97, 2002/03, and 2008/09) an identical consumption model has been estimated (Equation 1), the dependent variable for which is household per capita consumption deflated using the national consumer price index (CPI). Then, out-of-sample predictions are made for both 2002/03 and 2008/09 by replacing the original coefficients with coefficients obtained by applying the 1996/97 model parameters. Normally distributed errors are randomly assigned (Equation 2).

## **Equation 1**

> 13 “Auto-consumption” refers to the consumption of goods (in this case food) produced by the household.  Autoconsumption prices should, in principle, reflect the opportunity cost of consuming household produce rather than selling it.

8
formal demography — known as the ‘theory of stable populations’ — and methods for estimating migration are much less settled than those devised for measuring fertility and mortality.

Formal demography is anchored in the tradition of biology. It models population reproduction as the result of two biological processes, natality and mortality. The modern mathematical demography founded by Alfred Lotka simply excludes external migration<sup>3</sup> : “By a very natural abstraction, demographic analysis envisages as a point of departure the case of a closed population, that is to say, a population whose numbers receive new accessions only through births and suffers losses only through deaths, immigration and emigration being excluded” (Lotka 1998 [1939], p. 53). Basic analytical models—to begin with the most commonly used of them, i.e. the life table which describes the extinction of a generation “in the absence of external migration”—are constructed on the assumption of a “closed” population, i.e. a population that receives or sends no external migration<sup>4</sup> .

True populations however are not closed. States, or nations, define populations and borders separate national populations from one another. As soon as a population is delineated by a border, border crossing becomes one of the factors of its growth and reproduction. For Lotka this is not a reason for introducing migration in the fundamental demographic equations: “Demographic statistics is concerned primarily with human populations, and particularly with certain more or less isolated populations, as for example those of a nation [...] The practical problems [posed by migration across borders] are reduced more and more as the area included in the study expands, since emigration and immigration are plainly functions of the border periphery, whereas births and deaths are instead functions of the land

> 3 In this section we use the term ‘external’ rather than ‘international’ migration. The latter refers to nations, a modern division of the world which bears no meaning for most of history, while the former refers inclusively to any sort of territory.

5
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 14. Increased reliance on surface water and open defecation, as population growth outpaces growth in rural access to water and sanitation**

Population gaining access per year between 2002 and 2006 <!-- Start of picture text --> a. Water b. Sanitation<br>6 5<br>5 4<br>3.7<br>4 3 3.0<br>3.7<br>2.5<br>3 3.0 2<br>2.5<br>2 1<br>1 0<br>National Urban Rural<br>0 -1<br>National Urban Rural<br>-1 -2<br>Piped Water Standposts Flush Toilets Improved Latrines<br>Wells/Boreholes Surface Water Traditional Latrines Open Defecation<br>Annualized Growth Annualized Growth<br>Population gaining access per year (%) Population gaining access per year (%)<br><!-- End of picture text --> _Source_ : WHO–Joint Monitoring Program 2010; Census 2002; and Demographic and Health Survey 2006.

The sanitation sector lags far behind the water sector, even though some decline in the reliance on open defecation has been achieved at the national level. In 2006 62 percent of the population practiced open defecation versus 74 percent in 1996. Even though the improvement has been significant, the percentage of population practicing open defecation is still extremely high, in particular when compared to low income countries and middle income countries (table 9).

The lack of sewerage systems in Benin is a major challenge given rapid urbanization and increasing industrial growth. Only in Cotonou is there a septic sludge disposal plant, but its capacity is greatly exceeded. Most domestic wastewater is discharged into streets and gutters. The greater Cotonou metropolitan area, including Porto Novo, is home to more than 80 percent of the industries of Benin. In the absence of adequate waste-treatment facilities, industrial wastewater is also discharged into the open, into lagoons, or into the sea. In many cases it is left to filter into the groundwater without treatment (World Bank 2009d).

# Power

## **Achievements**

Benin has steadily increased access to power since the middle 1990s, particularly in urban areas. Electrification rates increased from 14.5 percent in 1996 (Demographic and Health Survey 1996) to 25 percent in 2009 (World Bank 2009b). While improvements in access are encouraging, overall access is below the average level of low-income countries, at 33 percent, and only half the level of middle-income countries, at 50 percent (table 11).

26
- Zu ̃niga Cordero, Alvaro.<sup> ́</sup> 2022. _Distributional National Accounts Costa Rica: inequality_ .

- _measures from the national to the local level, 2000-2020_ Working Paper. Paris School of Economics.

- Zwijnenburg, Jorrit. 2019. Unequal Distributions: EG DNA versus DINA Approach. _AEA Papers and Proceedings_ , **109** (May), 296–301.

25
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE operations. Recently, however, governments in Africa have given exclusive rights to third parties for services such as ground handling and ramp and warehouse services. A growing number of international operators are handling air cargo. These operators not only provide specialized skills but also provide business connection services with carriers and forwarders. They have provided standard quality air cargo handling and have adapted to the complex regulatory environments that are prevalent among African countries. One such company is Swissport Cargo, which offers services in 20 Latin American countries and 8 African countries, making use of joint ventures in which it holds a majority stake in Brazil, Philippines, South Africa, Kenya, and Tanzania.

The freight rates for air cargo from Europe to East Africa are surprisingly lower than for several other regions of the world (table 2.14). The pricing of cargo is determined by market conditions, and discriminatory pricing is used to improve price margins based on level of service. Prices are increasingly differentiated according to guaranteed delivery time. Freight rates computed per kilometer taper off with distance, in part because the block costs per kilometer decline and in part because the costs for cargo handling are fixed.

**Table 2.14. Air cargo freight rates**

|Trade routes|$/kg|
|---|---|
|South China–Western Europe|4.39|
|South China–West Coast United States|4.62|
|South China–Middle East|6.54|
|West Europe–Middle East|2.01|
|East Coast United States–Middle East|2.00|
|**West Europe–East Africa**|**3.45**|
|West Europe–West Africa|6.44|
|China–Central Europe|8.85|
|Source: World Bank 2009b.<br>Note: Excludes fuel surcharge which average $1.05 in May 2008.||

34
title.<sup>8</sup> For the most part, government agents (called “sub-registrars”) are not concerned with the legal validity of the document but only with the correct payment of the fee. Table 1 shows the composition of these three forms of charges for various States.

**Table 1. Composition of Stamp Duties and Registration Fees, Selected States, 1992-1993** <u>(as percent of total)</u>

|**State**|**Non-judicial Stamp Duties**|**Judicial Stamp Duties**|**Registration Fees**|
|---|---|---|---|
|AndhraPradesh|82.3|7.6|10.1|
|Assam|45.1|26.2|28.7|
|Bihar|15.1|60.3|24.6|
|Gujarat|85.2|2.6|12.1|
|Haryana|88.9|6.2|4.9|
|Karnataka|71.1|9.2|19.8|
|Kerala|81.6|5.7|12.7|
|MadhyaPradesh|63.3|7.9|28.8|
|Maharashtra|85.8|4.3|9.9|
|Orissa|74.5|9.6|15.9|
|Punjab|83.7|5.2|11.1|
|Rajasthan|76.5|4.7|18.8|
|TamilNadu|84.2|4.8|11.0|
|Uttar Pradesh|44.3|35.2|20.5|
|WestBengal|58.4|18.6|23.0|

Source: National Institute of Public Finance and Policy (1995), _Economic Reforms and the Stamp Act_ <u>(New Delhi, India).</u> Under the Indian Constitution, stamp duties and registration fees are divided into those imposed under the Union List (or those set by the central government) and those imposed under the State List (or those determined by the individual States). Under Entry 91 of the coming together List (I), the central government sets the rates of stamp duties on bills of exchange, bills of lading, cheques, promissory notes, letters of credit, insurance policies, share transfers, debentures, proxies, and receipts-in-brief. The rates are uniform across all States. The collection of the duties is the responsibility of the State government in which the transaction occurs, and the State government retains all revenues collected by the State, although the States sometimes allocate a portion of the duties to the local government in which the collection occurs.

Under Entry 63 of the State List (II), State governments have the exclusive power to fix the stamp duties for other instruments listed on the State List. The specific items taxed vary by

> 8  The stamp duty and registration fee department of Maharashtra is experimenting with a pilot program in which the payment of the registration fee provides legal protection of ownership and property rights, a system similar to the Torrens system used in various countries around the world (e.g., Australia, Thailand, the United Kingdom).  The Torrens system is a system of registration of titles to land introduced into South Australia by the Real Property Act (Act 15 of 1857-58), drafted by Sir Robert Torrens, whose essential feature is the guarantee by the government of property-registered titles.  The system has been generally adopted in Australia and British Columbia, and in its original or modified form in some other countries, including some states in the United States.  For a discussion of this system, especially its current use, see Lynden Griggs (2001), “Torrens Title in a Digital World”, _ELaw - Murdoch University Electronic Journal of Law_ , 8 (3).

3
a cast on the accuracy of the sample means for the later dates, as discussed above. Inequality is higher (in terms of the Lorenz dominance criterion) in the urban sector for all three years.

A further complication arises concerning the BBS poverty lines. Nominai monetary equivalents of the BBS caloric poverty lines are available.<sup>4</sup> However, there are substantial discrepancies between the implied rates of inflation and those indicated by the aforementioned CPIs, for both urban and rural areas. The BBS poverty lines are of about 15% lower real value in 1981/82 than the two later years, in terms of the CPI bundle of goods.5 It might be argued that the CPI bundle of goods is inappropriate lor adjusting a caloric poverty line. But the BBS poverty lines also have increasing purchasing power in terms of food (using the food component of the Dhaka middle income CPI), and also in terms of the nain sources of calories, rice and wheat (using open market prices). While, as a general rule, one may allow the possibility of poverty lines with different real purchasing power, reflecting changing social standards over time (or in different countries), the validity of doing so in this particular context is far from obvious.

For the purpose of this investigation, I have preferred to use poverty lines with constant purchasing power, as indicated by the CPI. The poverty lines used are a consumption per capita of Tk 200 for rural areas, and Tk 300 for urban areas, in 1981/82 prices. These are approximately equal to the BBS poverty lines for 1981/82.6 I shall refe. co these as the "BBS 1981/82 poverty lines". To test the sensitivity of ths results to this choice, I shall also give results for lower poverty lines, set at three quarters of the above levels. All calculations are based on the distributions of consumption
disposable income changes. Mode and car choice specific constants are added to the utility function and are set so that the shares for mode of commuting and car ownership that are given by the data for each quintile are replicated. Table 3 shows the variation by income quintile of the elasticities produced by the calibrated model.

# **TABLE 1: Basic data for the modes of travel**

||**Walk**|**Bicycle**|**Bus**|**Car**|
|---|---|---|---|---|
|Average trip length<br>(2 way kms),<br>_n_<br>_d_ (*)|0.8|5.57|22.4|26.0|
|Trip times (2 way hrs.),<br>_n_<br>_G_ (*)|0.43|0.70|1.86|1.4341|
|Speed(km/hr), _v_ (**)|1.86|7.96|12.04|18.13|
|Fuel price,<br>(RMB/liter),<br>_F_<br>_p_|n/a|n/a|n/a|4.26|
|Car equivalent traffic load of one passenger,<br>_n_<sup>(#)</sup>|0|0|0.083|0.5714|
|Annualized car ownershipcost(RMB/year), _O_|n/a|n/a|n/a|5750|
|Average monetary cost of trip per worker (RMB/2-way<br>trip),<br>_n_<br>_g_(**)|0|0.35|2.5|7.381|

(*) As reported in Zheng, Peiser, Zhang (2009, in press).

(**) Source: The Beijing Municipal Institute of City Planning and Design ( BMICPD ) and the _2005 Transportation Survey_ conducted by the Beijing Municipal Committee on Transportation.

(#) Sources: Interview with experts at BMICPD, the “2005 Transportation Survey” conducted by the Beijing Municipal Committee on Transportation.

From discussions with transportation planners and casual observations, it appears that a car in Beijing typically carries 1.75 people. Therefore, each person-trip by car is assumed to cause<sup></sup> 4 =0.5714 (=1/1.75) car-equivalent traffic units. A Beijing bus is believed to cause three times the congestion as does a car, and carries about 32.5 passengers on average. Hence, the car-equivalent traffic generated by a bus ride is 0.0923 (= 3/32.5). Mode 3 is transit which includes subway and bus, and about ten percent of transit trips are reportedly by subway. Therefore, we set  3  0.9  (0.0923)  0.083 . A typical truck in Beijing causes 1.7142 times the car-equivalent traffic load. Hence,<sup></sup> _T_<sup></sup> 1.7142. One truck vehicle trip is assumed to occur for each ten carequivalent non-truck traffic unit. Hence, _A_ =0.1 in (21).

19