rafmacalaba/datause-encoder-data huggingface dataset:
# _Other Determinants of Future Water Level and Water Flow_

# Projections for Land Subsidence

The Ganges Delta in Bangladesh is still in an active, dynamic state. Therefore, it is critical to include in the analysis land subsidence of the lower Bengal delta (the Ganges Delta in Bangladesh) in simulating future climate scenarios. Physical impacts of relative mean SLR (RMSLR) are caused by a combination of SLR scenarios associated with global warming and vertical land movement (subsidence or accretion). Experts agree that land subsidence in the lower Bengal delta is mainly caused by tectonic subsidence and isostatic adjustments, the rate of sediment loading or delta aggradation, compaction of Holocene sediments, compaction of peat layers, water withdrawal, and surface water drainage and management. However, the available estimates of land subsidence in the lower Bengal delta vary widely, from a fraction of a millimeter to a few centimeters per year (Pethick 2012; ADB 2011; Akhter et al. 2012; Syvitski et al. 2009; Nicholls and Goodbred 2004; Brammer 1996; Emery and Aubrey 1991). At present there is an intense controversy in Bangladesh regarding the estimates and projections of land subsidence in the coastal region. In light of the widely varying estimates, the hydrological modeling for our analysis was run for three alternative scenarios of land subsidence: 2mm per year, 5mm per year, and 9mm per year.

# Projections for Water Abstraction

Although Bangladesh has abundant water in the wet season, the supply of water is limited in the dry season. River water is abstracted for the production of _boro_<sup>28</sup> rice and for the supply of water to Khulna City.<sup>29</sup> Therefore, it is important to take into account the expected change in river-water abstraction in the future for the projections of water flow and water level in the hydrological modeling.

Bangladesh does not maintain a database on water abstraction for irrigation. The analysis uses secondary data on low-lift irrigation pumps<sup>30</sup> from the subdistrict level annual Irrigation Reports

> 28Rice grown in winter.

> 29 Other municipalities use groundwater. Khulna Water Supply and Sewerage Authority currently extracts

> 123,000m<sup>3</sup> per day of groundwater.

> 30 The capacity of these pumps varies from one to five cusec and they operate from six to ten hours per day.

20
**Poly, Research, and External Affairs**

# **Ppulation, Health, and Nutrition**

# WPS 48g

This paper -a product of the Population, Health, and<sup>Nutrition Division, Africa Technical Department</sup> - was written as part of the Africa Regional Study on<sup>Health Financing to aid the ongoing sector</sup> adjustmcnt dialoguc ir. Zaire. The study received outside<sup>financial support from SIDA, NORAD, and the</sup> U.S. Agency for Intemational Development. It was prcsented<sup>at a seminar in April 1990. Copies of this</sup> paper are availablc free from the World Bank, 1818 H Street<sup>NW, Washington DC 20433. Please contact</sup> Karol Brown, room J9-112. extension 35073.

After identifying 12 systems of health insurance in Zaire, Shcpard, Vian, and Kleinau prepared detailed case studies of four systems (two urban and two rural) and brief studies of four others. The case studies focused on the terms of the insurancc plans, their organization and management, resourcc mobilization, efficiency, equity, client perccptions, and the quality of services. Among the lessons Icamed:

* Simplc control methods, such as printed premium stamps, detailed descriptions of enrollces, and the enrollment of entire families helped reduce fraud and error.

* Appropriate investment strategies preserved the value of premium income. Investing in imported drugs was a hedge against crosion of the purchasing power of premia.

* Plans vary substantially in the services covcred. Consumers found coveragc of ambulatory care attractivc despite substantial premia or required copayments. Only half the plans covcred inpatient care.

* The most successful schemes had modest premia.

* Acceptablc quality of service is a precondition for successful implementation of an insurancc scheme.

* The implementation of voluntary schemes requircs publicity within the community at the outset.

* The risk that insurance plans would be ovcrcharged was limited by the decentralization and direct management.

* Financial analysis of the insurance system requires better accounts. Fcw insurance plans had good financial reports of the health delivery plan, much Icss of the insurance plan.

* A financial guarantor (for example, a development organization guarantceing the first year's services) boosts the public's confidence in the insurance system.

* Evidence of adverse selection and moral hazard was found in most plans. Their impact can be moderated by requiring that the entire family joins or by cnrolling cmployce groups.

* Informal associations exist that finance members' hcalth care through intcrest-free loans to pay for births, hospitalizations, and other emergencies.

The PRE Working Paper Serics disseminates the findings<sup>of work under way in thc Bank's Policy, Research, and Extemal</sup> AffairsComplex. Anobjective of the scries is to get thcse<sup>findings out quickly, even ifprcsentations arc less than fully polished.</sup> The findings, intcrprctations, and conclusions in thcsc<sup>papers do not necessarily reprcsent official Bank policy.</sup> Produced by t-hc PRE Dissemination Center
_35_ <!-- Start of picture text --> Asset Allocation under Jamsostek Investment<br>(%of total investment; 1994)<br>Bank Deposits<br>76%<br>Properties<br>0% Eut2es Bonds<br>14% 10%<br><!-- End of picture text --> Source: Factbook on Jamsostek 1994.

The Jamsostek program is superficially similar to many successful national provident funds, such as those of Malaysia, Singapore and Chile. But the appearance is misleading. There are significant differences. First, while the other programs are subject to strict prudential regulations to ensure the safety of members' assets, Jamsostek is virtually unregulated.

Second, unlike social security programs elsewhere, the investment income earned by the Jamsostek plan is fully subject to tax.

Third, Jamsostek by design and implementation is not a stand-alone program. While the other programs are capable of providing at least 40 percent salary replacement after a full career, Jamsostek will provide less than 10 percent, based on current contributions and past investment performance (see the table below). Members will need additional saving for post-retirement income.
# **3 Conceptual Framework**

Having established the prevalence of empty trips across many settings, I develop a conceptual framework for understanding the causes and effects of underutilization. I begin by reviewing empirical evidence for three key frictions that generate empty trips: geographic imbalances in trade, search and matching frictions, and regulatory barriers. I integrate these three frictions into a stylized model of a trucking market with fronthaul and backhaul legs. This model offers suggestions for diagnosing the presence of each friction in a given market, as well as predictions for the effect of potential interventions. Finally, I use the model to interpret one hypothetical and three empirical case studies. This discussion focuses on the main causes of empty trips and is not exhaustive. In Appendix C, I discuss additional factors which affect empty trips and offer potential extensions of the stylized model.

## **3.1 Geographic Imbalances**

Geographic areas vary in shippers’ demand for importing and exporting. An imbalance arises when some regions are systematically net importers, so loaded carriers who arrive in those regions face a smaller supply of jobs out. The “backhaul” effect means that some of these carriers must travel empty. Symmetrically, if a region is a net exporter, then some carriers must arrive empty to fulfill all the loaded trips out. In some countries, this imbalance can be significant.

Physical trade imbalances may result from value-based trade imbalances, or from differences in the value-weight density of imported versus exported products. For example, according to Jonkeren et al. (2011), Rotterdam and Antwerp import bulk cargo to supply interior manufacturing firms which produce final goods. The greater value-density of these ports’ export products generates an imbalance, as the weight and volume of manufactured exports is less than that of bulk imports. The relative mix of mining, agriculture, and manufacturing industries within a region therefore affects the region’s geographic imbalances.

**Measuring Physical Trade Imbalances** At the highest level, geographic imbalances can occur at the national level. I use trade data from the United Nations Comtrade database to summarize the potential for geographic imbalances. In this context, the relevant measure of the magnitude of trade is physical units such as weight and volume, which correspond more closely to the number of trucks, ships, and other vehicles needed than trade in dollars. In particulary, I use Comtrade’s estimates of weight (in kilograms) of imports and exports<sup>6</sup> .

> 6For about 20 percent of total trade by value, I do not observe weight, but I do observed trade value in dollars. I assume that these missing country-commodity-directional observations have the same value density

15
13 The fall of Brazilian government securities’ prices and capital outflows also occurred because of a global phenomenon: the rise in uncertainty and risk aversion caused by the growth slowdown in the industrialized nations, the terrorist attacks in the United States, and the corporate corruption scandals of the more mature capital markets around the world. This fact exerted additional downward pressure on Brazilian government bonds, and their prices moved in tandem with those of other assets worldwide (Figure 10). Favero and Giavazzi (2003) show how Brazilian spreads depend on domestic factors (the stance of fiscal policy) and on global conditions. The relationship is non-linear: when domestic fundamentals are sound, this relationship is not as strong, but when fiscal fundamentals are weak, the effect of global factors is amplified.

Figure 10 Co-movement of Brazil C Bond Prices and the Dow Jones Index Evidence of Global Factors’ Influence on Brazilian Asset Prices <!-- Start of picture text --> 90 12000<br>DJIA BR C Bond Price<br>(right scale)<br>80 11000<br>70 10000<br>60 9000<br>50 8000<br>40 7000<br>1998 1999 2000 2001 2002<br><!-- End of picture text --> Source: World Bank staff calculations Faced with mounting difficulties in rolling over the domestic debt, the central bank redeemed a fraction of debt falling due by printing money. Consequently, the monetary base expansion exceeded nominal GDP growth (Figure 11). The monetary effect of public domestic debt redemptions during the second semester of 2002 reached the tenor of 30 percent of base money (Figure 12). It is crucial to point out, however, that the monetary expansion originated by the treasury’s operation began in the second semester of 2001 and could have been interpreted as a leading indicator of the more turbulent episodes that were to unravel in mid 2002.
_Annex 6. Dictionary of variables included in BOS database_

|**Module in**<br>**BOS database**<br>**Variable in BOS database**|**Description**|**Category**|
|---|---|---|
||Unique identifier in the form||
|Firm identifier|XX0000j, where first 2 digits<br>correspond to ISO country<br>code.|Text|
|Country|Country full name|Text|
|Company name|Full company name in latin<br>alphabet|Text|
|Local Company Name|Full company name in local<br>language|Text|
|Year of Information|Year of the financial<br>information collected.|Numeric|
|Year of start of operations|Year of incorporation of the<br>firm (proxy of age)|Numeric|
|**Firm-ll**<br>Sector of operation (1 digit)|NACE rev. 2 classification - 1<br>digit (main category)|Numeric|
|**eve**<br>**characteristics**<br>Sector of operation description (1d-<br>description)|NACE rev. 2 classification - 1<br>digit (Description)|Text|
|Industry of operation (2 digits)|NACE rev. 2 classification - 2<br>digits|Numeric|
|Industry of operation description (2d-<br>description)|NACE rev. 2 classification - 2<br>digits (Description)|Text|
|Activity classification (4-digits)|NACE rev. 2 classification - 4<br>digits|Numeric|
|Activity description (4-d - description)|NACE rev. 2 classification - 4<br>digits (Description)|Text|
|National legal form|Legal form of the company<br>based on national law|Text|
|Corporate type (i.e., Corporatized, Non-<br>corporatized)|Categorical variable indicating<br>whether the firm is<br>corporatized or non-<br>corporatized|Categorical:<br>Corporatized<br>Non-corporatized|
|Firm size (number of employees)|Total number of workers in the<br>firm (permanent + temporal)|Numeric|
|**Performance**<br>**module**<br>Consolidation code|Indicator of the level of<br>consolidation of the financial<br>information provided. The<br>focus of the exercise is<br>unconsolidated, but when that<br>is not available, we provide the<br>consolidated information and<br>identifyaccordingly.|Categorical:<br>C - consolidated<br>U- unconsolidated.|
|Revenues (Unconsolidated)<br>in thousand US dollars|Operating revenues in<br>thousand US dollars|Numeric|
|Profit/loss (Unconsolidated)<br>in thousand US dollars|Net profit/loss after tax in<br>thousand US dollars|Numeric|

46
53 Chiswick (1976) develop-d **a** technique to include self employed workers in the estimation of an annual earnings function along with wage earners, and thereby avoid the selection bias due to analyzing only wage earners. Her approach attributed a **share** of self e*ployed earnings to entrepreneurial capital or risk taking. Based on an analysis of Bangkok from the 1971 Socioeconomic Survey of Thailand, sale **wage** earners (not self employed at all) received a 10.4 percent return on their years of schooling, whereas females received a 14.5 percent return. Including partial and full-time self employed Ln the sample reduced slightly the returns to schooling to 9.1 percent for men and to 13.0 percent for women. **The** urban estimtion sample Lncreased by 39 percent for males and by 53 percent for **females** with the inclusion of the self employed. In both cases, women's returns exceed those for men, but those who are self employed report slightly lower returns on their schooling than do wage earners. This is broadly consistent with the pattern of more educated women (beyond primary school) working more frequently in wage jobs than the less educated. It is uncertain whether underreporting of incomes by self employed biases such estimates of return to schooling and by how much. There are few studies of the relationship between wage rates and schooling that assess how the selective sample of wage earners biases findings (e.g. Anderson, 1982; Mohan, 1986; Griffin, 1987; King, 1986; Schultz,. 1988c). Moreover, these studies often **deal** with mn alone or women alone and thus do not help assess whether the bias due to **sample** selection **modifies** systematically comparLsons of male-female estLiated returns to education, **as** reported in Tables 1 and 2. Here is an important **issue** for public policy that has received-surprisingly little empirical study. Griffin (1987) analyzes the earnlngs of marrled women ln the rmilipplnes
17

**_C. Financial Needs:_ 0.12 percent of GDP on average per year.** The financing gap to reach this goal is the smallest of all MDGs, although this might be due to the lack of available data. The cost of achieving this target for the period 2008-2015 amounts to 0.14 percent of GDP on average per year, corresponding to US$ 26.6 million in total. Once the allocation of government expenditure is taken into account, the country needs an additional total financing amount of 0.12 percent of GDP on average per year (Figure 13), which translates into US$ 16.6, million in total (Table 9). For a detailed, yearly cost breakdown, see Annex 1.

<!-- Start of picture text --> Figure 13. MDG 6: Financing Gap Table 9. MDG 6: Cost, Budget and<br>-1.2 0.00 Financing Gap (2008-2015)<br>2008 2009 2010 2011 2012 2013 2014 2015<br>-1.70<br>-0.03<br>-2.0 -2.13<br>-0.04<br>-2.49 US$ 26.61 m<br>-0.05 -0.06 Total Cost<br>-2.8 -2.82 -0.07 MRO 7,285.70 m<br>-3.07 Budget US$ 10.05 m<br>-3.41 -0.08 -0.09 Allocation MRO 2,772.32 m<br>-3.59 -0.09 US$ 16.56 m<br>-3.6 -3.81 -0.11 -0.12 Financing MRO 4,513.4 m<br>-0.12 Millions of US$ gap 0.12% of non-oil GDP<br>% of non-oil GDP (secondary axis)<br>-4.4 -0.14 -0.15 US$ 1.00 p.c.<br>Source: Authors’ calculations.<br><!-- End of picture text --> _Source:_ Authors’ calculations.

# **MDG 7: Ensure Environmental Sustainability**

_Target 10: Halve, by 2015, the proportion of people without sustainable access to safe drinking water and basic sanitation._

**_A. Current Situation:_ access to safe water is still a major problem.** In Mauritania, the data concerning access to safe drinking water depend on the definition of the indicators. In 2004, only 32 percent of urban households and 17 percent of rural household were connected through a tap to a safe drinking water source. Conversely, these proportions rise to 64 and 42 percent respectively when the access to improved water sources is via a protected well, a public fountain, or an external tap. Still, more than half of the rural population still struggle to find safe drinking water, with clear adverse effects on physical well-being, notably amongst children (WHO/UNICEF 2006).

**Figure 14. MDG 7: Current Situation and Trends**

<!-- Start of picture text --> 70<br>Past performanceGovernment plan 65.6 66.2 66.867.4 68.0<br>Projected status quo 65.0<br>63 MDG-4<br>60.8<br>56 56.7<br>52.5<br>49 48.3<br>44.2<br>42 37.0 37.2 37.4 37.6 37.9 38.1 38.3 38.5 38.7 38.9 39.1 39.4 39.6 39.8 40.0<br>35<br>1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> _Source:_ Authors’ calculations.
gains, or other dynamic effects from trade are not reflected in this model. In addition, liberalization of services is not considered. We applied the relative changes in real household income to PPP-corrected GNI — assuming that GNI follows the same evolution as real household income — using 2001 as the base year, similar to the Linkage model.

An important point to note in this exercise is that trade liberalization is increasing the efficiency of the world economy, so that the redistribution that it entails is a positive-sum game. However, this is not for the same reason as for aid. In the case of aid, the overall gain was coming from transferring dollars to countries with lower costs of living. In the present case, the overall gain is the standard ‘gain from trade’. In effect, the world average welfare increases by approximately 0.4 percent as a result of the liberalization policy.

In terms of inequality, the redistributive effects of high-income country protection in world trade are smaller in absolute value that those of aid. The Gini coefficient of the international distribution would be lower by 0.06 percent in the absence of protection. However, the poverty sensitive measure like the Atkinson index with high levels of inequality aversion show much less variation than in the case of aid, suggesting that the redistribution that would take place through liberalization is not exclusively towards the poorest in the world.

16
to highly autocorrelated nonlinear processes that happened to track each other over the relatively short period NQ study, but differentially among regular and irregular food aid recipients. Given that periods when wheat yields are elevated are clustered together, the fact that conflict is high only in these periods does not force one to the conclusion that aid is causing conflict. The fact that aid was mostly sent to the countries that experienced conflict could just be a consequence of the fact that aid is mostly shipped to places experiencing humanitarian disasters such as conflict, exactly the simultaneity bias that the strategy is meant to circumvent.

An intuitive way to show graphically how such time trends influence NQ’s IV estimate is to reproduce the plots NQ use to explain and demonstrate their strategy, highlighting changes across decades. Figure 2 reproduces the NQ’s Figures 3 and 4, which show the relationship between US wheat production and the proportion of countries in each year who are experiencing a conflict. The bottom panel shows the relationship among only the 50% of countries that receive aid from the US most frequently, while the top panel is the 50% of countries that receive aid least frequently during the study period. NQ use these plots to show that that wheat production is related to conflict, but only among frequent recipients of US aid, presenting an intuitively appealing demonstration for what is effectively the reduced form in their IV strategy.

But given that wheat production displays pronounced trends, it is useful to group observations that are near each other in time. Our Figures 3 and 4 reproduce the preceding figure for the high food aid recipients, with different markers and colors representing different decades. As we know from the fact that both wheat aid and conflict followed an inverted-U shape trend during this period, all of the years with high wheat production and elevated incidence of conflict occur in the 1980s and 1990s (grey diamonds and dark blue squares, respectively), while all the years with low wheat production and high conflict occur in the 1970s and 2000s (light blue circles and black triangles, respectively).

14
relative size of its import market and its geographic proximity). The results are provided for two years, 1990 and 1998, to illustrate whether the South Asian countries demonstrate an increasing or decreasing tendency to trade with one another.

**Table 3: Trade Intensity Ratios of South Asia**

|**PARTNER**||||**EXP**<br>|**ORTING**<br>|**COUNT**<br>|**RY**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|
|<br>**COUNTRY**|**Ind**<br>|**ia**<br>|**Pakis**<br>|**tan**<br>|**Sri La**<br>|**nka**<br>|**Bangla**<br>|**desh**<br>|**Nep**<br>|**al**<br>|
||**1990**|**1998**|**1990**|**1998**|**1990**|**1998**|**1990**|**1998**|**1990**|**1998**|
|India|-|-|1.28|3.14|1.55|1.29|1.89|1.88|10.19|42.81|
|Pakistan|1.16|2.28|-|-|8.11|4.09|6.57|4.58|1.48|1.10|
|Sri Lanka|7.55|12.84|17.57|9.89|-|-|6.52|0.24|0.66|16.63|
|Bangladesh|15.94|21.86|16.34|9.71|4.95|0.94|-|-|2.82|9.72|
|Nepal|13.22|35.03|1.22|3.57|0.25|1.70|26.19|18.45|-|-|
|Maldives|6.67|2.41|2.51|2.80|44.00|40.00|N/A|N/A|N/A|N/A|
|Bhutan|0.00|16.34|0.22|0.54|N/A|N/A|2.32|4.36|N/A|9.04|
|US|1.02|1.26|0.84|1.3|1.75|2.17|2.06|2.15|1.58|1.55|
|UK|0.98|1.05|1.16|1.23|0.89|1.75|1.12|1.66|0.90|0.21|

Note: Calculations are based on data from the IMF Direction of Trade Statistics While the South Asian countries demonstrate TIIs (not normalized) greater than unity, some relatively increasing and some decreasing as seen from Table 3, when normalized for geographic proximity, their relative importance to each other becomes less pronounced. This is illustrates from statistics in Table 4 which reports the ratio of observed to predicted TIIs for each of the countries, thus demonstrating the magnitude of the difference in actual (not normalized) TIIs and the TII that would be predicted given the geographical distance (i.e. at a given point along the regression line). For example, India’s “real” trade intensities, normalized for geographical proximity, with the other South Asian countries are stronger than that implied by its bilateral trade shares. In 1998, India traded more than 3 times as much as should have been expected with Sri Lanka, more than 51⁄2 times as much as should have been expected with Bangladesh, and 8 times as much as should have been expected with Nepal.

However, for the peripheral countries, trade with the region is below what should have been expected given the size of the partners’ import market and their geographical proximity. For 13