rafmacalaba/datause-encoder-data huggingface dataset:
**-** **<u>Table 6. Sources of Income for rural, net buyer seller households, by North South</u>**

|||**Sou**<br>|**th**<br>|||**No**<br>|**rth**<br>||
|---|---|---|---|---|---|---|---|---|
||**_Net Bu_**|**_yer Rice_**|**_Net Sel_**|**_ler Rice_**|**_Net Buy_**|**_er Rice_**|**_Net Sel_**|**_ler Rice_**|
||1993|1998|1993|1998|1993|1998|1993|1998|
|Income from Agriculture|37%|40%|50%|60%|55%|56%|69%|65%|
|Sales|20%|29%|30%|42%|24%|26%|32%|37%|
|Own consumption|17%|11%|21%|18%|32%|30%|37%|28%|
|Business Income|21%|20%|10%|11%|14%|13%|9%|12%|
|Agricultural wages|19%|14%|18%|7%|4%|2%|2%|1%|
|Other wages|11%|11%|8%|8%|9%|9%|6%|8%|
|Other Income|11%|15%|14%|15%|17%|20%|14%|15%|
|Total Income <sup>a</sup>|8,415|16,033|9,131|14,631|6,891|9,740|6,734|10,192|

a: Total income is in 1998 prices, and is measured in 1000 dongs. Source: Author’s own calculations using VNLSS 93 and 98.

We also observe a drastic increase in the share of agricultural income for the Southern households from 1993 to 1998 (for both net buyers and sellers). As real income was also rising (which will be discussed in the next section) this increase in share is even more pronounced. It is due to increase in sales (especially rice income but also others) for net seller households, and due to increase in sales (of mainly food crops) coupled with the decline in own consumption for net buyers. In North, net seller households are also specializing as share of sales increases and own consumption of rice has declined. In sum, specialization is taking place where net sellers in South are selling more (and cultivating more) and net buyers are moving away from producing rice even further and cultivating other crops. In addition, shares of business income and non agricultural wages have increased for net seller households in North, which may be an indication of rice sellers moving away from rice and moving onto other sectors.

Rice is a labor intensive commodity. Thus, it is expected that as rice prices increase the wage rate in agriculture and/or employment in agriculture will increase. The implicit assumption underlying the increased employment is the existence of surplus labor in agriculture. However, as Table 6 illustrates, share of agricultural wages decline across the board. Since real incomes have increased, the decline in share may not necessarily reflect a decline in income from agricultural wages. A closer look reveals that for net buyer households in the South the income from agricultural wages has increased in real terms (especially among the lower deciles 1-5). However, for the rest there is a 15
- Trujillo, L. and Nombela, G. _Privatization and regulation of the seaport industry_

instruments to introduce private participation in seaports - must cover. Some of the more innovative international experiences are also described here. Section 4 is devoted to analyzing regulatory mechanisms over the different tariffs that seaports charge their users, while section 5 discusses regulation affecting quality and safety. Section 6 contains definitions and characteristics of some performance indicators for seaports, which are useful for regulators to obtain information. Finally, section 7 examines the roles that the different institutions responsible for seaports must play in the new context of this industry.

# **2. - DESCRIPTION OF SEAPORTS' INFRASTRUCTURE AND SERVICES**

# **2.1.** - **Economic analysis of seaports' activity**

Defined in broad terms, a seaport can be considered a single organizational unit that provides a service to ships. However, when its internal workings are analyzed in detail, it is clear that there are _multiple_ services being produced and demanded within a port area (services to ships, to cargo, and to passengers). Even for a type of service such as cargo handling, technologies can vary enormously depending on the type of cargo, up to the point that, for example, container loading can be regarded as a different service from bulk cargo handling. Therefore, instead of a single unit, a seaport is better characterized in economic terms by considering it to be a multi-product organization. Seaports offer many different services to ships, which in some cases can all be offered by a single company (small ports), but more often, are provided by independent firms working within the port area.

There are two basic characteristics that define the organization of seaports' activities. On one hand, the infrastructure where these activities are performed - berths, quays, storage areas, etc - is expensive to build _(see Box 1)_ and exhibits a problem of indivisibility, i.e. it is not possible to enlarge a port in a continuous way. Port infrastructures must be built with determined minimum dimensions, and in general, their full design is strongly conditioned by the physical characteristics of the coast where the port is located.

# **_Box 1._ Typical civil works unit costs for port infrastructure**

Dredging (confined space restricted by existent berth $ 7.5 / cu.m requirements) Quayside (35m wide berth) $ 54,000 / m Container yard paving and infrastructure $ 63.8 / sq.m Open storage yard $ 5s / sq.m Sheds $ 375 / sq.m _(") 1997 data, for a 14 metre draft 500-600 m long berth._ _Source:_ Drewry Shipping Consultants (1998) 6
# 5. **Policy Choices and Environmental Consequences: Scenarios for Five Cities**

**QJt1ztSh.edaXDJrtseSedA3**

<!-- Start of picture text --> 35 ............................................ .local<br>0.30 < - t +(*<br>az;_ -z<br>.m m.-~ -<br>0.15 . I<._. x sChina's<br>01 --_; x<br>1!B 1994 ZD 2M mm<br><!-- End of picture text --> Figure **21:** Changing Shares for Pollution-Intensive Sectors, **1988-2020** During the past decade, decisions on the pace of economic reform and environmental regulation have significantly affected the pollution -. z- ....................... Sintensity of industrial production in ,--3UChina. Because they reflect much of regional variety, our five cities provide a good focus for exploring the past results of these policies and their implications for future environmental conditions. To provide overall perspective, we also include projected pollution intensities and loads for China as a whole.

## **5.1 Trends in Pollution Intensity Factors**

In the first stage of the analysis, we use recent trends to project future levels of non-regulatory determinants of pollution intensity (pollution per unit of output) for COD, S02 and TSP. At the national level, we project the share of COD-intensive sectors to remain constant at 20% between 1994 and 2020; the share for S02intensive sectors to drop from 6.6% to 4.7%, and the TSP-intensive share to increase very slightly, from 32% to 32.8%. While only the S02-related changes appear significant at the national level, Figure 21 suggests increasing diversity in the structure of regional production. All pollution-intensive sectors have a rising output share in Beijing and a falling share in Guangdong. Liaoning has rising S02and TSP-intensive shares, but falling share for COD-intensive production. The pattern in Shanghai and Sichuan is mixed, with rising COD- and TSP-intensive shares but a falling S02-intensive share. Although these impacts are unintentional, they are part of 'business as usual' under the reform. On this margin, provinces <!-- Start of picture text --> . )12<br>QJ10 X -. B11<br>Q _ Se.<br>Qt; .. ___..h. . -- -<br>Q04 . - X a.<br>0(2<br>Qa)<br>Output Share of TSP-Intensive Sectors<br>0.50 ..........-.-....<br>0.40 *~ ~-----.S iha<br>0.35 ... . . _SK huan<br>0.25 ... Guangdong<br>01.S --- - Shanghai<br>*--0-.- Betin<br>o.OO i<br>1988 1994 2000 2010 2020<br><!-- End of picture text -->

**Figure 21: Changing Shares for Pollution-Intensive Sectors, 1988-2020**

22
**Figure 3.  Resident Population by Main Age Group at Beginning of Year, 1980-2003**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>below working age working age above working age<br>percent<br><!-- End of picture text --> Note: Working age here is defined as 15 to official pension age. The latter was 60 for men and 55 for women until 1996, but has reached 62 and 59 respectively in 2003. Calculations are based on legislation applicable in different years. Source: Central Statistical Bureau of Latvia and Calculations.

**Figure 4.  Old Age Dependency Ratio, 1980 – 2003**

<!-- Start of picture text --> 410<br>400<br>390<br>380<br>370<br>360<br>350<br>340<br>330<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>working age persons<br>above working age population per 1000<br><!-- End of picture text --> _Source:_ Demographic Yearbook of Latvia, 2003 and own calculations.

# **B. EMPLOYMENT AND UNEMPLOYMENT: INTERNATIONAL COMPARISONS**

As shown in Figure 5, the contraction of the labor force which accompanied transition ended only ten years later. Employment declined even more sharply than labor force participation during this time, as it did in the other Baltic states. Between 1990 and 2002 employment declined by 30 percent in Latvia. Estonia and Lithuania experienced, respectively, 30 and 25 percent declines in employment in the same period. The Russian financial crisis of August 1998 interrupted post-1996 reductions in ILO-style unemployment and more recent temporary increases in employment. However, since 2000 all three indicators: labor force participation, employment, and unemployment - have featured healthy trends. These recent trends compare favorably to the other Baltic countries. In Lithuania unemployment started to 7
It is interesting to find that Alzheimer’s is significantly driven by unemployment. This could be a subject for more extensive research. In this paper, we speculate this could be due to the high cost of Alzheimer’s. <mark>Total health care costs are more than three times higher for people with Alzheimer’s and other dementias than for other people age 65 and older, according to the Alzheimer’s Association’s 2009</mark> _<mark>Alzheimer’s Disease Facts and Figures.</mark>_ <mark>A substantial part of the costs derives from private insurance and out-of-pocket payments. Families and counties hit with large declines in employment were understandably financially disadvantaged to care for patients with Alzheimer’s.</mark> Table 5.3b: Other Diseases and Unemployment (IV with controls)

|VARIABLES|Diabetes|Alzheimer|Chronic<br>Lower Resp.|Liver|Kidney|
|---|---|---|---|---|---|
|Instr. Job Losses|0.272|1.067***|-0.141|-0.168|0.0588|
||(0.248)|(0.374)|(0.170)|(0.289)|(0.382)|
|Constant|-0.0770|-0.962**|0.175|-0.273|0.120|
||(0.175)|(0.382)|(0.117)|(0.214)|(0.175)|
|Controls|Yes|Yes|Yes|Yes||
|Observations|2,071|2,018|2,199|1,362|1,852|
|R-squared|0.040|0.044|0.098|0.041|0.063|

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

# **5.4 Deaths caused by external reasons**

We continue to look for impacts of unemployment on deaths caused by external reasons (Table 5.4a). First of all, unemployment does not seem to have an impact 22
first to examine the long-term economic impact of extreme weather conditions in developing countries. He finds in the context of Ethiopia that the loss in food consumption persists five years after a drought has occurred. Relying on Brazilian data, Mueller and Osgood (2009) also point to a five year persistence effect: they show that droughts can cause wages in rural municipalities to be lower than their peers for five years after the event. Following this evidence, we therefore assume that the impact of extreme weather conditions can affect individuals’ decision to migrate up to five years after their surge. This means that we compute the mean of the yearly average values of the SPI in each region during the five years preceding the year of the census, i.e., between 1997 and 2002. One may want to note that in the absence of panel data (and therefore of controls for regional fixed effects), the SPI is valuable. Given that SPI is defined relative to each region’s own rainfall distribution, any cross-regional variations in it are indeed truly capturing regional differences in shocks rather than regional in mean historical rainfall.

# **3.3 Region-specific variables**

We create two region-specific variables: one which allows us to distinguish between regions with higher and lower road density, the other which controls for the level of regional economic development at the beginning of the migration period (i.e., in 2001).

## **3.3.1 Road density**

Road density at the regional level is captured by the number of kilometers of road per square kilometer in each region. This information stems from the USGS (US Geological Survey) 15
Policy Research Working Paper 8922

# **Abstract**

Groundwater currently provides 98 percent of all the drinking water supply in Bangladesh. Groundwater is found throughout Bangladesh but its quality (that is, arsenic and salinity contamination) and quantity (that is, water storage depletion) vary across hydrological environments, posing unique challenges to certain geographical areas and population groups. Yet, no national-scale, multi-hazard groundwater risk maps currently exist enabling water resource managers and policy makers to identify areas that are vulnerable to public health. This paper develops, for the first time, groundwater risk maps at the national scale for Bangladesh that combine information on arsenic, salinity, and water storage, using geospatial techniques, linking hydrological indicators for water quality and quantity to construct risk maps. A range of socioeconomic variables, including access to drinking and irrigation water supplies and social vulnerability (that is, poverty), are overlaid on these risk maps to estimate exposures. The multi-hazard groundwater risk maps show that a considerable proportion of land area (5 to 24 percent under extremely high to high risks) in Bangladesh is currently under combined risk of arsenic and salinity contamination, and groundwater storage depletion. As few as 6.5 million (2.2 million poor) to 24.4 million (8.6 million poor) people are exposed to a combined risk of high arsenic, salinity, and groundwater storage depletion. The multi-hazard groundwater risk maps reveal areas and exposure of population groups to water risks posed by arsenic and salinity contamination and depletion of water storage.

This paper is a product of the Water Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gjoseph@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
**Table 6: Fiscal (pro)cyclicality, Instrumental Variables Regressions (2000-2016)**

||Cyclicality of real gen<br>|eral government prim<br>|ary spending<br>|||||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Variables|IV 2SLS|||||||
|Cyclicality<br>real GDP|of<br>0.329***|0.327***|0.322***|0.320***|0.328***|0.324***|0.327***|
||(0.067)|(0.063)|(0.063)|(0.063)|(0.068)|(0.063)|(0.068)|
|Constant|-3.128|-3.862|-4.644|-4.715|-4.009|-4.556|-4.040|
||(24.320)|(22.203)|(22.544)|(22.532)|(25.390)|(22.560)|(25.250)|
|Centered<br>squared|R-<br>0.141|0.145|0.147|0.148|0.144|0.146|0.145|
|Rmse<br>|156<br>|147|147|147|146|147|158|
|Weak ID<br>(stat>10)|test<br>103.953|119.327|118.917|120.440|100.334|119.050|100.753|
|OID<br>test<br>value)|(p-<br>0.813|0.803|0.189|0.177|0.329|0.426|0.948|
|Intruments|Initial Crgdp|Initial Crgdp|Initial Crgdp|Initial Crgdp|Initial Crgdp|Initial Crgdp|Initial Crgdp<br>|
||Initial revenue.ratio|Initial credit.ratio|Initial pol.corruption|Initial pub.service.corruption|Initial law.order|Initial resource.distribution|Initial<br>ethnicandrel.stability|
|Covariates<br>|Set2<br>|Set2|Set2|Set2|Set2|Set2|Set2|
|Observations<br>countries)|(#<br>95|108|108|108|108|108|94|

Notes: The year 1999 is considered as reference for the initial Crgdp and successive instruments. The full list of removed countries per columns is available upon request. With the collapse of the data, all BD variables refer not to the presence of a condition (i.e., a missing variable), but to the percent of time with the considered condition (i.e., the percent of time with a missing variable over 20002016).

17
Our main explanatory variable of interest is _Lkdt_ , which measures the local, fieldspecific labor demand for graduates. We also control for institution- and market-level variables, _Xjkdt_ and _Mjdt_ respectively, as well as department, field-category, and year fixed effects. Institution-level controls in _Xjkdt_ include cost proxies, namely the institution’s relative size in the department (its share of the department’s total SCP enrollment) and the institution’s degree of specialization in the field (the field’s enrollment share within the institution). We expect institutions with a large share of SCP students in the department (i.e.,“large” HEIs) to have low entry costs by virtue of their infrastructure and popularity. Similarly, we expect institutions that are more specialized in a field to have lower entry costs into it by virtue of already having field-specific resources (e.g., facilities and faculty). Market-level controls _Mjdt_ include the number of SCPs in the field and department provided by each of the following three types of competitors: public HEIs, private HEIs, and SENA. These variables seek to capture the effect of competition and market structure on entry decisions.

# **Identification**

## **Effect of local labor demand on entry**

As explained in Section 2.3, we construct two variables to measure local labor demand, _Lkdt_ : local GDP by field and field relative employment. Our GDP-by-field measure is a shift-share variable that captures the differential exposure of departments and fields to sector-specific national shocks. Although a sector’s shock is common to all departments, the exposure of a particular field and department to the shock is proportional to the sector’s importance for that field and department, as given by the sector’s time-invariant employment share. For identification purposes, the key feature of these shares is that they are based on the graduates employed in a department independently of where they studied. Similarly, our relative employment variable measures the proportion of SCP graduates from a field who work in a department independently of where they studied. If these two variables were based on the SCP graduates who obtained their degree in the department rather than those who work there, they might be correlated with local SCP demand and supply and might not be exogenous. As a

> 24 fields listed in Table A.1 yet the field-category fixed effects, _αf_ , refer to the four _aggregate_ field categories from that table. We use field-category fixed effects for a more parsimonious model—particularly given the inclusion of year and department fixed effects—and because some of the 24 fields have very few observations.

16
## **Table 5b. Further results – mixed group**

||Initiators|||||Receivers|||||
|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|10|
|Private<br>university|2.815***<br>(.805)|2.843***<br>(.876)|2.749***<br>(.803)|2.785***<br>(.825)|4.658<br>(3.006)|-1.201<br>(1.209)|-.851<br>(1.127)|-.899<br>(1.343)|-1.413<br>(1.057)|4.987<br>(5.290)|
|Amount||||||.626***|.593***|.637|.602***|.614***|
|received||||||(.047)|(.072)|(.045)|(.050)|(.049)|
|Particularized|-.925*|-.959*|-.990*|-1.047*|-.665|<br>2.028|<br>3.016**|<br>2.337|<br>.735|2.752|
|trust (trust in|(.508)|(.539)|(.529)|(.541)|(.707)|(1.282)|(1.363)|(1.476)|(.818)|(1.751)|
|friends)|||||||||||
|Benevolent||.203|||||.193||||
|self-image||(.326)|||||(1.093)||||
|Material||.513||-.711|||-3.681||-15.521**||
|utility||(.958)||(3.433)|||(2.597)||(6.895)||
|maximizer|||||||||||
|Socialism||-.256|||||-3.012**||||
|better||(1.032)|||||(1.194)||||
|Authoritarian||.043|||||1.747||||
|better||(1.568)|||||(2.315)||||
|Would likely|||.980|||||2.667|||
|know people|||(1.471)|||||(2.396)|||
|Would report|||-.484|||||-.564|||
|to people *|||(.943)|||||(1.466)|||
|trust in|||||||||||
|friends|||||||||||
|Utility max *||||.512|||||4.549**||
|trust in||||(1.181)|||||(2.223)||
|friends|||||||||||
|Private|||||-.687|||||-2.281|
|university *|||||(1.074)|||||(1.794)|
|trust in<br>friends|||||||||||
|Observations|44|44|44|44|44|44|44|44|44|44|
|R squared|.425|.441|.433|.438|.431|.699|.757|.711|.769|.709|
|F statistic|_5.61_|9.18|_3.93_|10.82||43.53|33.40|39.95|37.62|34.51|
|RMSE|2.544|2.653|2.595|2.583|2.565|4.481|4.257|4.515|4.033|4.467|

Note: *** (**) [*] denote significance at p<.01 (p<.05) [p<.10]. All estimates include the full baseline specification and a constant term.

# _8.5 Insignificant influences_

Other results (not shown but available from the authors) indicate that various factors included in the survey such as living standards, parents’ education, religion, being a party member, as well as fairness considerations and how much money receivers expected to be sent, were not significant in affecting behavior.

40
conducted by the Bank of Tanzania (2007), this is grossly contradicted by the facts. Given our focus on services and the importance of tourism to the Tanzanian economy, we had to correct this problem.

In our first step, we reduced imports and exports in the "Transport and communication" sector by the value of tourism imports and tourism exports, respectively. This yields levels of traditional exports and imports for this sector which are roughly in line with balance of payments data from the Bank of Tanzania for these sectors.

In the second step, we had to reallocate the tourist expenditures subtracted from Transportation and Communication to other sectors. Since most of the tourism imports are reexported, e.g., international air travel, we chose to calculate net tourism expenditures. We allocated the net expenditures to the various sectors of our model based on the Bank of Tanzania survey results.

The survey revealed that around 28% of total expenditure of non-package tourists is transport, 29% is accommodation, 12% is shopping and 31% is other expenditure. We therefore allocated the cost share of tourism input expenditures to the following sectors: 29% to “Hotels & restaurants,” 28% to road transport, railway transport and air transport in proportion to the three sectors’ contribution to GDP. The remaining 43% is on goods. We allocate these expenditures to the goods producing sectors in proportion to their share in total exports of goods. Major examples of tourist expenditure on goods include clothing, gold items, and food products such as cashew nuts and coffee.

This allocation of tourist expenditures implies that our SAM gets additional accounts representing “Tourism”. An activity account (the tourism sector) contains the tourism expenditure and produces an aggregate tourism commodity. The tourism sector in our SAM does not generate value-added itself, but demands domestically produced services and goods (as detailed above). Additional exports of tourism therefore indirectly increases value-added in the Tanzanian economy.

An additional commodity account contains the aggregate tourism commodity (the output of the domestic tourism activity) and tourism imports. The sum of the two is exported as gross tourism exports.

31