rafmacalaba/datause-encoder-data huggingface dataset:
circumstances did not push the IRR into the negative. It is an important message of the simulation because, conceptually, negative IRR paths could be consistent with equation (3) under certain circumstances. These, however, would be difficult to implement at the practical level.

**Table 2:  Descriptive Statistics for Selected Outputs**

||**Annui**|**ty calculat**|**ed****_with_**<br> **disco**|**unting**|**Annuity**|**calculated**|**_without_**<br> **dis**|**counting**|
|---|---|---|---|---|---|---|---|---|
||**Mean**|**Stdev**|**Min**|**Max**|**Mean**|**Stdev**|**Min**|**Max**|
|**Average wage**|||||||||
|_Reserves/GDP t=150_|-1.326|0.698|-2.480|-0.146|0.918|1.283|-0.050|4.341|
|_Reserves/GDP t=300_|-3.390|3.514|-17.241|-0.728|0.761|2.331|-1.301|8.856|
|_Balance/GDP t=150_|-0.031|0.013|-0.046|-0.009|-0.002|0.006|-0.008|0.004|
|_Balance/GDP t=300_|-0.022|0.017|-0.065|0.001|0.000|0.006|-0.012|0.013|
|_Average IRR(toyear 300)_|0.021|0.005|0.009|0.032|0.021|0.005|0.009|0.032|
|**Wage bill**|||||||||
|_Reserves/GDP t=150_|-0.959|0.561|-1.689|-0.124|1.180|1.400|0.277|4.823|
|_Reserves/GDP t=300_|-2.765|3.036|-15.027|-0.366|1.207|2.644|-0.557|9.164|
|_Balance/GDP t=150_|-0.023|0.010|-0.030|-0.009|0.004|0.000|0.003|0.004|
|_Balance/GDP t=300_|-0.020|0.013|-0.044|-0.004|0.002|0.001|0.001|0.004|
|_Average IRR(toyear 300)_|0.025|0.006|0.016|0.038|0.025|0.006|0.016|0.038|
|**GDP**|||||||||
|_Reserves/GDP t=150_|-0.959|0.561|-1.689|-0.124|1.180|1.400|0.277|4.823|
|_Reserves/GDP t=300_|-3.255|3.150|-15.027|-0.794|0.850|2.535|-1.993|9.164|
|_Balance/GDP t=150_|-0.023|0.010|-0.030|-0.009|0.004|0.000|0.003|0.004|
|_Balance/GDP t=300_|-0.020|0.013|-0.044|-0.004|0.002|0.001|0.001|0.004|
|_Average IRR(toyear 300)_|0.022|0.004|0.016|0.025|0.022|0.004|0.016|0.025|
|**Inflation**|||||||||
|_Reserves/GDP t=150_|1.191|1.142|0.297|4.058|2.706|2.113|1.146|8.115|
|_Reserves/GDP t=300_|0.928|2.102|-0.691|8.262|3.734|5.657|0.509|19.952|
|_Balance/GDP t=150_|0.005|0.009|-0.007|0.020|0.024|0.003|0.019|0.028|
|_Balance/GDP t=300_|0.001|0.002|-0.003|0.008|0.016|0.009|0.004|0.033|
|_Average IRR(toyear 300)_|0.021|0.005|0.009|0.032|0.021|0.005|0.009|0.032|
|**Swedish mechanism**|||||||||
|_Reserves/GDP t=150_|0.859|1.187|-0.040|4.028|0.928|1.295|-0.046|4.381|
|_Reserves/GDP t=300_|0.947|2.136|-0.471|8.270|0.968|2.254|-0.517|8.930|
|_Balance/GDP t=150_|-0.001|0.005|-0.007|0.004|-0.002|0.006|-0.008|0.004|
|_Balance/GDP t=300_|0.002|0.004|-0.009|0.012|0.002|0.005|-0.011|0.013|
|_Average IRR(toyear 300)_|0.019|0.006|0.002|0.030|0.019|0.006|0.002|0.030|
|**Equation 3**|||||||||
|_Reserves/GDP t=150_|1.381|0.196|1.057|1.841|1.548|0.280|1.102|2.176|
|_Reserves/GDP t=300_|1.057|0.460|0.498|1.851|1.203|0.533|0.572|2.266|
|_Balance/GDP t=150_|0.019|0.012|-0.007|0.036|0.021|0.012|-0.009|0.037|
|_Balance/GDP t=300_|0.008|0.008|-0.008|0.021|0.008|0.009|-0.009|0.025|
|_Average IRR(toyear 300)_|0.002|0.006|-0.007|0.0164|0.014|0.007|0.003|0.0305|

Source: Authors’ calculations.

23
dependency can be omitted. The estimation of the function is carried out through a multiple regression of productive factors in order to estimate the following the Cobb-Douglas function: where: _Q_ = number of TEUs<sup>15</sup> , _C_ = Net crane operation time, _B_ = Berth hours, _L_ = labor, α, β and γ are parameters to be estimated.

The data used for the empirical estimation are monthly -covering from May of 1984 to February of 1990 (both inclusive)- and relate to three terminals located at the port of Melbourne. The information about the different terminals was taken as if it were coming from only one since this was thought to be the only way to gather enough reliable data to carry out the estimation. Moreover, the authors claim that the similarities among terminals, regarding their geographical location and the equipment used as well, allow this course of action, at the same time that data confidentiality is secured.

_Tongzon (1993),_ as _Reker et al. (1990),_ estimates the production function of container handling, although, in this case, the author’s objective is to examine whether the new tariff policy at the Port of Melbourne improved port efficiency and, at the same time, assess the contribution of the different factors involved in port efficiency. This study considers monthly data from May of 1984 to February of 1990 for container berths in the port of Melbourne and estimates the following production function: where: _Y_ = number of TEUs per berth hour, _X1_ = number of cranes per berth hour, _X2_ = labor per berth hour, _X3_ = number of TEUs carried by land per berth hour, α, β and γ are parameters to be estimated.

As in _Reker et al. (1990)_ , the model specification estimated by _Tongzon (1993)_ results from various tests aimed at achieving the best adjustment of data on the basis of different criteria such as consistency with a-priori restrictions, coefficient significance and the absence of serial correlation.

The studies by C _hang (1978), Reker et al. (1990)_ and _Tongzon (1993)_ all apply a Cobb-Douglas functional form which –although easy to estimate- presents the important limitations described above in section 2.3. Therefore, we emphasize the idea that it is clearly preferable to use the so-called flexible functional forms which avoid placing these kinds of restrictions.

> 15 _Twenty feet equivalent unity._

13
accessibility or favorable natural endowments such as a river crossing, a coastal location or fertile volcanic soils. Those geographic settings are often associated with an increased probability of hazard events—floods, cyclones and volcanoes. Agriculture in most of southern Italy, for example, is difficult due to poor soil quality. An exception is the area around Mount Vesuvius near the city of Naples where rich volcanic soils have been farmed for centuries despite the risk of new eruptions. Globally an estimated 9 percent of population lives within 100 km of a historically active volcano, with high concentrations in Southeast Asia (Indonesia, Philippines) and Central America (Small and Naumann 2001). Similarly, low elevation coastal zones, often exposed to cyclones and storm surges, cover 2 percent of the world’s land area but contain 10 percent of the world’s population and 13 percent of the world’s urban population (McGranahan et al. 2007).

# _Land scarcity_

Competition for land in urban areas is intense. City managers often exacerbate land scarcity by restricting high density development as further discussed later in this paper. The desire to live close to jobs and amenities means that even marginal city areas such as floodplains or steep slopes will be settled—often, though not always, by poor people. In Santo Domingo’s largest slum, 45 percent of houses near a river flood when it rains (Fay et al. 2001, 2003). Housing prices reflect this risk with the poorest living in the lowest quality housing in the most at-risk areas. In cities such as Caracas or Rio de Janeiro, poor families occupy steeply sloped land prone to landslides. This sorting process, with low income households and squatters occupying the most hazardous urban land is not static. Detailed data for Cali, Colombia, show that localized hotspots of small scale disaster events change as inner-city neighborhoods gentrify, governments improve hazard management, and new informal settlements spring up at the periphery (ISDR 2009).

# _Externalities_

Land scarcity leads to higher land prices and therefore, usually, to higher density occupation. Larger building sizes in cities can increase damages and loss of life in severe earthquakes especially where building codes are not enforced. The collapse of larger buildings in dense urban areas may also cause neighboring buildings to be damaged even 4
**Figure 2:  Availability of Household Survey Data (POVCALNET and LIS)**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>LIS Database (# obs) PCN Database (#obs)<br>1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011<br><!-- End of picture text --> Notes: This figure shows number of household surveys available in each year, for the LIS and POVCALNET databases.

36
# About AICD and its country reports

This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a solid empirical foundation for prioritizing investments and designing policy reforms in Africa’s infrastructure sectors.

The AICD is based on an unprecedented effort to collect detailed economic and technical data on African infrastructure. The project has produced a series of original reports on public expenditure, spending needs, and sector performance in each of the main infrastructure sectors, including energy, information and communication technologies, irrigation, transport, and water and sanitation. _Africa’s Infrastructure— A Time for Transformation_ , published by the World Bank and the Agence Française de Développement in November 2009, synthesized the most significant findings of those reports.

The focus of the AICD country reports is on benchmarking sector performance and quantifying the main financing and efficiency gaps at the country level. These reports are particularly relevant to national policy makers and development partners working on specific countries.

The AICD was commissioned by the Infrastructure Consortium for Africa following the 2005 G8 (Group of Eight) summit at Gleneagles, Scotland, which flagged the importance of scaling up donor finance for infrastructure in support of Africa’s development.

The AICD’s first phase focused on 24 countries that together account for 85 percent of the gross domestic product, population, and infrastructure aid flows of Sub-Saharan Africa. The countries are: Benin, Burkina Faso, Cape Verde, Cameroon, Chad, Côte d'Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Uganda, and Zambia. Under a second phase of the project, coverage was expanded to include as many of the remaining African countries as possible.

Consistent with the genesis of the project, the main focus is on the 48 countries south of the Sahara that face the most severe infrastructure challenges. Some components of the study also cover North African countries so as to provide a broader point of reference. Unless otherwise stated, therefore, the term ―Africa‖ is used throughout this report as a shorthand for ―Sub-Saharan Africa.‖
7 _κ_ Equation ( ) shows that it is possible to estimate , based on firm-level exports and imports data. In particular, if _DV Ait_ can be controlled nonparametrically by _Dit_ ( _·_ ), then (7) can be reformulated into a regression with a classical measurement error term, _εit_ : where the coefficient on imports, _κ_ , has a structural interpretation as the share of imported inputs used specifically for the export production.

With an unbiased estimate of _κ_ , we can adjust firm-level imports to determine _DV ARit_ for the ordinary exporters:

# **3.2 Econometric Specification**

The import coefficient has a structural interpretation as the share of imports used for exports, when regress firm’s exports on imports according to (8). This is analogous to the production function estimation, where regressing firm’s output on inputs, resulting in the structural interpretations of the input coefficients as input shares or elasticities. Similar to the production function estimation which is subjected to simultaneity bias, estimation of _κ_ according to (8) could be inconsistent due to factors that affect both exports and imports of firms. One such factors is firm’s productivity: productivity increases could simultaneously raise both imports and exports, leading to an upward bias in the least squares estimate. In contrast, another factor could be currency depreciation, which might reduce imports while boosting exports, causing a downward bias. The net effect of these opposing forces is empirical and cannot be determined without further investigation.

To control for the potential bias in the regression of (8), firm-level exchange rate data can be constructed according to Kee and Tang (2016) and introduced directly in the regression. However, is unobserved and econometric to address. One firm productivity requires specific techniques way is to use an instrumental variable (IV) correlated with imports but unrelated to exports. Given that firm-level IVs are often difficult to identify, here we adopt the control function approach from Olley and Pakes (1996) (OP) to control for the unobserved productivity. Intuitively, firms make decisions on inputs and investments based on their productivity. Part of the investment is imported capital goods, which can be observed from customs transaction data. Thus, firm-level imported capital goods could serve as a for the unobserved similar to OP used investment in proxy productivity, firm production function estimation to proxy for productivity.

10
pay the cheapest premium, which only provides limited entitlements through the “basics benefit package” (Dartanto et al., 2020; Muttaqien et al., 2021).

While these outcomes may be taken as evidence of segmentation between formal and informal workers, other factors need to be considered. For example, many employees with “good” jobs in the formal sector have low wages, work without employment contracts, and receive little or no employment benefits despite qualifying for such benefits (World Bank, 2010). More importantly, the intrinsic aspects of informal jobs (e.g., autonomy, flexibility) have yet to be thoroughly examined. This constitutes a critical gap in knowledge as these characteristics are believed to encourage informality, particularly among certain groups. These include women, particularly those with caring responsibilities; young workers, who use informal jobs to acquire skills and work experience; and entrepreneurs, who aspire to start their own businesses. Whether these advantages fully offset the disadvantages of informal employment is a question that remains unanswered.

Likewise, the issue of heterogeneity in informal jobs has not yet been fully unpacked in the context of Indonesia. However, there is evidence to suggest that informal jobs are not always a dead end. Using the 2000 and 2007 waves of the IFLS, for example, the World Bank (2010) showed that 14 percent of formal workers transitioning to an informal job experienced wage growth. Among the “winners” in the informal sector are young workers who eventually found formal jobs, as well as older workers who chose to become informal entrepreneurs (World Bank, 2010). More recent evidence suggests that heterogeneity also manifests in terms of diversity in incomes. Applying quantile regressions on the last three waves of the IFLS, Ablaza and co-authors (2021) showed that the size of the formal-informal earnings differential varies substantially across the income distribution. Women, workers with low levels of education, and employment in the agriculture sector were all associated with larger differentials.

As mentioned in the previous section, informal workers may also be employed by formal enterprises although the extent of this arrangement remains unclear. In other words, how much 17
-21-

4). Econometric analysis suggests that the increase in capacity utilization between 1981 and 1983 led to an increase in private fixed investment of 0.7 percentage point of GNP. This is more than a nine percent increase over what it would have been without this increase in capacity utilization. Subsequent improvements in capacity utilization since 1983 added an additional half percentage point of GNP to investment (see Figure 5; the impact of improved capacity utilization is measured by the difference between line B and C).

TABLE 4: CAPACITY UTILIZATION IN PRIVATE SECTOR MANUFACTURING INDUSTRY 1977-87

||State Institute of<br>Statistics (SIS)|Istanbul Chamber<br>of Industry|
|---|---|---|
|1977|63.6a/||
|1978|61.1k/||
|1979|<br>57.1 b/|45.0|
|1980|55.5|.<br>51.1 b/|
|1981|<br>57.4|62.1|
|1982|<br>59.0 c/|66.8|
|1983|<br>61.0c/|69.6|
|1984|<br>62.0**_c/_**|72.0|
|1985|<br>62.9 c/|72.7|
|1986|<br>64.2 £/|72.0|
|1987||73.6 A/|

> **_A/_** July-December 1977 _/ Unweighted ./ Fourth quarter 4/ First two quarters only

The final factor is more directly related to fiscal policy. At issue is the composition of public investment. Since 1980, the Government has shifted the composition of its public sector investment program heavily towards sectors where it does not compete with private sector investment. Large cuts were made in public sector investment in manufacturing. At the
population are achieved. Thus, the program provides explicit incentives to enroll the target population of uninsured mothers and children, and to provide services that improve health outcomes of the eligible population.

Health targets are measured using ten specific indicators called tracers (table 1). The tracers are derived from best practice clinical protocols. The tracers include health outcomes such as share of newborns with low birth weight (that is, less than 2,500 grams) and APGAR scores greater than 6. They also include the use of priority services such as beginning prenatal care in the first 20 weeks of pregnancy, receiving Venereal Disease Research Laboratory (VDRL) test and tetanus vaccines prior to delivery, reproductive health counseling within 45 days of delivery, on-time and complete child immunization, and complete and on-time well-baby visits.

**Table 1: Tracers**

||Tracer|Description|
|---|---|---|
|1.|Early detection of pregnancy|First prenatal checkup before week 20<br>of pregnancy|
|2.|Effectiveness of obstetric care|Five-minute APGAR scores of over 6|
|3.|Effectiveness of prenatal care|Birth weight of over 2,500 grams|
|4.|Effectiveness of prenatal care|Mother given VDRL test and tetanus<br>toxoid vaccination prior to delivery|
|5.|Proper cause of death review of<br>infant and maternal deaths|Proper cause of death review of infant<br>and maternal deaths|
|6.|Immunization coverage|Children under 18 months of age given<br>measles vaccine or triple vaccine|
|7.|Sexual and reproductive health|Mothers receive counseling within 45<br>days of delivery|
|8.|Well-child follow-up until one<br>year of age|Children <1 with complete record of<br>preventive checkups|
|9.|Well-child follow-up from one to<br>six years of age|Children 1 to 6 years old with complete<br>record of preventive checkups|
|10.|Inclusion of the indigenous<br>population|Providers with staff trained to provide<br>care to indigenous population|

_Source: Manual Operativo (2005)_ Performance targets associated with the tracers for each province are set with the provinces in the annual agreement between the parties and are measured 6
each instrument depend upon staff and communication costs. Because of high SWIFT charges, the cost of a telegraphic transfer is higher than a draft or check (Table 6) and even more when compared to informal remittance channels such as Hawalla.

**Table 5  Cost of Remitting Money To Sri Lanka (2005)**

|Country|Currency|Telegraphic<br>Transfer|Draft/<br>Check|E-Remittance|
|---|---|---|---|---|
|United Arab|Diram|8-10|3-5|10-15|
|Emirates|||||
|(exchange houses)|||||
|USA (banks)|USD|30-50|25|10-62 (for USD 100-500 remittance)|
|UK(banks)|GBP|27.50|22.50|7-11 (for GBP 100-500 remittance)|
|Other European|Euro|10-75|25|7-11( for Euro 100-500 remittance)|
|Union (banks)|||||

_Source: Central Bank of Sri Lanka (2005)_ _The use of bank drafts for remittances is highly inefficient._ Overseas remittance companies issue drafts on Sri Lankan banks, which the remitter mails to the recipient through the postal system. When the recipient receives the draft, he or she presents it to a bank branch. The branch presents the draft to the bank’s main office for validation. When validated, the recipient’s account is credited or cash paid over the counter. Remitting money using bank drafts presents several challenges. Drafts may be lost in the mail and may be forged. In Colombo, banks will deliver the cash for remittances to the home of beneficiaries without surcharge. This is not the case in the countryside where the beneficiary will have to visit the nearest branch once, in some cases twice to obtain cash. The settlement time is long because of the large part of manual work and transportation involved in processing the remittance. Although inexpensive to the remitter, the service level is inferior to what informal operators can provide and to what formal systems in developed remittance markets offer. The long transfer time may encourage recipients to relieve liquidity constraints through borrowing money at high interest rates from informal lenders.

_There are also electronic fund transfers available to Sri Lankan migrants, but they are generally more expensive._ International Money transfer businesses (MTBs) remit through commercial banks. Furthermore, commercial banks have entered into partnerships with exchange houses and overseas banks in setting up electronic infrastructures that allow remittance instructions from the overseas partner to initiate a credit to the recipient’s account. Overseas workers are in some cases able to initiate transfers using the internet. Such transfers, provided the branch where the recipient is banked is on-line with the main office, can take place within a day.

_In order to compete with the informal providers, the formal operators must lower transaction costs and improve the speed and distribution reach of their services._ Banks must expand their international electronic transfers to rural areas. Branches must be connected to electronic payments 13
**Table 2: Regional FDI Inflows: Sectoral Breakdown, 2009**

(as percent of total FDI)

|**Regions**|**Agriculture**<br>**and mining**|**Manufacturing**|**Service**|**Other**|
|---|---|---|---|---|
|East Asia and Pacific|3.50|45.21|50.81|0.47|
|Europe and Central Asia|11.43|9.14|77.17|2.25|
|Latin America and Caribbean|14.42|28.47|52.01|5.11|
|Middle East and North Africa|38.25|30.50|29.10|2.15|
|**South Asia**|**3.95**|**22.28**|**72.04**|**1.73**|
|Sub-Saharan Africa|57.17|15.78|24.46|2.59|

Source: International Trade Centre and World Bank Staff Calculations based on 2009 statistics

**Table 3: Regional FDI Inflows: Sectoral Breakdown**

(as percent of GDP)

|**Regions**|**Total**|**Agriculture**<br>**and mining**|**Manufacturing**|**Service**|**Other**|
|---|---|---|---|---|---|
|East Asia and Pacific|2.13|0.07|0.96|1.08|0.01|
|Europe and Central Asia|2.26|0.26|0.21|1.74|0.05|
|Latin America and Caribbean|2.03|0.29|0.58|1.06|0.10|
|Middle East and North Africa|5.15|1.97|1.57|1.50|0.11|
|**South Asia**|**1.77**|**0.07**|**0.39**|**1.27**|**0.03**|
|Sub-Saharan Africa|4.72|1.51|1.05|1.63|0.00|

Source: International Trade Centre and World Bank Staff Calculations based on 2009 statistics

# **_Source Countries for South Asia’s FDI Inflows_**

**The number of developing countries investing in South Asia has increased in recent years, which reflects a global trend of more south-south FDI flows** .  Growth in the number of countries investing in South Asia may suggest greater trade linkages and knowledge spillovers between developing countries and industries, rather than the expansion of investments from a single country with only marginal additional knowledge spillovers.  As Table 4 shows, the number of developing countries with FDI positions in South Asia increased from 38 to 45 between the early and late 2000s, while the number of developed countries making investments in South Asia grew by only one (Greece).

**Table 4: FDI into South Asia: Source Country Counts**

||**Developed Countries**|**Developing Countries**|**Total**|
|---|---|---|---|
|2003-2006|23|38|61|
|2007-2010|24|45|69|

Source: World Bank Staff estimates from UNCTAD statistics and fDimarkets 13