rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7. Health Shocks and the Supply of Unprotected Sex**

||(1)<br>----------------|(2)<br>-------------- All|(3)<br>Women -----|(4)<br>-----------------|(5)<br>--------|(6)<br>--------|(7)<br>--------------------|(8)<br>----------------- Rou|(9)<br>nd 2 Women Only|(10)<br>-----------------------------|(11)<br>----------------|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Had Unprotect.<br>Sex|# Unprotect.<br>Sex Acts|Had Anal<br>Sex|Had Vag.<br>Sex|Had Oral<br>Sex|Had Unprotect.<br>Sex|# Unprotect.<br>Sex Acts|Had Unprotect.<br>Vaginal Sex|Had Unprotect.<br>Anal Sex|# Times Unprotect.<br>Vaginal Sex|# Times Unprotect.<br>Anal Sex|
|Mean of Dependent Variable^|0.157|0.346|0.198|0.826|0.176|0.086|0.129|0.079|0.015|0.108|0.022|
|**Panel A. Household Sickness**||||||||||||
|Somebody in Household|0.030|0.063|0.042|0.022|0.037|0.013|0.019|0.020|0.006|0.017|0.010|
|<br>(other than respondent) Sick|(0.013)**|(0.042)|(0.012)***|(0.013)|(0.013)***|(0.012)|(0.024)|(0.012)|(0.006)|(0.020)|(0.011)|
||[0.191]|[0.182]|[0.212]|[0.027]|[0.210]|[0.150]|[0.147]|[0.255]|[0.389]|[0.158]|[0.462]|
|Respondent Sick|-0.002|-0.015|-0.016|-0.085|-0.012|-0.005|-0.019|0.001|-0.010|-0.007|-0.011|
||(0.011)|(0.030)|(0.013)|(0.018)***|(0.012)|(0.013)|(0.021)|(0.012)|(0.006)*|(0.019)|(0.006)*|
||[-0.013]|[-0.043]|[-0.081]|[-0.103]|[-0.068]|[-0.058]|[-0.147]|[0.013]|[-0.648]|[-0.065]|[-0.508]|
|Observations|12293|12072|12293|12293|12293|5493|5365|5493|5493|5369|5477|
|Number of women|192|192|192|192|192|108|108|108|108|108|108|
|**Panel B. Sexually Transmitted**|**Infection(STI)**|||||||||||
|STI|0.012|-0.046|-0.069|-0.233|-0.017|0.025|0.031|0.028|-0.007|0.036|-0.005|
||(0.036)|(0.095)|(0.036)*|(0.047)***|(0.031)|(0.044)|(0.083)|(0.045)|(0.005)|(0.081)|(0.006)|
||[0.077]|[-0.133]|[-0.348]|[-0.282]|[-0.096]|[0.289]|[0.240]|[0.357]|[-0.454]|[0.334]|[-0.231]|
|First Day After STI|0.087|0.180|-0.029|0.073|0.048|0.052|0.036|0.060|-0.011|0.045|-0.007|
||(0.062)|(0.134)|(0.053)|(0.043)*|(0.045)|(0.078)|(0.107)|(0.079)|(0.005)**|(0.107)|(0.008)|
||[0.555]|[0.520]|[-0.146]|[0.088]|[0.272]|[0.602]|[0.279]|[0.764]|[-0.713]|[0.417]|[-0.323]|
|Observations|10393|10241|10393|10393|10393|4448|4372|4448|4448|4374|4440|
|Number of women|192|192|192|192|192|108|108|108|108|108|108|
|Sample|All|All|All|All|All|Round 2|Round 2|Round 2|Round 2|Round 2|Round 2|

Note: All regressions are fixed effects regressions with controls for the month and for the day of the week. Clustered standard errors at the individual level in parentheses. All regressions include controls for the round of data collection. Sickness is an indicator variable equal to 1 if respondent reports a cough, fever, malaria, typhoid, diarrhea, cuts, burns, or other illnesses. Unprotected sex includes unprotected anal and unprotected vaginal sex. The variables in Columns 8-11 (separate indicators for unprotected anal and unprotected vaginal sex) are only available for the Round 2 diaries. ^Means of dependent variables are means when all shocks are equal to 0. * significant at 10%; ** significant at 5%; *** significant at 1%
The hourly dispatch data for Bangladesh show that the system will still use fuel oil during evening hours when demand is at its maximum. During peak demand hours fuel oil output reaches up to 80% of fuel‐oil based generation capacity, providing up to 2.5 GW of energy. In the base 2025 scenario, fuel generation will decline during daytime hours. The reference case assumes that no renewable energy sources of energy, other than the existing hydro plant, will be added to the generation mix. As we discuss later, solar will replace daytime fuel oil generation, contributing to the reduction of carbon emissions.

The modeling of spinning reserve costs becomes complex when other reserve requirements are considered, as we have noted previously. The reference case and all other cases assume that there will be 15% capacity reserve margin over and above peak demand. Without any renewable generation capacity, available spinning reserves are projected to grow from 501 MW in 2015 to 880 MW in 2025. Making this maximum spinning reserve capacity available in the system means that all generators will have to run below full capacity to enable some flexibility. This implies a slight deviation from merit‐order dispatch, which is the key driver in short‐run spinning reserve costs. Between now and 2025, providing the full projected spinning reserve will cost between US$22 million (in 2017) and US$95 million (in 2024).

Table 2. Potential spinning reserve (MW)<sup>14</sup>

||**2015**|**2016**|**2017**|**2018**|**2019**|**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Generation costs (USc/kWh)**|3.35|3.26|3.49|3.81|4.01|4.28|4.58|4.96|5.28|5.93|6.59|
|**In‐built, full spinning reserve**<br>**potential (MW)**|501|576|622|598|550|558|643|706|738|815|880|
|**Annual cost of providing full**<br>**spinning reserve potential**<br>**(US$mln)**|13|9|22|22|31|15|27|31|43|95|69|

# Model Results –10% Renewable Target Policy Scenario

The Reference Case considered that solar and wind generation costs will far exceed projected system costs, and hence, none of these technologies would be deployed within the forecast period. For the high renewable scenario, renewable energy generation costs were reduced below USc6.5/kWh by 2025, which enables the deployment of solar up to a capacity limit. For this scenario, the maximum generation capacity was limited to 4.2 GW for solar and 500 MW for wind.

> 14 Source: World Bank calculation.

17
private debt can sometimes be transformed into public debt, either directly through bailouts or indirectly through countercyclical government spending in response to private deleveraging, suggesting that the line between public and private debt can blur (Mbaye, Badia and Chae 2018).

**Growth subject to downside risks.** Growth in LICs is expected to remain resilient, supporting their ability to service debt, but risks are tilted to the downside. LIC growth is expected to average 5.6 percent in 2018 and accelerate to just over 6 percent in 2019-20, supported by rising agricultural output and continued infrastructure investment (World Bank 2019). However, over the next decade, weaker growth in major emerging markets may slow global demand for metals, which dampens growth prospects for LICs that depend on metals for government and export revenues (World Bank 2018c). Downside risks to this outlook predominate and include the possibility of a faster-than-expected slowdown among major trading partners (including China, a major commodity consumer); a renewed plunge in commodity prices; a deterioration in international financial conditions; and the possibility of natural disasters, conflict, or severe weather events.

**Elevated debt, lower investment growth, increased risks.** Rising levels of non-concessional public debt, often at variable rates, make some LICs susceptible to a sudden increase in borrowing costs, especially when they have substantial refinancing needs in coming years or have borrowed in foreign currencies. As advanced economies continue to withdraw monetary policy accommodation, new debt issuances and debt rollovers may become more expensive, resulting in rising LIC debt service costs that could weaken investment and lower medium-term growth (World Bank 2015b, 2016, and 2017). Fiscal consolidation, while often necessary, can also dampen growth in the short term.

In the absence of sufficient lending made available at concessional terms, there is a risk that high public debt will lead to higher interest rates, crowding out private investment and slowing growth.<sup>10</sup> Similarly, rising interest payments to domestic creditors may encourage policy makers to engage in financial repression—using administrative or other means to channel domestic savings toward the purchase of public debt—which can dampen private sector investment and limit the development of domestic financial markets (Fry 1997).

**Substantial current account deficits.** Almost all LICs carry persistent, substantial current account deficits, with an estimated median of 6.8 percent of GDP in 2017 (Figure 4). Forty percent of LICs had current account deficits that widened by at least 3 percentage points of GDP over the last decade. Among metals exporters, rising deficits reflected the pickup in import-intensive mining investment, while in non-resource-intensive countries it often reflected high public investment.

10 Bevan (2012) argues that although evidence in the literature for the crowding out effect on investment in LICs is weak, it may be more important where financial depth is low.

9
<!-- Start of picture text --> Local Avg. + Polynomial in GEL 500 Intervals from Cutoff Local Avg. + Polynomial in GEL 100 Intervals from Cutoff<br>50000 100000 150000 10000 20000 30000 40000 50000<br>2008 Declared Revenue 2008 Declared Revenue<br>150000 50000<br>40000<br>100000<br>30000<br>2009 Declared Revenue 2009 Declared Revenue<br>20000<br>50000<br>10000<br><!-- End of picture text --> Since estimating Equation 2 requires data on revenue for two years, we prefer not to use a difference-in-difference approach to control for the pre-reform jump around GEL 100,000. The reason is that we have fewer years of outcome data since we are using one year of data as the explanatory variable and cannot use the same variable at the outcome (and 2008 and 2009 are the only pre-reform years we have with good data). This lack of data means that we cannot test whether the jump in declared revenue is constant in different pre-reform years. We thus focus on the GEL 30,000 threshold for estimating the impact of the reform on declared revenue. As businesses below the 30,000 GEL are exempt from income taxation, increases that remain below the threshold have no associated cost for entrepreneurs aside from potentially attracting more scrutiny of tax inspectors verifying the eligibility of their exempt status. In order to estimate this impact, we will run the following regressions Revenue2010 = a0 + a1*Indicator[Revenue2009<30,000] + a2*Revenue2009 + ε, where the coefficient a1 measures the reform impact (we also add higher order polynomial terms of Revenue2009 as additional controls). Note that we use declared revenue in 2009 as the explanatory variable for 2010, 2011, and 2012 declared revenue since 2010 and 2011 revenue may be endogenous to the reform. A caveat here is that the predictive power of 2009 revenues is likely declining as we look at revenue in future years, which may lead to less statistically significant estimation results when we use 2011 and 2012 revenues as the outcome variables.

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- 71 -

changes in the rate of inflation. Apparently, the benefit of nominal interest deductibility in times of inflation more than offsets the disadvantage of historic,cost depreciation, which tends to reduce the value of Z,. Conversely, **_t*_** for retained earnings is positive as shown in the second column. The cost of equity flnancing is not tax-deductible. In these cases, years of lower inflation (1985 and 1986) tend to lower **_tc._** The last column uses observed weights of debt and equity financing to calculate tc when both sources are used at the margin. Naturally, tc falls between the pure debt-financed and equity-financed cases, but is negative in all years. Thus, the corporate tax system actually subsidizes investment at the margin.

The last three columns of the Table report what tc would have been had the post-1989 tax structure been in place. Essentially this involves a reduction in u from .45 to .35. The qualitative comparisons among the various cases remain exactly as before, but the absolute magnitudes are all reduced. Positive values of _tc_ become smaller and so do negative values.

# _b. Profit-Making Pioneer Firms_

Table 2 shows tc's for a firm granted pioneer status in 1983. Ideally we would show the rates applying to margiral investments undertaken in each of the five years of the tax holiday period; but absence of pertinent data for 1982 precluded this, and so we report rates for only the final four years of the tax holiday. The last entry in each column shows Xc in the absence of pioneer status using 1983 data. (Here S.S. refers to steady state.) It is taken from Table 1.

Note that pioneer firm investments financed from all sources are subsidized by the tax system. However, the size of the subsidy is quite small. For debt-financed investments, the tax-induced subsidy is actually less than for non-pioneer firms. Such investments are penalized by pioneer status. Basically, the advantages of interest deductibility during the tax holiday no longer exist. For equity financing, this is not the case. Pioneer status converts tc from positive to negative. There is no loss in foregoing interest deductions. At the same time, firms benefit from being able to carry forward their initial and depreciation allowances until after pioneer status is finished. Thus, in principle it seems that pioneer status might serve either to increase or decrease investment incentives. Which way it works in practice will depend on which of these two effects is most important for the firm in question. If the tax burden on current revenues less interest deduction is larger (in a present value sense) than the tax savings from depreciation allowances, the first effect would be expected to dominate, and pioneer status would increase investment incentives, and _vice versa._ More generally, the granting of pioneer status tends to reduce the impact of the tax system on marginal investmnent decisions. If investment is penalized under the general tax laws, then pioneer status will reduce this disincentive. But if investments are subsidized, pioneer
_18_

# **Pension Assets and Liabilities of a Public Enterprise**

||Rph.Millions|US$ Millions|
|---|---|---|
|Pension Assets|187,600|85|
|Retirement|1.387.600|631|
|Obligations/a|||
|Unfunded|1,200.000|545|
|Obligations|||
|Earnings (1994)|798,000|362|
|Wage Bill (1994)|1.128,600|513|

Note: a. Pension plan and post-retirement health-care benefits Source: Pt. Telkom Prospectus ( 1995) Telkom's benefit policy is typical among public enterprises. But its prospects, and financial position, are not typical. In fact, it was selected for privatization on the basis of its exceptional growth prospect and strong balance sheets. Nonetheless, the existence of large and mostly unfunded pension liabilities played a role in depressing investors' demand and the valuation of Telkom's shares. For other public enterprises, whose prospects are generally not as bright as Telkom's but whose pension obligations are comparable, the privatization process may prove even more difficult. If unfunded liabilities were to be fully accounted for, many of the public enterprises, particularly state banks, would probably have negative net worth.

**_Contributions_** Rates of contributions for employer-sponsored plans are set by individual companies subject to funding rules. The division of contributions between the employer and employees is also left to the plan sponsor. There is a ceiling set, however, on the level of salary for which a pension may be set up and funded. This ceiling also sets a limit on tax-deductible contributions. irrespective of whether the sponsor or the worker makes the payments.

Such a restriction on pensionable salary is not unusual. Many countries impose similar restrictions. The salary ceiling of Rph. 5 million per month (or just over US$2.000) currently set in Indonesia, however, appears too low. It limits the potential for the pension system to mobilize resources. It may also penalize older workers under new pension plans who have relatively high income and little time to build up saving. _It might be_ **_useffil_** _to adjust this ceiling upward. say by 100 percent, and thereafter index the ceiling to the raite of inflation._

**_Vesting and Portabiiity_** Approved pensions are required to vest benefits fully, witlin 5 years. The vast majority of firms (more than 99 percent) in the formal sector, however, offers no vesting of benefits. In addition, the government, the largest employer in the country, does not vest benefits until the worker is 50 years old. (See Chapter 3.) A reality of the labor market today is that most workers are facing many impediments in seeking new employnment.

**_Disclosure_** Sponsors of approved pensions are required to provide reports to members at least annually. Independently audited financial statements must be submitted to regulators once a year. In addition, actuarial assessments must be made and submitted once every three years.
Estimates and projections of deaths by cause for major world regions are reported here. The sources for these estimates are country reports to the World Health Organization (WHO) and regression models.

Mortality rates will be reported for seven major causes-infectious and parasitic diseases, neoplasms, circulattty system diseawes, complications of pregnancy, certain perinatal conditions, injurv and poisoning, and other causes-and for more specific causes under some headings. For the major causes, predictive equations depending on the overall mortality level were previously estimated to supplement reported data; for the specific causes, WHO data and some new equations will be the basis.

Estimates were made for six age groups (0, 1-4, 5-14, 15-44, 45-64, and 65 and older) by sex for four years (1970, 1985, 2000, and 2015) and six country groupsindustrial market economies, industial nonmarket economies, Lafin America and the Caribbean (LAC), Sub-Saharan Afica, the Middle East and North Africa (IMENA), and Asia and the Pacific. Clsfication of countries as industral or developing (Annex A) is as of 1985.

The objective, approach, and background of the exercise are discussed; the methodology is described; current and projected distrbutions of death by cause are presented and contrasted among regions; and the reasonableness of and possible biases in the estimates are assessed.

# Background

The main objective of this exercise is to provide a comprehensive picture of the dsriution of deaths by underlying cause for 1985 and a gerneral perspective on how the distribution can be expected to change. We attempt to classify current deaths by both maor and specific cause, but project only the major causes into the future.

The approach is to summarize reports of causes of death to WHO and supplement these with regression estimates for nonreporting countries. An alternative would have been to consider diseases or disease groups one by one, building up a composite picture from the epidemiologicalliterature. Though disease experts have been canvassed for information on this literature, we do not rely primarily on this approach because it would lead to inconsistencies between estimates for different diseases and would provide overaU mortality rates at odds with other mortality data.

The approach taken follows work initiated by Preston (1976) and continued by Hakulinen and others (1986a, 1986b), whose age-sex specific equations for major causes of death are used here. This regression-based approach has also been applied to partcular areas, such as cancer deaths (Parkdn and others 1988).

Hakulinen and others estimated the distrbution of deaths by major cause as of 1980. We update their estimates to 1985, incorporate new data not available to them, attempt a more detailed clasification of causes, and provide perspectives on possible future trends.
transit through ports remain in the container terminal much longer than customs formalities or physical transfers would need.

The main reason for this is that the container terminal is in fact a warehousing option for cargo owners and is therefore used to meet some of their temporary or long term storage needs. These needs derive from a set of constraints - such as availability of cash flow, warehousing capacity outside the port, current inventory levels or cargo time sensitivity - as well as from strategic choices on risk exposure or inventory coverage that we will refer to as inventory management decisions. For example a shipper may leave his cargo in the yard because he has got no other warehousing possibility outside the port or he may leave it there because this would delay the payment of charges and duties and reduce hence temporarily the pressure on his cash flow. For the purpose of the study we will then define a **discretionary dwell time** as the sum of all idle times between vessel arrival and exit from container yard that are strictly storage times (no clearance process or handling operation is performed).

The complexity of research on dwell time is that these three constituents of total dwell time – operational dwell time, transactional dwell time and discretionary dwell time – are actually intertwined and overlaid. Yet we can proceed to a thorough analysis of total dwell time by taking two complementary approaches:

- The independent analysis of each of the three components defined here before to identify the structural bottlenecks and improvement prospects

- The analysis of interrelationships between the three dwell time constituents using shipment level data and the identification of behavioral patterns

<!-- Start of picture text --> Vessel<br>Transfer<br>Vessel berthing to the Delivery Exit<br>Operational arrival & onto from<br>dwell time at buoy container yard or ODCY truck yard<br>discharge<br>Domiciliation<br>Establishment Payment of NVOCCs, port<br>Transactional & Pre- and Lodging authority & inspection bodies<br>dwell time shipment of Customs<br>inspection declaration Completion of clearance procedures &<br>process payment of taxes & duties<br>Storage in<br>Discretionary the<br>dwell time container<br>terminal<br><!-- End of picture text -->

**Figure 1 – The components of cargo dwell time: sequence of operational, transactional and logistics operations**

5
# **2.2. Data and estimation**

There are two main options for estimating (7). One is based on regional panel data, while the other one is a country-per-country approach based on time series data.

The panel estimation technique rests on the assumption that a common production function exists for the Asian countries under analysis, with individual country effects to be controlled for. While this approach has been extensively used with state / provincial panel data for India (Hulten et al. (2005)), Italy (La Ferrara and Marcellino (2000)) and the US (Holtz-Eakin (1994)), the above assumption is dubious when applied to a set of countries as diverse as those in our sample. We report below tentative panel estimations that confirm such crosscountry heterogeneity.

We therefore give priority to individual country estimations, which more realistically do not assume that there is a common underlying technology for all countries. This has been the approach used by most non-infrastructure growth accounting studies.<sup>8</sup> Concerning any possible simultaneity in the estimations, note again that the left hand-side quantity in (8) – TFP growth – is computed directly from data, not estimated. In particular, we do not have to worry about the typical simultaneity problem in regression-based growth accounting studies, namely reverse causation from the growth rates of GDP to _K_ . The only possible remaining source of simultaneity would be an influence of TFP growth on _X_<sup>&</sup> investment in infrastructure, . Possible causes of simultaneity include endogenous _X_ responses of infrastructure policies to TFP growth, making it necessary to test the presence of reverse causation in the data.

Country-specific estimations, as opposed to panel estimations, call for longer time series in order to produce efficient estimators. Two sets of long time series can be considered. First, physical indicators of infrastructure stocks have been used in the literature. Canning (1999) uses indicators of telephones lines availability, electricity generating power and length of

> 8 See for example Barro and Sala-i-Martin (2004).

11