rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Occupational Pension Plans in Germany (1990-93)** (in percent of total).

|Type of Pension Plan|Assets|Employees|Companies|
|---|---|---|---|
|Book reserves|56.3|54.1|12.8|
|Pension funds|22.9|18.9|28.3|
|Life insurance|12.1|14.0|68.9|
|Support funds|8.7|13.0|1.6|
|Total|100.0|100.0|100.0|

Sources: Deutsche Bank Research 1996, Statistisches Bundesamt 1994 Note: Data for assets for 1993, for employee and company distribution for 1990 The share of book reserves in total assets of occupational schemes has been declining over the last decade. In 1981, book reserves accounted for 67 percent of all assets. Direct insurance has been growing the fastest with an increase from less than 5 percent in 1981 to 12 percent in 1993.11 But book reserves still are the most attractive form of pension scheme for sponsoring employers, as can be seen in the share of covered employees in table 3. By setting up book reserves, companies can reduce taxable profits, and the pension reserves can be reinvested in the company.Taxes become payable only when the reserves are released which increases profits; at the same time, pension payments are considered operating expenses which in turn offsets the increase in profits.

Contributions to support funds are tax-deductible up to certain limits depending on the type and average level of benefits promised. Companies who choose this option often take out reinsurance to cover the remaining unfunded liabilities. Support funds are not subject to investment restrictions; the most common forms of investment are loans to the sponsoring enterprise(s) and real estate. Due to the high degree of discretion for the sponsoring employer, support funds also have to be insured with the PSV.

The number of pension funds has been declining; their share of total second pillar assets, however, has been increasing. In 1993, there were 150 pension funds in Germany; their total assets amounted to about 3.5 percent of GDP. Pension funds are subject to the same regulations as insurance companies. The investment rules mandate the following limits as percentage of assets: real estate 25 percent, fixed income securities inside the European Economic Area 100 percent, foreign bonds 5 percent, European equities 30 percent, and foreign equities 6 percent; additional rules apply with respect to the issuers of securities. In 1994, German pension funds invested about 72 percent of their assets in domestic bonds, 4 percent in foreign bonds, 13 percent in real estate and 9 percent in equities. 1<sup>2</sup>

> " Deutsche Bank Research 1995

> **12** Deutsche Bank Research 1995

14
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Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood. _The American Economic Review_ , _104_ (9), 2633-2679. doi:10.1257/aer.104.9.2633 Darling-Hammond, L. (2005). Teaching as a profession: Lessons in teacher preparation and professional development. _Phi delta kappan_ , _87_ (3), 237.

doi:10.1177/003172170508700318 22
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Angola’s road indicators benchmarked against Africa’s low- and middle-income countries**

|Indicator|Unit|Low-<br>income,<br>nonfragile<br>countries|Resource-<br>rich<br>countries|Angola|Middle-<br>income<br>countries|
|---|---|---|---|---|---|
|Classified road network density|km/1,000 km<sup>2</sup>of land area|88|98|29|278|
|Total road network density [1]|km/1,000 km<sup>2</sup>of land area|132|128|41|318|
|GIS Rural accessibility|% of rural pop. within 2 km of all-season road|25|20|31|31|
|Main road network condition [2]|% in good or fair condition|72|68|58|86|
|Rural road network condition [3]|% in good or fair condition|53|61|40|65|
|Classified paved road traffic|AADT|1,131|1,408|884|2,451|
|Classified unpaved road traffic|AADT|57|54|10|107|
|Primary network overengineering|% of primary network paved with 300 AADT<br>or less|30|15|42|18|
|Perceived transport quality [4]|% firms identifying transport as major<br>business constraint|13|27|**—**|20|

_Source:_ AICD Road Sector Database on 40 Sub-Saharan African countries.

[1] Total network includes the classified and estimates of unclassified and urban networks.

[2] Main network for most countries is defined as result of adding the primary and secondary networks.

[3] Rural network is generally defined as the tertiary network and does not include the unclassified roads.

[4] Source: World Bank—IFC Enterprise Surveys on 32 Sub-Saharan Africa countries. GIS = geographic information system; AADT = average annual daily traffic. — = Not available.

**Figure 11. The condition of Angola’s main road network benchmarked against others in southern Africa**

<!-- Start of picture text --> South Africa<br>Mauritius<br>Swaziland<br>Malawi<br>Namibia<br>Botswana<br>Mozambique<br>Madagascar<br>Zimbabwe<br>Zambia<br>Angola<br>Lesotho<br>- 0.25 0.50 0.75 1.00<br>Percentage Road Network Length<br>Good Fair Poor<br><!-- End of picture text --> _Source:_ AICD Road Sector Database on southern Sub-Saharan African countries.

20
Bardhan, A. D. and S. Guhathakurta (2005), ‘Global links of subnational regions: coastal exports and international networks’, _Contemporary Economic Policy_ , **22** , 225–36.

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Beine, M., F. Docquier and H. Rapoport (2008), 'Brain drain and human capital formation in developing countries: winners and losers,' _Economic Journal_ , **118(** 528), 631-52.

Bhagwati, J. (1979), ‘International migration of highly skilled: economics, ethics and taxes’, _Third Word Quarterly_ , **1** (3), 17–30.

46
**Figure 2: Sub-Saharan Africa – Average Growth Rate of GDP per capita in HIPCs and non-HIPCs (in percent)**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>HIPC non-HIPC<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> 22
18 be calculated. One could estimate the government’s marginal propensity spend from years prior to the start of the project, thereby obtaining the counterfactual amounts _X_ 11 and _X_ 21 . The cost of intrasectoral fungibility could then be computed as  1 times _N_ 1 times the difference between the estimated counterfactual value of _X_ 13 and its actual value. Note that intrasectoral fungibility has no cost if  1=0, i.e. if the donor does not leverage any productivity increases in the target areas.

# V. **THE CASE OF TWO WORLD BANK HEALTH PROJECTS IN VIETNAM**

The World Bank’s Population and Family Health Project and National Health Support Project in Vietnam<sup>5</sup> are just two examples of donor projects that fit the scenario outlined above.<sup>6</sup> Around half of the budget of each project was directed to 37 of Vietnam’s 64 provinces, with the overall objective of improving primary care—especially maternal and child care—in commune and district health centers in the selected provinces.<sup>7,8</sup> The

> 5 Details of the projects are to be found in World Bank (1995; 2004; 2006; 2007), which can be downloaded from ~ ~ <u>http://web.worldbank.org/WBSITE/EXTERNAL/PROJECTS/0,,menuPK:115635 pagePK:64020917 piPK:64021009</u> ~theSitePK:40941,00.html.

> 6 In actual fact, the projects were cofinanced by the Vietnamese government, the World Bank (more precisely the International Development Association), and other donors, the Dutch government and the Swedish Development Agency (SIDA) in the case of the health support project, and the Asian Development Bank and the German Kreditanstalt für Wideraufbru (KfW) in the case of the population and family health project.

> 7 Both projects aimed to disburse around $ 130 million. The percentage allocated to the province-specific interventions was 47% in the case of the health support project and 49% in the case of the population and family health project.  The rest of the two projects’ funds were not targeted on specific provinces, and instead addressed national issues and programs. The rest of the health support project budget was directed at two areas: national priority programs concerning malaria, TB and ARI (40%); and health planning and management capacity in the health ministry (4%). The rest of the population and family health project budget was directed at family health and family planning issues nationwide, and included: a component on information, education and communication (10%); a component on contraceptive supplies (19%); a component on family planning (4%); and a component on service delivery model innovation (6%).

> 8 The population and family health project targeted 15 provinces (this grew to 18, and subsequently 20), and the health support project targeted a further 15 (this also grew to 18). The number of provinces grew during implementation, in part because some provinces split into two, and in part because new provinces were added to both projects. By the end,
benchmarks to construct al index. As the second best alternative, we have divided our sample countries into three categories based on IFC Emerging Stock Markets Factbooks 1989 and 1990. (a) Completely open:- All ten developed markets, Singapore, Malaysia, Portugal (b) Relatively easier access:- Thailand, Indonesia, Mexico, Philippines, Turkey (c) Virtually closed:- Korea, Taiwan, Brazil, India, Chile. We would expect the premium to be relatively higher for funds that invest in markets with difficult access.

Degree of spanning within host mark.t (SPN): As discussed in the companion paper, the availability of substitute assets (for a given fun,:) in the host market would determine the potential diversification benefits of the fund uneier nsideration. The natural proxy would be the residual volatility of a fund obtained from time series regressions on the U.S. index. Since this proxy would suffer from measurement error problem, we cor ider another proxy for diversification benefits, namely the host and home market retums correlation coefficient. A priori, we would expect higher premium for funds with the lower correlation.

Degree of substitution between the fund and the underlying assets (SUB): The results of the companion paper suggest that the degree of substitution between the fund and the underlying asset has ar, important bearing on the premium/discount. We use the ratio of volatility of price and NAV returns as a reasonable proxy to capture the degree of imperfect substitution. We would expect a higher ratio to have a lower premium effect.

Fund size (FSZ): The initial size of the offering could proxy investor demand, since the size is usually determined by the investment bankers to reflect market interest and conditions. We would expect a larger initial offering to command a larger premium.

22
# **References:**

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Alesina, Alberto, et al. "Fractionalization." Journal of Economic growth 8.2 (2003): 155-194.

Annan, Jeannie, Christopher Blattman, and Roger Horton. 2006. “The State of Youth and Youth Protection in Northern Uganda: Findings from the Survey for War Affected Youth.” Kampala, Uganda: UNICEF Banerjee, A., Duo, E., & Qian, N. (2012). On the road: Access to transportation infrastructure and economic growth in China. Yale Department of Economics mimeo.

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Brück, T. (2004a), “Coping Strategies Post-War Rural Mozambique”, HiCN Working Paper No. 02, Households in Conflict Network, University of Sussex, UK Brück, T. (2004b), “The Welfare Effects of Farm Household Activity Choices in Post-War Mozambique”, HiCN Working Paper No. 04, Households in Conflict Network, University of Sussex, UK (www.hicn.org).

Bruck, Tilman. 1996. “The Economic Effects of War.” Unpublished.

Bundervoet, Tom, Philip Verwimp, and Richard Akresh. 2009. “Health and Civil War in Rural Burundi.” Journal of Human Resources, 44(2): 536–63.

Bundervoet, T. and Verwimp, P. (2005), “Civil War and Economic Sanctions: An Analysis of Anthropometric Outcomes in Burundi”, HiCN Working Paper no. 11, Households in Conflict Network, University of Sussex, UK (www.hicn.org).

Casaburi, Lorenzo and Glennerster, Rachel and Suri, Tavneet, Rural Roads and Intermediated Trade: Regression Discontinuity Evidence from Sierra Leone (August 13, 2013). Available at SSRN:http://ssrn.com/abstract=2161643 or http://dx.doi.org/10.2139/ssrn.2161643 24
the pandemic, and 64 percent of those who continued working worked with reduced income (World Bank 2020).

To balance between the health and economic crises, nearing mid-2020 the government began transitioning to the “New Normal,” relaxing mobility restrictions and establishing health protocols to limit COVID spread. In early June 2020, Jakarta, Bogor, Depok and Bekasi began reopening workplaces, shopping centers, places of worship, and other public places with 50 percent capacity. Additionally, to support the poor and vulnerable, the government launched the National Economic Recovery (PEN) program, allocating a total of IDR 695 trillion<sup>7</sup> (i.e. 4.2 percent of the country’s GDP), to stimulate the economy via assistance programs for both households and micro, small, and medium enterprises (Sparrow, Dartanto, and Hartwig 2020).

# **3. Literature Review**

Existing studies have documented several factors that might cause the pandemic to affect male and female employment differently. The first factor is the sex-segregation across employment sectors and/or occupations (Coskun and Dalgic, 2022; de Paz Nieves, Gaddis and Muller, 2021). The pandemic crisis tends to hit high-contact sectors the hardest, and these sectors are highly populated with female workers (Alon, et al. 2021; Albanesi and Kim 2021).<sup>8</sup> Gender differentiated impacts may also arise through increased childcare needs and other domestic care responsibilities due to school closures during the pandemic, for which the burden has been disproportionately shouldered by females (de Paz Nieves, Gaddis and Muller, 2021; Alon et al., 2020; Beauregard et al., 2020). While these two factors are expected to induce more negative employment effects among females during the pandemic recession, studies have also demonstrated the possibility of the ‘added-worker effect’, where non-employed spouses (more often to be female, particularly in

> 7 It is equivalent to USD 149 billion at PPP exchange rate of 1 USD = 4,675.22 in 2020.

> 8 Using O*NET data, Albanesi and Kim (2021) showed that women are overrepresented in high-contact and inflexible occupations; 73% of workers in these sectors were female.

8
households, which can be either couples or individuals, make choices over time with regard to labor supply, private savings and retirement in an environment with uncertainty about future wages, returns on pension savings, fertility, divorce or widowhood and own survival. Men and women choose not only whether to work or not but also whether to work in the formal sector where pension contributions are mandatory or in the informal sector. The distinction between formal and informal sector work is important in Chile (and most countries across the world) because many individuals work in the informal sector and reach old age having contributed little to the system. In addition, women choose whether to work part-time or full-time. The model is estimated on longitudinal microdata from the _Encuesta de Protecci ́on Social (EPS)_ merged with administrative data on pension funds.<sup>4</sup> We use the estimated model to study how labor supply and savings behavior changes with the introduction of the 2008 pension reform in comparison to the previous pension system behavior and in comparison to alternative pension system designs. We estimate model parameters by the method of simulated moments using pre-reform data from the 2004 and 2006 EPS Surveys. The estimation results reveal substantial heterogeneity in how individuals value pecuniary and non-pecuniary dimensions of formal and informal work. Some individuals have strong comparative advantages to working in the formal or informal sectors, which dampens behavioral responses to pension rule changes.

Incorporating the pension rules explicitly in our model allows us to construct counterfactual simulations in which the reform did not happen, keeping everything else constant. In this way, we perform an ex ante prediction of the short-run behavioral impacts of the pension reform and compare our predictions to some evidence from other causal impact studies of the reform’s impacts. Consistently with other studies, we find that the more generous safety net induces negative wealth effects on the labor supply of men and women at older ages. Contrary to other studies that model reform changes at the individual level, our simulations show that household structure is an important determinant of pension policy reform responses. Even though many of the reform features were targeted at women, we find that men’s responses are sometimes greater than those of women, as men respond to the wealth effect of an increase in pension benefits accruing to their wives. In addition, household-level

> 4The longitudinal household survey data were collected by the Microdata Center under the supervision of David Bravo and the administrative data come from Chile’s _Superintendencia de Pensiones_ .

2
10

**Table A1. Change in produced assets under varying rules for genuine investment (I**<sup>**G**</sup> **<u>).</u>**

||Produced<br>capital in 2000,<br>$bn<br>(1995 dollars)|I<sup>G</sup>= 0<br>%difference|I<sup>G</sup>= 5% of<br>1987 GDP<br>%difference|I<sup>G</sup>>= 5% of<br>1987 GDP<br>%difference|Rent / GDP<br>Average<br>(1970-2000)|
|---|---|---|---|---|---|
|Nigeria|53.5|358.9%|413.6%|413.6%|32.6%|
|Venezuela, RB|175.9|272.1%|326.1%|326.1%|27.7%|
|Congo, Rep.|13.9|57.0%|78.0%|116.9%|25.2%|
|Mauritania|3.0|112.3%|153.7%|154.0%|25.0%|
|Gabon|19.7|80.3%|105.5%|130.4%|24.1%|
|Trinidad and Tobago|13.7|182.1%|238.3%|239.1%|23.6%|
|<br>Algeria|195.4|50.6%|80.9%|83.9%|23.3%|
|Bolivia|13.7|116.1%|169.8%|177.5%|12.8%|
|Indonesia|540.6|-26.5%|3.8%|32.1%|12.5%|
|Ecuador|37.7|95.3%|158.0%|158.3%|11.6%|
|Zambia|7.5|312.3%|383.4%|388.0%|11.5%|
|Guyana|2.1|149.3%|185.6%|191.2%|11.4%|
|China|2899.4|-62.1%|-45.0%|5.1%|10.8%|
|Egypt, Arab Rep.|159.7|-12.9%|28.1%|36.2%|9.5%|
|Chile|151.4|-3.0%|31.6%|54.0%|9.5%|
|Malaysia|305.2|-52.7%|-31.4%|6.6%|8.3%|
|Mexico|975.5|-1.5%|35.3%|42.2%|8.2%|
|Peru|132.3|37.2%|98.1%|103.9%|7.5%|
|Cameroon|24.1|-9.3%|54.8%|67.6%|6.5%|
|South Africa|349.5|50.7%|109.3%|115.8%|6.5%|
|Jamaica|13.4|39.9%|87.8%|99.6%|5.7%|
|Colombia|198.0|-19.7%|30.4%|39.3%|5.3%|
|Norway|456.6|-14.3%|24.6%|33.0%|4.3%|
|India|965.4|-52.9%|-18.3%|8.6%|3.4%|
|Zimbabwe|14.9|9.1%|64.8%|89.1%|3.3%|
|United States|16926.7|-39.8%|12.9%|26.1%|2.7%|
|Argentina|569.6|-6.9%|49.4%|53.9%|2.6%|
|Togo|3.6|-26.8%|22.7%|55.1%|2.6%|
|Pakistan|125.6|-50.7%|-1.7%|11.1%|2.2%|
|Hungary|149.1|-43.5%|8.7%|22.3%|2.2%|
|Morocco|93.8|-59.1%|-16.3%|7.8%|2.0%|
|Brazil|1750.5|-59.0%|-6.6%|9.1%|1.9%|
|United Kingdom|2400.1|-32.7%|27.3%|32.8%|1.6%|
|<br>Dominican Republic|33.8|-73.0%|-27.9%|1.2%|1.6%|
|Philippines|195.0|-58.4%|-14.5%|10.6%|1.5%|
|Honduras|12.3|-66.9%|-29.7%|8.9%|1.5%|
|Ghana|16.1|30.6%|73.2%|76.7%|1.0%|
|Fiji|3.6|-36.5%|26.9%|59.3%|0.9%|
|<br>Benin|4.6|-72.7%|-21.7%|10.6%|0.8%|
|Senegal|10.0|-44.0%|14.2%|27.5%|0.7%|
|Thailand|520.6|-86.3%|-63.6%|3.0%|0.7%|
|Haiti|2.8|-62.7%|109.2%|109.5%|0.6%|
|Korea, Rep.|1607.6|-93.5%|-68.6%|0.9%|0.6%|
|Israel|215.8|-72.8%|-31.3%|4.2%|0.5%|
|Cote d'Ivoire|16.1|-21.2%|71.1%|108.7%|0.5%|