rafmacalaba/datause-encoder-data huggingface dataset:
life-cycle will reduce the number of periods during which they can get pregnant and will therefore have an obvious negative eect on the overall average number of births. The increase in child mortality, on the other hand, can increase fertility through a replacement behavior. If there is a decreasing marginal utility from an additional child, the loss of a child will increase fertility in the periods after the loss.

The impact of the subjective beliefs on fertility outcomes is even more ambiguous. As described in the previous subsection, a woman compares the expected value of lifetime utility with and without a current pregnancy. With higher likelihood assigned to being infected, a woman assigns less likelihood to her and her child's survival and therefore perceives fewer periods during which she would get utility ows from having the additional child. Given that the non-pecuniary cost of pregnancy is not aected by beliefs about HIV status, this implies that the woman would be less likely to get pregnant if she assigns higher probability to being infected. On the other hand, if a woman has high valuation for children, she might choose to increase her fertility when she assigns some probability to being infected. She would do so in expectation that a larger share of her children will not survive. In addition, even without a change in the total number of births a woman gives, the presence of HIV might change the timing of her pregnancies. A woman might assign no or small likelihood to being infected in the present but decide to give birth at younger ages if she perceives a higher likelihood to being infected in the future. This will depend on her perceived hazard rates throughout the fertile stage of her life-cycle.

# 3 Data

## 3.1 Malawi Diusion and Ideational Change Project

Malawi is a landlocked country located in Southeast Africa. It has a population of about 15 million, comprised of dierent ethnic and religious groups. 81 percent of the population 16
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- Liu, Lili, and Michael Waibel.  2009. “Subnational Insolvency and Governance: Cross-Country Experiences and Lessons.” In _Does Decentralization Enhance Service Delivery and Poverty Reduction?_ ed. Ehtisham Ahmad and Giorgio Brosio. Edward Elgar **,** Cheltenham, UK; Northampton, MA, USA.

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Thirteenth Finance Commission Report. 2009. Delhi.

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were told that they would be eligible for the chance to win a 230 SDG credit transfer in a monthly survey should they complete the survey.

Prior to visiting households, the mobile phones were fully charged, enabled with SIM cards, tested to ensure they could make and receive calls and pre-loaded with 2 SDG of credit.<sup>5</sup> A team of 20 enumerators from the National Bureau of Statistics were trained in the World Bank office in Juba on the procedure for the household visits. This involved an orientation about the purpose of the survey and the protocol for household visits.

Visits to sampled households were carried out in 10 teams of 3 beginning the first week in November 2010. Each team covered one state capital. Most visits occurred in the morning or late afternoon. Initial plans called for randomly selecting one respondent from the household roster and alternating between male and female respondents to ensure gender balance. The pilot revealed this would be logistically challenging since randomly selected individuals not present at the time of the visit would require coordinating additional visits to the household until the respondent was found. In addition, as the survey focused on gathering information on general household and community-level characteristics rather than very specific or personal characteristics, the risk of systematically biased responses was not deemed large. As such, when enumerators visited each household, rather than taking the list of household members and randomly selecting one member from the list, the first adult member of the household encountered at the time of the visit was selected for participation. Each selected participant was trained in the use of the phone and given a calendar displaying the specific day he or she would receive a call, with the first call occurring four weeks after the initial household visit. Enumerators collected an alternative phone contact number if available, the language preference of the participant (Arabic, English, Dinka, or Nuer) and the participant’s preferred time of day for calls.

Each month’s SSEPS questionnaire consisted of two modules: a set of core questions that were repeated each month and a set of “special” questions that were changed on a month-to-month basis. The core questions fall into three categories: past changes and outlook, material deprivation, and security. The wording for the core questions was taken from Afrobarometer surveys conducted in 2008 and 2009, in

> 5 Facilities for mobile charging through community charging stations, usually powered by generators or car batteries, are common in urban areas.

9
respectively), credit performance (charge-offs, nonperforming loans, loan and lease loss allowances), and portfolio allocations (loans, unused commitments, deposits) to see if the COVID19 crisis caused significant distress in any of several areas of bank operations. For each ratio, we show for the entire U.S. banking industry the number of banks, mean, standard deviation (SD), and P10, P25, P50, P75, and P90 percentiles.

We also want to be sure that summary statistics for the industry are not masking any systemic risk being posed by large, systemically important institutions that could bring down the banking and financial systems. For this reason, we show additional summary statistics for the eight U.S. Globally Systemically Important Banks (G-SIBs).<sup>15</sup> This relatively small number of banks are those deemed by international regulators to pose global systemic risks and hold more than half of all U.S. banking assets. For the G-SIBs, we show the number of banks, mean, SD, minimum (min), P50, and maximum (max), given that some of the other percentiles are not sensible for a sample of only 8 observations per quarter.<sup>16</sup> While there are many statistics that may tell complex stories in Table 1, in the interest of brevity, we narrow our focus in the text to the simplest cases. With only one exception of a potentially misleading mean, for all variables and all time periods and for both all banks and the G-SIBs subsample, we discuss only the mean and the extreme statistic – P10, P90, min, or max – that would signal the worst performance problem if there were one. Thus, for the capital ratio, we focus on the mean and P10 for all banks, and the mean and the min for G-SIBs. In contrast, since

> 15 <mark>The Financial Stability Board (FSB) publishes a list of Globally Systemically Important Banks (G-SIBs) based on their scale and the degree of influence in global and domestic financial markets.  In 2020, 30 institutions qualified, including eight U.S. institutions –</mark> Citigroup, JP Morgan Chase, Bank of America, Bank of New York Mellon, Goldman Sachs, Morgan Stanley, State Street, and Wells Fargo.  The G-SIBs generally refer to bank holding companies (BHCs) that own commercial banks, rather than commercial banks themselves.  The Call Report data in Table 1 is for the largest or only commercial bank in each BHC.

> 16 We do not construct separate summary statistics for small banks, since the vast majority of U.S. banks have less than $1 billion in assets.  Thus, the summary statistics for the industry cover small banks well.

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CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE investment patterns are also very distinct, heavily skewed toward transport, while in other countries they are more skewed toward ICT.

**Table 10. Financial flows to Cape Verde’s infrastructure, average 2001 to 2006**

<u>$ millions per year</u>

||O&<br>Public sector|M<br>Public sector|ODA|Capital e<br>Non-OECD<br>financiers|xpenditure<br>PPI|Total capex|Total<br>spending|
|---|---|---|---|---|---|---|---|
|ICT|0|0|1|1|0|2|2|
|Power|34|9|0|1|0|10|44|
|Transport|2|28|45|1|0|74|77|
|WSS|4|12|9|0|0|22|25|
|Total|40|50|55|3|0|107|147|

_Source:_ Derived from Foster and Briceño-Garmendia (2009).

_Note:_ Capex = capital expenditure; ICT = information and communications technology; O&M = operations and maintenance; ODA = official development assistance; OECD = Organisation for Economic Co-operation and Development; PPI = private participation in infrastructure; WSS = water supply and sanitation.

**Figure 10. Cape Verde’s existing infrastructure spending is very high in GDP terms**

<!-- Start of picture text --> Sub-Saharan Africa<br>Middle-income countries<br>ECOWAS<br>Low-income countries, fragile<br>Resource-rich<br>Liberia<br>Cape Verde<br>Senegal<br>Ghana<br>Benin<br>Mali<br>Burkina Faso<br>Niger<br>Nigeria<br>Côte d'Ivoire<br>0 5 10 15 20<br>% of GDP<br>capex operations and maintenance<br><!-- End of picture text --> _Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ Capex = Capital expenditure; ECOWAS = Economic Community of West African States; GDP = gross domestic product.

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- _tech_ = domestic firm’s technology indicator as defined in previous section, where _tech_ ε {0, 1, 2, 3, 4}. The technology indicator serves as a proxy for R&D intensity which is unavailable;

- _skills =_ domestic firm’s share of high-skilled labor in firm’s total labor force;

- _size_ = domestic firm’s total number of permanent and temporary employees in natural logarithms;

- _aggl_ = region’s total number of manufacturing and services firms as percentage of a country’s total number of manufacturing and services firms. This measure is a proxy for urbanization economies (locational advantages) and covers both domestic and foreign firms<sup>15</sup> ;

- _exp_ = domestic firm’s share of direct or indirect exports in firm sales.

# **3.5 Measures of National Characteristics and Institutions**

In a third step, we turn to mediating factors at the national level. We include the following country-level variables into the model.

- _labor_ = measure of labor freedom in natural logarithms from the Heritage Foundation and captures labor market institutions. The variable ranges from 0 to 100 (highest labor freedom) and includes various aspects of the legal and regulatory framework of a country’s labor market, such as minimum wages; laws inhibiting layoffs; severance requirements; and measurable regulatory burdens on hiring, hours, etc. The measure is mainly based <mark>on data from the World Bank’s Doing Business study;</mark>

- _finance_ = measure of financial freedom in natural logarithms from the Heritage Foundation. The variable measures banking efficiency as well as a measure of independence from government control and interference in the financial sector with scores ranging from 0 to 100 (highest financial freedom). This measure relies on various underlying data sources, including (in order of priority) the Economist Intelligence Unit, the International Monetary Fund, the Organisation for Economic Co-

> 15 We included services and foreign firms in this measure as urbanization economies refer to spillovers due to the proximity of firms from all types of sectors and ownership.

17
excluded since their initial enrollment decisions would have depended on conditions in their natal homes rather than in their current residence. These criteria yield a sample of 4,699 children, of which 2,192 (47%) are girls and 2,507 (53%) are boys.

Table 1 provides some descriptive statistics for the sample, highlighting, in particular, the importance of the school entry margin in rural Pakistan. Also evident is the substantial gender gap in schooling, one that varies tremendously by region. In the more developed North Punjab, 92% of boys and 85% girls had enrolled in school by age nine, whereas the poorer southern part of Punjab and the entire province of Sindh have much lower enrollment rates (just above 50% for girls) and huge gender gaps favoring boys (17 and 24 percentage points, respectively). By contrast, dropout rates from primary school are much less differentiated by gender and region, perhaps because many girls who would have dropped out never entered school to begin with. Conditional on having enrolled in school and not completed the primary grades, 88% of 9-15 year-old boys are still in school compared to 81% of girls, with gender gaps of 5, 5, and 9 percentage points in North Punjab, South Punjab, and Sindh, respectively.<sup>3</sup>

# 3 Context

The term "village" in much of Pakistan, and indeed in large parts of South Asia, has a dual connotation. On the one hand, the administrative or revenue village is the lowest tier of the government’s administrative structure, at which public services, such as schools and health workers, are allocated. Most administrative or revenue villages, however, consist of several distinct clusters of habitations or hamlets; though most residents would also refer to these as "villages", we will call them "settlements." Among the 165 PHRS-II villages, for example, our village census records 1031 named settlements. However, villages of North Punjab typically are less geographically dispersed and have much fewer settlements than those of Southern Punjab and Sindh (see Table 2 below).

> 3Beyond primary school, strong gender-region differences in dropout rates re-emerge. Conditional on having completed primary school, 81% of 9-15 year-old boys are still enrolled in school compared to 78% of girls, with region-specific gender gaps of 1, 15, and 22 percentage points, respectively. Since post-primary school enrollment, conditional on reaching that decision point, is driven more by the location of middle schools than of primary schools, we will have little more to say about it in this paper.

4
only 10 years" (National Research Council, 1985, p. 121), a conclusion which is echoed by a British report which suggests that half the knowledge of current electronics engineers will be obsolete within five years (Economist, 1986).

In response the profession is exploring a return to distributed training, albeit starting at a higher initial academic level and for a more pressing motive. A notable example is the report on Lifelong Cooperative Education from the MIT Department of Electrical Engineering and Computer Science (Bruce, Siebert, Smullins and Fano, 1982), proposes measures for what it calls preventive maintenance for engineers, observing that even if obsolescent engineers could be replaced with new graduates, both the human cost and the cost to society would be unacceptable.

The most substantial movement in the direction of lifelong training is to be found in Latin America, where in most countries in-service training is provided by a national traiaing authority. The training is however not primarily a response to the need for skills-renewal caused by technological change, but an effort to bring workers closer to the state of the art in traditional skills and its coverage has been limited: including short courses as well as long ones, 6.1% of the labor force in Brazil, 4.6% in Colombia, and less in other countries, benefited from this type of training in 1973 (Corvalan, 1977).

The Latin American programmes apart, training provision remains heavily weighted to pre-employment training, especially in developing countries (ILO, 1983, p.29), and its type and duration continue to have an overwhelming impact on the life-chances of the individual.

Tnere are two reasons for this. First, national authorities are so preoccupied by the problems of implementing the traditional approach satisfactorily that they lack the resources and the will to explore the scope for replacing it with a life-long approach. Second, pre-employment training is more easily defined and classified and hence correspondingly easier to plan and implement.

The first problem may diminish as the expertise of national authorities develops. The second may eventually be mitigated by the more widespread implementation of modular training. Demand factors are however likely to have a more powerful effect. In many countries it is transparent that for some occupations there is little or no foreseeable demand for new workers but that there is a need for upgrading the performance of existing ones. This is obvious from labor force surveys and confirmed by detailed surveys of employers' views: Kelly and Palmer (1985), Kelly and Evans (1985), Kelly, Evans and Garcia (1985), Kelly (1986a). In theory, although not always in practice, this ought to cause the corresponding training provider to shift from pre-employment to in-service training.

- 17 -
Figure 7 provides a schematic of the equation above. Figure 7 contains only one prefiscal income concept (market income) and several postfiscal income concepts (disposable income, consumable income, and final income).

**Figure 7. Definition of CEQ Income Concepts**

<!-- Start of picture text --> Market income plus pensions<br>• Income from work (wages/salary + in-kind benefits)<br>• Income from capital<br>• Self-provision of goods/services<br>• Private transfers/remittances/alimony<br>• Private pensions<br>• Imputed rent<br>• Include Contributory social insurance old-age pensions, exclude<br>contributions to social insurance old-age pensions<br>-<br><!-- End of picture text --> <!-- Start of picture text --> Direct taxes and contributions<br>• Personal income tax<br>• Property taxes*<br>• Contributions to social insurance<br>fund<br><!-- End of picture text --> <!-- Start of picture text --> Direct transfers<br> Conditional or unconditional<br>cash transfers (Takaful Karama,<br>Social Solidarity)<br> Near-cash transfers ( tamween<br>program)<br><!-- End of picture text --> <!-- Start of picture text --> Net market income<br><!-- End of picture text --> <!-- Start of picture text --> +<br><!-- End of picture text --> <!-- Start of picture text --> Disposable income<br>+<br>Indirect taxes • Fuel<br>• Sales taxes - •<br>• VAT (added as simulation)<br>• Excise Taxes (fuels, tobacco, sugar, tea)<br><!-- End of picture text --> <!-- Start of picture text --> Subsidies<br>• Fuel<br>• Electricity<br><!-- End of picture text --> <!-- Start of picture text --> Consumable income<br><!-- End of picture text --> <!-- Start of picture text -->
-<br><!-- End of picture text -->

<!-- Start of picture text --> • Copayments and user fees for<br>education and health services<br><!-- End of picture text --> <!-- Start of picture text --> +<br><!-- End of picture text --> <!-- Start of picture text --> In-kind transfers<br>• Monetized value of<br>education and health<br>services<br><!-- End of picture text --> <!-- Start of picture text --> Final income<br><!-- End of picture text --> _Source:_ Adapted from Lustig 2018. * Not effective in 2015.

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