rafmacalaba/data-use-mentions-extended huggingface dataset:
13 in the questionnaire.<sup>6</sup> Accuracy tests on the sibling mortality data collected in the RDHS are discussed in more detail elsewhere.<sup>7</sup> Moreover, we disaggregate sibling deaths by gender, differentiating between women who lost a brother (BROTHERDEATH) and a sister (SISTERDEATH) in 1994. As discussed below in more detail, these variables are likely to capture the direct effects of genocide at the household most accurately. Women still living with their parents who recorded a sibling death during the genocide have a very high probability of having experienced the sibling death very closely. This may shape fertility negatively, for example through the stigma attached to having been part of a conflict victim household.

# **b) Marital status and sex ratios**

The third proxy is a specific form of marital status, namely whether or not a woman is a widow (WIDOW). This proxy is available in both the pre-genocide and post-genocide RDHS. However, the RDHS questionnaire does not record the husband’s cause of death or date of death. It is hence impossible to distinguish conflict widows from HIV/AIDS widows or other widows. Still, the majority of widows in the 2000 and 2005 waves are very likely to be genocide widows (Brück and Schindler 2009) – that is, formerly wives of Tutsi husbands or moderate Hutu husbands. Given the gender-unbalanced mortality during the genocide, widowhood can be assumed to be exogenous. This point is underlined in Fig. 4, which depicts the distribution of current marital status (single, married, divorced, widowed) for women of various age groups in 1992, 2000, and 2005. The proportion of widows relative to married and unmarried women increased considerably after the genocide, particularly for younger women. For instance, the proportion of widows among women of age 25-34 years doubled between 1992 and 2000. Moreover, the proportion of widows rises steadily with older birth cohorts; a similar pattern is apparent for divorced women. This
<br>(1<br>are obtained with<br>he empirical dist<br>riables in TA 2 st<br>od before standar|7.7<br>.9)<br>the population we<br>ribution of the err<br>andardized by th<br>dization. The imp|ights. Method<br>or terms. Both<br>ose in TA 1. F<br>uted poverty r|1 uses the normal<br>methods employ<br>ood expenditures,<br>ates for TA 2 use|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in TA 1. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA 2 use the estimated parameters based on the TA 1. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.7.

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have larger loans outstanding, borrowers who suffered especially large income losses, or borrowers who are worried that their temporary inability to repay will tarnish their credit score, are more responsive to the relationship offer.<sup>23</sup>

# **4 Debt Moratoria and Banking Relationships: The Product Adoption Experiment**

In this section, we examine whether debt forbearance offers extended by the lender can strengthen banking relationships in the longer term. This also tests the intuition that forbearance offers improve repayment because borrowers interpret them as an investment in the relationship that makes the option value of a continued association with the lender salient to the borrower.

> which of these correlated variables is responsible for the heterogeneity.

> 23 In Appendix Table B.8, we supplement this analysis with a conventional heterogeneous treatment effect analysis. Specifically, for each variable in the vector of borrower and loan characteristics _Zi_ considered in the machine learning analysis, we construct an indicator _I_ ( _Zi_ ) = 1[ _Zi > Zmedian_ ] equal to 1 if borrower _i_ has an above-median value of _Zi_ and zero otherwise. We then estimate variations of equation (1) that include interactions between the relationship offer treatment dummy _Ti_<sup>1and the loan and borrower characteristics indicators</sup><sup>_I_(</sup><sup>_Zi_) (along with both terms uninteracted).While specific</sup> estimates naturally differ between the two exercises, some of the main characteristics along which treatment effects are heterogeneous are similar in the two exercises, such as the remaining duration of the loan, and the extent to which borrowers care about maintaining a good credit score. Because some borrower characteristics used in the heterogeneity analysis are elicited in an endline survey, several months after participants received one of the treatment or control messages, we verify that the treatments have no effect on survey responses. The results, reported in Appendix Table B.9, show that there are no treatment effects on any of the survey outcomes.

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Figure 4: Fixed- and within-effects estimation <!-- Start of picture text --> Employment (2-digit) and GDP per capita (PPP) for all countries<br>Pooled (N=4,932) Between (N=140) Within (N=4,929)<br>oses Ss ° s. s = ss ose Ssss<br>GDP per capita, PPP (constant 1995) ‘GDP per capita, PPP (constant 1985) Change in GDP per capita, PPP (constant 192<br><!-- End of picture text --> Source: Authors’ calculations The first panel of Figure 4 displays the same lowess curve as Figure 2: the non-parametric lowess curve for the Gini index on GDP per capita, on pooled data. The smoothed values of the Gini index are obtained from local weighted regressions of the Gini index on GDP per capita according to the following equation: y,, = a+ $x, + €,,. The lowess curve has a U-shaped pattern showing two stages of diversification.

<!-- Start of picture text --> curve has<br><!-- End of picture text --> <!-- Start of picture text --> pattern showing<br><!-- End of picture text --> <!-- Start of picture text --> stages of diversification.<br><!-- End of picture text --> The second panel displays the non-parametric lowess curve of the average Gini index on average GDP per capita. The smoothed values of the Gini index are obtained from local weighted regressions of the country-average Gini index on the country-average GDP per capita according to the following equation: <u>7,</u> = aPE 4 BBE <u>TZ</u> + <u>=.</u> The lowess curve displays again a U-shaped pattern, this characterizing the between-country
the height of the child on all regressors and instruments. This test allows to detect whether instruments have additional power on the current height of the child, beyond through the prediction of the program probability. The value associated to this F test is 1.40, suggesting that our instruments do not have a direct effect on malnutrition. Thus it is valid to exclude them from the malnutrition equation.<sup>7</sup> The results of the instrumenting equation, which are

> 6 As the placement of the program is at village level, the ancillary regression has been run on the sample based on the four round panel dataset made of village level observations; the dataset, thus, being different then the one used for our main regression.

> 7 We also derived a chi squared test following Wooldridge (2001). According to the test result we cannot reject the overidentifying restrictions. Different set of instruments have been tried, the coefficient associated with Partage ranging from 0.3 to 1.2.

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for men and women beyond legal ownership of the business, and in doing so inform policy making (ILO, 2018c). Such questions were also recommended by the 20<sup>th</sup> ICLS in the revision of ICSE-18.

# Summary and ways forward

Since a large share of rural women’s employment is concentrated in contributing work in family farms/businesses, as well as informal wage employment that can be paid irregularly, strictly relying on individual earnings data to make comparisons on the value of women’s and men’s labor may not be very informative. Box 5 presents some recommendations from this section.

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Table 8 compares key features of the minimum wage legislation with the other five countries. It is clear that minimum wages provisions in Indonesia are at least as generous as in any of the other countries. Malaysia stands out as the only one of the six countries with no minimum wage. The other four countries either have more exclusions in coverage for example for apprentices or workers on probation (U.S., Korea) and/or permit certain non-wage benefits to be counted as wages (U.S. and in Korean law), and, at least at first glance have minimum wages set at lower levels relative to average manufacturing wages. The minimum wage as a ratio of an average manufacturing wage was 0.51 in Indonesia in 1991 and is likely to be slightly higher in 199432/. The real value of minimum wages fell through the eighties in the U.S., Mexico and Chile33/. In the first quarter of 1993, approximate calculations based on available data reveal that approx one-third of all manufacturing industry workers had monthly income below 1.2 times the minimum wage while about 15% had monthly incomes below 0.8 times the minimum wage34/. While not cast in stone, these estimates suggest that the minimum wage may be more binding in Indonesia than in the other countries being studied35/36/ Minimum wage provisions are usually designed to guarantee each worker a reasonable wage for his or her work effort and thus to reduce the incidence of poverty. Proponents also argue that they increase productivity. Neoclassical economic theory, on the other hand, suggests that such legislation could have unintended adverse consequences. Even in the case of full coverage, repeated minimum wage imposition as in the case of the U.S.37/ could be associated with a cycle of short run employment losses, inflation reducing the real value of the minimum wage and restoring employment, and then an increase in the minimum wage starts the process all over. In a growing economy with complete coverage, the
swiftly influence the distribution of demographic variables in countries. For example, the urban share of residents could have been heavily influenced by the internal migration of citizens returning to their home villages or to other rural areas or to take up remote work, or by people migrating from rural to urban areas in order to search for better livelihoods during the pandemic. These changes happened swiftly, in the aftermath of the onset of the pandemic in March 2020 in low- and middle-income countries (see, for example, evidence for Brazil (Garcia, 2021), SubSaharan Africa (FAO, 2020), and Peru (Dupraz-Dobias, 2020)). We take up such swift changes as a caveat while interpreting the data.

<mark>Data from figure 3.1 indicate that in Kenya and Indonesia, the urban share is well captured reasonably accurately in the online survey. For Brazil, the survey underrepresents urban areas. In Egypt and Sri Lanka, the survey overrepresents urban areas. Türkiye has a very high share of urban data points in the online survey, but location data are not available for Türkiye in the GMD. Overall, this mixed picture is consistent with the expectation that urbanization itself might be changing as a result of the COVID-19 pandemic, as noted above. Comparison with the Internet using population in GMD (figure 3.2) presents a different picture and shows that urban residents are underrepresented in the online survey. Female respondents are underrepresented in the online survey in all countries, which is consistent with many studies, particularly, Valliant and Dever (2011) for the United States. Younger people are overrepresented in online surveys in all countries and older people are underrepresented in the online survey, which is consistent with findings in Steinmetz et al. (2009) for the cases of Germany and the Netherlands, and by Valliant and Dever (2011) in the United States. These results are robust when we compare the online survey with the Internet-using population in GMD in figure 3.2.</mark> <
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**32. Policy measures taken by central banks in emerging and mature economies in recent months (especially by the ECB) are believed to have improved funding conditions markedly, but lending conditions still remain weak.** The slowdown in the associated deterioration of lending conditions in the first quarter of 2012 seems most pronounced in CEE, which has the tightest funding links with the European banks. Nonetheless, while improving, credit conditions continue to remain tight, reflecting economic uncertainty in the local and advanced country economies. In fact, a number of CEE, Asian, and Latin American subsidiaries of European and U.S. banks have seen their credit ratings downgraded following rating actions on the parent banks, including BBVA, Citigroup, Goldman Sachs, HSBC, KBC, Santander, and Unicredit (see appendix table A.3).

**Figure 19. Emerging Market Lending Conditions and Drivers**

<!-- Start of picture text --> a. EM Banking Lending Condition Indices b. IIF Data Index–Global Conditions by<br>Category<br>70 2009:Q4–2012:Q1 75 2009:Q4–2012:Q1<br>70<br>65<br>65<br>60<br>60<br>55 55<br>50 50<br>45<br>45<br>40<br>40<br>35<br>35 30<br>Credit Standards Demand for Loans Funding Conditions<br>GLOBAL AFME LATAM EUROPE ASIA Nonperforming Loans Trade Finance<br>Diffusion Index (50 = neutral)<br>Diffusion Index (50 = neutral)<br>Q4 2009 Q1 2010 Q2 2010 Q3 2010 Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012 Q4 2009 Q1 2010 Q2 2010 Q3 2010 Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012<br><!-- End of picture text --> Source: International Institute of Finance, Emerging
recent figures for a bigger sample of EMs and the three developed markets shown in Figure 4.

While the significant ownership shares of foreign investors in a large number of EMs is striking, it is worth noting that even these proportions may understate the importance of offshore demand in certain parts of the market, since offshore ownership is not necessarily evenly-distributed amongst domestic debt issuances. For example, foreign ownership may be concentrated in longer tenors even when overall foreign ownership as a proportion of all debt is quite low, making long-term yields much more sensitive to foreign demand than aggregate holdings data suggest.

Amongst the limitations of the foreign holdings data used for Figure 4 is the difficulty of gauging who the beneficial owners of debt are, given the wide variety of custodial arrangements that exist. The intermediary role of banks, including those with an onshore EM presence, is worth highlighting here; the true extent of foreign investor participation in a market will be understated by basic holdings data in cases where onshore banks warehouse bonds in order to satisfy foreign client demand, or to meet risk management needs arising from their own issuance of instruments, which provide foreign clients with exposure to local yields. The EM credit-linked note (CLN) market has existed since 1996 and as early as 2003 the notional value of all credit derivative contracts based on sovereign EM underlyings was estimated at US$250 billion (including local currency instruments, though these would have been only a small share).<sup>5</sup> Comprehensive market statistics remain elusive, but while the credit derivative market as a whole was hit hard by the financial crisis, it continues to evolve, including for local currency EM debt underlying instruments (e.g. total return swap products replicating local currency bond index returns).

**Figure 4: Foreign holdings of domestic government securities Table 1: Proportion of domestic government securities held by foreigners**

|90<br>% of total<br>% of total|90|**%**|**
.

- Peltoniemi, Janne (2007). “The benefits of relationship banking: Evidence from small business financing in Finland,” _Journal of Financial Services Research_ 31: 153–171.

- Rocha, Roberto, Subika Farazi, Rania Khouri, and Douglas Pearce (2010). “The Status of Bank Lending to SMEs in the Middle and North Africa Region: The Results of a Joint Survey of the Union of Arab Banks and the World Bank,” Mimeo, the World Bank, Washington, D.C., and the Union of Arab Banks, Beirut.

- Schiffer, Mirijam, and Beatrice Weder (2001). “Firm Size and the Business Environment: Worldwide Survey Results,” Discussion Paper 43, International Finance Corporation, Washington, DC.

- Scholtens, Bert (1999). “Analytical Issues in External Financing Alternatives for SMEs,” _Small Business Economics_ 12: 137-148.

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14 activity in Ho Chi Minh City to estimate ranges of future exposure. The project used simple depth-damage curves to estimate future vulnerability. As shown in Figure 4.1, we linked these components using a simple model built in the Analytica modeling environment.<sup>10</sup> This Analytica model also allowed comparison of the impacts of alternative adaptation and retreat measures as part of alternative integrated flood risk management strategies.

# **Measures of Risk (M)**

Based on our workshops and discussions with Steering Center for Flood Control, this project employed three measures of risk to compare the potential consequences of alternative flood risk management strategies in our study area. Two measures focus on population risk and one on economic risk. These are:

1. Risk to the poor, measured as the expected number of people affected annually by flooding;

2. Risk to the non-poor, measured as the expected number of people affected annually by flooding; and 3. Risk to economic value, measured in percent GDP lost annually to flooding.

The analysis employs two measures of population risk because equity issues are important to Steering Center for Flood Control. The population risk measures distinguish between a cohort called “poor” and those of higher income in the Nhieu LocThi Nghe catchment identified as “non-poor.” Many definitions exist for “low-income” and the literature lacks any consensus about the distinction between the poor and nonpoor even in the present. In this future this distinction becomes even less certain. As discussed in the uncertainty discussion below, this project treats the number of poor and non-poor in Ho Chi Minh City as fundamentally uncertain, drawing upon a range of data on poverty rates from the Vietnamese government and other sources to inform the range of possible future poverty rates.<sup>11</sup> Figure 4.2 shows risk to the poor population in what is described below as benchmark conditions.

The project employs a single measure of economic risk because the available data did not support any useful disaggregation. Note that
Policy Research Working Paper 10763

# **Abstract**

Quality early childhood education improves childhood development outcomes and has long-term implications for school readiness, workforce participation, and economic growth. Despite this, in Pakistan, the net enrollment rate of children ages 3 to 5 in early childhood education was only 31 percent in 2022. This paper estimates the cost of expanding access to early childhood education using an adapted version of the early childhood education Accelerator Costing and Simulation model. Using available administrative data, the paper presents cost estimates for three packages: (i) a business-as-usual package, (ii) a core service delivery package, and (iii) an augmented service delivery package. It considers how these costs might vary using alternate delivery mechanisms, such as community construction and vouchers. To ensure 100 percent net enrollment in early childhood education by 2035, Pakistan must increase the amount of the education budget spent on early childhood education from the existing allocation of 5.3 percent to 10.4 percent by 2035. This means increasing the early childhood education budget from PKR 71 billion (US$0.3 billion) in 2022 to PKR 418 billion (US$1.85 billion) in 2035, suggesting an average annual increase of 14 percent. Using alternate delivery mechanisms, such as community construction and vouchers, the required budget can be reduced to PKR 311 billion (US$1.37 billion) in 2035.

This paper is a product of the Education 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 ahasan1@ worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

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3.07 A major factor in the economic justification was out-migration of skilled workers from Egypt, Jordan, Pakistan, Bangladesh and Indonesia to the oil-based economies of the Middle East. By the time of the seventh Pakistan project, worker remittances were the largest single source of foreign exchange. A significant portion of the training investment supported the continuing supply of such labor. The return of workers as the oil-exporting economies contracted led to labor *'arket instability in the 1980s. In Jordan, it produced an oversupply of workers in certain skills categories that, together with continuing demand for skilled workers, led the government to establish a labor market information system and re-orient the number and size of courses of different kinds in vocational training centers. In contrast, in Pakistan, returning workers appear to be easily absorbed by the economy, with many choosing to establish their own businesses (Pakistaa Sector Study, 1988).

3.08 The most prominent rationale in the low income countries of Sub-Saharan Africa was the need to replace costly expatriate managers and skilled workers. The same rationale was important in Morocco and Algeria. Economic justifications for the low-income countries of Sub-Saharan Africa were also based on projections of growth. Economic analysis tended to be less complex in these smaller economies, and to be too optimistic. In the Cameroon, for example, a secondary vocational school was built to provide skilled workers for intended industrial expansion in the northern region. Industrial development was delayed, and graduates had to leave the region to find employment.

3.09 Equity Justifications: Only one of the sample of components was justified primarily on equity grounds: investment in boarding places for Tanzanian nonformal centers and technical secondary schools was intended specifically to increase the access of rural clients to the former and women to the latter. Increased access for rural groups to skills training was, however, an ubiquitous secondary justification: it was mentioned in connection with at least one (but not necessarily each) component in each country in the sample. Access for women figured