rafmacalaba/datause-encoder-data huggingface dataset:
# * EVOLUTION OF TEM WORLD SAM' S LENDZNG POLICY

As a development institution, the World Bank supports education by provid'ng financial resources, technical assistance, and policy advice. It contributed US$11.2 billion to support education and training through 396 projects in 100 countries between 1963 and 1990. The volume of Bank<sup>1</sup> lending for basic education- totalled US$2 billion since 1970, and accounted for over a quarter of the world's multilateral aid to primary education between FY81-86.3 The Bank employs more than 100 education professionals to assist Borrowers with the development and implementation of the education loans and to appraise more than 20 education projects per year. It also ham a small division of 10 professional staff to undertake educational research and policy work. This paper will review the evolution of the Bank's lendine policy, and the objectives, strategies and lessons of lending for basic education, which includes both primary and nonformal education.4 The Bank's policy of lending for education has gone through several phases over the past two decades. At the time of its inception, the World Bank was authorized under its Articles of Agreement (1945) to provide investment capital to support projects "for productlve purposes, including...the encouragement of the development of productive facilities and resources in less developed countries."5 In the 1950s, few development economists recognized the critical contribution of education to productivity and economic growth. In that decade, education was not considered a suitable object for Bank investment.

# Entry into Education Lending

In FY63, the World Bank began lending for education, stimulated b' the emerging view in development economics of education as an investment in human capital, and by the growing demand for financial assistance in education from its member-states. Initially, the objectives were to promote educational planning, build infrastructures, such as schools, and attract additional capital investment from other donor agencies.

I The word "Bank" used in this paper refers to the World Bank.

2 Basic education includes primary education for children and literacy and basic skills training for youths and adults. It does not include formal vocational training.

3 The World Bank's fiscal year starts on July lot. FY81-86 refers to the period from July 1, 1980 to June 30, 1986.

4 The discussion in this chapter is based on the following World Bank papers: 1971 Sector Working Paper) 1974 Sector Working Paper; Education Sector Policy Paper; Review of Bank Operations in the Education Sectorl The Dividends of Learning; and Primary Education: A World Bank Policy Paper.

World Bank, "Review of Bank Operations in the Education Sector," p. 1.

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ineligible) for humanitarian cash transfers that also qualify for the government cash transfer using the MoLSA-MPT. The result of the different overlap possibilities is presented in Table 3, showing the crosseligibility between different vulnerable groups in the humanitarian models (such as extremely vulnerable group - below first cut-off, extreme and mid vulnerable groups below the second cutoff, and all vulnerable groups below the third cutoff) and the different possible cutoffs for the government model (bottom 15 percent, 18 percent, 25 percent, 30 percent, 35 percent). Combining the humanitarian cutoffs with the government cutoffs gives a different proportion of overlaps. For instance, among the households categorized as extremely vulnerable by the SEVAT, 40 percent would be eligible for the government assistance under the MoLSA-PMT scoring if the government expanded the support to only those below the national poverty line i.e., those in the bottom 18 percent nationally.

If we extend the overlap analysis including the other two less but still vulnerable groups, the referral rate would reduce to 36 for the extreme and mid vulnerable groups and 32 percent when including all three vulnerable groups. Despite the decrease in share, it should be noted that the expansion of the humanitarian cutoffs increases the actual number of households referred. This is because the subset from which the overlap is calculated has now increased. From the single eligibility calculation in Table 2, only 27 percent of the households qualify as extreme vulnerable, 46 percent as extreme-and-mid vulnerable, and 63 percent as any vulnerable (extreme, mid, and vulnerable). Although expanding the humanitarian or government cutoffs would likely increase number of households referred, there are trade-offs in terms of referral success, referral failure, and failure to refer. For instance, an expansion of the humanitarian cutoffs beyond the extremely vulnerable group would increase the number of referrals but would also increase the probability of referring ineligible households, i.e., increasing the referral failure but increasing the absolute number of households referred. On the other hand, decreasing the humanitarian cutoff to capture only, for example, the extremely vulnerable groups would likely increase the probability of correctly referring eligible households, i.e., increasing the referral success, but decreasing the absolute number of those referred, and possibly failing to refer some eligible households. This trade-off reflects a concomitant political trade-off between maximizing the number of successful referrals (albeit at a lower success rate) and not mistakenly raising expectations (by referring fewer eligible households at a higher success rate but at a cost of not referring some eligible households). More so, expanding the eligibility coverage beyond the national bottom 18 percent to capture for example the bottom 25, or 30 percent would increase both the proportion and the actual number of cross eligible households, but obviously, increase the referral failure.

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urban labor market was never dominated by circulatory migrants with a high turnover of labor as in East Africa. Hence the massive increase in urban wages which took place in East Africa in the sixties driven by the desire to change the urban system from a migratory to a stabilized one was never necessary in Ghana. Secondly, the urban centers in Ghana were clearly not so differentiated from the rural ones as in East Africa. But this relatively modest differential was enough to sustain a significant stream of rural-to-urban migration through the sixties. "Between 1956 and 1967 modern sector non-agricultural employment grew on average by 3.3 per cent per annum whereas urban population grew at more than twice this rate" (Knight, p.<sup>2</sup> 14).

**Table 6.1: Expenditure of Urban and Rural Households 1961-62**

|Expenditure per household<br>(NC per month)|Urban|Rural|Urban<br>(Rural=<br>100)|
|---|---|---|---|
|All regions||||
|Consumption of own|2.6|8.5|31|
|produce||||
|Other expenditure|34.2|19.0|180|
|Total expenditure|36.8|27.4|134|
|By region||||
|Accra|46.3|||
|Ashanti|41.3|35.6|116|
|Brong Ahafo|42.5|30.7|138|
|Eastern|36.2|31.1|116|
|Northern|23.4|17.1|137|
|Volta|33.7|33.3|101|
|Western|27.9|26.5|105|
|Expenditure per capita (NC|9.0|6.0|150|
|per month)||||
|Average size of household|4.08|4.54|90|
|Percentage of single member<br>households|22.3|13.4||

_Sourc: P. T.F. Golding, 'An inquiry into household expenditure and consumption and sale of household produce in Ghana.' Economic Bulletin of Ghana, 1962, No. 4 Tables 6 and 7._ Notes: The data relate to a two-stage stratified sample of 990 households with income under NC 100 per month

# **6.2. The Course of Rural-Urban Earnings Differences during the Economic Decline**

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growth effect at all, might have typically small effects—too small to be detected by growth regressions. There may never have been a good reason to think that growth regressions could find that small signal amid the noise.

# **6. A way forward**

This analysis suggests that economists likely do not have the tools at this time to reliably measure the effects of remittances on broad processes of growth and development. Our timeseries data on changes in remittances are likely to reflect changing measurement practices more than they reflect changes in true flows. Even the overstated changes in existing estimates would not have large enough growth effects to be reliably detected in panel growth regressions. And while very recent remittance estimates may be getting more accurate, for many reasons it is difficult to measure their growth effects in cross section. One of these reasons is that in cross section, greater remittance inflows go hand-in-hand with greater labor outflows—the net effect of which is unlikely to be large or even necessarily positive for many countries.

None of this, however, means that remittances do not have broad effects on growth and development. It simply means that the empirical hunt for any such effects is likely to remain quixotic for the time being. Remittance measurement and accounting practices appear to be converging toward more accurate measures in the last few years (although there are some reasons why they may now over-count remittances in some cases), and remittances continue to (genuinely) grow. There may thus come a time many years in the future when we have crosscountry panel data on remittances of sufficient great magnitude and sufficiently little noise to measure any effects on growth that remittances may have.

There is large and rich research agenda on the poverty and development effects of remittances, discussed by Rapoport and Docquier (2006), Yang (2011), and Clemens and Ogden (2014), among others. There is mounting evidence that migration and remittances have first-order economic impacts on poverty in origin countries, on migrants and their families, and on global GDP. There is little evidence, however, that substantial growth in the GDP of migrants’ origin countries has been caused by remittances. The relatively small size of remittances, the noise in 28
research budget restrictions, Casey et al. (2019) only make use of surveys conducted with community leaders and a limited number of measures based on direct observation of public infrastructure. In this study, we do the same but also collect data from randomly selected households, including children’s test scores, and from the users and directors of the public infrastructure. One benefit of these additional data sources is that it provides a richer set of measures per outcome family. Another benefit is that these data allow us to explore a larger set of outcome families, such as additional dimensions of service provision, education, health, and women’s empowerment; outcomes that are central to many CDD interventions.

Second, the size of the experiment we study here is much larger. Casey et al. (2019) employ data from 236 villages (113 treatment, 113 control). In contrast, our measurement strategy builds on data from 735 villages (367 treatment, 368 control), significantly increasing statistical precision and decreasing the possibility for Type-II errors.

Finally, we contribute through replication. Similar to Sierra Leone, the Democratic Republic of Congo scores badly when it comes to development outcomes. However, Congo is different in important ways, including the proliferation of armed groups and continuing conflict and violence, which may affect the effectiveness of a CDD program. As such, this study contributes to generalizable knowledge by understanding the impact of a similar program in a different context, specifically a fragile and conflict setting.

In sum, we find very similar results as those reported in Casey et al. (2019). We find no evidence for software results, despite the change in context, the additional precision, and the wider set of software-related outcomes. In addition, we find positive effects on some hardware-related outcomes. Specifically, the positive effect on the stock and quality of local public infrastructure (0.23SD, p<0.01) as reported in Casey et al. (2019), is very similar to the positive effect on the infrastructure quality of health facilities (0.16SD, p<0.01) and primary schools (0.19SD, p<0.01) that we report in this study.<sup>16</sup> The similarity in findings related to the durability of material benefits of CDD programs is encouraging. Furthermore, the longer term positive effects on hardware but not on software-related outcomes largely mirror the results found in short-run studies (Casey, 2018). Improving local infrastructure in some of the most challenging environments, and at times in the context of

> 16 Note that results differ when it comes to economic welfare. Casey et al (2019) find a positive effect (0.24SD, p<0.01), while we find a negative effect (-0.08SD, p<0.05).

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Women of prime working age who have children at home face higher penalties on the time dedicated to all four activities, compared with the effects for younger and older women. The effects for all age groups, however, are smaller than those associated with marriage (Table 9). The presence of children is associated with a reduction of 11 minutes of market work, 23 minutes of personal care, and 36 minutes of leisure per day. At the same time, women in this age group see an increase of 1.6 hours of unpaid domestic work associated with having children at home. For mature women, the effect of children on the amount of time dedicated to the different activities is nearly half of that observed among women of prime working age.

Compared to women, the presence of children at home does not make a big difference in the way men allocate their time. Among men of prime productive age and mature men, unpaid domestic work increases by 11 minutes per day and leisure diminishes by 10 minutes, but the effect on market work and personal care is not significant. In contrast, the reduction of leisure time among young men is nearly twice as much as that experienced by their older counterparts (19 minutes per day) in the presence of children, but there is no effect on unpaid domestic work.

# **6. Discussion**

In this paper, we contributed to the discussion on gender equality and development by analyzing men’s and women’s time use patterns in 19 countries using recent time use data. Our analysis confirms and adds nuances to the findings of other scholars and those of the 2012 WDR. We focused on the allocation of time to four different activities: market or paid work, unpaid domestic work, self-care, and leisure, social and study activities.

First, we analyzed the average number of hours that men and women allocate to each activity on a daily basis. Our results showed that women specialize in unpaid domestic work and men specialize in market work, but the intensity varies widely across countries. Considering productive activities altogether, women work more hours than men in most countries and consequently, end up having less available time for leisure. When comparing our findings with earlier estimates of time use, we concluded that progress has been made, but marked differences persist. The gender gap in time allocated to market work diminished in 4 of 10 countries; the difference in unpaid domestic work hours declined in 6 of 10 countries; and the gap in personal activities and leisure narrowed in 3 of 7 countries with data.

Second, time use patterns based on individual and household characteristics are highly heterogenous. Men’s and women’s time allocation tend to converge as education increases, but differences persist even for the most educated. Gender gaps in all activities widen when comparing the time patterns of young women and men with those of individuals of prime working age. In the transition to adulthood, women allocate more time to unpaid domestic work than men, whereas men work longer hours in the market. Regardless of their marital status, men devote more time 19
ECONOMIC ANALYSIS OF WATER SUPPLY PROJECTS -- INDONESIA Year(O) refers to the year of project completion -- everything, with the possible exception of connections, is installed. Input data in the highlighted/coloured areas. To reach a paragraph directly, press F5 and type PARAx, when x is the number you want. To see the results, press F5 and type RESULT. Arrow keys move the cell pointer.

A. 'WITH' project situation: 1. Population served and piped water delivered by the project:

|Year(1)|house|yard|public|non-||total (press F9)|
|---|---|---|---|---|---|---|
||connection||standpipe|resid.|||
|no. of<br>persons|0|0|0|n/a||0|
|cubic<br>meter/year|0|0|0||0|0|
|Year(6)|house<br>connection|yard<br>|public<br>standpipe|non-<br>resid.||total (press F9)|
|no. of<br>persons|0|0|0|n/a||0|
|cubic<br>meter/year|0|0|0||0|0|
|Year(1 1)|house<br>connection|yard<br>|public<br>standpipe|non-<br>resid.||total (press F9)|
|no. of<br>persons|0|0|0|n/a||0|
|cubic<br>meter/year|0|0|0||0|0|
|Year(16)|house<br>connection|yard<br>|public<br>standpipe|non-<br>resid.||total (press F9)|
|no. of<br>persons|0|0|<br>0|<br>n/a||0|
|cubic<br>meterlyear|0|0|0||0|0|
|Year(21)|house<br>connection|yard<br>|public<br>standpipe|non-<br>resid.||total (press F9)|
|no. of<br>persons|0|0|0|n/a||0|

29
The ICC plots provide diagnostic information about the difficulty distribution of items which can be used to generate an ‘item bank’ for SBM in the same way as there are question banks for student assessment. Figure 4 (a) takes the two items at the extreme ends of the difficulty distribution and plots their ICCs. The solid curve without markers is the theoretical symmetric distribution, centered on a mean of zero being a rating of 5 and following an S shape to capture a normal ability distribution. The blue line shows the ICC for RES_2 ‘School has adequate financial resources to purchase educational material’ and it can be seen that it extends lower along the theoretical S-curve, to a nearly -1 level of difficulty. The red curve showing PRI_2 ‘School head has harmonious relationships with local authorities’ is only seen at the right side, it virtually has no discriminating power – it appears that all school principals in the Philippines have great relationships ! Figure 4 (b) shows the ICCs for all ‘autonomy’ items – this indicates a jumble of items with ICCs crossing one another and all concentrated on the right hand side. To the extent possible, it would be useful to see such a family of curves running parallel, with a left to right displacement showing easier items.

This section has shown that rigorous analysis of SBM implementation is possible – rather than relying on arbitrary indices, indices can be constructed which meet objective criteria regarding validity and reliability. However, in order for SBM related literature to adopt the suggested measurement technique, more empirical studies in different contexts need to be carried out and the process of item construction build successively on lessons learnt from earlier studies. The measurement of personality traits and political skills are two examples of fields where a large literature has emerged, and we turn next to the application of BFI and PSI to the same sample of 150 schools from the Philippines.

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# **Box 1.  Nairobi: Land Cover vs Land Use –Is There a Difference?**

# **Land use vs Land cover**

In 2005 the Government of Nairobi released a city land use map based on the manual digitizing of 2003 aerial photos. Land was divided into many more categories than used in this study, making it difficult to compare results. For example, the land use map has land owned by universities and churches labeled as ‘institutional’. But if these institutions have large fields or parking lots a land cover map would label part of this land as vegetation or barren. Despite these inconsistencies, the classification of informal settlements is loosely comparable. The land use map identified slums as areas with “small mostly temporary buildings that re randomly distributing in high density clusters” and warned that the distinction between residential formal and residential slum “was highly subjective.”<sup>38</sup> Similarly the algorithm in this study attempted to identify neighborhoods with small tightly packed roof tops and label them as “Residential shanty”. The graphs below compare the results, showing how the coverage of slum/irregular land changes as one moves away from the city center. In the first 4 kilometers the land use map claims almost no slums, while the land cover map identifies a small portion. After the 5 kilometer mark, the maps agree almost perfectly, with a steady rise in slum/irregular land followed by a tapering off at around 10 kilometers. Similar comparisons between a semi‐automated approach and manual digitization have been explored in Accra Ghana.<sup>39</sup> This study found the land cover layer derived from VHR imagery was able to accurately detect 94% of the land designated as ‘slum’ by the UN Habitat/Accra Metropolitan Assembly <u>(UNAMA).</u> <!-- Start of picture text --> Nairobi Land Cover c. 2003<br>Nairobi Land Use 2003<br>(semi‐automated classification)<br>(manual classfication)<br>60%<br>60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance from CBD (km) Distance from CBD (km)<br>Residential (Irregular)<br>Residential (Irregular)<br>Residential (Regular) Residential (Regular)<br>Commercial/Industiral Commercial/Industiral<br><!-- End of picture text -->

> 38 2005. Nairobi Land Use Memo. Download from Nairobi GIS Maps.

> 39 Engstrom et al (2015).Mapping Slums Using Spatial Features in Accra, Ghana. IEEE.

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# **Table 9 Export Effects, MFN Liberalization-based Preference Erosion and the ATC Phaseout**

||chang <br>|e in exports, perc<br>|ent<br>|
|---|---|---|---|
||Total|Total|Total ATC|
||preference loss,<br>unadjusted|preference<br>loss,adjusted|quota<br>elimination|
|_African LDCs_|_3.7_|_4.2_|_-0.1_|
|Botswana|-4.2|-4.2|-0.2|
|Madagascar|7.7|11.2|-1.9|
|Malawi|12.5|16.0|-0.3|
|Mozambique|-6.0|-7.8|-1.4|
|Tanzania|2.2|6.3|0.0|
|Uganda|0.0|1.5|0.0|
|Zambia|-1.7|-1.6|0.3|
|Other Sub-Saharan Africa LDCs|4.4|4.8|-0.1|
|_Asia/other LDCs_|_7.3_|_7.6_|_0.7_|
|Bangladesh|6.0|10.2|-8.2|
|Other Central/South Asia LDCs|7.5|7.3|1.8|
|_Other Low-income_|_6.6_|_6.1_|_1.4_|
|India|2.5|2.4|1.4|
|Vietnam|22.9|20.7|1.5|
|TOTAL|6.0|6.0|0.7|

Source: Model estimates, and Francois, Spinanger, and Woerz (2005).

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