rafmacalaba/datause-encoder-data huggingface dataset:
atmospheric distortions and similar temporal resolution for all selected periods. We downloaded images (30-meter spatial resolution) with less than 10 percent of cloud cover from mid-to-lateFebruary for all years falling within Bangladesh’s dry season. Although data from the European Space Agency’s Copernicus satellite, Sentinel-2 A/B offers better spatial resolution (10 meters), we did not use it because of lack of availability in earlier years (before 2014).

We used additional geospatial data. First, we use Protected Planet’s World Database of Protected Areas map to classify areas of Cox’s Bazar as wildlife refuges, national parks, or nonprotected (UNEP-WCMC & IUCN, 2021). We also use annual population data (2010-2020) derived from the Worldpop Population Counts unconstrained, UN-adjusted maps at 100m resolution (Lloyd et al., 2019). For estimates of economic productivity, we use the VIIRS 20142020 Nighttime Lights data produced by NOAA. This data has a spatial resolution of 15 arcseconds and is collected monthly. We derive annual estimates of nighttime lights by averaging across months.<sup>7</sup> Finally, we gauge remoteness using the Google Maps API and its static maps of main road locations.

To construct our data set, we draw a random sample of 10,000 pixels across the district and derive zonal statistics of the pixel’s land cover classes for 2010, 2014, 2017, and 2020, the annual population of the 100m grid the pixel falls in (2010-2020), and the nighttime lights score for the pixel (2014-2020). We also determine static characteristics, including whether the pixel is in a protected area, the pixel’s geodesic distance<sup>8</sup> to the nearest border of a camp that opened after Rohingyas arrived in 2017, and the pixel’s geodesic distance to the nearest main road.

# **4.3 Image Processing**

For the image preprocessing, we use some of the in-build <mark>radiometric enhancement techniques in ArcGIS pro to improve the quality of data visualization during image processing. These techniques enhance our ability to interpret the image correctly. For instance, we use the Normalized Difference Vegetation Index (NDVI), a s</mark> pectral enhancement technique, to

> 7 Elvidge, Baugh, Zhizhin, & Hsu (2013) argue that VIIRS better captures nighttime lights as compared to the DMSP maps. Hence, we opt for VIIRS over DMSP for this study.

> 8 <mark>A geodesic distance measures the shortest path between two points on a curved surface, like the Earth (ESRI, 2021).</mark>

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earlier investments completed before SY 2002/03. Second, it is possible that students from primary schools, which are not part of our sample, came into grades 5 and 6 in our sample elementary schools, which alters the student body at grade 5. Since TEEP also contributed to the conversion of primary schools to elementary schools by building new classrooms and staffing for grades 5 and 6, it is possible that attrition is different in the treated and control groups.<sup>16</sup> Third, as an observational analysis, we cannot eliminate bias due to timevariant unobservables.

To illustrate our empirical approach, let _D_ = 1 if a cohort is treated (located in TEEP area) and _D_ = 0 if a cohort is not treated (located in non-TEEP area). Let the outcome of being T C treated by TEEP and the counterfactual outcome at time be denoted by _t_ (Yt , Yt ). The gain from treatment is (YtT −YtC), and we are interested in the average effect of treatment on the treated (ATET), E(YtT −YtC|Dt = 1). With t= 1 denoting SY 2004/05 and t= 0 denoting SY 2002/03, we can write the standard DD estimator as

DD= E(Y1T −Y0C|D= 1) −E(Y1C −Y0C|D= 0) = E(Y1T −Y1C|D= 1) + B1 −B0, where Bt is the selection bias and Bt = E(YtC|D= 1) −E(YtC|D= 0). If the selection bias is constant over time (B1 = B0), which is also called the parallel trends assumption, the DD estimator yields an unbiased estimate of the actual program impact.

The condition B1 = B0 or E(Y1C −Y0C|D= 1) = E(Y1C −Y0C|D= 0) will not hold if the cohort characteristics or initial conditions affect subsequent changes of the outcome variables and

> 16 In SY 2002/03, total grade 5 enrollment was 94.1 percent of the total grade 4 enrollment in TEEP schools on average, compared with 95.4 percent in non-TEEP schools; and the total grade 6 enrollment was 94.6 percent of the total grade 5 enrollment in TEEP schools on average, compared with 95.5 percent in non-TEEP schools.

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family for additional income, and the cost to her family of obtaining additional income. When a wife enters the labor market, the household must compensate for the loss of her work at home. The cost of which depends on the number and ages of her children, and the composition of the household. In this study household composition variables are included as proxies of these costs (see definitions in Table A-1). The need for additional income, plus the factors that determine how much a woman needs to work in order to earn a given amount of income, appear to increase as her husband's income decreases, as non-labor income of the family decreases, and as the living standards aspirations of the family increase. This study included age, schooling, and annual income of the husband, annual non labor income and remittances received by the family.

Table 5 shows the results of the multinomial logit estimation of the sector of employment model.

Table 5 Women: Choice of Sector of Employment (multinomial logit coefficients) a/

|Independent|Sector|of Employme|nt|
|---|---|---|---|
|variables|Wage<br>Private|Wage<br>Public|NonWage|
|Constant|-6.675*|-12.341*|-6.750*|
||(11.78)|(12.68)|(14.67)|
|Some secondary|-0.034|1.070*|-0.416*|
|(6-9 years)|(0.18)|(2.92)|(3.00)|
|Secondary complete|0.375*|1.832*|-0.603*|
|(10 years)|(2.22)|(5.62)|(4.36)|
|Some post-second.|<br>0.379*|2.186*|-0.517*|
|(11-13 years)|(1.69)|(5.77)|(2.26)|
|Post-sec. complete|0.411|2.760*|-0.748*|
|(14+ years)|(1.61)|(7.19)|(2.72)|
|Diploma|0.378*|0.785*|-0.136|
||(1.88)|(3.54)|(0.58)|
|Training|0.852*|0.884*|0.671*|
||(7.43)|(5.62)|(5.91)|
|Annual remittances|-0.00002|-0.00003*|-0.00001|
|to household|(0.21)|(1.76)|(1.01)|

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Thailand and Vietnam.<sup>5</sup> To increase the capacities and quality of its education system, the Cambodian government received a first grant from the Global Partnership for Education (GPE I) of $57 million for the period 2008—2012, which included an expansion of the early education system (formal preschools, informal preschools, and parenting programs). Bouguen et al. (2013) evaluated the impact of the early childhood education and development (ECD) components of GPE I on child development outcomes.They find no impacts on outcomes overall, although the study period was marked by implementation issues including low individual take-up and delays in program implementation.

Aware of the shortcomings of the first early childcare expansion, the Government of Cambodia, with the support of the World Bank, launched another preschool education expansion program for the period 2014—2018. The plan was financially supported by a second GPE grant (GPE II) of $38 million, of which about $20 million were allocated to ECD programs. Our study focuses on the sub-component that includes the construction of formal community preschools.

# **2.2 Supply-side intervention: Formal community preschools**

Before GPE II, two distinct types of public preschools existed in Cambodia: state preschools (SPS) and “community preschools.” Since these “community” preschools were unstructured and lacked uniform quality standards, we refer to them here as informal (community) preschools (IPS).<sup>6,7</sup> GPE II introduced a new type of community preschool with a structured age-appropriate educational program and uniform quality standard, which we refer to as (formal) community preschools (CPS).

SPS were financed by the Ministry of Education, Youth and Sport (MoEYS). SPS teachers benefited from two years of formal training in a MoEYS teacher training center in Phnom Penh. They received a monthly salary of roughly $250 to teach for three hours a day, five days a week. Almost all SPS were attached to a public primary school and had access to classrooms equipped with teaching and play materials, along with better overall infrastructure (including sanitation facilities).<sup>8</sup> In contrast, IPS were typically not attached to a primary school. The local community established an IPS and covered operational costs. This included the IPS teacher stipend, which was at the discretion of the local commune council. Constrained by council budgets, it varied from $30 to $50 per month at the time of

> 5Source: Data from UNESCO Institute for Statistics.

> 6According to government data (MoEYS, 2017), of the 7,241 preschool facilities in Cambodia in 2016, 55% were SPS, 39% were IPS, and 6% were private preschools. However, these preschools are not evenly distributed across the country, and 38% of the 1,646 communes in Cambodia had no preschool facility.

> 7See Bouguen et al. (2013) for an impact evaluation of each type of preschool developed in the context of the GPE I.

> 8The program designers and research team did not anticipate the presence of SPS in the experimental villages. Villages with a preexisting SPS were not supposed to be eligible for the program.

8
# **_Effect of Firm Selection on Firm Level Decomposition Findings_**

Remember that for conducting firm level analysis we had to work only with 9,379 specific firms that exist every year over the 2005-2012 period. One consideration pertaining to the firm level decomposition findings is the degree to which they are representative of the energy patterns of the whole manufacturing industry. Since these firms account for 72.5% of energy consumption and 66.5% of production the manufacturing industry, we do not expect firm selection has a significant negative impact on the quality of our findings above. However, in order to get an educated idea about this, we below run traditional sector level decomposition by first using the whole universe of firms and second by using only those specific 9,379 firms<sup>9</sup> .

The decomposition findings presented in Table 2a and Table 2b are very similar. Therefore, we conclude that not being able to cover all firms in the firm level decomposition does not pose a big problem for the representativeness of that decomposition of the whole manufacturing industry.

**Table 2a: Decomposition Results at Sector Level for the 2005-2012 Period (including all firms)**

|**Dman**|**Dact**|**Dstrs**|**Dints**|
|---|---|---|---|
|1.278|1.538|0.981|0.847|

**Table 2b: Decomposition Results at Sector Level for 2005-2012 Period (including only 9,379 firms existing in the data set over the whole period)**

|**Dman**|**Dact**|**Dstrs**|**Dints**|
|---|---|---|---|
|1.273|1.520|0.971|0.862|

# **VI. DISCUSSION OF FINDINGS**

In this section, we first discuss our firm level decomposition findings by comparing with the findings of the only firm level study in the literature - Petrick (2015) for the German manufacturing industry. Although the time period of that study is different from ours, we still conduct such a comparison as it is the only firm level decomposition analysis in the literature and we believe that energy dynamics in an industry is not likely to change significantly over a short period of time. Comparison by each decomposition component is given in Table 3. First, unlike a significant 27% increase in the energy consumption in Turkey over the 2005-2012 period, energy consumption in Germany increased by a much smaller amount (5%) over the 2000-2007 period.

> 9 We use the multiplicative version of the Log-Mean Divisia Index Method-I decomposition introduced by Ang and Liu (2001).

16
unavailable) in the survey data we use are reasonably comprehensive, including both cash spending and imputed values for consumption from own production. But we acknowledge that even the best consumption data need not adequately reflect certain “non-market” dimensions of welfare, such as access to certain public services, or intra-household inequalities. For these reasons, our poverty measures need to be supplemented by other data, such as on infant and child mortality, to obtain a more complete picture of how living standards are evolving.<sup>5</sup> Our poverty measures are estimated from the primary (unit record or tabulated) survey data. For the poverty measures without an urban-rural split we have used 560 household surveys for 100 low- and middle-income countries, representing 93% of the population of the developing world.<sup>6</sup> Only for a subset of these was it feasible to do the urban-rural decomposition. This was done for 87 countries using 208 household surveys. (Details on the specific survey used can be found at: <u>http://iresearch.worldbank.org/PovcalNet/jsp/index.jsp.)</u> We have not used any secondary sources for measuring poverty at each survey round (unlike all other compilations of distributional data and global poverty measures that we know of), although we do use other data sources for interpolation purposes, given that the surveys of different countries do not coincide in time. Households are ranked by either consumption or income per person. The distributions are weighted by household size and sample expansion factors so that a given fractile (such as the poorest decile) should have the same share of the country-specific population across the sample. Thus our poverty counts give the number of people living in households with per capita consumption or income below the poverty line.

As in past work, we have tried to eliminate obvious comparability problems, either by reestimating the consumption/income aggregates or even dropping a survey when there is little option. However, there are problems that we cannot deal with. It is known that differences in survey methods (such as in questionnaire design) can create non-negligible differences in the

> 5 The annual _World Development Indicators_ provides data on a broad set of indicators, including poverty measures, but also measures of health and education attainments (World Bank, 2006).

> 6 Coverage varies across regions, from 78% in Sub-Saharan Africa to 98% in Eastern Europe and Central Asia and South Asia.

4
variety of global polls of individuals conducted by Gallup, Latinobarometro, and Afrobarometro. We also capture the perceptions of country analysts at the major multilateral development agencies (the European Bank for Reconstruction and Development, the African Development Bank, the Asian Development Bank, and the World Bank), reflecting these individuals’ in-depth experience working on the countries they assess. Other data sources from NGOs (such as Amnesty International, Reporters Without Borders, and Freedom House), as well as commercial risk rating agencies (such as the Economist Intelligence Unit and DRI-McGraw Hill) base their assessments on a global network of correspondents typically living in the country they are rating.

As in our past work, we combine the many individual data sources into six aggregate governance indicators. The premise underlying this statistical approach should not be too controversial – each of the individual data sources we have provides an imperfect signal of some deep underlying notion of governance that is difficult to observe directly. This means that as users of the individual sources, we face a signalextraction problem – how do we isolate the informative signal about governance from each individual data source, and how do we optimally combine the many data sources to get the best possible signal of governance in a country based on all the available data? The statistical procedure we use to perform this aggregation, known as the unobserved components model, is described in detail in our past work (see for example Kaufmann, Kraay and Mastruzzi (2004)). The main advantage of this approach is that the aggregate indicators are more informative about unobserved governance than any individual data source. Moreover, the methodology allows us to be explicit about the precision – or imprecision – of our estimates of governance in each country. As we discuss in more detail throughout the paper, this imprecision is not a consequence of our reliance on subjective or perceptions data on governance – rather imprecision is an issue that should be squarely addressed in all efforts to measure the quality of governance.

An important innovation this year is that we have obtained permission from the majority of our data sources to report the underlying indicators that go into our aggregate indicators. The sources we have made available on our website are noted in Table 1. A number of our data sources, such as Freedom House and the Heritage Foundation have always been publicly available through the publications and/or websites of their 7
Teachers’ professional development is essentially a process, not an event. There is no once-for-all therapy for any problem that a teacher might encounter in his/her career. It basically starts from teachers’ pre-service training and continues through various forms of non-formal and informal learning that build up and consolidate their knowledge base and skills continuously. Other aspects of education reform, even those not specifically designed to affect teachers, could also have significant effect on teachers, such as the afore-mentioned accountability. An interesting example comes from Shanghai, the top performer in 2009 PISA. Shanghai’s success may be partly due to upgrading teaching standards and raising teacher pay (Cheng 2010).

# <u>Quality Element 5: Attention to Early Childhood Development</u>

Early Childhood Development (ECD) constitutes another structural quality element in education planning and reform. Early childhood development is the foundation of a person’s learning and well-being (Heckman 2004). Recent scientific research has established that a tremendous burst of synapse formation occurs early in life, whereas the synapse number declines when a child grows up to primary school age, and apparently the decline extends into adolescence in some areas of the brain, as shown in Figure 6. This implies that the best time for nurturing sensing pathways, language abilities and higher cognitive function is the early years of childhood. That could explain why early childhood care and development during these early years strongly influence the extent to which a child’s cognitive and socio-emotional abilities may develop to their fullest potential. A plethora of evidence also suggests that experience-based brain development _in utero_ and during the early years of life can set brain and biological pathways that affect an individual’s health, behavior, and learning for a lifetime (Mustard 2007: 39).

**Figure 6: Human Brain Development: Synapse Formation**

<!-- Start of picture text --> Sensing Language<br>Pathways Higher<br>(vision, hearing) Cognitive Function<br>-6 -3 0 3 6 9 1 4 8 12 16<br>Months Years<br>AGE<br>Conception<br><!-- End of picture text --> Source: Shonkoff and Phillips 2010 15
34 Total Dynamic HeadTDH = Lift + 10m friction and pumping to storage Pumped volume Q (m<sup>3</sup> /day) = Population x 20 liters/capita/day/1000 Diesel system costs Installed engine cost $3000 for 2.5 kW engine Pump cost ($) = _275_ + 25(TDH) + 75Q/4, but at least $750 Solar system costs Installed array cost $12/Wp Pump cost ($) = 275 + 25(TDH) + 75Q/4, but at least $750 Wind stem cost Installed system cost $500/i<sup>2</sup> swept area of rotor WelU cost 2000(Q)Y/4, or $2000 for Q< 16. Storage cost l000(Q*0.3)

# Renewable technology system sizing

Array size in peak watts: Wp = Z x Q x TDH x PEAK Swept area of windmill rotor in m<sup>2</sup> = 1.13Q x TDH x PEAK / v<sup>3</sup> ' = design month mean windspeed (m/sec) PEAK = Qmax/Q Omax = peak daily demand during design month Z = sizing factor from table below.

# SIZING FACTOR (Z)

||PUMPiDesign Month Daily Insolatio<br>|EFFI4kWh/m0SklWh/m<br>2<br>6kWh/m0|n**I**<br>l|
|---|---|
|40%I1.97<br>1.61<br>1.33||
|50%I1.60<br>1.30<br>1.08||
|**I**60%I1.28<br>1.04<br>0.87|**I**|

Source: IT Power. Solar Powered Pumping Systems: Their Performance. Cost and Economics July 1986.
small effect of maternal education on whether the mother does any paid work at all (-1, 5 and 1 percentage points in the OLS, IV and biprobit specifications), but in all cases the effect is not significant. Third, in timeuse we do find and an increase in time allocated to paid-work in the IV specification (59 minutes more) but the estimate is imprecise and is not consistent with the negative correlation in the OLS regression. Taken across the three measures it is likely that this channel, where education affects paid work and time outside the house, which is so prominent in the discussion on developed countries, is missing in this low income country environment.

Given that education does not appear to increase mothers’ outside options, we should also not expect to see any changes in the decision making role for the mother who has some years of schooling. Our survey asks two questions about the mother’s decision making in their children’s education: whether the mother was principally responsible for the child’s enrollment decision and whether the mother was principally responsible for the choice of school. The effect of maternal education is uniformly small and insignificant in all three specifications (OLS, biprobit and IV) for both these questions. Finally, we are unable to detect a causal effect of mother’s education on enrollment or school choice even though there is a large positive effect of maternal education on enrollment and a large negative effect on enrollment in public schools in the OLS specification. This is consistent with Black and others (2005), Behrman and Rosenzweig (2002) and a detailed survey by Strauss and Thomas (1995) that points out the inconclusive nature of many studies relating child outcomes to maternal education.<sup>10</sup> Results on schooling expenditures also confirm that on the _intensive_ margin (allocations conditional on school enrollment) maternal education has little impact on child inputs other than the time allocations discussed previously. We estimate the impact of maternal education on disaggregated schooling expenditures (books and supplies and uniforms and shoes) and total educational expenditures (Table 6). For all three outcome variables we are unable to detect a causal impact of maternal education on child allocations. Finally, we examined whether the causal impact of maternal education on time allocations for educational needs

> 10 At the same time, we want to caution against reading too much into this result. Given the relatively small sample, the precision of our estimates does _not_ allow us to rule out that the OLS and IV coefficients are statistically the same, and therefore rule out the positive enrollment effects of maternal education.

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