rafmacalaba/datause-encoder-data huggingface dataset:
teaching and during which there is not a “large group” or “all” students—which per the Stallings manual is defined as 6 or more students—that are off-task, namely being “disciplined by the teacher,” who are involved in “social interaction” or who are “uninvolved;” we call this Share of Time Teaching and Learning. The instrument also records the availability of materials in the classroom, infrastructure and the classroom environment, from which we create two aggregates: Availability of Materials and Classroom Infrastructure (again, which we shorten to “Materials and Infrastructure”), and Classroom Environment (see Box 3; more details are in Appendix 1 Table 2).

|**Box 3: Summary of variables derive**|**d from Stallings observation system**|
|---|---|
|**Time-on-task (Level 1)**<br>||
|Share of Time Teaching|Share of 10 snapshot observations in which teacher is engaged in<br>teaching activity.|
|Share of Time Teaching and Learning|Share of 10 snapshot observations in which teacher is engaged in<br>teaching activity and in which there are not 6 or more students who<br>are off-task.|
|**Teacher practices and classroom enviro**|**nment (Level 1)**|
|Classroom Environment|Average of 2 0/1 variables capturing whether pupil work or other<br>materials are displayed on the walls.|
|Availability of materials and Classroom<br>Infrastructure|Average of 5 0/1 variables capturing whether the classroom has a<br>blackboard and chalk; whether there are reading books in the<br>classroom; whether the classroom has electricity; whether students<br>have textbooks/other printed material; or whether students have a<br>notebook/writing material.|
|Source: Authors and World Bank (2015)||

# **_Classroom Assessment Scoring System_**

The CLASS system involves trained raters observing a video clip and subsequently coding the totality of what they observed along 11 dimensions (these include, for example, “positive climate,” “behavior management,” and “analysis and inquiry”). Scores are given on a 7-point scale ranging from low (=1) to high (=7). The CLASS manual provides detailed descriptions and examples of behaviors that fit each of these scores, and to be certified each rater had to pass an exam that requires them to be within 1 point of three expert-scored videos at least 80 percent of the time. In our case, certification also required “recalibration” after every 2 weeks or 20 segments (whichever came first). This process involved coding an additional video where raters had to be within 1 point of another expert-scored video at least 80 percent of the time.<sup>14</sup> The scores on these 11 dimensions are then aggregated into three domains: Emotional Support, Classroom Organization, Instructional Support (see Box 4; more details are in Appendix 1 Table 3). The score for an additional domain, Student Engagement (which cuts across the other dimensions) is derived separately based on how students behave during the observation.

14 For this exercise, native Kiswahili speakers were trained in Washington, DC USA using the CLASS protocol. All the raters were certified as CLASS raters. Videos were coded in Washington, DC, USA.

10
_Panel b. Poverty Rates by CEQ Income Concepts using the AROP Poverty Line (equivalent to US$20.5 2017 PPP per day in 2020)._ <!-- Start of picture text --> Direct Taxes and<br>Transfers<br>31<br>29<br>29.1<br>27<br>25<br>25.3<br>23<br>21 22.2<br>19 Indirect Taxes<br>and subsidies<br>17<br>15<br>Market Income Pensions Disposable Income Consumable Income<br>2020 2018<br>Percent<br><!-- End of picture text --> _Source:_ Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology.

**Overall, Bulgaria's fiscal system leads to a decline in inequality, with direct transfers, education, and health transfers still making the most significant difference.** Bulgaria's fiscal policy in 2020 contributes to reducing market income inequality. The Bulgarian fiscal system has shown greater capacity to reduce inequality compared to countries with similar income levels (Annex 2, figure A.1 panel a). When considering all taxes and transfers (direct taxes and transfers, subsidies, indirect taxes, and the monetized value of health and education benefits), the Gini coefficient declines from 0.454 to 0.322—13 Gini points. Excluding the monetized value of education and health services, the improvement in inequality is still significant, with the Gini falling from 0.454 to 0.385, i.e., 7 Gini points.

**The cumulative impact of direct transfers, direct taxes, and social contributions (excluding pensions) results in a 5-point reduction in the Gini coefficient when comparing market income plus pensions with disposable income** . Most of the impact comes from direct transfers, especially child benefits (noncontributory and means-tested), unemployment insurance, disability benefits, and other noncontributory and means-tested transfers. The direct tax effect is non-significant in terms of inequality reduction. Also, subsidies and indirect taxes reduce the Gini coefficient from disposable to consumable income by 1.9 Gini points, with an individual positive effect of VAT. The most considerable effect comes from in-kind transfers (health and education) that help reduce inequality by 6.3 Gini points (See Figure 4 panel a). Specifically, the most significant effects come from health and upper-secondary education. Similar results were obtained in the 2018 CEQ.

**However, the redistributive impact of direct taxes and transfers continues to be among the lowest in Europe.** Overall, Bulgaria's redistributive impact through direct taxes and transfers ranks among the lowest in Europe. With results second only to Hungary, Bulgaria falls within the group of countries achieving minimal redistribution through direct taxes and transfers, excluding contributory pensions. The

21
19 assumed that the national accounts are accuratt, their implied growth rate of about 0.5% per year is more plausible.<sup>Assessimiints</sup> of growth during the 1980s consistent with national accounts data<sup>**(only**using the household surveys to</sup> measure relative inequalities) suggest<sup>that the proportion of the population</sup> deemed to be poor has remaired fairly stable<sup>over recent years, while absolute</sup> numbers or poor have increased.

Per capita growth rates in Bangladesh<sup>have been below the average for</sup> South and South-East Asia in the 1980s,<sup>and few observers expect this to</sup> change in the 1990s. Furthermore, the growth<sup>rates needed to prevent an</sup> increase in the absolute numbers of poor<sup>in Bangladesh, or to attain any given</sup> rate of poverty reduction, are higher<sup>than similar calculaticns have suggested</sup> would be needed for some other low-income<sup>countries in Asia. At a widely</sup> assumed poverty line for Bangladesh,<sup>the growth rate of real consumption per</sup> capita has to be at least equal to<sup>the rate of population growth before the</sup> absolute numbe of the poor can start to fall appreciably<sup>without a shift in</sup> relative inequaiities. Such a growth rate<sup>has not been achieved in recent</sup> times, but it is expected over the next<sup>ten years or so by some observers. By</sup> contrast, India and Indonesia will only<sup>require a per capita growth rate of at</sup> least half the rate of population growth<sup>to further reduce the numbers of poor</sup> assessed bv local poverty lines; both countries<sup>have done a good deal better</sup> than this over the last 10 years, and<sup>that is expected to continue. Recent</sup> growth in Bangladesh has thus been relatively<sup>low in a country where it needs</sup> to be relatively high to avoid an increase<sup>in the number of poor.</sup> Certain changes in relative inequalities<sup>could, in principle, wipe out</sup> povertv alleviation through growth. It appears<sup>that a fairly substantial</sup> distributional change would be needed<sup>to do so for the simple headcount index</sup>
# **1. Introduction**

The financing of small and medium enterprises (SMEs) has attracted much attention in recent years and has become an important topic for economists and policymakers working on financial and economic development. This interest is driven in part by the fact that SMEs account for the majority of firms in an economy and a significant share of employment (Hallberg 2001).<sup>1</sup> Furthermore, most large companies usually start as small enterprises, so the ability of SMEs to develop and invest becomes crucial to any economy wishing to prosper.<sup>2</sup> The recent attention on SME financing also comes from the perception among academics and policymakers that SMEs lack appropriate financing and need to receive special assistance, such as government programs that increase lending. Various studies support this perception. A number of papers find that SMEs are more financially constrained than large firms.<sup>3</sup> For example, using data from 10,000 firms in 80 countries, Beck, Demirgüç-Kunt, Laeven, and Maksimovic (2006) show that the probability that a firm rates financing as a major obstacle is 39% for small firms, 38% for medium-size firms, and 29% for large firms. Furthermore, small firms finance, on average, 13 percentage points less of their investments with external finance when compared to large firms.<sup>4</sup> Importantly, lack of access to external finance is a key obstacle to firm growth, especially for SMEs (Beck, Demirgüç-Kunt, and Maksimovic 2005). On the policy side, there are a large number of initiatives across countries to foster SME financing including

> 1 According to data collected by Ayyagari, Beck, and Demirgüç-Kunt (2007) for 76 developed and developing countries, SMEs, on average, account for over 50% of manufacturing employment. 2 See, for example, Beck and Demirgüç-Kunt (2006) and references therein.

> 3 See, for example, Schiffer and Weder (2001), IADB (2004), Beck, Demirgüç-Kunt, and Maksimovic (2005), and Beck, Demirgüç-Kunt, Laeven, and Maksimovic (2006). See Cressy (2002) for a review of the literature on funding gaps.

> 4 See Beck, Demirgüç-Kunt, and Maksimovic (2008).

1
The equation is estimated using the ordinary least squares (OLS) method with robust Huber-White standard errors. All standard errors are clustered by country. We also provide estimation results separately for small and large firms (split sample estimation). Unless stated otherwise, the data source for all the variables discussed in the remainder of the paper is ES.

# 3.3 _Dependent variable_

In separate regressions, the dependent variable is the level of labor productivity and its annual growth rate. Labor productivity is widely used in the literature dealing with firm performance and efficiency, in part due to data availability issues (see, for example, Frazer and Van Biesebroeck 2019, Islam et al. 2019).<sup>2</sup> The level of labor productivity is defined as the log of the total annual sales of the firm (deflated and in 2009 USD) during the last fiscal year prior to the year ES was conducted divided by the total number of workers employed at the firm at the end of the last fiscal year ( _Labor Productivity_ ). In our baseline sample, the mean value of labor productivity (in logs) equals 10.01, and the standard deviation is 1.67.

The growth rate of labor productivity equals annual sales per worker (in 2009 USD) in the final year (last fiscal year prior to the year the ES was administered in the country) minus the same in the initial year (3 fiscal years ago prior to the year the ES was administered in the country) and divided by the average value of sales per worker in the initial and final years (Haltiwanger growth measure). This ratio is divided by the number of years between the final and initial years and multiplied by 100 to arrive at the average annual percentage change in labor productivity ( _Growth Rate of Labor Productivity_ ). By construction, the growth measure has the advantage that it is bound

> 2 In the ES, information on capital stock is missing for several firms. This prevents us from using capital stock in the regressions or using total factor productivity (TFP) instead of labor productivity.

15
returns to education for girls reflect women’s comparative advantage in skill-intensive occupations as opposed to brawn-intensive occupations and structural change in the economy in favor skill-intensive occupations. However, the evidence also consistently shows that there is a substantial gender wage-gap against the women due to the fact that _Y_ 0<sup>_cd_</sup> _< Y_ 0<sup>_cs_</sup> in most of the cases. It is well-known in the literature that return to education plays a prominent role in intergenerational educational persistence (Becker (1991), Solon (1999), Solon (2004)). As we will see below, in contrast, the intercept _Y_ 0<sup>_cj_</sup> matters much less.<sup>14</sup> For the main analysis of the mechanisms underlying intergenerational persistence in schooling, we assume that the parents are aware of the fact that the labor market returns are higher for girls. We are not aware of any studies on eliciting parental belief about returns to education for sons vs. daughters in our study countries.<sup>15</sup> In fact, data on what parents state (which may differ from the true belief) when asked about relative returns to education in a survey are also not available in the major data sets we are aware of in India (such as NSS, IHDS, REDS, NHSF) and China (CHIP, CFPS, CHNS). The only data set we are aware of is the Gansu Survey of Children and Families in China. An analysis of the Gansu survey shows that about 55 percent of mothers disagree that returns to education (in terms of higher income) are higher for sons. We also explore the implications of the alternative assumption where parents believe that the returns to education are lower for girls notwithstanding the evidence noted above. This alternative assumption, however, leads to implausible conclusions, as we discuss below in the context of urban India in section (4.1). The consumption sub-utility function of the parent is given by: available evidence on rural and urban areas below which is consistent with the national-level results of Bargain et al. (2009).

> 14This refers to educational mobility; the wage gap clearly has important implications for income and poverty.

> 15In a widely-cited study, Jensen (2010) shows that parents in the Dominican Republic underestimate the returns to education, but he does not analyze possible gender differences in parental belief. In a related study in the context of India, Jensen (2012) finds that women’s education responds positively to labor market opportunities, providing a basis for the argument that labor market returns are important for parental decisions regarding women’s education in India.

9
to estimate the source size of the hot object. Radiant heat (measured in MW) is then calculated with temperature and source size using the Stefan-Boltzmann law.

In case of single-spectral infrared detection, Planck curve fitting is thus no longer feasible; we instead set temperature at 1810K, which is the temperature of an object with peak radiant emissions at the M10 wavelength. Alternative temperature assignment rules are considered (see Figure A.11 and corresponding discussion in Annex section A.4.5).

Finally, if we do not have infrared detection and have yet determined that the site is flaring (state _S_ 1), we measure DNB radiance and, as above, set temperature to 1810K to apply the Stefan-Boltzmann law and derive the associated RH. Note however that in such instance, the observed radiance aggregates radiance from gas flaring and any other source of lights not filtered out by the SMI such as facility or automotive electric lighting; the resulting measure is then an overestimate of RH from gas flaring.

When a site is deemed inactive (state _S_ 3), a value of _RH_ = 0 is then assigned for each day of these 7-day periods.

Finally, when a state of production with venting is identified (state _S_ 2), we make the assumption that the site is producing at a level equivalent to the median of its historical production since being taken over by Daesh. If such level is equal to zero or is not defined because there was no detected infrared signal, we instead set venting production at the median pre-war level: we assign the median RH of all measured RH prior to July 2012.

# **A.3 Identifying oil assets and assigning control A.3.1 Identifying flaring sites**

All nighttime VIIRS data over Syria and Iraq, spanning March 2012 through November 2016, were processed with the VIIRS nightfire algorithms [14]. The detections were ingested into a spatial database for analysis. Sites were included when (i) one multispectral band detection 28
not included because they are possibly a ff ected by migration.<sup>9</sup> In contrast, age, education, and caste status can be regarded as exogenous to the migration decisions of adult males. Equation (4) is estimated using correct sampling weights.<sup>10</sup> Regression estimates for equation (4) are summarized in Table 2 where we show α as well � as the average and standard error of δ s, β s and χ s. The coe ffi cients<sup>�</sup> δ i and η i are large and jointly signi fi cant. There is considerable variation across districts not only in average log income and consumption but also in the income or consumption premia associated with education and high caste. These results are used to construct, for each of the 16,000 or so work migrants in the census, a measure of the income or consumption they can expect to achieve in each of the possible destination districts. Formally, this measure is calculated as: where Es<sup>handH</sup> s<sup>haretheeducationandhighcastedummyformigranth.</sup> Age is ignored from the calculation since work migrants typically migrate around the same age, i.e., in early adulthood.

Formula (5) can be decomposed into two parts:<sup>�</sup> δ s, which measures the average income level � in district s, and �η sz<sup>h</sup> ≡ β<sup>�</sup> s(Es<sup>h−</sup> Es)+ χ s(Hs<sup>h−</sup> Hs) which captures individual-speci fi c variation in income. Migration models predict that, other things being equal, the choice of migration � destination should depend on E[ys<sup>h</sup> |z<sup>h</sup> ]. This means that if we regress the choice of destination separately on<sup>�</sup> δ s and �η sz<sup>h</sup> , they should have the same coe ffi cient.

The same methodology is used to construct other variables that may a ff ect the choice of

> 9 The literature has often emphasized that migrations often serve an important role in household formation. For migrants, the prospect of forming a large, successful household is likely to be one of the purposes of migration.

> 10 The 1995/96 NLSS survey adopted the following sampling strategy. Within each district a small number of wards were selected at random. Within each ward, 12 randomly selected households were interviewed. Because the wards di ff er widely in terms of population, applying sampling weights is essential in order to obtain consistent estimates of δ s.

16
of the direction and possible magnitude of the poverty effects from climate change in Mexico, rather than actual forecasts.

The rest of this paper proceeds as follows. Section 2 introduces key definitions and data sources applied. Section 3 presents the empirical methodology followed by a discussion of the results in Section 4. Section 5 concludes.

# **2 Data**

This study is carried out in 2,069 municipalities from all states in Mexico, representing 84% of the total number of municipalities in the country. Missing municipalities are largely from Oaxaca and Puebla where climate model projections could not be fitted into smaller municipalities. Both states display pockets of high poverty (see Figure 3). However, their highly fragmented political geography, especially Oaxaca, it made unfeasible to analyze climate data (available at a resolution of 50 x 50 km approx) in some small municipalities.

The analysis uses five types of information: (i) income and geographic data, (ii) climate and weather data, (iii) poverty rates, (iv) climate change scenarios, and (v) population and output (GDP) projections. Per capita GDP and geographic controls come from the National Institute of Statistics and Geography (INEGI). Daily precipitation in millimeters and temperature come from meteorological stations and the National Weather Service (Servicio MeteorolÃşgico Nacional - SMN). Historical Climate data were aggregated at the municipality level from a gridded historical dataset derived from observational data produced by the Climatic Research Unit (CRU) of University of East Anglia (UEA). These datasets were accessed through the World Bank Climate Change Knowledge Portal (CCKP)<sup>5</sup> .The CRU TS 2.1 Global Climate Dataset is comprised of 1,224 monthly time series of climate variables, including temperature and precipitation, for the period 1901-2009, and covering the global land surface, excluding Antarctica, at 0.5 degrees resolution. Poverty rates were obtained through small area estimation techniques using data from the 2000 Census on Population and Housing and the Count of Population and Housing 2005. Population projections come from the National Population Council (CONAPO). All data are available at the municipal (county) level (See Annex 1 for summary statistics).

> 5http://climateknowledgeportal.wordlbank.org.

5
2 Many economists have written on the effects of demography on saving. Most commonly the empirical studies look at dependency ratios -- the share of the population under 15 or over 65 -- as one of the right-side variables in a regression with the aggregate saving rate on the left. The results vary according to the sample and the choice of other explanatory variables, but generally higher dependency ratios significantly lower saving rates (Leff 1969, 1971, 1980; Mason 1987; Mason and Fry 1982; Lahiri 1988; Rossi 1989; see Hammer 1986 for a survey). This empirical result is fully consistent with the model and simulations that follow.

The contributiun of this study is to employ an explicit model of lifecycle saving to examine the effects of actual and predicted changes in demographic structure for particular economies. Explicit life-cycle models have been used to predict saving rates analytically, which has required the researcher to assume steady-state populations (Tobin 1967; Hammer 1987). Only with the numerical simulation procedures used here is it feasible to examine in detail the effects of the great deviations from steady-state that occur during demographic transitions. In the simulations shown below, the greatest fluctuations of saving rates occur when the population systems deviate farthest from steady-state.

# Demographic Proiections

Demographic forecasters predict hopefully that by the middle of the next century all the developing countries will have begun phase two of their demographic transitions, in which net reproductive rates (NRR) fall to 1. (The NRR is the number of female children per woman that survive to childbearing age.) Eventually, this will cause aggregate birth rates to