rafmacalaba/datause-encoder-data huggingface dataset:
2

|**Contents**|**Page**|
|---|---|
|I. Introduction ......................................................................................|......................................3|
|II. How Is Diversification Measured? ..................................................|......................................4|
|III. Diversification, Growth and Volatility ..........................................|......................................5|
|IV. Patterns of Diversification .............................................................|......................................6|
|V. Patterns of Quality Upgrading ........................................................|......................................7|
|VI. Country Case Studies .....................................................................|......................................9|
|VII. Conclusions ..................................................................................|....................................10|
|Appendix I. Definitions of Main Indices .............................................|....................................20|
|A. Herfindahl Index .................................................................|....................................22|
|B. Theil Index ..........................................................................|....................................22|
|C. Product Quality ...................................................................|....................................23|
|Appendix II. Region Definition ...........................................................|....................................26|
|Appendix III. Case Studies ..................................................................|....................................27|
The panel structure of our data allows us to address the two empirical challenges described above by controlling for an array of time-varying omitted variables and relying on within household comparisons. First, we account for a wide range of city-wide and local time varying omitted variables through the inclusion of week and municipality-by-year fixed effects. Second, we include household fixed effects in our estimation which rules out bias through time-invariant omitted variables. Importantly, since our estimates are identified out of the within household time variation in air pollution, they are also free of sorting bias provided that changes in air pollution exposure over time are uncorrelated with household specific vulnerability (Wooldridge, 2005).

We find that, even at moderate concentrations, air pollution reduces labor supply. The effect is concentrated among households with dependents more susceptible to pollution, i.e., small children and elderly adults. The magnitude is economically significant. For instance, an increase in PM2 _._ 5 of 10 _μ_ g/m<sup>3</sup> is associated to a reduction of almost 2 hours worked per week. The effect of air pollution on individuals with susceptible household members appears linear. In contrast, the effect of pollution on labor supply of workers in households without susceptible individuals is non-linear, and only appears to respond to high levels of pollution (above 75 _μ_ g/m<sup>3</sup> ). This observation points to the extensive margin as a potential source of non-linearities in the relationship between air pollution and labor supply: as pollution levels increase, the effects on labor supply expands beyond households with susceptible individuals to the rest of the population.

These findings are consistent with evidence suggesting that children and elderly adults’ health is more susceptible to air pollution, and thus may be affected at lower concentrations than healthy adults (U.S. EPA, 2009, Ch. 8). Our results suggest that the mechanism linking low levels of air pollution to labor supply is the increase in demand for caregiving: healthy adults reduce hours of work to take care of sick dependents. However, at higher concentrations, the link is more direct: pollution directly harms the health of those who participate in the workforce. To corroborate these findings, we use auxiliary data from the Demographic and Health Survey (DHS) on health outcomes for the same city and period. We find that PM2 _._ 5 is associated with an increase in respiratory diseases among small children.

Finally, we examine whether households respond to this shock by re-allocating caregiving

> Muney, 2010; Graff-Zivin and Neidell, 2012). Other studies focus on NO _x_ (Deschenes et al., 2012), SO2 (Hanna and Oliva, 2015) and aerosols (Jayachandran, 2009). Among the few studies focusing on PM2 _._ 5 are Zweig et al. (2014), Chang et al. (2014), Adhvaryu et al. (2014), Lavy et al. (forthcoming) and Li et al. (2015).

3
**Figure 4 GDP  per Capita Growth in Past and Current Crises**

<!-- Start of picture text --> 8.0<br>6.0<br>4.0<br>2.0<br>World (3 crises)<br>0.0<br>World (2009)<br>World (2009) Projected<br>-2.0 LICs (3 crises)<br>LICs (2009)<br>LICs (2009) Projected<br>-4.0<br>-5 -4 -3 -2 -1 0 1 2 3 4 5<br>Growth rate<br><!-- End of picture text --> Source: World Bank (2010 a) Note: The figure plots the average per capita GDP growth in the world and in Low-income countries 5 years before and 5 years after the global crises (centered at zero on the horizontal axis) of 1975, 1982 and 1991, and the current crisis. Also shown in dashed lines are WEO projections until 2013.

# **The Changing Global Landscape**

The long term shifts in the global economic landscape, as well as the performance of developing countries in the aftermath of the crisis, have cemented the emergence of a multi-polar growth world. Developing countries contributed around 40 percent of global growth in the past decades, and in 2010 their projected contribution will approach 50 percent (see Figure 5). Because developing countries are growing faster, they are also increasing their share in global GDP. Whereas developing country GDP represented approximately 17 percent of global GDP in 1980, as of 2008 that share had increased to 29 percent, when measured at market exchange rates and close to 45 percent if purchasing power parity (PPP) weights are used. Those that are contributing the most to this new global economic landscape are the developing countries that are also members of the G-20; China, and to a lesser extent India, have been the main drivers of these shifts. In 1980, China accounted for 1 percent of global GDP. As of 2008, China‘s share had increased to 6 percent of world GDP (11 percent in PPP terms), accounting for a larger share 9
5 Unlike the PHL estimates from the Africa Post-harvest Loss Information System (APHLIS)<sup>8</sup> and FAO , which are based on national extrapolations from purposively sampled (and often older) in depth case studies of both on-farm and off-farm post-harvest loss, the estimates reported in this study are based on recent nationally-representative samples of self-reported (on-farm) post-harvest loss per household. This has a number of advantages. First, possible sample selection bias is avoided, given the nationally-representative nature of the sample. Second, harmonization in the survey methodology facilitates comparison across countries.

Third, while subjective and thus more prone to measurement error, self-reported loss estimates are also more likely to reveal the losses that farmers deem important. They are more likely to be incentive compatible and informationally relevant from a behavioral and operational perspective, than objective measures, which are in effect also hard to come by and also subject to multiple sources of measurement error and framing bias given non-harmonized definitions and measurement protocols.<sup>9</sup> Finally, even though most likely largely limited to on-farm losses, the use of household survey based estimates is still relevant in the African context, given that the bulk of food losses in SSA is concentrated on the farm.

Results are reported for three LSMS-ISA countries, Malawi, Uganda and Tanzania, where the systematic roll out of the survey data collection throughout the year enables the construction of _annual_ on-farm post-harvest loss estimates. These countries are also explicitly covered in APHLIS. For Tanzania, the analysis of the drivers of farmers’ post-harvest loss and the use of improved storage practices across different agro-ecological and market environments is further conducted. Finally, while farmers in LSMS-ISA surveys report the post-harvest loss for all their crops, the study here focuses on on-farm post-harvest grain loss, in particular this of maize. Grains continue to provide the bulk of (rural) households’ caloric intake in East and Southern Africa (where maize makes up 40

> 8 <u>www.aphlis.net</u>

> 9 In addition to practical considerations—continuous data collection efforts throughout the post-harvest season would be needed—there are also methodological challenges such as distinguishing between irreducible and avoidable losses. Some amount of spillage, such as discarded amounts that do not fit market or consumption standards and natural weight and volume shrinkage, is unavoidable. In an economic sense, it may not even be truly lost if used as animal feed.
# **1. INTRODUCTION**

Governments throughout Latin America and the Caribbean (LAC) are embracing policies for extending the school day. The motivation is twofold. First, most countries in the region have achieved universal primary education: In 2012, the primary completion rate was 95 percent (World Bank Group, 2015). Given that, countries are now exploring options for improving the quality of education, and – rightly or wrongly – view more time in school as a critical input to learning. Second, keeping children in school into the afternoon has great political appeal, both because it provides child care for more of the day and because school lunch programs can serve as a social safety net. What is more, demographic shifts in many LAC countries facilitate this transition from partial school days to full-time school, as fewer primary school aged children mean less need for double-shift schools. Indeed, across the region, the student population age 4-18 is expected to drop 8 percent between 2010 and 2025 (Bruns & Luque, 2015).

To inform the policy dialogue regarding the extension of the school day, this paper brings together a diverse array of impact evaluation evidence on academic, labor, and social outcomes from 19 studies across six countries. It also uses examples from around the region to characterize heterogeneity in extended school day programs. Finally, it provides relative cost effectiveness estimates of extending the school day. Specifically, we seek to answer three main research questions:

1. What is the impact of extending the school day on student academic outcomes?

2. What is the impact of extending the school day on social and labor outcomes?

3. What is the relative cost effectiveness of extending the school day vis-à-vis other proven interventions?

Across studies, the effect on student learning has been the outcome most widely measured, both because it is the top policy priority for ministries of education and – endogenously – because many countries have the systems in place to quantify this impact relatively easily. Other effects on schooling variables, such as repetition and dropout rates, also feature prominently. Less frequently measured but still of interest is the increase in the labor market participation rate of parents (particularly mothers), as this increase in productivity is one of the justifications for adding hours of instruction to the school day. The same is true for the impact of extending the school day on 2
Table 4 shows a slightly higher number on DUTYMOM for Vietnamese parents compared to Dev7, but a greater difference, more than ten percentage points for TIGERMOM. VOLUMOM refers to parents volunteering in various non-academic activities, such as field trips or carpentry and yard work. Vietnamese parents appear to have a slight advantage with regard to VOLUMOM, yet a much higher one when considering TEACHMOM, which refers to parents volunteering as assistants to the teacher - 38.28% compared to 12.18% for Dev7. Vietnamese parents also appear to be much more active in fund raising, looking at FUNDMOM, though they may have less formal influence through school committees.

# **2.6 Teacher Characteristics**

Conventional measures regarding student-teacher ratios and teacher certification show some advantage for Vietnam over Dev7 as shown in Table 6.

**Table 6: Teacher characteristics and management**

|||Dev7 cou|ntries|Vietn|am|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|**Teacher number**|**s and teacher management**|||||
|PROPCERT|Proportion of<br>certifed teacher|0.6757<br>(0.4042)|35130|0.7961<br>(0.3978)|4586|
|SMRATIO|Mathematics<br>teacher-student ratio|188.1791<br>(158.6256)|33985|120.9773<br>(43.6092)|4777|
|SC35Q02|Professional development<br>in math in last 3 months|40.5068<br>(40.8546)|39550|49.0086<br>(45.1706)|4762|
|STUDREL _(r)_|Teacher student<br>relations|0.3794<br>(1.0178)|25870|0.0186<br>(0.8883)|3253|
|TCH<br>~~I~~NCENTV|Teacher appraisal<br>linked to incentives|-0.0317<br>(1.0301)|41394|0.2687<br>(0.6336)|4882|
|**Quality assuranc**|**e of mathematics teachers **|**through . .**|**.**|||
|TCH<br>~~M~~ENT|Teacher mentoring<br>as quality assurance|0.8566<br>(0.3505)|40734|0.9859<br>(0.1181)|4882|
|TCM<br>~~P~~EER|Teacher peer review<br>of lectures, methods etc|0.7916<br>(0.4061)|41095|0.8382<br>(0.3683)|4882|
|TCM<br>~~O~~BSER|Principal or senior<br>staf observations|0.8015<br>(0.3989)|41170|0.9785<br>(0.1451)|4882|
|TCM<br>~~I~~NSPE|Observation of classes|0.5882|41020|0.8664|4882|
||external inspector|(0.4922)||(0.3402)||

Notes: The variables relate to the questionnaires administered to schools and students in the rotated booklet, marked with _(r)_ . For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically different at the 95% significance level. Figures in parenthesis represent standard deviations.

The overall student-teacher ratio is not much different for Vietnam and Dev7 and stands at roughly 20 students per teacher. However, there are more specialized mathematics teachers per student in Vietnam, as shown by the values for SMRATIO (121 in Vietnam compared to 188 for Dev7). There is a higher percentage of certified teachers in Vietnam and higher 13
# **Biogas: Clean Energy Access with Low‐Cost Mitigation of Climate Change**

E. Somanathan<sup>1</sup> and Randall Bluffstone<sup>2</sup> Keywords: climate change. fuelwood. biogas. carbon credits. JEL Classifications: _D12, O13, Q41, Q54, Q56_ 1 Economics and Planning Unit, Indian Statistical Institute, 7 SJS Sansanwal Marg, New Delhi 110016, India. E‐mail: som@isid.ac.in 2 Department of Economics, Portland State University, Portland OR, USA.
The next two panels of Table 2 give the percentages below the line and numbers of poor. Through most of the 1990s, about half of the population of the developing world was relatively poor. The proportion fell over time, from 63% in 1981 to 53% in 1990 and 47% in 2005. But the decline was not continual; the aggregate incidence of relative poverty rose slightly in both the late 1980s and late 1990s. The trend rate of decline over the period as a whole is -0.56 percentage points per year (with a standard error of 0.10). Projecting this trend rate of decline over 1981-2005 forward to 2015, the proportion living in relative poverty would be 40.5% (standard error=2.4%).

The trend decline in the incidence of relative poverty has not been sufficient to reduce the number of poor by this measure, which rose from 2.3 billion to 2.6 billion over 1981-2005 (Table 2). The turning point appears to be around 1987.

Figure 3 shows the simultaneous rise in relative poverty and fall in absolute poverty. As one would expect, the proportion of the relatively poor that are also absolutely poor has fallen over time, given economic growth. In 1981, 82% of the relatively poor were absolutely poor; by 2005 the proportion had fallen to 53%.

South Asia saw the largest absolute increase in the number of relatively poor. East Asia experienced a falling count of both the absolutely poor and the relatively poor (though with a more rapid pace of progress against absolute poverty). Comparing Tables 1 and 2, we see changes in the regional profile of poverty, although it is notable that the two regions with the highest incidence of absolute poverty also have the highest relative poverty rate. In 2005, SubSaharan Africa (SSA) had the highest incidence of absolute poverty, with South Asia in second place (Table 1), but South Asia emerges as the region with the highest incidence of relative poverty (Table 2), with SSA second. Latin America and the Caribbean (LAC) had the third highest relative poverty incidence, but came fourth in absolute poverty. As expected, the share of global poverty in LAC rises from 3.3% to 9.6%. The largest decline in share is for SSA, which falls from 28.4% to 16.4%; South Asia’s share falls from 43.3% to 36.1%.

Also comparing Tables 1 and 2, we find that the aggregate headcount index of relative poverty for 2005 is 1.88 times the aggregate index of absolute poverty; in 2002, the ratio was 1.63. It is of interest to compare these numbers to the corresponding ratios from Chen and Ravallion (2004), using their parameterization of the Atkinson-Bourguignon relative poverty lines. For the latest year in the Chen-Ravallion series (2001) the aggregate measure of relative 17
**INCENTIVE INDICATORS**

# **NOMINAL PROTECTION, 1965-94**

The estimates of nominal protection and the other incentive indicators are based on the methods used and the estimates for the period 1981 -89, in the research project study of sugarcane and sugar by Goldar and Gulati (1991). This is supplemented by the findings of a later research project study focusing on gur and khandsari by Kalra and Gulati (1992). In the first section, we first comment on the nominal protection estimates for sugar and molasses, both of which are internationally traded, so that references prices can be estimated from their international prices. The section on sugar also provides separate estimates of nominal protection to khandsari. The second section deals with the nominal protection to sugar cane. Sugarcane is not traded internationally, and in addition a large part of the production is used to produce gur, which is internationally traded but in negligible quantities relative to Indian production. As regards the cane that is supplied to the sugar mills, the extent to which it is protected or disprotected is the indirect combined result of the protection or disprotection of sugar and molasses. In order to estimate a reference price for sugarcane, it is therefore necessary to estimate what the sugar cane price would have been at the same exchange rate with free trade in sugar and molasses, and also with free trade in the principal tradeable inputs which affect the margin required to transform cane into the tradable products. As regards the cane that is used to produce gur and khandsari, the domestic markets are segmented and the prices paid for the cane differ substantially from the prices paid by the sugar mills, and so we have made separate NPC estimates which reflect the differences between these domestic prices and the reference prices of the cane consumed by the mills.

## **_Nominal protection: mill sugar_**

# Methods

Following the Goldar and Gulati study, we have made estimates for the three states with the largest sugar production: UP, Maharashtra, and TN. Between them, they accounted for about 70 percent of national sugar production in the early 1990s, and represent diverse farming and other conditions, including private and parastatal mills in UP buying from smallholder farmers and competing under severe handicaps with unregulated khandsari units, mills in Maharashtra which are owned by cooperatives of the sugar farmers who supply them with cane, and a number of private mills in TN which are to a large extent supplied from their own estates. The price comparisons for the three states respectively use wholesale prices in Kanpur, Bombay and Madras, in each of which sugar is traded in large quantities. Marketing costs, trading margins, and transport costs have also been estimated for each of these three markets. Since sugar has been imported in some years and exported in others, NPCs have been estimated both under the hypotheses that the commodity is an import substitute, and on the hypothesis that it is an exportable.. The price comparisons are averages for the Indian "sugar year", covering the period 29
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18
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