rafmacalaba/data-use-mentions-extended huggingface dataset:
0|1|118,669|
|Household size|2.96|1.49|1|21|118,669|
|Urban|0.74|0.43|0|1|118,669|

_Notes:_ The average daily maximum temperature, and average precipitation and average pollution during the month leading to the interview are used. The sample has information from 4 DHS rounds for Cambodia: 2000, 2005, 2010, and 2014 Our dataset spans four DHS rounds conducted in Cambodia from 2000 to 2014, enabling a comprehensive exploration of the intricate relationships between these variables and health outcomes.

# **3 Empirical Strategy**

Our regression model investigates the relationship between environmental hazards and child health in Cambodia, focusing on diarrhea and cough incidence. To that end, we create temperature, precipitation, and pollution bins using each month’s average maximum temperature, average precipitation, and average pollution. This binned specification is standard in the environmental economics literature (as reviewed in Deschenes, 2014; Dell et al., 2014). A binned specification allows us to flexibly assess the hazard-morbidity link, accounting 7
22 DASGUPTA AND GROVER petition that non-primary regions provide to domestic industries, thereby making them relatively less attractive. This hypothesis is borne out in the Indian data at the aggregate level during 2000-09 (Stylized Fact 2) and in the evidence provided in Table 1, which shows a relative decline in output, employment and plant count in the Secondary region during 2000-09. Unlike in the model, however, internal trade costs did not fall uniformly but rather for a subset of districts across all the regions that got connected along the GQ network. This can lead to re-allocation _within_ each region as districts that experience a lower internal trade cost due to two opposing forces: while districts along the highway network are not protected from import competition,they benefit from increased market access due to better connectivity. The overall outcome on reallocation within (and across) regions depends on the net of the two effects.

We define districts “on GQ” as those that are within the 10 kilometer range of the highway network.<sup>24</sup> Given that a large part of GQ passes along the coast, it is not surprising that 38 of 68 “on GQ” districts are in the primary region. Following the empirical specification in the previous section, we additionally introduce a third dummy variable, GQ status, and include the full set of double interaction terms and the triple interaction terms of the form Period _×_ Region _×_ GQ status. As before, we allow the coefficients to be region, period and GQ-status specific. Specifically, we run the following regression: where _αg_ is a dummy variable that takes a value of one if the district is on GQ.

# **5.2 Results**

Our estimations in Table 3 suggests that following key points: _First_ , prior to GQ upgrades, economic activity in secondary region did not vary by their location on a potential highway network. Said differently, there were no pre-trends in economic activity within the secondary region. While output, employment and plant
countries such as Brazil, Mexico, China and Russia and there were only a few documents on the subject from the Middle East North Africa or South Asia. It is unclear whether the lack of literature in these regions is due to lack of importance of SPSPs or a result of low priority accorded by sector practitioners.

# **3.2  Summary of Literature Review:  Electricity SPSPs**

For electricity, literature from 32 countries with SPSPs was reviewed, with a focus on dealers of solar panels and other household electricity generating equipment as well as networked services.<sup>9</sup> The level of SPSP activity is high in 15 of these countries, primarily in rural areas and small settlements. Data on micro-power systems show that the total number of such systems in developing countries has more than doubled over the past decade. This gradual expansion took place over a period in which public investment in power was declining and private funding for large-scale investments was inconsistent. An estimated 10-50 million clients are served by the 7,000 SPSPs currently operating in the electricity sector<sup>10</sup> . About 85 percent of these are in Asia. In Africa, the Middle East and Latin America the number of SPSPs identified in each region was less than 500. In Latin America and the Caribbean, SPSP activity, particularly in large countries such as Brazil, was focused largely in remote regions of the country and provided only a small fraction of electricity service. In Africa, SPSP activity was largely limited by access to financial markets, high transaction costs, and the monopolistic rights granted to national utilities. That said, a few post-conflict countries such as Mozambique demonstrated a notable incidence of SPSPs. The maps in Annex 1 shows countries which were found to have significant levels of SPSP activity in water supply and electricity according to available literature.

> 9 For the purposes of this study, literature on battery recharging businesses, categorized as “point source” systems in the typology, was not reviewed.  This level
the Democratic Republic of Congo and Burundi (National Bank of Rwanda 2013).

In sum, the contribution of newly registered companies to the increase in exports would appear to be significant, particularly in non‐commodities. The absolute magnitude of this contribution is an area for further study.

> 15 Relying on (Gathani & Steolinga, Understanding Rwanda's Agribusiness and Manufacturing Sectors, 2012) and (Gathani & Stoelinga, Understanding Rwanda's Export Sector, 2012). figures cited in this section refer to cleaned customs figures for the year 2010.

29
%|27.6%|35.7%|40.5%|
|**Commercial/public**<br>|**Sector**||||
|Total GHG emissions from cooling and street lighting<br>(milliontons)|0.083|0.087|0.091|0.094|
|GHG mitigation from the options considered in this study<br>(milliontons)|0.019|0.024|0.031|0.036|
|% reduction of GHG emissions due to the options<br>consideredinthis study|22.5%|28.1%|33.7%|37.9%|
|**(b) Georgia**<br>**Residential Sec**<br>|<br>**tor**||||
|Total GHG emissions from heating, lighting, refrigeration,<br>TV and washing machine use (million tons)|1.56|2.19|2.9|3.58|
|GHG mitigation from the options considered in this study<br>(million tons)|0.09|0.19|0.35|0.52|
|% reduction of GHG emissions due to the options<br>|5.7%|8.9%|11.9%|14.6%|
|considered in this study|||||
|**Commercial/public**<br>|**Sector**||||
|Total GHG emissions from heating, lighting and street<br>lighting (million tons)|0.24|0.31|0.39|0.45|
|GHG mitigation from the options considered in this study<br>(million tons)|0.04|0.08|0.14|0.20|
|% reduction
KR‐I than other regions across the entire distribution. For instance, the average impact on a household living in an IS‐affected governorate would have been more than double that of the total country (‐27 % vs. ‐13%) and in KR‐I this would have been 5 percentage points higher ‐i.e. ‐18%.

# **Incidence of losses and income mobility**

The IICs provide information about consumption gains or losses incurred by the average household within each percentile of the consumption distribution, and are thus useful in identifying which consumption groups experience relatively larger or smaller changes. However, they can hide a significant amount of heterogeneity in the absolute size of impacts, even among households with very similar baseline or initial per capita household consumption levels.

An alternative is to examine the size of the income change experienced by the households in each consumption group. We do this using matrices constructed by deciles of per capita consumption, keeping the upper and lower limits of each decile fixed at the business as usual consumption levels. In other words, we examine how many households within relative narrowly‐defined groups (i.e. business as usual per capita household consumption deciles) experience a consumption gain or loss between business as usual and crises scenario in 2014 large enough to push them up or down by one consumption decile or more, where consumption deciles are defined on the basis of the business as usual distribution.

19
teachers’ classroom practice; that is, how teachers are actually trained and which specific components of this training effectively improve teacher behavior and subsequently student learning. This is true despite the fact that the evidence we have suggests that there is much more variation in effectiveness across teacher training programs than across education programs more broadly (Evans and Popova, 2016; McEwan, 2015); in other words, teacher training programs vary enormously, both in their form and in their effectiveness.

This paper has two objectives. The first is to fill this gap in information on the essential characteristics of training programs by proposing a survey instrument – the In-Service Teacher Training Survey Instrument (ITTSI) – to document the design and implementation details of inservice teacher training programs. The instrument was piloted on a sample of programs from lowand middle-income countries whose impact has already been evaluated, and the resulting data analyzed using a combination of quantitative and qualitative methods. The second objective is to use those data to characterize the current evidence on in-service teacher training in low- and middle-income countries.

Findings suggest that characteristics positively associated with program impact on student learning include the provision of textbooks and other reading materials alongside the training, as well as linking participation to incentives such as promotion or salary implications, and the training having a specific subject focus, among others. Meanwhile program implementers themselves most commonly mention the provision of mentoring follow-up visits, engaging teachers for their opinions and ideas, and designing programs in response to local context as responsible for positive impacts on student learning.

This paper proceeds as follows. Section 2 summarizes a sample of the theoretical literature on in-service teacher training and provides insights from impact evaluations of programs in highincome countries. Section 3 describes the methodological approach, including the search strategy, 4
# Figure 4 **Learning: Transactions without Assistance**

<!-- Start of picture text --> p<0.001<br>71% 13%<br>N = 1,461 N = 378<br>Bank or mobile payroll Bank or mobile account<br>.9<br>.6<br>.3<br>0<br><!-- End of picture text --> Notes: The figure displays the share of respondents that made at least one transaction at a banking or mobile money facility outside the study location by treatment, measured in administrative data at endline. Error bars show 95% confidence intervals of the mean. Horizontal bars show p-values for _t_ -tests of equality between the treatment conditions.

36
Figure B.1: Missing tender identifiers <!-- Start of picture text --> CE MG<br>5<br>0.04<br>4<br>0.02<br>3<br>0.00<br>2<br>0.02<br>1<br>0.04<br>0<br>2009 2011 2013 2015 2017 2019 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>PB PR<br>0.04 0.04<br>0.02 0.02<br>0.00 0.00<br>0.02 0.02<br>0.04 0.04<br>2014 2015 2016 2017 2018 2019 2020 2021 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>RS PE<br>0.04 0.04<br>0.02 0.02<br>0.00 0.00<br>0.02 0.02<br>0.04 0.04<br>2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Year Year<br>Null Tenders (%) Null Total Tenders Value (%)<br>%<br>%<br>%<br><!-- End of picture text -->

**Notes:** This figure presents the percentage of observations by year for which we were unable to build a unique identifier (id_licitacao_bd) in the _tender_ table. To calculate the percentage of “Null Total Tenders Value ” we use the variable valor_corrigido winsorized at percentiles 0.01 and 99.9.

48
The essential piece of information that is required for these calculations is an estimation of the labor intensity of exports and import substitutes. The first row in table 14 is an export weighted average of gross output per worker in three sectors: garments and textiles, footwear and "other" light industries. It is based on a survey of 200 State-owned enterprises (SOEs) by the Ministry of Finance and shows that each worker produces on average $3,885 worth of output per year. The second row shows similar information for private foreign companies in light industry. According to data from the Ministry of Planning and Investment, the average gross output per worker in this sector is slightly higher than in SOEs, reaching $4,300. Finally, row three is an average between the first two rows. It is the best available estimate of the labor-intensity in Vietnam's exports and indicates that each worker produces $4,078 worth of output per year.

But when exports increase, so do imports. And while higher exports create jobs, higher imports tend to displace domestic workers. Thus, the last row in table 14 also shows estimates of the labor-intensity in Vietnam's industries that compete with imports. Due to the lack of comparable production and trade data, this sector is assumed to contain the following heavy industries: chemicals, machinery and equipment, and iron and steel. In these sectors, average output per worker is estimated at $9,200, indicating that the export sector is slightly more than twice as labor-intensive as import-substituting enterprises. This is well in line with previous estimates in developing countries and it is similar to the situation observed in Brazil, Indonesia or Thailand in the 1970s (see Krueger, 1983). It indicates that an expansion of trade according to the lines of Vietnam's comparative advantage should be beneficial to labor demand.

**Table 14: The labor intensity of exports and import substitutes**

|Sector|Output/worker<br>(dollars)|
|-
Figure 5: Evolution of Weighted Firm Degree by 2014 Percentile <!-- Start of picture text --> 125 80<br>100<br>60<br>75<br>40<br>50<br>20<br>25<br>0 0<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>2014 Percentile: <25th 25-50th 50-75th 75-90th >90th 2014 Percentile: <25th 25-50th 50-75th 75-90th >90th<br>firm average by 2014 percentile firm median by 2014 percentile<br>Supplier and customer links (wgt. degree) Supplier and customer links (wgt. degree)<br><!-- End of picture text -->

**Note:** The graph tracks the average (left panel) and median (right panel) number of links for firms in the bottom 25th percentile, the 25th-50th percentile, the 50th-75th percentile, the 75th to 90th percentile, and above the 90th percentile of the weighted degree distribution (i.e. the firm link count) in 2014 over time, focusing on the balanced sample of actively covered agribusiness firms.

Our findings also indicate signs of strategic diversification and concentration. Within narrowly defined industries, agribusiness firms maintain a stable number of relationships and do not exhibit a tendency to diversify these specific relationships (see top and bottom left panel, Figure 6. Instead, these firms demonstrate a strategic pattern of maintaining a constant number of firms per partner-industry, for both customer and supplier connections. This observation suggests a preference for deepening existing relationships rather than expanding their network within the immediate industry sectors of their partner firms.

Conversely, there is a clear trend toward industry diversification, as evidenced by the increasing number of distinct industries to which firms maintain supplier or customer relationships. This trend is illustrated in the left panels of Figure 6, which show a steady rise in the variety of industries connected to these firms over time,
Akin to the analysis discussed in the previous paragraphs, we use as a proxy for recall length the time in months between the interview day and the first day of fieldwork. This proxy has two advantages in the case at hand. First, productivity measures are composite indicators whose components – output, land area, labor inputs – each have different timing patterns, so that there is no obvious single recall length measure. Second, time between the interview and the beginning of fieldwork has a simple practical interpretation and can be determined by survey designers. We test the effect of the recall length both at the plot level and aggregated at the farm level in the Malawi IHS4 and Tanzania NPS 2012/13 data using an OLS specification as in equation 5.<sup>13</sup> The results are in line with the prediction. Maize yield at the plot-level increases by 3.1 percent with each additional month of fieldwork in Tanzania NPS 2012/13 and by 5.1 percent in Malawi IHS4 (at the household level: 2.2 percent and 5.2 percent, respectively; Figure 16, Table A.19, Table A.20). Output (maize harvest) per day of labor input is also generally positively associated with fieldwork length, with the effect ranging from null to 3.1 percent per month (Tanzania NPS 2012/13: null effect at the plot-level, 1.9 percent at the household-level; Malawi IHS4: 2.7 percent at the plot-level, 3.1 percent at the householdlevel; Table A.19; Table A.20; Figure 16). Finally, output per worker is also positively associated with fieldwork length, with the effect size ranging between 2.4 and 5.1 percent per month (Tanzania NPS 2012/13: 2.4 at the plot-level, 2.5 percent at the household-level; Malawi IHS4: 5.1 percent at the plotlevel, 3.0 percent at the household-level; Table A.19;
. We find that individuals who received large transfers (either in cash or via direct deposit) made significantly more transactions (pvalue is 0.022). Although the overall mean is low, individuals who received the large transfer were also more likely to correctly report the fees for the basic account and to trust NBS more. All these variables were expected to be affected by treatment. While other variables in panel B could also have changed with the transfer, we find that they did not: excluding the percentage of individuals who correctly predicted the fees in the basic NBS account, the number of transactions in the six and 12 months after the large transfer, and the percentage who trust NBS, the p-value of the F-test that the remaining characteristics in panel B are jointly zero is 0.419. In particular, while households that received the transfer (particularly in cash) seem to have larger balances in 2016 than those without the transfer, these differences are not statistically significant (p-value is 0.375). The structural model described in the next section uses balance data to explain the decision of account ownership and for this reason it can only be estimated with individuals with available balance data. Although we requested balance data for the full sample of 747 individuals with accounts, the bank only provided 8
_Post_×_pre-US|-0.650***|-0.173***|-0.698***|-0.267**|-0.432***|-0.296***|
||(0.153)|(0.052)|(0.145)|(0.104)|(0.104)|(0.075)|
|No. Obs|6,334|6,334|6,334|5,442|2,040|3,690|
|R-squared|0.631|0.611|0.647|0.653|0.516|0.610|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS. columns (4) to (6) are on a set of firms with positive patenting * at _t_ . Standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

39
of the population. The sample of the remaining members therefore will be less representative of the population at the present time as well as of the population evolving over time. However, as we will detail later in the paper, this lack of representativeness is mitigated because all LSMS-ISA surveys incorporate new individuals who have joined interviewed (and tracked) households, such as newborns or immigrants.

The loss of representativeness over time can be addressed by tracking original individuals. The extent to which individual tracking can mitigate representativeness issues depends on decisions regarding the characteristics of the tracking targets and the geographical scope of the tracking. Conceptually, deciding which individuals to track depends on the characteristics of the population that need to be represented over time. Aspirations of studies that focus on a specific subpopulation are different from LSMS-ISA panel surveys, which intend to describe the national population.

However, tracking all individual movers can be very expensive. The more ambitious the tracking goals (e.g., the greater the distance away from their original home that an individual must be tracked), the more that costs will increase. Generally, LSMS-ISA individual tracking targets are defined based on the individual’s age as well as on their relationship to the household head. Malawi, Tanzania, and Uganda track and follow up with movers that are biologically related to the household head, excluding therefore servants or guests at the time of the previous survey. Moreover, all three surveys impose a lower bound under which individuals moving out of the households are not tracked (12 years old in Malawi and 15 years old in Tanzania and Uganda).

The geographical scope of the tracking may also impact the representativeness of the panel survey in future rounds, if those individuals or households that move the farthest are also very different from others. How far to track is generally established based on administrative boundaries, geographic boundaries, or distance (Witoelar, 2011). Panel surveys that rely on local tracking only search for households in the same EAs or nearby. Distant tracking allows for tracking
air pollution. A better understanding of the interplay between air pollution and poverty could be crucial for several reasons. Studies from high-income countries on the mortality and morbidity associated with air pollution may not be directly transferable to low-income countries and communities, where the nature of occupations and health care differ substantially. The health and productivity implications of unsafe air pollution will also impact the socio-economic development prospects of low- and middle-income countries. This is especially pertinent in low-income countries, which – as this study shows – still tend to have relatively low pollution levels compared to more industrialized middle-income countries. In these countries, it will be important to ensure that development progress does not come hand in hand with intensifying air pollution and the associated adverse effects.

Against this context, this study explores the global prevalence of unsafe outdoor air pollution, and how it interacts with poverty. It uses global high-resolution data on ambient air pollution (outdoor concentrations of PM2.5), population distribution, and poverty to provide aggregate exposure estimates (see Section 3.1 on data sources). It also shows that pollution levels are most hazardous in middle-income countries, where economies tend to rely more heavily on polluting industries and technologies. This study does not cover indoor air pollution, which is another pervasive risk to health and well-being, especially in developing countries.

Overall, this study contributes to the literature in two ways. It offers the first global population PM2.5 exposure estimates based on the WHO’s recent guidelines, revised in September 2021, with detailed national and subnational estimates. It also offers the first global estimates of PM2.5 exposure and its interaction with national and subnational poverty levels.

The rest of this study is organized as follows. Section 2 reviews the existing evidence on air pollution, health impacts and development outcomes. Section 3 summarizes the data sets and analytical methods used in this study. Section 4 presents results, and section 5 concludes.

2