rafmacalaba/data-use-mentions-extended huggingface dataset:
sector by 1.06 - 1.14 percentage points whereas the shares of agriculture and manufacturing decline, respectively, by 0.65 - 0.7 and 0.42 - 0.43 percentage points over the period 1970 – 2006. Almost one-third of the observed increase in GDP per capita is explained by the expansion of electricity infrastructure, which is a relatively high estimate.

Turning to India, Rud (2012) analyzes the effect of electricity provision on industrialization between 1965 and 1984, by using the introduction of a new irrigation technology as a natural experiment. Agricultural productivity is linked with timely irrigation that could be cheaply supplied by electrified pump sets. As the demand for these irrigation technologies increases, so does the demand for electricity. Hence, the availability of groundwater explains the divergence in electricity network expansion and hence industrial outcomes. The author finds that electrification has a larger impact on industrialization than groundwater availability. An increase of one standard deviation in the measure of electrification is associated with an increase of around 14 percent in manufacturing output for a state at the mean of the distribution. In addition, network expansion encourages the entry of small firms. These results suggest that the expansion of electricity networks could be used as a means of promoting industrial development, although the effectiveness of electrification will depend on the presence of other development constraints, such as lack of transport infrastructure.

Using panel data of rural Ethiopian villages, Fried and Lagakos (2021) show that rural electrification not only facilitates structural change but also alters migration patterns by slowing out-migration from rural villages. Non-electrified villages are 2.3 percent less likely to receive migrants, while electrified villages are 7.0 percent more likely. Similarly, non-electrified villages are 4.7 percent more likely to be net senders of migrants, while electrified villages are a dramatic 20.9 percent less likely to be net senders of migrants.

Electrification also lowers market entry costs for firms, increasing competition, and forcing unproductive firms to exit more often. Kassem (2021) studies the
Upper Urban|0.134|-0.0746|
||(0.0938)|(0.0601)|
|Upper Rural|0.0632|-0.301***|
||(0.105)|(0.0748)|
|Borders Urban|0.488***|-0.0291|
||(0.0808)|(0.0910)|
|Borders Rural|0.683***|0.433***|
||(0.0692)|(0.0499)|
|Region*Owner|Yes|Yes|
|Region*Not-owner|Yes|Yes|
|Constant|7.122***|7.653***|
||(0.101)|(0.0737)|
|Observations|11,841|23,970|
|R-squared|0.505|0.411|
|Robust standard errors in parentheses.|||
|*** p<0.01, ** p<0.05, * p<0.1|||
|**Source**: Authors’ calculations based on HIECS 20|15 and 2017/18.||
|**Note**: Regression run using total expenditures.|||

23
<!-- Start of picture text --> gs Ln[Women-owned establishments per population, 1994]<br>=<br>oo<br>Le<br>co<br>8 t<br>a2§ (~) MN<br>5O ——<br>A<br>ESTz (©)<br>Bq<br>Be af 63)<br>2 Sy, CoA(eu)<br><eap<br>= 1992 1996 2000 2004<br>§ Year of First Election Under 73rd Amendment<br>Bubble sizes represent state population.<br><!-- End of picture text --> <!-- Start of picture text --> Women-owned share of establishments, 1994<br>x<br>Ow<br>Oy :<br>#<br>c<br>2s d<br>(€) :<br>—<br>wo<br>So<br>$ N (#)<br>s =<br>xe]<br>o ~ D — Ai<br>é a BK) CH<br>2 @) \K<br>1992 1996 2000 2004<br>Year of First Election Under 73rd Amendment<br>Bubble sizes represent state population.<br><!-- End of picture text --> <!-- Start of picture text --> @ Ln[{new women-owned establishments per population, 1994]<br>=<br>c<br>Bo<br>on (m) MN<br>2<br>3a2<br>25 a<br>=<br>cL28<br>| @<br>BB<br>o-= eyaa fa<br>i= Aah vie BH<br>% 1992 1996 2000 2004<br>= Year of First
uses GDP per capita, under-5 mortality, life expectancy, rural population shares, and regional dummies predicts welfare well, and that adding more information does not lead to any relevant gains. National accounts data stand out as the single best predictor of welfare, even, but to a lesser extent, in data-deprived countries. This suggests that notwithstanding the gaps between welfare from surveys and national accounts (Deaton 2005; Pinkovskiy and Sala-iMartin 2016, Deaton and Schreyer 2022, Prydz et al. 2022) and measurement issues with GDP in autocratic countries (Martinez 2022), GDP provides valuable information on welfare in contexts where household surveys are unavailable. However, about half of the countries are so datadeprived that they not only lack an income or consumption distribution, but also GDP. For these countries, we use World Bank income groups as a proxy, resulting in two tiers of models depending on whether GDP data is available for the country of interest.

These two tiers outperform models based on remote sensing data (e.g., nighttime lights, vegetation), which do not meaningfully lower the out-of-sample error if added to the models. This suggests that, on average, remote sensing data are worse predictors of welfare at the national level than GDP, which is consistent with evidence suggesting that remote sensing data might produce household welfare estimates very different from survey-based ones (Van Der Weide et al. 2024). This suggests that a relatively simple model using readily available data outperforms more complicated models using costly data.

2
growth. Yet, as emphasized by Baland and Robinson [4], the real issue is to better understand the determinants of child labor so as to evaluate its welfare implications. More generally, it is crucial to jointly investigate the factors driving schooling and child labor decisions.

This paper aims at analyzing the determinants of primary school enrollment, attendance and child labor in Bolivia from 1999 to 2007, identifying how the substitution and complementary relationships among such activities evolve over time.

The unprecedented use of Bolivia’s national household survey MECOVI for several years allows for an in-depth historical analysis of the recent trends of schooling and child labor. Due to the lack of empirical literature on this specific issue for Bolivia, this study represents a contribution that aims at filling the gap.

Results at the descriptive level reveal that enrollment became progressively more widespread in Bolivia. Nonetheless, the attendance figures are discouraging, as about 40 percent of the enrolled children did not go to school.

Triprobit estimations show that the increase in enrollment is led by indigenous and children living in urban areas, whereas poverty and indigenous are

> 1 GDP data are from the United Nations National Accounts Main Aggregate Database, data on poverty are from MECOVI 2007.

> 2 More specifically, this represents the second Millennium Development Goal (MDG) as established by the United Nations.

> 3 According to the MECOVI surveys employed, more than 50 percent of the total Bolivian population declare themselves belonging to indigenous groups.

2
Table B2: Firm distribution and frequency Malaysian firm‐level data by year

|**Year**|**Freq. **|**Percent**|**Cum.**|
|---|---|---|---|
|2010|34,896|47.31|47.31|
|2015|38,861|52.69|100.00|
|Total|73,757|100.00||

Table B3: Summary statistics for the variable used for Malaysia (2010 & 2015)

|**Variable**|**Observations**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Index * (D/T)|73,757|0.09|0.03|0.03|0.14|
|R&D|73,757|0.07|0.26|0.00|1.00|
|Assets Purch.|73,757|0.04|0.20|0.00|1.00|
|Assets Used|73,757|0.00|0.05|0.00|1.00|
|Assets Prod.|73,757|0.00|0.05|0.00|1.00|

36
this method has yielded predictions that are highly correlated with up-to-date census-based estimates in Mexico, Sri Lanka, and Tanzania (Masaki et al., 2022; Newhouse et al., 2022). The methodology we use in this paper deviates from the official approach endorsed by the World Bank’s Poverty Global Practice, as described in Corral et al. (2022), which is based on the EBP under a unit (household) level mixed model, and census micro-data as covariates (referred to as census-EBP). The main difference, besides the use of geospatial (instead of population census) covariates, is that our modeling approach utilizes only grid cell covariates, but the outcome is still modeled at the unit (household) level. This is why sometimes this latter model is referred to as the unit context model.

We explore the use of the unit context model in Chad, Guinea, Mali and Niger, which lack recent census data. We further leverage the availability of recent census data for a fifth country in West Africa, Burkina Faso, to conduct an evaluation exercise. The evaluation exercise compares estimates of headcount poverty rates obtained with a unit-level model and the empirical best predictor using census covariates, with poverty rates obtained using the empirical best predictor under the unit context model with geospatial covariates.

As noted above, an alternative approach to small area estimation using geospatial covariates is to use an area-level model (Fay-Herriot, 1979), case in which both the direct estimates of poverty rates and the geospatial covariates are aggregated at the target area level. Hence, in the evaluation exercise presented in Section 4 we also produce estimates under a Fay-Herriot model as a way of providing additional evidence about the validity of the estimates produced under the unit context model.

Using geospatial data instead of census data in SAE and the unit context model has been criticized in recent literature (for example, Corral et al., 2021). This is due to the possible introduction of omitted variable bias (relative to the unit-level model)
FAO analysis of pesticide composition revealed high shares of toxic chemicals (for example, carbamates and organophosphates in insecticides, and dithiocarbamates and inorganics in fungicides) which have been known to cause cancer, genetic damage, fetal damage, and severe allergic responses in exposed populations (Zahm, Ward and Blair, 1997).

**Figure 1:  Trends in Pesticide Use, 1992-2001**

<!-- Start of picture text --> Pesticide consumption (metric tons)<br>18,000<br>16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Year<br><!-- End of picture text --> _Source_ : Department of Agricultural Extension, Plant Protection Wing, Bangladesh Many pesticides used in Bangladesh are also banned or restricted under international agreements (Meisner, 2004; NOVIB, 1993; SOS-arsenic.net, 2004, SUNS, 1998). Pesticide suppliers in Bangladesh even continue to sell the 12 particularly controversial pesticides known by activists campaigning worldwide as the “dirty dozen” (SUNS, 1998; SOS-arsenic.net, 2004). In addition, substantial anecdotal evidence suggests that users’ lack of information have led to widespread overuse or misuse of pesticides. As a result, pesticide poisonings and ecological damage have become common in Bangladesh (Ramaswamy, 1992).

# **3.  Survey Data**

The research reported in this paper is based on a large survey of Bangladeshi farmers, carried out by the World Bank in the summer of 2003. We used structured questionnaires to collect information on pesticide use and practices, risk perceptions, knowledge, 4
well consider this option if they recognized the potential health benefits for their children. By the same logic, of course, other family members would benefit from extending their outside time during peak cooking periods.

# **6.  Comparison with Results for India**

A recent study of IAP exposure in India (World Bank, 2002) provides a useful point of comparison for our results. The India study also considers different age-sex groups, cooking vs. living areas, and cooking vs. non-cooking times. Its conclusions are similar to our findings in some respects: _Whereas women, in their traditional capacity as cooks, suffer from much greater average daily exposures than other family members, adult men experience the least exposure. Among noncooks, those who are most vulnerable to the health risks of IAP — young children and elderly people — tend to experience higher levels of exposure because they spend more time indoors._ (World Bank (2002), p. 3) As Table 4 shows, we also find large differences between women and men in exposure to air pollution.<sup>9</sup> However, we find essentially no difference in exposure for women and young children of both sexes. We also find relatively small differences between women’s exposures and exposures for adolescents of either sex. The essential difference between our results and the India results lies in our findings for pollution in living areas. We find average living-area pollution concentrations to be much closer to cooking-area concentrations, and our 24-hour monitoring data indicate that daily pollution cycles are close to identical for the two areas. As a result, time spent in living areas does not provide much relief from pollution exposure. Time spent outside the house therefore emerges as the key variable in our analysis, and adult males have much smaller pollution exposure simply because of their outside orientation.

> 9 As we note in our previous paper (Dasgupta, et al., 2004), average PM10 concentrations are smaller in our study.

15
<mark>2. We calculate the probability of selection into the phone survey for each household (</mark> pi <mark>), as the total number of LSMS-ISA households for which contact was attempted in that household’s region (</mark> mr <mark>) divided by the total number of LSMS-ISA households in that region (M</mark> r).

pi =<sup>mr</sup> Mr <mark>3. We multiply the existing LSMS-ISA sampling weight with the reciprocal of the probability of selection in the phone survey, as calculated in step #2.</mark> 1 wi,phone = wi,pre pi <mark>4. Using the entire LSMS-ISA sample, we run a multivariate logistic regression in which the dependent variable is a binary variable that is equal to 1 for LSMS-ISA households that were successfully interviewed for the phone survey and equal to 0 otherwise. The independent variables included represent a range of household, dwelling, and head of household attributes that predict the likelihood of a completed phone survey interview.</mark> 18
literature that demonstrates that development of large transport infrastructure on average leads to economic activity. By focusing on the above-mentioned parameters, this paper focuses on when the resulting WEBs should be expected to be higher/lower.

The specification addresses some of the discussed concerns about the placement of transport corridors in this analysis. However, it does not fully control for the endogenous placement of transport corridors and endogenous selection by international organizations. If countries make the decision of building these corridors in areas connecting existing economic hubs, then the inclusion of these economic hubs in the analysis would lead to endogeneity issues. We address this issue by performing two sensitivity analyses (robustness checks).

The first sensitivity test excludes points that are close to the nodes of the corridor. Typically, transport corridor projects are designed to connect two or more cities, which could create endogeneity bias if the nodes are included in the analysis. An approach followed in the literature to deal with the potential bias is to remove the nodes (see, for example, Michaels (2008) and Ghani, Goswami, and Kerr (2016)). The nodes typically fall at the ends of the corridor; hence, in one approach, 20 percent of the corridor area around its ends is excluded. However, it is also possible that the transport investment was done to connect nodes along the corridor that have economic potential. If this is the case, then the exclusion of the nodes at the ends of the corridor is not suitable. To address this issue, we use the Global Human Settlement data to identify urban centers along the corridor that existed before the start of the project. To remove possibly problematic nodes, all areas with an average human settlement value of 2.5 or above are excluded. By excluding these nodes from the analysis, attention is restricted to the areas along the corridor that got connected by chance. This allows the analysis to correct for the endogenous placement of the corridor.

The second sensitivity test involves excluding partially treated locations surrounding the corridor. To
. In the third column, the standard error of the difference is reported.

**Table S1.3: average value of household total consumption per capita by household structure - all commodities**

|Household type|PSF survey<br>Mean<br>(N)|ESPS survey<br>Mean<br>(N)|Difference<br>PSF-ESPS<br>(Std. err.)|
|---|---|---|---|
|Single person households|1,086,946|1,000,344|86,602|
||(98)|(540)|(84,672)|
|Other one cell households|531,182|535,947|-4,764|
||(147)|(901)|(52,169)|
|More than one cell 'nuclear' hh.|392,651|351,172|41,479|
||(549)|(3,971)|(22,022)|
|Other households|367,697|301,915|65,783|
||(968)|(8,147)|(14,561)|
|All households|429,115|371,231|57,884|
||(1,762)|(13,559)|(12,859)|
|All individuals|328,135|284,917|43,218|
||(13,988)|(123,486)|(3,519)|

Source: « Pauvreté et Structure Familiale » (PSF) and « Enquête de Suivi de la Pauvreté au Sénégal » (ESPS) surveys, authors' calculations. Note: Summary statistics of total consumption per capita, by household types. In the first two columns, the sample size is reported between parentheses. In the third column, the
rates is among those who had attained secondary and post-secondary (10+2) levels of education, followed by those with levels of education below the secondary level, across years in both rural and urban areas. The FLFP rate is highest among illiterates and college graduates in both areas.

This creates a U-shaped relationship between level of education and FLFP in India, across all rounds of NSSO survey data during the post liberalization period.

**Figure 3: FLFP Rate by Level of Education**

<!-- Start of picture text --> Rural Urban<br>60.0 60.0<br>50.0 50.0<br>40.0 40.0<br>30.0 30.0<br>20.0 20.0<br>10.0 10.0<br>0.0 0.0<br>Illiterate Below Secondary College and Illiterate Below Secondary College and<br>Secondary above Secondary above<br>1993‐94 2004‐05 2011‐12 1993‐94 2004‐05 2011‐12<br><!-- End of picture text --> Source: Authors’ own estimate based on NSSO data.

Klasen and Peters (2013) also find a U-shaped relationship between education and FLFP in urban India. They argue that illiterate women and women with very low levels of education tend to participate more in the labor market, while women with intermediate levels of education seem to withdraw from the labor market. Women with very high levels of education do enter the labor market in order to seek better paid work. Sudarshan and Bhattacharya (2009), in their study on the Delhi labor market, argue that the relationship between education and FLFP only becomes significant when considering levels of education at the college level or higher.

16
born 60 years later in 19901995, and who were ages 18-23 in 2013, we conclude that that there has been an increase of 6.8 years of average educational attainment across generations over the period.<sup>7</sup> This way of plotting the data is useful for viewing long-term trends, and also for verifying the moment when different benchmarks were reached. For example, as the figure shows, the cohort born around 1950—who turned 18 in 1968 and 63 in 2013—attained 6.5 years of schooling, which is equivalent to having completed the primary level. Our interpretation (subject to the caveats mentioned in footnote 7) is that already in 1968, the new entrants into the labor force in Latin 6 The year of birth of each cohort appears at the bottom of the Figure. For illustration, take again the case of El Salvador, where the latest household survey was held in 2013. In these data it is possible to identify, for instance, all individuals in the 78-82 age group, who by definition were born between 1935 and 1940, and thus turned 18 during the period 1953-58 -which is the age at which individuals are expected to have exited US and either continued to tertiary or entered the workforce. Since household surveys report the number of official years of schooling and the maximum level reached for all individuals, it is possible to determine the average schooling level of the cohort. Additionally, since schooling occurs at early ages and typically stops by ages 20-24, it is possible to interpret the result as the education acquired by the cohort during schooling age. The same exercise can be performed for all individuals who were age 18-22 in 2013, and were therefore born in the 1990-1995 period, as well as for all cohorts in between the two extremes. The Figure plots the average years of schooling for subsequent cohorts born between 1935 and 1995 for the 18 Latin American countries for which we have data –the data points correspond to unweighted regional averages.

> 7 Evidently,
- <mark>ORGI (Office of the Registrar General and Census Commissioner). 2011a. Census of India</mark> _<mark>2011.</mark>_ <mark>New Delhi: Ministry of Home Affairs, Government of India. http://censusindia.gov.in/</mark>

- <mark>______. 2011b.</mark> _<mark>Administrative Atlas</mark>_ <mark></mark> _<mark>2011.</mark>_ <mark>New Delhi: Ministry of Home Affairs, Government of India. http://censusindia.gov.in/2011census/maps/administrative_maps/admmaps2011.html.</mark>

- <mark>Pesaresi, Martino, Daniele Ehrlich, Stefano Ferri, Aneta Florczyk, Sergio Freire, Matina Halkia, Andreea Julea, Thomas Kemper, Pierre Soille, and Vasileios Syrris. 2016.</mark> _<mark>Operating Procedure for the Production of the Global Human Settlement Layer from Landsat Data of the Epochs 1975, 1990, 2000, and 2014.</mark>_ <mark>Luxembourg: Publications Office of the European Union.</mark>

- <mark>Pesaresi Martino, Michele Melchiorri, Alice Siragusa, and Thomas Kemper. 2016.</mark> _<mark>Atlas of the Human Planet 2016</mark>_ <mark>:</mark> _<mark>Mapping Human Presence on Earth with the Global Human Settlement Layer.</mark>_ <mark>JRC Science for Policy Report, European Commission, Brussels.</mark>

- <mark>https://ghsl.jrc.ec.europa.eu/atlas2016Overview.php.</mark>

- <mark>Picard, Richard R., and R. Dennis Cook. 1984. "Cross‐validation of Regression Models."</mark> _<
do not exhibit much variation with respect to income. For references to this extensive literature we refer the reader to the introduction of Gardner and Oswald (2007).

> 4 There is a fair amount of evidence on the causes of poor mental health among adolescents. Patel et. al. (2007) identify a range of risk factors including poverty and social disadvantage, unstable romantic relationships, violence and abuse, poor physical health, and inadequate education. Das et. al. (2008) are unable to confirm a relationship between poverty and mental health using survey data on mental health from 5 developing countries. Engaging family and education environments (such as parents encouraging children to express their emotions and schools providing a safe learning atmosphere), on the other hand, can reduce the probability that adolescents develop mental disorders (Lewinsohn, Rohde and Seeley 1998; Patel et. al. 2007; Saluja et. al. 2004).

3