rafmacalaba/data-use-mentions-extended huggingface dataset:
revised network, we find that domestic output shocks have a more pronounced negative effect on sector-regions with higher levels of informality, compared to results based on formal sector data alone. Our results suggest that a 1 percentage point decrease in the formal sector share corresponds with an underestimation of the reduction in output by 5 percentage points. By contrast, for import shocks, the bias goes in the opposite direction. The economy appears less exposed to import shocks once the informal sector is taken into account. This discrepancy arises because import shocks primarily affect larger formal firms, which carry less weight in the overall firm network once informality is incorporated.

Finally, we consider the sensitivity of our results with respect to alternative assumptions about the linking patterns of informal firms. We conduct bounding exercises where we further restrict the degree of integration of informal firms with the formal sector relative to small formal firms. Drawing on survey evidence and the existing literature (B ̈ohme and Thiele, 2014; Gadenne

> 6We provide evidence showing that the linking patterns of small formal firms are similar to what we would expect from informal firms – they link more locally and buy more from intermediaries relative to their larger peers.

> 7This can include wedges introduced by the VAT system itself (De Paula and Scheinkman, 2010; Gadenne et al., 2022).

4
.000|-0.000|0.001|-0.000**|-0.000|-0.001|
||(0.002)|(0.000)|(0.001)|(0.000)|(0.000)|(0.001)|(0.000)|(0.000)|(0.001)|
|Eligible Child|0.010|-0.001*|-0.012***|-0.000|-0.001|-0.005|-0.000|-0.001|-0.005*|
||(0.007)|(0.000)|(0.003)|(0.000)|(0.001)|(0.005)|(0.000)|(0.001)|(0.003)|
|FDR q-value|0.872|0.872|0.747|0.872|0.872|0.872|0.872|0.872|0.872|
|Observations|224,925|224,925|224,925|224,925|224,925|224,925|224,925|224,925|224,925|
|Mean|0.098|0.001|0.049|0.000|0.002|0.098|0.001|0.003|0.064|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dummy for being employed in a certain industry category indicated in column heading on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6
from self-employment, so the authors estimate a production function from which they are able to derive a shadow wage for labor. This permits them to come up with a comprehensive labor earnings differential between the rural and urban populations. They then control for differences in hours worked, which they find to be an important component of the total urban-rural income gap.

Having isolated the difference in hourly earnings between rural and urban households, Shi, Sicular and Zhao (2002) control for differences in personal characteristics, as well as occupation. Once these differences are controlled for, the unexplained portion of this income gap falls to about 50%. They reason that the remaining 50% of this earnings differential must be either compensation for higher urban living costs, or the consequence of a labor market distortion. Once they have taken into account differences in living costs, the authors conclude that the apparent labor market distortion is about 42% of the rural-urban labor income differential and 48% of the hourly earnings differential. When applied to the average wage differential (2.15 yuan/hour = 3.43 yuan/hour urban – 1.28 yuan/hour rural), this amounts to an _ad valorem_ rate of apparent taxation on rural wages of 81% = 100% * (.482 * 2.15) /1.28 .

Clearly there may be other, unobserved factors inducing this rural-urban wage differential, in which case estimation of the labor market distortion via subtraction of known factors is biased in the direction of overstating the _hukou_ -related distortion. In fact, rural-urban wage differentials persist in market economies which do not have a household registration system. Therefore, it is useful to consider an alternative approach whereby one estimates the direct impact of household registration status on the observed wage difference among households. Shi (2002) takes this approach to the problem, using the same CHNS data set. He finds that only 28% of the ruralurban wage difference can be explained directly via the coefficient on the _hukou_ registration 5
the estimated change in fires in the preferred specification.

## **6.2 Changes in crop choice induced by MNREGA’s role as insurance**

Table 5 provides no evidence that the overall production of non-fire associated crops, which includes higher value, higher variance crops, increases as measured either by area in production or total tons. Existing work (Gehrke, 2013; Raghunathan and Hari, 2014) has suggested that farmers do in fact shift into higher variance, higher value crops such as cotton after the implementation of MNREGA. I find no evidence of such a change but more importantly, the production process of the crops that farmers may have shifted into (Gehrke, 2013) does not typically include fire (Jain et al., 2014; Bhuvaneshwari et al., 2019). It is therefore difficult to explain the observed increase in fires as being driven by MNREGA’s implicit insurance provision leading to transitions in crop type. In Appendix 3.11, I show that the absence of an impact of MNREGA on crop production in the average persists when examining outcomes by quartiles of the mechanization index. That is, there is no differential impact of MNREGA on crop production in different quartiles of the mechanization index.

# **7 Implications for air pollution**

The primary concern regarding the use of fires to clear crop residue stems from concern that this practice may increase air pollution. Previous estimates of the contribution of crop burning to pollution in Delhi suggest that substantial amounts, from 17% to 60% of particulate emissions in Delhi in the winter months are the result of upwind crop burning (Liu et al., 2018; Bikkina et al., 2019). In a direct examination of the impact of upwind crop burning on infant mortality in India, Pullabhotla (2018) suggests that an increase of five upwind fires in a given year increases the infant mortality rate by approximately 10%.

The availability of satellite measures of emissions due specifically to biomass burning enables me to directly examine how
by

> 6 Ibid.

> 7 That is, if _y i_ − _x i_ ′β ˆ θ > 0 , then yi can be increased toward + ∞, or if _y i_ − _x i_ ′β ˆ θ < 0 , yi can be decreased toward -∞, without altering the solution β<sup>ˆ</sup> θ .  In other words, it is not the magnitude of the dependent variable that matters but on which side of the estimated hyperplane the observation is.  This is most easily seen by considering the FOC to

> This can be seen both as a strength and a weakness of the method.  In the present context, with data from Côte d’Ivoire, the advantages seem to outweigh the disadvantages, since the reliability of data from LDCs generally is questionable.  However, to the extent that a given outlier represents a feature of the "true” distribution of the population, one would prefer the estimator to be sensitive to such an outlier—at least to a certain degree. 8 See Velenchik (1997) and Verner (1999a,b).

5
households with per capita consumption or income below the international poverty line. The primary data come in various forms, ranging from micro data (the most common) to specially designed grouped tabulations from the raw data, constructed following our guidelines.

We draw on 850 surveys for 125 countries. Taking the most recent survey for each country, 2.1 million households were interviewed in the surveys used for 2008. The surveys were mostly done by governmental statistics offices as part of their routine operations. Not all available surveys were included. A survey was dropped if there were known to be serious comparability problems with the rest of the data set. As in past work, we have tried to eliminate obvious comparability problems, either by re-estimating the consumption/income aggregates or the more radical step of dropping a survey. However, there are problems that we cannot deal with. For example, it is known that differences in survey methods (such as questionnaire design) can create non-negligible differences in the estimates obtained for consumption or income.

Following past practice, poverty is assessed using household per capita expenditure on consumption or household income per capita as measured from the national sample surveys. When there is a choice, we use consumption in preference to income, on the grounds that consumption is likely to be the better measure of current welfare on both theoretical and practical grounds.<sup>19</sup> Of the 850 surveys, 521 allow us to estimate the distribution of consumption expenditures; this is true of all the surveys used in the Middle East and North Africa, South Asia and Sub-Saharan Africa, though income surveys are more common in Latin America.<sup>20</sup> Our data are national for almost all countries. The exceptions are China, India and Indonesia, for which we do an urban-rural split. (Given that Method 2 allows different lines for urban and rural areas we plan to do urban-rural splits for more countries in future applications.) The measures of consumption (or income, when consumption
2.9438|1.4527|-6.9018|
||VILLAGE|0.1403|0.4584|-11.2676|-1.7407|-8.1092|-1.6368|
||PRIVATESCL|0.1714|0.0832|4.4454|-2.5786|10.9349|-11.4833|
||STU<br>~~F~~EES|25.7233|16.6104|4.5328|-23.7404|-|-|
||RATCMP15|0.3909|0.2216|7.6894|-12.933|3.6968|-11.777|
||SCHAUTON|-0.2542|-1.0419|-10.9579|-0.3955|-10.3639|0.5217|
||EXC1<br>~~B~~AND|0.4710|0.1678|0.29099|-2.4563|-0.118|0.1559|
|**TOTAL WEALTH**||||-14.57471|-109.5998|4.7213|-125.7274|
|||||-14%|-103%|4%|-100%|
|**SUM TOTAL (ED WE**|**ALTH & WEALTH)**||||**106.4774**<br>**100%**||**125.2960**<br>**100%**|

Notes: The ’Mean values’ for Dev7 and Vietnam are taken from the whole data set and represent the same values used for Section 2 - Endowment Differences. They are included here to reiterate the categorization of variables into WEALTH and ED WEALTH, depending on their comparative values between Dev7 and Vietnam. Underlined values represent those variables mentioned within the
al. (1995) developed an alternative GMM estimator in which the unobservable fixed effect of firms is approximated by the history of patents prior to the period of the estimation sample, namely a pre-sample mean estimator. Blundell et al. (2002) used this estimator in a dynamic context finding elasticities around 0.5. This pre-sample mean estimator is the closest approach to endogenous growth models as it takes into account previous patents as a determinant of current patent counts.

At the aggregate level, Botazzi and Peri (2003), using cross-sectional data for Europe, find elasticities close to 0.8 for the relationship between patents and R&D. Moreover, they find spillovers within 300 kilometers. Bosch et al. (2005) used a panel of 49 countries, using various techniques to estimate elasticities that vary between 0.44 and 0.96. The estimates include static and dynamic specifications, as well as the pre-sample mean estimator, models of count data (with Poisson and negative binomial distributions) and linear GMM.

As mentioned, we address the possibility of multiple sources of patent spillovers. In particular, we consider the following variables: (i) stock of patents from countries of the same income group or region, and (ii) the stock of patents in the remaining income groups or regions of the world. We denote by SP the stocks of patents. In a first stage, we estimate global knowledge spillovers on national patent counts by estimating the following function, which generally corresponds to count-data estimators: where subscript i indexes countries, j represents income group and t is time (year). The variable SP 8
Figure 19: Uncertainty about sales and probability innovating <!-- Start of picture text --> (a) Investment digital (b) Innovation<br>.3 .25<br>.25 .2<br>.2 .15<br>.15 .1<br>0 20 40 60 0 20 40 60<br>Uncertainty about sales Uncertainty about sales<br>Std. Deviations Std. Deviations<br>Probability of investing in digital platforms<br>Probability of creating a new product/service<br><!-- End of picture text --> Notes: Panel (a) is the bin scatter of the probability of investing in digital platforms controlling for size, sector, severity of the crisis and wave. On average, an increase in 1 standard deviation in uncertainty is associated with 0.15 percentage point decrease in the probability of investing in digital platforms (p-value: 0.000) Panel (b) repeats the same for product innovation. Because we have this information only for wave 2, it does not control for wave. On average, an increase in 1 standard deviation in uncertainty is associated with 0.07 percentage point decrease in the probability of innovating (p-value: 0.020)

# **4.6 Stylized fact 6: As the COVID-19 pandemic evolved, governments have expanded the support for businesses.**

Relying on the first wave of BPS data covering more than 120,000 firms in 60 countries, Cirera et al. (2021c) document the wide range of policy support measures, the reach of these policies, the alignment of measures with firm needs, and their targeting and effectiveness. Despite the plethora of measures launched around the globe, they show that only one in four firms received any type of public support at the time we conducted the surveys. These results show stark differences by income levels: the probability of receiving some public support is 11% in low-income countries, 15%
is the lowest during the _Monga_ season. As for the individual sources, the pattern is more pronounced for microcredit and informal sources than for formal sources.<sup>3</sup> As shown in Figure 3, the

> 2 While the panel surveys also include data from 1991/92, we could not use them because they are not suitable for calculating season-specific income that would be consistent with incomes calculated from 1998/99 and 2010/11 data. So, the analysis of this paper is restricted to 1998/99 and 2010/11 survey data only. Nevertheless, using the 1991/92 along with the 1998/99 and 2010/11 panel surveys, our results on the seasonality of income and borrowing still hold.

> 3 This is not surprising, given that formal financial institutions lend little but mobilize more from their rural branches.

7
average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. The two indices of contributions to the clinic are composed of variables that measure support and contributions once from the perspective of key informants in the villages and once by health personnel. In the community survey, we ask about meetings between the clinic and community as well as labor contributions to the clinic. In the clinic survey, we ask about labor or financial contributions as well as disputes between the community and the clinic. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table D.8:** Community development and political engagement (CDPE)

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**CDPE index**(SUR)||0.132<br>(0.049)<sup>_∗∗∗_</sup><br>[0.033]<sup>_∗∗_</sup>|0.116<br>(0.060)<sup>_∗_</sup><br>[0.114]|0.150<br>(0.056)<sup>_∗∗∗_</sup><br>[0.025]<sup>_∗∗_</sup>|508|
|(1) Projects with local council/chief|0.054<br>(0.226)|0.047<br>
_Figure 9: Index of gender/disability equality measuring the extent to which officials promote gender equality and inclusion of children with disabilities._ <!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>Gedenr/disabilitity equity index<br><!-- End of picture text --> Source: Calculations from CDG–World Bank 2022 microdata. Principal factor analysis of the following items: “Schools should try to promote gender equality.”, “When jobs are scarce, men should have more right to a job than women.”, “Teachers found to have a sexual relationship with a secondary school student should be suspended.”, “Children with disabilities deserve the same level of access to public schooling as children without disabilities.”, “In most cases, accommodations should be made so that children with disabilities can be included in regular classrooms with children who do not have disabilities.” The index is normalized to have a zero mean and a unitary standard deviation across the whole sample; a score of 0.16 means that the average score in the country is 16% of a standard deviation above the global mean.

# 4.5 Officials favoring equality also tend to reject violence toward children.

The survey included three items about violence toward children. Officials were asked if beating of children by parents or teachers could be justified and about their perceived frequency of children affected by sexual violence. Officials displaying more favorable views about gender equality and inclusion of children with disabilities are also less likely to agree with statements justifying violence toward children (Table 1). Their estimates of the prevalence of sexual violence toward children were also lower. This is important since it suggests that officials’ views align with values espoused
publications,. About 88 % of diarrheal cases globally are attributed to water, sanitation and hygiene (Pruss-Ustun et al, 2004). This is a conservative approach where malnutrition impact on early childhood diseases is omitted. If considered, this additional indirect impact would approximately double the mortality attributed to water supply, sanitation and hygiene (WSH) (World Bank, 2010). However, a major part of these losses are in the form of acute respiratory mortality that was accounted for in the indoor air pollution section. To avoid double counting and be on a conservative side we considered only direct impact of inadequate water supply, sanitation and hygiene (WSH).

Mortality for children under 5 and diarrheal-based child mortality are high in India. Baseline health data for estimating the health impacts of inadequate water supply, sanitation and hygiene are presented in Table 4.1. The Office of the Registrar General (2004) indicates that 14 percent of child mortality was due to intestinal diseases. A baseline diarrheal mortality rate of 14 percent of under-5 child mortality is thus used for diarrheal mortality estimation.

For diarrheal morbidity, however, it is very difficult or practically impossible to identify all cases of diarrhea. The main reason is that substantial numbers of cases are not treated or do not require treatment at health facilities, and are therefore never recorded. A second reason is that cases treated by private doctors or clinics are often not reported to public health authorities. Household surveys therefore provide the most reliable indicator of total cases of diarrheal illness. Most household surveys, however, contain only information on diarrheal illness in children. Moreover, the surveys only reflect diarrheal prevalence at the time of the survey. As there is often high variation in diarrheal prevalence across seasons of the year, extrapolation to an annual average will result in either an over- or underestimate of total annual cases. Correcting this bias is often difficult without knowledge of seasonal variations.

In spite of all these difficulties a reasonable estimate has been made of the number of cases and prevalence of
equal number of randomly selected households in the control communities who had applied for the program. Figure 1 illustrates the location of the treated and control communities in Jordan.

## **_Jordanian Host Community Sample:_**

A key concern with refugee assistance programs is the potential for social backlash from host communities. The HSP was intentionally designed to mitigate such effects among landlords, by disbursing funds directly to landlords for housing improvements, as noted above. The social cohesion component of the design focuses instead on measuring the impacts on attitudes of Jordanian neighbors in the community, who are were not eligible for HSP assistance, rather than on landlords. The Jordanian neighbor sample consists of 1,455 Jordanians living near the treated home location at the time of implementation. The sample was identified in 2022, a year after program aid had ended, through in-person surveying.

Enumerators used a randomized algorithm to select a Jordanian neighbor living close to the original home of the refugee participant (from among the set of nearest neighbors). This selection was based on the refugee participant’s home location at the time of study randomization, irrespective of any subsequent moves. The algorithm was successful at identifying and surveying a close neighbor: the median distance between the selected neighbor and the original refugee participant’s home is just 63 meters (roughly 200 feet).

# **3.1 Data on refugee outcomes**

The study collected three rounds of surveys tracking participants’ outcomes during (“midline”), immediately after (“endline”), and 1.5 years after all assistance was delivered (“follow-up”). The outcomes to be examined were specified in detail in the pre-analysis plan and divided in three main groups including:

1. _Proximate outcomes:_ This comprises outcomes that were expected to change directly due to program implementation, and include housing expenditures and a housing quality index. As

11
adds to its system, the more attractive it becomes to student clients. On the other, a larger client base incentivizes more local tutors to register in the system. As the number of participants on either side grows, the growth generates value for both groups, creating two-sided network effects.

A tremendous amount of data accumulates on digital platforms with expansion of the positive network effect. Data on consumer behavior and preferences become the secret weapon to further propel business growth.<sup>14</sup> The data can either be utilized to improve a platform’s efficiency as an exchange facilitator or to develop other related businesses. Flipkart, an Indian e-commerce platform, provides customers with more targeted advertisements based on their transaction patterns. Ant Financial incorporates data gained through Alibaba’s Taobao.com Marketplace in its loan assessment model to offer microloans to merchants on the 4