rafmacalaba/data-use-mentions-extended huggingface dataset:
is endogenously determined. Families are prone to support child-labor bans in situations where children compete with adult workers in the labor market and children’s incomes are not a large share of the family income. Otherwise, families would be against childlabor bans. Fertility decisions lock families into the child-labor equilibrium and childlabor bans can be triggered if some exogenous change induces parents to have smaller families; such as skill-biased technology changes.<sup>6</sup> They predict that even in countries that initially have strong opposition to child-labor restrictions, the support for childlabor bans may increase over time once the ban is in place. This happens if the cost of schooling is sufficiently low and the value of child work (opportunity cost) is not too high. If these two conditions are not met, families and children themselves would be worse off.

The evidence of consequences of child labor has increased in the last 15 years or so. Studies have found that child labor can harm individuals on different dimensions. Tyler (2003) used U.S. data from the 1980s and found that working while studying is detrimental to learning among high-school students. Bezerra et al. (2009) and Emerson, Ponczek, and Souza (2016) reached similar conclusions for Brazil.

Using data from Brazil, Emerson and Souza (2011) show that very early entry into the labor market harms individuals’ outcomes in adult life, over and above the effect on schooling. However, this negative effect is reversed when entry into the labor market occurs after age 14.

Also for Brazil, Lee and Orazem (2010) found that a simultaneous effect of an early entrance into the labor force and a premature school dropout resulted in worse health outcomes.<sup>7</sup> Beegle et al. (2009) investigated the medium-term consequences of child labor on schooling, the labor-market, and health outcomes in rural Vietnam and found child labor had a negative effect on school attendance and educational attainment but a positive effect on labor-market outcomes such as
# **_1. Introduction_**

The COVID-19 pandemic significantly affected lifestyle and mobility worldwide, ranging from a dramatic decrease in travel to an unprecedented increase in teleworking. These impacts resulted from governmental measures, with some affecting whole sectors in the economy, and some affecting crucial infrastructure sectors, such as transport. After the outbreak of COVID-19, the local governments of many cities have suspended the operations of buses and subways to prevent people from being infected with COVID-19, reducing the gathering of crowds, and required new safety standards, including requirements to wear masks. These measures implied a major reduction in service capacity compared to the pre-COVID-19 era, by affecting ridership due to a dramatic decrease in travel demand levels as well as imposing regulations that have consequences for service capacity. Such restrictions have significantly affected people's mobility behavior, with individual choices to refrain from traveling in order to reduce exposure and risk of contamination.

One of the sectors that has experienced a major impact is transportation, which is the focus of this paper. Urban travel has declined all over the world, but not uniformly for all modes; public transportation has taken the hardest blow, as shown by survey-based data (Molloy _et al.,_ 2020; Astroza _et al.,_ 2020, Tirachini and Cats, 2020). As a result, there has been a widespread reduction in public transport ridership: according to recent estimates, passenger numbers in cities around the world have been at the peak of the pandemic down 70% to 90% (Google, 2020). Much of the changes in travel demand has been attributed to individuals' adjustments in their daily travel activities such as replacing out-of-home activities with in-home activities. The impact of COVID-19 on public transport varies depending on the stage of the coronavirus spread. The loss of public transport demand can mostly be attributed to reduced travel demand resulting from a combination of public health restrictions, working from home, online schooling, shift to online shopping, transit service cuts, and loss of employment and fear
larger size.<sup>13</sup> The measure used is based on adding up all contributions to obtain the total overall or sum for relevant categories. This allows us to test whether, by increasing voice and participation, reservation of leadership positions for females prompts individuals to contribute more towards public goods.

Before discussing descriptive evidence, it is worth noting that a precondition for our empirical strategy to be valid, i.e. for OLS estimates to be interpreted as a causal effect, is that allocation of reserved seats across villages is indeed random. The fact that the last round of the panel survey was conducted in 1999 allows us to test this for the 2000 election. Descriptive statistics for village and household characteristics in 1999 in panel 1 and 2 of table 2 reveals little if any bias in this regard. Some 30% (71 out of 233) of the sample villages were reserved in this period. Reserved and unreserved villages are similarly sized and have similar levels of access to infrastructure and public goods (drinking water, anganwadi, food for work programs), with a significant difference only in the number of health workers. Household characteristics such as education, age, assets, income, and its composition, are not significantly different between reserved and unreserved villages either.

# **3.2 Leader qualifications and process characteristics**

Having established that villages are otherwise comparable, we note that characteristics of pradhans differ between reserved and unreserved panchayats. The top panel of table 3 highlights that those on reserved seats have much lower educational qualifications; 25% as compared to less than 6% on unreserved seats are illiterate, and only 29% and 6% of them (vs. 62% and 22% respectively) completed at least secondary or high school. This also implies that very few „unreserved‟ pradhans had been involved in politics before; the share of those who held GP positions or ran for pradhan or ward member before is significantly higher in unreserved compared to reserved villages (24%
This indicates that the vast majority of the improvement from utilizing the partial registry, relative to the other three methods considered, derives from its ability to training a much richer and more accurate predictive geospatial model, rather than the increased predictive accuracy in the villages it covers.

In this context, our findings provide convincing evidence that partial registries that collect a limited set of indicators for all households in a sample of villages, if collected properly, can greatly enhance the accuracy of geospatial predictions of village welfare. Many surveys routinely undertake full listing exercises in sampled enumeration areas, which could be extended to collect information on welfare proxies. Combining household surveys, partial registries, and geospatial data has to our knowledge yet to be implemented. Yet the cost would be relatively modest; a rough estimate is that the marginal cost of interviewing all households in 450 villages would be between $24,300 and $72,900.<sup>10</sup> Moreover, this strategy appears to offer a large improvement over existing feasible methods when targeting social assistance programs to poor villages in contexts where conventional data sources are incomplete or outdated. These partial registries could be integrated into current systems of data collection, to help existing surveys benefit more from the wealth of publicly available geospatial data.

> 8 Aiken et al, 2022 and Van der Weide et al. (2022).

> 9 Although Engstrom et al (2022) finds that the model performance is very sensitive to the size of the sample training data.

> 10 These are based on an estimated marginal cost of $3 to $9 per household to conduct face-to-face surveys in Malawi, and the average of 18 households per village in the census extract.

5
STEPS, the World Bank’s Europe and Central Asia Household Health Survey, and the WHS. Table 4 summarizes the key details of these survey ‘families’. For 863 of the surveys identified, we were able

> 6 The SDG indicators are listed at <u>https://unstats.un.org/sdgs/indicators/indicators-list/.</u>

> 7 STEPS survey reports and fact sheets are available at <u>http://www.who.int/ncds/surveillance/steps/en/.</u> 8 The indicators being used to monitor progress on the NCDs are listed at <u>http://www.who.int/nmh/global_monitoring_framework/en/.</u> 9 See also the discussions of UHC service coverage indicators at <u>http://www.who.int/healthinfo/universal_health_coverage/UHC_Meeting_Nov2015_Report.pdf.</u> 10 http://www.ihsn.org/.

> 11 http://microdata.worldbank.org/index.php/home.

> 12 http://ghdx.healthdata.org/.
raw materials, and electricity.<sup>9</sup> The correlation between reported profits and the difference between revenues and expenditure on all of the above categories is very high: 87 percent for monthly data and 97 percent for annual data. Moreover, in the majority of cases the reported profits are higher than revenue less expenses on the above categories, as expected (see the left panel of Figure 7, where most observations lie below the 45 degree line). Additionally, there is a significant positive relationship between firms with higher reported profits and firms with greater output per worker (right panel of Figure 7). These results, therefore, give us additional confidence in using self-reported profits as a measure of profitability and firm performance.

The distribution of profits among the sampled firms is quite wide, ranging from zero to over US$10,000 a month. The distribution of profits is approximately log-normal with half of the firms reporting profits of 8 Katayama et al (2009) argue that most firm-level productivity measures (i.e., output per a specific unit of input) have little to do with measuring technical efficiency or the likelihood of firm survival, unless data on physical quantities are available and the firms are relatively homogeneous in their input and output characteristics. The authors show that commonly-used revenue-based proxies to productivity indices (e.g., firm revenue deflated by an appropriate price index less the cost of inputs) are contaminated by variation in factor prices and demand elasticities and are therefore likely to give rise to spurious results regarding firm efficiency or performance.

9 For each of these questions, the respondents were asked to provide numbers for the last month, the last full year (2010), and expected outturn for the current year (2011). The length of recall period did not seem to affect the responses very much: for profits and revenues, respectively, their annualized values (12*the monthly outturn of May/June) were quite close to the values reported for 2010 and
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _24_ The model explains around 95% of variation in the data with only 11 indicators. At this point there are diminishing returns associated with additional complexity. Next, being able to project the covariates accurately or have reasonable scenario values for them is critical to the application and limits the data that can be used. Most importantly, the model cannot rely on future unknowns for which we have only historical data.<sup>26</sup> Others have also put forward broader arguments in favor of parsimony. Baylis et al. (2021) argue that the use of domain knowledge of economic mechanisms is critical to interpretability of prediction results in the agricultural domain. Zhou et al. (2022) focus more concretely on narrowing down on inputs in their call for transparent machine learning models for food security tailored to specific policy needs, by proposing to carve out the path for future efforts and adoption with a simple prototype model. McBride et al. (2022) similarly observe that related but distinct objectives for food insecurity modeling will require choosing to use distinct data sources and urge for careful consideration of the purpose and use cases when making such decisions.

Expanding the set of input features well beyond what can intuitively be grasped immediately diminishes usability from these perspectives. Andr ́ee (2021a) explain that food crises result from complex interactions between conflict, poverty, extreme weather, climate, and food price shocks; see the analyses by Misselhorn (2005); Headey (2011); Singh (2012); D’Souza and Jolliffe (2013) for more focus on individual elements, that compound in the presence of long-standing structural factors, as pointed out also by Maxwell and Fitzpatrick (2012). The vast complexity of food insecurity provides ample directions for more data and rich opportunity to develop a complex model. The intrinsic assumption behind the relatively simple model developed here is that while the exact causal mechanisms that describe how food crisis shocks play
# **References**

- Agrawal, A., Cashore, B., Hardin, R., Shepherd, G., Benson, C., Miller, D.C., 2013. Economic Contributions of Forests, Commissioned Background Paper for a report to the UN Secretary General ed. United Nations Forum on Forests, New York, NY.

- Angelsen, A., Jagger, P., Babigumira, R., Belcher, B., Hogarth, N.J., Bauch, S., Börner, J., Smith-Hall, C., Wunder, S., 2014. Environmental Income and Rural Livelihoods: A Global-Comparative Analysis. World Development 64, Supplement 1, S12-S28.

- Arnold, J.E.M., Dewees, P.A., 1995. Tree management in farmer strategies: responses to agricultural intensification. Oxford University Press, Oxford.

- Arnold, J.E.M., Dewees, P.A., 1997. Farms, trees and farmers: responses to agricultural intensification. Earthscan, London.

- Bakkegaard, R.K., Agrawal, A., Animon, I., Bosselmann, A., Hogarth, N., Miller, D.C., Persha, L., Rametsteiner, E., Wunder, S., Zezza, A., Zheng, Y., 2016. National socioeconomic surveys in forestry: Guidance and questionnaires for measuring the multiple roles of forests in household welfare and livelihoods. FAO, CIFOR, IFRI, and the World Bank, Rome.

- Balk, D.L., Deichmann, U., Yetman, G., Pozzi, F., Hay, S.I., Nelson, A., 2006. Determining Global Population Distribution: Methods, Applications and Data. Advances in Parasitology 62, 119-156.

- Barton, G.A., 2002. Empire Forestry and the Origins of Environmentalism. Cambridge University Press, Cambridge
<!-- Start of picture text --> 50 r :<br>Ml Data<br>Model<br>46 |B Model<br>p<br>3<br>= 30 .<br>8<br>‘5 20<br>=<br>10<br>)<br>0-5 510 10-15 15-20 20-25 25-30 30-35 35-40 >40<br>Residence-workplace distance (km)<br><!-- End of picture text -->
that the reduction in formal sector borrowing is primarily driven by a decline in borrowing from cooperative banks, while the greater share of informal sector financing is primarily due to a greater percentage of loans obtained from friends and family, rather than high-interest loans from moneylenders or credit from shopkeepers and traders. These results indicate that, in the absence of bank credit, most households are in fact able to increase their relative reliance on internal financing through family networks, rather than having to resort to loans from moneylenders –the most

> 19These estimates are based on self-reported data, in which borrowers may tend to underreport high-interest rate informal sector loans (Karlan and Zinman, 2008). To mitigate this concern, the next section reports evidence based on questions asking about relative shares of financing obtained from different lenders rather than loan amounts. 20Results distinguising between different informal sector lenders are reported in the Supplemental Appendix.

19
On the revenue side of the economy, if labor does not change (a fair assumption if we assume that workers displaced in the fossil fuel industry get absorbed by the renewable industry -– i.e., perfect labor mobility), then this approach implies that the cost should be reflected in capital rents ( _Rt_ ) of<sup>12</sup> Since we are substituting out one form of capital for another, _Rt_ is the only variable that can shift in this case. The cost is thus only equal to the difference between one source of energy compared to another. While nominal GDP is likely increasing in this scenario, real GDP should be falling due to higher costs. Given that the stock of productive capital is the same in the two cases, total energy costs would then be equal to: Unfortunately, the capital stock and valuation of electricity asset data is not readily available, hence the methodology we propose through investment deflator.

# 3.2.3 Approach through quantities

For this approach, one will need to modify the production function in the case of viewing – it as an investment. GDP will increase in the short run (due to the investment values — not because there are more laborers or more productive capital generated i.e., the total electricity output is the same before and after the transition). However, we know that if we increase investment, then capital stock will also increase. This will lead to an increase in potential GDP. To ensure that we do not have an increase in potential GDP (since both sources of electricity yield the same output) we need to modify TFP as follows: where ∆is the change in capital due to additional investment in alternative energy sources. This modification will ensure that potential GDP remains as before (i.e., capital stock increases but TFP falls). A lower TFP will result in a higher marginal cost and hence lead to higher prices, but a lower real wage. Hence this change will have
suggests that the unbanked are far from a homogeneous group. While they tend to be poorer and less educated as a group than the banked, the unbanked of Bogotá also include individuals from high income groups.

# **3.2 PAYMENT SERVICES FOR THE UNBANKED**

13. **The majority of the unbanked receives their salary in cash** , while the banked are more likely to receive paychecks or have their salaries deposited directly into savings accounts (see Figure 3.7). Handling cash payments makes the unbanked more susceptible to loss and robbery than the banked. Unlike in Brazil, where the majority of respondents prefers to be paid in cash, over 90% of the banked individuals in Colombia who receive payments by check and direct deposits responded to the question how they would like to be paid that they prefer the existing system.

**Figure 3. 7  Payment of salaries to the banked and unbanked**

<!-- Start of picture text --> 0.3%<br>Other<br>0.1%<br>Deposit in checking 0.5%<br>account 4.4%<br>5.0%<br>Check<br>9.4%<br>Deposit in savings 5.0%<br>account 52.4%<br>89.2%<br>Cash<br>33.8%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Population by income level<br>Banked Unbanked<br>Source: DANE household study of Bogotá , 2003<br><!-- End of picture text --> 14. **Cashing checks is costly for the unbanked.** One third of the people paid by check are unbanked, corresponding to 5% of the unbanked. The unbanked cash their checks in banks in 83% of the
and Xu 2017). We use learning outcomes in this paper with these caveats in mind.

# II. DESCRIPTIVE STATISTICS SUPPLEMENT

Several statistics demonstrate the coverage and detail of the database. Supplement Table III presents country-subject-level observations by year. The data are spread over time, slightly weighted towards recent years since countries are increasingly participating in assessments. A related feature of the data is a large influx of data in particular testing years. This is more prevalent for developing regions which participate in sporadic assessment.

32
methodology to address the input prices bias stemming from unobserved plant-level input prices, henceforth, TFPQ à la De Loecker et al..

_Estimation:_ We employ the standard specification in the literature where i represents the set of logged values of productivity measures of plant _i_ at time _t_ : wages, i ; TFPR, t t i ; and TFPQ, t t i , using both the Foster et al. and à la De Loecker et al. methods, price, i ; marginal yy ww cost, m i ; and mark-ups, i . d is the log of population density in location ( ) where plant ( ) is located tt at time ( ). For each country included in our study, we adopt a comparable spatial scale used in the literature: ll(ii)ii μμ dd ll ii analysis in Chile and Colombia is at municipality level, while in Ethiopia it is carried out at the towns-level. In tt Indonesia, we use district as the primary spatial unit of analysis.<sup>11</sup> i includes plant-level controls, such as plant age and size (that is, the number of workers i , included in wage and price regressions only). are pair-wise XX 4-digit-industry-year fixed effects to help factor in any sectoral or time trend. Base estimations pool the jjii ll TTFF observations (OLS), and standard errors are clustered at the level of the spatial unit. Later we will exploit the cross-sectional variation to attempt to control imperfectly for firm selection.

Both quantity and quality effects described in the previous section can lead to inconsistent estimates. Though previous studies suggest that attempting to remedy this endogeneity does not drastically change the elasticity magnitudes (see meta-analysis by Grover, Lall and Timmis, 2021; Duranton 2016 for Colombia), we follow the literature by: (i) employing geological variables such as land fertility (Combes et al. 2010), land suitability for the construction of tall buildings (Rosenthal and Strange 2008; Combes et al. 2010) as instruments
Policy Research Working Paper 10191

# **Abstract**

This paper presents new evidence on the levels and trends of vaccine hesitancy in developing countries based on harmonized high-frequency phone surveys from more than 120,000 respondents in 53 low- and middle-income countries. These countries represent a combined 30 percent of the population of low- and middle-income countries. On average across countries, one in five adults is hesitant about the COVID-19 vaccine, with the most cited reasons for hesitancy being concerns about the safety of the vaccine, followed by concerns about its efficacy. Between late 2020 and the first half of 2021, there tended to be little change in levels of hesitancy except in Iraq, Malawi, and Uzbekistan, where hesitancy increased. COVID-19 vaccine hesitancy is higher among female, young, less educated, and rural respondents, after controlling for selected observable characteristics. Country estimates of vaccine hesitancy from the high-frequency phone surveys are correlated with but lower than those from earlier studies, which often relied on less representative survey samples. The results suggest that vaccine hesitancy in developing countries, while less prevalent than previously thought, will be an important and enduring obstacle to recovery from the pandemic.

This paper is a product of the Poverty and Equity Global Practice and the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at jdayton@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the