rafmacalaba/data-use-mentions-extended huggingface dataset:
, are the ecosystem services of highest dependency for banks’ portfolios (see Figure 4). This finding is in line with the studies analyzing financial sector exposure in Brazil (Calice et al.,2021), Netherlands (van Toor et <u>al., 2020) and France (Svartzman et al., 2021). Of every dollar that banks lend to corporates, 31 and 21</u> cents are either highly or very highly dependent on climate regulation, and flood and storm protection, respectively. Both services are essential for climate mitigation and adaptation, demonstrating the close linkages between nature loss and climate change. Mauritius, India, and the United Arab Emirates have a particularly high exposure to those ecosystem services (Figure 6).

Banks’ portfolios in lower-income countries are more exposed to water-related ecosystem services. These differences are more evident in the case of surface and groundwater. For instance, of every dollar that banks lend to firms, 26 cents are either highly or very highly dependent on surface water in lower-middleincome countries. This compares to 20 cents per dollar in the case of upper-middle-income and 12 cents per dollar for high-income countries. The divergence is on a similar order of magnitude for groundwater but to a lower extent for water quality and water flow maintenance (Figure 7). Pakistan, Argentina, and loan-level data and daily regulatory reports of all securities held at the security issuance level (NGFS-INSPIRE, 2022). Using significantly more aggregated information, our estimate is 6 percentage points lower.

> 13 It should be noted that the indicated dependencies on the global sectoral averages are from the ENCORE database. However, ecosystem service dependencies may vary depending on the project design and should ultimately be evaluated at the project level. For example, a real estate investment that adheres to the passive house standard (a green building standard) may rely much less on ecosystem services such as water compared to traditional real estate projects.

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main providers of goods and services inside the family. This assumption was made because of simplicity. Estimating the joint decisions of several members regarding labor market status and time allocation is a complex exercise. Even with this simplifying assumption, the model exhibits the spouses’ optimal strategies when facing utility maximization over time usage and labor income. Following this idea, the model includes the distributions of home production productivity by gender. The identification of these distributions relies on the mean and variance of home production time reported by husbands and wives in the RRS survey, conditional on labor market states.

**Second, the paper provides a methodological contribution within the search literature; the model presented here goes beyond the existing search model literature that either focused on intensive and extensive labor market decisions while ignoring household production (Flabbi and Mabli 2018), or focused on the simple binary choice between employment or household production (Choi 2018** ). This paper combines these two strands of search literature in a dynamic model that explains the intensive and extensive margins of spouses’ decisions, as well as spouses’ time allocation over the full range of activities available to them (work in the market, work in the household, and enjoying leisure time).

**Finally, the model allows us to decompose gender gaps from two potential sources: the structural labor market and the preference parameters.** Therefore, the model can measure the effects of gender roles on males’ and females’ willingness to participate in the labor market.

**Additionally, our estimated structural model is useful for evaluating the ex-ante impact of gender-specific policies on labor market gender gaps, including the gender-disaggregated labor market status and time allocations of Roma couples.** These may include gender policies affecting

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safety net that protects previous gains in reducing poverty. The aim of targeting the program on the population that is currently experiencing poverty is to reduce deprivation now. However, risk-induced vulnerability involves fluctuations in welfare, that is, the likelihood of households currently above the poverty line to fall into poverty after exposure to unforeseen shocks. In the realm of social assistance, this means the method described here can help enhance the targeting of the RNU to contribute more effectively to the ability of households to cope with risks, a crucial step toward increasing the resilience of the population to risks and shocks (Skoufias, Vinha, and Beyene 2024).

Accounting not only for poor households, but also for households at risk of becoming poor reflects a recognition that expanding the safety net to cover a portion of nonpoor households is essential for sustaining the gains of past spells of poverty reduction during economic downturns and shocks. In practice, using vulnerability rates along with poverty rates, instead of poverty rates alone, increases program eligibility among households in urban and peri-urban communes. For instance, urban Dakar’s eligibility rises by 84 percent with respect to the eligibility rate if only the poverty rate is considered. Nonetheless, rural areas still exhibit consistently higher program eligibility rates because of their comparatively high poverty rates. Taking risk-induced vulnerability into account broadens the scope of social protection to encompass households that, in the face of a shock, could slip into poverty, a scenario that the current targeting mechanism may not address sufficiently.

The paper is structured as follows. Section 2 explains the use of poverty maps in support of the targeting of social programs, and discusses of the concept of the vulnerability to poverty. Section 3 describes the data and the methods used in estimating poverty and vulnerability and clarifies the assumptions involved in adding vulnerability to the concepts behind poverty mapping. Section 4 provides estimates of poverty and vulnerability using data on Senegal, the distribution of communes by poverty- and risk-induced vulnerability rates, and several robustness checks. Section 5 provides background on the RNU,
rather save some for investing and savings. There will likely always be some individuals with very low or negative income each year due to a negative capital shock or for other reasons. Yet this may not imply that their consumption falls below any problematic threshold. This poses a challenge for our study because it is not obvious that income poverty can be ended. For that reason, we convert income distributions to consumption distributions by applying the following formula: ln(c m m m )) cc) = llcc(iic<sup>0.93</sup> + 0.68 + 0.26 ∗ln(iic (1) This is derived by exploiting 150 surveys spanning 16 countries where for a given year, there exists both an income aggregate and consumption aggregate in the World Bank’s Poverty and Inequality Platform. For each of these surveys, the distributions are converted to 100 quantiles of matching income-consumption pairs (i.e. the income and consumption level at a particular quantile for a particular country-year). The functional form in equation (1) is chosen as it reflects a scenario where both the consumption and income distributions follow a 3-parameter log normal distribution while allowing for these distributions to differ based on a country’s median income. The fit gives an R<sup>2</sup> of 0.965 and hence highly accurately converts income to consumption distributions. Note that the conversion is not done at the household level, meaning that we do not convert a particular household’s income level to a consumption level. Rather, we convert the 2022 distributions to 1,000 quantiles and convert each of the income quantiles to consumption based on the equation above. More details about the income-consumption conversion are available in the appendix.

For about 50 economies home to less than 2% of the world’s population, we have no prior income or consumption data at all. These tend to be small high-income countries that do not share microdata as well as very closed countries, such
on the relative importance of the two channels of increasing diversification across levels of development. Section 4 analyzes the empirical relationship between income, discovery, and diversification.

# _2) Discovery and development: a part of structural change based on factor endowments_

Rather than equation (1), consider the fundamental framework for analyzing output and growth in economics, namely the aggregate production function. Stated in its most basic, generalized form, national income ( _Y_ ) depends on an economy’s factor endowments, such as physical capital ( _K_ ), human capital ( _H_ ), and unskilled labor ( _L_ ): When countries grow, their relative endowments of factors of production change, which in turn determines patterns of production across income levels. As pointed out by Leamer (1984), less developed countries with a relative abundance of _L_ will specialize in traditional labor-intensive goods. However, as they grow, their stock of capital (both human and physical) increases, causing them to shift production towards more capital-intensive goods.

This theoretical perspective has its own implications for what we would expect to find in our discovery data. Discoveries may be capturing the structural transformation that occurs with growth, in that discoveries will be concentrated in certain industries at low levels of development and in others at higher levels of development. If this factor-endowments story accurately characterizes discovery patterns across income levels, we would expect the relative frequency of 5
fixed effect and (ii) shocks to consumption persist over time. Lanjouw et al. (2011) argue that correlation between error terms will almost certainly be positive if the condition (ii) holds.  In their study using Vietnamese and Indonesian data they present empirical support in favor of this assumption.

> 4 We replicate the procedure 50 times in this paper ( _R_ =50). Section (5) discusses the robustness of replicating the procedure 150 and 300 times.

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include non-food consumption, housing, and household roster. Although not done in this paper, one could potentially conduct an analysis at the question level to find ways to make the module more efficient in terms of time and the amount of information the module extracts.

For survey practitioners interested administering individual questionnaires to adult household members, we estimate that the additional time spent on individual data collection for all adult household members in a given household ranges from 37 to 48 minutes in Cambodia, 22 to 44 minutes in Ethiopia, and 81 to 191 minutes in Tanzania. These numbers do depend on how many modules are administered per person which explain why Tanzania has a higher additional duration compared to others given its higher number of individual modules administered. However, the individual questionnaire, on average, only takes 13 to 25 minutes to administer to each adult, depending on the country. The top time-consuming individual questionnaire modules include land, labor, health, and education.

Furthermore, combining the paradata with the total survey budgets allows us to provide estimates of each minute of survey data collection. For instance, the unit cost estimate is $1.71 in Ethiopia, 28
Although the NFHS contain fairly rich data on investments in children, these questions are asked only for children born in a few years preceding each survey and there are no pre-ultrasound data on investments.<sup>20</sup> For this reason, we utilize the 1999 round of the Rural Economic and Demographic Survey (REDS) that reports data on health investments for all children alive at the time of survey to analyze effects on health investments.<sup>21</sup> For the mortality and postnatal health specifications (presented in the next section), we pool all births of the surveyed women to create a child-level data set. The birth hazard sample is a retrospective mother-year panel that we create from women’s retrospective birth histories; women enter this panel in the year of marriage and exit in the year of survey. The sample used for the fertility stock estimates simply pools all surveyed women to form a woman-level data set. Summary statistics for mortality, (actual) fertility, and child health investments are in Table 1. In Section 5.1.1, we examine the robustness of our results to data-related issues, including gendered recall bias. The maximum recall period is 20 years in our NFHS sample and is 26 years in our REDS sample.

# **4 Empirical Strategy**

The hypothesis we test in this paper is that the availability of prenatal sex-detection technology (in particular, ultrasound), which led to large-scale selective abortion of female fetuses, also modified conception and investment decisions in a gendered manner, with implications for excess female mortality (EFM).

As discussed in Section 1, we exploit exogenous variation in the supply of ultrasound scanners. The first supply shock was the first imports of ultrasound scanners in the early stages of India’s import liberalization (tariff reduction), and the second was initiation of local production following industrial delicensing policies.<sup>22</sup> However, the wave of economic liberalization in India that led risk, and more so
birr) resulted in 109 firms being excluded because their revenue exceeded the cut-off. The annual turnover is estimated by multiplying the self-reported average monthly revenue by the number of months of operation in a year.

The resulting sample consists of 1,523 nonfarm household enterprises in 2018/2019 and 1,374 enterprises in 2021/2022, which are disaggregated by sex of the owners. Female businesses are defined as those owned exclusively by women, while male businesses are those owned exclusively by men or jointly by women and men. Jointly-owned firms are classified as male firms because the presence of a male owner allows for easier entry into male-dominated sectors, which are more profitable (Alibhai et al., 2017). The estimates in the paper use household weights.

# **_3.2 Descriptive statistics: Gender and nonfarm enterprises_**

> 7 See Marchese (2021) for examples of these cases from Latin America.

> 8 The presumptive tax threshold at 500,000 birr is half the size of the VAT and turnover tax threshold at 1 million

> birr.

> 9 There were other ESS surveys that were conducted prior to the current presumptive tax schedule took effect in 2017 and are therefore excluded from the analysis.

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Migrant households with a short-term contract are affected by higher school fees (row 4), while those with a permanent or long-term contract are not (not shown).

Next, we divide the sample by two subjective indicators: whether the household head is planning to stay in the city for a long time, and whether the household head is depressed based on Center for Epidemiological Studies Depression Scale (CES-D10) questions.<sup>42</sup> Estimation results, however, are similar for these different groups, even though the depressed migrant workers appear to be more impacted by changes in school fees (with the estimated coefficients being larger than those in Table 3, column 3) (row 6).

We then compare the impacts for migrant households with more than one child and migrant households with only one child. The estimation results show that households with only one child (row 7) are more likely to be affected by school fees. Migrant households with both spouses in the city (row 8) or with only one spouse (not shown) are both sensitive to school fee changes. The same result holds for both employees (not shown) and the self-employed (row 9).

Overall, the estimation results in Table 6 suggest that the vulnerable groups (including the poor, the uninsured, those without permanent contracts, and to some extent, the depressed) are more sensitive to changes in school fees. These results are consistent with our theoretical results. _School fees and child migration during the economic crisis_ Our theoretical results suggest that child migration would increase in response to the reduced school fees in 2009 (Propositions 1 and 3), but would decrease during the economic crisis in this same year (Corollary 3.3). Which effect would dominate child migration? Estimation results using the 2009 wave of the RUMiC survey show that the non-IV estimates (Table 7, row 1) are negative, and are between 60 percent and twice larger in absolute magnitude than those for 2008 (bottom of Table
**Weight Calculations for Panel Surveys with Sub-Sampling and Split-off Tracking**

Kristen Himelein<sup>1</sup> _Development Research Group, World Bank 1818 H Street NW, Washington DC, 20433, USA_

**Keywords** : survey weights, panel surveys, attrition, tracking surveys

**JEL classification:** C83

**Sector:** POV

> 1 This paper was written as part of the Living Standards Measurement Study – Integrated Surveys on Agriculture project and benefited from comments from Peter Lynn, Steve Herringa, Stephanie Eckman, Kathleen Beegle, Jonathan Kastelic, and Raka Banerjee, as well as background research by Anthony Tamasuza.
.3|5.3|10.6|5.2|10.0|7.4|
|21 Sichuan|2.5|4.8|7.4|13.6|7.7|8.4|8.0|
|22 Guizhou|1.7|3.3|4.7|7.7|7.7|6.3|7.1|
|23 Yunnan|2.2|4.0|6.5|11.4|7.9|7.6|7.8|
|24 Shaanxi|2.9|5.0|7.8|14.2|7.8|7.2|7.5|
|25 Gansu|3.5|5.2|8.4|14.9|6.7|7.2|6.9|
|26 Qinghai|4.3|6.3|8.1|13.6|4.8|6.3|5.5|
|27 Ningxia|3.7|6.2|8.9|15.3|7.1|6.5|6.8|
|28 Xinjiang|3.1|6.2|11.0|17.7|8.7|7.6|8.2|
|**Total China**|**3.6**|**6.6**|**10.9**|**22.9**|**7.9**|**9.8**|**8.9**|

Data source : _China Statistical Yearbooks_ and authors’ calculations _._ The data of Chongqing are included in Sichuan for a better coherence; Hainan (an island and a Special Economic Zone) and Xizang are excluded for
). Conducted by the Department of Labour (DOL) at the Ministry of Labour, Immigration and Population (MOLIP), this unique countrywide survey collected detailed information on the population aged 5 years and above on the topics labor force participation, child work and school-towork transition.<sup>9</sup> The objective of the survey was to provide information on the national labor market to be used in the development, management and evaluation of labor market policies and programs. A twostage sampling design was used with enumeration areas and households serving as the primary and ultimate sampling units, respectively. Estimates derived from the survey are reliable at national, rural-

> 9 The data used here are from 2015 and do not fully capture the socioeconomic improvements Myanmar experienced until the early 2020s before being hit by multiple crises.

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# **6. Conclusion**

This paper estimates the differential effects of exposure to COVID-19 on educational outcomes. We take advantage of the municipal-level warning index that the Guatemalan government developed, with lower values denoting fewer COVID-19 cases and higher values greater infections, to implement a difference-in-differences strategy using student-level administrative panel data. We are therefore able to estimate how the pandemic affected dropout, effective promotion, and school switching behavior (private to public and vice versa) across the entire school system.

Results show that municipalities with a higher warning index had significantly larger dropout, lower promotion rates, and a greater share of students switching from private to public schools. These effects are more pronounced during the first year of the pandemic. We also find differential effects by level of instruction, with greater losses for the youngest children who are enrolled in initial and primary education. These results are robust when using the warning index and time spent in different color categories, and are also consistent across specifications, still significant after multiple hypothesis adjustments, and mostly validated by placebo experiments.

The literature on the consequences of the pandemic has grown over the past few years, especially in terms of quantifying differences among population groups. While this study takes a step in this direction with respect to extensive margin educational outcomes, we do not have information to determine effects on intensive margin results such as the quality of education. Some research has already found generalized learning losses (García, 2020; Tadesse and Muluye, 2020; <mark>Bundervoet</mark> _et al_ ., 2022; Marín _et al_ ., 2022; Singh _et al_ ., 2022), but a similar analysis would be warranted to better understand other impacts from the pandemic. These potentially unequal effects are especially relevant in terms of education since they have immediate impacts as well as 29
# **Using Administrative Data to Evaluate Municipal Reforms:**

**An Evaluation of the Impact of Minas Fácil Expresso**<sup>*****</sup>

Miriam Bruhn, _World Bank_ David McKenzie, _World Bank, BREAD, CEPR and IZA._ _Keywords:_ Entry reform; Difference-in-Differences; Informality.

_JEL codes:_ O17; O12; D22 ; L26.

> * This evaluation arose from a request from the State Government of Minas Gerais, Brazil. We are grateful to Gustavo Andrade, Alex Barbosa, Henrique Costa and Rita Gozer for their help in understanding this program, for providing data, and for working with us to design the evaluation at the 2011 DIME-FPD workshop in Rio de Janeiro. All views expressed in this paper are those of the authors alone, and do not necessarily represent the views of the World Bank or its member countries.

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other sources of significant measurement error which affect one sensor, but not the other because of their different timing. For example, auroral activity is a significant concern for night lights retrievals but not NO2.

Third, there are other potential instruments available which corroborate the lights-based estimate. In Table 5 Column 3, we appeal to the results of Pinkovskiy and Sala-i Martin (2016), who showed that measurement errors in PovcalNet survey means are uncorrelated with those of lights. Thus, these represent another potential instrument, in which we can have some particular confidence. Survey means cover a smaller sample of countries than satellite-based signals, so they are perhaps not the best instrument for our purposes. However, by including both lights and survey means as instruments, we now have an overidentified model, and can test those overidentifying restrictions with a standard Sargan (1958)-Hansen (1982) _J_ -statistic. We find that it is not possible to reject the null hypothesis that both overidentifying restrictions are valid, with a _p_ -value of 0.273. Of course, validity of overidentifying restrictions does not necessarily imply instrument exogeneity (e.g. Deaton (2010)). But we may also see that the estimate of _β_ changes only marginally with the inclusion of this additional instrument.

**Robustness Checks.** In Table 5, Columns 4 and 5, we further scrutinize our lights-based estimate in Column 2 by including electricity and CO2 as additional instruments. Again, we may not reject the validity of overidentifying restrictions. Moreover, all instruments are jointly

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