rafmacalaba/data-use-mentions-extended huggingface dataset:
in most values surveys. Income is in fact unavailable in the _Ecosocial_ survey, which contains however information on a series of household goods and assets. We use therefore information of households’ assets to construct a measure of households’ permanent income – the long-term level of economic wellbeing, purged from short-term volatility and measurement error (Torche 2009).

To do so, we match assets in _Ecosocial_ with assets from an “external” household survey in each country that contains information on both assets and households’ income.<sup>2</sup> Using these external surveys, we run a regression model predicting logged per capita household income by means of the set of household goods and assets, controlling for the household head’s education, and the log of household size. The coefficients obtained for the household goods and assets are then used in _Ecosocial_ to predict, using the same set of assets and household characteristics, (logged) per capita income for each household. This technique closely resembles the two-sample instrumental variable approach used to examine intergenerational income elasticity, when information on parental income is unavailable in the focal survey (Arellano and Meghir 1992, Angrist and Krueger 1992). To achieve comparability across countries, we convert each income variable in 2005 USD PPP terms.

Next, we use the permanent household income as a regressor in our models. We construct two alternative measures of social class from the values of predicted household income that we call _absolute_ and _relative_ measures of class. Absolute class is based on fixed monetary thresholds, and distinguishes four classes: lower class (households with a monthly per capita income below $4 in PPP terms), lower-middle class (households with an income between $4 and $10), middle class (households with an income between $10-$20), and upper-middle class (households with an income above $20). We refrain from introducing an upper class with income above $50 or $100 because of very few households earning these incomes in Latin America (typically
one category; and temporarily closed by mandate and choice into a second one. Similarly, we group plants that granted leave without pay and with pay into one group.

Change in sales is only available for businesses open or partially open, or that closed less than 4 weeks prior to the time of the survey (temporarily or permanently). We set change in sales -100 for businesses that have been temporarily closed for more than four weeks at the time of the interview.

For comparison purposes, size and sector in each country is obtained from the pulse survey data, even if in some countries these variables are available from the sampling frame. In some countries where the survey was a follow-up from the Enterprise Survey, size excludes part-time workers. To compute the percentage change in employment, we subtract workers laid off from workers hired, but we exclude observations with measurement error in the question on workers hired (number identical or higher than the size of the firm).

We trim the top 1% in the number of workers hired and in the percentage change in sales relative to the same period of last year. We also trim the top and bottom 2% in the predicted changes to sales in the three scenarios (pessimistic, regular, optimistic). To study expectations and uncertainty, We only use subjective probability distributions where the probabilities for the three scenarios total 100.

# **B.5 Predicting mobility trends**

For countries not covered in the Google mobility reports (Google, 2020), we use the stringency of lockdown restrictions in Hale et al. (2020) to predict mobility trends. Mobility around transit stations is highly correlated but not only explained by the stringency of lockdown restrictions (Figure A4). We fit a linear regression model of mobility on stringency of lockdown restrictions including a dummy for whether the observation is before or after COVID-19 was officially declared a pandemic by the World Health Organization (March 11th), the interaction of the two variables, and country and date fixed effects.
cross-sectional surveys are therefore pooled to increase the sample size in hopes of improving the precision of the estimates.

To answer the question on the extent to which agricultural productivity affects poverty, the analyses are conducted using five measures of poverty: whether or not a household is poor (i.e. poor/non-poor based on the national poverty line), per capita annual consumption expenditure, poverty gap, poverty severity, and relative deprivation in terms of per capita consumption expenditure. The annual consumption expenditure variable includes aggregate expenditures on food and non-food products. The poverty gap and severity of poverty are measured using the Foster-Greer-Thorbecke index (Foster et al. 1984), where the latter is the square of the former. The Foster-Greer-Thorbecke index is typically a summary statistic but, following Mason and Smale (2013), this study constructed a household-specific version of the index for use in a regression model. Both the poverty gap and poverty severity take values of zero for non-poor households and a fraction for poor households. Relative deprivation in terms of consumption expenditure—which compares a household’s mean consumption expenditure to that of better-off households—was

> 5 Datt and Ravallion (1998), for instance, find that higher real wages resulting from increases in agricultural productivity helped reduce absolute poverty levels in India. Also in India, Saxena and Farrington (2003) report that agricultural labor wages rose by 3 percent per annum following an increase in agricultural productivity during the 1970s and 1980s.

> 6 Otsuka (2000) and Biswanger and Quinzon (1986) observe that much of the positive impact of Asia’s green revolution in technology on inequality and poverty resulted from lower food prices accruing from output expansion. Schuh (2000) also suggests that world agriculture’s greatest achievement in the fight against poverty came via the supply of affordable food to the masses.

5
Panel D is constructed similarly, but it is instead taken from a private post-job survey conducted for oDesk company contacts. The results in both panels indicate that there are no performance differences for diaspora-based contracts relative to their peers. Effects are very small in economic magnitude and not statistically significant. The appendix shows that this null result holds regardless of whether employer search is used and for four other measures of performance: obtaining a wage rate increase on the contract, being hired again on oDesk, being rehired by the same company contact, and the worker’s wage rate on the next contract that he or she signs.

We interpret these results as suggesting that workers in India operate in a competitive environment where they are paid market rates, regardless of whether or not a contract is diaspora-based. These results have strong implications for our four hypotheses of what determines initial location choice. First, they are consistent with taste-based preferences existing on the part of company contacts, but they are not consistent with significant levels of taste-based preferences among workers in India. Second, the null results for performance, especially the lack of rehiring of workers, do not align with stories about ethnic Indians having special information advantages or screening capabilities for workers in India. Similar to observable traits examined in Table 6, the future performances of the hired workers are not different for ethnic Indians. Third, the very small wage declines suggest that greater bargaining power by ethnic Indians in their home region is not likely. Finally, the overall lack of wage or performance consequences is not consistent with explanations related to productivity advantages of ethnic collaborations.

Table 8 next repeats this analysis, but instead uses variation across contracts initiated by ethnic Indians living outside of India. Conceptually, this analysis shifts from the worker’s perspective to that of the hiring ethnic Indian. This sample provides a nice contrast with Table 7. This analysis is also informative about whether ethnic Indians have a general “technology” advantage in their work that is being captured in our study of their connections to India
Rural-Urban Migration in China (RUMiC) longitudinal survey and a random selection of approximately 35% of respondents from the 2015 survey round who were provided detailed information regarding the costs and future benefits of participating in the health insurance and pension programs available in their respective cities. Respondents were also informed as to whether social insurance programs were portable or not in the event that they moved home or to another city, and how to contact the representatives in local social-protection bureaus responsible for enforcing laws and regulations governing employer participation. In the following wave of the RUMiC survey, implemented later in 2016, respondents were then asked about their actual and planned participation in these insurance programs.

Over the full RUMiC sample, the average information intervention effect is not statistically significantly different from zero. Consistent with a pre-specified plan to examine

> 2While many migrants had prior experience with health insurance through China’s New Rural Cooperative Medical System (NRCMS), evaluations have found that this system increased health system participation, but afforded little protection against financial risk (Wagstaff and Lindelow, 2008; Wagstaff et al., 2009).

> 3Gallagher et al. (2014) find that a high share of migrants understand that they are eligible to participate in social insurance, but it is unlikely that migrants understand program details or benefits.

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but not all, of these variables. Revenues are non-judicial stamp tax revenues as before, and the tax rate changes are computed from the information in Annex II. We do not have detailed State by State data for the number of immovable property transactions recorded in the Registrar’s office and hence subject to stamp tax. Instead of actual declared values for the immovable property transactions, we use real estate price changes based on data provided by the Housing Development Finance Corporation (HDFC) for commercial and residential real estate prices in Mumbai, Calcutta, Bangalore, and Chennai. These price data are based on the sales price of new developments checked against a buildup of developer costs, and are not based on values declared to the registrar for payment of stamp duty. As such, we would not expect them to be directly affected by underdeclaration. From the price data per square foot for a variety of different properties, we create an average index price and observed percentage changes in the index for each city. There are no data recording the number of real estate transactions and subsequent registrations, and there are no state level data on GDP in the construction sector. We use real GDP growth as a very rough proxy, in order to capture changes in transaction levels.

Figures 13 to 16 suggest that run ups in property values drive stamp tax revenues, just as expected. Analyzing those time periods in which tax rates decline, we also note that predicted revenues tend to decline much more than actual revenues, even when property values shifts are taken into account. Likewise, in the case of duty rate increases, we find that the predicted growth in revenues is consistently higher than the actual. While the available data are not detailed enough to provide conclusive hypothesis testing, the evidence is broadly consistent with the commonly held view that underdeclaration responds to shifts in the stamp duty rate. This pattern reinforces our earlier finding on the relationship between shifts in tax rates and the resulting growth in revenues. Together this evidence strongly suggests that underdeclaration responds to
of contextual and demographic factors on this relationship, including, _inter alia_ : political regime type; level of development; perceptions of public sector corruption; coverage of broadband subscriptions; distribution of political power among social groups; population distribution between 15 and 44; human capital development; measures of government effectiveness; and indices of conflict and fragility.

This analysis utilizes entropy balancing as the primary estimation strategy. In recent years, this method has been used for impact analyses on cross-sectional or panel data in various fields (Kinda and Thiombiano, 2024; Balima and Sy, 2021; Sawadogo, 2020; Gottschalk et al., 2020; Baborska, 2020; Pek et al., 2019). Importantly, this method has several advantages compared to the other matching methods and avoids multicollinearity issues. Propensity Score Matching (PSM) and Ordinary Least Squares (OLS) regressions on the matching sample are used as alternative strategies. Additional robustness checks were performed using alternative GTMI data (GTMI 2022) and by considering the possibility of a slower diffusion of the technology. In addition, a sensitivity analysis considering the role of governance as well as political and institutional factors in the success of GovTech initiatives in boosting citizen participation is performed. Also, an Africa-focused analysis was conducted to test whether the adoption of digital platforms in an African context has an impact on citizen engagement.

This study finds a significant and positive impact of the implementation of GovTech platforms allowing citizens to participate in policy decision-making and provide feedback on public service delivery. These results are robust with respect to the use of alternative data and identification strategies and the rate of diffusion of technology. In addition, the findings show that democracy and the equal distribution of political power have strong and positive effects on citizen engagement. Likewise, public sector corruption has a negative and significant impact on citizen engagement. The analysis also provides evidence that

> for example. (World Bank, 2023. Amplifying People’s Voices - Opportunities for Mainstreaming Citizen Engagement Through Digital Technologies (English). Washington, D
# **3.3 Data**

In December 2019, we conducted a baseline survey of female respondents, any cohabiting partners, and children aged 0 to 6, through face-to-face interviews. The baseline questionnaire asked women participants about their household composition, demographic background, economic activities of the respondent and any co-resident partner, consumption, dwelling and assets, knowledge and practices on child health and nutrition, input into productive decisions of the respondent and any co-resident partner, freedom of movement, acceptance towards domestic violence, sharing of housework, happiness, mental health,<sup>8</sup> and time use. We directly interviewed the woman and cohabiting partner. We also asked about the primary caregiver for each child for each hour over the past 24 hours. We measured child development using the Development Milestone Checklist (DMC III), which was adapted and validated in Burkina Faso for children aged 0 to 8 (Prado et al., 2014). The DMC III includes questions on gross motor, fine motor, and language skills. Due to the time demands of administering the DMC, we randomly selected one child aged 0 to 2 and one child aged 3 to 5 from each household to include in the sample, instead of administering it to all children in each household.<sup>9</sup> The baseline sample includes 2,150 households and 3,126 children. The baseline survey occurred approximately one month after the first childcare centers opened, we therefore control for baseline outcomes in our econometric specifications.

In February 2021, we conducted an endline survey by phone, due to COVID-19 social distancing requirements. We interviewed each female respondent about herself, her partner, and her children. We were able to contact 1,990 respondents from our baseline sample (a 92.6% tracking rate). We have some differential attrition by treatment status with a slightly higher attrition rate in the treatment group (8.6%) compared to the control group (6.4%)
# 2 Two debates on the e¤ects of school meals

Proponents of school meals have argued that school meals can foster educational achievement, thereby increasing people’s earnings potential and boosting economic growth. In answering the question of exactly how school meals can foster educational achievement, there have been two major lines of argument. The older argument is that school meals increase educational achievement by improving child nutrition. The second, more recent argument, is that school meals provide incentives for families to send their children to school.

The argument that school meals improve educational achievement through nutrition relies on two links: ...rst, that school meals improve nutrition; and second, that improved nutrition leads to better educational achievement. Most evidence on these two links is non-experimental, though there are some experimental studies on the link between school meals and nutrition.<sup>7</sup> Since child nutrition, child health and schooling re‡ect household preferences in human capital investments in the child, they might be correlated without any direct causal relationship between them. The evidence on the e¤ect of school meals on nutrition is mixed. The counter-argument to the e¤ectiveness of school meals at improving children’s nutritional status is that families adjust to school meals by reducing resources allocated to children who bene...t from the school meals, transferring them to other members of the households.<sup>8</sup> A few economic studies have tried to infer causal relationships between childhood nutrition and educational achievement by using longitudinal data or various exogenous shocks to nutrition as identi...ers.<sup>9</sup> While these studies document a positive relation between the two factors, they primarily document the e¤ects of early childhood nutrition, this is nutrition before a child reaches 18 months. Since school meals take place in schools, they do not reach children under the age of 18 months.

The more recent argument on school participation is a more promising motivation for funding for
Observations|52|52|52|
|Adjusted_R_<sup>2</sup>|0.941|0.898|0.629|

OLS regression with all variables expressed as percentages. Standard errors in parentheses * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01

# **5. The BRI: Infrastructure improvements and border effects**

A large component of transport times between countries in Central Asia and with the rest of the world is the time spent at borders. De Soyres et al. (2018) produced separate estimates of changes in border delays based on reductions in importing and exporting times from the ‘trading across borders’ section of the World Bank’s Doing Business Database.<sup>10</sup> Table 12 combines these estimates with those of the preceding section to give combined reductions in transport time. The table indicates that the estimated reduction in border times yield greater average time saving than do the physical investments alone (the simple average of numbers in table 12 is a 39% reduction in travel times, as compared to a 15% average reduction in the previous experiment, the average of numbers in table 4). Furthermore, there are considerable differences in the geographical pattern of these changes. For example, reduced border delays bring Kyrgyzstan very large reductions in transport times, with many falling by over 60%. China‐3 has lower travel times with Pakistan and improved access to the rest of the world through Gwadar.

> 10  For any border, the data on “Border Compliance” and the total delay is assumed to be the sum of export time from the exporting country and the import time from the importing country. Documentary compliance is not included as it does not relate to travel time.  All data are available at http://www.doingbusiness.org/data/exploretopics/trading‐across‐borders.

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III.4. Which approach is better, NISR (2015) or NISR (2016)?

Both NISR (2015) and NISR (2016) produce a similar poverty trend (and more or less similar rates). But NISR’s (2016) approach is the one that produces comparable poverty estimates, using a common poverty line and a consistent COLI, one which uses the same budget weights, source data on prices, and base month.

# **IV. Poverty trend based on International Poverty Lines**

Rwanda’s poverty rates measured at the $1.90 poverty line are 60.3 percent in EICV3 and 60.4 percent in EICV4 (Povcalnet as of March 2018). In contrast, Rwanda’s official poverty rate declined from 46 percent to 39 percent between the two surveys. This section explores the reasons behind these differences.

As mentioned earlier, poverty measurement typically hinges on the estimation of a nominal welfare aggregate, adjustment for spatial and temporal price differences, further adjusted for household composition differences. When comparing national and international poverty measurement, we find that both estimates are based on the same nominal consumption expenditure. Thus, any differences in national and international poverty trend are driven by differences in the price index used for spatial and temporal deflation, and the adjustments made for household age and size composition. In the case of Rwanda, we find three key differences in the latter two adjustments.

First, the international measure uses household expenditure per capita while the national measure uses household expenditure per adult equivalent. Second, the international poverty measure uses an average inflation rate for each of the EICV surveys and deflates household expenditure with this average inflation rate derived from survey-weighted monthly CPIs. In contrast, the national poverty measure first deflates household expenditure by a price index that adjusts for spatial and monthly price variations _within_ a survey year, and then adjusts for inflation _between_ survey years. Third, the two aggregates are deflated to different base periods. The international measure uses 2011 as the base
the above factors in establishing expected loss amounts.

To implement such a process which reflects preconditions for improvements in the insurance markets, it is recommended that a Caribbean privately funded insurance services office be established. Such an institution which could operate at a regional or sub-regional level, would be well positioned to develop insurance plans by asset class and advise on prospective loss costs taking into account individual risk characteristics. This would permit primary insurers, at their option, to use such standardized class plans and loss costs in pricing their products. At the request of insurers, such an office would also provide reinsurers with pertinent data which could facilitate the negotiation of reinsurance contracts. Additionally, functions such as inspections of individual properties would be conducted when requested by individual insurers. A key function would also be to maintain updated insurance and risk data on the local industry.

# _Factors Affecting Catastrophe Insurance Demand_

Insurance policies cover the 'natural' catastrophe perils of earthquake, volcano and storm; the last embraces declared hurricanes (>75 mph sustained winds), windstorm, flood and stormn surge. Volcano and flood cover is practically unattainable in some areas recognized as particularly prone to these events (e.g.: Montserrat).