rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>0.287<br>**Notes:** This table reports the treatment efect of the employment and cash arms on a variety of outco<br>Each column shows the OLS estimates of Equation (1) including dummies for each treatment arm and <br>dependent variable (ANCOVA), camp and enumerator fxed efects, and sociodemographic controls as d<br>LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014). Each row presents the coefcients of th<br>group (which received a small weekly cash payment).<br>All outcomes are standardized and drawn from the six-week followup survey, in which we ask only a su<br>Psychosocial Index collected at endline. The ‘PS Index’ presented here is therefore only an index of thi<br>index of two questions asking i) how the respondent feels today on a six point scale, and ii) how many d<br>values for ‘Stress’ represent _reduced_ stress. All remaining outcomes are likewise presented such that hig<br>‘Cognitive’ represents an index of three arithmetic questions, and ‘Days Healthy’ measures the number <br>Standard errors are clustered at the block level. The row labeled “Test Cash=Work” refects the unadj<br>the Cash and Work arms, while the row labeled “Shrp. q-val Cash=Work” refects the adjusted p-value<br>multiple hypothesis testing of Anderson (2008) for all outcomes in this table.|

49
- European Banking Authority (EBA), September 2012. Results of the Basel III monitoring exercise based on data as of 31 December 2011.

- European Banking Authority (EBA), October 2012. Final Report on the Implementation of Capital Plans following the EBA’s 2011 Recommendation on the Creation of Temporary Capital Buffers to Restore Market Confidence.

- Feyen, E., Kibuuka, K., and Ötker-Robe, I., 2012. Bank Deleveraging: Causes, channels, and Consequences for Emerging Market and Developing Countries, World Bank Research Working Paper 6086.

- Financial Stability Board (FSB), June 2012. Identifying the Effect of Regulatory Reforms on Emerging Market and Developing Economies: A Review of Potential Unintended Consequences.

- Furceri, D. and A. Mourougane, 2009. The Effect of Financial Crises on Potential Output: New Empirical Evidence from OECD Countries. OECD Economics Department Working Paper 699.

- Giannetti, M. and Laeven, L., 2012. The Flight Home Effect: Evidence from the Syndicated Loan Market During Financial Crises. Journal of Financial Economics 104:1, pp. 23-43.

International Monetary Fund (IMF), April 2012. Global Financial Stability Report.

International Monetary Fund (IMF), October 2012. Global Financial Stability Report.

- McKinsey Global Institute, 2012. Debt and deleveraging: Uneven Progress on the Path to Growth.

- Le Lesle, V., 2012. Bank Debt in Europe: Are Funding Models Broken? IMF Working Paper 12/299.

- Takáts, E., 2010. Was it Credit Supply? Cross-border Bank Lending to Emerging Market Economies during the Financial Crisis, Bank for International Settlements mimeo.

26
B2C level in the year 2017, distinguishing by region, as well as the corresponding _t_ through 2047. Applying these to the two cases under consideration, we obtain the forecast for B2C Internet use for the period 2018‐2047. We transform such forecast estimates back to 1‐to‐7 values, to maintain consistency with the original WEF data.

As Figure 8 shows, B2C adoption converges gradually to the maximum value of 7 in Case 1, as expected. Although MENA and SSA countries<sup>14</sup> lag the rest of the world in the extent to which B2C is adopted, they eventually converge to the maximum level. Indeed, for the average MENA country, B2C exceeds 6 by the year 2023 and 6.9 by 2035. In Case 2, in which adoption never reaches its full potential, B2C internet usage converges to 5.8.

<!-- Start of picture text --> Figure 8<br>B2C Internet Adoption (1‐7)<br>7.0<br>6.5<br>6.0<br>5.5<br>5.0<br>4.5<br>4.0<br>3.5<br>MNA (hi) MNA (low) SSA (hi)<br>SSA (low) World (hi) World (low)<br>Source: Author’s calculation.<br><!-- End of picture text --> In turn, we use the above B2C forecasts to project how the average number of travelers per country of origin —that is, not the total number of visitors— will increase as the use of digital platforms grows in the

> 14 As stated in the introduction, we pay particular attention to MENA and SSA countries, as the two regions have only recently been catching up with the rest of the world in terms of the use of digital tools.

Page **
Table B1: Summary Statistics of pooled household-level data

||N|Mean|Median|SD|Min|Max|
|---|---|---|---|---|---|---|
|**Consumption (per day USD in 2017 PPP terms):**|||||||
|Real per capita consumption expenditures|167,101|3.96|2.90|8.03|0.06|2304.38|
|Nominal per capita consumption expenditures|167,260|3.38|2.14|8.82|0.04|2521.88|
|**Spatial defator**|||||||
|Spatial defator<br>|167,128|1.00|0.98|0.13|0.76|1.75|
|**Demographic:**<br>|||||||
|Household size|167,357|6.55|6.00|3.80|1.00|62.00|
|Age of household head|167,253|40.31|40.00|18.46|0.00|119.00|
|Household head is male|167,303|0.78|1.00|0.41|0.00|1.00|
|**Marital status (of household head):**|||||||
|Married|167,293|0.75|1.00|0.43|0.00|1.00|
|Never married|167,293|0.04|0.00|0.18|0.00|1.00|
|Living together|167,293|0.07|0.00|0.25|0.
that they were comfortable completing the task.

Our WTA format is also likely to be incentive compatible because it asks about a collective, rather than private good. Lloyd-Smith & Adamowicz (2018) modify the theory in Vossler et al. (2012) to show that incentive compatibility carries through under the same sufficiency conditions when the elicitation format is WTA in a public good referendum. They also show empirically that the WTA format for public goods is preference revealing. This finding is relevant for us because our household survey meets the conditions described by Lloyd-Smith & Adamowicz (2018): the local forest resource is a collective good, and the vote centers on the single issue of the community accepting payment for collectively giving up aspects of it. The audience for the vote is the community government, which enters into an enforceable contract if the vote is positive.

The question would also be incentive compatible for leaders if they respond to the referendum question with a public good in mind and on behalf of constituents. It is possible, however, that leaders could respond to the question as they might for a private good, in which case for the leaders’ survey there could be incentive to strategically overstate WTA. It is possible that leaders could view themselves as acting as a representative of the community selling an asset to an external party, with the audience for their vote the program administrator. Carson & Groves (2007) and Lloyd-Smith & Adamowicz (2018) describe two types of strategic responses in this type of private good stated preference WTA context: for existing goods, there is incentive to respond no to an offer to indicate more price sensitivity, and for new or future goods there is incentive to respond yes to secure a future chance to participate. Since this is an existing program, the former seems more likely in our case. This would cause leaders to be less likely to vote yes, which would cause us to overestimate the costs of program participation.

This issue could also possibly affect the more widespread use of referenda questions to solicit
this methodology by displaying the trajectory of urban growth in Uganda’s capital city, Kampala. The figure displays five-year increments in built-up pixels, showing how Kampala’s urban extent gradually expanded within the specific grid cell where it is located.<sup>6</sup> We first calculated the baseline

> 6Note that our regression analyses use data that ends in 2014 due to the extent of availability of the drought measure that we use. Figures where we only display data based on the World Settlement Footprint database allow us to go beyond 2014.

8
to increase male and female land owners’ or SLMs’ awareness/knowledge could be a promising and low cost way to quickly identify options that could help bring the country closer to full registration of all transfers, helping to ensure the sustainability of the large investment made under LTR. Using LAIS, together with databases maintained by other institutions, to regularly and in real time monitor the indicators reported here, among others, could help RNRA to document the benefits already generated by LTR. Without maintenance it will be difficult to sustain these benefits, and manage the process of doing so.

Establishing a monitoring system that builds on planned real-time integration of the Land Administration and Information System (LAIS) with data on court cases or disputes by Ministry of Justice, land lease fees by the Revenue Authority, and land use by the Ministry of Agriculture promises to enhance these benefits. Regular monitoring and publication of key statistics at high levels of spatial disaggregation will allow the Rwanda National Resource Authority (RNRA) to measure progress in addressing remaining challenges, manage the process of doing so and, if data (at more aggregate levels to protect confidentiality of the underlying information) are made public, significantly improve the basis of information (e.g., on land prices) for private sector decision-making.

14
, specifically ratings of her morality, respect, social standing, and economic mobility.

_Endline Survey Measures._ The endline survey assessed economic and psychosocial outcomes, both relating to personal and relational processes. The outcomes and their constructions align to the extent possible to the policy experiment, as reported in Bossuroy et al. (2022). Due to budget and logistical constraints, this endline survey was a condensed and slightly altered version of the follow-up surveys used in Study 2, i.e. the broader policy experiment. We construct composite indices across all outcomes within each domain (economic and psychosocial) in order to address multiple hypothesis testing. All outcome indices are standardized such that each index has mean 0 and standard deviation 1 for the control group (Kling et al., 2007).

**_Economic outcomes_** . The _food security index_ is a composite of two measures. _Household food security frequency_ over the past 12 months is assessed with two items adapted from the Food Insecurity Experience Scale (FIES, e.g., having gone a whole day without food, 0=Almost every month to 4=No, never) (Ballard et al., 2013). _Dietary diversity_ is captured with an abbreviated measure of the food consumption score that is a weighted sum of the number of days in the past week that the respondent has eaten vegetables and meat, where meat is given a weight of four and vegetables of one (WFP, 2008).

_Business engagement index_ assesses whether the program participant had an off-farm business or not, the number of off-farm businesses owned or operated in the past 12 months and the number started in the last 12 months, the number of businesses the beneficiary intends to expand (vs maintain or abandon), the sum of days worked on all businesses in the past month, an index of self-reported healthy business practices (e.g., keeping a sales ledger), and the total value 40
# **4. Estimation Results**

This study estimates the impact of electricity consumption on the value added using a cross-country data set that includes an unbalanced panel of annual observations of 126 countries during 1996-2014. It runs separate regressions for the three aggregate sectors of agriculture, manufacturing, and services, which have data for both electricity consumption and value added, as well as for such control variables as capital and labor. Estimating elasticities for individual sectors helps understand sector-specific patterns, as the sectors vary substantially in terms of intensity of consumed electricity and possibly its impact on growth. Although the data set includes more advanced economies than developing countries due to limited data availability, the latter are responsible for the most variations (see Figure 2). Finally, the study uses several control variables to account for time-variant observed characteristics that affect the value added, such as import and export shares in GDP, shares of respective sectoral value added in total, capital, and labor. All control variables are measured at the country level and are sector neutral, with the exception of sectoral share in the value added. Both export and import shares in GDP are important because they show the global competitiveness of a country, the openness of its economy, and its dependency on imports, reflecting a lack of countries’ productive capacities. Finally, capital and labor are the main components used in production, and they capture country-specific and time-varying effects, such as business cycle fluctuations.

The empirical strategy includes various econometric approaches to estimate the impact of electricity consumption on the value added with the two-step system panel GMM as a preferred estimator. In particular, the study applies the fixed-effects approach, two-step difference and system panel GMM estimation methods. The OLS estimators are prone to reverse causality and endogeneity bias, capturing only a correlation between the two variables despite an addition of several controls. The fixed-effects approach improves the OLS coefficients by removing country fixed effects that might generate omitted variable bias in the regression results. For example, a country with abundant oil resources would have
) after intensive analysis of household surveys.

> 31 Indeed, Birdsall (2015) proposes that the World Bank can invest more resources in supporting researchers, particularly in poorer countries. Clemens and Kremer (2016) even suggest that the World Bank has had more influence on policies in poorer countries through its policy advocacy than its lending portfolios; see also Gavin and Rodrik (1995) for a similar viewpoint. Ravallion (2016) further argues that in its activities related to data and research, the World Bank has not fully reached its potential as a “knowledge bank”. It is not clear to what extent that similar arguments can apply to other international organizations like the UNDP that are also well known for their technical assistance.

33
of analysis is that they are also the smallest administrative unit where the location of a mine can be identified.

In the analysis, we introduce two sets of explanatory variables: variables related to the location and magnitude of mining activity, and control variables. In line with the distribution formula of the _Canon_ , and using plant-level mining data from the Peruvian Ministry of Energy and Mining, we distinguish between three types of districts within mining departments:<sup>5</sup> _producing districts_ , which hosted a mining facility during 2004-2006; _non-producing districts in producing provinces_ , which, despite not hosting any mining activity, receive a larger share of the _Canon_ than districts in provinces where there is no mining production; and _non-producing districts in non-producing provinces_ , which still receive a share, albeit the smallest, of the _Canon_ because they are located in a producing department. Our final sample consists of 87 producing districts, 453 non-producing districts in producing provinces, and 742 non-producing districts in non-producing provinces (Table 2).

For all districts, we use information about the amount of _Canon_ they received during 2002-2006; the amount of other government transfers during 2002-06; and the value of mineral production at the district and province level during the same period.<sup>6</sup> In addition, we also use in the analysis time invariant district characteristics such as altitude, area, and the location of provincial capitals, and district socioeconomic characteristics from the 1993 Census, prior to the mining boom (for more details, see Table 1).

Table 2 presents summary statistics of these three groups of districts. Of interest are the two socioeconomic outcomes for which we have information in both 1993 and 2007: illiteracy and basic necessities uncovered. Overall, in 1993, producing districts seemed to display a slightly higher literacy rate than-non producing districts; however this difference disappears once we control for basic district characteristics (Table 3). And there are no significant differences regarding the index of basic necessities uncovered between producing and non-producing districts. Of
ownership), and community characteristics (fraction of population working in agriculture, access to schools and roads, and so on<sup>8</sup> ). μ is the unobserved component for household i.

M is an indicator for having a migrant or receiving remittances. To account for potential endogeneity, in the two-stage least squares regressions, 1993 and 1997 migration networks, defined as the percentage of households in the village with migrants, will be used as instruments for M in 2007. Only 1993 network will be used as instrument for M in the 2000 equation.

As female migration may have a differential impact on the outcomes, we include ―female_it‖—a dummy variable which denotes households with only female migrants, the omitted category being those households with at least some male migrants. Alternatively, this variable can refer to the female share among migrants, for each household with migrants. The coefficients of interest related to the impacts of migration are beta and gamma. Gamma, in particular, captures the differential impact of female migrants.

For robustness check, we will present estimations of probit model in addition to linear probability for binary variables and also estimate a non-linear relationship in the form of IV probit in estimating the probability of household head being employed, which is a binary outcome. We will discuss the robustness of the results with respect to various control variables.

# **6. Results**

This section first presents the estimated impacts on labor supply and child outcomes, and how the impacts vary with the gender of the migrant. The results for migration and remittances are interpreted together throughout this section.<sup>9</sup> This is because the results for migration and remittances are qualitatively very similar. Also, households receiving international remittances identified in the data are a strict subset of households with international migrants, therefore constituting a smaller number of observations. Subsequently, a discussion of robustness of the base results follows. The main results are

> 8 Other proxies for community characteristics that have been
boundaries visible in the drone imagery. Four sub-blocks were then randomly selected and fully listed.

- For larger EAs with greater than 400 households in hosts, a fanning protocol was developed for listing based on a ground-truthing exercise described in detail below.

# _Ground truthing and developing the listing methodology for camps_

For the Rohingya camps, we had access to publicly available high-quality drone imagery from IOM’s Needs and Population Monitoring program for the Round-12 data that had been used as the sampling frame. The maps in theory could allow us to geographically locate the exact 200 blocks that had been sampled for the survey. In addition, the field team intended to use the IOM-assigned Block IDs (6-digit codes) and the Local Block Names specified in the NPM data to identify blocks inside the camps.

The field team was instructed to go to the campsite management office and ask staff or volunteers for assistance in locating the sample blocks in the camp. Printed IOM drone maps were provided to navigate inside the camps using prominent buildings and other geographical landmarks.

The first challenge faced in the field was inconsistencies in block names used across the camps. The field team had mixed experiences in being able to identify the correct blocks using the NPM Block IDs and Local Block Names provided. Some camp authorities were able to identify blocks using the IOM-assigned IDs, and some were identified using the local block names. However, in some camps neither of these options was feasible. This was explained by the following circumstances:

- The IOM NPM data used majhees<sup>35</sup> as their main point of contact and did the block mapping based on “majhee boundaries.” The majhee governance system is an informal system, and thus it was realized that these assigned boundaries and local names are relatively fluid and subject to change.

- Half of the camps in Ukhia and Teknaf were managed by UNHCR and the other half by IOM. Thus, while the NPM block IDs and names were familiar to IOM-governed site management, camp managements governed primarily by the
*

A century ago, after millennia of imperial rule, the founding President of a new Republic died. This unexpected death was followed by a dozen years of warlord fragmentation and an increasing incidence of famines. This period also witnessed the spread of Communist ideology into China from Russia and the founding of the Chinese Communist Party. Are there connections between warlord-period famines and the Communist triumph in China? Using prefecture-level data, we find a sharp increase in the number of famines when China fell into 30
Indirect subsidies (electricity subsidy)|90.7|134.9|67.2|
|Education transfers|2,950.9|2,974.8|<br>99.2|
|Health transfers|1,400.5|1,498.8|<br>93.4|
|Disposable Income|107,335.6|81,589.7|<br>131.6|

Source: Amounts of taxes paid are taken from 2019 Table of Government Financial Operations (TOFE); for SSC values come from NSSF, Report on Ten-Year Achievements 2008-2017 and Action Plans 2018; Education transfers are taken as MoEYS budget, health transfers are taken as MoH expenditures less donor funding. Notes: Values in yearly amounts. *External statistics of SSC: Employment Injury Insurance and Health insurance not available for the most recent years, value provided for 2017 and uprated to account for inflation to 2019. **There are no observations in the CSES data of income from interest, royalties, or dividends. ***Only one case of property sale in the CSES data.

14
Household leverage is substantially lower in Emerging Europe than in more advanced European countries. The 2010 LITS survey provides comparable data for five advanced European economies: France, Germany, the UK, Sweden, and Italy. In these countries 40% the households which own their home have a mortgage compared to just 6% in Emerging Europe. Also, 53% of households in advanced Europe have a credit card compared to 26% in Emerging Europe.

The limited use of bank credit in Emerging Europe suggests that debt overhang in the household sector should not be a major impediment to overall future economic growth in the region. To assess whether household debt may limit economic activity in selected countries, we need to examine which type of households use bank debt and how debt affects their consumption and investment. Further, we need to look in more detail at whether household debt is more prevalent in countries that were the most severely hit by the crisis.

# <u>The Distribution of Debt across Households</u>

Consumer credit and mortgages in the region seem to be concentrated among those households which are least vulnerable to economic shocks. Table 4.4 examines the distribution of credit cards, mortgages and FX mortgages across household types. We focus on three household characteristics which should capture their resilience to economic shocks: the income level of the household, whether the household depends on wage income or is self employed, and the education level of the household. The table suggests that households with a mortgage or a credit card are substantially more wealthy. For example households with a mortgage have an average income of more than 5,700 USD per year, while households without a mortgage have an average income level of just around 4,000 USD. Households with a credit card or mortgage are also much more likely to have a household member with higher education. We find that selfemployed households are not more likely to be indebted than households with wage income, which confirms that the more risky (self-employed) households are not more exposed to bank