rafmacalaba/data-use-mentions-extended huggingface dataset:
policy emphasizes the latter criterion, but the Bank’s Articles of Agreement require lending decisions to take into account a country’s ability to repay the loan.

Finally, we consider the role of vulnerability to shocks that may reduce per-capita incomes. For a given level of measured income and institutional quality, countries that are more exposed to shocks seem less likely to meet the criteria for graduation. If they were to graduate, they might be more likely to return to the Bank for assistance because of a realized shock, and so they may be less willing to graduate in the first place. We include two measures of vulnerability to shocks in our analysis. One is the degree of export concentration: countries with exports concentrated in fewer products are likely to face greater volatility and to be more vulnerable to idiosyncratic exogenous shocks. Concentration is measured using a Herfindahl index of export lines.

A second measure of vulnerability is country size. The Bank’s IDA classifies countries with under 1.5 million people as small states subject to higher vulnerability to natural disasters and macroeconomic shocks. The same rationale may apply even to middle- or high-income small states.<sup>6</sup> We therefore include an indicator variable for these small countries, with population data taken from the WB-WDI.

In addition, we include one measure of actual shocks: a dummy variable representing whether the country has suffered a systemic banking crisis within the previous 5 years. Data for that variable are drawn from Laeven and Valencia (2008).<sup>7</sup> All regressions control for a linear time trend. This time trend could reflect the net effect of various factors, such as any tendency to interpret the graduation policy more or less stringently over time. For example, if the IBRD was more capital-constrained in the 1980s than in the 2000s, it may have rationed lending and graduated the wealthier above-threshold countries sooner in that early period than in the later decade. This change would show up in our regressions as a negative
Supplementary Table 2. Directory of variables in the plot-crop-level dataset

|**Variable name**|**Label**<br>|**Data processing notes**<br>|**Additional notes**|
|---|---|---|---|
|_crop_name_|Crop name|Absent of a harmonised list of<br>codes for crops, this variable<br>is coded a string.||
|_plot_id_obs_|Plot ID (panel<br>identificator)|Variable created by assigning<br>a numeric code to each unique<br>plot. The first number of the<br>code determines the country.<br>UGA: plot is equal to the<br>parcel ID in the**Plot-level**<br>**dataset**||
|_plot_id_merge_|Plot ID (to<br>merge with<br>raw data)|ID to merge with raw LSMS<br>data|Can be merged with the following<br>variables in the raw data<br>(concatenated variables are separated<br>by hyphens):<br>ETH: concatenation of_holder_id_,<br>_parcel_id_and_field_id_(wave 1)_,_<br>concatenation of_holder_id_,_parcel_id_<br>and_field_id_(wave 2)_,_concatenation<br>of_holder_id_,_parcel_id_and_field_id_<br>(wave 3)_,_concatenation of_holder_id_,<br>_parcel_id_and_field_id_(wave 4)_,_<br>concatenation of_holder_id_,_parcel_id_<br>and_field_id,_OR concatenation of<br>_holder_id_, and_field_id_for<br>households answering section 12c<br>(wave 5)<br>MWI: concatenation of_case_id_and<br>_plot ID_(wave 1), concatenation of<br>_y2_hhid_and_plot ID_(wave 2)_,_<br>concatenation of_y3_hhid_,_garden ID_<br>and_plot ID_(wave 3)_,_concatenation<br>
Table S1.7: EFFECTS ON LABOR SUPPLY IN ULTRA-POOR HOUSEHOLDS

||_Either r_|_espondent_|_Wome_|_n’s labor_|_Men’_|_s labor_|
|---|---|---|---|---|---|---|
||works for|hours in|works for|hours in|works for|hours in|
||a wage|wage-labor|a wage|wage-labor|a wage|wage-labor|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment|-0.082***|-433.971***|-0.101***|-235.044***|-0.017|-67.471**|
||(0.015)|(44.818)|(0.013)|(24.954)|(0.013)|(30.639)|
|Mean in control|0.702|1614.405|0.589|764.414|0.430|692.373|
|Adjusted R-squared|0.170|0.195|0.262|0.257|0.140|0.135|
|Observations|13464|13464|13464|13464|7778|7778|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if either the main female respondent, or the male head of the household was engaged in income generating activity where s/
- <mark>Jolliffe, D., Mahler, D. G., Veerappan, M., Kilic, T., & Wollburg, P. (2023). What Makes Public Sector Data Valuable for Development?</mark> _<mark>World Bank Research Observer</mark>_ <mark>, 38(2): 325–346.</mark>

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- <mark>Kraay, A. (2019). The World Bank human capital index: a guide.</mark> _<mark>The World Bank Research Observer</mark>_ <mark>,</mark> _<mark>34</mark>_ <mark>(1), 1-33.</mark>

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registration status, since the sampling of the surveys is limited to persons who had permanent registration status.<sup>26</sup> In order to trace the wage changes during the period 2002 and 2004, I construct a sample of the panel individuals interviewed both in the VHLSS 2002 and 2004 who were 15-59 years of age in 2004 and had wage jobs as the most time consuming job in both years.<sup>27</sup> This resulted in a sample of 1,746 individuals. Relative to using the repeated cross section data, there exist advantages in analyzing the panel data component of the VHLSS. First, since the panel data model traces the changes in wages of the same individuals between 2002 and 2004, it controls for any impacts on wages resulting from time-invariant observable and unobservable personal characteristics. Second, the panel data approach adjusts for the “composition effect”, i.e., any impacts resulting from the difference in the composition of wage workers between two years. For instance, there is some evidence that the individuals who moved from a non-wage job in 2002 to a wage job in 2004 were less skilled on average than those who had a wage job in both years.<sup>28</sup> If the unobservable component of the selection process to wage employment and the export variable are correlated, the coefficient for the export variable using cross section data may be biased even controlling for observable personal characteristics.

> 26 For instance, the number of workers employed by foreign firms computed from the VHLSS 2004 (0.7 million) in 2004 is smaller than that taken from the _Enterprise Survey_ data (1.0 million), most likely because the VHLSS data exclude migrants who hold a temporary or no registration status. According to the _Vietnam Migration Survey_ 2004 data, only 10.2 percent of the migrants who work for foreign firms possess permanent registration status (Fukase, 2013b).

> 27 Since there exist some errors in the panel identifiers in the original
- Dupas, Pascaline and Jonathan Robinson. 2013. “Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya.” _American Economic Journal: Applied Economics_ 5(1): 163-192.

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## <mark>4.1.4 Overall bias</mark>

The results so far have examined the separate sources of bias introduced at each stage of the phone survey selection process. Column 3, 6, 9, and 12 in Table 4.4a present the cumulative bias for the Ethiopia, Malawi, Nigeria, and Uganda HFPS interviewed samples. It is clear that the successfully interviewed HFPS samples in all four countries are biased across many different household characteristics. Overall, the bias is skewed towards urban households (except in Uganda) as well as households that are relatively better off in terms of material well-being. Household heads that are successfully interviewed are better educated and more likely to working in formal (wage) employment compared to the overall LSMS-ISA sample. These results are mimicked in Table 4.4b when disaggregating the Ethiopia sample into the urban and rural components.

# <mark>4.2 Testing Effectiveness of Weighting Adjustments</mark>

The results above have clearly demonstrated the bias present in the sample of successfully interviewed households in the HFPS in the four countries. This bias would compromise the external validity of the results obtained from the HFPS which would not be fully representative of the general population. Furthermore, the bias in all four countries skews towards households that are generally better off and therefore would not accurately reflect the situation for poorer households, a segment of the population that is likely most vulnerable to the COVID-19 crisis.

However, since the samples for these two surveys are derived from representative samples in the ESS, IHPS, GHS-Panel and UNPS, there is potential to reduce the bias by harnessing the detailed information available in the F2F surveys to conduct the weighting adjustments described in Section 3.1.2. Implementing these weighting adjustments will likely counteract the bias but will not fully eliminate it. This section presents results from an assessment of the effectiveness of the adjustments performed on the HFPS in each country.

Table 4.5a presents three different characteristic profiles representing: (i) the full
terms dictionary includes 347 green terms, of which 308 are considered strict green because they are directly linked to the green economy. Another 39 terms are added to generate the broad green list of terms. These terms are not explicitly linked to environmental sustainability and climate change, but they could become directly linked if green technology or practices are adopted. These are added to the database to capture tasks that could be green. For example, the statement “land use” might not be directly linked to the green economy, but if greener technologies and practices are adopted in land use then this statement would be linked to the green economy. Any green terms clusters (a grouping of similar terms) can include only strict green terms, only terms that could be green that are used for the broad green definition, both, or neither. That said, the strict green list of terms is a subset of the broad green list of terms. Table 1 shows a sample of terms in the dictionary.

17 To build the GEP, the O*NET team reviewed the literature and identified 12 green sectors. Work activities identified as green included those that reduce the use of fossil fuels, decrease pollution and greenhouse gas emissions, increase the efficiency of energy usage, have to do with recycling materials and developing and adapting renewable sources of energy (Dierdorff et al. 2009). To identify green tasks within occupations and develop a definition of green jobs, the United States Department of Labor conducted representative surveys of job incumbents and interviewed industry experts. This information has been integrated into the United States standard occupations classification (SOC) and can be matched with labor force surveys.

18 Including the O*NET GEP, Burning Glass Technologies (BG) green list, US Bureau of Labor Statistics (BLS), GTP survey, IAB Janser (2018) list, UN Environmental GS, and the European Skills, Competences, Qualifications and Occupations (ESCO) skills taxonomy.

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# **3. Data and Methodology**

Ideally, to determine how perceptions change over the life‐cycle, we would like to follow individuals over time. However, long‐running panels are rare in both developed and developing countries. In order to identify age patterns using repeated cross‐section data, Deaton and Paxson (1994) and Deaton (1997) suggest an identification strategy that instead of tracking individuals follows the evolution of cohort averages over time. The procedure consists in dividing the individuals in the sample in cells defined by time‐invariant characteristics such as year of birth and averaging the variable of interest over individuals belonging to each cell; then, the authors estimate a linear model using the variable of interest as a dependent variable, and cohort, age and year dummy variables as independent variables. Since we want to control for certain individual characteristics that change over the life‐cycle and are likely to affect the degree of pro‐immigration attitudes, we estimate the models using stacked micro‐data from the cross‐section surveys instead of averaging over time‐invariant characteristics. As a robustness check, we also construct a birth cohort panel and estimate the model using the standard decomposition approach as suggested by Deaton (1997) and the results are very similar to the ones reported in the paper.

To identify the effect of age on attitudes toward migration, we append household surveys from multiple years and estimate the following Probit model separately for each country: 10
groups. The regional and income distribution of the countries included in the dataset for 2005 is presented in Table 2.<sup>9</sup> For the analysis, I combine data from several sources which are explained in the previous section. In Table 3, I present the list of variables included in the analysis. The export data is obtained from 2006 to 2008 whereas the explanatory variables are from 2005 to 2007 when possible. Although some of the variables are available for a longer time span, the analysis is limited by the availability of the data for TTRI and MA-TTRI from WTI which are only available for 2005-2007. Data from global competitiveness report on the quality of infrastructure is only available for 2008. The last column in Table 3 shows the expected signs of the relationships between the explanatory variables and export sales. Descriptive statistics for the variables used in the analysis are presented in Table 4. In the table, I present both within and between country variations for each variable. The data shows that for most of the variables within variation is quite small compared to the overall variation which is due to the few number of time periods. This also shows why using fixed effects method in panel regressions would give imprecise results.

# **4. Analysis and Results**

Collinearity between the explanatory variables could make the estimation results imprecise and make it difficult to identify the relationship between each explanatory variable and export sales. I present the correlation matrix between the variables in Table 5. The table shows that investment climate indicators are highly correlated with each other. Thus in each estimation, I include only one investment climate indicator.

The first group of estimation results with the pooled OLS method is presented in Table 6. GDP is a strong correlate of high export performance which is a common finding of gravity models. On the other hand, large and remote countries export less. Ten percent increase in the remoteness of the country decreases export sales by 4-5 percentage points. These findings are in according with the results of Djankov
by the National Bureau of Statistics (NBS). The HBS is a nationally representative sample of 22,178 households sampled between May 2000 and June 2001. The HBS is a much richer survey than the census, containing information on a wide range of outcomes including demographics, education, health status, and ownership of durable assets. The detail of the HBS allows the construction of a rigorous consumption aggregate, but the survey is not representative at the district, let alone ward, level. Log per capita household consumption can be modeled in the HBS sample using variables that exist in both datasets and then the parameter estimates can be used to simulate per capita consumption for each household in the census.

7
19 livelihood system, at least among those closer to the metropolitan area. It concerns here largely households in the Mekong River Delta, who have their livelihood systems built around floods (“living with flood”, very much as in Bangladesh (Beck, 2005)). Comparison of self‐reported flood measures with the riverine flood measure used here (taken from the DFO) supports this interpretation. The flood incidence based on the latter measure is much larger than the flood incidence based on the former, indicating that not all floods recorded in DFO are actually also experienced as negative events by the households themselves. It is not so much the flooding as such which is detrimental, but rather its duration and level, rendering only a subset of the DFO observed floods really damaging.

# _Immediate, lagged and persistent welfare effects by event_

For a more detailed shock by shock comparison of the welfare effects, the study turns to the Maximum Likelihood welfare estimates, which also correct for spatial correlation (Table 1, column 4). While they qualitatively correspond to the OLS estimates in column 3, the size of the effects changes at times substantially and the coefficients are also more precisely estimated, yielding additional insights. This is expected given that both the tests for spatial lags and errors (not reported here) point to the existence of spatial correlation (within and beyond the cluster). To provide some further intuition to the findings, it is simultaneously explored how households cope with each of the shocks. In particular, the immediate, lagged and persistent effects of the different natural disasters on a household’s asset holdings, its reception of official disaster relief and domestic and foreign remittances, and its degree of income diversification as captured in the Herfindahl index,<sup>38</sup> are given in Table 2. The effects of natural disasters on asset holdings and income diversification are estimated using OLS with suitable corrections for heteroskedasticity and cluster design, while left censored tobits are used for the household assistance regressions, as many
The intervention aims to: (a) present information about the importance and benefits of the four aspects of conscientiousness at work; (b) increase the motivation to change conscientiousness states; (c) provide instructions to activate conscientiousness states; and (d) prompt behavioral practice using reminders to activate the conscientiousness states.<sup>15</sup> The intervention consisted of two components. The first component was a group training session with an average of 24 participants that lasted 2 hours, for eight groups in total. The length of the session and the number of participants were similar to other training sessions that workers received on safety and security. The session was hosted by a professional consultant with experience delivering training of this kind in Senegal.<sup>16</sup> The training was conducted in the local language, Wolof, to ensure that workers fully understood all of the material. During the session, the trainer explained different concepts regarding non-cognitive skills to workers in the treatment group, in particular conscientiousness, with an emphasis on how improvements in such skills can lead to long-term benefits for the worker. The control group did not receive any additional training beyond the standard training provided by the company to all workers, which includes an initial security training at the start of the contract and periodic sessions throughout the duration of the project.

The second component consisted of weekly reminders for eight weeks via short phone calls (less than 1 minute) to activate non-cognitive skills change. Calls were made by a survey company. Reminders were randomized across weeks and all workers received the same set of reminders each week. During each call, workers were given personal skills reminders, such as, “Make sure not to leave your place of work at any time without replacement”, that target conscientiousness. Phone calls were also conducted in Wolof.

A mid-line survey was conducted in January 2020, and an end-line survey was conducted by phone in May 2020. Our sample started with the 386 individuals interviewed at baseline. At mid-line