rafmacalaba/data-use-mentions-extended huggingface dataset:
|Ethiopia<sup>b</sup>|9|IBT|3.68|0.67|0.23|
|---|---|---|---|---|---|
|Mali|1|IBT with fixed<br>charge + VAT|2.29|0.37|1.04|
|Niger|1|IBT|3.97|0.48|0.37|
|Nigeria<sup>c</sup>|10|Fixed rate/m<sup>3</sup>,<br>some with a<br>fixed rate|2.05|0.40|0.80|
|Uganda|1|Fixed rate/m<sup>3</sup>|1.89|0.37|0.86|
|El Salvador<sup>d</sup>|1|VDT, with fixed<br>charge for 10 m<sup>3</sup><br>or less|0.85|0.19|0.32|
|Jamaica<sup>e</sup>|1|IBT|2.51|0.55|1.69|
|Panama<sup>f</sup>|1|IBT|1.18|0.26|0.20|
|Bangladesh|23|Depends on<br>utility (IBT, 16<br>with flat rate)|0.49|0.12|0.26|
|Vietnam|3|IBT (+ VAT for<br>some)|2.70|0.62|0.38|

_Source:_ IBNET database, World Bank, and other resources.

_Note:_ Since, there are no available data on OPEX cost-reflective tariffs for El Salvador and Jamaica, we use the value for
in other census enumeration areas and interviewed the same week. Our key restriction is that the average acquisition of these peers correlates with household diary and recall measurements only through the household’s own consumption. Third, while we allow survey errors in recalled consumption and diary acquisitions to be dependent, we channel their dependence through household usual consumption. This assumption is standard in nonparametric identification with measurement error (Schennach, 2013), and its interpretation along with possible threats to its validity are discussed using the consumer demand model above.

Our first key insight is that diary and recall reports are not rank preserving, meaning that they do _not_ order the same household identically in the data. We rule out simple explanations for this result. For example, we show that answering the additional recall module does not affect the accuracy of diaries: households randomized to, and households excluded from, the recall module have the same distribution of acquisitions in diaries. Besides, important departures from rank invariance are found across the support of diary and recall measurements and of the income distribution, suggesting that survey effects are unlikely to arise only due to households with specific demographics and income. These results imply that the same household could end up above or below the poverty line depending on the survey mode employed, which has distribution-wide implications for welfare ranking relevant to policies using proxy means tests derived from consumption data. The departure from rank invariance is also worrisome because it is among the

> 3These assumptions are rejected in our data and would be likely violated in most empirical settings (as conjectured by Aguiar and Bils, 2015).

> 4When the between-group component of inequality is of interest, however, the same argument can be used to claim that data from diaries dominate recall measurements.

3
project implementing partners for international aid organizations (Bhargava, 2007).<sup>10</sup> These efforts gained recognition as the pace of economic and social development in Rajasthan intensified. Between 1991 and 2001 literacy rates for females doubled from 20% in 1991 to 44%, and literacy rates for scheduled castes doubled from 26% to 52% (Census of India, 1991 and 2001). These achievements were met with national as well as international acclaim and insights from several Rajasthani programs shaped national policies such as the Sarva Shiksha Abhiyan at the national level.

Over time, some NGOs expanded the scope of their activities even further to include political mobilization of the rural poor (Bhargava, 2007).<sup>11</sup> A particularly notable NGO in this regard is the Social Work and Research Center (SWRC) in Tilonia, Rajasthan (John, 2003).<sup>12</sup> Offshoots of this organization eventually spread all over the state of Rajasthan. PRADAN (Professional Assistance for Development Action) was established in Alvar in 1987, and also noteworthy was PEDO (People’s Education and Development Organization). A distinctive feature of PEDO and its offshoots is the focus on promoting _sangathan_ (organization), _sangharsh_ (agitation) and _samrachna_ (constructive work) across the state (Rajvansi, 2007: 326). While the SWRC has retained a broad focus on economic self-reliance and environmental issues at the local level, some of the offshoots have addressed other issues. Both PRADAN and PEDO for example, have focused on women. They favored the creation of new institutions and platforms such as women’s self-help groups, cooperatives and collectives.<sup>13</sup> The efforts of these groups have been successful enough to influence national legislation. The National Right to Information Act, passed in in 2005, makes it mandatory for the government to share information with any citizen of India who requests it. The law was largely shaped by citizens’ movements,
results with all variables and quarter dummies, but without country fixed effects. Column two includes country dummies to ascertain whether these features can predict changes within countries during the crisis. The first mobility factor seems to be the only consistent predictor of GDP growth according to the first two columns of Table 4. The inclusion of country dummies seems to improve the precision of some coefficients and increases r-squared and adjusted r-squared quite substantially.

Column three presents the group lasso-selected features and their coefficients. The group lasso selects both sets of fixed effects and all other candidate features except for the religion index and the first labor index. The overall predictive power as measured by r-squared decreases only slightly, and the adjusted r-squared goes up slightly.

At this stage, it might be useful to compare the relative strength of the real-time indicators’ predictive power across the two samples of countries used in our analysis. The first is a sample of 61 countries with different income status. The sample in question includes 34 high-income, 18 upper-middle-income, eight lower-middle-income and one low-income countries. The second sample consists of 39 countries with relatively more high-income states (29 high-income countries).

> 9 We have obtained the information on the internet users per capita (most recent year available) from the World Telecommunication/ICT Indicators Database. See https://www.itu.int/en/ITUD/Statistics/Pages/publications/wtid.aspx.f

12
Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” arXiv. . https://doi.org/10.48550/ARXIV.1810.04805 Hansen, Stephen, Michael McMahon, and Andrea Prat. 2018. “Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach.” _The Quarterly Journal of Economics_ 133 (2): 801–70.

Hjort, Jonas, Diana Moreira, Gautam Rao, and Juan Francisco Santini. 2021. “How Research Affects Policy: Experimental Evidence from 2,150 Brazilian Municipalities.” _American Economic Review_ 111 (5): 1442-80.

<mark>Jolliffe, Dean, Daniel Gerszon Mahler, Malarvizhi Veerappan, Talip Kilic, Philip Wollburg. 2023. “What Makes Public Sector Data Relevant for Development?”</mark> _<mark>World Bank Research Observer</mark>_ <mark>38 (2): 325-346.</mark> Kleinberg, Bennett, Maximilian Mozes, Arnoud Arntz, and Bruno Verschuere. 2018. “Using Named Entities for Computer-Automated Verbal Deception Detection.” _Journal of Forensic Sciences_ 63 (3): 714–23.

Lo, Kyle, Lucy Lu Wang, Mark Neumann, Rodney Kinney, and Daniel Weld. 2020. “S2ORC: The Semantic Scholar Open Research Corpus.” In _Proceedings of the 58_<sup>_th_</sup> _Annual Meeting of the Association for Computational Linguistics_ , 4969–83. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.447.

National Science Board, National Science Foundation. 2019. “Publications Output: US Trends and International Comparisons. Science & Engineering Indicators 2018.” _National Science Foundation_ .

Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. 2019. “PyTorch: An Imperative
GDPPC is constant GDP per capita (2000 US$); DEMOG stands for a demographic variable; it is either the growth rate of population between 15-64 years old, or the age dependency rate; TRADE measures trade openness (exports plus imports in percentage of GDP); AGR is agriculture value added in percentage of GDP; GOVERNANCE QUALITY stands for bureaucracy quality index or corruption index; it is excluded in the basic specification.

We also include both regional and time dummies. The regions are defined in Table A4. The time dummies are annual.

World Bank’s World Development Indicators Database and the International Country Risk Guide (ICRG) Database are the main data sources.<sup>15</sup> The income level of a country is expected to be one of the significant factors determining actual tax collection.<sup>16</sup> As presented in Section II, higher-income countries tend to collect more taxes in percentage of GDP. Thus, it is expected that _GDP per capita_ to have a positive and significant impact on tax collection, as well as on fiscal revenue (Bahl, 1971; Fox et. al., 2005; Piancastelli, 2001).

_Higher age dependency_ and _higher population growth_ are expected to distort tax collection capacity of countries and lower the share of productive population (Bird et al., 2004). Thus, these two variables are expected to have a negative impact on taxes and total fiscal revenues.

_Trade openness_ is one of the variables commonly considered as an important determinant of taxation (Rodrik, 1998; Piancastelli, 2001; Norregaard and Khan, 2007; Aizenman and JinJarak, 2009). The changing size of international trade has expected to have two opposite effects on taxes. On the one hand, higher trade openness is expected to lower taxes collected on imports and exports; thus, it may have a negative impact on taxes and fiscal revenue. On the other hand, given that because higher trade openness is associated with higher economic growth rates, we expect
I1 2) The total debt to total assets ratio is higher for the larger firms, but the difference is not ver large. This is because the ratio of short term borrowing to gross working capital is higher for the small firms as indicated by the ratio of short term debt to inventories. However, the ratio of long term borrowing to fixed assets is significantly lower for the smaller firms, which also suggests that the smaller firms find it more difficult to access long term finance. The debt-equity ratio also shows that larger firms are more leveraged than the small firms. However, of the total liabilities of the small firms almost 40% are in the form of trade dues and current liabilities whereas these form only 25-27% of the total liabilities of the larger firms".

3) For the smaller firms almost two-thirds of their non trade debt consists of short term borrowing whereas for the larger furms the situation is quite the reverse, with two-thirds of their borrowing being long-term (see matl which gives the percentage of long term debt relative to total debt, excluding trade dues). If trade dues are included in total borrowing, short term debt represents more than 80% of total debt for small firms, versus 57% for large firms (see mat2). This also suggests that access to long-term finance is harder for small firms. The maturity structure of debt matches the maturity structure of assets as represented by the ratio of fixed assets to total assets which is 27.3% for the small firms and about 45% for the larger firms.

4) When one compares the large firms with the entire set of public limited companies, there appears to be a positive relationship between the cash flow ratio and the debt maturity ratio. If the cash flow ratio is interpreted as a performance measure, then this suggests a positive correlation between performance and maturity. However, the result is quite weak as the difference between the maturity structure of the debt of the large firms and of all public limited companies is small. Comparison
|
|Not in labor force|0.09|0.07|0.08|0.08|0.21|0.02|0.06|0.09|
|Unemployed|0.06|0.00|0.00|0.00|0.04|0.02|0.01|0.00|
|Employed in Agriculture|0.34|0.66|0.52|0.72|0.14|0.42|0.41|0.47|
|Employed in Industry|0.09|0.08|0.09|0.05|0.14|0.14|0.15|0.10|
|Employed in Services|0.42|0.18|0.31|0.15|0.48|0.40|0.38|0.34|
|**Access to basic services**|||||||||
|Improved water|0.68|0.81|0.78|0.57|0.88|0.59|0.79|0.78|
|Improved sanitation facility|0.46|0.30|0.33|0.04|0.32|0.20|0.29|0.37|
|Access to electricity|0.47|0.53|0.80|0.36|0.91|0.81|0.44|0.58|

Source: International Urban Poverty Database. Note: Real per capita consumption expenditures are deflated using a spatial deflator calculated based on the WorldPop 250m dataset and the DOU method. 40
and violence index|0.007|0.007|‐0.129**<br>‐0.152**|‐0.159*|
||(0.939)|(0.927)|(0.024)<br>(0.011)|(0.087)|
|Migration index|‐0.050|‐0.002|‐0.013<br>‐0.025|‐0.022|
||(0.478)|(0.975)|(0.845)<br>(0.716)|(0.834)|
||_Main outcome_|_s(experiments) – Psy_|_chological and behavioral_||
|List experiment cattle index|0.269*|0.258|0.108<br>0.117|‐0.142|
||(0.094)|(0.174)|(0.382)<br>(0.206)|(0.507)|
|List experiment argument index|0.140|0.135|‐0.303**<br>‐0.304**|‐0.439**|
||(0.378)|(0.419)|(0.017)<br>(0.014)|(0.038)|
|Observations|555|547|968<br>948||

_Note: P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no
||0.26|
|_⇢r_|CES Parameter for Ecosystem Services||0.123|
|_✓_<sup>_R_</sup>|E↵ectiveness Index of Protected Lands||10|
|_c_<sup>_o,r_</sup>|Cost of Recreation Services||0.0296|
|_✓_<sup>_o_</sup><br>0|Total factor Productivity Index in 2004||1.854|
|__<sup>_o_</sup>|Total Factor Index Growth Rate||0.0225|
|_Preferences _|_and Welfare_|||
|_↵f_|AIDADS Marginal Budget Share at Subsistence||0.189|
||Income for Services from Processed Food|||
|_↵l_|AIDADS Marginal Budget Share at Subsistence<br>Income for Services from Processed Livestock||0.035|
|_↵e_|AIDADS Marginal Budget Share at Subsistence<br>Income for Energy Services||0.112|
|_↵w_|AIDADS Marginal Budget Share at Subsistence<br>Income for Services from Processed Timber||0.036|
|_↵r_|AIDADS Marginal Budget Share at Subsistence<br>Income for Ecosystem Services||0.049|
|_↵o_|AIDADS Marginal Budget Share at Subsistence||0.579|
||Income for Other Goods and Services|||

24
42

# **Tables**

## Table I: Data Sources and Variables

|**Topics**|**Variables**|**Time Period**|**Source**|
|---|---|---|---|
|**Trade Policy**|**[1]** List of Harmonized System 8-digit (HS8) codes of products<br>subject to import bans,and bans’ start and end dates|March<br>2020-<br>October 2022|Sri Lanka’s Extraordinary Gazettes on<br>Imports and Exports,digitized byhand|
|**Trade Flows**|**[2]**Import values and weights at HS8 product-month-year level|January<br>2017-<br>October 2022|S&P Global Market Intelligence’s Pan-<br>jiva data platform|
||**[3]**Export values at exporting frm-HS8 product-quarter-year level|January<br>2017-<br>October 2022|S&P Global Market Intelligence’s Pan-<br>jiva dataplatform|
||**[4]** Rice production, cultivated area, and yields at district-season<br>level<br>**[5]**Production, cultivated area, and yields by crop at district-season<br>level (maize, groundnuts, potatoes, onions, cinnamon, cloves, tea)|Maha 2013 to<br>Yala 2022<br>Maha 2020 to<br>Yala 2022|Sri Lanka’s Department of Census and<br>Statistics (DCS)<br>DCS and Sri Lanka Tea Board’s annual<br>reports|
|**Agricultural**<br>**production,**|**[6]**Production by crop at national level|2022|Central Bank of Sri Lanka|
|*
in one sitting prior to any plot visits. The only contact with these households was made during the time that the field teams returned to the EA for the post-harvest visit to the treatment households. At the conclusion of each interview, one maize plot was then selected at random, and the enumerator accompanied the farmer to the selected maize plot, whose area and plot outline was obtained using the handheld GPS device. This protocol mirrors the current interview flow in the household surveys that have been supported by the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative, including the IHS5 for Malawi. Table A1 provides an overview of the fieldwork implementation timeline, and the distribution of interviews with treatment and control households over time.

# **3. NCME as a Window on Behavioral Anomalies**

NCME in self-reported plot areas has been widely reported (e.g., Carletto et al., 2013; Carletto et al., 2015; Dillon et al., 2019; Abay et al., 2019; Gourlay et al., 2019, Abay et al. 2021). This analysis corroborates prior findings and allows us to make more nuanced observations and link these patterns to behavioral anomalies observed in the broader economics literature. These findings lay the foundation for section 4's exploration of the causal impacts of providing objectively verifiable information that might resolve NCME.

Table 1 reports the descriptive statistics for sampled households and plots. Most households are male-headed and literate with about three-quarters of them relying on farming as main source of livelihood. On average, the plots are half an acre while the average farm size is roughly 1.5 acres. Table 1 shows that the randomization worked. Most of the observable characteristics are balanced across the treatment and control groups, and we cannot reject the null hypothesis of jointly zero coefficients associated with the regressors in Table 1.<sup>9</sup> Most importantly, pre-treatment self-reported plot area appears to be statistically similar across the control and treatment group
/ obesity are serious nutrition risks that can lead to poor health outcomes, including an increased risk of non-communicable diseases (NCDs). In 2018, NCDs were linked to 67 percent of all deaths in Bangladesh (WHO 2018).

In addition, Bangladeshis are at increased risk of micronutrient deficiencies. While data remains sparse, in 2011-12, the prevalence of vitamin A deficiency and anemia was 20 and 19 percent in preschool-aged children respectively, 21 and 17 percent in school-aged children, and 5 and 26 percent in non-pregnant non-lactating women respectively (ICDDR’B, UNICEF, GAIN, IPHN, 2013). Additionally, the national prevalence of zinc deficiency was 45 percent in pre-school-aged children, and 57 percent for non-pregnant and non-lactating women (ICDDR’B, UNICEF, GAIN, IPHN, 2013).

The quality of Bangladeshi diets has moderately improved and in the past twenty years, the proportion of severely food insecure households has decreased from 21 percent in 2000 to 11 percent in 2018-2020 (FAO, IFAD, UNICEF, WFP and WHO 2021). In 2019, 38 percent of children of 6 to 23 months had adequate dietary diversity as compared to 24 percent in 2011 (NIPORT and ICF 2020). In addition, the share of dietary energy consumption from cereals, roots and tubers has reduced from 81 percent in 2005 to 78 percent in 2017<sup>12</sup> with rice as the most frequently consumed staple. Despite a reduction in the share of dietary energy consumption from rice from 83% in 1991, rice alone still accounted for 68% of dietary energy consumption in 2016 <mark>(Cabinet Division of the People’s Republic of Bangladesh and WFP 2019). T</mark> his proportion is high, also compared to the country’s FBDGs, which recommend that 55 percent of dietary energy comes

> 12 FAOSTAT. FAO Estimates. Last accessed 16/06/2022.

14
the changes over time in position of individuals in the distribution. Consequently, the results in terms of inequality or pro-poorness of growth might differ substantially between the conventional poverty analysis which relies on cross section data and the mobility profiles approach (or mobility statistics), based on longitudinal data. Jenkins and van Kerm (2008) show in the case of Britain that whether patterns of income growth became more pro-poor under the labour government in the year 1999-2003 (with respect to the Conservative period 1992-1996) depend on the perspective used (and the definition of income growth). According to conventional analysis using a cross-sectional perspective, income growth became more pro-poor using both absolute and proportionate growth definitions. However, from a longitudinal perspective, an increase in progression of income growth is most clearly apparent using absolute income growth definition. The picture of greater progressivity is more muted when viewed in terms of proportionate changes<sup>10</sup> .

The sensitivity of the conclusions to how income growth is defined raises questions about how changes in income distribution over time should be assessed. In this context it is also important to bear in mind that the negative slope of the mobility profiles, which is associated with a pro-poor definition, describes a form of regression to the mean which can reflect also the effects of measurement errors and transitory variations. It is therefore essential to apply methods to mitigate the cause of potential spurious impact in the analysis.

# **_Measurement errors in the analysis of mobility based on panel surveys_**

All income distribution statistics are sensitive to measurement errors and transitory variations in income, but the issue is particularly relevant when estimators are based on change measured at the household level. The observed mobility or growth rates for poor versus non-poor can be genuine – attributable to genuine economic phenomena – or reflecting the effects of measurement errors and transitory variations in income. If there is measurement error of the "classical" form (uncorrelated with the true value and over time), then the expected income increase is positive for someone with
11 recently, Bujumbura was also the political capital of the country. Excluding Bujumbura from the analysis is common in studies in Burundi given the different dynamics in the city (Fransen et al, 2017; Verwimp and Van Bavel, 2014).

The survey is a follow-up to a survey conducted with the same households in 2011. However, most of the variables related to social cohesion were only collected in 2015, hence the analysis in this paper focuses on that round.

# **4.2 Measuring social cohesion**

Social cohesion is a multi-dimensional concept. Therefore, we use a series of different variables to measure it, including: (A) measures of support across households, (B) feelings towards conflict and reconciliation, (C) trust in others and on specific groups, and (D) participation in different community organizations.

Table 2 reports means for all the dependent variables and the Appendix provides a description of all the variables included in the analysis (Table A1). We present the results for all respondents and then separately for those in more/less ethnically diverse communities, those in communities with more/less pre-war land availability and those in communities with better/worse attitudes towards return migration. In order to construct an index to proxy migration-related divisions at the community level we add the four responses to the return question in Table 1, Panel C (i.e. help the country, fit in, bring new ideas, and receive preferential treatment), in a way in which higher values indicate worse attitudes, and create a dummy to indicate that the community is above or below the mean for this variable.

Table 2 – Means of independent variables

||All|Ethnic di<br>|versity<br>|Land av<br>(pre-19<br>|ailability<br>93 war)<br>|Negativ<br>toward<br>|e attitude<br>s return<br>|
|---|--