rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 10 reports estimation results on self-reported early season labor application intensity (i.e., labor per hectare, in person days). The first two columns include household and hired labor, while the last two columns show estimates for household labor only. There is some similarity with the observed spillover effects of plot size information on fertilizer use intensity. We see statistically significant asymmetric response, downward adjustment in self-reports of early season labor use intensity among those farmers who had overestimated plot size pre-treatment, but not among those who overestimated.

The labor use patterns differ in important ways from those with respect to fertilizer, however. In particular, the information treatment had no significant impact on labor intensity selfreports at mean plot size and pre-treatment measurement error. Furthermore, a significant inverse relationship exists between labor intensity and plot size in the absence of information treatment and is not statistically significantly affected by the corrective information. Measuring labor applications in smallholder farming is challenging (Arthi et al., 2018); the evidence here suggests one added complication: the possibility of correlated measurement error with plot size (Abay et al. 2019).

The findings in Tables 9 and 10 suggest that farmers employ simplifying mental models – e.g., optimal prediction error (Hyslop and Imbens, 2001) – to track some variables. This has two important implications in terms of understanding survey data generating processes. First, the presence of NCME in one production input likely propagates to other production inputs. Given the empirical regularity of NCME in smallholder plot sizes and farm sizes (Carletto et al., 2013; Carletto et al., 2015; Desiere and Jolliffe, 2018; Abay et al., 2019; Dillon et al., 2019; Gourlay et al., 2019), this implies likely NCME in fertilizer, labor, seed, and other inputs that have been less well studied. Second, simplifying mental models naturally generate correlated measurement error, which further complicates econometric correctives, as replacing an erroneous self-reported variable with an accurate measure
The literature seems to suggest that women tend to be more ethical and less adapted or tolerant to corruption. Indeed, women are more likely to be reciprocal in gift exchanges (Croson and Buchan 1999, Buchan et al. 2008), and therefore less involved in corruption. Another reason for lower corruption for women is their weaker access to the personal networks traditionally dominated by men (Goetz 2007). Furthermore, since women tend to be more risk averse (Croson and Gneezy 2009) and corruption entails risk of being detected and punished, women are naturally more reluctant to participate in corrupt activities. Women, including female top executives, also tend to have lower overconfidence (Lundeberg et al 1994, Barber and Odean 2001, Huang and Kisgen 2013), which makes them less likely to underestimate the probability of being caught, again reducing their susceptibility to corruption. Thus, it is not surprising that firm-level data (from Georgia) and cross-country individual data (World Value Survey) show that women are less involved in bribery and are less likely to condone taking bribes, while cross-country evidence shows that corruption is less severe in countries with higher presence of women in the parliament, government, and the labor force (Dollar et al. 2001; Swamy et al. 2001). The negative effect of corruption on the level of technical efficiency is found to be particularly strong for female-headed firms (relative to male-headed firms) in 14 Central and Eastern European countries (Hanousek et al. 2017).

Since women business leaders are likely to be risk averse and apprehensive of the risks of expropriation associated with corruption, we suspect that in corrupt environments they are more reluctant to invest in innovations and long-term investment, and the effects of corruption on productivity performance should be especially pronounced and more negative.

_The corruption hypothesis_ : the effects of corruption on productivity performance should be more adverse for female than for male business leaders _._

# **Access to personal networks and female business leader effects**

Business leaders organize various inputs into outputs
IPL are all in per capita terms (Jolliffe and Prydz, 2016), we have no ability to directly estimate the equivalent IPL in square-root of household size terms.

The approach we follow instead is to solve for the value of the IPL based on the squareroot allocation of household resources that keeps the overall headcount unchanged (from the percapita allocation). More specifically, let the global poverty rate in a reference year (in our analysis, this is 2017) be given as: where z is the value of the IPL in per-capita, per-day terms (expressed in 2011 PPP US dollars).<sup>12</sup> is the global poverty rate obtained from a global welfare probability density function, of daily consumption per capita, PP<sup>∗</sup> _y(pc)_ , in revised 2011 PPPs. For our sample of 162 countries, ff(yy(. )) is equal to 11.6 percent (Table 4). We then find the value of on the density function of PP<sup>∗</sup> consumption that has been allocated to the individual based on the square root of household size, zẑ )): ff(yy(r This substantial increase in the value of the square-root poverty line, relative to the percapita poverty line, is expected. Recall that the per-capita allocation rule divides total household

> 12 We first examine the IPL value of $1.90 (2011 PPPs) and then also consider the reported higher value lines of $3.20 and $5.50 (World Bank, 2018; Jolliffe and Prydz, 2016).

> 13 When we carry out this analysis at higher-value lines, we repeat this method and solve for the values of the square-root poverty line that keep the poverty rate the same as when evaluated at the higher-value, per capita lines (i.e., $3.20 and $5.50).

12
# **4.4. Limitations**

This study has limitations that are important to consider when drawing conclusions from our evidence. Due to its rapid introduction and promotion on a large scale, our findings about COVID-19 vaccine hesitancy might not be applicable to other long-established vaccines (e.g., MMR, DTaP, flu) or new vaccines (HPV). Recruitment and survey administration on Facebook, while innovative for mirroring national demographics using Facebook targeting and quota sampling, cannot provide a representative sample, leading to under-representation of older age groups, the poorest, and least educated, who might be disproportionally impacted by the COVID-19 and future pandemics. Self-selection bias may introduce systematic differences between respondents and non-respondents based on unmeasured factors and affect the findings. As such, the generalizability of the findings from our meta-analysis to offline populations remains to be tested and the impacts of our studies likely represent upper-bound estimates of effects. Despite these limitations, the sampling strategy of stratifying by demographic characteristics allowed for greater representation and timely data collection among key subgroups (e.g., women) who can be harder to reach in online settings.

The study measured self-reported vaccination intentions, not actual behavior. While behavioral intentions are commonly used to predict behaviors in experiments, and one meta-analysis found that a medium-to-large change in behavioral intention ( _d_ = 0.66) led to a small-to-medium change in behaviors ( _d_ = 0.36) (Webb & Sheeran, 2006), the extent to which changes in intentions translate into increased vaccinations across these international contexts remains unknown and warrants further investigation, when feasible (e.g., Alhajji et al., 2023). Indeed, some research has identified differential impacts of messaging interventions on vaccine intentions compared to behavior (Saccardo et al., 2024).

Lastly, we were limited in exploring additional study-level moderators (e.g., World Value Surveys [2024]; Hofstede’s cultural dimensions [Culture Factor Group, 2024]) given scarcity of data available for
the firm‐level innovation variables from the World Bank Enterprise Survey and average this binary information by country and sector. Admittedly, doing so has problems as the variable is initially dichotomous and would much depend on the number of firms included in the sample. Nonetheless, it would be worthwhile to conduct this analysis in order to obtain a first impression of the potential direction that the econometric correlations may take. We undertake this analysis for all sectors, but for now focus on the computer and related services sector given that our interest lies in the responsiveness of firms in a sector that is data‐intensive. Figure 1 showed that the computer and related services sector is an intense user of software.

8
# **3.1 Historical Predictors of Rural Non-Farm Employment**

We begin by estimating the following specification to estimate the role of individual characteristics on rural non-farm employment: The unit of observation in equation(1) is the individual _i_ , located in district _d_ , and observed in year _t_ . We aggregate the NSS data across the four rounds in the spirit of a repeated cross-section and restrict the sample to rural individuals aged between 15 and 65. **X** contains a host of individual characteristics such as a quadratic in age, dummies for literacy, secondary education, gender, religion and caste.<sup>2</sup> **Z** contains time-varying district-level predictors of interest such as the level of urbanization, fraction of manufacturing workers, contemporaneous and lagged rainfall shocks, and banking infrastructure.<sup>3</sup> _Y_ denotes our outcome of interest. We consider two categories of outcomes: the first – is broad employment type namely dummies indicating whether the individual is a participant in the labor force (LFP), and conditional on participation, whether the individual is self-employed, or works as a casual or salaried worker. We also use an aggregate indicator of any non-farm employment. In addition to these broad employment categories, we also consider dummies indicating sectoral employment in the farm, manufacturing, trade, construction and service sectors. As all our outcomes of interest in this section are binary, we estimate linear probability models, with the _β_ and _γ_ coefficients capturing the change – in the likelihood of observing a “success” namely, whether the individual is employed in the non-farm employment categories of interest. All specifications include district ( _α_ ) and survey round ( _δ_ ) fixed effects. The former flexibly captures time-invariant district-specific unobservable factors predicting non-farm employment such as soil fertility, societal attitudes or institutional quality in the district. The latter captures time-varying factors which equally affect all individuals in a year such as global demand for farm products or aggregate GDP

> 2 The full
data), we decided to use extent since our flood data assumes no protection. Protection is more likely to impact the depth, rather than the extent, of the flood results.<sup>2</sup> We then overlay this flood layer with the population density data set, to estimate the number of people per population grid cell that are exposed to floods. As the population density data set is at a lower resolution (1km) than the flood data (90m), we estimate the percentage of the population grid cell which is flooded, and multiply this percentage by the population in that grid cell. For instance, if a population grid cell has 500 people, and 10% of that cell is flooded (based on the flood data), then we estimate 50 people to be exposed to floods in that cell. In doing so, we assume that the population is evenly distributed within a grid cell.

We run this analysis for all the scenarios presented in Table 2, and aggregate our results at the district level to estimate the number of people affected. To include the poverty dimension, we use the poverty headcount rate in each district to estimate the percentage of poor people exposed. For instance, if 20,000 people are exposed to floods in District X, and District X has a poverty headcount rate of 20%, 1,000 poor people are exposed to floods in that district. In this analysis, we assume that poverty is evenly distributed within a district.

# **4.2. Slum and urban expansion exposure in Ho Chi Minh City**

For the HCMC analysis, we estimate the general exposure to flooding, for the whole province of HCMC and in each of its 24 districts. The flood maps used here are based on a model of HCMC, and are not the same map as used in the figurative example in Section 4.1.

Exposure to flooding was again evaluated using flood extent (we also evaluate flood depth, for full results,
# **5 Annex: Detailed RPSR Indices**

A. Generation and transmission planning index for Kenya and comparators, 2015

||**Kenya**|**Senegal **|**Tanzania **|**Uganda**|**International**<br>**Benchmark**|
|---|---|---|---|---|---|
|**Generation Planning**|86%|43%|43%|43%|56%|
|Countryhas ageneration masterplan|||||94%|
|Countryhas an overall energy plan|||||65%|
|Competent entityis responsible forproducingtheplan|||||88%|
|Inter-governmental committee oversees theplanningunit|||||29%|
|Powergeneration systemplan is mandatory|||||19%|
|Plan leads to timelyinitiation ofprocurement|||||38%|
|Planning process is transparent andparticipatory|||||59%|
|**Transmission Planning**|75%|100%|75%|75%|72%|
|Competent entityis responsible forproducingtheplan|||||100%|
|Explicitlylinked topowergenerationplans|||||88%|
|Plan is mandatory|||||29%|
|Planning process is transparent andparticipatory|||||71%|

- B. Generation and transmission procurement index for Kenya and comparators, 2015

||**Kenya**|**Senegal **|**Tanzania **|**Uganda**|**International**<br>**Benchmark**|
|---|---|---|---|---|---|
|**Procurement of Generation**|100%|50%|100%|95%|85%|
|There is
# _Labor regulation_

The chart (Figure 6) suggests that this element of the business environment becomes more costly to firms as GDP per capita rises. Most of the SAR countries report elevated levels of this constraint as compared with other countries at their level of development. This is especially in the case for Nepal, India and Sri Lanka.

# _Courts_

Problems with the courts look unusually serious in Pakistan (Figure 7). By contrast, this dimension of the business environment appears less problematic than might be expected at their level of GDP per capita in Nepal, India and Bhutan. The contrast between Pakistan and India along this dimension of the business environment is very striking.

# _Inadequately educated labor_

Firms in the SAR countries do not identify problems posed by inadequate access to skilled labor as especially serious – as compared with countries at their level of development (Figure 8). The only exception is the case of Bhutan. Indeed for Nepal, Afghanistan, India and Pakistan, the costs of this element of the external environment are rated below that of their comparators.

# _Telecoms_

The chart (Figure 9) highlights the fact that for most countries in the world, telecoms is a low-ranked constraint. This is also true of firms in the South Asia region. Problems appear more serious in Bangladesh, Afghanistan and Sri Lanka than is typical of countries at their levels of development.

# **10.  A comparison of business environment constraints between formal sector firms and rural and informal sector firms**

Surveys have been conducted of rural firms in Bangladesh, Pakistan & Sri Lanka and of informal sector firms in Afghanistan, India and Pakistan. However, the sample for India is much larger (over 2,000 firms as compared with about 200 and 400 firms for the other two countries with informal sector surveys). We therefore confine the 34
with occupation. We calculate the change in employment separately for low-paid and highpaid occupations. Low-paid occupations are those that pay below the median on average and high-paid occupations are those that pay above the median on average. The table shows that employment increased by a similar amount in both low-paid (0.28 percentage points) and high-paid (0.24 percentage points) occupations. Furthermore, there is clear heterogeneity within both low-paid and high-paid occupations: virtually all the employment change comes from low-TFP firms. Columns (5) and (6) also highlight that the employment elasticity is similar in low-paid and high-paid occupations. At low-TFP firms it is close to -0.50, while at high-TFP firms it is close to zero within both occupation groups.

Finally, in Panel C we study worker heterogeneity by education. Since we do not observe education directly, we again rely on occupation information in our data. First, we use the Hungarian Labor Force Survey<sup>27</sup> that has detailed information on education and occupation for the same individuals for a large sample of workers. We calculate the mode of the education level for each four-digit occupation. Then we assess the employment change by the modal education-level in each occupation.

The table shows that the employment increase mainly comes from the lowest-skilled workers with primary or lower-secondary education. There is also a slight increase in employment for workers with tertiary education and no change workers with upper-secondary education. When we look at employment changes within an education group, we find clear indication for firm heterogeneity in all cases. Employment at low-TFP firms increased within every group and the elasticities vary between -0.22 and -0.69 (see column 5). These elasticities are statistically significant in all cases at the 5% level. At the same time, there is no evidence for significant employment change at high-TFP firms in any education group. The employment change is close to zero in all cases and the elasticities are statistically insignificant at the conventional levels. Overall, these
>30<br>20<br>25<br>20 15<br>2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014<br>Greece Ireland Portugal Spain Greece Ireland Portugal Spain<br><!-- End of picture text --> Source: OECD, Revenue Statistics‐ Comparative tables.

Given the need to support growth, encourage labor force participation and maintain export competitiveness, particularly in countries where the export basket may be in direct competition with lower wage countries (probably also producing lower complexity products as shown in Table 1), GIPS may want to rethink the composition of the taxes and expenditures of their systems. For example, lower taxes on labor in GPS, by reducing labor costs, could promote export competitiveness. Another area where further consideration is desirable is corporate taxation. Given low investment rates, corporate taxation/ exemptions and tax collection could be further rationalized where tax rates are high and collection rates low. At the same time, public expenditure composition has been shifting dramatically since the crisis. With capital expenditures at a low not seen in over a decade, government size as a share of GDP larger in every single country relative to the 2007 values, social transfers consistently higher, and the share of interest expenditures in GDP having risen, it is time to think about what growth enhancing fiscal policy should look like. To conclude, despite the substantial adjustments that have occurred, fiscal adjustment by GIPS is not

> 39 The negative effect is ascribed to the non‐Keynesian impact of consumers reducing spending as they expect higher taxes in the future.

22
Seats for international travel within Africa (seats per year)|**978,337**|1,237,153|467,203|263,363|3,144,782|6,314,557|
|Seats for intercontinental travel (seats per year)|**493,740**|585,763|18,304|6,864|2,755,352|7,707,063|
|Seats available per capita|**0.050**|0.093|0.050|0.032|0.210|0.954|
|Herfindahl-Hirschmann Index—air transport market (%)|**17.19**|9.75|32.63|28.42|39.47|16.66|
|**Quality**|||||||
|Percent of seat-km in newer aircraft|**73.2.**|79.3|95.8|87.3|80.2|83.8|
|Percent of seat-km in medium or smaller aircraft|**27.9**|48.6|72.0|64.5|20.8|32.8|
|Percent of carriers passing IATA/IOSA Audit|**0**|33|0|0|11.1|33.3|
|FAA/IASA Audit Status|**No audit**|No audit|No audit|0|Failed|Passed|

_Source:_ Bofinger 2009Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). n.a. = Not applicable. _Note:_ The Herfindhal-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market
lower-achieving students experienced much larger losses. Considering principal-provided data, presented for the EU countries on Figure 1A, students at the 1<sup>st</sup> achievement decile (lowest achieving students) in schools affected for more than eight weeks suffer a loss of nearly 25 points compared to less than 15 points for the top students (9<sup>th</sup> decile). Similarly, for students in schools affected between 2 and 8 weeks, the lowest achieving students lost more than 20 points, while those at the top lost less than 10 points. Figure 1A also shows that in schools not affected or affected by less than two weeks, the lowest achieving students lost only 10 points, while those at the top lost around 5 points. Overall, for EU countries, these results suggest that longer school closures affected the low-achieving students more, extending inequalities.

9
3 Future Directions 31 tem could theoretically prove very relevant at reducing ethnic tensions and deterring conflict, yet there is insufficient evidence on the subject.

A new avenue of research should evaluate the potential of a stronger judiciary to reduce corruption. To what extent can a stronger judiciary hold accountable corrupt politicians and government officials? What interventions could achieve greater judicial independence–and would they lower corruption in society? What is the specific impact of a corrupt judiciary over development outcomes such as poverty, violence and overall corruption? These questions remain unanswered. Big data may offer new ways to evaluate the independence or quality of the judiciary, for example, by identifying outlier decisions in comparison to similar decisions based on fact patterns and legal precedent. Empirical research on these topics may become promising in the fight against corruption.

For empirical evidence to truly contribute to development, greater diversity in the countries and contexts of study is essential. Our review shows that few countries–mostly in South Asia, Europe, and North America–concentrate most research on justice. There is little research on the role of justice in Sub-Saharan and North Africa, Latin America, the Middle East, and East and Southeast Asia. Extending the geographical presence of research is important for a variety of reasons. On the research side, it provides greater external validity to ongoing research and brings new perspectives, contexts and challenges into consideration. On the policy side, these regions tend to concentrate a significant number of the poor as well as the conflict-afflicted areas.

Challenges related to the unreliability and unavailability of data pose a conundrum: countries that stand to gain the most from data-driven technologies lack the resources and data infrastructure to take advantage of them. However, with sufficient training, support, and testing of new innovations to collect and analyze data there is the capacity to overcome such challenges. Addressing these will be essential to ensuring that the promise of technological innovations and data-driven decision-making reach those who need it the most.

# **3.B Data for Better Judiciaries**

To