rafmacalaba/data-use-mentions-extended huggingface dataset:
discussing how different welfare estimation methods differ for different parts of the welfare distribution. This is important because, as we stated in the introduction, this paper is mainly concerned about poor people and poor countries. Moreover, by shifting the analysis to the distribution of welfare, we can also borrow from welfare economics and the very rich literature on distributional analysis and use instruments such as stochastic dominance or pro-poor curves to refine our assessment of the difference between welfare estimation measures. This is what this section is about. We will start by introducing a possible strategy to estimate standard errors at the social and quantile levels and we will finish by introducing pro-poor curves for comparing welfare estimation methods.

# **5.2.1 Statistical inference (Society)**

The questions we address are the following: Are the differences between means of welfare estimation methods statistically significant? And can these statistical tests help us to discriminate between welfare estimation methods? Most analysts work with sample surveys and welfare estimates such as _LV_ or _CS_ will contain a sampling error. For each sampled household _i_ , one can estimate the net impact on wellbeing: ∆ _νi_ = _f_ (∆ _p_ 1 _, ...,_ ∆ _pk_ ; ∆ _m_ = _tri_ ) where _tri_ refers simply to the transfer to the individual _i_ . Since the required information to assess _νi_ comes only from the sampled households _i_ , the variable ∆ _ν_ will be _i.i.d_ . In general, the sample size of national household surveys is large and, based on asymptotic theory and the case of large sample size, the distribution of any estimator will converge to the normal distribution. We need, therefore, to start by finding a proper approach to the estimation of the standard error of the difference between means of welfare estimation methods.

Suppose that we want to estimate the mean difference between _LV_ and _PV_ , the upper and lower bounds of our welfare estimation methods. Let ∆ denote the estimator of the mean _PV,LV_ difference, _N_ the simple size and
In what follows, we will present results using questions that aim to evaluate responses of state capacity in each of those areas. In addition to presenting results for an exhaustive set of individual outcomes, we also construct indices that aggregate those outcomes for each system. The construction of these indices follows closely the methodology in Anderson (2008). For each index, we first code all components so that higher values indicate a "better" outcome, then standardize all transformed variables and finally construct the index as a weighted average of components.<sup>15</sup> The indices for the household survey and their underlying components are as follows:

# • **Government officials’ survey:**

1. _Staff Capacity Index_ (6 items): Responses on staff skills in using computers and software.

2. _Management Index_ (5 items): Responses on management willingness and ability to attract, retain, and promote staff.

3. _Performance Culture Index_ (8 items): Responses about staff shared values, commitment to deliver, pride in serving, and ability to withstand political pressure.

# • **Household survey:**

1. _Urban Planning Systems Index_ (3 items): Responses about local government use of revenues and planning.

2. _Fiduciary Responsibility Index_ (8 items): Responses about government honesty and bribe payments.

3. _Infrastructure Management Index_ (12 items): Views on government capacity to maintain infrastructure and access to state services.

4. _Accountability & Transparency Index_ (21 items): Views on local government transparency, participation in political meetings, ease of access to information.

5. _Views on Taxation and Fees Index_ (11 items): Normative views on taxation and fees (e.g., is it wrong not to pay taxes?) and positive views about whether individuals are punished for not paying fees and taxes.

In Table 1 we present correlations across the summary indexes from the household survey on government performance in different areas. All the measures are broadly positively
_Table 17: Total estimated value of crop production lost (LSMS-ISA) and total aggregate economic losses (EM-DAT) due to floods_

|Country|Year|Total aggregate<br>flood loss (2022<br>dollars) in LSMS-<br>ISA|Total aggregate<br>flood loss (2022<br>dollars) in EM-<br>DAT|
|---|---|---|---|
|Malawi|2018 - 2019|346 million<br>[95 ; 598]|_missing_|
|Nier|2011|9 million<br>[ 3.00 ; 15 ]|_missing_|
|g|2014|81 million<br>[ 0 ; 205 ]*|3 million|
|Nigeria|2019|352 million<br>[ 208 ; 496 ]|_missing_|

_Note: missing entries correspond to cases where either no event was reported or information on the population affected was missing in the EM-DAT. 95% confidence intervals for estimated totals are calculated with LSMS-ISA microdata *Negative lower bounds were cut at 0._ 38
likely to be headed by a male. They tend to be smaller and have more children and fewer household members of working age and that are in

> 18 Authors’ calculation using the Nepal Labor Force Survey 2017/2018.

> 19 This likely places the sample in the middle of the income distribution. According to the 2014/2015 Nepal Household Budget Survey, the average monthly household income for the third consumption quintile group is estimated at 24,516 NPR and for the fourth consumption quintile group (second richest group), it is at 32,042 NPR.

9
**Figure 10: Reform-2: Tax maneuver and formalization of 50 percent of informal employees. Percentage change in consumable income w.r.t. no-reform scenario by decile, age group, household and location types.**

<!-- Start of picture text --> 1<br>0<br>-1<br>-2<br>-3<br>1<br>0<br>-1<br>-2<br>-3<br>Scenario A Scenario B Scenario C<br>Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.<br><!-- End of picture text -->

**Effects of the tax maneuver on employment, income distribution and fiscal budget based on a probabilistic model**

Another mechanism of selecting individuals that undergo formalization is approximating the behavioral decision to formalize as a response to tax changes through a probabilistic model. To relax the assumption of random selection of those who formalize used in the previous section, a logistic model was used to estimate the probabilities of being formally/informally employed based on changes in wages (before and after-tax changes) and personal characteristics.<sup>21</sup> These estimates render a more precise picture of the possible effect of the tax maneuver than the previous exercise for two reasons. First, they give an estimate of the share of individuals who would formalize due to the tax reform, and second, they indicate which individuals -according to personal characteristics- are more likely to formalize. These data are then used as input for RUSMOD to calculate the effect of formalization on

> 21 The authors also tried simulating the transitions between non-employment and formal employment, but that lack of information in the survey regarding the reasons for not working and no information to simulate those changes did not allow doing so.

26
of poverty. In fact, there was no change over the decade, with the headcount index staying at 26.5%. Distributional shifts working against the poor exactly offset the gains from growth. What does the empirical evidence suggest about the relationship between initial inequality and the growth elasticity of poverty reduction? Support for the intuition that higher inequality countries tend to have lower (absolute) elasticities was first presented in Ravallion (1997) and subsequently verified by Ravallion (2001) and Kraay (2005). These papers have used regression-based methods (in which rates of change in poverty are regressed on rates of growth both on its own and interacted with initial inequality). We will return to this approach shortly, but first it is instructive to look at the empirical relationship seen in a more flexible way.

A simpler non-parametric method is to calculate the elasticity as the log difference in the headcount index divided by the log difference in the mean, all based on successive household surveys. There is clearly a lot of noise in such a measure. To help reduce the noise, I smoothed the period-specific elasticities by taking the simple average of two-period elasticities (across three surveys). I also trimmed 15 extreme elasticities (below –20 or above 20). Figure 3 gives the results for the “$1 a day” poverty rate. The elasticity is negative in 80% of cases. We see a rather weak tendency for the elasticity to rise (become less negative) as inequality rises, from an average of about –4 at the lowest Gini index to roughly zero at the highest. The correlation coefficient is 0.26, which is significant at the 1% level. The two high positive elasticities in Figure 3 are almost certainly measured with large errors, and this is exaggerating the slope of the line of best fit. Dropping these two observations, the correlation is still significant at the 1% level, and the line of best fit passes through an elasticity of zero at Gini index of about 60%
a considerably higher incidence of 74.1%. The chosen solution only uses an average of 3.9 questions per household, allowing 94.6% of the population to be sampled to achieve an incidence of 78.3%. Further average length reductions yield little benefit in terms of respondent numbers, and decrease accuracy so much that the overall effect on incidence is negative.

This case study suggests that TrESt has significant potential for raising incidence in a suitable setting. The case study relies on limited information and should be interpreted as a stylized example rather than as a fully calibrated use case. Nevertheless, it provides an illustration of the relationship between targeting survey coverage and household-level accuracy, and that it can be beneficial for desired policy outcomes to prioritize the former over the latter. Another caveat is that the low travel time between households in the urban setting under consideration here is a key environmental factor that promotes a close link between questionnaire length and incidence. Whether similar benefits would accrue where population density is lower could be assessed with enumeration meta-data for rural and peri-urban localities. Even when the number of questions is similar, the simulation suggests that the TrESt approach has a considerable advantage over a standard PMT, likely due to a more efficient orientation of survey resources to households with more uncertain eligibility status, and due to the greater predictive power of the non-linear, national-level machine learning model.

# **6 Discussion**

We proposed a selection of practically feasible methods that expose a tradeoff between accuracy and survey length, all based on variable sequences that order questions in order of predictive power. One simply truncates questionnaires to a certain length for all surveyed households, another deploys prediction intervals to generate a household-level early stopping criterion. For the most effective variable sequence – generated by stepwise selection – both approaches produce remarkably similar results in a simulation of a 40% programme coverage simulation with data from Indonesia, despite relying on different mechanisms. Compared with a policy baseline PMT that requires 22.8 questions to achieve an EER of 26
3

# Introduction

Sub‐Saharan Africa (Africa hereafter) lags behind all other regions of the world in household access<sup>2</sup> to electricity, per capita power consumption, and installed generation capacity. Africa accounted for 13 out of the top 20 countries in the world with the largest numbers of people without access to electricity identified in the Global Tracking Framework report published in 2015 (World Bank and IEA 2015). The report found that the percentage of people living in households with access to electricity in 2012 was about 35 percent. However, disparity between urban and rural areas was stark—69 percent of urban residents but only 15 percent of rural residents were estimated to have had access to electricity. The region with the second lowest access rate, South Asia, had more than double the percentage of people living with electricity (79 percent), and nearly five times the percentage share of rural residents with electricity (70 percent).

The global initiative, _Sustainable Energy for All_ , brings diverse partners together to work toward universal access to electricity by 2030. Africa is likely to be the last region of the world to reach this target. Years of underspending, serious shortcomings in operational efficiency, underpricing, high costs of small‐scale operation, over‐reliance on expensive oil‐based power generation, and the inability of many customers to pay for electricity services have contributed to much lower generation capacity per capita than in other regions. Existing customers face frequent power outages, and new connections in some countries have barely kept with population growth.

Benefits of electrifying households are many. Electric lighting is safer, more convenient, and far more efficient than kerosene lamps. Electricity enables households to use electronic forms of communication, and refrigeration makes food safer as well as reduces the frequency of grocery shopping and cooking. Studies analyzing data from household surveys have found positive effects of electrification on income (Kumar and Rauniyar 2011; Khandker et al. 2012; Khandker, Barnes, and Samead 2013; Van de
In the case of South Asia, the BPS includes data for six countries: Afghanistan, Bangladesh, India, Nepal, Pakistan, and Sri Lanka, over three waves of data collection. The first one was conducted at the onset of the COVID-19 crisis (between May and July 2020), a follow-up round was collected between May and August 2021, and the third and final round was collected between May and July 2022. The last round was not implemented in Sri Lanka and Afghanistan due to fragile political situations in the two countries (see Figure A1 for the timing of the surveys and Table B1 for the observation count in each round).

# **3.2 Measuring Informality**

Starting from the second round, the BPS in South Asia also captures the multidimensional nature of informality by including six questions relating to business license, registration under company law, contributions to employee provident funds, value-added tax (VAT) registration, the firm holding a bank account, and the separation of this account for business use.<sup>6</sup>

# **3.2.1 Informality Composite Index**

To measure the multidimensionality problem of informality, we follow an approach similar to Medvedev and Oviedo (2016) in constructing an informality index that relies on a combination of informality indicators. We take the first component of a Tetrachoric Principal Component Analysis (PCA), given that our informality variables are binary, and build three scores to capture distinct aspects of informality. Such an approach helps reduce the multidimensionality of informality into one index and allows us to build a continuous quantitative measure. The first index, _PCA 5_ , uses all five measures captured in the BPS, except for the bank accounts mix, as this variable is conditional on having a bank account. We build two sub-indices, for robustness checks. _PCA 1_ restricts to operational indicators that measure the extensive margin of informality (Ulyssea, 2018, 2020) accounting for aspects relating to business license, VAT registration, registration under company law
Kristjanson et al. 2005). This reflects the importance of infrastructure but may obscure social aspects of inaccessibility, like women’s avoidance of overnight travel. Less common are composite indices built off a combination of the above (Babu et al. 2014).

Reported travel times to specific services from survey instruments, sometimes further divided by travel modality, are very popular measures of accessibility among economists (Babu et al. 2014, Jacoby 2000, Minten 1999).

Questions regarding travel times are common to Living Standards Measurement Surveys promulgated by the World Bank and hence data are available for many countries. Despite their popularity, reported travel times contain serious inaccuracies, biases and limitations. Respondents may misreport times (a.k.a. recall bias) and idiosyncratic household conditions (i.e. disabilities, physical fitness, schedules of nearby bus) create many outliers (Roberts et al. 2006). Assessing Nepal’s 1996 Living Standards Measurement Survey, Jacoby (2000) notes that reported travel times within wards vary widely around the ward median value for these reasons. Similarly, when comparing reported travel times to a locally validated cost time model, Ahlström et al. (2011) found responses differed by up to 30% (+/‐) of the mean modeled travel time for a given district. These findings imply that while such data are appropriate for household level analysis, aggregation and generalization from them is problematic. Reported times also do not work for assessing new or planned infrastructure and are prohibitively expensive to collect at scale.

Some researchers employ road network analysis within a GIS to calculate travel times along roadways (Delgado and Baltenwick 2000). Such analysis work well where road network data sets are complete, terrain’s impact is easy to model and off‐road travel is insignificant. Only a few researchers have calculated their own cost time models in a GIS. Notable examples of such models include Kosmidou‐Bradely and Blankespoor’s national mobility model for Afghanistan (2019), the continent‐
# **Annex IV.  Are Gross Revenues from Rice a Good Proxy for Rural Income?**

This policy note assumes that farmers with smaller plots or lower gross revenues from rice are worse off. This is because there are limited alternative activities (off-farm or beyond rice) that would compensate for small land size or low rice revenues. Although this is clearly an over-simplification, the evidence suggests that the assumption holds when looking at asset variables (e.g. quality of housing) in the survey and farmers with smaller plots of land or lower gross income from rice are worse off.

In the absence of an income variable from both surveys, the approach to determine if gross revenues from rice are a good proxy for wealth is two-fold: (i) use asset variables in the 2003 and 2007 surveys (the building materials of the farmers’ houses, the quality of the walls and flooring), and (ii) draw a subsample in 2003 that is limited to those respondents where rice accounts for a larger share of their income (50 percent or more) and look at assets in this sub-sample only. In both cases, there is evidence showing that farmers with small paddy fields and lower gross revenues are less well-off.<sup>20</sup> The findings from both 2003 and 2007 surveys suggest gross revenue from rice is a good proxy for rural income. Farmers in the poorer quintiles 1 and 2 report having houses of inferior quality materials, with soil floors and bamboo walls, as opposed to the higher quintiles 4 and 5, having non-soil floors and wooden walls. In both 2003 and 2007 surveys, half of those farmers with soil-floor houses belong to quintiles 1 and 2 (respectively, 27 percent and 24 percent in 2003 and 2007, see Tables IV.1-6). Likewise, 33 percent and 37 percent of the farmers in quintile 1 had bamboo walled housing, while 48 percent and 45 percent of the farmers having wood-walled houses belong to higher revenue quintiles 4 and 5.

#
necessary, because fixed and operational costs are not negligible while returns tend to be small, and the licensing to patenting ratio is very low. Moreover, returns from spinoffs need time to mature and contract research only develops in gradual steps. Despite evidence that specific TTO activities that favor new venture creation include early-stage technology and licensing for equity, such a strategy is the least likely to be favored by the university and thus unlikely to be used. Evidence from the United States confirms that TTOs are concentrated in short-term cash maximization, because the main mechanism favored by most TTOs was licensing for cash (72 percent), with licensing for an equity stake and sponsored research less popular at 17 percent and 11 percent, respectively (Markman et al. 2005).

Russian TTOs and/or their parent institution tend to provide accompanying innovation services. Half of TTOs report offering both incubation and prototyping services. The presence of auxiliary innovation services is welcome (Figure 11), as most research results need further development before they reach a phase where they can be commercialized. In the United States, from inventions licensed in a five-year period, TTOs reported that 45 percent were at the proof of concept stage, 37 percent were laboratory prototypes, 15 percent were manufacturing-ready technologies, and 12 percent were market-ready inventions (Thursby et al. 2001).

Patent applications through TTOs are made mostly in Russia but patenting abroad is negligible. On average, each TTO surveyed applied for 34 local patents. The estimated total of 1,715 patents represents around 5 percent of total patent applications made in Russia. Patent filing in the European Patent Office and U.S. Patent and Trademark Office is almost nonexistent – on average, not even 1 patent is filed annually.

The TTOs surveyed helped initiate around a fifth of all the start-ups based on research discoveries. Out of the 973 IP-based start-ups reported by the MoES, 200 were founded using licenses of the TTOs’ parent institution technology, an average of 3 start-ups per TTO.

# **III.
each partition, whose predictions are combined using neighborhood averaging of local model predictions. The advantages over M5 are that it can produce smoother transitions across numeric outputs, and much faster runtime. Both being of high importance to the current application. The advantages over Random Forest are that the cubist model has linear regressions at terminal nodes and so it can extrapolate slightly out of range, while Random Forests can only interpolate using medians or averages of typical values associated within the ranges of the input data.
"Do gender dynamics in intrahousehold decision making shift with male migration? Evidence from rice-farming households in Eastern India." _Gender, Technology and Development_ 27, no. 2 (2023): 157-183.

Ruppert Bulmer, Elizabeth, Ami Shrestha, and Michelle Marshalian. "Nepal Jobs Diagnostic." _World Bank Group_ (2020).

Ruppert Bulmer, Elizabeth. “Job Quality in Nepal is Improving, but Women Lag Behind.” _World Bank Jobs and Development Blog_ (2020). https://blogs.worldbank.org/jobs/job-quality-nepal-improving- women-lag-behind Samman, E. “Quantitative measurement of gendered social norms.” _London: ALiGN_ (2019).

Kathmandu University, School of Arts; Interdisciplinary Analysts, and The Asia Foundation. “A Survey of the Nepali People in 2018.” (2019).

School of Arts, Kathmandu University, Interdisciplinary Analysts, and The Asia Foundation. “A Survey of the Nepali People in 2020.” (2020).

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