rafmacalaba/data-use-mentions-extended huggingface dataset:
. The index is standardized by subtracting the control group’s mean and dividing by its standard deviation. Food Diversity is defined as the number of food categories consumed during the past 12 months. Reduced Coping Strategy Index is a measure of food insecurity and is defined following WFP’s guidelines from the five coping strategies that the household may have used in the seven days prior to the interview (Rely on less preferred foods [weight 1]; Borrow food, or reply on help from a friend or relative [weight 2]; Limit portion size at meal-times [weight 1]; Restrict consumption by adults in order for small children to eat [weight 3]; Reduce number of meals eaten in a day [weight 1]) and its values vary between 0 and 56. Total Asset Value is defined as the sum of non-food expenses in last 12 months and expenses on durables, which is defined as money spent on cellular phones, televisions, motorized vehicles, air conditioners, refrigerators, propane heaters, water tanks, computers and tablets, livestock, and other durables in the last 12 months.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers; durables are winsorized at the top 10% as pre-specified.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%).
* p<0.01, ** p<0.05, * p<0.1||

Notes: Averages of temperature and rainfall are calculated at the household level for each billing cycle in the data set. The regression is weighted by sampling probabilities and clustered at the town and village level. The sample is trimmed to include approximately 365 days of temperature and rainfall data for each household.

**Table 8: Nonlinear Least Squares Estimates Electricity Production Function**

|δ<br>**Lamba: Heating and Coo**|2.971<br>(0.23)<br>**ling energy services**|**Lamba: Lighting ener**|**gy services**|
|---|---|---|---|
|Constant|-3.287<br>(0.30)|Constant|-3.481<br>(0.34)|
|Temperature|0.0642<br>(0.04)|Temperature|0.081<br>(0.02)|
|Rain|0.0376<br>(0.04)|Rain|-0.061<br>(0.05)|
|**Lambdas: Domestic end-**|**use energy services**|**Lambdas: Business en**|**d-use energy services**|
|Constant|-1.938<br>(0.27)|Constant|-2.977<br>(0.63)|

48
>@ -030 —t, BS I<br>& i B42 — Sop so<br>035 a 2 pr<br>8 9 410 WW 12 $13 #14 #415 #16 #17? 18 #19 20 241 8 9°70 1 12 $13 #14 #415 #16 WF 18 #19 20 21<br>Years of education Years of education<br>0.45 co : De :<br>OAQ es a ot A ss<br>i... ae Ee -<br>Bo4g — ee<br>3 Po §<br>fq «(0.00 3<br>s $4 a<br>= -0.05 : ~ 8<br>By AQ . — a £<br>Fi S16 : : : 7<br>E 145 ——— ~ - 2<br>3a20.20 ” a 243a©a fa;<br>-0.30 ; ; ~ oe ~ — 32 12 : et Oe<br>a<br>OBB i 440 ee<br>le} 41 2 3 4 5 6 7 8 9 10 aa 12 13 fe} 4 2 3 4 5 6 7 8 9 10 “1 12 13<br>Difference in years between aspired and expected education Difference in years between aspired and expected education<br><!-- End of picture text -->
<!-- Start of picture text --> Fig 2 Share of total owned area by holding<br>size: all India, 1953-54 to 1982<br>* 100%<br>180%.<br>6> 6 ha<br>o 60%.<br>a 40% ^! r| m C] -2 - hha<br>0 31 -2ha<br>e! 20% ^0.4-1 ha<br>0% E<0.4ha<br>1953-54 1961-62 1971-72 1982<br><!-- End of picture text -->

One would expect the distribution of operational holdings to be less skewed than ownership holdings, on the assumption that larger owners generally rent land to smaller ones. While this is generally true, ground reality is more complex, and landlords and tenants cannot be divided into mutually exclusive classes (cf. Neale 1990). Many with relatively large operational holdings lease-in land from smaller owners (the phenomenon of so-called 'reverse tenancy'). Where there has been rapid technological change (Haryana, Punjab) operational holdings have become more concentrated owing to the displacement of tenants, increase in hired labor, and diversification of assets away from land, especially in irrigated areas. At least until the early 1980s, the following trends could be observed from all-India data. Although operational land remains very unequally distributed, inequality has lessened over time. The number of small and marginal operational holdings has increased owing to population growth and subdivision, particularly in the northern (former _zamindari)_ states of Uttar Pradesh, Bihar, Orissa and West Bengal, but the average size of marginal holdings also increased. Larger holdings fell proportionately in number and area but their average size did not decline. The most significant net gains overall were for medium holdings, except in Kerala. Figures 3 and 4 illustrate the changing structure of operational holdings and share of total area operated by size of holding since independence.

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As a result of the above factors taken together, women have fewer assets and lower capacity for mobilizing resources than do men. This is compounded by lower access to credit and to most public and private services which also tend to favor men: extension agencies, credit, institutions allocating agricultural inputs and outputs, marketing boards, fertilizer programs, contract farming schemes etc. In some cases, access to private transfers and informal networks may be less differentiated across gender. In a study of northern Mali, Christiaensen and Boisvert (2000) find that female-headed households are less vulnerable to drought shocks in part due to community solidarity which more readily kicks in for them. In particular, widows and married women may have access to support from brothers or uncles. Official food aid and family food transfers are found to provide significant insurance mechanisms. However, these results may not be generalizable to all of Mali. Elsewhere in West Africa, where similar questions have been posed, the opposite has been found. For example based on fieldwork in Ghana and Côte d‘Ivoire, MacLean (2010) identifies patterns of reciprocity and gift giving within and across families and shows that the elderly and women participate less in them than the young and male, and thus tend to rely more on formal systems of social security than informal ones.

Using rural household data collected by IFPRI in Mali‘s Zone Lacustre, a few studies have examined the degree to which shocks are reflected in consumption (Skoufias and Quisumbing 2003; Harrower and Hoddinott 2004). Harrower and Hoddinott find that idiosyncratic shocks such as the illness of a prime age adult household member are not reflected in household per capita consumption regardless of the gender of the shock‘s recipient. However, this does not reveal whether actual individual consumption alters differentially by gender following a shock as one study found was the case in rural Southern Ethiopia. Dercon and Krishnan (2000) document imperfect risk-sharing between spouses whereby wives loose much more weight than husbands following a negative shock.

In sum, men and women
data on women’s and men’s property ownership are available from multiple DHS rounds, only the most recent survey is used. Data sets released after June 18, 2020 are not considered.

> 6 For example, in many parts of Africa, village chiefs, kinship groups and extended families may engage in specific aspects of property transactions (Pande and Udry 2005; Doss et al 2015). Based on data for Senegal, Lambert et al (2014) show that even though 17 percent of women inherited some land from their parents, only 2 percent have land to bequest to their heirs, suggesting that women’s land ownership rights are often revocable.

> 7 DHSs conducted under the 7th and 8th rounds collected additional information on the availability of title deeds. However, these data are not yet available for a large cross-section of countries.

8
significantly associated with both productivity and firm growth. A one-standard deviation increase of the Enterprise Survey’s Web indicator is associated with a 0.4 percentage point increase in employment growth and a 14 (9) log point increase in labor productivity (TFP). Moreover, the Global Competitiveness Broadband indicator is also positively and significantly associated with the productivity level. Increasing it by one standard deviation is associated with around a 10-log point increase of labor productivity and TFP. The general consistency between Enterprise Survey and Global Competitiveness on the importance of broadband renders further support to the findings of the previous literature on internet as a general-purpose technology (Clarke, Qiang, and Xu 2015).

_Human Capital._ The _Enterprise Survey_ and the World Bank HLO indicators yield robust patterns in terms of the associations between firm performance and human capital. A onestandard deviation decrease in the Enterprise Survey’s Skill Shortage Obstacle indicator is associated with an increase of TFP of 6 percentage points and a decrease in firm growth of 0.4 percentage points. The same conclusion is observed based on the World Bank’s HLO measure: a one-standard deviation increase in the HLO measure is associated with an increase in productivity of 16 log points, and a decrease in firm growth by 1.0 percentage points. In addition, the Global Competitiveness indicator of Quality of Education System is significantly associated with TFP. A one-standard deviation increase of the Education System indicator is associated with a 9-percentage point increase in TFP. The consistent findings of the productivity-boosting effect of human capital by all three sources underscores the critical importance of human capital. The negative association between our human capital measures and firm growth is suggestive of the trade-off between the quantity and quality of jobs.

_Access to Finance._ Access to finance tends to be positively associated with both productivity and firm growth. The Enterprise Survey’s Overdraft indicator is positively associated with both the productivity level and firm growth (it is significant when regressed alone and insignificant when all three groups of BE indicators are included). A
in constant USD excluding emergency aid, food aid, and debt relief (OECD Table 2a). All covariates are in logs, except for democracy. Democracy measure taken from Acemoglu, Naidu, Restrepo, and Robinson (forthcoming) and updated in accordance with their methodology. GDP and population data is taken from the WDI database. All regressions use recipient-year observations and include a constant term and period dummy variables, which are not reported. Recipient level cluster-robust standard error reported in parenthesis. Significance levels : _∗_ : 10 percent _∗∗_ : 5 percent _∗∗∗_ : 1 percent Table 2 shows that there is a strong relationship between the lagged CPIA measure and current aid after controlling for the lagged aid level, population, and income per capita.<sup>4</sup> The table shows OLS

> 4Our aid data are from the OECD, Table 2a, that provides aid data in the donor-recipient-year dimensions. We adjust our aid measure and focus on development aid ignoring emergency and food aid and debt forgiveness by following the procedure described in Annen and Kosempel (2009).

14
Figure 4. The effects of COVID-19 exposure on educational outcomes by color _Source_ : Authors’ calculations from Guatemalan educational administrative records. _Notes_ : The figure presents the estimates for β1 and β2 and their 95% confidence intervals from Equation (2) that capture the effect of more time spent in the orange and red warning categories, respectively. We present results for all four specifications considered throughout the paper and which may be found in Appendix Table A.2.

We find that effects are of the same sign as when using the warning index in Table 3. That is, being in the orange or red category has a negative effect on educational outcomes compared to time spent in green and yellow. However, these estimates capture the effect of an additional 1 percent of time spent in each category, so are not directly comparable to the results using the warning index. Our interest lies in determining whether the educational consequences of time spent in orange and red categories differ between themselves. We find that the effects of time spent in orange and red categories are statistically identical for all outcomes and specifications.

We also explore differential effects by year and color in Appendix Table A.3, confirming that effects seem to be larger during the first year of the pandemic (2020) than the second year 24
the Family Care Indicators (FCI) (Hamadani et al. 2010), which measures the frequency of learning and play activities with children, as well as the amount and variety of play and learning materials available at home. In the endline survey we used a revised version of the FCI, with additional self-report and observational items from the HOME-SF (Bradley and Caldwell 1984), enabling us to expand the Family Care Indicators for an older age group. Using these items (which are highly correlated to each other), we construct a latent factor index of cognitive stimulation using all the items using principal components analysis.

# 2.3.3 Child development outcomes

We consider three developmental domains potentially affected by the intervention: language, executive function, and socio-emotional development. Different test instruments available at baseline reached an age limit and endline, and were complemented by measurements that could cover a wider age range.

<u>Language: At baseline we measured both receptive and expressive language for children from 0</u> to 71 months using the Spanish version of the Preschool Language Scale (PLS-4). However, because a large proportion of children at endline were older than 71 months and could not be administered the PLSIV, in the endline survey we applied the “Test de Vocabulario en Imágenes” (TEVI-R), a direct assessment for receptive vocabulary that has been adapted from the Peabody PPVT and normed for the Chilean context and was administered to children 36 months of age and 9
# **7. Cost-benefit analysis**

The final part of our study focuses on a basic cost-benefit analysis of the subsidy by monetizing the benefits of the subsidy, taking into account its potential to increase formal employment, and the cost of the subsidy for the government. Whereas such cost-benefit analyses are rarely conducted in ALMP evaluations (as asserted in Card et al. 2018), they provide important information for policy makers. We use the data from September 2018, the latest available month in our dataset. The analysis is straightforward in the sense that it includes the government's direct (opportunity) costs – i.e. the monetary benefits in terms of social security contributions and tax payments it sacrifices to implement the subsidy – and its direct benefits, i.e. the additional social security contributions and tax payments it would receive due to the subsidy’s effect in increasing formal employment.

The design does not take into account costs and benefits from the perspective of workers, for example, in the sense of an increase in income of informal workers due to formalization. Similarly, it does not account for any increase in tax revenues to the government caused by an increase in consumption as a result of income increase. Finally, costs that the government could bear, for example, to provide social assistance to individuals or households with no formal employment are not considered in this analysis.

The cost of the subsidy for the government is the amount of revenues sacrificed to cover part of the social security premiums for workers in eligible firms. As defined, the government would forgo 6% of the social security premium payments (based on the minimum wage) for eligible firms that the firm would otherwise have needed to pay to the government. The benefit of the subsidy for the government is the increase in social security premiums and tax payments due to the subsidy’s effect on increasing workers that are registered to the Social Security Institution by the firms. The net benefit of the subsidy for the government at time is thus
have liked more days of work and still had fewer than the 100 days stipulated by the Act. We have no choice but to ignore this aspect of the scheme‘s performance since the National Sample Survey (NSS) did not ask how many more days of work the household wanted; all we know is whether the household wanted work on the scheme.

Table 1 gives the participation rate, the demand rate and the rationing rate across states, as well as the rural headcount index ( _H_ )—the proportion of the population living below our updated versions of the Planning Commission‘s latest poverty lines. (The table also gives the female share of employment, to which we return later.) ―Demand‖ is defined as either getting work on the scheme or seeking work but not getting it. For India as a whole, 45% of rural households wanted work on the scheme. Of these, 56% got work—a national rationing rate of 44%. The rationing rate varied from 15% in Rajasthan to 84% in the Punjab. Only three states have rationing rates under 20%. There is clearly a large excess demand for work.

A striking observation about the data in Table 1 is that participation rates are only weakly correlated with the rural headcount index across states, as can be seen in Figure 1. If MGNREGS worked the way the Act intended then this weak correlation would be surprising, as one would expect the scheme to be more attractive to poor people, and hence have higher take up in poorer states. The same point holds for public spending on MGNREGS. Table 2 gives summary statistics on spending per capita for 2009/10 and 2010/11. The correlation between MGNREGS spending per capita and the poverty rate is -0.02 using spending in 2009/10 and 0.04 for 2010/11.

Why is MGNREGS not more active in poorer states? We postulate that being a poor state has two opposing effects on participation. First, there is an
|32.12|327.33|139.68||
||0.34|0.00|0.00|0.00||
|Year FE|No|No|No|No|No|
|Observations|1386|1386|1386|1386|1386|
|R‐squared|0.099|0.111|0.109|0.147|0.217|

Note: Firm drops out=1. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance.

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

# Conclusions

This research leveraged factory‐level data on compliance from the ILO‐IFC Better Work Vietnam program to assess the relationship between transparency on working conditions and firm compliance of labor standards in Vietnam. It exploited a change in the policies of Better Work Vietnam when, in 2015, the 29
2.714***|_0.239_|<br>2.753***|_0.095_|
|North East|1.340**|_0.164_|1.117|_0.114_|1.136|_0.154_|1.347**|_0.188_|1.212|_0.267_|1.172|_0.142_|<br>1.201***|_0.077_|
|Round 50|||||||||||||0.955|_0.031_|
|Round 61|||||||||||||1.006|_0.040_|
|Round 64|||||||||||||0.821***|_0.035_|
|Round 66|||||||||||||0.820***|_0.035_|
|Round 68|||||||||||||0.831***|_0.038_|
|_cons|0.013***|_0.007_|0.009***|_0.005_|0.020***|_0.014_|0.056***|_0.045_|0.014***|_0.010_|0.027***|_0.020_|<br>0.021***|_0.006_|
|_N_|25,2|56|28,7|93|28,3|41|26,|928|26,3|41|26|,250|1|61,909|

Notes: *** p<0.01, ** p<0.05, * p<0.1 In the NSS, data for OBCs were not
|that have been estimated from open-source data.<br>Our estimation algorithm<br>is designed to support<br>expansion of GBIF species maps with the continuing increase in occurrence reports. We test the maps<br>sets to compute species density counts for a high-resolution global grid and compare the resulting global<br>distributions. We find close similarity in the global comparisons for mammals, ants and vascular plants.<br>Where there are regional differences, we trace them to technical differences in estimation methods or<br>cases where the boundaries of existing expert maps could be revised to reflect species-level patterns in<br>We use our occurrence maps database to explore two important issues for priority-setting in the Global<br>Biodiversity Framework.<br>First, we estimate the incidence of endemism across broad species groups<br>because it identifies countries’ stewardship responsibilities for species that reside entirely or almost<br>entirely within their boundaries. We find a heavy incidence of endemism for 610,694 species in our<br>(55.6%) for90% habitat. We also find thatendemism varies widely by species group. In a high-resolution<br>spatial analysis, we identify candidate “hotspot” areas for protection within countries where endemic<br>Our second exploration focuses on species whose small habitat areas increase their extinction risk. Our<br>algorithm produces area estimates for all mapped species, offering a low-cost complement to traditional<br>risk indicators whose species coverage<br>is limited by their resource intensity.<br>Using a conservative<br>standard for minimum critical habitat size, we find 117,934 vulnerable species. Our global assessment<br>finds a highly-skewed spatial distribution that isonly modestly correlated with country size. We find many<br>skewed distributions within countries as well, with 90% ofsmall-occurrence region species concentrated<br>in less than 10% of WB level-2 administrative units for 50 of 167 entities analyzed. This creates
% revenue|10|69|0|
|**US cents**||**Niger (2006)**|**Predominantly**<br>**thermal**|**Other developing**<br>**regions**|
|Effective power tariff|Residential at 100 kWh|18.17|10.27||
|Effective power tariff|Commercial at 100 kWh|15.75|11.73|5.0–10.0|
|Effective power tariff|Industrial at 50,000 kWh|18.17|11.39||

_Source:_ Fragile and nonfragile countries’ figures as of 2005, calculated using the AICD power database (www.infrastructureafrica.org/aicd/tools/data), which contains household data. For the CAR, national access is for 2008 (World Bank 2010a); power outages are for 2005 (Rosnes and Vennemo 2009); collection ratio is for 2010 (World Bank 2010b); cost-recovery ratio, system losses, and revenue per unit are for 2009 (NIGELEC 2009).

_Note:_ kWh = kilowatt-hour; MW = megawatts.

# **Challenges**

Despite the fact that its cost-recovery rates are high at present, NIGELEC’s financial situation is threatened by the potential increases in Nigeria’s power costs since Niger relies highly on Nigeria for power. Under an agreement made between Niger and Nigeria dating back to 1977, Nigeria’s state electricity company supplies 90 percent of Niger’s electricity needs to NIGELEC, with a preferential pricing of $0.03 per kWh (World Bank 2007b); Nigeria offers Niger a $0.05 subsidy for every unit of power imported. In 2009 Nigeria subsidized an estimated $26.5 million (or around 3 percent of its revenues) for power imports. In the long run under a scenario where Nigeria increases the price of power to its recovery levels ($0.08 per kWh