rafmacalaba/data-use-mentions-extended huggingface dataset:
card. The need for a specific voter ID can undermine citizens’ ability to exercise their voting rights. Online voter registration is only possible in India, and online voting is not permitted in any of the surveyed countries.

# **3.2.1. Legal requirements**

Economies around the world have adopted different electoral systems to fit their political frameworks. Voting is compulsory in nearly 13% of the world’s economies. In 85% of the economies, casting a ballot during elections is not mandatory, while in 2% of the economies there are no elections at all<sup>12</sup> (International IDEA 2017). In all four economies included in this report, voting is not mandatory and therefore there are no sanctions for nonvoters. In Chile, India, and Vietnam, although voting is voluntary by law, it is compulsory for citizens to be on the voter registry list. The Constitution of Ghana, on the other hand, states that citizens are entitled to be registered as a voter, which does not constitute an obligation to be included on the registry.

# **3.2.2. Non-discrimination provisions**

The Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), adopted in 1979 by the United Nations General Assembly, mandates that women should not be discriminated based on their marital status. Such practice impairs women from exercising their rights and freedoms in the political, economic, civil, and social spheres. _Living Life_ attempted to capture variations on procedures and requirements based on gender. The data found that in Ghana, Chile, and Vietnam voter registration provisions are equal for women and for men regardless of their marital status. However, in India, married women must state the name of their husbands when applying to register to vote. Unmarried women should indicate the names of their mothers and fathers when registering to vote. This is not required for married or unmarried men. This provision is established in India’s 1960 Registration
Surveys (DHS). Health and nutrition indicators have been measured for different strata of households defined in terms of a proxy indicator of “wealth” constructed by principal components analysis from the DHS (following a methodology introduced by Filmer and Pritchett, 1999).<sup>6</sup> Again the indicators do not include the crude death rate since this cannot be measured reliably with the sample sizes of a typical DHS. The differences in death rates for children indicate that on average the poorest quintile in developing countries (in terms of the proxy wealth indicator) has an infant mortality rate that is 17% higher than average (implying _DRD_ =1.2), though this rises to 35% ( _DDR_ =1.4) if one focuses on low and middle income countries only.

It is not clear how well the DHS “wealth index” reflects actual wealth or the most widely used indicator of living standards, namely aggregate expenditure on consumption (including imputed values for consumption in kind). So it is of interest to also see what can be learnt from surveys for which more familiar consumption or income measures are available. Using nine such surveys for developing countries, Wagstaff (2000) provides estimates of the infant and under-5 mortality rates grouped into quintiles according to household income or expenditure per person. Using the poorest quintile, the implied values of _DRD_ are in the range 1.1 to 1.8.<sup>7</sup> What about adult deaths? While there is currently nothing like the NLMS available for developing countries, there are some useful clues to likely differentials in overall (child + adult) death rates.<sup>8</sup> The demographic surveillance system implemented in Matlab thana in Bangladesh by the International Center for Diarrhoeal Disease Research has been an important source of data

> 6 Details on the methodology can be found at <u>http://devdata.worldbank.org/hnpstats/pvd.asp.  The</u>
and obtained, if needed, assistance to do so to receive their grant. Although this makes ‘having a bank account’ more a project output than an outcome, this is an important achievement as experience with a formal bank and improved financial literacy might have lasting impacts for the beneficiaries.

For the beneficiaries of cash grants, we also see an increased probability by 13 percentage points (from a control group base of 29 percent) to keep books on sales and purchases. This may partly also follow from the increased formalization, which requires to do bookkeeping. This is also an outcome with potentially lasting effects.

The survey also asked the entrepreneurs several questions regarding their ability and plans to innovate by introducing, for example, new or modified products, services, management or operational processes, and their plans to use the internet. To analyze impacts on innovation, we aggregated the answers using Principal Component Analysis. At least based on OLS, we find sizeable positive effects in this case for both beneficiaries of cash and matching grants. We find impacts in the order of 0.18 and 0.15 units of standard deviation for beneficiaries of cash grants and matching grants, respectively. If we use the ANCOVA specification, the effects for beneficiaries of matching grants turn insignificant but are borderline, and the size of the effect does not change much. Hence, it seems that even beneficiaries of matching grants, maybe partly in anticipation, increased their innovation potential.

Overall, we cannot find significant heterogenous effects at this stage. With respect to gender, for women, we see slightly lower effects on formalization and slightly higher effects for bookkeeping and innovation, but these differences are statistically not significant. There is also no difference for banking. There is otherwise no systematic pattern that emerges with respect to the sector of activity, the size of the capital stock at baseline, or the size of the grant firms received.

23
21 panel data on how changes in the distribution of various sources of income are related to changes in the ownership of land.'<sup>8</sup>

# VI. Conclusion

This study has used decomposition analysis on a nationally-representative data set from rural Egypt to examine the impact of five sources of income - including nonfarm income -- on rural income inequality. Three key conclusions emerge.

First, the study shows that if policymakers are interested in reducing poverty and improving income distribution in rural Egypt then they should focus on nonfarm income. Not only does nonfarm income account for almost 60 percent of the total per capita income of the rural poor (i.e. those in the lowest income quintile group), but it also has a favorable impact on overall income distribution. Of the five sources of income examined in this study, a 1 percent marginal increase in nonfarm income will lead to the largest decrease in overall rural income inequality. A 1 percent increase in nonfarm income will cause overall rural income inequality to fall by 0.068, which is equivalent to a 12.8 percent decline in the Gini coefficient of overall income. By contrast, a 1 percent increase in any other inequality-decreasing source of income - such as transfer or livestock income-will only lead to a 3.2 percent decline in the Gini coefficient of overall income.

Second, as de Janvry and Sadoulet (1993) have suggested, this study affirms the close tie between land, nonfarm income and the poor. Nonfarm income is an inequalitydecreasing source of income in a land-scarce, labor-rich setting like rural Egypt because inadequate land access "pushes" poorer households out of agriculture and into the
|0.711|0.803|0.144|0.014|0.712|0.775|0.176|0.040|0.719|0.795|
|Endline control mean|2.|444|2.7|75|2.4|44|2.7|75|0.|168|0.|225|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.031|0.496|0.224|0.570|0.006|0.094|0.030|0.077|0.007|0.285|0.059|0.299|
|Observations|1,606|1,606|400|400|1,606|1,606|400|400|1,606|1,606|400|400|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (columns (5) to (12)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and their matched controls. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

32
--|---|---|---|
|_2003_||||||||||||
|Central Asia|6.47|0.32|14.38|47.41|0.03|3.02|23.31|1.75|1.23|2.08|16,730|
|Southeast Europe|5.58|1.19|6.75|4.71|0.18|6.25|21.54|16.95|35.92|0.94|35,946|
|Europe and Central<br>Asia|5.19|0.63|5.23|20.39|0.27|6.49|21.79|24.92|12.63|2.48|455,874|
|_2008_||||||||||||
|Central Asia|4.36|0.13|8.42|61.70|0.02|3.16|16.69|1.99|0.51|3.03|70,187|
|Southeast Europe|6.08|1.59|6.88|8.11|0.45|7.50|24.35|24.53|18.56|1.96|94,668|
|Europe and Central<br>Asia|4.66|0.63|4.54|29.09|0.32|6.80|18.95|23.69|7.10|4.22|1,343,808|

_Source_ : United Nations Commodity Trade Statistics Database.

_Note:_ Column headings reflect Standard International Trade Classification categories, revision 3.

12
of the product and the remaining economic rent can be imputed to the unpriced input, i.e. water (the residual claimant).

49. A basic version of the residual imputation approach, the budgeting method, was, for example, used by Chang and Griffin to estimate agricultural costs of irrigation water transfers to urban uses (1992). Linear programming based representative farm models were applied by Hamilton et al. (1989) to analyze the potential of allowing some irrigation to move to hydropower use in critical low flow periods. Taylor and Young (1995) estimated FDB by developing a discrete sequential stochastic program to model the sequential nature of crop production decisions and the uncertainty of water supplies and rainfall. Sunding et al. (2002) used three alternative models to evaluate the impacts of water reduction in irrigated agriculture in California‘s Central Valley for improving water quality in the San Francisco Delta estuary. Based on discrete sequential stochastic programming combined with a hydrological model, Houk et al. (2007) estimated foregone benefits from water transfers from agriculture to instream flows in the Platte River for critical habitat purposes in Nebraska.

50. **Issues with Deductive and Inductive Techniques** . Although conceptually straightforward, in practice several problematic issues arise with the residual imputation approach which so far have not been much examined in the literature (Young, 2005). They include accurately specifying the physical production function, specifying technologies, and assigning correct prices for the non-water purchased inputs as well as the outputs. Furthermore, non-water _owned inputs_ pose a particular challenge. Both production theory and empirical evidence suggest unresolved problems in properly identifying and pricing inputs owned by the firm. Theory suggests that owned inputs such as household labor, equity capital, management and entrepreneurial skills, and land and possibly other (non-water) natural resources should be accounted for. Empirical observations of land market transactions and more elaborate hedonic pricing using farm sales data and econometric techniques show that typical residual imputation studies yield higher valuations than do market transaction-based studies. If,
sophisticated technologies (i.e., _MAXf,j ≥_ 3) even in such an advanced economy as South Korea.<sup>20</sup> To explore the cross-country differences in technology sophistication, the top panel of Figure 4 also plots the histogram of _MAXf,j_ in Senegal (left) and India (middle). There are stark cross-country differences in the distribution. The mean and variance increase uniformly with the level of development in the country (i.e. Senegal, India, South Korea). Additionally, the cross-establishment distribution of _MAXf,j_ is most skewed to the right in Senegal, and least in Korea.

The bottom panel in Figure 4 shifts the focus to the most widely used technology. In particular, it shows the histogram of _MOSTf,j_ in fabrication across food processing establishments for each country. By construction, the distribution of _MAXf,j_ stochastically dominates the distribution of _MOSTf,j_ , as _MAXf,j ≥ MOSTf,j_ . However, the distributions of MAX and MOST differ significantly. For example, in 65% of Indian food processing establishments the most sophisticated technology used in fabrication is ’machines controlled by operators.’ Yet, this technology is the most widely in only 35% of establishments.

The gap between MAX and MOST in this example motivates a deeper exploration of whether MAX and MOST are statistically distinct across a broad range of business functions and countries and, if that is the case, their relative importance in shaping the relationship between technology sophistication and productivity across establishments.

# **4 Technology Sophistication at the Business Function**

We use the FAT dataset to examine technology at the business function level. We explore two issues: the range of technologies used in each function and the comparison between the most widely used and the most sophisticated technology available in the business function. We then compare our findings with the predictions of state-of-the-art models of technology in production and draw conclusions about relevant extensions.

## **4.1 The Array
_Social Capital, Household Welfare and Poverty in Indonesia_ 14 situations. Within each sub-district, four villages were selected based on location criteria (upland/lowland and near/far to growth center). Within each of the 48 villages, 25 households were selected randomly to participate in the household survey.<sup>7</sup> The subdistricts were Sarolangon Bangko and Batang Han in Jambi, Banyumas and Wonogiri in Jawa Tengah, and Timor Tengha Selatan and Ngada in NTT. In each sub-district, 200 households were interviewed for a total sample of 1,200 households.<sup>8</sup>

**Table 1: Selected Socio-economic Indicators of the Three Study Areas**

||**Jambi**<br>I<br>___<br>_I|**Jawa**<br>**Tengah**<br>_____<br> <br>|**Nusa**<br>**Tenggara**<br>t**Timiur**|**Indonesia**|
|---|---|---|---|---|
|Population ('000)<br>|2,370|29,653|3,577|194,755|
|Area ('000 km<sup>2</sup>)<br>|44.8|34.2|47.9|1,919.3|
|Population Density (people/kn<sup>2</sup>)|53|867|75|101|
|% Urbanized<br> <br>|27.2<br>|31.9<br>|13.9<br>|35.9<br>|
|% of Households<br>with Access to Electricity|30.5|71.1|14.5|57.2|
|Gross Primary Enrollment
# **1. Introduction**

Corporate savings is an important component of the overall savings in Egypt, representing about 50% of total savings (Figure 1). It is important to understand why companies in Egypt save so much, what types of companies are most likely to save, and how financial savings interact with firm real decisions on investment into productive assets and with external finance constraints. This analysis may suggest more effective policies to support higher growth and development in Egypt. This paper uses two datasets - Investment Climate Assessment and data for publicly listed firms on EGX to analyze the determinants of corporate savings behavior.

**Figure 1. Composition of Savings in Egypt**

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>95/96 96/97 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 06/07<br>Household savings Corporate savings<br>Government Savings Total savings (% of GDP)<br><!-- End of picture text --> Source: WB Staff calculations based on National Accounts

# **2. Defining Corporate Savings**

Figure 2 presents a rough outline of the flow of funds in firms, which is helpful in defining corporate savings measures. Sales revenues minus costs comprise gross operating income (GOI), which is a very crude proxy for the cash flow available to firms. Out of the GOI the firm must pay interest on loans and taxes (and perhaps subtract other expenses not included in the GOI cost calculations). Then we arrive at the net income, which is a proxy for cash flows available to firms.<sup>1</sup> The net income can
misunderstood and the probability underestimated. A 1- in-100 year flood has, on average, a 1 percent probability of occurrence in any given year, which translates to a 10 percent probability in a decade, or 50 percent in a lifetime (68 years). These are significant probabilities that lie well within government planning horizons. For comparison, the Dutch flood protection system protects against events up to 1-in-10,000 years. Further, these probabilities apply independently to any given river basin or microclimate (and we consider hundreds of thousands of locations for this study). This means that, globally, hundreds of 1-in-100 year flood events happen every year.

# Global settlement footprint data since 1985

Considering the rapid urbanization patterns observed in the recent past, annual high-resolution settlement footprint data are crucial for monitoring the evolution of people’s exposure to natural hazards. In this study, we use the WSF-Evo data set developed by DLR<sup>11,48</sup> , which enables us to track the flood exposure of new urban development in cities and small rural settlements alike (figure 2). WSF-Evo outlines the global settlement extent from 1985 to 2015 on a yearly basis at about 30x30-meter resolution based on archived Landsat imagery<sup>49</sup> . For each year, we gather all available Landsat-5 and -7 scenes acquired over the given area of interest and extract key statistics—temporal mean, standard deviation, minimum, maximum—for different spectral indices after performing cloud and cloud-shadow masking. Among others, these include: the normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and modified normalized difference water index (MNDWI). Temporal features prove generally robust if computed over at least 7 clear cloud-/cloud-shadow-free observations. Accordingly, if this constraint is not satisfied for a given pixel in the target year, we enlarge the time frame backwards (at 1-year steps) until the condition is met.

The 2015 edition of WSF-Evo, generated by jointly exploiting both optical
more employees. The impact on sales in the third round is positive, but not statistically significant, although an aggregate index of sales and profits shows a significant increase. By this last survey round, existing firm applicants are 19.6 percentage points more likely to be operating a firm, have 4.4 more workers per firm, and are 20.6 percentage points more likely to have reached 10 or more workers. Winning also significantly increases sales by 338,000 naira per month (US$2153), which is a 66 percent increase on the control mean.

This setting has several favorable characteristics for considering the accuracy of counterfactuals. It involves a large treatment effect, and a relatively educated set of individuals. The intervention is a relatively simple one – the main benefit of winning being the $50,000 grant winners receive – so that it should be reasonably easy for the control group to also understand what exactly the treatment is. This contrasts with job or business training programs, where the control group is unlikely to have accurate knowledge of what the exact content of the intervention is, making it harder for them to think about likely impacts.

# **3.2 Eliciting Business Owner Counterfactuals**

The last follow‐up survey elicited counterfactuals for business ownership, employment, and sales for both groups. Applicants in the control group were asked the following questions: _Imagine you had won the YouWin! Competition in the first round. I would like you to tell me what you think you would be doing right now in terms of running a business or working._ _a) What do you think is the percent chance (out of 100) that you would be running a business today if you had won YouWin!?_____________________ _b) If you had won YouWin!, and were running a business today, how many employees in total do you think you would have today?_ _c) If you had won YouWin!, and were running a business today, how much do you
25128<br>0.83|
|Treatment<br>0.47<br>0.46<br>0.07<br>0.10<br>0.58<br>0.38<br>0.14<br>0.44<br>0.15<br>20.4<br>11853<br>659<br>68<br>0.43<br>0.49<br>3.26<br>1.70<br>7.32<br>0.23<br>0.34<br>341<br>5922<br>-0.04<br>100<br>ans|
|Control<br>0.46<br>0.43<br>0.10<br>0.09<br>0.58<br>0.36<br>0.17<br>0.52<br>0.07<br>20.1<br>11416<br>399<br>77<br>0.46<br>0.51<br>4.18<br>2.18<br>9.05<br>0.17<br>0.44<br>336<br>7617<br>0.04<br>100<br>Me|
|_Variables used in Stratification_<br>Small Firm<br>Medium Firm<br>Large Firm<br>Outlier in export data<br>Exported in last three years<br>Export Practices Index<br>_Other Variables_<br>Firm located in Antioquia region<br>Firm located in Cundinamarca region<br>Firm located in Valle de Cauca region<br>Firm age (years)<br>Firm sales in 2016 (millions of pesos)<br>Firm profits
sri lanka country risk profile 13

# 50-year precipitation flood in Rathnapura

<!-- Start of picture text --> 120 Subsistence line<br>Increase of 7,500 (0.6% of regional pop.)<br> in income subsistence<br>100 Poverty line<br>Increase of 11,300 (1.0% of regional pop.)<br> in income poverty<br>80<br>60 Pre-disaster income<br>(FIES data)<br>Post-disaster income<br>(modeled)<br>40<br>20<br>0<br>0 250 500 750 1000 1250 1500 1750 2000<br>Income (USD per person, per year)<br>120 Subsistence line<br>Increase of 0 (0.0% of regional pop.)<br> in consumption subsistence<br>100 Poverty line<br>Increase of 11,000 (0.9% of regional pop.)<br> in consumption poverty<br>80<br>60 Pre-disaster consumption<br>(FIES data)<br>Post-disaster consumption<br>(modeled)<br>40<br>20<br>0<br>0 250 500 750 1000 1250 1500 1750 2000<br>Consumption (USD per person, per year)<br>Population (,000)<br>Population (,000)<br><!-- End of picture text -->

**Figure 3:** Per capita income (top) and consumption (bottom) in Rathnapura district, before and immediately after 50-year precipitation flooding, in US$. Income losses take into account the lost productivity of disaster-affected assets, while consumption losses additionally include reconstruction costs, post-disaster support, and savings.

Therefore, income losses describe better than asset losses the real impacts of disasters on Sri Lankans’ wellbeing and prospects.

To return to a historical example
, both surveys were conducted at different times compared to our survey. The RAMV survey was undertaken in 2018 in response

> 10See Ib ́a ̃nez et al. (2022) for specific survey and sampling details.

> 11Refer to Ib ́a ̃nez et al. (2022) for further details.

> 12Since the VenRePS and RAMV surveys lack information regarding children and adolescents within households, our analysis concentrates only on the household and household head characteristics that are available in all three surveys.

20