rafmacalaba/data-use-mentions-extended huggingface dataset:
increase of 1 unit in the ICT index<sup>16</sup> will increase process innovation by 0.11 units and product innovation by 0.09 units. The coefficients are large and significant in Ghana and Kenya; while only significant for product innovation in Tanzania and process innovation in Uganda.

The table in Appendix C displays the results including organizational innovation. The sample is, however, significantly reduced given the fact the organizational section of the survey was only implemented to medium and large firms. In any case, the results confirm previous findings where ICT acts as an innovation enabler taking into account the effects from organizational innovation.

# **6.2.3 Innovation and productivity**

The final stage of the structural model is to estimate the impact of product and process innovation on productivity, proxied by sales per worker. Column (1) in the tables show the results of estimating the pooled and country by country regressions of the productivity equation. The coefficients for the ratio of capital to labor and on labor are significant in most specifications and with the expected signs.

The results for the innovation variables are, however, not statistically significant. Only in the cases of DRC, Tanzania, Uganda and Zambia; we find that product or process innovation are significant, and in some cases the coefficients show a puzzling negative sign.

One important element when looking at the relationship between ICT, innovation and productivity relates to the definition of innovation. So far, the paper has used the standard definition of innovation followed in most innovation surveys that relates to whether the innovation is new to the firm. In addition to the subjective nature of the question about “the introduction of significant changes” to measure innovation, the fact that only new to the firm counts as innovation is translated into very large innovation rates in these countries. Even considering that firms further away from the technological frontier may find it easier to implement incremental innovations, the actual measured rates are unreasonable high as compared to OECD and emerging market rates
# **4 Materials and Methods**

## **4.1 Data**

**Wealth data–** Our wealth data comes from recently published estimates of relative wealth on a 2.4 _×_ 2.4 km grid for populated areas in 103 countries around the world.<sup>25</sup> These data use machine learning algorithms to combine satellite data, mobile phone data, and data from Facebook users to estimate relative wealth (RWI) within countries. The algorithms are trained on Demographic and Health Survey (DHS) collected measurements of wealth. The gridded wealth data provide estimates for each grid-cell of how wealthy that grid-cell is relative to others in the same country as well as an estimated error for that estimate. The relative wealth indicator ranges from -2.5 to 2.5 over the full data in our sample, with smaller ranges within individual countries.

As a robustness check we also re-estimate our model with the Demographic and Health Survey (DHS) derived wealth indices that the gridded wealth estimates were trained on.<sup>36</sup> **Pollution data–** We use estimated pollution concentrations from the global dataset produced by the Van Donkelaar research group<sup>29</sup> . This provides monthly average pollution estimates on a 0.01 _×_ 0.01 decimal degree covering the entire planet. We use the average of estimates spanning 2015 to 2020 and for each wealth grid cell assign it the value of the nearest PM2 _._ 5 gridcell. To calculate the average we collapse the monthly pollution estimates in three different ways. In our primary analysis we calculate the average across all years and months from 2015 to 2020. Secondly, we calculate the median across all months and years. Finally, we use the maximum monthly average level of particulates for each grid-cell for the month-of-year that had the highest average pollution from 2015 through 2020. For example, Delhi is assigned the average December PM2 _
26 A: Service and merchandise export growth has slowed down 26 B: India’s share in goods exports has plateaued in the recent years <!-- Start of picture text --> 70 India's Share in Global Exports<br>60<br>3.5<br>50<br>3.0<br>40<br>2.5<br>30<br>20 2.0<br>10 1.5<br>0 1.0<br>-10 0.5<br>-20 0.0<br>Goods Services Goods Services<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Note: Data is from the Reserve Bank of India and World Development Indicators. Years are fiscal years. All national accounts data is measured in constant INR.

- Comparing the product-specific average growth of exports in the boom period preceding the global economic crisis, 2003 to 2008, to a more recent period, 2012 to 2016, we note that total export growth rates declined by 26 percentage points during this period. Decline in exports growth was most pronounced for commodity exports, reflecting the effect of declining commodity prices after 2014, but it also extended to other product groups.<sup>47</sup>

# **Figure 27: Export growth rates before and after the Global Financial Crisis**

Difference in Growth Rates between 2003-08 and 2012-16

> 47 Growth rates are for export values in current USD.

44
The incidence of different policy instruments helps us to better understand the capacity of each to attain the objectives of poverty reduction or equity. Figure 14 presents the incidence of government spending (or tax collection) across quintiles of a selection of the policies studied in the CEQ exercise. It is evident, for example, that the most equity-enhancing education expenditure is that devoted to primary and preparatory education. About a quarter of spending in this category is estimated to be received by the poorest quintile. In contrast, the spending on tertiary education appears to be regressive. About 40 percent of this spending is benefiting the richest quintile, whereas households in the poorest quintile receive only 5 percent of the benefits.

Finally, Figure 14 provides evidence that the reforms recently implemented by the GoE tackled some components of subsidies spending that were benefiting the better off. Almost three-quarters of the subsidies to gasoline were received by the top 20 percent of households, while only 2 percent were received by the bottom 20 percent. Electricity and kerosene subsidies are also enjoyed by the richest quintile, while LPG subsidies tend to be more equitably distributed.

# **Figure 14. Concentration Shares of Benefits and Payments in Egypt 2015, by Fiscal Policy and Market Income Quintile**

<!-- Start of picture text --> 10%<br>21% 20% 20%<br>29% 27%<br>39% 40% 41%<br>48%<br>55%<br>75%<br>25%<br>17% 17% 16% 19%<br>10% 10% 11%<br>2% 7% 4% 5%<br><!-- End of picture text --> _Source:_ Based on HIECS 2015 and administrative data. _Note:_ LPG = liquefied petroleum gas; PIT = personal income tax. Subsidies reflect only the direct effects.

32
Daughters are just as likely to provide transfers as other children, but tend to provide less on net.

The one caveat about these results is that the older cohorts we study still had an average of 3.4 children each, so the constraints created by more stringent family planning starting in the 1970s has not yet been reached. How will transfers evolve for later cohorts, those who have fewer children but more human capital and higher life-cycle incomes? This remains an important question for future study as China’s population ages and new social insurance and pension systems mature.

# **References**

- Altonji, J. G., Hayashi, F., and Kotlikoff, L. J. (1997). Parental Altruism and Inter Vivos Transfers: Theory and Evidence. _Journal of Political Economy,_ 105(6), 1121-1166.

- Becker, G. S. (1974). A Theory of Social Interactions. _The Journal of Political Economy_ , 82(6), 1063-1093.

- Benjamin, D., and Brandt, L. (2000). Aging, Wellbeing, and Social Security in Rural Northern China. _Population and Development Review,_ 26(2000), 89-116.

- Cai, F., Giles, J. and Meng, X. (2006). How Well do Children Insure Parents Against Low Retirement Income? An Analysis Using Survey Data From Urban China. _Journal of Public Economics,_ 90(12), 2229-2255.

- Chou, R. J. (2010). Filial Piety by Contract? The Emergence, Implementation, and Implications of the "Family Support Agreement" in China. _The Gerontologist,_ 51(1), 3-16.

- Cox, D. (1987). Motives for Private Income Transfers. _The Journal of Political Economy,_ 95(3), 508-546.

21
collection, adequate sanitation and electricity. However, Venezuelan heads of the households are more likely to be employed and be self-employed than Brazilian heads, although the months worked is substantially lower. Overall, the summary statistics reveal that Venezuelans who register for social assistance are poverty-stricken and work in low quality jobs, although they are more educated and the overall results seem to be consistent all across Brazil including Roraima and Amazonas.

<!-- Start of picture text --> Brazilian Venezuelan<br>3.06% 10.45%1.66%0.28%<br>17.74%<br>15.32%<br>48.52%<br>20.91%<br>72.28%<br>9.78%<br><R$89 >=R$89 & <178 Brazilian Venezuelan<br>>=R$178 & <R$522 >=R$522 & <R$1045<br>>=R$1045 Fundamental High School<br>Graphs by nationality College<br>(a) Household Income (b) Education Level<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Figure 6: Fraction of those registered in _Cadastro Unico_<sup>_ ́_</sup> by income brackets, education and nationality 23
incomes to account for part of the NAS-HHS gap increases national and global inequality considerably, as measured by the Gini coefficient. Moreover, the hypothesis that as economies develop, inequality first increases and then decreases, also known as the Kuznet’s curve, is much more strongly supported in our cross-sectional sample of ‘top income’-adjusted distributions.

Because of the large gaps between survey and national accounts data, and particularly the large variation in gaps across countries and over time, the prospect of reliably filling gaps in poverty data with estimates of poverty imputed from national accounts growth rate or aggregates, is limited. The errors (differences) of estimates based on national accounts data, in comparison to survey data, are very large. As long as household surveys appear to be the preferred method of measuring poverty and inequality, national accounts data offer only partial hope for filling data gaps. Ultimately, more frequent and properly sampled household surveys – designed to capture the full incomes and consumption of all households, potentially with integration of tax records and administrative data as countries get richer – appears to be the best approach for improving our understanding of poverty and inequality.

29
Although these considerations have not gone entirely unrecognized (see for example Atkinson, 1991; Atkinson and Micklewright, 1992; Deininger and Squire, 1996)3, conventional poverty analysis tends to rely only on welfare measures which are as _comprehensive_ as possible, without going to the same lengths to establish perfect _comparability._ As a result, little is known about the biases in poverty comparisons caused by departures from comparability. Taking consumption as our measure, we use data from Ecuador and Pakistan to illustrate that changes in calculated poverty rates across consumption definitions can be very substantial.<sup>4</sup> We also present an example of the use of the robust procedure to deal with a problematic survey design used in El Salvador. In this example, a different consumption definition was used for different households within the same survey.

While we show that robust comparisons of poverty are possible across datasets with different definitions of consumption, this is at the cost of restricting oneself to the headcount measure-with its well-known drawbacks.<sup>5</sup> However, it can be argued that moving beyond simple counts of the poor to the development of _poverty profiles_ will ultimately be of greater use to those concerned with the relative poverty of population subgroups. By virtue of their relation to poverty rates, profiles based on the aggregation consistent poverty indicator are similarly robust to different consumption definitions.

The latter fact gives us a baseline to explore the possible tradeoffs involved in aggregation when there is measurement error in the data. When measurement errors are not perfectly correlated, aggregation can be beneficial. However, non-food expenditures are often thought to be particularly poorly measured, and this might make more comprehensive consumption measures less reliable than, say, food alone. Typically, the net impact of aggregation is impossible to assess because we do not observe true expenditures. Using our robustness result we show that, in these data, aggregation is beneficial. The more comprehensive the consumption definition, the sharper the poverty profile, with more clearly defined differences between poor and
# **Table 4. What We Know (and Don’t Know) About Financial Literacy and Financial Education**

||**What we know**|**What we don’t know**|
|---|---|---|
|**Financial**<br>**literacy**<br>**levels**| **National surveys**have found that<br>financial literacy tends to be associated with:<br>-Gender, age (elderly/youth), level of income<br>and education, race/ethnicity, and region<br>(part of country, urban/rural)<br>In Africa and Pakistan, the**FinScope**<br>**surveys**indicate:<br>-Awareness of and access to different<br>financial products can vary greatly by<br>country (e.g. microfinance, mobile banking,<br>types of insurance)<br>-Most people cite lack of income and savings<br>as primary reason for not having a bank<br>account<br>-Business literacy among entrepreneurs may<br>be somewhat lacking<br>-Most people also seem to have a desire to<br>obtain financial education<br>FinScope surveys on access to finance for<br>SMEs in Africa indicate that**business**<br>**literacy**among entrepreneurs may be low,<br>based on lack of record-keeping and other<br>factors<br>Studies in developed countries show that<br>financial literacy are**correlated with**:<br>-Retirement planning (found across many<br>national studies)<br>-Investments (participation in stock markets,<br>portfolio diversification)<br>-Debt and credit performance
al., 2011). The World Bank mainstreamed the SCI in its monitoring and assessment framework and has adopted it as a baseline indicator in various projects at the country level.<sup>3</sup> The SCI is based on publicly available data, and this has various advantages over other indexes of statistical capacity. A key advantage of the SCI is that it can provide assessment of a country’s statistical capacity in an internationally comparable and cost-effective manner.

Existing efforts in building indexes to assess statistical capacity have focused on the practical aspects such as data collection, organization, and legal issues, paying little attention to the underlying theoretical principles that are indispensable for the construction of a reliable, transparent, and consistent statistical capacity index. For example, the UNECE, in a recent Global Assessment report, discusses only the legal basis, description of the statistical system, data source, and processing of the target country (UNECE, 2014). The FAO, in its guidelines for assessing country capacity in producing agricultural statistics, provides instructions on completing the questionnaires and on compiling the assessment indicator (FAO, 2014), but pays no attention to the axiomatic principles of these indicators. The U.S. Census Bureau developed and recently updated (2017) the Tool for Assessing Statistical Capacity (TASC) with a primary objective of measuring the overall capacity of an NSS by providing a breakdown of the areas of strength and weakness. However, the focus of this instrument is on measuring the capacity of an NSS to conduct household-based surveys and censuses.<sup>4</sup> To our knowledge, only Sanga, Dosso, and Gui-Diby (2011) discuss the technical framework behind the African Statistical Development Index (ASDI).

> 3 For other recent examples that use the SCI, see: Beegle et al. (2016) for an analysis of the relationship between good governance and statistical capacity in African countries; Tapsoba, Noumon, and York (2017) for the impacts of statistical capacity on
. Identifying those vulnerable to falling into poverty can assist in determining which policies should be prioritized and developed. The analysis can also inform the design and implementation of adaptive social safety net systems that protect households when shocks arise. Additionally, it can provide evidence on how to best achieve sustainable growth patterns. To the best of our knowledge, we are the first ones to estimate the vulnerability rate in El Salvador via a probabilistic approach.

**To address vulnerabilities in El Salvador, the government should invest in adaptive social safety nets and risk mitigation strategies.** Our analysis reveals that vulnerability in El Salvador is higher than poverty, especially in rural areas. Additionally, households' exposure to individual-level risks plays a critical role in both rural and urban areas. Therefore, it is recommended that policy makers invest in risk mitigation and coping strategies. It is vital to reduce households' exposure to risks, such as crime, illness, or job losses, in the first place and to increase their access to mitigation and coping mechanisms in the second place. One possibility to do so is enhancing the adaptiveness of El Salvador's social safety net and developing a

> 4 Own estimations from El Salvador’s household survey.

3
of the poor using a much more comprehensive sample, based on household data from 73 low- and middle-income countries in the 2000s. That analysis primarily focused on analyzing historical trends in poverty at the country level, however, and only scratched the surface by profiling a few key characteristics of the poor.<sup>4</sup> Furthermore, little is known about those living just above the extreme poverty line, who account for a considerable share of the population. In the 89 countries considered in this study, 20%, or around a billion people, are estimated to live on more than the extreme poverty line of $1.90 per person per day (in 2011 PPP terms) but less than $3.10 per day in 2013. Better understanding the characteristics of this group, which we term the “moderate poor”, is important because many of them are materially deprived and considered poor by national standards. Identifying the characteristics that distinguish the moderate poor from the extreme poor may also provide a measure of insight into key factors that drive reductions in extreme poverty.

This paper presents a comprehensive demographic profile of the extreme and moderate poor by age, gender, household composition, educational attainment, urban/rural residence, and employment in the agricultural sector. The profile draws on the richest and most updated collection of household survey data on poverty assembled to date – the Global Micro Database (GMD). From this database, surveys were pooled across 89 countries, each collected since 2009. The results are based on the same welfare aggregates that are used to compute the regional and global poverty estimates published by the World Bank, which are often used by the countries themselves to estimate national poverty.<sup>5</sup> This paper is also be the first to compare the extreme poor and moderate poor for a large number of countries, and to document patterns of missing data in this type of global analysis. The analysis “lines up” survey-based poverty estimates to a common year
were precisely estimated, and a significant improvement (or worsening) in any individual well-being measure analyzed here can be rejected at conventional significance levels.

Second, this lack of change in several key well-being measures is in contrast to the substantial monetary and non-monetary gains made in much of the rest of the world over the same time period, even in places with little economic growth. The World Bank’s global database illustrates that there were large improvements in four of the five indicators tracked here in countries with the most similar multidimensional poverty rates in the pre-

> 2Author’s calculations using data from the World Development Indicators.

> 3The high estimate of monetary poverty is corroborated using a variety of approaches, including estimates of severe food insecurity and projections based on non-resource GDP growth.

3
fluctuated wildly – in 2007 it stood roughly at the same level as in 2001. These patterns are consistent with a volatile, but roughly constant relative demand for skilled labor over this period. The evolution of the high school-dropout wage premium and relative supply in Table 3.2, in turn, signals the presence of substantial demand forces for this middle education group for the two decades, since despite large increases in the relative supply the wage differential fell relatively little.

The evolution of supplies and premiums from Honduras reveal a great deal of volatility, in part due to changes in the underlying household survey. Table 3.1 shows that the relative supply for skilled labor increased over the two decades, with virtually no change in the wage skill premium in the 1990s indicating a relative demand for skilled labor growing at the same rate as its relative supply. In the 2000s, however, the fall in the wage premium is consistent with a slow or even decreasing relative demand for this factor. Table 3.2, meanwhile, indicates that the high school-dropout wage differential fell by 1.8 percent per year over the decade of 1990, with a small concurrent increase in its relative supply over the same period. For the decade of 2000, the corresponding wage premium only exhibits a small increase, but with a substantially larger reduction in the relative supply of high school graduates. Both patterns are consistent with a decrease in the relative demand for this type of labor over the two periods. It should be noted that this fall in the relative supply of high school graduates is one of the few observed for the countries and periods under analysis.

Table 3.1 and the evolution of the underlying variables (see the Appendix below) indicates that the wage premium increased substantially in Mexico in the late 1980s and early 1990s, even when ignoring the hike around 1995 (the “Tequila” financial crisis). The fall in the wage premium that characterized most of the Latin American economies in the 2000s started in Mexico a few years earlier