rafmacalaba/data-use-mentions-extended huggingface dataset:
10 Economic Forum Survey on employers, who were asked to rate a country’s quality of education using a scale of 1 to 7 (see Figure 5). In both mathematics and science education as well as in management schools, educational quality in Thailand is well below regional counterpart institutions. The quality of education in Malaysia was perceived as comparable to Korea and Hong Kong SAR, China. There are drawbacks to these indicators, however, as they lack a standard for measurement, rely heavily on subjective judgments and are a small sample. Employer surveys provide some insights into how the supply of skills have been matched to the demand, but more reliable measurements are needed to provide an accurate understanding of the basic skills that an individual needs for success.

**Figure 5: Quality of Education as Perceived by Employers**

<!-- Start of picture text --> Quality of the Math & Science Education, 1-7 (best) Quality of Management Schools, 1-7 (best)<br>7 7<br>6 6<br>5 5<br>4<br>4<br>3<br>3<br>2005 2006 2007 2008 2009 2010 2011<br>2005 2006 2007 2008 2009 2010 2011<br>Korea, Rep. Malaysia Thailand Malaysia Thailand Hong Kong<br>Singapore Korea<br>Singapore Hong Kong<br><!-- End of picture text --> <!-- Start of picture text --> Source: World Economic Forum<br><!-- End of picture text -->

# _The economic benefits of human capital_

The economic benefits of human capital can be estimated from education produced in schools or elsewhere (the home, for example). There are both private – accruing to the individual in the form of better jobs, higher wages, for example – and social – meaning they affect not only the recipient of education but others as well – benefits. Better educated mothers have
elements of EU policy, the government established the digital SAR registry platform, using it to implement the EU-supported producer support grant targeted to small farmers (< 120 ha) among many others.

# **2.1 Ukraine’s agricultural sector**

With more than 40 million ha of some of the most fertile agricultural land globally, Ukraine has traditionally been a key global supplier of agricultural commodities, especially sunflower oil, maize, and wheat. Before the war, agriculture contributed about 10% to GDP and 42% of the country’s exports. The sector’s structure was shaped by de-collectivization in the early 2000s when some 7 million landowners were provided with land shares of about 4 ha each. Some 20 million ha of Ukraine’s agricultural land is farmed by formalized large farms, often firms with links to foreign capital markets (Deininger _et al._ 2018) that lease land from owners. About 12 million ha is estimated to be cultivated by small and household farms many of which lack the resources to register formally and may also not be covered by the country’s agricultural statistics. In 2002, about 9.2 million ha was state or communal land and an unknown share of this land was ‘privatized’, often in non-transparent ways that imposed significant losses to the public (Nivievskyi 2020). The country has one annual growing season with winter crops (wheat, rye, barley, rapeseed) sown in fall, summer crops (barley, maize, sunflower, soybean) in the spring and harvest from July to November.

In 2019 and 2020, Ukraine enacted far-reaching land governance reforms including allowing sales of agricultural land through the stepwise lifting of a moratorium on such transactions that had been in place since 2001. This was expected to allow use of land as collateral for credit to deepen domestic capital markets and, in the agricultural sector, support diversification, , especially by smallholder producers, away from land- and capital-intensive bulk commodities with little value or employment added. Complementary institutional reforms
gender gap in the intended use of output is not primarily an artifact of the way the data were collected (i.e. via self-reports) but rather, a reflection of the differences in male and female farmers’ labor allocation across crop and, particularly, livestock products, which differ in their intended use of output. Third, in all three countries included in this study, farmers are less likely to report producing only or mainly for sale at the beginning of the agricultural season than at the end. In Malawi, the differences in the intended destination of output between the beginning and the end of the season are mostly driven by farmers’ reallocation of labor across agricultural products, particularly between subsistence crops and livestock, where the latter is more commercialized. However, changes in the self-reported intended use of output also play a role, as farmers are more likely to report a given crop as being intended for sale at the end of the agricultural season than at the beginning. Though caution is warranted when generalizing to other countries, this suggests that, under the revised standards, rural employment ratios in Sub-Saharan Africa may be higher at the end than at the beginning of the agricultural season, at least in countries with agricultural production systems that are similar to the ones reported on in this paper.<sup>5</sup> This heightened sensitivity of agricultural employment to the timing of data collection also poses challenges to comparisons of rural labor market indicators, especially when the underlying surveys are fielded at different points during the agricultural calendar.

The revised standards have significant implications for measures of rural employment in Sub-Saharan Africa, in terms of its prevalence and sectoral distribution, and these can be illustrated for the two countries, Malawi and Nigeria, for which we have nationally representative data. Measured male and female rural employment-to-population ratios drop by 18 percentage points for males and 12 percentage points for females in Nigeria, and by 12 and 19 percentage points, respectively, in Malawi (corresponding to relative declines of approximately 20 to 30 percent
‐02 Rosenzweig, M. R. 2010. “Microeconomic Approaches to Development: Schooling,Learning, and Growth.” Yale Economic Growth Center Discussion Paper No. 985, 2010.

Schultz, P.T. 2004. “Evidence of Returns to Schooling in Africa from Household Surveys: Monitoring and Restructuring the Market for Education.” _Journal of African Economies_ 13 Suppl.2: ii95‐ii148.

Schady, N. R. 2003. “Convexity and Sheepskin Effects in the Human Capital Earnings Function: Recent Evidence for Filipino Men.” _Oxford Bulletin of Economics and Statistics_ 65(2): 171‐196.

Schwiebert J, 2015, Revisiting the Composition of the Female Workforce ‐ A Heckman Selection Model with Endogeneity, mimeo Soderbom, M., F. Teal, A. Wambugu, and G. Kahyarara. 2006. “The Dynamics of Returns to Education in Kenyan and Tanzanian Manufacturing.” _Oxford Bulletin of Economic and Statistics_ 68(3): 261‐288.

Teal J.F., S. Baptist, 2014, Technology and Productivity in African Manufacturing Firms, World Development Vol. 64, pp. 713–725 25
household, and other (extremely basic, necessary) items for everyone in the household.

We next define as “vulnerable”, anyone with a probability of being poor higher than 0.5 across the 6,000 states of the world simulated (Pritchett et al., 2000; Chaudhuri, 2003). We also calculate the expected poverty gap and squared expected poverty gap (equation 3, with _α_ = 0 _,_ 1 _,_ 2; Christiaensen and Subbarao (2005)) to show vulnerability to extreme poverty. Finally the Calvo-Dercon “no-compensation” vulnerability measure (Calvo and Dercon, 2013), with the _θ_ risk exposure reaction parameter set to 2, is also calculated.<sup>13</sup> Finally, we note that we include only idiosyncratic shocks that negatively affect consumption, though a positive draw from the rainfall distribution would decrease poverty. To the extent that there are any other positive shocks experienced by households, our vulnerability estimates could be overestimates.

> 12Though noting that the HICES 2011 contained questions about 20 different shocks, including “other, specify” and there were no others with a frequency higher than 3 or 4 percent.

> 13In robustness checks, we vary the value of _θ_ parameter.

12
*GDP per capita growth**|0.04<br>-0.01 **-0.04**|0.05<br>0.01<br>**-0.06**|**0.13**<br>**-0.13**<br>0.00|-0.02<br>0.03<br>**-0.01**|**0.10**<br>**-0.10**<br>0.00|0.06<br>0.01<br>**-0.08**|0.04<br>0.02<br>**-0.06**|**0.05**<br>-0.02 **-0.03**|**0.05**<br>0.03<br>**-0.08**|
|**Change in predicted**||||||||||
|<br>**probabilities following**<br>**a one unit increase in**<br>**CPIA score**|’<br>-0.03<br>0.07<br>-0.03|<br>**0.50**<br>**-0.22 -0.28**|0.07<br>-0.07<br>0.00|0.05<br>-0.03 -0.02|0.05<br>-0.05<br>0.00|**0.17**<br>0.13<br>**-0.30**|0.
<!-- Start of picture text --> food, bev. & tobacco<br>textiles<br>clothing<br>leather<br>wood<br>paper & printing<br>coke<br>chemicals<br>pharma<br>rubber & plastics<br>non-metalic minerals<br>basic metals<br>fabricated metals<br>computers<br>electerical equip.<br>machinery<br>motor vehicle & o. transport<br>furniture<br>other manuf.&repair<br>-.3 -.2 -.1 0 .1<br>% change in M empl AC % change in F empl AC<br><!-- End of picture text --> Source: Authors’ own elaboration using the EIFBS. Weights are used. Gender gap is the difference of the number of male and female employees before COVID (BC) and after COVID (AC).

Other heterogenous effects pertain to firm behavior which captures the extent to which good managerial practices, innovation, the adoption of advanced technologies and worker training have weighed in various ways on the gender gap. These behavioral traits have provided an opportunity for firms to adapt their business models and show greater resilience in coping with the COVID-19 crisis in Egypt (El-Haddad and Zaki 2023a). But do they also positively reflect on the gender gap post-COVID?

**Table 2: Distribution of employment by firm technology adoption and** **<u>worker training</u>**

||Total<br>male<br>empl. BC|Total<br>female<br>empl. BC|Total<br>empl. BC|Total<br>male<br>empl. AC|Total<br>female<br>empl. AC|Total<br>empl. AC|Gender<br>Gap BC|Gender<br>Gap AC|% change in<
products or reduce costs incessantly. The literature on industrial clusters report several cases in which negative external shocks turned out to be blessings in disguise in the sense that they triggered multifaceted improvements within clusters.<sup>1</sup> In many other clusters, however, negative external shocks have worsened the downward trend in profitability, and firms are struggling to survive (e.g., Altenburg and Meyer-Stamer, 1999; Kennedy, 1999; McCormick, 1999; Akoten and Otsuka, 2007).

Thus, a cluster may either enter the quality improvement phase or stay as a survival cluster. A major hypothesis is that among the important determinants of a cluster’s fate is the management knowledge of the entrepreneurs in the cluster. This study examines to what extent basic management training can improve the business practices and business results of firms in a survival cluster. The evaluation of the full effect of the training will require several rounds of follow-up surveys. At this stage of research where we have completed only the first post-training survey, we can only analyze the short-run effects.

According to the literature on technology diffusion, the same technology is adopted by different adopters several years apart, and a major explanation for such a phenomenon is that different adopters put different values on the new technology (e.g., Hall and Khan 2003). Likewise, management training participants will be heterogeneous in both incentive and in the ability to put the knowledge they learn from the training into practice. Moreover, their business results will be subject to idiosyncratic shocks. Thus, we expect that the effect of the training on business results will vary considerably among the participants.

If management training proves to be useful for at least some participants, a question arises as to whether the benefit is large enough to justify the cost of the training. For this reason, we focus on the short-run private benefit because the benefits and losses for non7
Elvidge 2000; Sutton and Costanza 2002; Ebener, Murray, Tandon et al. 2005, Doll, Muller, and Morley 2006; Henderson, Storeygard, and Weil 2012; Sutton, Elvidge, and Ghosh 2007; Chen and Nordhaus, 2011; Ghosh, Anderson, and Powell et al. 2009). Night lights are seen as indicators of where people – especially urban people – are concentrated, and more importantly, where economic activity (particularly non‐agricultural) is concentrated.

The second is population. Nightlights seemed to capture industrial and commercial activity reasonably well, but it performed poorly in capturing significant portions of total GDP in countries that have relatively large agricultural sectors.

Both types of methods use national or subnational GDP measures, and distribute the measures to each pixel based on the intensity of the indicator: night lights or population density. In the latter case, the assumption is normally that GDP per capita is uniform across people in the national or subnational unit for which data are available.

Often the distribution happens using separate measures for rural and non‐rural. If the two are distinguished, often nightlights are used with non‐rural GDP distribution while population is used to distribute to rural areas the agricultural GDP. Because this is a mechanistic approach, in whatever way the population data set has an error, there will be a corresponding error in the gridded agricultural GDP. Furthermore, in whatever way the true distribution deviates from the assumption of perfectly even distribution of agricultural GDP per person this will also produce errors.

Nordhaus (2006) uses subnational GDP data to distribute the GDP based on population per pixel. He then does post‐distribution analysis at a pixel level with climate and geographic variables. He does not distinguish between urban and rural populations, even though data for most countries show a gap between rural and urban income. Kummu, Taka, and Guillaume (2018) use a similar methodology, and Yetman, Gaffin, and Xing (2004) use it to generate a future gridded GDP based on
(Figure 3.3). The left‐hand panel shows that for non‐exporters the productivity gap between servicified firms and non‐servicified firms initially increased then partially rebounded. When looking at exporters in the right‐hand panel, the TFP gap between the servicified and non‐servicified exporters has grown over time. Goods exporters (without service affiliates) recovered quickly after the crisis, while servicified exporters have struggled to do so.

**Figure 3.3: Firm‐level TFP developments in Turkey (2007‐2014)**

<!-- Start of picture text --> TFP ACF non-exporters TFP ACF exporters<br>2007 2008 2009 2010 2011 2012 2013 2014 2007 2008 2009 2010 2011 2012 2013 2014<br>Goods non-exporters Goods exporters<br>Servicified goods non-exporters Servicified goods exporters<br>1<br>1<br>.995<br>.995<br>.99<br>.99<br>TFP changes .985 TFP changes<br>.98 .985<br>.975 .98<br><!-- End of picture text --> Source: EIS data from Turkey; author’s calculations. Note: TFP based on Ackerberg et al. (2015)

**While most industries show negative “productivity premiums” for firms with service affiliates, some show positive performance.** Underperformance and overperformance can be measured by the extent to which servicified firms have a higher or lower productivity level than non‐servicified firms. This productivity premium is computed for each manufacturing sector and plotted in Figure 3.4 for non‐ exporting (top panel) and exporting (bottom panel) firms. A positive (i.e. higher than zero) productivity premium means that servicified firms in that industry have a higher productivity level than non‐servicified firms. The opposite is true for negative values. The dashed lines separate sectors with positive versus negative productivity premiums. For both exporters and non
<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 20 40 60 80 100 120<br>Share of population with flood exposure (percent)<br>PPP)<br>Average consumption (2011 USD/day<br><!-- End of picture text --> _Figure 22. Scatterplot of share of population exposed by income (2011 PPP USD/day)_

# **5.5. From global aggregates towards local flood risk assessments**

The results in previous subsections that are presented at the subnational, national, regional and global scales are aggregates of flood exposure maps that were generated as part of this study for all countries. These exposure maps with higher spatial resolutions have been retained and can be used to derive more granular insights at the local level.<sup>2</sup> For illustration, Figure 23 shows the population flood exposure estimates for Vietnam in an aggregate form at the national and subnational scales, as well as in its high-resolution raster format for example areas. The raster displays the number of people exposed to flood risk of each 90m x 90m pixel. The map shows a high concentration of flood-exposed populations in Hanoi, as well as urbanized areas along the Red River.

The granularity offered by these pre-processed population exposure maps could be valuable for various applications; e.g. for the development of disaster mitigation plans prior to a flood event, or to estimate the number of affected people during flood events in order to prepare the scale-up of shock-responsive social protection systems. However, several limitations of these maps should be noted: First, poverty estimates are not available with the same resolution and are not incorporated in these maps. Second, while the maps can help to identify the scale of overall vulnerability and
, 1952 ) . Among several methods proposed in the literature, the ordered quantile normalization most consistently transforms an underlying variable to follow a normal distribution (Peterson and Cavanough, 2019). We utilize the Ordernorm function in the Bestnormalize R package to implement this transformation. The method produces an exactly normal distribution of the transformed variable when the original welfare measure contains no ties, irrespective of its underlying distribution. While a normally distributed outcome variable does not guarantee normality of the residuals, it helps make the distribution of residuals closer to normal, and the ordered quantile normalization leads to smaller discrepancies between the official national poverty rate estimated from the survey and the weighted mean of the small area estimates.

To calculate small area estimates of the poverty gap, poverty severity, or inequality, it is critical to retransform the estimates back to the original welfare metric, as is done in the ELL method by exponentiating log welfare in each simulation. However, this retransformation is not necessary when estimating headcount poverty rates. This is because households’ poverty status is preserved under any monotonic transformation applied to both the welfare measure and the poverty line threshold. We therefore set the poverty line to the percentile of the transformed welfare measure that corresponds to the assumed national poverty rate, which is 4 percent in Sri Lanka and 20 percent in Tanzania. Because the ordered quantile normalization transformation is based on household ranks within the distribution, applying it entails information loss regarding the shape of the distribution between household ranks, and alters the estimated parameters of the model. To verify that this loss of information is minor compared with the benefits of normalizing the welfare measure, we also consider in the robustness section below a more traditional Box-Cox normalization procedure designed to minimize skewness.

> 18 When the underlying distribution is left-skewed, the skew-minimizing transformation becomes , which is not monotonic. log(−yy+ kk)

16
**Venezuela.** ( _Guarantee Fund of Deposits and Banking Protection-FOGADE, BANAP, Charter of Deposit Guarantee and Bank Protection Fund_ ) The fund in Venezuela was established in 1985. It is officially administered and jointly funded. Central bank and government have borne losses and have refinanced the DIS in the past. The board has seven members of which four are from the government, one from the banks, one from the labor union, and one from the insurance agency’s employees. In 1994 the premiums were raised from 0.5% to 2.0% due to a substantial assistance to troubled banks. The Fund has selectively made payments over the legally stated limits. Inter-bank and foreign currency deposits are not covered. The coverage limit since 2002 is Bs 10 millions, which was Bs 250,000 in 1985, Bs 1 mil in 1994, and Bs 4 mil in 1995.

_Sources_ : FOGADE (2004), Garcia (1999), Kyei (1995).

**Vietnam.** ( _Deposit Insurance of Vietnam_ ) Deposit Insurance of Vietnam was created in July 2000. It is government legislated and administered. The maximum coverage has been VND 30 millions which is calculated per depositor per institution.

_Source_ : Own survey of deposit insurers.

**Zimbabwe.** The deposit insurance system of Zimbabwe was created in July 2003. The coverage limit was Zimbabwe $ 200,000 as of 2003. There is no co-insurance and coverage is calculated per depositor per institution. It is jointly administered.

_Source_ : Barth, Caprio, and Levine (2004), Own survey of deposit insurers.

76
-0.0425|0.161**|
||||(0.0803)|(0.0700)|
|Constant|1.680***|-11.70***|0.565***|-0.301|
||(0.0300)|(1.394)|(0.0698)|(0.255)|
|Observations|1,579|1,574|601|601|
|R-squared|0.010|0.452|0.026|0.091|
|Number of frm|223|223|152|152|
|Firm FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. The data on tariffs comes from the _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII). **_Note:_** Table normalise the tariff variable to estimate the effect on market power. Robust standard errors clustered at three digit industry level in parentheses. _∗∗∗_ Significant at 1%. _∗∗_ Significant at 5%. _∗_ Significant at 10%.

23
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