rafmacalaba/data-use-mentions-extended huggingface dataset:
inundation due to climate change. Of this, 5,088 km (94%) will be exposed to an additional inundation depth of up to 0.5 m and 29 km (less than 1%) to more than 1 meter.

The total cost for raising embankments to offset the additional risk due to climate change is Taka <u>6,727 or US$ 96 million</u> **<u>.</u>** This cost has been calculated using cost estimates available from the BWDB for earth work (Taka 125/ m<sup>3</sup> ), compaction (Taka 60/ m<sup>3</sup> ), and turfing (Taka 25/ m<sup>2</sup> ). Table 9 provides the itemized cost of height enhancement of affected embankments for various inundation depths. As expected, the bulk of the costs is for the required earthwork. (See Table 9.)

# <u>Drainage within Coastal Polders</u>

Bangladesh has 123 coastal polders (i.e., areas with a dyke) to protect low lying coastal areas against flood and salinity intrusion. Water regulators are used to prevent drainage congestion in coastal polders.<sup>29</sup> The higher precipitation level during the monsoon and the rising sea level under the climate change scenario is expected to increase drainage congestion in coastal polders.

For this analysis, trial simulations were conducted with 4 coastal polders to determine the average number of additional vents necessary for draining water if the water level inside the polder increases due to climate change conditions and creates drainage congestion. Each simulation consisted of adjusting the length of opening under the climate change scenario to reduce the water depth inside each polder to the level under the baseline scenario. The adaptation measure is the additional length of opening needed to ensure same drainage conditions under both scenarios. Additional numbers of vents are computed by dividing the length of opening with 1.
# **1. Introduction and Overview**

The World Bank's Human Capital Index (HCI) captures the size of the income gains when today's better-educated and healthier children become tomorrow's more productive workers. Specifically, a child born today can expect to be HCI ×100% as productive _as a future worker_ as she would be if she enjoyed complete education and full health. But this implicitly assumes that when today's child becomes a future worker, she will be able to find a job—which may not be the case in countries with low employment rates. Moreover, even if today's child is able to find employment in the future, she may not be in a job where she can fully use her skills and cognitive abilities to increase her productivity. In these cases, human capital can be considered _underutilized,_ because it is not being used to increase productivity to the extent it could be. For example, unemployed future workers may be underutilizing their human capital, as are those out of the labor force. Likewise, engineers driving taxis are underutilizing their human capital because, even though they are employed, they do not hold jobs in which their education increases their productivity. Moreover, there is a stark gender gap in terms labor market outcomes: in many countries, women face inferior job and income opportunities compared to men, even with the same human capital. As such, simply considering the HCI by sex may give a distorted view in terms of realizing the potential of human capital investments.

This paper introduces two Utilization-Adjusted Human Capital Indices (UHCIs) that, as their name suggests, adjust the HCI for labor-market underutilization of human capital in a consistent way across a large number of countries. The UHCIs are designed to complement the main HCI, and _not_ to replace it. In part this is because they have different purposes: the HCI is an index of supply of a factor of production (in the future), whereas the UHCIs are a hybrid between
2.962*|-1.738*|0.406|-1.083|
||(0.006)|(0.675)|(0.088)|(0.064)|(0.679)|(0.319)|
|DTRI DR|1.003*|-0.679|1.829|0.352|-2.031**|-2.320***|
||(0.090)|(0.318)|(0.116)|(0.656)|(0.016)|(0.009)|
|WEF NRI|1.114***|1.355***|1.897***|1.212***|1.186***|1.536***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp + imp|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|3717|3717|3717|3717|3717|3717|
|P-R2|0.986|0.967|0.962|0.963|0.934|0.973|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex Table A2. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter
the intervals collected in the LFS and the distribution of wages across these intervals.<sup>12</sup> Only 3.3 percent of employees failed to report wages in the 2018 LFS. To convert wages into a continuous variable, in the baseline case, we obtain point estimates by assuming that wages are uniformly distributed on the reported interval and impute a random variable.<sup>13</sup> To address missing data, we use the standard practice of imputing mean wages conditional on observable characteristics, such as age, gender, occupation, marital status and urban status. In our analysis, we compare real wages net of taxes and deflated with the consumer price index (CPI), using 2015 as a base year. Specifically, we use the CPI estimates published by KAS (Harmonized Indices of Consumer Prices, 2018).

**Figure 2. Distribution of nominal wages reported in intervals (2018 euros)**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Euros<br>Percentage<br><!-- End of picture text --> _Source: Own estimates using the 2018 Kosovo LFS._

**We also analyze the sensitivity of the results to alternative imputation techniques** . In particular, we predict wages conditioning on observable worker characteristics using the 2017 HBS, in which the exact net wages are reported, and we use non-parametric matching techniques ( <mark>Ñopo</mark> , 2008) to impute these wages into the 2018 LFS. We also use the 2017 HBS to identify poor households because official poverty in Kosovo is measured based on a consumption aggregate, and this can only be constructed with HBS data.

# **Another empirical challenge is to establish causality since identifying sources of exogenous variation**

**is not possible with available data.*
</sup> It is lower but still substantial for the sales tax19% for corporations and 14% for the self-employed. This proportion has been stable over the last ve years, and is based on a tax register that is updated on a regular basis. However, it is possible that the tax register contains inactive rms whose owners decided not to deregister, as deregistration requires a personal visit to the tax oce and the payment of any outstanding tax obligations. Even among rms covered by third-party information, i.e., shown to be economically active, 12% did not le their income tax for the 2014 tax year.

> 13The corresponding gures in Brockmeyer and Hernandez (2016) are comparable, albeit lower, as they use an algorithm based on tax declarations and registration/deregistration records to construct snapshots of the tax register at dierent points in time, but do not directly observe the tax register. The gures in this paper are the authority's own estimates based on all available data.

8
**Table S4: Estimations of extensive margin**

||OLS estimation<br>Reduced-form estimation<br><br>|
|---|---|
||(1)<br>(2)|
||Panel A: Weighting bilateral routes by the interaction<br>of exporter and importer trade in industry<br>Dependent variable is Δ (0,1) [exports > US$100k]|
|Δ Log country-industry labor<br>productivity|-0.001<br>(0.004)|
|Δ Log estimator for technology|-0.050|
|flows from the United States|(0.034)|
|Observations|241,792<br>241,792|
|Importer-Industry-Yr FE|Yes<br>Yes|
|Exporter-Importer-Yr FE|Yes<br>Yes|
||Panel B: Excluding sample weights|
|Δ Log country-industry labor|-0.002|
|productivity|(0.004)|
|Δ Log estimator for technology|-0.023|
|flows from the United States|(0.027)|
|Observations|241,792<br>241,792|
|Importer-Industry-Yr FE|Yes<br>Yes|
|Exporter-Importer-Yr FE|Yes<br>Yes|

Source: Author's analysis based on data described in text. Notes: See Tables 1 and 3. Estimations test the extensive margin of trade through linear probability models. The dependent variable is a dichotomous indicator variable taking unit value if bilateral exports exceed $100k. The $100k threshold is chosen due to WTF data collection procedures discussed in the text.
compilation of Household Budget Surveys. Eurostat publishes consumption means from household budget surveys, which are available in five-year intervals between 1988 and 2015. The Eurostat compilation provides consumption data for all 28 EU Member States and also for Montenegro, the Former Yugoslav of Republic of Macedonia, Turkey and Norway.<sup>7</sup> For the United States, we supplement our database with annual data from 1981-2015 from the Consumer Expenditure Survey.<sup>8</sup> To facilitate comparisons with per capita national accounts data, we express all survey values (consumption and income) in annualized per capita basis. In cases where household survey consumption data are only available at the household level or in per adult equivalent terms, we adjust them to express in per capita terms. By converting all survey means to per capita terms, we align survey means with national accounts data.

Our comparisons of the gaps in levels in Section 3 are based on converting all data to local currency units. To accomplish this, we combine per capita mean consumption or income (expressed in 2011 PPP values) from each national survey with (i) inflation measures, (ii) currency conversion rates (in the case of currency devaluations and change of national currency) and (iii) the PPP values from PovcalNet that were used to convert the national survey data into 2011 PPP US dollars.<sup>9</sup> For the analysis in Section 4, where we examine how potential adjustments to account for the gaps between household surveys and national accounts affects global poverty and inequality measurement, we extract detailed information on the Lorenz curves for each welfare vector from PovcalNet. We are then able to construct national and global

> 7 We obtain the Eurostat consumption data from the Eurostat Household Budget Survey Database available at <u>https://ec.europa.eu/eurostat/web/household-budget-surveys/database</u> (accessed on November 5, 2020). Eurostat provides survey means per adult equivalent in constant currency
The GCD compiles household expenditure surveys across countries and harmonizes product categories across all surveys at the 2-digit COICOP product level. 14 Merging this dataset with our core sample, we obtain consumption data for 89 low and middle income countries which account for over 50% of the world population. While this extended sample does not contain place of purchase information, we can use it to characterize food consumption levels and food Engel curves. Food products are defined as all items pertaining to the COICOP 2-digit category ‘food and non-alcoholic beverages’. This category is a good proxy for the set of products which

> 12We assign the following probabilities that the place of purchase is formal to each category: 10% in category 2; 20% in category 3; 50% in category 4; and 90% in category 5. Other categories are unchanged from the baseline assignment rule.

> 13We use crosswalks to convert survey-specific product categories to COICOP categories when necessary. This could not be done for three countries: Brazil, Chad and Peru. For these countries, we use survey-specific product categories at the 3 and 4 digit levels.

> 14 Aggregate statistics are available at http://datatopics.worldbank.org/consumption. Appendix B.3 provides further information on the dataset and our merge.

9
2012)). The rationale for such findings is that workers with lower levels of human capital are less costly for firms to replace when recovery from the downturn materializes.

Table 7 presents the impacts of GFC foreign shocks on non-labor inputs and productivity: materials, materials per worker, capital, capital per worker, labor productivity, and TFP.<sup>41</sup> Regarding non-labor input use, columns (1) and (2) show that materials and materials per worker decline as a result of the negative demand shock suffered by firms in Brazil whereas there is no evidence of capital-labor substitution in columns (3) and (4).

Finally, our estimates show clear evidence of a negative impact of GFC foreign shocks on firm productivity. The effects in columns (5)-(8) are persistent and consistent across productivity measures. Our TFP estimates are obtained as the part of firm revenues deflated by industry price indexes (to derive quantities produced) that cannot be explained by the contribution of labor, intermediates, and capital. Such estimates, referred to as revenue TFP 40The measures are based on Brazil’s RAIS starting worker-level database. Our regressions rely on a firm sample where the top and bottom 1 percent of the distribution of firms’ continuous outcome variables (except firm worker composition shares) are dropped.

> 41The measures are based on Brazil’s RAIS and PIA manufacturing survey. The definitions of these variables are provided in Appendix A. Our regressions rely on a firm sample where the top and bottom 1 percent of the distribution of firms’ continuous performance variables are dropped.

20
# **1. Introduction**

After decades of stagnant or declining commodity prices when agriculture was considered a ̳sunset industry‘, recent increases in the level and volatility of commodity prices and the resulting demand for land have taken many observers by surprise. This phenomenon has been accompanied by a rising interest in acquiring agricultural land by investors, including sovereign wealth and private equity funds, agricultural producers, and key players from the food and agri-business industry. Investors‘ motivations include economic considerations, mistrust in markets and concern about political stability, or speculation on future demand for food and fiber, or future payment for environmental services including for carbon sequestration. Some stakeholders, including many host-country governments, welcome such investment as an opportunity to overcome decades of under-investment in the sector, create employment, and leapfrog and take advantage of recent technological development. Others denounce it as a ―land grab‖ (Zoomers 2010). They point to the irony of envisaging large exports of food from countries which in some cases depend on regular food aid. It is noted that specific projects‘ speculative nature, questionable economic basis, or lack of consultation and compensation of local people calls for a global response (De Schutter 2011). In a context of diametrically opposite perceptions, the objective of the present paper is to provide greater clarity on the numbers involved and the factors driving such investment. This is done by quantifying demand for land deals, and exploring the determinants of foreign land acquisition for largescale agriculture using data on bilateral investment relationships. This work is an important first step to assess potential long-term impacts and discuss policy implications.

The analysis of large-scale land deals is relevant for a number of key development issues. One such issue is the debate on the most appropriate structure of agricultural production. The exceptionally large poverty elasticity of growth in smallholder agriculture (de Janvry and Sadoulet 2010, Loayza and Raddatz 2010) that is reflected in rapid recent poverty reduction in Asian economies such as China, and the fact that the majority of poor are still located
of the capital stock used in the _government sector_ . It is based on estimates of the capital-output ratio for the Indian economy in 1975 (Arestoff and Hurlin). Overall, the preferred estimate for the initial capital stock is CNY 147 billion in 1952 prices, representing an overall capitaloutput ratio of 2.2.

# **_Depreciation rates_**

In the absence of published deprecation rates, it has been necessary to make assumptions about the appropriate depreciation rates for capital in China. Depreciation rates serve to measure the extent to which capital is used in a given period. Ideally, this can be measured through collecting the prices of the same type of asset as the asset ages. Here it has been assumed that assets decline in value at a constant geometric rate. In this case, the complete retirement of an asset only occurs after an infinite time period. However, given the observed rate with which price declines with age, the proportion of assets in use after a decade is small except for structures. Many studies have used accounting data to estimate the depreciation rate. This is inappropriate as such figures are a reflection of arbitrary rates set by the tax authorities.

# _Comparison with other estimates of depreciation in China_

Analyses of the performance of the Chinese economy needed to assume a depreciation rate in order to transform annual investments into a capital stock. A literature search found 36 studies (Table 4). These papers presented 42 estimates or assumptions for the national depreciation rate as some studies had different depreciation rates for different periods. Eleven of the studies were in Chinese, the remainder in English. The average depreciation in the English language studies was 5.6%.

A simple meta-regression of the results suggests that there are several trends in the reported assumptions for the depreciation rate. _First_ , there is a significant tendency for assumptions about depreciation rates to increase in papers published more recently. The estimated coefficients from the meta-regressions suggest a depreciation rate of 3.2% in 1988, rising to 7.3% for papers published
|None|
|23|Rolled steelplate|Venezuela|28-Oct-93|28-Dec-95|None|84.05%|
|24|Corrugated rod|Brazil|29-Nov-93|11-Aug-95|None|57.69%|
|25|Corrugated rod|Spain|29-Oct-93|10-Oct-94|None|None|

Source: Based on data from the 2002 Countervailing Duty Agreement, _UPCI’s Annual Report 2002_ , Resolutions published in the DOF.

27
; the results do not suggest that the implied re-weighting has much impact on the consumption PPP, as we show in Chen and Ravallion (2010).

Figure 1 plots the poverty lines for developing countries compiled by RCS against log household consumption per capita from national accounts, also at 2005 PPP; there are 75 countries with complete data. The Figure also gives a nonparametric regression of the national poverty lines against log mean consumption. Comparing expected values conditional on mean consumption (as estimated by the nonparametric regression in Figure 1), the range is from $1.25 to $30 per day. The mean line for the poorest 15 countries in terms of consumption per capita is $1.25 while the mean for the richest 15 is $25 a day.

> 8 For a fuller discussion of these issues see Deaton and Heston (2010) and Ravallion (2010).

> 9 The greatest bias is probably in the ICP survey for China, which was confined to 11 cities. Although the survey included some surrounding rural areas of these cities, it clearly cannot be considered representative of rural China; evidence on this point is provided by Chen and Ravallion (2008b) which discusses our corrective.

6
> 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, 2013, and shows that this procedure has mild impacts on the profile of the poor.

There are five main findings. First, the poor tend to be rural and young, slightly more so than previously thought. About 80 percent of the extreme poor and 76 percent of the moderate poor live in areas classified, according to national definitions, as rural. These shares are likely overestimated, because in many cases the welfare aggregates have not been adjusted to account for the lower cost of living in rural areas. Data from South Asia, however, suggest that the magnitude of this rural bias is modest.<sup>6</sup> With respect to age, nearly 45 percent of the extreme poor are children under 15 years old, and over 60 percent of the extreme poor live in households with three or more children. Second, gender differences in poverty rates are muted. This is because poverty status is identified at the household level, whereas gender disparities are most apparent in individual-level indicators such as education, autonomy in decision making and labor market outcomes. Third, a primary school education is not sufficient to exit poverty. A sizeable minority of the extreme poor -- about

> 4 In addition, as discussed below, the welfare aggregates used to profile the poor in that study were not the same ones that were used to construct the World Bank’s official poverty estimates.

> 5 Countries in Europe and Central Asia and Latin America and the Caribbean are exceptions, where the income aggregate used for global poverty monitoring has been standardized across the region and may differ from the aggregates used for national poverty estimates. In other regions, the welfare aggregates used are not consistent across countries, and vary for example in their treatment of housing and
, the benefits of domestic policy cooperation may not outweigh the extensive costs of contracting. However, even if contracting costs are assumed to be symmetric across domestic and trade policy instruments, an optimal agreement may still allow discretion over domestic policies. This is because the underlying rationale for the international agreement is trade-related (the terms-of-trade externality). Thus, while governments may use a domestic policy as a secondary trade barrier when their trade

> 30 For example, while some countries like the US maintain some data on export cartels (see Dick, 1992) many countries do not even track the number of export cartels operating in or from their region.
et al 2012; <mark>Shi et al. 2018; Falcheta et al 2019)</mark> . Such data have been shown to give rise to valid indicators of energy production, energy consumption, and carbon emissions (see Jasiński 2019 and the survey in Zhu et al. 2019). Our findings are consistent with the view that remote-sensing data may also be useful as a proxy for electricity access and, to some extent, even the reliability of electricity (i.e., a reduction in the frequency of blackouts) in developing countries such as Malawi.

> 4 The DiD estimator also requires that (a) the intervention’s design permits the identification of both a treatment and a control group (i.e., households that benefited from the program and those that did not benefit), and (b) there is sufficient data available on the outcomes for both groups, and both before and after the implementation of the project. For the project studied in this paper, (a) is fulfilled based on the variation in the geographical locations of project components, and (b) is achieved by using existing, nationally representative household surveys as well as data from satellite images. Both types of data are available for years before and after the implementation of the project.

> 5 Our data only allow us to study the impact of the project as a whole. Thus, we are unable to disentangle the effects of individual project components (including of components focused on the supply side and those focused on the demand side).

2