rafmacalaba/data-use-mentions-extended huggingface dataset:
empirical evidence on the precise magnitude of such indirect impacts for the long-term income growth of the beneficiaries (reviewed in Alderman and Yemtsov 2013). However, all of these studies typically cover the effect of one program in one country, and it is hard to generalize across the world.

To approach the issue of cross-country comparability and comprehensiveness, we limit our attention to just the first, direct, channel of transfer impact on the current wellbeing of beneficiaries. For this we use household survey information and compute what the household’s income would be without the transfers it receives from social protection programs. Thus, generally the transfer value is subtracted from individual or household welfare<sup>30</sup> , providing a synthetic counterfactual. Of course these results are more indicative than authoritative since behavioral effects, for example, are not simulated.<sup>31</sup> In the short run, if households reduced their efforts to earn market income in response to income transfers through social protection, then this method would give an overestimate of the impact on poverty. Evidently _any_ medium term improvements in household market income discussed above, imply that the direct assessment would be an underestimate of the poverty and inequality impact. It is difficult to know where exactly the balance would come out, but our estimates can provide an anchor for discussion and sensitivity analysis.

Using the ASPIRE dataset and an international poverty line of $1.25 per day per capita in PPP, Figure 2 below uses this simulation to estimate how many people are moved out of poverty as a result of social protection transfers (by income groups of countries) in the ASPIRE surveys. If these estimation from surveys in ASPIRE are extrapolated to the world population in developing and emerging countries, almost 150 million people worldwide are prevented from falling into extreme poverty thanks to social protection.<sup>32</sup> This is a sizeable dent in global poverty which counts 1.4 billion people (at $1.25 a day). Taking
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _38_ beyond what seemed likely from initially observed data alone.

# _B2. Ensemble_

Let _Q_ be the quantity of interest, simple linear difference calculation using fixed-weight averages of _P_ . The ultimate goal of the multiple imputation strategy will be to obtain an estimate _Q_<sup>ˆ</sup> (B3)

**P** denoting the _true_ price index population. Since **P** is unknown, _Q_ is unknown. The amount of uncertainty in the estimate _Q_<sup>ˆ</sup> thus depends on what is known about **P** mis. Since we can only recreate it with uncertainty based on information in **P** obs, the idea is to summarize a distribution of _Q_ under varying estimates of **P** mis. In other words, the possible functions _Q_ given what has been observed in **P** obs have a posterior distribution _P_ ( _Q|_ **P** obs) which in turn can be decomposed into two parts

In this, _P_ ( _Q|_ **P** obs _,_ **P** mis) is the posterior distribution of inflation in the hypothetically complete price data and _P_ ( **P** mis _|_ **P** obs) is the posterior distribution of the missing price data given the observed price data. Suppose that _P_ ( **P** mis _|_ **P** obs) is used to draw various likely price data sets for **P** mis, denoted as **P**<sup> ̇</sup> mis. Then, associated inflation _Q_ can be calculated from ( **P**<sup> ̇</sup> mis _,_ **P** obs). By repeating this process multiple times, one can obtain the posterior
# **Rising Food Prices and Coping Strategies: Household-level Evidence from Afghanistan**

Anna D’Souza * Dean Jolliffe ‡

- Economic Research Service, USDA; Contact: adsouza@ers.usda.gov ‡ World Bank; Contact: djolliffe@worldbank.org

JEL Codes: D12, I3 Keywords: food security, high food prices, nutrition, poverty, Afghanistan _Acknowledgments:_ Findings from this analysis provide background information for the World Bank’s poverty assessment for Afghanistan, and have informed the Ministry of Economy (MoEc), Government of Afghanistan (GoA). The authors are grateful to GoA Central Statistics Organization for granting access to the data for this research, as background for poverty assessment. The authors wish to thank for comments Amanullah Assil, Cheryl Christensen, John Hoddinott, Mark Nord, John Pender, Byron Ponce-Segura, Ismail Rahimi, Shahla Shapouri, seminar participants at ERS and the World Bank, Kabul office; and conference participants at the 2010 annual meeting of the Agricultural and Applied Economics Association. D’Souza is a Research Economist at ERS, USDA; Jolliffe is a Senior Economist at the World Bank, and also holds affiliations with the Institute for the Study of Labor (IZA) in Bonn and with the National Poverty Center (NPC) at the Ford School of Public Policy, University of Michigan. The views expressed here are those of the authors and may not be attributed to ERS-USDA, the World Bank, IZA, or NPC.
.494)|
|Lower secondary|0.186|(0.389)|0.179|(0.383)|
|Upper secondary|0.098|(0.297)|0.097|(0.295)|
|_School attributes_|||||
|Bilingual instruction|0.245|(0.430)|0.251|(0.433)|
|Private institution|0.228|(0.420)|0.221|(0.415)|
|_Municipality attributes_|||||
|COVID-19 positivity rate|0.000|-|18.837|(5.028)|
|Observations|8,199,383||8,259,519||

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : This table shows pre- and post-COVID averages for students and their standard deviation in parentheses.

# **4. Identification strategy and empirical approach**

Given the available data, we estimate results using a difference-in-difference approach with twoway fixed effects. Since we have two measures of the intensity of COVID-19 exposure, we propose two forms of estimation. The first uses the average warning index for the municipality in each year as the main explanatory variable and the second uses the amount of the year spent in different color categories, mainly orange and red. Therefore, the base category is green and yellow. Figure 3 shows the distribution of the warning index in post-COVID-19 years (2020 and 2021) and the
## **Table 3. Examples of studies analyzing relationships between infrastructure investments, income inequality and poverty reduction**

|**Study**|**Methodology**|**Findings**|
|---|---|---|
|Calderon and<br>Chong (2004)|GMM on dynamic panel data set for<br>101 economies for the year 1960-1997|Infrastructure stock is negatively linked with inequality; the relationship is<br>more prominent in low-income countries.<br>Estimated elasticity-1.3830**|
|Calderon and<br>Serven (2004)|GMM on dynamic panel data set for<br>period 1960-2000 from 121 countries|Infrastructure investment played a larger role to lower income inequality in<br>East and South Asia, where it did only a modest role in Sub-saharan Africa.<br>Estimated elasticity:-0.0464**|
|Calderon and<br>Serven (2010)|Panel data analysis of 87 countries for<br>1960-2005 period focusing on Sub-<br>Saharan Africa; GMM technique for the<br>econometric analysis; two scenarios for<br>infrastructure shocks|The increased access to infrastructure services (i.e., improved quantity and<br>quality of infrastructure) would decrease income inequality; the income<br>inequality, measured in terms of Gini Coefficient, is found strongly negatively<br>correlated with the synthetic indices used as measures of infrastructure quantity<br>and quality; the correlation values range from-0.47 to-0.56.|
|Chatterjee and<br>Turnovsky<br>(2012)|A general equilibrium model of a closed<br>economy|Public spending on infrastructure would reduce income inequality in the short-<br>run, whereas it would increase in the long-run.|
|Sasmal and<br>Sasmal (2016)|Panel data analysis for Indian states|The lager
<!-- Start of picture text --> 60 60<br>54<br>51 51 50<br>48<br>42 42 44<br>40<br>40 40<br>34<br>20<br>20<br>0<br>0<br>Hospitality Knowledge Retail Manufacturing Other Agriculture<br>intensive services Micro (0-4) Small (5-19) Medium Large (100+)<br>services (20-99)<br>% of firms that have online sales<br>% of firms that have online sales<br><!-- End of picture text --> Figure 11: Predicted probability of having online sales

# **5.1 The intensive margin of digital sales**

To explore the size of the gap, this section starts by examining how dissimilar is the use of technologies across firms and how that has changed with the pandemic. Figure 12 and Figure 13 show a bright story about how the pandemic has pushed more firms into the digital sphere. The total value of online sales as a share of 2019 sales has increased over time, as captured by the different rounds of the BPS survey. This comes from more firms establishing online sales channels, as well as online sales having a more prominent role in total sales. At all percentiles of intensity of online sales in wave 1, the value of online sales has increased. For example, while the median firm did not generate any online sale during wave 1, during the latest wave, it generated 7.9% of its 2019 sales via online channels; the 75th percentile went from 2.3% to 28.8%, and the 90th percentile from 19.9% to 64%.

15
a forward‐looking view of risk beyond the historical events used in their development.

**2. A methodology for deriving vulnerability relationships for the impact of drought on poverty in Ethiopia**

For our chosen area of focus – the impact of drought hazard on the welfare of rural households in Ethiopia – we aim to determine the validity of applying a derived set of damage functions onto a forward‐looking view of drought risk. The methodology follows a purely empirical approach using a survey‐weighted regression model combining historical household survey data with historical data on drought hazard. Within catastrophe risk modelling, there are two principal methods used to derive damage functions; empirical and analytical derivation. For example, content for the Global Earthquake Model<sup>6</sup> demonstrates statistical methods for empirical derivation of damage functions from historical damage and loss data, and also analytical methods based on numerical models of response of different structures/structural components (for further information, see D’Ayala et al, 2014 and Rossetto et al, 2014). The analogy for drought damage to crops, for example, would be the use of mechanistic agro‐meteorological models based on a process approach versus statistical methods using historical crop loss and drought hazard data.

This model will also attempt to separate out observed relationships for different vulnerability‐ determining characteristics, analogous to how catastrophe risk models incorporate distinct damage functions for different classes of exposed asset; for example, by structural ‘class’ for buildings or by crop type. For example, a set of primary building characteristics that determine seismic response (such as those shown in table 2.1 from the Global Earthquake Model project) will form the basis of a

> 6 http://www.nexus.globalquakemodel.org/gem‐vulnerability/posts/guidelines‐for‐empirical‐vulnerability‐ assessment

6
# **Figure 4 PROPER rating system (Indonesia)**

|**Rating**|**Criteria**|
|---|---|
|**GOLD**|Levels of pollution control for air and hazardous waste similar to those for<br>water pollution; extensive use of clean technology; pollution prevention;<br>recycling.|
|**GREEN**|Pollution discharges 50% below regulatory standards; proper disposal of<br>wastes; good housekeeping; accurate emission records; reasonable<br>maintenance of wastewatertreatment system.|
|**BLUE **|Pollution discharges equal to or below regulatory standards.|
|**RED**|Some pollution control effort but pollution discharges exceed regulatory<br>standards.|
|**BLACK **|No pollution control effort or serious environmental damages.|

# **Figure 5 Industrial EcoWatch System (Philippines)**

|**Rating**|**Criteria**|
|---|---|
|**BLACK**|•<br>Discharge beyond standard and has no wastewater treatment system<br>and/or air emissions control system;<br>•<br>Violation of toxic and heavy metals discharge regulations;<br>•<br>Under a “Cease and Desist” Order;<br>•<br>Willful manipulation of effluent discharge or air emissions such as<br>dilution of discharge, use of by-pass or direct discharge without<br>treatment;<br>•<br>Verified obstruction of inspection activities;<br>•<br>A legitimate and verified public complaint of pollution and no effort has<br>been exerted to address this complaint.|
|**RED**|•<br>Violation of applicable effluent/air emissions standards|
|**BLUE**|•<br>Pollution level consistently within
(percent)|(percent)|(percent)|
|Lowest 10|9.4|10.8|8.1|10.5|
|Second 10|11.1|11.7|6.0|11.8|
|Third 10|9.7|8.4|4.7|18.1|
|Fourth 10|11.5|7.1|6.1|11.4|
|Fifth 10|10.5|9.2|8.7|13.9|
|Sixth 10|10.0|7.3|7.2|9.9|
|Seventh 10|9.7|12.3|13.1|20.7|
|Eighth 10|9.1|10.2|10.5|18.8|
|<br>Ninth 10|9.2|8.8|13.9|12.5|
|Top10|9.8|14.8|21.7|20.4|
||100.0||100.0||

Notes: All values based on observed – not predicted – data. Households ranked into decile groups on the basis of observed per capita household expenditure (excluding remittances). For those households receiving internal remittances (from Ghana), column (2) shows the percent of total per capita household expenditure (including remittances) coming from internal remittances. For those households receiving international remittances (from African or other countries), column (4) shows the percent of total per capita household expenditure (including remittances) coming from international remittances.

Source: Calculated from 1998/99 Ghana Living Standards Survey (GLSS 4).
- Blackman, Allen, Eduardo Uribe, et al. (2013). “Voluntary environmental agreements in developing countries: the Colombian experience”. _Policy Sciences_ 46. Publisher: Springer, 335–385.

- Bloom, Nicholas et al. (2010). “Modern Management: Good for the Environment or Just Hot Air?*”. _The Economic Journal_ 120, 551–572.

- Botzen, W. J. Wouter, Olivier Deschenes, and Mark Sanders (2019). “The Economic Impacts of Natural Disasters: A Review of Models and Empirical Studies”. _Review of Environmental Economics and Policy_ 13, 167–188.

- Boustan, Leah Platt et al. (2020). “The effect of natural disasters on economic activity in US counties: A century of data”. _Journal of Urban Economics_ 118, 103257.

- Bret, Lauriane et al. (2021). “Towards a model assessing supply chain resilience strategies”. _Procedia CIRP_ . 9th CIRP Global Web Conference â Sustainable, resilient, and agile manufacturing and service operations : Lessons from COVID-19 103, 14–19.

- Brucal, Arlan and Antoine Dechezleprêtre (2021). _Assessing the impact of energy prices on plant-level environmental and economic performance: Evidence from Indonesian manufacturers_ . Paris: OECD.

- Brucal, Arlan, Beata Javorcik, and Inessa Love (2019). “Good for the environment, good for business: Foreign acquisitions and energy intensity”. _Journal of International Economics_ 121, 103247.

- Brucal, Arlan and Shilpita Mathews (2021). “Market Entry, Survival, and Exit of Firms in the Aftermath of Natural Hazard-related Disasters: A Case Study of Indonesian Manufacturing Plants”.

- Cai, Ruohong et al. (2016). “Climate variability and international migration: The importance of the agricultural linkage”. _Journal of Environmental Economics and Management_ 79, 135–151.

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dietary diversity; and (iii) per capita daily protein intake.<sup>28</sup> The first measure is related to United Nations Millennium Development Goal 1.9 – to reduce by half the proportion of population below a minimum level of dietary energy consumption. The second two measures reflect different dimensions of household food security that relate to nutrient intake. Several studies from developing

> 26 Expenditure on food away from home accounts for approximately 2% of household food expenditure. 27 The food price index is based on a reference bundle of goods consumed by relatively poor households; the reference bundle was constructed to reflect regional diversity in consumption patterns. See Diewert (1993) for a discussion of the Laspeyres price index, as well as other indices.

> 28 Since consumption data were collected at the household level, it is not possible to address the issue of individual nutritional status, which depends on the intra-household allocation of resources. There is some evidence that in times of difficulty, households allocate more food to children. (Block _et al._ , 2004)  In the robustness tests, we include the number of children in the household as an additional control; the results do not change.

11
grown at the national level; being a household-level survey, large-scale farming operations which grow commercial crops are not included.

Figure 19: Popularity of Crops Grown in Sudan, 2009 and 2014/15

- (a) Crop Grown as a Proportion of Total Agricultural Households, 2009

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>sorghum millet groundnuts tea Vegetables wheat coffee beans roots & other<br>tubers crops*<br><!-- End of picture text --> _Note:_ * Other crops include cotton, maize, and rice.

- (b) Crop Grown as a Proportion of Total Agricultural Households, 2014/15

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>sorghum millet seasam groundnutvegetables beans wheat roselle fruit dates other<br>crops*<br><!-- End of picture text --> _Source:_ Authors’ calculations using NBHS 2009 and NHBPS 2014/15.

_Note_ : * Other crops include cotton, maize, sunflower, potato, gum arabic, and spices.

There are key differences between crops grown by poor and non-poor households, with subsistence crops more likely to be grown by the poor (Figure 20). While sorghum is grown almost equally by poor and non-poor households, millet is grown more predominantly by the poor; sesame and groundnut are grown more predominantly by the non-poor. Apart from roselle, non-poor households predominantly grow almost all the cash crops listed in the survey. This is driven by the extent to which production constraints, such as access to land, input and output markets, and credit and insurance markets, are binding for poor and non-poor households
Finally, to whom do these countries offer bilateral tariff preferences? Figure 8 illustrates the export recipients in 2014; consider again the data for the European Union. The EU offered a bilateral tariff preference to developing country exporters in 89 percent of these preference possible products and to middle income countries in 95 percent of products.<sup>43</sup> On the other hand, it offered a tariff preference to high-income countries in only 32 percent of available products.

In general, high-income economies in Figure 8 tend to provide the largest share of their realized offerings of preference possible products to developing countries, primarily through GSP-type programs (see again Table 3) but also increasingly through FTAs. With the exception of Australia, high-income economies offer the fewest preferences to other high-income economies; they offer much more to emerging and developing countries. Indeed, Japan (and Saudi Arabia) offered zero tariff preferences to other high-income economies in these data. However, this pattern may also change if the Trans-Pacific Partnership (TPP) (which includes Australia, Canada, Japan, and the US) and the Transatlantic Trade and Investment Partnership (which includes the EU and US) negotiations result in FTAs that include the standard comprehensive reciprocal tariff reductions between members.

Figure 8 also reveals substantial variation for the bilateral preferences offered by emerging economies. India has the most preference possible products and yet offers the fewest preferences overall (see again column 3 of Table 4). Argentina and Brazil also tend to offer relatively few bilateral preferences to the countries in this sample; on the other hand, China and Turkey offered the most preferences in the set of emerging economies and have levels of offerings comparable to some high-income countries. Mexico, on the other hand, stands out by way of the concentration of its tariff preferences toward high-income economy exporters - recall its NAFTA participation (with the US and Canada) and its bilateral FTAs with the EU and Japan.

The lowest panel of Figure 8 reveals the pattern of bilateral tariff preferences that developing countries offered in 2014
comprehensive literature review, we identify three typologies of market failures which could potentially require corrective actions through state ownership: i) market power generated by structural barriers to competition, ii) under provision in the presence of positive externalities or uncertainty, and iii) risks connected to large/irreversible negative externalities.

- **Competitive Sectors** : these are sectors in which it is economically viable for multiple firms to compete to provide a good or service. Inherent market features, such as cost structure or demand characteristics, make entry into these sectors largely unproblematic. Furthermore, firms in commercial sectors are typically engaged in the provision of goods or services the consumption of which is either rivalrous or excludable. Given the competitive nature of these markets and private sector firms’ ability to achieve economic efficiency without encountering significant market distortions, there is no strong economic rationale for SOE participation in them.

The BOS database excludes firms operating in sectors considered to provide public goods, which in some cases cannot be priced. In total, the data set excludes all 4-digit disaggregated sectors (classes) falling in the NACE Rev. 2 divisions of Public Administration & Defense, Education, Human Health & Social Work, and Activities of Extraterritorial Organizations; 2 disaggregated sectors in Finance & Insurance (pension funding and central banking), 8 disaggregated sectors within Arts, Entertainment & Recreation, and 6 within Other Services; a total of 52 out of 615. The exclusion is consistent with the harmonized definition of SOEs as entities that provide goods or services which can be traded at economically significant prices. Please refer to Dall’Olio et. al. (2022) for a more detailed discussion of the sector taxonomy.

# 3. Data

## 3.1 Bureau van Dijk (BvD) – ORBIS

Bureau van Dijk (BvD) provides two ways to access ORBIS data: the BvD-ORBIS online interface (Orbis.bvdinfo.com) and the BvD historic vintage files. The interface is the most straightforward way to access the data for each firm. By
where where  _it_   _N_  _itn_ is the scale elasticity such that  _itn_   ln _D_ <u>0 </u> _<u>yit</u>_ , _xit_ , _t_ <u></u> _n_  1  ln _xitn_ If the output is only one, then a _translog_ output-oriented distance function can be defined as Given the technical efficiency measure in Equation  5 , the technical efficiency change (TEC) between periods _t_  1 and _t_ can be estimated by following Coelli et al. (2005): The technical progress (TP) index can be obtained from equations  6 ,  9 , and  11 as follows: From equation  3 , the scale elasticity can be written as The index of scale efficiency change then can be calculated by using equations  10 and  14 .

# **4. Empirical analysis: Database, variables and results**

This section examines the productivity spillovers from FDI in the Latin American countries by using a unique and extensive country-level panel data covering the period 1996–2006. The intra-country productivity spillovers are examined through the FDI and trade variables, and the roles of labor skills and R&D effort in extending spillovers from FDI and trade are evaluated to test the absorptive capacity of productivity spillovers.

7