rafmacalaba/data-use-mentions-extended huggingface dataset:
the size distribution of an indicator of economic welfare. In the section, we review two types of approaches to modeling the size distribution of economic welfare across a population. The first includes purely statistical models such as _POVCAL_ (Chen, Datt, and Ravallion 1991) and _SimSIP Poverty_ (Wodon, Ramadas, and van der Mensbrugghe 2003). These two tools rely on a parameterization of the _Lorenz curve_ and, arguably, offer the simplest way of simulating the poverty effect of macroeconomic shocks and policies. The second approach relies on _unit record data_ and includes (1) _PovStat_ (Datt and Walker 2002), (2) the _maximum value or envelope model_<sup>2</sup> (Chen and Ravallion 2004; Ravallion and Lokshin 2004), and (3) the _household income and occupational choice model_ (Bourguignon and Ferreira 2005).

Section 3 discusses poverty and distributional analysis within a general equilibrium framework (Dervis, de Melo, and Robinson 1982; Decaluwé and others 1999).

Section 4 reviews _modular_ approaches to linking macroeconomic models to models of income distribution. The section focuses on the 123PRSP model – the name stands for one country, two sectors, and three commodities, and the model is built to feed into the macroeconomic framework for Poverty Reduction Strategy Papers – (Devarajan and Go 2003); the Poverty Analysis Macroeconomic Simulator I (PAMS I) (Pereira da Silva, Essama-Nssah, and Samaké 2003); PAMS II (Essama-Nssah 2004); a macro-micro simulation model for Brazil that uses the investment savings–liquidity money (IS-LM) framework for macroeconomic analysis (Ferreira and others 2004); and the integrated macroeconomic model for poverty analysis (IMMPA) framework, which links a dynamic computable general equilibrium (CGE) model to unit record data (Agénor, Izquierdo, and Jensen, forthcoming).

Concluding remarks are presented in Section 5. An annex provides a summary description of the tools reviewed in this paper.

Table 1 presents a synoptic view of the modeling approaches reviewed in this paper
To explore more this angle, we use GDELT, a data set that contains a snapshot of global news published and distributed every 15 minutes and translated from 65 languages. The GDELT database covers 98.4 percent of the global news (GDELT 2020). This data set records the main themes of each news piece, and in that way, allows us to estimate the volume of news about a given topic in a given country over time. Since news events compete for limited space in media attention, increased coverage of certain events happens at the expense of coverage of other events (Eisensee and Stromberg, 2007). Increased coverage of news related to the COVID-19 pandemic may imply a displacement of news covering other topics, and therefore indicate how much public debate is focused on the pandemic. To the extent more media attention is focused on the pandemic, it may be harder for citizens to “return to normal” slowing the economic recovery associated with the reopening process. At the same time, media coverage is a reflection of the pandemic and economic situation itself. Hence, while causality is difficult to ascertain, press coverage is likely to both shape and be influenced by the evolution of the pandemic and the economic activity.

Table 5 presents the result of this exploratory analysis based on Specification (5). Using data from GDELT, we include a daily indicator of relative frequency of different themes in the news for every country as an additional regressor. There is a strong and significant correlation between the increasing coverage by the press of themes related to “quarantine” and “pandemic” and the electricity consumption. At the same time, a higher presence of the theme “corruption” in the news might indicate a trend to the “return to normal,” which is reflected in the positive and significant coefficient on this indicator. These correlations indicate that the news media can play an important role in shaping public perception and response to the current global health emergency and might affect as well as reflect the reopening trajectory.

# *
productivity constraints and the potential maize-based production and income gains, across crop-specific soil suitability profiles.

Classifying the sampled plots into three suitability classes, namely highly-suitable, moderatelysuitable, and marginally-suitable, and leveraging plot-level crop-cutting-based maize yields allows for comparison of the distributions of observed maize yields by suitability class. We then extend this analysis by estimating stochastic frontier models of maize yield separately for each suitability class, using both the MAPS-based and AFSIS-based suitability classifications, to understand differences in (i) returns to factors of production, (ii) technical efficiency, and (iii) potential yield measures. Compared to observed yields, the potential yield estimation provides a unique overview of maximum yield gains that can be achieved in each suitability class by increasing the efficiency with which the current set of inputs into agricultural production are utilized. Pairing the household

> 21 Note that the GAEZ data is spatially derived, and therefore does not possess the same level of granularity as the MAPS-based data. For example, the GAEZ potential yields under the high input regime are not statistically different across any of the MAPS-based soil suitability classifications.

27
of achievement trends before the pandemic and the dependency of results on regression specifications. Thus, we perform multiple robustness checks. We first estimate equation (1) on the pooled data from all rounds of PISA with comparable achievement data (8 rounds for reading, 7 for mathematics, and 6 for science). Next, we test for non-linear trends using quadratic terms for time. Then, we re-estimate it for shorter periods deleting data from 2000, then from 2003 and 2006, and so on, leaving at the end the 2018 to 2022 comparison only. Moreover, we estimate results for the OECD countries only, and we use different weighting methods: one estimating results for the whole population of 15-year-olds in participating countries and then using the so-called senate weights which weight each country equally, which is standard approach in OECD reports (see, for example, OECD 2009). Finally, we try different regression specifications, including additional control variables. We compare these results to see if the main estimates of the learning loss are robust to different regression and sample specifications.

The pandemic could affect students in different ways. Disruptions could affect health, economic, and social well-being. Thus, after estimating the overall effect of the pandemic on achievement, we use information on the number of weeks of school closures to show how achievement differs from the time trend depending on the country-average school closure duration. We estimate the following regression model: i = ∑ + ∑ ∗t t 2022 2022 w i i (2) nn nn ii ii=1 ii ii=1 ii ii ii ii YY αα ββ + ττDD + ππDD ∗wwt + γγXX + εε The model is specified as before, but the interaction term between a dummy denoting 2022 data and a measure of the length of school closure in weeks provides an estimate of how the departure in 2022 depends on the length of closures. In this case, captures the departure of achievement in 2022 from country-specific time trends assuming no school closures. In a sense, ττ it captures
stock and will thus prefer to wait or even attempt to disinvest from their existing stock. Therefore, exporters, and particularly those focusing on sales of goods that are hard to resell, should expect a further reduction in trade if the importer uncertainty is accompanied with a major hit to the real economy.

To summarize, based on guidance from previous literature we expect the following parameters to matter for the aggregate response of trade in the downturn and in the recovery. First, importer uncertainty should matter more for exporters than domestic uncertainty. Second, the size of the shock should also matter, with a potentially non-linear effect. Third, liquidity constraints and large adjustments in wealth, that tend to accompany major uncertainty shocks, will be aggravating the contraction of expenditures. Finally, the adjustment costs should also matter. We expect that countries specialized in goods entailing high adjustment costs (such as durables or investment goods) should experience an heightened impact of uncertainty on exports. Exports of non-durables on the other hand could potentially be boosted due to the fact that consumers have a higher disposable income that they can devote to these goods, due to the savings on durable goods.

# **3 Producer and consumer uncertainty: VAR analysis**

As a preliminary test of the hypotheses outlined above we focus on US data and conduct a vector autoregression (VAR) analysis analogous to that of Bloom (2009). He estimates a standard VAR model that includes stock market volatility used as a proxy for uncertainty and the following additional variables: the S&P500 stock market index, federal funds rate, average hourly earnings, consumer price index, hours in manufacturing, employment in manufacturing and industrial production. His sample covers the period June 1962 to June 2008 which comprises seventeen uncertainty shocks that are depicted in Figure 1. For reasons discussed in Section 4.2 of this paper he works with a stock market volatility based measure of uncertainty. Rather than working with stock market volatility explicitly he chooses to only work with periods of exceptional uncertainty during which the
substantial share of the industry.

**_Fortune 500 Companies_** In some of the descriptive analyses, we complement our firmto-firm link data with information on the U.S. Fortune 500 agribusiness companies. We sourced these data from the March 2022 list of U.S. Fortune 500 companies, utilizing fuzzy string matching techniques to identify relevant entities in the FactSet database, based on company names and their headquarters’ state in the U.S.. We cleaned and reviewed the matching results manually. Table A.5 presents these firms along with their primary industry classifications, offering a detailed view of the leading agribusiness companies that shape our analysis.

# **3.2 Methodology and Definitions**

## **3.2.1 Descriptive Statistics of the Agribusinesses Network**

To assess the role and importance of individual nodes in the agribusiness supply chain network, we primarily rely on the measure of degree centrality. This refers to the combined number of incoming (indegree or supplier) and outgoing (outdegree or customer) ties or links of a node (firm). For a more nuanced understanding, we calculate the weighted degree centrality, where the weights are determined by the duration of activity for each link and the frequency of interactions between two nodes.<sup>14</sup> If an agribusiness maintains two customer and five supplier ties that are active for the entire year, its weighted degree centrality is seven. In some graphs, we distinguished between indegree (i.e., supplier) and outdegree (i.e., customer) links.

Since we are unlikely to observe the complete set of links for firms that only appear as counterparts in other firms’ reporting, for more accurate insights, our analysis prefocuses on that are monitored in FactSet. This dominantly firms actively encompasses

> 14Two nodes can form multiple supplier or customer relationships. An alternative measure is given by the unweighted degree centrality which corresponds to a 0 or 1 dummy indicating whether at least one relationship is
One criticism of the current income classification is that the thresholds are dated and somewhat arbitrary. Some feel that it is a less relevant classification now than in the past since the majority of the world’s extreme poor now live in countries classified as middle income (e.g., Kanbur and Sumner, 2012; Ravallion, 2012). Indeed, over 70 percent of the world’s total population—some 5 billion people—lived in countries classified as middle income in FY16; less than 10 percent lived in low income countries (see Figure 2). However, Table 2 shows that the estimated incidence of extreme poverty is considerably higher among low income countries as a whole (47.2%), compared with lower middle countries (18.7%) or upper middle income countries (5.4%).

**Table 2. Extremely poor population in each income group, 2012**

||Extreme poverty<br>headcount<br>(% living below US$ 1.90 a<br>day at 2011 PPP)|Share of population<br>(%)|Share of extremely poor<br>population (%)|
|---|---|---|---|
|Low|47.2|8.3|30.1|
|Lower middle|18.7|39.4|56.3|
|Upper middle|5.4|32.8|13.6|
|High|0.0|19.5|0.0|
|World|12.7|100.0|100.0|

Source: World Development Indicators and PovcalNet, accessed on December 8, 2015.

The use of market exchange rates for converting GNI to a common currency is also felt to be sub-optimal. The common suggestion is to use purchasing power parities (PPP); some argued that, at least for a period of time, there
Table (F2) Impacts of the Program on Dwelling Characteristics and Household Composition

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Housing quality index|0.09|(0.19)|[1.00]|-0.01|(0.99)|1,321|
|Housing Material quality index|0.22|(0.22)|[1.00]|-0.14|(1.08)|1,321|
|Has access to electricity {0,1}|-0.02|(0.02)|[1.00]|0.94|(0.24)|1,321|
|Has access to grid (if electricity=1) {0,1}|0.02|(0.07)|[1.00]|0.77|(0.42)|1,321|
|Has access to generator (if electricity=1) {0,1}|0.01|(0.01)|[1.00]|0.01|(0.09)|1,321|
|Number of rooms in dwelling|-0.26|(0.16)|[1.00]|3.47|(1.06)|1,321|
|Number of occupied rooms|-0.19|(0.17)|[1.00]|3.37|(1.08
29); and, “CPHS-NSS-PLFS” denotes the model using variables common across all three surveys (see footnote 30).

Nevertheless, the first two rows in Table 3 underscore potential differences between the imputed consumptions into the CPHS and PLFS: Consumption imputed into the CPHS is about 7 to 16 percent higher than the PLFS. It should be noted, however, that the two consumption estimates are not a strict like-to-like comparison: the consumption imputed into the CPHS is based on demographic as well as asset variables, whereas imputations into the PLFS are based only on slower-moving demographic indicators (asset variables are unavailable in PLFS). To construct comparable vectors of imputed consumption across the surveys, we select a set of demographic indicators that are family; share of 0 to 18 years old members in family; share of 61+ years old members in family; female headed households; log (age of household head); any member with higher than middle to high school level of education; share of members with middle to high school level of education; any member with diploma to post graduate level of education; dummy variables for Muslim; Christian; Sikh; Jain; Buddhist; Zoroastrian and other religions; scheduled castes; other backward classes; other castes; principal industry code of the household; household type; any regular salaried member in the household; household size and an interaction between the two variables. For urban sample we also include a dummy for cities that had over a million population in the 2011 census.

> 30The list of variables used in imputation are the same as in Table 2 of the main text

52
>.4<br>0.4 0.4<br>0.3<br>.2<br>0.1<br>0 0.1 - 0.0<br>0.0 -0.1<br>-.2 -0.2 -0.3<br>2000 2002 2004 2006 2008 2010 2012 2014 RUS IND CHN POL CZE IDN TUR HUN BRA BGR ZAF<br>1.5<br>1.25<br>1<br>.75<br>.5<br><!-- End of picture text --> Source: Penn World Tables; author’s calculations. Note: the TFP numbers from the Penn World Tables may not reflect the most recent GDP figures for Turkey, since they were recently revised by the government.

**Productivity in services seems to be low for Turkey, especially compared to manufacturing.** Although productivity, and particularly TFP, is hard to compute for services, a rough indication of labor productivity is possible by looking at revenue per worker (left‐hand panel of Figure 2.2), as well as the ratio of revenue to the wage bill (right‐hand panel of Figure 2.2), for services firms.<sup>5</sup> Turkey performs below most

> 4 TFP refers to the portion of output not explained by the amount of inputs used in production, including capital and labor. It measures how efficient capital and labor are used with the help of new technologies.

> 5 Due to the lack of variables such as value‐added to compute labor productivity or TFP, revenue per worker serves as a crude indicator of productivity. Usually this measure is correlated with labor productivity or TFP.

6
Stanley, M. H., Amaral, L. A., Buldyrev, S. V., Havlin, S., Leschhorn, H., Maass, P., and Stanley, H. E. (1996). _“Scaling behaviour in the growth of companies.”_ Nature, 379(6568), 804-806.

- Tarfasa, S., T. Ferede, S. Kebede, and D. Behailu. (2016). “ _Determinants of growth of micro and small enterprises (MSEs), Empirical evidence from Ethiopia_ ”

- Van Ark, B., O'Mahony, M., and Timmer, M. P. (2008). _“The productivity gap between Europe and the United States: trends and causes.”_ The Journal of Economic Perspectives, 22(1), 25-44.

Van Ark, B. and E. Monnikhof (1996) _“Size Distribution of Output and Employment: A Data Set for Manufacturing Industries in Five OECD Countries, 1960s-1990.”_ OECD Economics Department Working Papers, No. 166, OECD Publishing, Paris.

- Van Biesebroeck, J. (2005). _“Firm size matters: Growth and productivity growth in African manufacturing.”_ Economic Development and Cultural Change, 53(3), 545-583.

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**Figure 2.4. PIQ-D index by EMDE groups and regions**

<!-- Start of picture text --> A. PIQ-D by groups, simple averages B. PIQ-D by regions, simple averages<br>Index Index 2009-2013 2017-2021<br>2009-2013 2017-2021<br>0.08 0.15<br>0.10 0.09<br>0.039<br>0.04 0.06<br>0.028 0.022 0.026 0.05 0.05 0.04 0.04<br>0.012<br>0.008 0.02<br>0.00 0.00<br>0.000 0.00 -0.01<br>-0.009 -0.05 -0.02 -0.03 -0.03<br>-0.04 -0.06<br>Commodity Commodity EMDEs LICs -0.10<br>exporters importers without LICs EAP ECA LAC MNA SAR SSA<br>C. PIQ-D by groups, GDP-weighted averages D. PIQ-D by regions, GDP-weighted averages<br>Index 2009-2013 2017-2021 Index 2009-2013 2017-2021<br>0.08 0.071 0.15<br>0.11<br>0.041<br>0.04 0.027 0.020 0.10 0.09 0.08 0.08<br>0.002 0.05<br>0.05<br>0.00 0.01<br>0.00<br>-0.04 -0.039 0.00 -0.01<br>-0.08 -0.055 -0.05 -0.04 -0.05<br>-0.10<br>-0.12 -0.099 -0.12 -0.10<br>Commodity
would be proportionally larger than a subsidy on in-natura foods, despite the price elasticity of in natura foods being higher in absolute terms.

_Figure 2_ - Price elasticity by food type <!-- Start of picture text --> 0.00<br>-0.50<br>-1.00<br>-1.50<br>-2.00<br>-2.50<br>-3.00<br>-3.50<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>In Natura Ingredients Processed Ultra-processed<br>Source: Estimates based on POF 2017/2018.<br>Price elasticity<br><!-- End of picture text --> 15
-  43  -

2001 to 69 percent and in EU-oriented exports, accounting for 67 percent of Bulgaria’s ICT exports, from 33 percent in 2001 to 66 percent in 2003. More significantly, there was a huge change in the composition of ICT EU-oriented exports: three four-digit SITC sectors— radiotelegraphic/ radiotelephonic products (7643), telecommunications equipment (7648),<sup>21</sup> and parts of apparatus—now accounting for three-fourth of ICT exports contributed only 33 percent in 1999.

Last but not least, while import intensities of both networks remain very high indicating lack of significant assembly operations, they began falling in 2002-03. Based on network trade intensities of CEEC-10 that successfully have entered into supply chains, the value of the share of imports of parts in network’s exports of parts and final products below 100 percent suggests significant involvement in division of labor based on production fragmentation.

Hence, despite a strong rebound and signs of entry of some firms, Bulgarian producers remain marginal suppliers in global ‘producer-driven’ networks. The share of networks’ products and parts in Bulgarian total exports remains low and reached its peak 1996 level only in 2003. Despite significant gains in EU markets, their shares in EU imports of both networks’ products are well beyond the share of total Bulgarian exports in total EU external imports.

# **C** **_._** _Bulgaria’s producer-driven network trade in regional perspective_

Hence, Bulgaria has a long way to catch up with most other CEEC-10 in terms of participation in ‘producer-driven’ network-based trade. The share of ‘producer-driven’ network products in total exports of manufactured goods, excluding chemicals, in 2003 puts it on a par with Latvia, and except for Serbia and Montenegro is much higher than in SEE-3 economies. It is, however, much lower than in Romania (Table 32).

**Table 32:** _Features of ‘producer-driven’ network trade of CEEC-10 and SEE-5 economies in 1996, 1999 and 2003 (
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tablets should be checked and correlated against records or, where this is not possible, against the recalled date of supply (Ross in WHO !.tjal 1986).

# 4.4 Nutritional status indicators

Anthropometric measurements on pre-school age children are technically straightforward and, if well controlled, can be reliable and accurate. Such measurements could be part of the WIHS core questionnaire. The WHS will be able to make use of the excellent training manuals on anthropometry now available. The task of training interviewers to weigh and measure children is facilitated by well illustrated guides like the one produiced bv the IJNHSCP (United Nations 19$6), and the tape-slide sets produced by the LSHTM. However, indicators based on weight-for-age, height-for-age, weight-for-height, mid-upper arm circumference etc. are all best regarded as both non-specific and indirect. This is because body size at a given time is the end result of the interaction of nutritional and infectious disease processes and hence is an indicator of the combined or sequential effects of both. In the absence of other information (e.g. on disease status) therefore, anthropometric indices cannot be interpreted by themselves so as to implicate a deficiency of nutrients or energy supplv as primary causes of poor health status.

During the past few years, progress has been made in relating anthropometric measurements to other measures of outcome - in particular, with mortality experience. Thus, for all of the indicators mentioned above, we can say that (i) There is a non-linear relationship between anthropometric measurements and mortality during a fixed period subsequent to measurement. That is to say, the probability of death rises more rapidly with increasing deficit in body size (Kielmann and McCord - - (1978) weight-for-age; Chen et al (1980) weight and height-forage; Sommer and Lowenstein (1975) - arm circumference and height).

(ii) The shape of the function relating mortality with anthropometry is not constant, but depends upon the pattern of iafectious diseases to which the population is exposed.

31