rafmacalaba/data-use-mentions-extended huggingface dataset:
percent of the shock will be adjusted in the first period.

Figure 4: Distribution of estimated pass-through of shocks in Juba’s prices (all regions, all markets) Figure 5: Distribution of estimated speed of adjustment to equilibrium after a shock to prices (all regions, all markets) <!-- Start of picture text --> Pass-through Coefficient - Juba/All Products Speed of Adjustment Juba/All Products<br>0 .5 1 1.5 2 -1.5 -1 -.5 0<br>Pass through Speed of Adjustment<br>Source: Authors’ calculations based on WFP data. Source: Authors’ calculations based on WFP data.<br>2<br>1<br>1.5<br>1<br>Density .5 Density<br>.5<br>0 0<br><!-- End of picture text -->

# **3.5. Determinants of Price Differences**

As documented above, price differences for narrowly defined products are heterogeneous across the country, as well as the degree of integration of relevant regional markets. This stresses the importance of understanding the drivers of these differences. Understanding whether price differences, for example, are driven by distance, poor infrastructure, fuel costs, or other factors 14
growth, but this consensus should not be interpreted as indicating that the extensive margin does not play a role. For example, Besedes and Prusa (2010) show that differences along the extensive margin (at the product-destination level) are particularly prevalent across developing countries but are often accompanied by a very short duration of most new export relationships. A handful of recent studies using firm-level customs datasets study the role of the extensive margin in export growth for certain, including Alvarez and Fuentes on Chile (2011), Eaton, Eslava, Kugler, and Tybout on Colombia (2008), and Lederman, Rodriguez-Clare, and Xu on Costa Rica (2011).

> 6 Amiti and Freund (2010) use trade flow data for China to study the importance of the intensive margin.

5
growth in the 1990s and their assets reached 9 percent of GDP by 1995. They invested heavily in government securities and accounted for almost 4 percent of government debt. Insurance companies probably held close to 1 percent of government debt, while pension funds, which had been allowed in the early 1990s to invest in government bonds through a special service of the central bank, probably held 3 percent of the total.<sup>20</sup> Thus, retail investors are estimated to account for 20 percent. The share of the banking system fell to 44 percent , while foreign investors accounted for 21 percent of the total (Table 3.6).

**Table 3.6: Holders of Government Debt, 1980-1995**

|(percent of total)|1980|1985|1990|1995|
|---|---|---|---|---|
|Banking System|83.9|69.4|62.9|43.9|
|Mutual Funds|0.0|0.0|0.0|3.9|
|Insurance Companies|0.0|0.0|0.0|0.8|
|Pension Funds|0.0|0.0|0.0|3.1|
|Retail Investors|0.0|0.0|17.0|20.2|
|Foreign Investors|16.1|30.6|20.1|20.6|
|Other Government Entities||||7.4|
|Total|100.0|100.0|100.0|100.0|

Source: Estimated on the basis of Bank of Greece, Ministry of Finance, IMF and OECD data.

# **3.4 The Search for Efficiency and International Integration (1996-2000)**

The attractiveness of government securities to domestic non-bank investors peaked in 1996 when they accounted for 79 percent of
quickly in the first several weeks, eventually reaching roughly double the initial figure. In contrast, the recall-surveyed households were asked at the end of the season to report on the household members who were working on each of the household plots; they reported an average of 2.8 workers per household. Figure 3 indicates that there are differences in the rates of increase by traits. Thus, women show a steeper curve, indicating that they tended not to be reported at the baseline, but were subsequently working on the plot during the season. Table 10 shows that, nonetheless, women were not systematically forgotten, except, perhaps, to a small degree in the recall ALT.

Second, although a person, by survey design, may not be more or less likely to be reported because of her or his gender, there is evidence that children, who may be less important in household farm production and who may be expected to be marginalized within the household, are more likely to be forgotten.

Third, there is little difference across arms of the study in those who self-identify as farmers, whereas we might expect that people who identify farming as their livelihood would be less likely to be forgotten in end-of-season surveys. Indeed, the share of farmers is statistically significantly larger in the recall ALT group than in the weekly visit data. However, given that most household members in the sample—whether identifying as farmers or not—are active in agricultural work, this occupational designation may have little practical meaning.

Finally, we look to actual work patterns to uncover the types of household members who may be underrepresented in the recall surveys. While the average number of household members working for an above-mean number of weeks is 1.7 in weekly visit households and 1.8 in weekly phone households, these data are much closer to but still somewhat lower than the 2.8 workers per household reported in recall surveys. Table 10 shows this finding more straightforwardly. Those who work infrequently during the season are dramatically underrepresented whether at
.029|-0.297**|
|Count of Agricultural Assets|-0.019|-0.019|-0.017|-0.021|-0.02|-0.042|
|CV of Flowering Season Rainfall (1999-2014)|1.303|1.047|-1.836|1.048|2.006**|†|
|Constant|6.262***|6.337***|3.859**|6.387***|5.850***|3.586***|
|_Random Error Term (v)_|||||||
|Constant|-1.949***|-1.857***|-3.140***|-1.807***|-1.718***|-2.033***|
|N|840|840|106|634|736|100|

Notes: Standard errors clustered at EA level; † = insufficient variation for inclusion of variable or high correlation with other covariates; ° = Binary variable; *** p<0.01, ** p<0.05, * p<0.1 18
||0.0243*|-0.0140|-0.479|0.0345+|0.121+|0.0524|0.212+|
|Constant|2.476**|-11.33**|2.647**|-9.422**|2.123**|-9.474**|-8.189**|-9.595**|-10.19**|-7.082**|-8.160**|-8.186**|
|Country fixed effects|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Observations|1,181|1,181|1,181|1,181|1,181|1,181|1,181|1,182|1,181|1,181|1,181|1,181|
|Adj R-squared|0.566|0.599|0.561|0.577|0.575|0.604|0.571|0.576|0.595|0.595|0.587|0.587|

Note: (1) Data source: EU 2011 Poverty Map (DG-REGIO and World Bank), 2010 Farm Structure Survey (Eurostat), and EU National Statistic Institutes (Eurostat); (2) Symbols: ** p<0.01, * p<0.05, + p<0.1; (3) Missing observations in agricultural indicators are treated as zero;(4) Luxembourg and Cyprus Republic are not being analyzed here;(5) CAP payment data in Croatia, 5 regions in Estonia and 1 region in France (Seine-Saint-Denis) is missing; 13
than in ESPS (29.5% for PSF versus 26.6% for ESPS) and very large imbalances appear between the Matam (PSF: 12.8%; ESPS: 3.4%) and Thies (PSF: 6.4%; ESPS: 14.2%) regions. In the face of this, we decided to follow a strategy initiated by DiNardo, Fortin and Lemieux (1996)<sup>2</sup> . Pooling together the PSF and ESPS data, we

> 2 See Nichols (2008).

10
3.1: Annual Changes in Main Components of the Labor Force**

|year ending the|Working|Out|of the labor fo|rce||Labor force||
|---|---|---|---|---|---|---|---|
|second quarter|age||non|||||
|<br>of...|<br>population|TOTAL|<br>available|available|TOTAL|unemployed|employed|
|2007|1,176,542|340,166|63,419|276,747|836,376|127,495|708,881|
|2008|1,233,282|185,131|365,100|(179,969)|1,048,151|88,111|960,040|
|2009|1,733,858|1,484,506|379,854|1,104,652|249,352|771,767|(522,415)|
|2010|951,655|(476,747)|(209,674)|(267,073)|1,428,402|120,851|1,307,551|

**Source: Own calculations using data from ENOE.**

It is difficult to ascertain the origin of the acceleration in working age population but a likely suspect is a change in migration patterns. Two forces may have played a role: (i) the recession in the United States may have forestalled migration from Mexico to the United States, and (ii) for the same reason, Mexican migrants in the United States may have returned to their country of origin. Preliminary evidence suggests that even though the crisis more strongly affected Hispanics in the United States, there was no net change in migration. Workers in the United States did not return
2 40<br>40<br>20 20 1 20<br>0<br>0 0 0<br>1990s 2000s 2010s SAR SSA LAC ECA<br>1990 1995 2000 2005 2010 2015 2018<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Source: IMF WEO, Peace Research Institute of Oslo (PRIO), Roser (2019), Haver Analytics, World Bank _Pink Sheet_ , World Bureau of Metal Statistics, World Bank staff calculations.

B. Agriculture includes 23 commodities, Industrial Metals includes 7 metals, and Energy includes coal, crude oil and natural gas. Index 100=2000.

D. Only conflicts in which at least one party was the government of a state and which generated more than 25 battle-related deaths are included. The data refer to direct violent deaths only and exclude outbreaks of disease or famine. Country-level data unavailable.

E. Unweighted averages. 2001 LICs includes 61 countries.

F. Committed debt relief under the assumption of full participation of creditors. Bars represent average debt relief per region in US$ billions for all Highly-Indebted Poor Country (HIPC) and Multilateral Debt Relief Initiative (MDRI) LICs. Diamonds reflect average debt relief per region relative to countries’ GDP.

The commodity boom of 2001-11 supported above-average growth in those 2001 LICs that were, or became, commodity exporters. Exports of primary commodities in these countries rose by onehalf of GDP between 2001 and 2011. Higher export earnings helped improve fiscal positions, with government revenues of commodity-exporting LICs rising by close to 4 percentage points of GDP, on average, and fiscal deficits narrowing by around 1 percentage point of GDP, between 2001 and 2011. This, as well as debt relief, allowed a doubling of social expenditures between the 2000s and 2010s. The commodity-driven growth surge was accompanied by a decline in inflation to single digits and an annual 3 percent real exchange rate appreciation between 2001 and 2011, on average,
mobile telecommunications services and maritime transportation services. 12 We supplemented this information based on the policy notes discussed in section II. The Telecommunications Management Group (2007) provided extensive details in telecoms. These questionnaires and papers provided us with data and descriptions and assessments of the regulatory environment in these sectors. In the case of professional services, a World Bank team commissioned separate surveys of the regulatory environment in several East African countries, including Tanzania. These surveys were conducted by local consultants, who interviewed the professional associations in the in 2009. For reasons discussed below, we used engineering services as our proxy for all professional services.

Mircheva (2008) then estimated the ad valorem equivalents of barriers to foreign direct investment in fixed line and mobile telecommunications, banking, insurance and maritime transportation services. The process involved converting the answers and data of the questionnaires into an index of restrictiveness in each industry. Mircheva followed the methodology of Kimura, Ando and Fujii (2004a, 2004b, 2004c) to generate these estimates. The methodology involves classification of the possible restrictions into separate categories with unique weights summing to one, where the weights are determined based on the significance of each category. Next, Mircheva assigned a score to each potential restriction, where the score reflects the level of restriction imposed by the economy. Mircheva estimated two indices: an index of “regulatory barriers” (RB index) where the regulatory barriers impose costs on both domestic and multinational firms in a non-discriminatory manner; and an index of discriminatory barriers against multinational service providers, which we call the foreign discriminatory index (FDR index).13 This methodology further involves building on the estimates and methodology explained in the volume by C. Findlay and T. Warren (2000), notably papers by Warren (2000), McGuire and Schulele (2000) and Kang (2000). For each of these service sectors, the authors evaluated the regulatory environment across many countries. The price of services is then regressed against the regulatory barriers to determine the impact of any of
* We study the nutritional outcomes of women and children. For women, the two variables we employ are the body mass index (BMI) (also known as the <mark>Quetelet index)</mark> , defined as a woman’s weight (in kilograms) divided by her height (in meters) squared, and an indicator for being underweight, which is set equal to one if a woman’s BMI is lower than 18.5 and zero otherwise. The DHS excludes values of BMI that are smaller than 12 and greater than 60 on the grounds that these are almost certainly measurement errors. We do the same for the consumption surveys. BMI is computed by the DHS for samples of women aged 15 through 49. For the LSMS surveys we restrict women to the same age range.  We exclude all women who report being pregnant at

> 18 Several countries had to be excluded due to older survey data that did not contain many of the key variables needed, namely the Central African Republic, Chad, Comoros, Madagascar, Sao Tome and Principe, and South Africa.

11
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data with vectors , , , , . In our case, the latent variable is (unobserved) true sales, which depends on a number of causes or factors X (the structural equation 1), and which is measured with error through indicators Y (the measurement equation 2). In the standard MIMIC model, it is also assumed that , with a diagonal covariance matrix. If and are jointly normally distributed, maximum likelihood estimation can be applied to the reduced form of the MIMIC model to obtain parameter estimates.<sup>13</sup> Our MIMIC model will be a more general version of the standard MIMIC model. First, we will allow for correlation between the error terms and u ( ), and between ( non-diagonal). Second, we allow indicators not only to depend on the latent variable but also on other factors Z as well ( ). And third, we will allow for truncation given that reported sales to the tax office (an indicator in our model) should not exceed the true sales of the firm (the latent variable). We first discuss the derivation of our structural equation and next continue with a discussion of our derivation of two measurement equations for the MIMIC model.

# _A. Structural Equation: Production Technology_

Assume that the production technology of a firm can be represented by a Cobb-Douglas production function: 13 For more details about the identification and estimation of the MIMIC model, see Joreskog and Goldberger (1975).

12
interaction term between inflation and distance to school. The coefficient on the interaction term is statistically significant and

> 8 The complete difference-in-difference estimates are presented in the appendix (Tables A1-A7). A summary of the regression results of the impact of inflation (β1 coefficients, difference-in difference) is provided in Table 4. 9 Attendance rates for children of primary school age (6-13) and secondary school age (14-18) are reported.

9
- Finally, the _Open Knowledge Repository_ (OKR) makes all World Bank publications available to the public at large. Long-running periodical series and the annual workplans of various World Bank units determine which documents are to be disseminated through this repository. However, as the name of the database indicates, all the documents in it can be deemed related to knowledge.

The matching exercise was conducted for documents filed in the IB database between July 1, 2015, and June 30, 2019, a period that corresponds to the World Bank Fiscal Years 2016 to 2019, or FY16-FY19 for short. In the case of the WH database, the matching concerned all budget codes that had been deemed completed in FY16-FY19, plus all active codes as of June 30, 2019. Including the latter group is justified because documents may be filed in the IB database before the corresponding budget codes are closed. Similarly, to account for delays linked to administrative clearances and production processes, all publications released through the OKR database after July 1, 2015, were included in the exercise.

An important challenge was to determine which of the documents in the IB database had substantial knowledge content. Documents are filed there under one of more than two dozen headings, such as Country Economic Memorandums, or Policy Notes, or Sector Strategies (table A.1). However, several thousand documents were not tagged to any of these ten categories and needed to be individually checked to determine whether they qualified as knowledge products.

Similarly, only a fraction of the budget codes in the WH database support knowledge work. ASA codes in principle do, so they were all included in the matching exercise. But knowledge work is also supported by budget codes funding DEC research, as well as by the preparation of regional and sectoral strategies, country partnership frameworks, and training, among others. Conversely, a considerable amount of knowledge work is produced without mobilizing earmarked budget resources.

The matching process focused first on budget codes, which are at times mentioned in the documents filed in the
# **3 Data and sample**

# **3.1 Data**

Our main data comes from seven waves of the Future of Business (FoB) survey that collected data from business owners and managers from over 84 countries.<sup>12</sup> The FoB survey is a collaboration between Facebook, the Organisation for Economic Co-operation and Development (OECD), and the World Bank to survey micro, small and medium (MSME) sized businesses on Facebook.<sup>13</sup> In May 2020, Facebook adapted the standard bi-annual approach of the FoB survey to instead run monthly waves of the survey to provide timely insights on the impact of the COVID-19 pandemic on small businesses. The partners developed a short enterprise survey instrument to collect data from business owners and managers who have a Facebook Business Page. The questionnaire is designed to measure the impact of COVID-19 on some key dimensions: operation of the business, revenues, employee adjustments, expectations and uncertainty about the future, and preferred methods of policy support during COVID-19. A number of demographic questions (for example, gender, age, and marital status) as well as time spent on work and domestic and care responsibilities for the respondent and their spouse, and breadwinner status within the household were also included.

The COVID-19 monthly waves sampled from over 84 countries around the world where the Facebook small business penetration is sufficient to achieve a meaningful sample.<sup>14</sup> The first of these monthly surveys Wave I from 28-31 May 2020 surveyed around 26,000 mostly small businesses.<sup>15</sup> The monthly survey waves are a repeated cross-section of approximately

> 12The sample for this paper consists of approximately 150,000 business owners from 50 countries as we restrict the sample to those countries with sufficient sample size to conduct gender-disaggregated analysis in each survey wave.

> 13The country level aggregated data set from the Future of Business Survey is provided for free through a Creative Commons license and is
might be preferable to uˆ<sup>′</sup> if the two variables xˆ1 and xˆ2 are highly correlated. Indeed, the additional complexity of building indicator uˆ<sup>′</sup> might not be worth its additional benefits. Clearly, this framework aimed at minimizing well-being comparison errors can be applied to well-being measurement at large. It can also guide the improvement of monetary indicators, which also face data constraints and thus yield erroneous comparisons in practice.

# **2.7 Improving on current indicators**

It seems unlikely that the monetary approach yields indicators whose well-being comparison errors are minimized. Even though income or consumption are important determinants of well-being, several non-monetary dimensions like health and longevity are arguably at least as important.<sup>15</sup> Furthermore, longevity has been regularly shown to substantially affect cross-country well-being comparisons.<sup>16</sup> The fact that multidimensional poverty measures ignore longevity was placed under crude light during the Covid-19 crisis.<sup>17</sup> Also, consumption and income indicators capture in a noisy way the control that people exert over economic resources.<sup>18</sup> Therefore, the challenge is to build a multidimensional index making fewer well-being comparisons errors than monetary indicators.

Consider first the case when the goal is to identify low well-being across individuals within a given region, say for the purpose of targeting social spendings. For this case, the indicator should make reliable inter-personal comparisons (Objective 1). Thus, synthetic indicators are of no use and one must consider objective indicators. As a result, one must use a set of variables for which the joint distribution is available. At global level, given the current data constraints discussed in Section 2.4, this means to either ignore the monetary dimension or ignore the health dimension. The global MPI made the former choice while the World Bank’s MPM made the latter choice.<sup>19</sup> Currently,
determinants of land use–including land abandonment (Meyfroidt _et al._ 2016), grazing intensity (Dara _et al._ 2020), and crop cultivation <mark>(Munteanu</mark> _<mark>et al.</mark>_ <mark>2017)–</mark> over rathe r long time periods (Matasov _et al._ 2019). We build on insights from recent studies using satellite imagery by using interpretation of readily available imagery from the European Union’s Copernicus Sentinel 2 constellation to construct an indicator of whether or to what extent a VC is directly affected by the war. Imagery also allows us to construct the two main outcome variables considered in our analysis, i.e., the area grown with winter crops and, for fields growing winter cereals, the NDVI as a measure of yield, for the 2019-22 time span at VC level. At the same time, we follow the literature on conflict impacts in terms of using a robust econometric framework to estimate conflict impacts as discussed below.

# **2.2 Estimation strategy**

To estimate conflict effects at a high level of spatial disaggregation and in a way that allows appreciation of possible heterogeneity, we construct a panel data set covering 10,125 village councils (VCs) over the period 2019-2022. This allows using a difference-in-differences approach to estimate conflict impacts on a wide range of variables. Indexing village councils by and year by , the model to be estimated is v v vv v v tt (1) vv vv where v is the outcome variable of interest in village YY = αα + ββC + γγXX + λλ at year + εε ; C v is a an indicator for presence of different types of conflict at village level that takes a value of zero for years prior to 2022 and for villages YY vv tt that did not suffer direct conflict-related damages in 2022 based on either satellite imagery or public sources as discussed in more detail below; v is a vector of time