rafmacalaba/data-use-mentions-extended huggingface dataset:
ii) three large Eastern European countries (Czech Republic, Hungary and Poland) with a range of emigration rates, mostly to Western Europe, (iii) two middle income countries with large emigration rates (Mexico and Turkey) and (iv) three non-OECD countries with large immigration rates (Argentina, South Africa and Singapore). We focus on the 1990-2000 ‡ows for several reasons. First, they are the most recent ‡ows that one can construct for both immigration and emigration (based on censuses) hence their skill composition and size is more relevant and possibly closer to later migration ‡ows during the 2000’s.<sup>9</sup> One could also assess with our method the labor market impact of the total stock of immigrants and emigrants across countries (reported for 1990 and 2000 in Table A3 of the Table Appendix). However the stock is accumulated over many decades and re‡ects migration that took place in the far past. Hence the migrant stock is less relevant to establish recent labor market e¤ects of immigration and emigration.

Several features of migration ‡ows are worth emphasizing. First, the US, Canada and Australia have much larger immigration (between 4.4 and 10.6%) than emigration (between 0.2 and 1.3%) rates among the highly educated portion of the labor force. The US is the only country with a comparable rate of immigration of less educated (5.8%) while both the immigration and emigration of low skilled is small in Canada and Australia. In Western Europe, high skilled immigration rates range from 0.2% (Greece) to 8.5% (the UK) while the emigration rates range between 1.1% (France and Germany) and 8.1% (Portugal). Em-

> 8See http://www.cia.gov/cia/publications/factbook.

> 9Clearly it would be best to have the ‡ow of migrants between 2000
analysis of land-cover change would have important policy and research benefits. Accurate analysis of avoided deforestation and forest degradation impacts, improved targeting, or meaningful cost-benefit analysis would all require improved cover-change data. The Mexican government is working toward this objective through its MAD-Mex system. Investments in strengthening this type of system will also support much needed new research efforts to better understand under-researched areas, such as the long-term impacts of PES and its relative effectiveness and cost-effectiveness compared to other land conservation initiatives.

24
9|7|0.9|9|1.0|11|1.0|6|0.9|15|1.0|
|XVIII Instruments, clocks and recorders|19|1.4|21|2.7|12|1.4|13|1.2|6|1.0|17|1.1|
|XX Other miscellaneous manufactures|27|1.9|27|3.5|20|2.3|25|2.3|12|1.9|29|2.0|
|Total -- above all sectors|1,399|100.0|778|100.0|855|100.0|1,068|100.0|615|100.0|1,472|100.0|

Note: Exclude arms sector XIX Source: WTO antidumping database.

51
a short questionnaire is necessary for the majority of the respondents. Some compromises are inevitably made. The problem is that the tradeoffs being made with respect to the quality and consistency of labor force and employment data may not be considered at the time because the good practice approaches are often not even tested against the “shortcut”. Only after the survey is complete does it become clear that the shortcut actually cost more because the data have too much non-sampling error to be reliable, or even usable. The result of shortcuts can be poor quality data on employment outcomes, leading to ill-informed policy choices.<sup>11</sup> In the remainder of the paper, we demonstrate how serious these non-sampling error biases can be, using the case of Uganda.

> 11 Although failure to properly screen and measure economic activity is most common in censuses, it is also found in SSA in CWIQ and DHS surveys, and in some cases in multipurpose integrated household surveys.  Both the CWIQ and the DHS surveys were not designed to measure individual or household economic activity, but in both surveys employment status is often used by analysts as an the key independent variables to explain the demographic, health, and service delivery outcomes for which the surveys were designed.

10
Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

Table A.2. Adjusted Wald test of means by displacement status and gender, for age groups

|Country|Children Pop<br>Female<br>IDPs v non-I|ulation (0-17)<br>DPs||IDPs<br>Females v M|ales||Non-IDPs<br>Females v M|ales||
|---|---|---|---|---|---|---|---|---|---|
||MPI|H|A|MPI|H|A|MPI|H|A|
|Ethiopia|56.06***|56.27***|1.19|0.78|0.26|4.56**|0.44|0.40|1.48|
|N.E. Nigeria|1.00|1.53|3.16*|3.30*|3.14*|0.52|0.04|0.06|3.25*|
|Somalia|5.29**|6.01**|0.94|0.09|0.14|0.08|0.81|0.80|0.01|
|South Sudan|2.74|2.90*|2.70|0.37|0.19|2.53|5.33**|5.63**|0.03|
|Sudan|163.21***|162.68***|6.37**|0.39|0.25|1
7 relative importance of both indices.<sup>13</sup> The regression explained 76.8 percent of the observed variation in the monthly rainfall records from these stations (R‐squared = 0.768), which provides some confidence in the predictive power of the method.<sup>14</sup> The IEW index carries substantially more weight in predicting rainfall during the wetter months (April‐November), while the IDW index is also important in predicting rainfall during the dry months (December‐March). An estimate of monthly rainfall for each grid cell was obtained by applying the estimated coefficients in Table A1 to their respective IEW and IDW indices. By way of illustration, Figure 1 presents the interpolated rainfall map for June 2006, both based on the regression prediction method and the IDW and IEW indices separately.

# _Drought maps_

While Vietnam does receive large amounts of rainfall on average, moderate and severe droughts occur across the country, albeit with diverging frequency. They are more common and more severe in the Northeastern part of the country and the Red River Delta, but have also been reported in the Mekong Delta in the South, despite widespread irrigation.<sup>15</sup> In keeping with the study’s focus on exogenous measures of a natural hazard, the focus here is on meteorological drought, defined as rainfall deficits over extended periods of time.<sup>16</sup> Unlike agricultural drought indices, meteorological ones also allow exploration of the (indirect) effects of droughts on urban households for example through water shortages and electricity outages.

A multitude of meteorological drought indices has been proposed in the literature (see Keyantash and Dracup (2002) for a review). Following Dilley et al (2005), the authors of the World Bank study “ _Natural Disaster Hotspots: A Global Risk Analysis_ ”, the cumulative precipitation anomaly index is taken. This is the deviation in precipitation from a long‐term mean or median for a specific period
levels in the central and northeastern parts of the country. The maps of estimated numbers of stunted children (bottom row) show rather different patterns to the prevalence maps. This is because these maps combine both relative frequency of stunting with the absolute number of children living in different Districts and Regions. This means that many parts of Tanzania with high prevalence, particularly in the southwest, do not feature prominently because they are very sparsely populated, while other regions of only intermediate prevalence nevertheless contain large numbers of stunted children because of higher population density.

# **Discussion**

Given the importance of reducing stunting through convergent multisectoral approaches, there is a growing need to generate high‐resolution predictions of stunting at the local level. This study has demonstrated the use of cross‐sectional, georeferenced, household survey data in a geospatial modeling framework to generate highly granular predictions of the landscape of stunting in Tanzanian children. The map reveals the considerable spatial heterogeneity in stunting across Tanzania, in particular the stark urban‐rural divide, with cities having notably lower average levels of stunting relative to even nearby rural areas. Similarly, areas proximal to major transport routes across the country tended to have lower average levels of stunting than the most remote rural areas. A rigorous model performance assessment has demonstrated that the map is predicted with reasonably high accuracy at the level of individual pixels, and an accompanying map of uncertainty indicates those places where predictions could be made with more or less certainty.

Aggregation of this pixel‐level map to administrative units provides a simple summary of prevalence rates within subsections of the country that may correspond to the units at which operational planning to counter childhood stunting is carried out and programs implemented. This aggregation also allows the straightforward estimation of the number of children stunted within each unit and, taken together, the two measures (prevalence and number stunted) provide distinct and complimentary information for decision making.

7
|**Table 2. Targeting of NAIVS over time**|2008/09<br>season|2010/11<br>season|
|---|---|---|
||(1)|(2)|
|_Panel A: All villages_|||
|% of beneficiaries who are eligible|27%|46%|
|Targeting differential|-6%|30%|
|% of beneficiaries who are related to government officials or influentials|25%|22%|
|_Panel B: Villages in the study (Arusha)_|||
|% of beneficiaries who are eligible|24%|46%|
|Targeting differential|-10%|30%|
|% of beneficiaries who are related to government officials or influentials|21%|22%|
|_Panel C: Villages not in the study (Kilimanjaro)_|||
|% of beneficiaries who are eligible|57%|64%|
|Targeting differential|-2%|6%|
|% of beneficiaries who are related to government officials or influentials|43%|30%|
|_Panel D: Villages not in the study (Morogoro)_|||
|% of beneficiaries who are eligible|7%|31%|
|Targeting differential|-2%|51%|
|% of beneficiaries who are related togovernment officials or influentials<br>Note: Data come from baseline and follow-up Villager survey. Arusha (north) villages we<br>Kilimanjaro (also in the north) and Morogoro (south) were not part of the whole study. Ben<br>respondents.|27%<br>re part of the study, <br>eficiary status is self-r|14%<br> while villages in<br>eported by survey|
As an alternative measure of firm risk, we use the rating of the first earmarked loan issued to each firm _f_ .<sup>15</sup> We classify firm risk as an indicator variable that equals one for firms with earmarked rating below the median in that month and zero otherwise. The results are presented in Table A2 in the Appendix. Overall, we confirm that riskier firms operating in regions where the banking sector is more concentrated experienced larger increases in the price of new working capital loans. These firms pay an additional 1.84 pp in the interest rate of working capital loans, which correspond to a 6.5 percent increase relative to the mean.

# **5.6. Alternative measure of market power**

In the analysis, we refer interchangeably to market power and market concentration. That is, we identify regions where the banking industry concentration, measured by the Herfindahl Index at the 2-digit zip code, is above the median of that year. To the extent that a small number of large banks dominate these markets, they might have more power to set prices. As a robustness check, we run our baseline specifications using two alternative measures of market power and confirm that our results remain unchanged.

The first measure corresponds to the banks’ Lerner Index, which captures the lender’s ability to set prices above its marginal costs <mark>(Bikker & Haaf 2002)</mark> . As the Lerner Index uses aggregate bank balance sheet data, one limitation in our set up is that this measure is calculated at the bank-time level. Thus, we cannot exploit important heterogeneity within a lender across different locations where firms operate at a given point in time. Despite this limitation, we estimate Equation (3) defining M l as equal to one for banks with a Lerner Index above the median and zero otherwise. The results of this exercise are summarized in Panel A of Table A3 in the Appendix. We confirm the main finding: after issuing an earmarked loan
ownership declined, the prevalence of renting increased, and the size of households increased, which are all consistent with the widespread issue of internal displacement (e.g., TFPM 2019). And access to services declined dramatically, where essentially no households had access to an electricity network and only 25 percent had access to a water network, which is consistent with the reporting of humanitarian agencies (e.g., OCHA 2018).

> 26The sample excludes the month of displacement (so as to avoid capturing impacts of displacement), but the results are qualitatively identical when the month of displacement is included, when only postdisplacement rounds are included, and when only post-displacement rounds well after displacement are included. There were 8,086 displaced households in the total WFP survey, but 60 of the households only responded to a single survey in the month of displacement and were excluded from the specification.

> 27The specifications also include month-year indicators and district fixed effects. The violence data are not included because households that only answered post-displacement rounds do not provide their origin districts.

> 28Alternatively, in specifications not reported, when regressing each food security variable separately on the indicator for whether the displaced household was reached both before and after first displacement, only one variable out of 14 is statistically significant at the five percent level, which is similar to what would be expected by chance.

15
# **Appendix A: Comparison of data from disaster inventories and microdata**

Data from disaster inventories such as EM-DAT and from microdata such as the LSMS-ISA differ in a number of meaningful ways. This includes their coverage of different shock types and sizes, the affected population, and time frame, as well as their detail, accuracy, and comparability across countries. These dimensions determine the respective strengths and weaknesses of each data source and can give rise to differences in estimates of disaster incidence and severity.

Disaster inventories collate reports from a variety of data sources on a per-event basis and are subject to the coverage and detail of information provided in these underlying sources.<sup>37</sup> Typically, this data comes aggregated, most often at the country-level. On the other hand, microdata, in our case, is based on household surveys that interview a sample from the target population and population-level estimates are formed based on this sample.

## **Shock coverage**

Coverage of shocks in disaster inventories varies depending on the underlying source data for each event. While large, salient shocks are likely reported across one or more sources that disaster inventories rely on (such as news reports or data bases from international organizations) and are therefore likely covered in disaster inventories, this is somewhat less likely for small or idiosyncratic shocks.<sup>30,37</sup> Such small shocks may therefore go unrecorded. For similar reasons, some shock types are more likely to be covered in disaster inventories than others.<sup>37,42</sup> Conversely, microdata is based on reports directly elicited from those affected by the shocks in question. They are therefore more granular and able to cover small and localized shocks. At the same time, shock recording in microdata sources is subject to the design of the survey questionnaire such as the list of shocks covered, and the number of shocks recorded.

## **Population coverage**

Disaster inventories are not limited to a
most lucrative enterprise in the household, which is therefore likely responsible for most of the reported variation in household enterprise earnings.<sup>11</sup> Respondents who are self-employed also report basic demographic information, which allow us to consider age and gender of firm owners.<sup>12</sup> Thus, to identify if a household owns an enterprise, we generate a variable that takes a value of 1 if the respondent is (i) self-employed or works in a family business and (ii) a household head. When we consider demographics of the firm owner, we further limit the sample to only those respondents who are self-employed. The variable is treated as missing if the respondent is not a household head. We call this the “household head definition.” Given the importance of separating farm and non-farm businesses in the analysis, we use the sector of employment of the respondent to limit the sample to only agricultural (or non-agricultural) enterprise when considering the behavior of farm (or non-farm) enterprises.

**The outcome variables of interest are income changes and use of coping strategies.** Some countries provide information on total income decline in the household, while others ask about income decline in farm enterprises, non-farm enterprises, and wage earners. The respondent provides a subjective response to the perception of income change since the pandemic began; the results are not triangulated with actual earnings data. We use four variables to capture the use of coping strategies: sold assets, received cash or in-kind assistance from government entities, received cash or in-kind assistance from private (non-governmental) entities, and received any kind of assistance from any entity. These variables are reported at the level of the household. Again, they are subjective responses that are not triangulated with data monitoring actual use of coping strategies. Each variable is asked separately; none are imputed. Not all variables are included in the surveys of all countries in the sample. The variables of interest that are reported for each country are provided
1

# **Introduction**

According to UPU data, on average across countries, postal employees account for approximately 0.2 percent of the labor force. The global operating income of post offices is $240 billion, accounting on average for 0.4 percent of GDP. Development of postal services and broader development have traditionally gone hand in hand. A “postal Jipp curve” can be constructed linking letters posted per capita with GDP per capita (data from UPU postal database and World Bank World Development Indicators).<sup>2</sup> About 73 percent of the variation in the number of letters per capita can be predicted by GDP per capita (Figure 1). This suggests that there is a significant relationship between economic development and expansion of postal networks.

# **Figure 1.**

<!-- Start of picture text --> 1000<br>100<br>10<br>1<br>0.1<br>0.01<br>100 1000 10000 100000<br>GDP per Capita (USD)<br>Letters/Capita/Year<br><!-- End of picture text --> A belief in the economic and social importance of the sector has been one factor behind pushing for universal access to postal services. The European Union (EU) defines universal service to post in the following terms:

> 2 Letters/capita is clearly not a directly equivalent measure of telephones per capita that the original Jipp curve concentrated on —it is more of a measure of use (equivalent, perhaps, to minutes of call time/capita) than of infrastructure rollout.  Another measure might be post offices/capita. However, as post offices are public, whereas the great majority of telephones are private, this is not a good comparator, either.  For example, Hong Kong could probably get away with very few really large post offices to serve the entire city, obviously it couldn't
L’effet de l’ˆage et de la taille sur la performance financi`ere et  ́economique des pme. _Journal of Small Business & Entrepreneurship_ , 23(2):287–306.

- Syverson, C. (2011). What determines productivity? _Journal of Economic literature_ , 49(2):326–65.

- Tovar, B., Ramos-Real, F. J., and De Almeida, E. F. (2011). Firm size and productivity. evidence from the electricity distribution industry in brazil. _Energy Policy_ , 39(2):826–833.

- Wooldridge, J. M. (2010). _Econometric analysis of cross section and panel data_ . MIT press.

- Xiao, Z., Gao, J., Wang, Z., Yin, Z., and Xiang, L. (2022). Power shortage and firm productivity: Evidence from the world bank enterprise survey. _Energy_ , 247:123479.

30
this analysis relies three groups of BE indicators to gauge their effects on firm performance. How should the three groups of BE indicators be associated with firm performance differently? Does it matter whether the indicators are _de jure_ or _de facto_ ?

The DB indicators are de jure measures, and are designed to reflect what a typical small or medium-sized firm (by international standards) would do based on the official laws and regulations. The actual BE that a firm faces could differ systematically from what is implied by the DB measures based on varying enforcement by various branches and/or agents of the government, or by the same government officials with respect to different firms. The enforcement could reflect whether the firm is well connected to the government, and/or 4
disruptions during the crisis, financial constraints, international trade and foreign ownership, skills shortages, and the presence of women workers.

We emphasize that the decomposition analysis is descriptive and not causal. The descriptive approach is consistent with previous research (see, for example, Nix et al. 2016, Essers et al. 2021, and Islam and Amin 2023).

# **2. Literature review**

In this section, we review the literature on the impact of the COVID-19 pandemic and earlier shocks on firms. The focus is on studies that compare the impact on small vs. large firms. There are several other studies that deal with the impact of recessions and crises on small firms alone. Some of these studies are discussed in Section 3, where we motivate our main explanatory variables.

# 2.1 _Small vs. large firm performance during crises_

Recent studies have explored how firms of different sizes performed during the COVID-19 pandemic. Using firm-level survey data on 23 economies in ECA collected by the World Bank’s Enterprise Surveys, Bruhn et al. (2023) found that small firms suffered larger declines in output and employment due to the COVID-19 pandemic than large firms. Using data from the US, Bloom et al. (2021) showed that the smallest offline firms experienced sales drops of over 40 percent due to the pandemic, compared with a 10 percent drop for the largest offline firms. Ding et al. (2021) used data on 61 mostly developed economies and found that the stock market response to the COVID-19 shock was less negative for large corporations than for small firms. Dörr et al. (2022) found that in Germany, small firms experienced stronger financial distress than large firms due to 7
% GDP) Revenue, excl. grants ( % GDP)<br>Revenue, excl. grants ( % GDP) Revenue, excl. grants ( % GDP)<br><!-- End of picture text --> _Notes:_ The sample consists of 182 countries. The dotted line represents the result of a bivariate linear regression of revenue on the indicator shown on the x-axis (see Table A2 in the appendix). _Source:_ Authors’ analysis based on the variables and data sources described in Table 1.

The indicator of technological readiness (see Table 1, Panel D) is countries’ score on the “Technological Readiness Index” from the World Economic Forum’s Global Competitiveness Report.

10
- District-level share of SC population: district-level share of population that belongs to a Scheduled caste out of the total population in the district from 2011 Census of India.

- District-level share of female population: share of female population out of the total population in the district from 2011 Census of India.

- District-level all-cause mortality rate in 2019: mortality rate for deaths from all causes in 2019 from SHRUG.

- District population: district population from 2011 Census of India.

- Zones: we combine neighboring districts in Haryana into the following five zones:

- Zone A: Ambala, Kurukshetra, Panchkula, Yamunanagar

- Zone B: Jhajjar, Sonipat

- Zone C: Karnal, Jind, Panipat

- Zone D: Faridabad, Gurgaon, Nuh, Palwal, Rewari

- Zone E: Bhiwani, Charkhi Dadri, Mahendragarh, Rohtak

14