rafmacalaba/data-use-mentions-extended huggingface dataset:
.853***|5.860***|0.0910***|0.101***|0.214***|0.213***|
||(0.00207)|(0.00787)|(0.00804)|(0.0173)|(0.00242)|(0.00556)|(0.00258)|(0.00680)|
|Obs.|3,882|3,882|2,116|2,116|2,050|2,050|2,050|2,050|
|R<sup>2</sup>|0.304|0.304|0.521|0.521|0.297|0.302|0.289|0.289|
|_δpv −βpv_||0.0986||-0.616||1.302***||0.183|
|S.E.||0.583||0.909||0.223||0.321|

_Notes_ : * p _<_ .1, ** p _<_ .05, *** p _<_ .01. Household-level data, restricted to households engaged in agriculture. S.E.s allow for clustering at the level of enumeration area (EA). All columns include fixed effects for if a household was located within 10 km. of a population center with at least 20,000 inhabitants, the state-level administrative center, and a market. Additionally EA-level, state-level, month, and river sub-basin fixed effects are included. Borno state is excluded.

32
# **1 Introduction**

The COVID pandemic was a major shock to the global economy. Global trade declined dramatically at the onset of the pandemic (Brenton et al., 2022) but trade bounced back sharply from late 2020 onward (Meijerink et al., 2020). Surveys of firms around the world, namely the World Bank’s Business Pulse Surveys, documented their responses to the COVID shock including their access to and use of government support programs (e.g., Constantinescu et al., 2022; Cirera et al., 2021). Despite the key role of government agencies that promote – trade Trade Promotion Organizations (TPOs)– highlighted in a recent survey by Srhoj et al. (2020), little is known about the impact of the pandemic on their activities. To address this gap, the World Bank implemented a survey of TPOs in 57 countries between fall 2021 and spring 2022 with the objective of better understanding their responses to a crisis such as the COVID pandemic as well as their functioning more broadly.

The early literature in the 1990s was critical of TPOs’ capacity in developing countries to promote exports (Hogan et al., 1991), as they tended to be inadequately funded, lacked leadership, and were too bureaucratic and not client oriented, while hiring staff with links to the government instead of the export sector. However, as TPOs reformed, the more recent literature suggests that they can successfully promote exports when sufficiently funded (Lederman et al., 2010), or when focusing on firms’ extensive rather than intensive export margins, both at the market, product and exporter/non-exporter levels (Volpe Martincus and Carballo (2008), Cruz (2014) and Broocks and van Biesebroeck (2017)). TPO returns are also higher when they spend a large share of their budget on marketing activities in a few sectors (Olarreaga et al., 2020), or when TPO activities are combined with investment promotion (Harding and Javorcik, 2012)
and Schmidt (2020): “a sense of shared purpose and trust among members of a given group or locality and the willingness of those group members to engage and cooperate with each other to survive and prosper”.

To operationalize this definition, this paper focuses on the presence or absence of major intergroup cleavages, which according to Chan, To and Chan (2006) and King, Samii and Snilstveit (2010) represents a key dimension of social cohesion, as strong divisions (whether by income, ethnicity, political party) can polarize the society and strain social solidarity. In the first part of the paper, I use different measures of refugees’ socio-economic integration and cultural assimilation to test whether the refugee inflow has caused the emergence of new cleavages (native vs. newcomer) within the Greek society as a whole. I notably use survey data on self-reported trust and selfperceived discrimination to measure the extent of social exclusion of the refugee population. I also use census data to measure educational and occupational inequalities between the two groups. In the second part of the paper, I focus on community-level measures of social cohesion, understood either as absence of societal conflict and division or, more positively, as civic participation. In partic7
out of poverty. Specific policies need to be targeted at the poor where they live, especially the rural poor clustered in fragile environments and remote areas. This will require a variety of measures including involving the poor in these areas in payment for ecosystem services, targeting investments directly to the rural poor, reducing their dependence on exploiting environmental resources, and tackling their lack of access to affordable credit, insurance, land, and transport. Where possible, efforts should be made to boost rural employment opportunities, especially for those poor households dependent on outside labor employment. These measures can complement other actions to improve livelihoods by increasing economic mobility, including physical or institutional migration out of low-return agriculture and the informal sector.

# **Ecological scarcity and poverty in poor economies**

From an economic perspective, ecological scarcity has been defined as the loss of myriad benefits, or “services”, as ecosystems are exploited for human use and activity (Barbier 1989, pp. 96-7; see also Barbier 2011, ch. 9). Certainly, at a global level, ecosystems and their services are in decline. An important indicator of the growing ecological scarcity worldwide was provided by the Millennium Ecosystem Assessment (MA 2005), which found that over 60% of the world's major ecosystem goods and services were degraded or used unsustainably. Some important benefits to humankind fall in this category, including fresh water, capture fisheries, water purification and waste treatment, wild foods, genetic resources, bio-chemicals, wood fuel, pollination, spiritual, religious and aesthetic values, and the regulation of regional and local climate, erosion, pests and natural hazards.

For many developing economies, ecological scarcity also is manifesting itself in another way – it is contributing to the economic vulnerability of the rural poor. Increasing ecological scarcity is disproportionately affecting the world's poor in rural areas, who depend critically on many ecosystem goods and services for their livelihoods (Barbier 2005 and 2010; MA 2005; TEEB 2010; Wunder 2008). As shown
trends in the bond data or the presence of other confounding and possibly unobserved variables that influence bond prices as well as natural resources.

The wealth accounting indicators used in this study were compiled on an annual frequency and will be made available together with the accompanying report (Lange et al., 2021). The values are comparable and qualitatively consistent with the publicly available 5-year frequency data until

> 6Monthly or quarterly data, on the other hand, might be more informative as they capture seasonal variations and are aggregated enough for higher signal-to-noise ratios to emerge.

7
# Appendix C. Hazard Simulations

The Centre for Disaster Protection and the World Bank asked Verisk’s AIR Worldwide (AIR) to develop a series of stochastic catalogs of relevant climate variables in Malawi. These would support the ongoing effort to develop a standardized approach to ex ante estimation of the welfare impacts of moderate and severe droughts in SSA, complementing the ongoing empirical analysis. The catalogs would allow a forward-looking assessment of possible future events that goes beyond the reliance on the history of past events (i.e., would allow a more robust probabilistic view of future events).

AIR’s methodology, data sources (table C.1), data validation, and results are set out in Blanchard and Sousa (2021) and summarized below, with extracts largely taken directly from the paper.

## **Developing the Stochastic Catalogs for Malawi**

Under the scope of this project, AIR developed stochastic catalogs of three drought-related variables for Malawi— precipitation, soil moisture, and vegetation index.

The 10,000-year stochastic catalog produced for each variable includes 10,000 samples of annual time histories for the variable of interest, which all reflect conditions that are likely under the current (and near-current) climate.

**Table C.1. Data sets used to generate the stochastic catalogs**

|**Variable**|**Description**|**Data set**|**Resolution,**<br>**frequency,**<br>**period covered**|**Source**|
|---|---|---|---|---|
|Atmospheric<br>moisture|Atmospheric<br>reanalysis of the<br>global climate|ECMWF Reanalysis<br>v5 (ERA5)|0.25°, three-<br>hourly, 1979–<br>2018|ECMWF|
|Precipitation|Atmospheric<br>precipitation<br>
**-** **<u>Appendix table A5: PSG and non PSG participants 50 km from the frontline based on farm survey data</u>**

|||<br>**PSG pr**|<br>**ogram ben**|<br>**eficiaries**||<br>**Eligible**|<br>**non-benef.**|_Δ_**Sig**|
|---|---|---|---|---|---|---|---|---|
||**To**|**tal**||**Size class**||**T**|**otal**||
||**2022**|**2023**|**<20**|**20-50**|**50-120**|**2022**|**2023**||
|**Panel A: Farm characteristics**|||||||||
|Total area cultivated (ha)|65.9|76.2|16.3|47.5|106.5|59.6|80.2|*|
|Members working full time on farm|1.44|1.69|1.59|1.62|1.75|1.66|1.70|*|
|Members working part-time on farm|0.84|1.02|1.16|1.05|0.96|0.78|0.87||
|Permanent employees|0.39|0.74|0.00|2.14|0.67|0.69|1.64||
|<br>Have tractor <
to measuring vulnerability. Indeed, let us consider the most interesting and realistic case where productive assets contribute towards the income generation process and can also serve as buffer-stock in order to face a non-anticipated drop in income (Deaton, 1991, Carroll, 1992). Empirically though, many studies find little evidence supporting the buffer-stock hypothesis in developing countries.<sup>19</sup> For instance, Dercon (1998) shows that, given subsistence constraints and agent heterogeneity, rich households will accumulate assets more quickly than poor ones who will pursue low-risk, low-return activities. Interestingly enough, the evidence suggests that households with lower endowments are less likely to own cattle and returns to their endowments are lower. So, in presence of imperfect markets for credit and insurance, few households are able to smooth their consumption. What is more, when assets are mainly made up of productive assets, selling these assets would induce a permanent loss in income for the household who

> 16 See Echevin (2010a) for a more complete version of this section and application to other countries.

> 17 Sahn and Stifel (2003) show that an asset index obtained from a factor analysis on household assets using multipurpose surveys from several developing countries is a valid predictor of child health and nutrition and, thus, long term poverty.

> 18 Echevin (2010a) provides such empirical evidence using Ghana Living Standard Surveys.

> 19 See, among others, Rosenzweig and Wolpin (1993), Morduch (1995), Fafchamps et al. (1998), Kazianga and Udry (2006), and Hoddinott (2006).

13
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data reports the extent of underreporting in sales to the tax office applying the indirect approach across cities, industries and firm sizes, with underreporting calculated as . Estimated underreporting is much smaller than revealed by the direct approach (table 1) and averages about 15%. If survey sales reflect true sales and firms tend to underreport sales to tax office, the indicator should be between 0 and 100, but we observe negative values for 96 firms.<sup>25</sup> This suggests the presence of serious measurement error and/or underreporting in the survey sales.

|**Av**|**erage % of Sales**|**T**<br>**Underre**<br>|**ABLE 2**<br> **ported by**<br>|**the Indirec**<br>|**t Approac**<br>|**h**<br>|
|---|---|---|---|---|---|---|
|||Obs.|Mean|Std. Dev.|Min|Max|
|**_City_**|_Ulaanbaatar_|139|14.9|41.5|-125.9|91.3|
||_Darkhan_|50|18.2|55.3|-121.2|91.3|
||_Edernet_|46|13.5|54.2|-154.3|90.1|
||_Hovd_|20|16.4|49.8|-132.8|86.7|
|**_Industry_**|_Manufacture_|131|18.0|46.2|-121.2|91.3|
||_Construction_|66|12.4|47
# **5. The long–run impact of COVID-19 on incomes, poverty, and inequality**

In this section we carry out microsimulations to provide rough estimates of the potential impact of the COVID-19 pandemic on future incomes and indicators of poverty and inequality through the educational channels discussed in previous sections. The exercises are necessarily based on several restricting assumptions and hence should be taken only as informed back-of-the-envelope calculations, aimed at informing the public debate on this very relevant issue.

## **5.1. Methodology**

We start from an income distribution in baseline year _to_ . We choose 2019, the most recent year with available microdata from a national household survey for most countries. Data for 2021 is not yet available for most countries, and 2020 was a very unusual year to use it as baseline.

For simplicity, and to focus only on the impact of the pandemic through the educational channel, we assume that without the COVID-19 pandemic the educational structure and income distribution _T_ years after _to_ ( _tT_ ) would be identical to those in the baseline year. For simplicity in the explanation, we focus on year 2045, _i_ . _e_ . 25 years after the COVID-19 shock. In section 5.3 we extend the analysis to the dynamics of the impact over time. Again, for simplicity, we initially assume that the pandemic in 2020 affected human capital accumulation of children and youths aged 5 to 20. Consequently, in year 2045 the workers who were affected by the pandemic back in 2020 are those aged 30-45. This will be our “treatment group” _G_ .<sup>13</sup> The key step of the methodology consists in changing the years of education of each person _i_ in country _c_ belonging to group _G_ and simulating her labor income _xic_<sup>_V_</sup> . In particular, we subtract _kic_ years (or fraction of a year) of education

> 13 This approach is similar to one that assesses the counterfactual impact on the current generation of adults
the influences of selected components of the World Bank’s Ease of Doing Business measures on GDP growth. Like Djankov _et al._ (2006), they also artificially create a rankings system but do so by creating a dummy variable of the 20 most regulated economies. They find countries with lower levels of regulation are more likely to stimulate growth. While the results are encouraging, the creation of a dummy variable reduces the richness of the data available for analysis and the authors do not disaggregate between developed and developing countries.

This paper expands on Busse and Groizard (2008), Djankov _et al._ (2006) and Eifert (2009) by using a more robust panel data set as opposed to cross-sectional analysis, considering the aggregate impact of reforms (as captured by the official World Bank rankings as opposed to artificially creating rankings), disaggregating impacts to developing countries and focusing specifically on the impact of Doing Business reforms on FDI inflows as opposed to the impacts on GDP growth.

# **3. Data and Methodology**

The study estimates the influence of the Doing Business Rankings (DBR), across approximately 84 countries, using data from the period when rankings officially began in 2006 to 2009. While there were 175 countries ranked in 2006, the number used in regression analysis varies due to the use of an unbalanced panel. The 2006 and 2007 rankings were gathered from the 2007 Doing Business report, the 4
**Table 5:** Food Security Impacts

|**Outcome**|**Obs**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|
|---|---|---|---|
|**Food Security**||||
|Food groups eaten in last 7 days|2113|7.302|-0.054<br>(0.081)|
|Food groups eaten in last 24 hours|2113|5.534|0.061<br>(0.082)|
|Meals skipped in last 7 days|2109|0.379|-0.012<br>(0.057)|

Note: Robust standard errors reported in parenthesis. *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively.All regressions include controls for number of boys 6-16 years old and stratum fixed effects.

21
14 Nowrasteh, Forrester, and Blondin

**Table 5.** EFW, Polity IV, and Freedom House Indexes, Jordan v. Kuwait

||**Total**<br>**index**|**Size of**<br>**gov.**|**Legal**<br>**system**|**Sound**<br>**money**|**Internatl.**<br>**trade**|**Regulation**|**Polity II**|**Political**<br>**rights**|**Civil**<br>**liberties**|
|---|---|---|---|---|---|---|---|---|---|
||||||_Panel A._|_Jo_<br>_rdan_||||
|1975|4.83|1.53|.|5.65|5.76|6.40|−10.00|6.00|6.00|
|1980|5.13|4.77|1.81|6.82|5.76|6.49|−10.00|6.00|6.00|
|1985|5.50|4.60|2.74|7.74|6.00|6.42|−9.00|5.00|5.00|
|1990|5.43|6.12|3.10|6.20|4.81|6.93|−4.00|5.00|5.00|
|1995|6.14|5.71|4.63|7.15|6.
in urban areas. Although the wealthiest households pay larger absolute amounts on medical items, as a proportion to non-food household spending, the poorest households spend about 1.5 times more on medical care than do richer households. Of total non-food expenditure, drugs and medicine consume about 3 times more among the poorest quintile than among the top quintile. User fees-non-food expenditure ratio--a measure of affordability--is much larger for the poorest 20 percent (2%) than for the richest 20 percent (0.60%). The top quintiles spend more on other category which mainly comprises charges for physician's home visits.

One indirect indication of quality gap is the difference in private expenditures on medical care across quintiles of household income for the same level of services. According to the findings in Table 3 the top quintile pay more in absolute amounts and buy better quality, particularly instruments and drugs. Another indicator is the quality of medical attention received by the lowincome population who use public health facilities. The 1993 NHS suggests that the poor are more likely to use Township medical centers when sick, but the probability of being treated by qualified doctor is less than 20 percent. Whereas, the rich are more likely to seek treatment in hospitals where the probability of consulting trained doctor is more than 60 percent. A third indicator is the paucity of qualified medical personnel in poorer provinces which exacerbates the quality of medical attention. The calculation of relative medical personnel ratio (RPR) in Table 4 shows a clear undersupply of qualified doctors in poorer provinces and an oversupply of such personnel in richer provinces.
Many SIDS are highly exposed to tropical cyclones, because of their geographical position, and are highly vulnerable because of the small size of their landmass and lack of economic diversification. The EM-DAT records 185 incidences of tropical cyclone disasters in the 16 Caribbean SIDS during the 28-year period between 1995 and 2022. It also recorded 74 incidences in the 12 SIDS in Oceania during this period.<sup>5</sup> The EM-DAT records total estimated damages, comprising all damages and economic losses related to the disaster, but these data pertain to less than half of the recorded tropical cyclone disasters. Table 1 shows the total damages caused by tropical cyclones and recorded in EM-DAT in the Caribbean and Oceanian SIDS from 1995 to 2022, by country and region, estimated in 2022 US dollar prices. These data must be underestimates of the actual damage caused by tropical cyclones, given the absence of data on total damages for more than half of the tropical cyclone disasters recorded by EM-DAT. It is likely that some SIDS provide more comprehensive data on damages than others; for example, data from Cuba appears to be more comprehensive than that from Haiti.<sup>6</sup> The data in disaster databases is unavoidably imperfect, and are subject to problems such as over or under reporting of certain hazard types and other forms of bias (Gall, 2015).

**Table 1 Total Recorded Damages caused by Tropical Cyclones in SIDS in the Caribbean and Oceania; 1995-2022 (US dollar millions, 2022 prices)**

|**Caribbean**|**Damages USD M.**|**Oceania**|**Damages USD M.**|
|---|---|---|---|
|Cuba|18,374|Fiji|1,080|
|The Bahamas|8,478|Vanuatu|557|
|Dominican Republic|4,586|Tonga|253|
|Haiti|3,241|Samoa|170|
**Table 2 Input and output data disaggregated by Income group**

||Obs|Mean|Std. Dev.<br>**LAYS**|Min|Max|Coef. Variation|
|---|---|---|---|---|---|---|
|High‐ Income|48|10.818|1.176|7.630|12.905|9.199|
|Upper Middle‐Income|38|8.094|1.771|4.004|11.900|4.570|
|Lower Middle‐Income|45|6.556|1.635|3.424|10.214|4.010|
|Low‐Income|26|4.528|1.228|2.275|6.890|3.688|
||**Ex**<br>|**penditurep **<br>|**er school‐ag**<br>|**e individual**<br>|||
|High‐Income|53|7488.619|4104.003|1238.602|22448.080|1.825|
|Upper Middle‐Income|45|1612.518|768.749|395.142|3449.911|2.098|
|Lower Middle‐Income|48|581.917|410.910|84.865|2062.425|1.416|
|Low‐Income|29|120.597|54.282|29.981|266.736|2.222|

# **4. Results**

The stochastic frontier model discussed in section 2 is estimated using the data presented in section 3. Equations 1 and 2 are simultaneously estimated using the maximum likelihood method discussed by Battese & Coelli (1995). The results allow
%. Recent data on concentration indicate that at the oblast level, the average 4-firm concentration ratio is above 95%.

In large part, the existing level of horizontal dominance in Russian manufacturing markets is a legacy of Soviet centralized planning. Horizontal integration is also increasing. But less so through _de novo_ expansion than through mergers and acquisitions. The recently announced mergers among aluminum companies and the earlier proposed mergers of oil companies are cases in point. Horizontal integration is also increasing through the creation of financial industrial groups (FIGs). The WB-RAS survey found that enterprises with higher market shares are more likely to be registered as members of a FIG; indeed, the average oblast market share of firms associated with a FIG was 53%, while the average oblast market share for firms not associated with a FIG was 42%.

Surprisingly, empirical investigation of the effects of horizontal dominance on the performance of Russian firms is exceptionally sparse. This is in contrast to the vast literature on this issue for many developed and developing countries, including transition economies. Arguably, this is due to the paucity of comprehensive data on Russian firms in the short period since the onset of reform; and what data do exist suffer from the fact that Russian financial accouniting practices diverge from International Accounting Standards. Rectifying this data vacuum and carrying out systematic empirical analysis of such data should be seen as a major challenge to the Russian authorities if policy-making in this area is to be strengthened. In one of the few empirical studies that have been undertaken, support can be found for the notion that, as in other countries, in Russia, the greater the level of market concentration the higher are industry profits.<sup>7</sup> The same study found that capital intensity is negatively related to profitability. Data from the WB-RAS survey indicate that firms with lower capital productivity are more likely to be registered as "dominant" (generally defined as having market shares above 35%--see below) by the Ministry of Anti-Monopoly Policy and Support