rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 2: Migration outflows and forced displacement from Donbas, annual averages (a) Pre-conflict migration outflows (2008-12) (b) IDP outflows after the conflict (2014-19) <!-- Start of picture text --> Kharkiv (5,540)<br>Donetsk (11,227)<br>Kyiv City (3,197)<br>Luhansk (6,720) Dnipropetrovsk (1,918) Kyiv City (30,743)<br>Zaporizzhia (1,139)<br>Kyiv (1,038)<br>Odesa (825)<br>Poltava (636)<br>Sumy (432)<br>Cherkasy (394) Donetsk (84,006)<br>Vinnytsia (349) Kharkiv (26,354)<br>Chernihiv (349)<br>Kherson (325)<br>Zhytomyr (254)<br>Khmelnytsky (250)<br>Mykolaiv (245)<br>Kirovohrad (231) Dnipropetrovsk (14,003)<br>Lviv (213)<br>Ivano-Frankivsk (116)<br>Volyn (112)<br>Rivne (109)<br>Zakarpattia (101) Kyiv (11,935)<br>Chernivtsi (95)<br>Ternopil (79)<br>Zaporizzhia (11,064)<br>Luhansk (43,912)<br>Odesa (7,261)<br>Poltava (4,492)<br>Kherson (2,764)<br>Sumy (2,229)<br>Vinnytsia (2,203)<br>Cherkasy (2,173)<br>Lviv (2,148)<br>Mykolaiv (1,631)<br>Chernihiv (1,454)<br>Zhytomyr (1,404)<br>Kirovohrad (1,297)<br>Khmelnytsky (1,223
Policy Research Working Paper 7949

# **Abstract**

This paper uses comprehensive and comparable firm-level manufacturing census data from four Sub-Saharan African countries to examine the extent, costs, and nature of within-industry resource misallocation across heterogeneous firms. The paper finds evidence of severe misallocation in which resources are diverted away from high-productivity firms toward low-productivity ones in all four countries, although the magnitude differs across countries. The paper shows that a hypothetical reallocation of resources that equalizes marginal returns across firms would increase manufacturing productivity by 31.4 percent in Côte d’Ivoire and as much as 162.7 percent in Kenya. The paper emphasizes the importance of the quality of the underlying data, by comparing the results against those from the World Bank Enterprise Surveys. The comparison finds that the survey-based results underestimate the extent of misallocation vis-a-vis the census. Finally, the paper finds that the size of existing distortions is correlated with various measures of business environment, such as lack of access to finance, corruption, and regulations.

This paper is a product of the Trade and Competitiveness Global Practice Group and the Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at xcirera@worldbank.org and rfattaljaef@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors
# **1 Introduction**

The Coronavirus Disease 2019 (COVID-19) pandemic has disrupted economic activity in India. Until mid-March 2020, the economy was mainly hit by disruptions in cross-border connections. For example, tourism arrivals in India declined due to strict travel restrictions and some value chains were interrupted, especially with China. When COVID-19 started to spread in India through domestic contagion, the Indian authorities enacted a series of measures to combat the pandemic, including a national lockdown from March 25 onwards that strongly disrupted economic activity across the country. When restrictions were stepwise eased in May, economic activity slowly recovered. Shutdowns and other non-pharmaceutical interventions to contain the spread of COVID-19 have high economic costs and consequently tend to be accompanied by policy responses to mitigate their economic impact (Gourinchas 2020). In line, both the Reserve Bank of India and the Government of India announced measures to assist individuals and companies that were negatively affected. Adjusting containment measures and policy responses to mitigate their economic impact require an assessment of the magnitude of the economic situation in near real-time. In addition, since the impact can vary at different locations, an assessment at high spatial granularity is needed.

Indicators traditionally used to monitor the economic situation are available only with substantial lags and often at the national level only, and hence provide little insights into the immediate effect of strong and sudden policy measures like a national lockdown. In response to such problems, economists have suggested different proxies that are available at a higher frequency and with shorter publication lags, as well as at a higher spatial granularity. Two of these are electricity consumption and night light intensity. Electricity is an input to activities throughout the economy, from industrial production to commerce and household activity, so changes in consumption reveal information about these activities in real-time (Cicala 2020a, 2020b). Similarly, nighttime light intensity contains information about economic activity at high spatial granularity. Such proxies have become especially important during the COVID-19 pandemic, as it makes data collection through surveys, which are fundamental for the traditional
) employ a difference-in-difference model to analyze household-level outcomes, Phan _et al._ (2016) use a GMM model to analyze province-level outcomes. We employ a more rigorous, combined RDD and difference-in-difference model with district fixed effects and analyze a richer database consisting of household surveys, firm surveys, and nighttime lights data. The time period that we analyze is also (more than) twice longer and more updated than those in the other studies.

5
, J.-P., Comtois, C. & Slack, B. _The Geography of Transport Systems_ . (Routledge, 2013). doi:10.4324/9780203371183.

87. Barthelemy, M. Spatial Networks. _Physics Reports_ **499** , 1–101 (2011).

# **Data Availability**

GHS-SMOD global human settlement layer is available online for download by tiles or as a single file.

- Download by tile: https://ghsl.jrc.ec.europa.eu/download.php?ds=smod

- Download as single file: https://cidportal.jrc.ec.europa.eu/ftp/jrcopendata/GHSL/GHS_SMOD_POP_GLOBE_R2019A/GHS_SMOD_POP2015_GLOBE_R2019 A_54009_1K/V2-0/GHS_SMOD_POP2015_GLOBE_R2019A_54009_1K_V2_0.zip

GHS-POP global population multitemporal grid raster data is available online for download by tiles or as a single file.

- Download by tile: https://ghsl.jrc.ec.europa.eu/download.php?ds=pop

- Download as a single file: https://cidportal.jrc.ec.europa.eu/ftp/jrcopendata/GHSL/GHS_POP_MT_GLOBE_R2019A/GHS_POP_E2015_GLOBE_R2019A_5400 9_250/V1-0/GHS_POP_E2015_GLOBE_R2019A_54009_250_V1_0.zip

# **Code Availability**

The Python code used to complete the analysis and produce the figures in this study will be available in the following online repository [https://github.com/jesuslovesyiyi/global-road-network-resilience-to-floods].

26
the UN’s World Population Prospects, which contains dozens of variables on population, health, and demographics. We compliment GDP data from the sources above with estimates from the Madison Database (Bolt and Van Zanden 2024). We also use country and region classifications by the World Bank and data on political rights, civil liberties, and freedom status from the Freedom House.

In addition, we use remote sensing data available from the Google Earth Engine. We use data on nighttime lights, precipitation, temperature, impervious surface, cropland, the normalized difference water/snow/vegetation index, and the enhanced vegetation index. While spatial coverage of remote sensing variables is not a concern as they cover the earth’s entire surface, temporal coverage is at times limited. Nighttime light data, for example, dates back to 1992. To fit into this exercise, the remote sensing data need to be aggregated to the country-year level. We first aggregate them to annual data by calculating the mean, max, min, and standard deviation of each location (e.g., a pixel) over a year. Afterwards, we aggregate them spatially by taking the mean, max, min, and standard deviation of the annual data for a country. This gives 16 features for each type of variable. Nighttime lights are also converted to a per capita level by dividing the sum of lights by the population size. We weigh each grid equally, and hence the indices reflect, for example, the mean temperature in the territory of a country, not the mean temperature experienced by a person in a country. Given that many of the variables impact welfare through agriculture, it is not clear that population weighting (which would give a dominance to urban areas) would make them more related to welfare distributions. Yet we also add population-grid weighted estimates of temperature and rainfall from Gortan et al. (2024).

4
likely to be found in a formal job throughout these two years.” The results are especially strong for younger cohorts (between 25 and 35 years old and 35 and 45 years old). De Brauw _et al._ (2015) find no overall impact on labor force participation of BF beneficiaries using longitudinal household-level panel data collected in 2005 and 2009. The authors, however, do find an increase in the intensive margin of informality (through

4
12 We can test this assumption for 2018, when the last population census took place in Colombia. Figures 2 **B.** and 2 **D.** show the municipal distribution of, respectively, the observed number of the Venezuelans and the predicted cumulative figure for 2018 (the predicted 2018 inflow is reported in Figure 2 **C.** ). Reassuringly, not only the distribution looks very similar, but also their correlation is 0.67. We are thus confident that our predicted migration shock has predictive power.<sup>11</sup> Secondly, the 1993-based predicted inflow measure needs not to be correlated with contemporaneous schooling outcomes through any channel different than actual Venezuelan migration. Regarding this assumption, it is worth noting that, because our estimates include fixed effects by municipality as well as by department _×_ year, they are confounded neither by time-invariant differences across municipalities nor by annual aggregate department-level shocks.<sup>12</sup> This is, however, not enough to achieve identification. It may well be the case that pre-shock migrants disproportionally settle in places with characteristics that explain future educational outcomes. Indeed, as noted by Goldsmith-Pinkham et al. (2019), identification in the Bartik/Shift-Share-type instruments comes mainly from the cross-sectional (“share”) variation, so it is important to check the extent to which the initial shares (of migrants) are correlated with potential confounders prior to the current migration wave. To this end, following Belloni et al. (2014), we use machine learning to select the most robust determinants of Venezuelan settlements according to the 1993 census and include in our main specification the interaction between each of these and the year fixed effects. By doing so, we flexibly control for municipal-specific trends, parametrized by a large set of pre-determined characteristics that predict early settlements.<sup>13</sup>

> 11In other words if we were to rely on a 2SLS strategy, the first stage would most
access the financing required to support their export activity, computed as the proportion of firms that needed a loan but did not apply for it owing to complex application procedures, unfavorable interest rates, excessively high collateral requirements, insufficient maturity and loan size, and anticipation of a negative decision by financial services providers. Results are displayed in Columns 11-20 of Table 9. Consistent with the literature, we find a detrimental impact of the proportion of loans requiring collateral on firm entry and first-year survival rates, with the coefficient on import uncertainty remaining unchanged across specifications. This is also broadly the case for the second variable capturing difficulties related to access to finance, which translate into depressed second- and third-year survival rates of entrants.

Furthermore, as described in Section 2.1, stages of the import process could be plagued by rent-seeking and corrupt practices, with Customs officials accepting informal payments for instance in exchange for modifying the classification or valuation of the imported good for tax purposes. Inspectors from other government entities such as SPS agencies involved in the clearance process could also engage in kickbacks and other illegal transactions that would ultimately influence the time required to clear foreign inputs at the border.<sup>35</sup> Accordingly, we check whether our results are immune to the inclusion of two indicators

> 34Using data for Chinese firms, Manova, Wei, and Zhang (2015) show that credit-constrained firms are less likely to penetrate foreign markets, in line with findings by Minetti and Zhu (2011) and Muˆuls (2015) for firms in Italy and Belgium respectively. Besides preventing firms from breaking into new markets, lack of access to finance also reduces export intensity (Bellone, Musso, Nesta, and Schiavo, 2010; Kiendrebeogo and Minea, 2016), and survival (Brenton, Cadot, and Pierola, 2012). For instance, Cadot, Iacovone, Pierola, and Rauch (2013) find that firms operating in a foreign market with limited presence of other firms from the same origin are less likely to obtain a
It is essential for the mechanism of the model to assume that when a government defaults, international lenders cannot seize the international reserves held by the central bank. Otherwise, the relevant statistic would be the foreign net position, as in Arellano (2008). To illustrate this point, suppose that after a default, a central bank cannot maintain control of its reserves. Therefore, accumulating reserves would increase the repayment value but not the default value. Thus, buying one reserve asset would decrease the probability of default to the same magnitude as increasing debt by one bond, but in the opposite direction. Subsection 2.3 describes the case of Argentina in 2015, which is consistent with this assumption and guarantees that vulture funds will not be allowed to seize the reserves held by an independent central bank.<sup>7</sup> The model is solved numerically to evaluate its quantitative predictions regarding the levels of international reserves, public debt, and sovereign spreads. I calibrate the model using data for Mexico, a typical emerging economy with an independent central bank, which is a common reference for studies on reserve accumulation. The central bank’s discount factor is disciplined by matching the money market interest rate, and the government’s discount factor is calibrated internally by targeting public debt.<sup>8</sup> I find that, under the benchmark calibration, model simulations account for 75% of the average level of reserves observed in Mexico from 1994 to 2017. Moreover, I also find that in periods of high income and low spreads, the government increases borrowing and the central bank accumulates more reserves, which accounts for a high and positive correlation between reserves and debt that is consistent with the pattern observed in the data.

Finally, I contrast the baseline model with an economy in which the central bank is as impatient as the government is (i.e. consolidated government). I show that, in accordance with Alfaro and Kanczuk (2009), a sovereign default model with a
of the survey was only administered to one-third of the sample. This significantly reduces the number of identifiable ECT beneficiaries when analyzing specific outcomes and reduces the overall sample size.

Several recommendations arise from this discussion: (1) collecting experimental data by aligning the purposes of policy and research in future implementations of ECTs would allow to collect stronger evidence; (2) survey data such as the CIMS (2020) are extremely valuable in the context of missing experimental data and they should pursue efforts to minimize data issues (e.g. missing responses, small sample size); (3) qualitative data would be a complementary source of information for our study and would allow us to gain further insight into how households allocate the amount of the ECT across their various needs.

# **7. Conclusion**

This paper took stock of lessons from a COVID-19 ECT program that was administered to vulnerable informal sector workers in Sierra Leone. It discussed how cash transfers are increasingly being instituted as a policy measure to support informal sector workers in the wake of the pandemic and reviewed emerging evidence on the impact of these measures in other countries. It also described the setting and context of the ECT program and presented analysis on the relationship between receiving the ECT and various socioeconomic outcomes and measures of subjective well-being for households with urban informal sector workers in Sierra Leone. While the ECT was found to be positively associated with effectively shielding vulnerable informal sector workers from income and job losses in the short-term, the analysis was inconclusive on the medium-term outcomes related to income, suggesting the need for further experimental data. The ECT was also found to be positively associated with measures of subjective well-being, especially on households receiving the transfers being less concerned about 26
**|**-0.99*****|**6.81*****|**5.80*****|**-1.00*****|
||(0.071)|(0.075)|(0.10)|(0.085)|(0.086)|(0.12)|
|No. Observations|9449|9070|18519|9449|9070|18519|
|||**Estimates**|**of Mobility**|**fromQuadr**|**atic CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|6.81|5.80||**_IGME0 _**|0.55|0.65|
|**_ES5 _**|9.39|8.82||**_IGME5 _**|0.485|0.556|
|**_ES10_**|11.61|11.37||**_IGME10_**|0.414|0.464|
|**_ES16_**|13.82|13.82||**_IGME16_**|0.330|0.353|

Notes: (1) The data used are IHDS 2012 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; *** p
|teristics. Across all regressions, the dependent<br>ipbursements from the World Bank expressed<br>heterogeneity. At the bottom of the table we<br>, as well as the associated p-value. Column 1<br>he World Governance Indicators (with higher<br>_closure requirements_ as measured by Djankov<br>_enness_ as reported by Chinn and Ito (2006).<br> polity2 from the Polity IV Project. Column<br>ed in the World Development Indicators. All<br>d time fxed efects. Percentage changes are<br>nd 99th percentile. Standard errors clustered|**credit**<br>**markets**<br>(5)|3.7033*<br>(2.0589)<br>2.0798*<br>(1.1430)<br>1,435<br>0.0963<br>YES<br>YES<br>-1.624<br>.477|
|---|---|---|
|diferent charac<br> quarterly aid d<br>e dimension of <br>low the median<br>ruption from t<br> presence of _dis_<br>_ital account op_<br>y the variable <br>GDP as report<br>ude country an<br>zed at the 1st a|**democratic**<br>**institutions**<br>(4)|3.3812**<br>(1.4936)<br>1.8541<br>(1.8224)<br>1,582<br>0.1131<br>YES<br>YES<br>-1.527<br>0.554|
|ountries with<br>n we interact <br>edian in som<br>ove versus be<br>
relationship.

In our setting, bilateral transactions between well-owners and neighboring farmers take one of two forms: spot contracts, in which groundwater is sold on a per-irrigation basis throughout the season, and long-term (i.e., seasonal) contracts, which specify ex-ante the price and area irrigated over the entire season. We develop a model in which spot contracts are fully state contingent and thus ex-post efficient, but, due to the classic hold-up problem, ex-ante inefficient. In particular, planting incentives of water-buyers are distorted. Long-term contracts, by contrast, are assumed immune from hold-up, but lead to ex-post

> 1The National Mission on Micro Irrigation, initiated in 2006, is perhaps the largest subsidy program of its kind in the world. When combined with complementary subsidies recently offered by several states, including Andhra Pradesh, smallholders may be eligible to defray up to 90% of a system’s cost.

> 2Due to the high degree of land fragmentation, groundwater markets are pervasive in India . The 2011-12 India Human Development Survey (Desai and Vanneman, 2011) indicates that, of the 83% of agricultural households nationwide that do not own a borewell, 37% purchase irrigation (groundwater).

> 3During the wet ( _kharif_ ) season, groundwater is typically used as a buffer against insufficient rainfall or shortfalls in surface water flows rather than as the sole source of irrigation.

1
RDHS wave, we compare fertility among Hutu and Tutsi. Of the nationally representative sample of women surveyed in the 1992 RDHS, 8.6 percent are Tutsi.<sup>14</sup> Descriptive statistics point towards differences across both groups: On average, Tutsi women have 0.58 fewer children, they marry 1.73 years later and give birth to the first child

> 12 A negative binomial model would be our preferred alternative, as it relaxes the assumption of equidispersion. Yet, this model has difficulties to converge with the data at hand and the estimate of ln(alpha) is large and negative, which prevents the prediction of probabilities. Following Long and Freese (2006), we resort to the Zero-Inflated Poisson model.

> 13 Although the Vuong test, fitted probabilities of the count variable, AIC, BIC, and log likelihood all indicate a slightly better fit of the Zero-Inflated Poisson model over the Poisson model, the latter allows for a more forward interpretation of the estimated coefficients and remains our preferred model.

> 14 This self-reported ethnicity variable very likely underestimates the Tutsi population in 1992. Historical accounts on ethnic violence before 1994 suggest that many individuals tried to hide their ethnic identity in order to avoid discrimination and persecution (Desforges 1999). This may have also been the case in the RDHS data collection.