rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 11004

# **Abstract**

The Middle East and North Africa is the most water scarce region in the world. Although studies have explored the effect of extreme weather events on agriculture, much less is known about the effect on businesses. Using geocoded firm-level data from the World Bank’s Enterprise Surveys across the Middle East and North Africa region, this study analyzes the effects of precipitation shocks on firm performance. The findings show that firms in areas that experience negative precipitation shocks have lower sales, labor productivity, and investment. The study tests a number of channels identified in the literature. Poor infrastructure, such as water and electricity outages, and lower access to finance that occur as a result of negative precipitation shocks are found to be key channels. Negative precipitation increases the share of temporary workers, possibly explaining the drop in labor productivity. A new channel of governance is also uncovered—negative precipitation shocks increase the time spent by senior management in dealing with regulations and expectations of solicitations of bribes. The results also show that firms respond to precipitation shocks by adopting greener practices, suggesting scope for adaptation in the region.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region and the Office of the Chief Economist, Planet Vice Presidency. 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://www.worldbank.org/prwp. The authors may be contacted at aislam@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
037|0.123***|0.172***|0.124**|0.164***|0.284***|0.325***|
||(0.036)|(0.033)|(0.025)|(0.038)|(0.059)|(0.029)|(0.029)|(0.025)|
|explained|0.068***|0.014|0.054***|0.115***|0.114***|0.083***|0.135***|0.122***|
||(0.013)|(0.014)|(0.011)|(0.019)|(0.031)|(0.016)|(0.016)|(0.015)|
|unexplained|0.041|-0.052|0.068***|0.057*|0.010|0.080***|0.149***|0.203***|
||(0.033)|(0.033)|(0.025)|(0.034)|(0.057)|(0.025)|(0.025)|(0.021)|

_Source:_ Calculations based on Cambodia Socio-Economic Surveys _Note_ : Standard errors are in parentheses. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.
Surveys,<sup>76</sup> Demographic and Health Surveys (DHS),<sup>77</sup> and Multiple Indicator Cluster Surveys (MICS).<sup>78</sup> The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner (UNSD 2014). There are only a few cases where modules or questions on forced displacement have been integrated into survey instruments (most notably in Somalia, Uganda, West Bank and Gaza, Azerbaijan, Bosnia and Herzegovina, Serbia, Ghana, as well as health surveys in Albania, Ukraine and Moldova).

There are several challenges associated with ‘mainstreaming’ forced displacement into household surveys: (a) there is huge demand for adding sector-specific or thematic modules to international surveys; (b) it is relatively difficult to convince national statistical agencies to modify their county-specific surveys; (c) disaggregating survey results by specific vulnerable groups (e.g. refugees, IDPs, migrant populations) requires these distinctions to be integrated into the sampling frame and sometimes there is insufficient information to do this or a lack of resources to expand the sample size; (d) lack of access to displacementaffected areas; and (e) difficulties associated with integrating an inherently political topic into less controversial surveys.

# **_Border crossings_**

The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration
assumed rate of return to education (Penn World table) is in fact relatively high. Similar findings emerge from the new World Bank Human Capital Index (HCI) report. The HCI for Serbia is higher than the average for its region and income group and what would be predicted for its income level. Yet its low labor market engagement, the increasing skills mismatch, and the large share of youth who are functionally illiterate make it clear that education quality and equity could be improved.<sup>15</sup> And in fact, according to the HCI, the country does not do so well when quality of education is taken into account.

15 Compared to 13.7 percent in 2014, in 2017 29.3 percent of companies reported problems with hiring new workers, mostly due to skills shortages.

25
derived from a combination of current survey and geospatial data with older, traditional censusbased small area estimates. These are important knowledge gaps given that subnational monetary poverty estimates play a significant role in resource allocation in many countries.

In addition, little is known about how the accuracy of predictions compares for target areas – the area at which we wish to produce estimates – that are covered by the sample and those target areas that lie outside the sample. This distinction is important for two reasons. First, in some contexts, the official household survey used for poverty measurement may not cover all target areas. If predictions for non-sampled areas are inaccurate, this would strengthen the case for both exercising caution when utilizing out-of-sample estimates for policy and for expanding survey data collection to cover all target areas. Second, comparing the accuracy of non-sampled and sampled areas sheds some light on the benefits of using Bayesian or Empirical Bayesian methods, which utilize the sample data as a prior to inform the estimates, rather than relying only on purely synthetic predictions.

Finally, the optimal statistical method to use when combining survey and satellite data remains unsettled. Bayesian methods effectively combine sample and big data in a well-established framework (Steele et al., 2017; Steele et al., 2021; Pokhriyal and Jacques, 2017). There are significant barriers, however, to their widespread use. These include computational complexity, a lack of familiarity among many practitioners, and required distributional assumptions on priors and hyperpriors that introduce additional complications. An appealing alternative is to use Empirical Best Predictor (EBP) models – a name coined by Jiang and Lahiri (2001) and Jiang et al. (2002) – in part because it is a well-established method in the existing literature on small area estimation.<sup>5</sup> This paper makes four main contributions. First, it assesses the gain in accuracy and precision, relative to direct survey estimates, from combining survey data with geospatial indicators to generate small area estimates of monetary poverty. Second, it
# **c. 100% price increase, medium-bound elasticity**

<!-- Start of picture text --> 4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Chile Ukraine Moldova<br>South Africa Bangladesh Indonesia<br>Russian Federation Bosnia and Herzegovina<br>Income Gains (%)<br><!-- End of picture text --> _Source:_ Based on national household budget surveys and estimates of Goodchild, Nargis, and Tursan d’Espaignet 2018.

_Note:_ Deciles are calculated based on household per capita consumption, excluding rent and lumpy expenditures.

Page 17
|Female _×_ Treat|-0.003|0.010<sup>_∗_</sup>|-0.009|-0.458<sup>_∗∗∗_</sup>|-0.062|
||(0.004)|(0.006)|(0.028)|(0.071)|(0.041)|
|Female|0.001|-0.002|-0.026<sup>_∗_</sup>|0.358<sup>_∗∗∗_</sup>|0.059<sup>_∗∗_</sup>|
||(0.002)|(0.002)|(0.016)|(0.044)|(0.026)|
|R-squared|0.010|0.012|0.145|0.956|0.739|
|Mean|98.95%|81.76%|59.59%|10.49|7.70|
|Observations|22,560|8,231|6,217|20,450|43,011|

_Source_ : Iranian Households Income and Expenditures Surveys (HIES). Note: The table presents estimated coefficients of _T reat × P ost_ 2007 ( _γ_ in equation (1)) in panel A, and estimated coefficients of _F emale × T reat × P ost_ 2007 ( _γ_ 1 in equation (2))) in panel B for enrollment rates and years of schooling. The sample for this analysis is children of the age group that officially corresponds to each level. In panel B, I examine gender differences by estimating equation (2). For college enrollment, I limit the sample to high school graduates (HSG) who aged below 24. The time period is 1995â2013. I control for age, age-squared
# Capital Markets, Temporary Migration and Entrepreneurship: Evidence from Bangladesh<sup>∗</sup>

Laurent Bossavie<sup>1</sup> , Joseph-Simon G ̈orlach<sup>2,3</sup> , C ̧a ̆glar Ozden<sup> ̈1,3</sup> , and He Wang<sup>1</sup>

> 2Bocconi University, BIDSA, CEPR, IGIER and LEAP

> 3CReAM, IZA

Originally published in the Policy Research Working Paper Series on _February 2022._ This version is updated on _January 2024._ To obtain the originally published version, please email prwp@worldbank.org.

> ∗We would like to thank the participants at the Migration and Organizations Conference at the University of Pennsylvania, Economics of Migration online seminar series for helpful comments and suggestions. We are also thankful to Rubaba Anwar for providing excellent field coordination support during the implementation of the survey, and to the Bangladesh Bureau of Manpower, Employment and Training for granting clearance to carry out the Bangladesh Return Migrant Survey (BRMS). Funding for this research was provided by the Rapid Social Response Multi-Donor Trust Fund, the DEC Research Support Budget (RSB), Knowledge for Change Program (KCP) and the Multi-Donor Trust Fund for International Trade within the World Bank.
# Early Warning, Early Action: The Use of Predictive Tools in Drought Response through Ethiopia’s Productive Safety Net Programme

Mareile Drechsler<sup>1</sup> and Wolter Soer<sup>2</sup>

**JEL codes:** H84 Disaster Aid, Q54 Climate, Natural Disasters and Their Management, Global Warming, I38 Government Policy, Provision and Effects of Welfare Programs

**Keywords:** Early Warning, Disaster Risk Management, Disaster Risk Financing and Insurance, Productive Safety Net Programme, Shocks and Vulnerability to Poverty

This paper is a product of the Disaster Risk Financing and Insurance Program (DRFIP), a partnership of the World Bank’s Finance and Markets Global Practice Group and the Global Facility for Disaster Reduction and Recovery, with funding from the UK Department for International Development. 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.

Many thanks, first and foremost, to Ato Tadesse Bekele, Engida Mena and Zinet Ahmet at the National Disaster Risk Management Commission (NDRMC) for their support of this project. Thanks also to John Aylieff, Samir Wanmali, Alemtsehai Alemu and Ezgimelese Tecleab at the World Food Programme (WFP) for their cooperation, provision of data and instructive comments. Stephen Anderson and Mark Lawrence at the Food Economy Group (FEG) provided helpful background information regarding the HEA methodology and LIAS tool. Many thanks are also due to Zerihun Mekuria at FEWS NET, who shared LIAS data used in this analysis and provided key background information on the sources and uses of LIAS data. Catherine Porter (Heriot-Watt University) conducted economic analyses and simulations of indicative drought-related poverty used in this paper. Background data and information were provided by Kelly Johnson, Segen Tewelde, Adeyabeba Mitiku Beyene, Elizabeth Farmer and Laura Campbell. Many thanks to Daniel Clarke and Ruth
# **Implementing 30x30:**

# **Lessons from Country Case Studies**

## **Susmita Dasgupta Brian Blankespoor David Wheeler**

_Funding_ : This research was funded by the Global Environment Facility.

Acknowledgements: We acknowledge the use of georeferenced species occurrence data provided by GBIF, which was downloaded following their citation guidelines

- (https://www.gbif.org/citation <u>guidelines#derivedDatasets). The GBIF occurrence data was</u> accessed via Google BigQuery on February 17, 2024.

We extend our gratitude to Pritthijit (Raja) Kundu for his valuable assistance with the graphics.

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 of the World Bank or the governments they represent.

_Keywords:_ Biodiversity Conservation, Protected Areas, Endemic S pecies, Kunming-Montreal Global Biodiversity Framework _JEL classification: Q57_
services than services provided cross-border (Francois and Hoekman, 2010). In our Krugman and Melitz versions, our FDI firms interact under monopolistic competition with endogenous productivity effects in the use of their outputs through the Dixit-Stiglitz mechanism from additional varieties of business services. As such, our model incorporates the economic theory and the substantial and growing empirical literature based on firm level data showing that foreign direct investment and the wide

> 2 See Caliendo and Parro (2015) and Caliendo _et al_ . (2017).  Limão (2016, pp.316, 319) concludes that he believes that the small estimates of the gains from PTAs from these simulation studies are explained by the fact that they only consider the tariff impacts of the PTAs.

> 3 The estimates of Hummels _et al_ ., (2007) and Minor (2013) show the trade facilitation costs alone (what they call the “time in trade” costs) are greater than tariffs as an obstacle to trade for most countries. The World Economic Forum (2012) found that the cost of trading is a more important obstacle to trade development than trade policies.

> 4 Arkolakis _et al_ . (2018) have recently produced an important single sector, heterogenous firms model on foreign direct investment, but do not compare the impacts to a Krugman or Armington style model. The model of Bernard, Jensen, Redding and Schott (2018) is a general theoretical model of heterogeneous multinational firms.

2
for market capitalization are negative in the high-income sample. At the same time, we recognize that this does not necessarily rule out a causal link. Our process for indentifying potential instruments simply failed to come up with candidates that worked well for stock market capitalization. In short, the positive coefficient for that variable in the OLS regressions might or might not reflect a causal link with firms‘ labor growth but our analysis did not uncover an instrument that could reveal the ̳true‘ relationship.

We also present LIML results for private credit/GDP in Appendix C. Although the statistical tests from the GMM models supported the relevance and validity of our instruments (implying that recourse to LIML is not necessary), the LIML results for low-income countries are similar to the GMM results, though significance levels are a bit lower. Still, the one result that is consistent across all estimation techniques in this section is the positive, significant relationship between private credit and firm labor growth in low-income countries.

# **C. Rajan-Zingales Methodology**

Another widely used method for identifying a causal link between financial sector development and growth comes from Rajan and Zingales (1998), focusing on how industry-level growth rates vary with both dependence on external sources of finance and the level of financial sector development in a country.<sup>14</sup> The key identifying assumption is that capital markets in the United States are relatively frictionless, and thus an industry‘s reliance on external finance in that country is a reliable indication of its true technological demand for external financing. Industrylevel dependence on external finance computed from U.S. data is therefore the key exogenous source of variation in their analysis.

The equation that we estimate is: where is the percentage change in the number of workers employed by firm _i_ in industry _j_ in country _k_ over the two years prior to the enterprise survey, as described above. represents

> 14 See, for example, Fisman and Love (2007) and Kroszner, Laeven, and Klingebiel (2007)
better education, higher incomes, and a higher likelihood of working than respondents to the mobile phone survey.<sup>16</sup><sup>_,_17</sup> These differences suggest that one of the most important caveats of analyses using internet-based surveys is the need to account for the overrepresentation of certain groups in the survey modality. Given the difficulty of trying to accurately measure the income and employment status of the population to properly re-weight the surveys to make them more representative of the general population, we focus on ensuring that all differences between the internet and mobile phone surveys are qualitatively identical even when restricting the samples of each survey to be more comparable. For example, in our analysis of estimating the difference in responses to sensitive questions between the two modalities, we further check whether that difference survives restricting both modalities only to lower income households, those located in less urban governorates, older respondents, and so on. Similarly, if one were to use the modality to estimate changes in response to large shocks, it is important to separately estimate the change for households that are over and underrepresented in internet-based surveys.

And lastly, we also have to address the possibility that the significant non-response rates in the internet-based surveys might result in significant non-observable sample selection that also limits the generalizability of the results. Although the response rates of random digit dialing mobile phone surveys similar to the one used here are also very low<sup>18</sup> , it is important to better understand and verify that the low response rates are not

> 16The largest difference in the table is in the share of non-working respondents who are not currently looking for work. Given the much smaller difference in the shares of households that are working, the difference is driven by the omitted category- a larger share of internet respondents who are not working and are not looking for work (e.g., students, retired, etc.). Although this is difficult to precisely interpret, it could be consistent
productivity of green/clean investment resulted from environmental regulations in China. Cao et al. (2021) report that regulated firms are more motivated (or obligated) to invest in green/clean technologies to meet existing environmental regulations. Using sample data from environmentally dirty industrial firms (e.g., coal mining and washing, oil and gas mining, petroleum processing, coking and nuclear fuel processing, power, thermal production and supply, and gas production and supply) for the 2000-2007 period, they find that investments in green/clean technologies led to an increase firms’ productivity. Using firm-level panel data of Chinese industrial enterprises for the 1998-2007 period, Peng et al. (2021) provide the evidence of a positive relationship between the deployment of new green/clean technologies resulted from flexible environmental regulations and productivity of the firms.

10
compliance at the level of individual shipments. We show that the agency inspects infrequent shipments more often than other shipments, even after controlling for available indicators of underlying risk. We argue that this behavior likely represents a response to ambiguity about expected compliance rates among infrequently observed shipments.

We formalize these insights using a tractable form of a Choquet Expected Utility framework that represents ambiguity with a single parameter. Under our assumed objective function, search plays two roles: detecting non-compliant shipments and discouraging noncompliant behavior on unsearched shipments. The presence of ambiguity unsettles the inspecting agency’s belief about the efficacy of the discouraging role of search. The agency that is pessimistic about compliance reacts by increasing search on those shipments in which there is more ambiguity. Both our empirical evidence and discussions with operational experts support this view: inspecting agencies mitigate ambiguity with additional search.

We develop a quantitative model to illustrate, numerically, the role of ambiguity in a realistic parameterization of the distribution of search activity and firm-level compliance. Counterfactual changes in ambiguity - which are plausibly attributable to IT - change the distribution of search activity and the level of search activity needed to maintain compliance. Our results are indicative of the kinds of benefits that accrue to countries that successfully implement risk management in their border inspection agencies.

# **Appendix A: Summary statistics**

Summary statistics for the 2013-16 data are presented in Table 4. The mean annual number of import shipments per combination of product, origin country, and importing firm is 5 _._ 55 and the mean number of inspections per triplet is 0 _._ 40. The mean observation was inspected 7 _._ 8 times per 100 shipments. 54 percent of _ofk_ triplets had not been observed in the previous year. The mean observation saw 4 _._ 23 lagged shipments in the previous year.

Among the risk control variables, we see that the mean lagged inspection rate is 3 percent and the average logged unit value is 7 _._ 77 (Serbian dinars per kilogram). The mean
# **Annex 1: Global Supply Chain Stress index for selected maritime regions (in TEUs)**

<!-- Start of picture text --> Black Sea China Sea<br>200000 800000<br>150000 600000<br>100000 400000<br>50000 200000<br>0 0<br>West Med Europe North America West Coast<br>200000 1200000<br>1000000<br>150000<br>800000<br>100000 600000<br>400000<br>50000<br>200000<br>0 0<br>Persian Gulf East Med<br>250000 250000<br>200000 200000<br>150000 150000<br>100000 100000<br>50000 50000<br>0 0<br>Red Sea North America East Coast<br>30000 600000<br>25000 500000<br>20000 400000<br>15000 300000<br>10000 200000<br>5000 100000<br>0 0<br>Source: World Bank.<br>2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01 2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01<br>2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01 2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01<br>2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01 2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01<br>2016-01 2016-08 2017-03 2017-10 2018-05 2018-12 2019-07 2020-02 2020-09 2021-04 2021-11 2022-06 2023-01 2023-08 2024-03 2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01<br><!-- End of picture text --> 22