rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8795

# **Abstract**

This paper evaluates whether mangroves can mitigate the impact of hurricanes on economic activity. The paper assembles a new, regionwide panel data set that measures local economic activity using nightlights, potential hurricane damages using a detailed hurricane windstorm model, and mangrove protection by mapping the width of mangrove forests on the path to the coast. The results show that hurricanes have negative short-run effects on economic activity, with losses likely concentrated in coastal lowlands that are exposed to both wind and storm surge hazards. In these coastal lowlands, the estimates show that nightlights decrease by up to 24 percent in areas that are unprotected by mangroves. By comparison, the impact of the hurricanes observed in the sample is fully mitigated in areas protected by mangrove belts of one or more kilometers.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice in collaboration with the Environment and Natural Resources Global Practice. 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/research. The authors may be contacted at oishizawa@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 of the World Bank or the governments they represent._ Produced by the Research Support Team
**Capturing Sensitive Information from Difficult-to-Reach Populations: Evidence from a Novel Internet-Based Survey in the Republic of Yemen**<sup>_∗_</sup>

By Sharad Tandon<sup>_‡_</sup> and Tara Vishwanath<sup>_§_</sup> Keywords: Conflict; Measurement; Yemen JEL Classification: D12; I31; I38; O10; O53

> _∗_ The views expressed here are those of the authors and may not be attributed to the World Bank.

> _‡_ The World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, standon3@worldbank.org.

> _§_ The World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, tvishwanath@worldbank.org.
Capitalizing on the harmonized data used in this study, we also estimate pooled regressions to examine the overall association between cognitive skills (as measured by reading proficiency scores and complexity of computer use on the job) and earnings across all of the eight countries together.<sup>24</sup> We estimate a country-level fixed-effects model (allowing for differences in average relationships between skills and earnings across countries) with robust standard errors.<sup>25</sup> Our results are presented in Table 9. Column 1 of the table presents results for reading proficiency scores with the usual controls; in column 2 we add schooling to the model with reading proficiency scores; and in columns 3 and 4 we estimate the relationship between the computer complexity dummy variables and earnings before and after controlling for completed years of education. Finally, in column 5 we include all cognitive skills in the model with schooling. As reported above, we find that the estimated effect of cognitive skills is lower in magnitude but continues to be positive and significant after controlling for education. In the full model, the returns to education are 4 percentage points, and hourly earnings increase by a similar magnitude for every standard deviation increase in reading proficiency scores. For computer skills, we find that as complexity of computer use on the job increases, and after controlling for schooling and reading proficiency, hourly earnings increase by 28 to 51 percentage points.

**Table 9. Pooled regression estimates for cognitive skills with country fixed effects, workers age 25 to 64**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Gender|-0.302***|-0.268***|-0.294***|-0.262***|-0.260***|
||
were targeted at reducing the impact of food price increases. Consideration should be made also of the potential shock to food prices stemming from reduced application of inorganic fertilizer and related reductions in local food production. Input subsidy programs have the potential to address issues fertilizer affordability, though studies have shown that the cost of input subsidy programs can outweigh their benefits (Jayne and Rashid, 2013) and that increasing fertilizer use alone may not be profitable due to associated expenses in acquiring the input as well as lack of complementary inputs such as improved seeds, irrigation and credit (Liverpool-Tasie, Omonona and Sanou, 2015).

This study has some limitations. Though the experience of farmers in six countries is represented, and represented at various points in time, the findings would be strengthened with the inclusion of additional country coverage as well as additional data points for each country. Data on the agricultural experience is not as well represented or as harmonized across countries immediately after the onset of the COVID-19 pandemic as it is in later years, which limits our ability to analyze precise time trends at the onset of that crisis. Additionally, given the mode of implementation of the surveys, through mobile phone, the sample may be biased towards farmers that are less remote and/or less poor. Though the use of survey weights addresses this concern in part, the findings may be seen as a lower bound as households without mobile phones, and whom price shocks may be expected to impact most severely, are excluded from the HFPS sample.

# **5. Conclusions**

This study examined recent trends in inorganic fertilizer use by smallholder farmers in six SubSaharan African countries over the 2018 – 2024 period in view of price increases following multiple global and local crises such as the COVID-19 pandemic, the Russian invasion of Ukraine and, in some countries, conflict and insecurity. Using high-frequency longitudinal phone survey data from these countries implemented around the same period, in conjunction with face-to-face surveys conducted on the same sample of households before the crisis period, this study documents
**Figure 10. Extensive Margin of Trade, Russia and Brazil, 1994–2011**

<!-- Start of picture text --> RUS BRA<br>250<br>40<br>200<br>30<br>150<br>20<br>100<br>10<br>50<br>0 0<br>Retained New<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: World Bank Trade in Services Database.

Note: Extensive margin is an indicator that assesses whether exports are growing because a country is exporting new goods and services or reaching new markets rather than exporting more of the same goods and services to the same markets.

34
the specific predictors that are used, as well as their estimated coefficients, are provided in Appendix A, Tables A.5 to A.7 for the TZNPSs 2019/20, 2020/21 as well as TA 1.

As discussed above, for comparison and robustness testing, we show the results using two estimation methods relying on different assumptions about the error terms in Tables 2–6. Method 1 uses the normal linear regression model, and Method 2 uses the empirical distribution of the error terms.

# _Across-year imputation_

Table 2 provides the estimates of poverty rates in the experimental TA 1 using the NPS 2020/21 as the base survey. This is the benchmark imputation run, where the most recent full-size household consumption survey is used as the base survey, and the experimental treatment arm using the standard full set of consumption items is used as the target survey, allowing the comparison of estimates to the observed “true” poverty rate (i.e., the poverty rate that is estimated based on the actual consumption survey data). The estimation results show that all Models 1–9 provide adequate poverty estimates that are not statistically significantly different from the true poverty rate of 21.0 percent. Estimates from Models 1–9 range from 20.4 to 23.1 percent, all within the 95 percent confidence interval (CI) of the true poverty rate [16.6, 25.4], and within one standard error [18.8, 23.2]. Estimation methods across all models thus perform similarly, without consistent ranking across models in terms of prediction errors or significance.

# _Across-year imputation with lighter questionnaires_

Similar results emerge in Tables 3 and 4 in the comparisons of the observed TA 1 poverty rate (21.0 percent with a confidence interval 18.8–23.2) and the predicted poverty estimates from the experimental TA 2 and TA 3 samples (using the NPS 2020/21 as the base survey). Table 3 (for 18
> -25 -

Figure 8 <!-- Start of picture text --> KOREA: HOUSING UNITS PER HOUSEHOLD<br>Ratio for Cities. 1960-1985<br>65.0<br>64.0'<br>61.0<br>60.0<br>t A k<br>59 .0 $<br>58.0<br>-l<br>$;7.0- \A/-<br>54.0-<br>s3.0-<br>0 5.0<br>i960 1965 1970 1975 1980 19 SS<br>a url-ts/hiousehsolds<br><!-- End of picture text --> 57. With the achievement of price stability and pursuit of financial liberalization, the financial markets are at a threshold in terms of housing finance policies. There are major opportunities for a large, decentralized and efficient mortgage market for the first time. analyses of the new flow-of-funds data which separate the household sector from the non-incorporated business sector shows the strong growth of household net financial assets which have more than doubled in five years under stable prices. Confirming the very slow decline of the "orepression ratio" between UMM rates and banking rates, households have stayed away from monetary (or deposit) institutions and favored the investment and finance companies. Life insurance has grown very rapidly. The demand for long-term assets is growing.

58. What the housing market least needs is increased government intervention. Housing finance liberalization and the rapid expansion of the mortgage market should contribute powerfully to the rapid demise of the chonsei which has outlived its usefulness and has such negative effects on the rental markets. The pursuit of public policies based on the fragmented thinking which has characterized past directed credit policies should be changed. For instance,, recent proposals for large public rental programs ignore the large benefits of genuine housing finance reforms and'should be integrated with them. Similarly, there is ample reason
Table A25: Robustness of impact on SRH knowledge index to differential attrition

|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|---|
|||Main|Trimming|treated|Im|puting cont|rol|IPW|
|||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1|(Safe Spaces)|0.31**|0.36***|0.30**|0.30**|0.30**|0.30**|0.31***|
|||(0.12)|(0.12)|(0.12)|(0.12)|(0.12)|(0.12)|(0.12)|
|T1|+Leaders|0.21*|0.45***|0.10|0.17|0.17|0.19*|0.21*|
|||(0.11)|(0.10)|(0.11)|(0.11)|(0.11)|(0.11)|(0.12)|
|T2|(Safe Spaces + IGA)|0.31***|0.48***|0.27**|0.29**|0.29**|0.29**|0.30**|
|||(0.12)|(0.10)|(0.
# **4. Data**

This paper uses data from two sources to analyze the behavior of corporate savings in Egypt: the ICA (Investment Climate Assessment data collected by the World Bank) and data for listed firms.

To create our listed firms datasets we use the data on the 50 largest companies listed on EGX (Egyptian Stock Exchange).<sup>3</sup> We excluded 14 companies that are classified as banks or financial holding companies as their savings have different meaning from those of industrial companies. We are left with a small sample of 36 companies with balance sheet and income statement data available from 2001-2008.

Each of the two datasets has its own advantages and disadvantages discussed below.

ICA data:

- Advantages:

- Data are available for over 1000 small, medium and large firms.

- There are 3 waves of survey (2003, 2005, 2007).

- It contains rich data on firm characteristics and access to external finance.

- There is exact data on investment into machinery, equipment and land (i.e. physical savings), as well as data on the stock of existing machinery and equipment.

- Disadvantages:

- There are no data for liquid assets, so we cannot measure financial savings directly.

- The data quality may be an issue and there are no reliable balance sheet data.

> 3 As of January 2010 there are 224 listed companies on EGX. We obtained from the Ministry of Investment data on the 50 most actively traded companies in 2008, with data going back to 2001.  Hence, the sample is biased towards the large companies.  Future work on this topic could explore results with a different sample.

6
and competition from the informal sector affect the firms’ decision to become exporters, which is a part of GVCs. Second, several constraints hinder women’s participation. Indeed, ILO (2021) argues that women’s access to finance and markets, their land and property rights, and business registration and informality are key issues to be addressed to increase women’s participation in the labor market. In addition, the World Bank’s Women, Business and the Law index providing the link between legal gender equality and women’s economic inclusion shows that the MENA and South Asia regions have the lowest index score (World Bank 2020b). The workplace indicator shows that, in many countries, the law does not prohibit gender-based employment discrimination that 17
costs for the apprenticeship were only one seventh of vocational school costs.30/ The results of Borus's research related to only two years posttraining labor market experience, and were thus silent about outcomes over the longer term. A more recent paper (Ziderman 1988), employing a similar methodology, followed up a larger sample of training course completers over a ten year period (including the three years of army service). Again, no significant earnings differentials were found between individuals who had attended vocational schools and between apprenticeship and industrial school completers, respectively.31/ On grounds of cost-effectiveness, these findings indicate the desirability of a redirection, at the margin, of vouth training for the skilled manual trades in Israel, away from vocational schools (principally the masmam track) towards non-formal job-related training modes.<sup>32</sup> / Yet it 30 Based on the then current regime for apprenticeships of one day of classroom instruction per week and five days training on-the-job; the subsequent move towards a system of three days classroom and three days training will have raised average apprenticeship costs towards those of the industrial school.

31 For a number of the follow-up years, completers of the teenager courses did sustain lower earnings than individuals emanating from vocational schools. Although not in line with those of Borus, this particular result is not altogether unexpected. Most of the students enrolled in these teenager courses came from very low socio-economic backgrounds, many were drop outs from secondary schooling and the courses, though full-time, lasted for up to a year only (compared with three to four years for the other training modes).

32 Indeed, in comparison with other countries, the ten per-cent of youth aged over 15 that is attached to the labor market (including those both studying and working) is inordinately low: for details, see the ILO Year Book of Labor Statistics.

- 44 -
. These models assume a convenient ad-valorem form of transportation costs, also known as the Samuelson iceberg. Therefore, the ad-valorem formulation became a natural default for transportation costs in the empirical literature. In addition, most datasets with the direct measure of transportation charges report the value but not the quantity of the imported goods.<sup>19</sup> This further strengthened the reasons to utilize the ad-valorem transportation cost as the per-unit measure could not be calculated without the information on the units.

The knowledge about the functional form of transportation costs has been greatly enriched by a seminal work by Hummels and Skiba (2004), who utilized the direct measures of transportation costs using the data from six countries (Argentina, Brazil, Chile, Paraguay, Uruguay, the US) importing from the rest of the world. They showed that the data do not support the widely assumed ad-valorem 19In the past, several studies have also employed measures of transportation costs constructed using matched partner c.i.f./f.o.b. ratios from the IMF Direction of Trade Statistics (DOTS) and UN COMTRADE data. Hummels and Lugovskyy (2009) found that the ratios from these sources are subject to significant measurement error and are unrelated to actual variation in shipping costs.

23
One possibility is that these firms are not very productive, making it unprofitable for them to expand. To explore this possibility, we take a closer look at the performance of the formal manufacturing sector following a two-pronged strategy. We begin by summarizing the recent evidence for Ethiopia and Tanzania documented in Diao et al. (2021). We then turn to UN Industrial Development Organization data to gauge whether the results for Ethiopia and Tanzania are indicative of what is happening in the other African countries for which we have data.

Diao et al. (2021) provide an in-depth analysis of the formal manufacturing sectors in Ethiopia and Tanzania. The core of their analysis rests on two newly created panels of manufacturing firms using formal firm census data, one for Tanzania covering 2008-2016 and one for Ethiopia covering 1996-2017. In both cases, the panel covers firms with 10 employees or more. In both Tanzania and Ethiopia, large firms, exporters and foreign firms all have significantly higher levels of labor productivity. These results are consistent with a very large theoretical and empirical literature on manufacturing firm performance in Africa and elsewhere. For example, see Bigsten and Soderbom (2006) for a summary of early research on the manufacturing sector in Africa based on the Regional Program for Enterprise Development and Harrison and Rodriguez-Clare (2010) for a summary of the literature on the impact of trade and foreign direct investment on firm performance.

However, the firm and sector-level results for Tanzania and Ethiopia suggest that the bestperforming firms are not absorbing employment. In Tanzania, labor productivity growth in large firms is on the order of 8 percent per year, and 13 percent per year for large exporters. By contrast, labor productivity growth in firms that start with fewer than 50 employees ranges between negative 3 percent and zero. By contrast, employment growth in these small firms is as high as 13 percent while employment growth averages zero percent in firms with 50 or more employees. In Ethiopia, the picture is somewhat better although labor productivity growth is not as high as it is
distinctions in the Indian context relate to establishment size. In manufacturing, the organized sector is comprised of establishments with more than ten workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act. Unorganized establishments do not pay taxes and are generally outside the purview of the state, thus approximating common definitions of the informal sector (e.g., Kanbur 2011).

For services, there is no simple legal distinction as in manufacturing. Service establishments, regardless of size or other characteristics, are not required to register and thus are all officially unorganized. There are various existing methodologies to comparably differentiate small-scale, autonomous establishments from larger employers which constitute the organized sector, as generally defined. We assign establishments with less than five workers and/or listed as an ―own-account enterprise‖ (OAE) to the unorganized sector. OAE enterprises are firms that do not employ any hired worker on a regular basis. The choice of five employees as the size cutoff recognizes that average establishment size in services is significantly smaller than in manufacturing. Using this demarcation, the organized sector makes up approximately 25% of employment in both manufacturing and services.

The organized manufacturing sector is surveyed by the Central Statistical Organisation every year through the Annual Survey of Industries (ASI), while unorganized manufacturing and services establishments are separately surveyed by the National Sample Survey Organisation (NSSO) at approximately five-year intervals. The survey years we employ are the most recent data by sector for which the young establishment identifiers are recorded. Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. Districts are administrative subdivisions of Indian states or territories. We use the provided sample weights to construct population-level estimates of total establishments and employment by district and three-digit NIC industry. Employment is formally defined as ―persons engaged
# 5. Using poverty and vulnerability maps to update RNU quotas

The Government of Senegal began developing the first RNU in 2015. The goal was to provide a unified information database to coordinate the various social projects and programs and serve as the foundation for targeting beneficiaries. The identification of households entering the RNU database relied on the poverty rates from the 2015 poverty map to provide commune eligibility quotas. Subsequently, communities provided lists of local households considered among the poorest in the area. Then, one by one, households with the lowest score in a proxy mean test were granted access to social programs until the commune quotas were met. Between 2015 and 2022, the RNU collected socioeconomic information on 550,000 households, representing nearly 29 percent of all households in the country. Because of the lower poverty rates in urban areas and in Dakar Region, the RNU includes a larger share of rural households.<sup>19</sup> In 2022, the government planned an expansion of the RNU to reach 1 million households. Recognizing that households are exposed to recurrent and severe shocks, one of the goals was to include households that were vulnerable though they might not be poor. Expanding the eligibility criteria to cover the poor, but also the vulnerable presents data and methodological challenges. Most methods for estimating vulnerability rely on household panel data, which are not available in Senegal. Furthermore, even if methods to estimate vulnerability from cross-sectional data exist, they fail to provide estimates at the level of geographical disaggregation necessary for targeting RNU regional eligibility quotas. The methodology described here represents a solution because it provides eligibility quotas that account for commune poverty rates as well as the probability that poverty rates will rise in the face of shocks.

How does the use of a vulnerability map affect the expansion of the RNU? While the implementation of the first RNU relied on commune poverty rates, the expansion of the RNU relies on commune vulnerability rates to determine the number of households in each commune that may be registered. Commune vulnerability includes the poverty