rafmacalaba/datause-encoder-data huggingface dataset:
# A Data-Driven Approach for Early Detection of Food Insecurity in Yemen's Humanitarian Crisis

Steve Penson,<sup>a*</sup> Mathijs Lomme,<sup>a,b</sup> Zacharey Carmichael,<sup>a</sup> Alemu Manni,<sup>c</sup> Sudeep Shrestha,<sup>b</sup> Bo Pieter Johannes Andrée,<sup>a*1</sup>

**Keywords** : Agriculture and Food Security, Crisis, Early Warning Systems, Food Price Analysis, Vulnerability, Economic Monitoring

**JEL** : C01, C14, C25, C53, O10

1 a World Bank, b ACAPS, c FAO, *email: spenson@worldbank.org and bandree@worldbank.org. Funding by the World Bank’s Food Systems 2030 (FS2030) Multi-Donor Trust Fund program (grants TF0C0728 and TF0C0828) is gratefully acknowledged. This paper has been prepared as background to the Joint Monitoring Report (JMR), a multi-partner monitoring initiative in Yemen. We would like to thank the peer reviewers on the Quality Enhancement Review for the Yemen Data Driven Identification of Food Security Crises through the JMR, Alan Fuchs, Alexandra Christina Horst, and José Lopez; and the peer reviewers on the Decision Review meeting on Expansion of Real Time Food and Energy Price Monitor, Sergiy Zorya, Nick Haan, Kamau Wanjohi, whose comments helped improve this paper. The authors would like to thank members of JMR Core Development Team including teams at ACAPS, UNICEF, FAO, WFP, WHO and the World Bank. Thanks also to those who contributed to the contextual analysis and methodology development and review including Francesca Marini, Artavazd Hakobyan, Faiza Hesham Hael Ahmed, Nic Parham, Maliha Hussein, Oleg Bilukha, Elijah Odundo, Ismail Kassim, Peter Hailey, Dan Maxwell, Rebecca Semmes, Riham Abuismail, Fawad Raza, Gaurav Singhal, Hussein Gadain, Andres Chamorro, Alia Jane Aghajanian, Olaf De Groot, Emily Henderson, Patrick Vercammen, Felix Leger and Seb Fouquet. Finally, we would like to thank participants to the Joint Monitoring Report workshops including the Yemen Social Fund for Development (SFD) as well as humanitarian, development, and donor partners who have supported the research throughout. This paper reflects the views of the authors and does not reflect the official views of the World Bank, its Executive Directors, or the countries they represent.
PRE FACE Population projections for all countries are prepared by the Population and Human Resources Department of the World Bank every year. They are published first in summary form in the Bank's World Development eRort and later in greater detail as technical notes or working papers and, in alternate years, as a separate book.

This set of projections was prepared for World DeveloRment ReRort 1988. It appears for the first time in the Policy, Planning, and Research (PPR) Working Papers series. (Last year's edition appeared as a set of Population, Health, and Nutrition (PHN) Technical Notes.) No other publication is planned this year.

As was done last year, separate working papers cover each of the Bank's four regions: Africa (sub-Saharan); Asia (and Oceania); Europe, the Middle East, and North Africa (including the U.S.S.R.); and Latin America and the Caribbean (and Northern America). Combined, these papers cover all countries of the world.

The data format of these papers generally follows previous editions. Only minor changes have been made in the specific demographic indicators included.

We intend these papers to provide a convenient and up-to-date reference on the likely demographic future of each country, to facilitate the Bank's work of assisting them in planning more productive futures. We welcome comments on their utility, and how this might be enhanced.

Anthony R. Measham Chief Population, Health and Nutrition Division iii
<mark>al., 2013) for an ensemble of five Global Climate Models (GCMs) from the CMIP5 project (Taylor et al.,</mark> 2012).<sup>3</sup> <mark>Because Peru’s most important highway is along the coast, we also overlay the network with coastal flood data.</mark><sup>4</sup> <mark>But since there are no probabilistic data we simply identify the links exposed to coastal</mark> floods and do not calculate disruption losses linked to coastal floods.

<mark>Finally, some parts of Peru are susceptible to landslides triggered by high rainfall. Thus, we use landslide susceptibility maps provided by Ingemmet to associate landslides to flood events.</mark><sup>5</sup> <mark>In places where landslide susceptibility is high, we add landslide impacts to flood impacts for high return period floods.</mark>

# 2.3. Vulnerability of critical links

Here we define the vulnerability of a link as the economic consequences of a hazard event. It is therefore broader than structural damages to the infrastructure and includes impacts on both road users and the government or concessionaries.

Assessing the vulnerability of exposed roads to floods was the most difficult part of this exercise because of a lack of local data on critical parameters such as flood duration, structural damages, and traffic rerouting. Instead of working with best guesses we built several scenarios for each unknown parameter and relationship.

## _Flood duration_

Data on flood duration and on the relationship between flood duration and depth were not available. This relationship generally depends on many factors other than flood depth, such as water velocity and topography. Thus, we constructed simple curves based on information about past floods on the Carretera Central, the only highway for which data were available (Figure 2). We validated those curves with expert consultations.

> 3 The GCMs used are GFDL ‐ ESM2M, HadGEM2 ‐ ES, IPSLCM5A ‐ LR, MIROC ‐ ESM ‐ CHEM, and NorESM1 ‐ M. For this study, we used climate projections based on one representative concentration pathway (RCP), RCP8.5. 4 2013 Dartmouth flood observatory flood extend v2 data set.

> 5 http://www.ingemmet.gob.pe/.

7
The marginal effects differ between the models with and without the water quotas. Including the water quotas reduces the absolute values of all the significant marginal effects of precipitation in the model. The reverse is true for almost all the absolute values of the temperature marginal effects. Including the water quota reduces the sensitivity of farm profits to precipitation but increases the sensitivity to temperature. Similar results were found by Mendelsohn and Dinar (2003) in their estimation of the Ricardian model in the USA.

# **Forecasts**

We then apply three climate scenarios from Atmospheric Oceanic Global Circulation Models (AOGCM) for Israel (Mendelsohn and Williams 2004). We use the 2020, 2060, and 2100 forecasts of the Parallel Climate Model (PCM) (Washington et al. 2000), Center for Climate System Research (CCSR) (Emori et al. 1999), and Canadian Climate Centre Model (CCC) (Boer et al. 2000) to forecast percent changes in average annual farm profits in each of those decades. The models predicted an absolute change in temperature and a percentage change in precipitation for the country which was then applied to each farm. The climate coefficients in Table 3 are then used to predict the change in net income per hectare for each new climate. The forecasts in Table 5 demonstrate the importance of specifying the model correctly. The two models, with and without irrigation water, show different results. The forecasts with irrigation water quota in the model show lower absolute welfare effects. That is, by omitting water quota, the Ricardian model overstates both losses and gains from climate change scenarios.

Comparing scenarios over time reveals that the models are highly sensitive to temperature. According to the model with irrigation water quota, farm profits tend to increase at first with small changes in temperature across all three climate scenarios. Over time, as temperatures climb even higher, farm profits decline in all three climate scenarios. A very different picture, however, emerges with the model that omitted irrigation water quota. In this case, higher temperatures lead to increasing farm profits over time. The biased model predicts global warming is strictly beneficial to Israeli agriculture.

# **Conclusions and Policy Implications**

This paper estimates the economic effect of climate on Israeli agriculture using the Ricardian technique. An economic survey of farms throughout Israel was conducted for this study. Net annual income is regressed on climate and other control variables across 10
a heavy reliance on self-employment, often informal in nature, data on income and earnings are likely to carry significant measurement errors.

For these reasons, in this paper we utilize educational attainment as the relevant metric to analyze intergenerational mobility. There are several advantages to doing this. First, educational attainment is a strong proxy for the overall economic status. (See Black and Deveraux 2011 for a review of evidence particularly in this context.) There are positive and non-trivial returns to education in almost all labor markets. Even in the post-transition countries in our sample, where there is some degree of notional universality of education at least up to the secondary level, there is significant heterogeneity in attainments and there are positive returns to higher levels of educational attainments. Second, education is often also the most important conduit for the transmission or reproduction of societal privilege or advantage and as such, it is a useful way of looking at social mobility. Bowles and Gitnis (2002 JEP), for example, report that the “combined inheritance processes operating through superior cognitive performance and educational attainments of those with well-off parents ... explain up to three-fifths of total intergenerational transmission of economic status”. Finally, educational attainments are measured much less noisily than other direct measures of income in the setting we are studying.

Occupation is another candidate labor market characteristic that could be used. In the sociological literature, one’s occupation is linked very closely to status, prestige and class and intergenerational mobility from the lens of occupation would entail movement to occupations with different social prestige or class mobility. However, from a pure welfare perspective, it is often difficult to order occupations based on their economic returns. The most prestigious occupation may not be the best from a remunerative standpoint. Conversely, a relatively less prestigious occupation may pay well. For example, a stable job in the public service may be prestigious but not pay as well as an individual self-employed in an agri-enterprise. Due to this inherent rank-indeterminacy of occupations, we focus on education.

The LITS asks respondents about their education as well as the educational attainment of both their mother and their father. We construct parental educational attainment as the maximum of these two educational attainment levels. Attainments are recorded in eight discrete categories in the 2016 round of the LiTS. We consolidate these into four broad categories to make the distinction between each a bit more explicit and meaningful and also to facilitate comparability with earlier rounds of LiTS which will allow us to carry out a more in-depth analysis of the mobility measure. We also restrict the sample to those above the age of 24 to minimize potential life cycle biases arising from the inclusion of students still in the process of attaining their maximum desired levels.

27
<!-- Start of picture text --> Access to External Finance Participation in Training Programs<br>(1) (2) (3) (4) (5) (6) (7) (8)<br>MScoreoveralt 0.363°** 0.331°** 0.625*** 0.391***<br>(0.112) (0.110) (0.105) (0.113)<br>Log(Employees) (0.030)0.026 (0.030)0.035 (0.030)0.028 0.122***(0.027) 0.126***(0.027) 0.129***(0.027)<br>Log(Cap/Employees) 0.053°** 0.053*** 0.053*** 0.0003 0.0002 0.0004<br>(0.012) (0.012) (0.012) (0.012) (0.012) (0.012)<br>Log(Mat/Employees) (0.018)0.017 (0.018)0.020 (0.018)0.017 0.059°**(0.019) 0.060***(0.019) 0.061***(0.019)<br>DuighEdu —0.076* -—0.072* —0.071* 0.072* 0.069* 0.081**<br>(0.042) (0.043) (0.041) (0.038) (0.038) (0.038)<br>MScore yonitoring 0.164 0.350°**<br>(0.110) (0.114)<br>MScorers:7 0.272*** 0.250°**<br>(0.083) (0.085)<br>R-Square 0.01 0.12 0.11 0.12 0.04 0.19 0.19 0.18<br>N 727 700 700 700 727 690 690 690<br>Region Dummies Yes Yes Yes Yes Yes Yes Yes Yes<br>Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes<br>Notes: Columns (1) — (4) use a dummy indicating whether a firm was able to finance through different external sources (such as bank loans, newly<br>issued bonds, newly issued equity, etc.) as the dependent variable. Columns (5) - (8) use a dummy indicating whether a firm has participated in govt.<br>training program or not as the dependent variable. ‘MScoregyerai’ is the overall management score of a firm as calculated following Bloom et al.<br>(2018). ‘MScoreygonitoring’ 1s the data driven performance monitoring management score. ‘MScore74.7’ is the targets and incentives management<br>score. ‘Employees’ is the total employees of a firm. ‘Cap/Employees’ is the capital per employee of a firm. We use fixed assets of a firm as an indicator<br>for capital. ‘Mat/Employees’ is the per employee materials consumed bya firm. ‘DuighEdu’ takes a value 1 if more than 20% workers of a firm have a<br>higher education degree. Robust standard errors are in the parenthesis. Intercepts included but not reported. *,**,*** denotes 10%, 5% and 1% level of<br>significance, respectively.<br><!-- End of picture text --> <!-- Start of picture text --> 14<br>12<br>1<br>08<br>0.6<br>an<br>°<br>0.2<br>Overall Monitoring —_Incentivesandtargets Overall Monitoring —_Incentivesandtargets<br>Sdes per employee Profits<br><!-- End of picture text --> <!-- Start of picture text --> 0.7<br>0.6<br>05<br>04<br>03<br>02 !<br>0.1<br>-0.1<br>0.2<br>Overall Monitoring Incentivestargets and Overall Monitoring Incentivestargetsand<br>Innovation Adoption of sophisticated technology<br><!-- End of picture text --> <!-- Start of picture text --> 0.7<br>0.6<br>05<br>04<br>0.3<br>0.2<br>0.1<br>0<br>01<br>02 Overall Monitoring _incentivesand Overall Monitoring _Incentivesand<br>targets targets<br>Accessto external finance Training programs<br><!-- End of picture text -->
# **Table 1, continued: Manufacturing Firms in Core versus Non-core Regions**

||**Core**|**Non-**<br>**core**|**p-value**<sup>**+**</sup>|**Core/**<br>**Non-**<br>**core**|
|---|---|---|---|---|
|**Investment climate, continued**|||||
|**_Transport and tradefacilitation_**|||||
|Average time to clear direct exports through customs(days)|8.9|8.1|0.668|1.10|
|Average time to clear imports from customs(days)|13.2|10.0|0.142|1.32|
|Average time of inventoryof most important input(days)|26.8|21.0|0.039**|1.27|
|% of firms identifyingcustoms & trade regulations as a major constraint|18.5|11.4|0.043**|1.63|
|% of firms identifying transportation as a major constraint|18.6|19.1|0.907|0.98|
|**_Regulations and Tax_**|||||
|Senior management time spent in dealing with requirements of<br>government regulation(%)|14.4|8.1|0.000***|1.78|
|Average number of visits or required meetings with tax officials|1.3|1.1|0.066*|1.19|
|% of firms identifyingtax rates as major constraint|49.4|40.3|0.019**|1.23|
|% of firms identifyingtax administration as major constraint|42.0|29.7|0.003***|1.41|
|**_Permits and Licenses_**|||||
|Average time to obtain operatinglicense(days)|55.0|49.4|0.652|1.11|
|Average time to obtain import license(days)|41.0|27.1|0.312|1.51|
|Average time to obtain construction-relatedpermit(days)|147.0|61.1|0.119|2.41|
|% of firms identifyingbusiness licensingandpermits as major constraint|29.3|20.2|0.035**|1.45|
|**_Corruption_**|||||
|% of firms expected topayinformalpayment topublic officials|23.5|11.0|0.000***|2.13|
|% of firms expected togivegifts toget an operatinglicense|19.2|8.9|0.008***|2.16|
|% of firms expected togivegifts in meetings with tax officials|21.0|11.7|0.041**|1.79|
|% of firms expected togivegifts to secure agovernment contract|19.9|21.6|0.732|0.92|
|% of firms identifyingcorruption as a major constraint|46.8|38.8|0.041**|1.21|
|**_Access to Finance_**|||||
|Finance from internal sources(%)|59.4|56.4|0.570|1.05|
|Finance from banks(%)|24.4|26.6|0.664|0.92|
|Finance from trade credit(%)|10.6|9.1|0.707|1.16|
|% of firms with line of credit or loans from financial institutions|46.7|39.2|0.043**|1.19|
|% of firms usingbanks to finance investments|32.7|37.4|0.383|0.87|
|% of firms usingbanks to finance expenses|40.9|31.2|0.026**|1.31|
|%of firms identifyingaccess to finance as a major constraint|41.6|26.0|0.000***|1.60|

Source: Own calculations. Data: Enterprise Survey Indicators. NB:<sup>+</sup> t-test of difference in means. p*<0.1, p**<0.05, p***<0.01.

The percentage of manufacturing firms in the core identifying business licensing and permits as major constraints is 45 percent higher. On average, senior management has to spend more time in dealing with requirements of government regulation and the average number of visits or required meetings with tax officials is also higher. The percentage of manufacturing firms identifying taxes and tax administration as a major constraint is more than 20 and 40 percent 11
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33
# PRE Working Pager Series

||Aubth<br>o||Contact<br>for par|
|---|---|---|---|
|WPS633 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of Thailand|Virabongse<br>Ramangkura<br>Bhanupongse<br>Nidhiprabh|March 1991<br>a|R. Luz<br>34303|
|WPS634 Trends in Developing<br>Country<br>Exports, 1963-88|Bela Balassa|March 1991|WDR Office<br>31393|
|WPS635 Exchange<br>Rates and Foreign Trade<br>in Korea|Bela Balassa|March 1991|C. Cuskelly-Young<br>39413|
|WPS636 Economic Integration<br>In Eastem<br>Europe|Bela Balassa|March 1991|C. Cuskelly-Young<br>39413|
|WPS637 Poverty in Poland, 1978-88|Branko Milanovic|March 1991|WDR Office<br>31393|
|WPS638 Researching<br>the Trade-Productivit<br>Link: New Directions|y James Tybout|March 1991|D. Ballantyne<br>37947|
|WPS639 The High Cost of Protecting<br>Uruguay's Automotive<br>Industry|Wendy E. Takacs|March 1991|D. Ballantyne<br>37947|
|WJPS640 The Impact of Policy in African<br>Agriculture: An Empirical Investigat|William Jaeger<br>ion|March 1991|A. Yideru<br>34663|
|WPS641 Intertemporal<br>Substitution<br>in<br>Consumption:<br>Evidence<br>for Some<br>High- and Middle-Income<br>Countries|Karsten N. Pedersen|April 1991|M. Hileman<br>31284|
|WPS642 How a Change in Brazil's Sugar<br>Policies Would Affec**t**he World<br>Sugar Market|Brent Borrell|April 1991|A. Kitson-Walters<br>33712|
|WPS643 Regional Integration<br>among<br>Developing<br>Countries,<br>Revisited|Andras Inotai|April 1991|D. Ballantyne<br>37947|
|WPS644 Trade and Payments<br>Arrangements<br>in Post-CMEA<br>Eastem and Central<br>Europe|Constantine<br>Michalopoul<br>David Tarr|os<br>April 1991|M. Colinet<br>34698|
|WPS645 Poverty, Policy, and Industrializa-<br>tion: Lessons<br>from the Distant Past|Ben Polak<br>Jeffrey G. Williamson|April 1991|WDR Office<br>31393|
|WPS646 The Developmental<br>Effectiveness<br>of Aid to Af rica|Tony Killick|April 1991|S. King-Watson<br>33730|
|WPS647 Growth Rates and Aggregate<br>Welfare: An International<br>Compariso|Nanak Kakwani<br>n|April 1991|B. Rosa<br>33751|
|WPS648 Who Paid the Bill? Adjustment<br>and<br>Poverty in Brazil, 1980-95|M. Louise Fox<br>Samuel A. Morley|April 1991|WDR Office<br>31393|
The second specification uses all six rounds of data from 1998 through 2003 to estimate the differential effects of cumulative transfers on productive assets. Cumulative transfer quintiles are constructed for each household-wave observation based on the total transfer amount a household has accumulated at that point in time. These new variables are constructed as indicator variables for the quintile a household belongs to in each round. It is important to note that they are constructed on the distribution of transfers across all waves of data and not within each round. We then use the cumulative transfer quintile dummy variables to estimate the following specification: where _Qijt,p_ equals 1 if household _i_ in community _j_ in period _t_ falls in the _p_ -th quintile of the total cumulative transfer distribution. We specify the effect of cumulative transfers non-linearly so as to allow for jumps in investment due to the potential lumpiness of certain types of investment.

One potential concern with the specification in equation (2) is that actual total cumulative transfers are determined in part by household decisions to send their children to school. If households decide their children should work instead of going to school, then part of the income generated from work may be invested. In this case, we would obtain a biased estimate of the impact of the transfer amount on investment.

In order to correct for this potential endogeneity we use the potential transfer the household would have received if all eligible children were enrolled in school. To compute potential transfers, we take household composition and children’s enrollment status at baseline and apply the program rules, assuming the child progresses one grade per year with no school drop outs and no repetition. Our IV is therefore constructed from pre-intervention household demographic structure interacted with timing of incorporation into the program (which was determined randomly). Since the potential transfer amounts include no household behavioral response but are highly correlated with actual transfers, it is a valid instrument of the actual transfer amounts. We also control for the 9