rafmacalaba/datause-encoder-data huggingface dataset:
interregional trade from highly distorted initial estimates on interregional shipments. It shows the model may have great potential in the estimation and reconciliation of interregional trade flow data, which often are the most elusive data to assemble. In addition, solutions from the aggregated model exactly provides the data needed for a MRIO model and the solution from the extended model exactly provide the data needed for an IRIO model. This will greatly reduce the data processing burden in such analysis. Therefore, application of the model will further facilitate quantitative economic analysis in regional sciences.

Lessons from the experiments in this study shaped our view on approaches for applying the model to real data from a particular nation’s statistics. A logical conclusion is that widely available and disparate survey data on the economy, including commodity flows data and incomplete geographic data, can effectively be used to substantially narrow the margins for error in obtaining feasible solutions to interregional input-output systems. It is also evident that data on region-to-region commodity flows represent a limiting factor in determining the optimal sector dimensions to be solved in the modeling framework.

However, there are important questions not yet answered by the current study. First, test results from the data set aggregated from GTAP also show that our model’s ability to improve the IO transaction estimates of individual regions from national averages may be limited. Continuing research on the real underlying causes and means of improvement are needed to further enhance the model’s capacity as an estimating and reconciliation tool in building interregional production and trade accounts. Second, the relative importance of regional sector output, value-added, exports, imports and final demand as model input in the accuracy of a model solution is also not analyzed, and could be addressed with minor changes of the current model. Third, the approach employed in this study draws primarily from regional science and constrained matrix balancing literatures. How insights from economic geography theory can help define a bounded solution needs to be explored. Finally, the robustness of the model’s performance should be further tested using other data sets.

24
# Weather index insurance and shock coping: Evidence from Mexico’s CADENA program

Alain de Janvry, Elizabeth Ramirez Ritchie, Elisabeth Sadoulet<sup>_∗_</sup>

## **JEL Classification:** Q54, Q10, I32, O13, H84.

**Keywords:** Disaster Risk Financing and Insurance; Financial Institutions; Climate Change and Disaster Risk; Shocks and Vulnerability to Poverty

> _∗_ We would like to thank Artemio Couti ̃no of SAGARPA who provided administrative CADENA data, as well as information about program rules. We would also like to thank Alan Fuchs at the World Bank for contributing his knowledge of the program and facilitating contacts with SAGARPA officials. A UC Mexus small grant generously funded interviews with beneficiary farmers. This paper is a product of the Disaster Risk Financing and Insurance Program (DRFIP), a partnership of the World Banks 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` . The corresponding author may be contacted at alain@berkeley.edu
have tried more complex security designs, usually pay-through structures. These instruments may be too complex for the markets in which they were introduced. There are a number of countries still in a take off phase, including India, Mexico and South Africa. In these cases, there have been pilot issues after many long years of development. It remains to be seen whether they will be successful.

# **VII. Conclusions**

## **_1) Lessons Learned_**

# **a)** You can’t skip the basics

A country must have a sufficient legal, regulatory and primary market infrastructure in place before mortgage securities will take hold. A good framework is a necessary not a sufficient condition.

The sheer difficulty of developing infrastructure is one reason why there has been only limited success in introducing mortgage securities in emerging markets. The legal and regulatory complexities of mortgage securities and specialized institutions are formidable even in sophisticated developed economies (e.g., the Germans still don’t have the right tax structure). It is the case that many pieces of the puzzle have to be put into place before a picture emerges and in a number of countries, the introduction of mortgage securities is still a work in progress.

It would be easy if setting up an appropriate legal and regulatory framework were sufficient to establish a market. This is far from being the case. Many countries have devised a framework for securitization or mortgage bonds, but the time lapse between the creation of the legal infrastructure and the actual development of regular issues can be very long – between 4 and 10 years. The development of a satisfactory legal framework for mortgage securities is also often complex and time consuming (often requiring further amendments), notably in civil code legislative environments where the concept of a trust may be missing as a convenient, flexible and bankruptcy-remote special vehicle to issue mortgage backed securities.

In rare cases, technical flaws in the framework explain the difficulty (ex: Chile’s securitization law in 1999 where SPVs had to buy portfolios before issuing securities). More often, exogenous obstacles stunt the actual use of a framework, however well designed. For instance, in Poland the length of the lien registration process – up to two years in some jurisdictions and the courts in charge of the registration in the land book are overloaded and appear to be a strong impediment to the issue of mortgage bonds.<sup>20</sup> In

> 20 From 2000, when mortgage banks started operating, and 2002 only $60 million were issued.

33
We use the Hidalgo et al. (2007) indicator of proximity based on co-exporting probabilities in the world. A product that is co-exported with another product by many countries is considered an outcome-based measure of relatedness. This understanding reflects the idea that co-exporting has similar requirements in terms of institutions, infrastructure, resources, technology, or some combination thereof. For example, producing and exporting computers is expected to require competencies, technology, inputs, and production factors that are similar to those used to produce televisions. Hence, most countries should export both computers and televisions, yielding considerable proximity between the two products. Alternatively, because the necessary requirements for the production and export of products such as cheese or natural gas are very different from those for computers, the proximity value between these two products and computers is likely to be low.

Bilateral proximity (for each pair of products _i_ and _j_ ) is calculated based on the probability that countries with comparative advantage in one of the goods ( _i_ or _j_ ) also have comparative advantage in the other. Following Hidalgo et al. (2007), revealed comparative advantages (RCAs) are defined using the index in Balassa (1965). A country is said to export a good with comparative advantage when the ratio of the export share of that product in the country’s export basket to the analogous worldwide export share is greater than 1. We define Pr( _i|j_ ) as the ratio of the number of countries with RCA in both _i_ and _j_ over the number of countries with RCA in _i_ and Pr( _j|i_ ) as the ratio of the number of countries with RCA in both _i_ and _j_ over the number of countries with RCA in _j_ . We calculate proximity as the minimum of those two pairwise conditional probabilities:<sup>2</sup>

7
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 2: Regional log Formal Employment - 2000, 2010

|Change in log formal employment:||1991-2000|||1991-2010||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Panel A: RAIS<br>|||||||
|Regional tariff change|2.934***|2.659***|2.762***|4.983***|4.326***|4.380***|
||(0.842)|(0.825)|(0.760)<br>|(0.866)|(0.812)|(0.795)<br>|
|Formal employment pre-trend (86-90)|||-0.413**|||-0.216|
||||(0.199)|||(0.187)|
|State fixed effects (26)||✓|✓||✓|✓|
|R-squared|0.035|0.273|0.300|0.081|0.400|0.405|
|Panel B: Census<br>|||||||
|Regional tariff change|2.319**|1.667***|1.430***|3.330**|2.340***|2.074**|
||(0.939)|(0.563)|(0.537)<br>|(1.424)|(0.886)|(0.863)<br>|
|Formal employment pre-trend (86-90)|||0.224***|||0.262***|
||||(0.071)|||(0.099)|
|State fixed effects (26)||✓|✓||✓|✓|
|R-squared|0.113|0.542|0.563|0.097|0.555|0.568|

Positive coefficient estimates for the regional tariff change imply larger declines in formal employment in regions facing larger tariff declines. 475 microregion observations. Standard errors (in parentheses) adjusted for 112 mesoregion clusters. Census results weighted by the inverse of the squared standard error of the estimated change in log formal employment. Pre-trends computed for 1986-1990 period. *** Significant at the 1 percent, ** 5 percent, * 10 percent level.

64
average _telesecundaria_ -non- _telesecundaria_ inequality in math scores, and 33 percent of average inequality in Spanish scores. The effects of _telesecundaria_ education were less notable in the mid-range group, where _telesecundarias_ eliminated 0.4 percent of average inequality in math scores and 10 percent of inequality in Spanish scores (Table 8). These numbers are the minimum positive effect of _telesecundarias_ on improving student achievement.

For both groups of disadvantage, _telesecundarias_ appeared to be more effective in improving Spanish scores than in improving math scores. This finding is particularly interesting given that CONAFE’s primary school interventions appeared to be more effective for math education than for Spanish education. Data on secondary education lacks a modality identifier, so we cannot determine the portion of indigenous students in this sample. Since no highdisadvantage group could be constructed, however, it is likely that few or no indigenous schools appear in this sample, and that other factors must explain the difference in performance between Spanish and math exams. This finding contrasts with an IDB evaluation, which found that _telesecundaria_ education was effective in both math and Spanish instruction, but that _telesecundarias_ were slightly more effective in math than in Spanish (Castro, Wolff and García 2001).

It may be that self-selection bias of rural students biases analyses of _telesecundarias_ — perhaps the best-achieving rural students make extra effort to attend _telesecundarias_ , while lower-achieving rural students do not. If those students’ observable background characteristics reflect their propensity to learn in school, then the p-score matching should eliminate this potential bias. If observable characteristics do not identify student propensity to learn, however, then this bias may affect these results, and imply that the CONAFE students discussed here have 31
**Figure 15 – Land Cover Classification and Distance to the City Center – Addis Ababa**

<!-- Start of picture text --> Addis Ababa Land cover c. 2012 Addis Ababa Land cover c. 2012<br>(Percent) (Total)<br>100% 140<br>80% 120<br>100<br>60%<br>80<br>40% 60<br>40<br>20%<br>20<br>0% 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance to CBD (in kilometers) Distance to CBD (in kilometers)<br>Residential (Regular) Residential (Irregular) Residential (Regular) Residential (Irregular)<br>Commercial/Industrial Commercial/Industrial<br>Total Land Cover<br>Total Land Cover in sq kms<br><!-- End of picture text -->

**Source: Author’s calculations**

# **Dakar, Senegal**

Located on the Cap Vert peninsula the metro area of Dakar is home to roughly one‐third of Senegal’s population and 80% of the nation’s economic activity. Home to the 9th busiest port in Africa, the majority of the city’s economic activity is concentrated in the huge industrial zone that stretches east from the Port of Dakar to Rufisque, along Hann Bay.

The population of Dakar has tripled since the 1970s with much of this growth occurring near the periphery of the city (in Rufisque and Pikine). The rapid urbanization of this land stems from not only natural growth, but also a considerable influx of immigrants from neighboring West African countries drawn to the city in search of economic opportunity. But prosperity is not a guarantee/ competition is stiff. Roughly one‐third of Dakar’s population lives under the poverty line and almost half the population is under the age of 18.<sup>52</sup> Due to the land constraints from its geographic location, the spatial distribution of the land cover in Dakar is decidedly different than the African cities previously discussed in this paper. The map in Figure 16 shows spatial distribution of Dakar’s land cover. The commercial and industrial land is primarily concentrated directly in the core, and along the south coast of the peninsula. Regular residential land is located north of the city center, and extends west. Irregular residential land on the other hand, is mostly found in small pockets scattered throughout the city, particularly near the airport and near Rufisque. When plotted by distance from the city center, as shown in Figure 17, the irregular land cover classes in Dakar appear to peak at only 2 to 3 kilometers from the city < <mark>city center</mark> ?>. Limited to a peninsula, the

> 52 UNEP (2014) Assessing Urban and Peri‐urban Agriculture in Dakar.

> (http://apps.unep.org/publications/pmtdocuments/‐Building_Urban_Resilience__Assessing_Urban_and_Peri‐ urban_Agriculture_in_Dakar‐2014EN_UPA.pdf).

25
argued by some). Put differently, the issue of increasing women’s participation in the labor market may need to be thought through at the fundamental level of the growth model itself. A key policy question, in that context, is thus how public policy can promote greater flexibility in employment practices.

# **2.3.4 An Improved, but still Insufficient, Innovation Capability**

Morocco’s innovation capability has improved in recent years. However, innovation activities continue to face a number of obstacles (see Djeflat, 2012). In principle, the country has a policy to promote innovation that is inclusive: it includes measures to assist the different phases of the process and the various stakeholders (companies, researchers, etc.), according to proven measures at the international level (support startup of innovative enterprises, the creation of eco‐industrial clusters, and research‐industry collaboration on R&D projects), and through facilitation institutions, such as establishing a single administrative point for grants or financing of innovative projects via the Moroccan Center for Innovation. The number of researchers per thousand individuals in Morocco’s labor force is relatively high, compared to countries such as China, Brazil, Malaysia and Chile (see WIPO, 2011). Equally important, businesses fund almost 30 percent of R&D, and employ about 10 percent of R&D personnel.

However, policies to promote innovation are not performing well and the means employed remain relatively modest. R&D expenditure, which averaged 0.7 percent of GDP in recent years, is still modest. According to the Global Competitiveness index of the World Economic Forum (2018), Morocco is ranked 78 out of 140 countries for its innovation capacity. It is ranked higher for R&D expenditures (51) and quality of research institutions (51), but worse for international co‐inventions (90) and patent applications (97). Indeed, while the overall number of patent filings has increased significantly in recent years, the proportion of patents filed by Moroccan residents has actually declined, from about 32 percent in 2014 to less than 9 percent in 2017 (Figure 2.17).<sup>1</sup> The fact that non‐residents file the vast majority of patents in Morocco raises some fundamental questions about the dynamism of R&D activities by nationals. Similarly, according to the global innovation index of INSEAD‐WIPO (2018), Morocco is ranked 76 out of 126 countries. In terms of the innovation input sub‐index (institutions, human capital and research, and infrastructure), the ranking is 84, and in terms of the innovation output sub‐index (knowledge creation, impact, and diffusion), the ranking is 69. Among lower middle‐income countries, it is ranked No. 10 out of 30 countries, behind Vietnam, India, Armenia, and the Philippines.

21
farmers and policy makers who are interested in making adjustments in anticipation of future climate changes because continental scale adaptation measures would be misleading due to a wide variety of agro-economic conditions across the continent. The purpose of this paper is to provide differential adaptation measures suitable for each location across the landscape. We make use of the Food and Agriculture Organization’s (FAO) typology of Agro-Ecological Zones (AEZs) of Africa. Specifically, we focus on the choice of crops and irrigation in African cropland by 16 AEZs. The results of this analysis are then extrapolated from the sample of farms explored in this study to all of Africa using the AEZ classification of farms.

We begin by analyzing the choice of crops and irrigation as a function of climate and other control variables using a sample of over 9000 farmers from 11 countries in Africa who grow crops. We then use the FAO classification of African cropland into 16 AEZs to examine AEZ specific adaptation strategies. We use these zone specific adaptation strategies to see how adaptations would be applied across Africa.

The next section develops a simple theoretical model of crop and irrigation choice. We use a logit to explain irrigation choice and a multinomial logit to examine crop choice. In the following section, we describe the data used in this paper which is based on GEF/World Bank project in Africa and the FAO classification of Agro-Ecological Zones. In the rest of the paper, we present empirical results and simulation results of the impacts of climate change on these decisions based on two climate models. We conclude the paper with a summary of key results and a discussion of relevant policy insights.

# **2. Economic Theory**

Farmers are observed to make many management decisions on their farms. We assume that they make these choices to maximize profit. Through generations of learning by doing, most farmers know what choices work best on their farms. With changing conditions, of course, farmers must determine how to adapt, how to change these choices. Farmers are commonly observed adjusting to changes in government policy, market prices, availability of new varieties, and changes in access as these changes occur. This paper does not address the short term problems farmers face keeping up with rapidly changing conditions. Rather, we focus on long-term adaptations that farmers make after they have had time to learn about the new conditions and adjust to them.

3
Managing Financial Integration and Capital Mobility— Policy lessons from the past two decades Joshua Aizenman and Brian Pinto<sup>1</sup> JEL classification: E63, F02, F33, F36, G01.

Key words: Bretton Woods II, Financial crises, Macroeconomic trilemma, Self-financed growth, Self-insurance.

> 1 University of California and NBER; and the World Bank, respectively.  This paper is part of a broader investigation at the PREM Anchor of the World Bank on financial integration and economic growth in developing countries.  The views herein 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, or the NBER.
AMI **I** I VIAT1'I()NS SAU ADA Agricultural Develupment Authority S13P ADB3P Agricultural l)evelopment 13ank uf Pakistan SCD AMIRI Agricultural NMaciniiery Research histitute SI) APCOM Agricultural P'rices Commission SDIR AR] Agricultuial Research Institute SRPO ATI Agricultural Training Iistitute SSC AUP Agricultural University of Peshawar WAPDA AUF Agricultural university of Faisalabad AZRI Arid Zone Research Institute BAC Balochistan Agricultural College BARB Balochiistan Agricultural Research Board BOR 13oard uf Revenue CEC Cotton Export Corporation CollectoDistrict Collector CRD Crop Reprting Directorate DC District Council DRIP Drainage and Reclanation Institute of Pakistan EngD Engineering Directorate EPA EnvironmeLntal Protection Agency EW Economic Wing FBC Federal Bank of Cooperatives FID Fertilizer Import Department FS Forest School FSCD Federal S-eed Certification Department FRI: Farm-to-Market FWMC Federal Water Managernent Cell GCP Ghee Corporation of Pakistan IWARSI InternL ational Waterlogging and Research Institute KARINA Karrakoram Agricultural Research Institute for Northerr. Areas LG&RDD Local Government and Rural Development Department MFD Marine Fisheries Deparument MIINFAC Ministry of Food, Agriculture and Cooperatives MlRI Mona Irrigation and Reclamation Institute MLG&RD Ministry of Local Govermnment and Rural Development ,MOW&P Ministry of Water and Power NCBs National Commercialized Banks NFC National Fertilizer Corporation NFML National Fertilizer Marketing Limited OFWNMD On-Farm Watle Managenent Diretorale PACO Pakistan Agricultural Census (P-ganization PAD Provincial Agriculture Departnk nt PAED Provincial Agricultural Extens5n Department PARB Punjab Agricultural Research 1hoard PARC Pakistan Agricultural Resew-ia Council PADSC Punjab Agricultural Development and Supplies Corporation PASSCO Pakistan Agricultural Services and Storage Corporation PCCC Pakistan Central Cotton Comunittee PCD Provincial Cooperatives Depatment PDF Provincial Department of Food PEPA Pakistan Environmental Protection Agency PFD Provincial Forest Deparmnta PFI Pakistan Forest Institute PFsD Provincial Fisheries Departnent PID Provincial Irrigation Dcparlmcnt PIRI Punjab Irigation Rescarch Institute PLD Provincial Livestock Dcpartment PPPD Pakistan Plant Protection Department PSC Punjab Seed Corporation RECP Rice Export Corporation of Pakistan SARO Sindh Agricultural Rescarch Organization SASO Sindh Agricultural Services Organization Sindh Agricultural University State Bank or Pakistan Soil Conservalion Directorate Statistics Division Statistics Directorate Sindh Regional l'lanning Organization Sindh Seed Corporation Water anl Power Development Authority 17