rafmacalaba/datause-encoder-data huggingface dataset:
Unfortunately, the monthly weather data from CRU do not allow to identify weather events, such as floods, droughts, and storms, which often depend on extremes within a short time period. Instead self‐ reported data from the commune surveys are used, which include questions about the five main emergencies in the last three years.<sup>15</sup> Based on this data, those communes that have experienced floods, droughts and storms within the 12 months before the survey are identified. Although the data also include information on crop diseases, fires and epidemics, these events are not included as they are supposedly less directly related to weather conditions.

Overall, more than a fifth of all rural households reports some weather event. The variation between zones and years is lowest for the occurrence of storms (Figure 9). In line with earlier findings suggesting that 2010 is the hottest and driest year, the self‐reported data indicates a higher occurrence of droughts in 2010 – mostly in the _Wet‐cold_ zone. Surprisingly, the share of communes reporting floods is highest in 2010 in the _Dry‐hot_ and _Wet‐cold_ zone, which is in contrast to the rather dry conditions in that year and the severe flood events observed in the _Wet‐cold_ zone in 2014. The self‐reported data does not correspond to the flood events experienced by communes in. Auxiliary regressions, however, reveal that self‐reported floods indeed are related to higher annual and seasonal rainfall levels, number of wet months and maximum rainfall (Table A.4).

Figure 9 _Weather events in rural communes by climate zone in 2010, 2012 and 2014_ <!-- Start of picture text --> a. Floods b. Droughts c. Storms<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014<br>.25 .25 .25<br>.2 .2 .2<br>.15 .15 .15<br>.1 .1 .1<br>share of communes affected share of communes affected share of communes affected<br>.05 .05 .05<br>0 0 0<br><!-- End of picture text --> Notes: Bars show the average share of communes in each climate zone being affected by the event based on self‐reported data. Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

> 15 These emergencies are listed by year and month, including crop diseases, storms, floods, droughts, fires and epidemics.

18
# **5. Hypothesis Evaluation**

# **5.1. The Jordan Compact**

The JC was signed in February 2016 between the country of Jordan and countries in the EU as a response to the Syrian crisis.<sup>4</sup> The JC includes measures to stimulate economic growth and create job opportunities in Jordan, particularly in sectors that can absorb both Jordanian workers and Syrian refugees, facilitating Jordan's access to European markets, through the relaxation of ROO for Jordanian exports (Abreha and Robertson, 2023). To achieve these goals, the JC focused in boosting the agriculture, construction, and manufacturing industries in Jordan by integrating Syrian refugees into 18 EU-focused Special Economic Zones (SEZs) to stimulate exports. In this study we focus on manufacturing industries, whose production in SEZs has tarifffree access into the EU market if: i) these industries have at least 15% Syrian employment, and ii) 200,000 work permits for Syrians in SEZs are issued.

The JC faced several challenges to implementation. The first problem is that targeted sectors are typically informal sectors, affecting the issuance of work permits. In 2017, only two manufacturing companies in SEZs were exporting to the EU and only 60,000 permits have been issued. The number of exporting companies increased to eleven by 2021. Second, commuting constraints impede integration. SEZs are far away from urban centers where the Syrian refugee population lives. The Jordanian government is aware of the commuting challenges faced by workers and is actively working on improving infrastructure to better connect urban centers and SEZs. Efforts to formalize sectors are also ongoing to facilitate better integration.

# **5.2. Approach**

To assess the true impact of the JC in boosting Jordan’s manufacturing exports to the EU we conduct a difference-in-differences regression analysis. Figure 19 suggests China is a natural control to use. Formally, we estimate the following regression equation E E 0 1 2016 3 2016 + ε ee ee ee ee EEtt = ββ + ββ ∗PPEEPPtt + ββ EEUU + γγPPEEPPtt ∗EEUU + λλXX where E E is the value of Jordan’s exports in manufacturing at year , 2016 is a dummy equal to one for year in 2016 and onwards, EU is a dummy variable equal to one if destination ee of exports is EU and zero if destination of exports is China in year EEtt , and tt PPEEPPtt 2016 is a dummy equal to one if exports flow to EU as of 2016. tt is the parameter of interest. is the set ee tt PPEEPPtt ∗EEUU of pre-trends controls. Robust standard errors at the country level. For the analysis, we exploit all ee γγ XX data available in WITS disaggregated by sector and destination country, ranging between 20022022.

> 4 Signed in February 2016 at the London Conference, which was hosted by the UK, Germany, Kuwait, Norway, and the United Nations.

17 | P a g e
# _4.5. Clearing & Settlement Infrastructure Reforms_

**A country specific vision should be developed for clearing and settlement infrastructure.** Only Morocco ́s CSD has the versatility required by wholesale and OTC government debt markets. All other countries require formulating a roadmap for a phased upgrade of their existing systems. An alternative option for some countries, such as Egypt, would be to follow the same strategy as with the RTGS system and develop a state-of the-art system. The rationale is the mutual dependency of both systems and the future need to have similar levels of IT and operational performance.

**Table 13: Stylized re-cap of bottlenecks and reform options for MENA public debt markets**

|**STRUCTURAL**<br>**CONSTRAINTS**|**High debt in buy-and-**<br>**hold portfolios**|**Excess liquidity**|**Concentrated demand**<br>**and dominance of**<br>**banks**|
|---|---|---|---|
|**STUMBLING**<br>**BLOCKS**| **Legacy issues:**<br>**illiquid and**<br>**uncertain valuation**| **Fixed exchange**<br>**regime**<br> **Lack of**<br>**predictability of**<br>**government cash**<br>**flow**| **Legacy of state owned**<br>**banks**<br> **Inefficient credit**<br>**markets**<br> **Captive demand**|
||_Shallow money markets_|||
|**CONSEQUENCES**|_Lack of competition in pr_|_imary markets_||
||_Opaque and thin second_|_ary markets_||
||_Dysfunctional mutual fun_<br>|_ds_<br>||
|**ACTIONS**<br>**DIRECTED AT**<br>**STRUCTURAL**<br>**CONSTRAINTS**| Predictable and regular<br>issuance<br> Consolidation of<br>maturities for<br>benchmarks<br> Balanced debt structure| Enhanced liquidity<br>forecasting<br> Market friendly<br>operations (effective,<br>accurate and timely)<br> CB and MoF<br>coordination| Competition in auctions<br> Revised framework for<br>mutual funds<br> Foster foreign investor<br>participation|
|**ACTIONS ON THE**<br>**MICROSTRUCTURE**| Liability management to<br> Repo framework<br> Price collection and disse<br> Hedging tools for interes<br> Upgrade the C&S infrast|ols<br>mination (fixing)<br>t rate and FX risks<br>ructure||

46
measures to assess impacts. This implicitly means that we do not take into account the potential spill-over effects of the increase in food prices for the food items included in the analysis on the prices for items not included.

A difficult question is whether increases in consumer prices do translate into increases in producer prices. At least two factors may dilute the impact of rising food prices on the incomes of farmers. First, production costs for farmers as well as transport costs are likely to be rising due to higher costs for oil-related products. Second, market intermediaries may be able in some cases to keep a large share of the increase in consumer prices for themselves without paying farmers much more for their crops. Because it is difficult to assess whether producers will benefit substantially from higher food prices, especially in the short term, we could consider our estimates obtained when considering only the impact on consumers as an upper bound of the impact of the rise in prices on poverty, and interpret the results obtained when factoring in a proportional increase in incomes for net sellers or producers as a lower bound of the impact.

The rest of the paper is structured as follows. Section 2 presents basic data on cereals production and consumption in Mali based on an analysis of food consumption and production using the 2006 ELIM survey for a number of food categories. In section 3, we provide estimates of the overall impact of higher food prices on poverty. A brief conclusion follows.

# **2. Patterns of food consumption and production**

# **_2.1. Food consumption_**

Cereals prices are the focus of this paper, and within cereals, we focus further on rice, millet and sorghum, and corn as these goods represent a large share of total consumption and have also experienced increases in prices in recent months. Table 1 provides summary data on rice consumption. Table 2 provides similar data for the consumption of millet and sorghum, and in table 3 data are provided on the consumption of maize.

We see important differences in the weight of the various cereals in the overall consumption basket of the population, as well as differences between various types of households in their consumption patterns. Consider first rice. For those households who consume rice, total consumption is at about 153,000 FCFA per year. Since ninety percent of the population consuming rice, the average household consumption of rice in the population as whole is only slightly lower at about 146,000 FCFA (about $300 at the current exchange rate). Knowing that the poverty line is at 149,000 FCFA per person per year, and that the average size
# 3. **Data**

We have selected our sample of countries and banks as follows. We want to include a number of Asian countries to see whether the gross safety net subsidies are different between countries that have been heavily affected by the East Asian financial crisis of 1997 and countries that have not. As a second control group we include a number of highly developed Western countries, which are expected to provide us a benchmark for a low degree of risk taking. To assess the impacts of the crisis we need data for the crisis years 1997-98 as well as for some years before the crisis. Within each country we restrict our sample to the number of exchange listed banks, since we need data on bank market capitalization and dividend yields.

We have collected data on daily stock market capitalization and annualized dividend yields from Datastream. Total deposits at year end, net loans at year end and ownership data are taken from BankScope. For missing observations we have consulted Bloomberg. Ownership data is collected as follows. We distinguish between four concentrated ownership forms. State-owned (the state, treasury, military or other government-institution owns shares in the bank), family-owned (a family or individual owns shares in the bank), company-owned (a manufacturing company owns shares in the bank), and owned by another financial institution (another financial institution owns shares in the bank). Banks that have no concentrated owners (dispersed ownership) are classified as being widely held. We define a number of ownership dummnies that are related to the above classification of ownership and that are based upon different thresholds of shareholdings. Our threshold for a majority shareholding is 50% of shares, and our threshold for a major shareholding is 20% of shares.

We have also collected country-specific data. GDP per capita figures and inflation rates are taken from International Financial Statistics (IFS) of the International Monetary Fund (IMF). As a proxy for the quality and enforcement of the legal system of a country we have collected figures from the Law and Order index of the International Country Risk Guide (ICRG), published by Political Risk Service. The Law and Order index ranges from 0 to 6, with a higher figures indicating a higher quality or less risk. Law and Order are assessed separately, with each sub-component comprising zero to three points. The Law sub-component is an assessment of the strength and impartiality of the legal 11
# **IV. Sensitivity Analysis**

## **Sensitivity to Investment Potential of the Regions**

In our central scenario, results differ across regions due to a significant extent to the inherited FDI of the regions—the more existing FDI, the more the regions are capable of attracting new FDI for the same elasticities. In this scenario we augment the assessment of how regions may adapt and attract FDI based on the ranking of their investment potential. For investment potential ranking we use the rankings of Expert RA, which we explain in Appendix B. We use the investment potential rankings to adjust a parameter in our model (etaf) that reflects the responsiveness (elasticity) of foreign investment supply to an increase in the price of their product in the region. We assign higher values of etaf to regions with above average investment potential rankings and conversely for low investment potential rankings. We present these results in the first row of new results in table 19, where we also indicate how the elasticity etaf varies across regions based on the investment rankings.

The principal result is that the estimated gains for Moscow, St. Petersburg and Tumen increase and the estimated gains for Siberia, Northwest, North, Central and the Far East decline. Despite smaller estimated gains in this scenario, Far East and Northwest are still estimated to receive above average gains. The results suggest that the gains for a region could vary considerably depending on whether it succeeds in creating an atmosphere conducive to investment.

## **Sensitivity to Results to a 50% Cut in the Barriers to Foreign Direct Investment**

We perform sensitivity analysis with respect to the extent of liberalization of barriers to foreign direct investment. In this scenario, we cut in the ad valorem tax equivalence of the barriers to FDI in the services sectors by 50 percent of the cut we executed in our central scenario. In this scenario, we allow for improved market access and a fifty percent cut in tariff barriers. We find that the gains to the economy are reduced to about 4.2 percent of consumption or 2.4 percent of GDP. St. Petersburg and the Northwest regions still are the regions that gain the most, but the rainking of gains among other regions changes slightly—reflecting different relative gains from FDI, versus tariff reduction or improved market access.

23
Matsumoto, T., & Yamano, T. (2011). Optimal fertilizer use on maize production in East Africa. In Emerging Development of Agriculture in East Africa (pp. 117-132). Springer Netherlands.

Nghiem, H. S., Nguyen, H. T., Khanam, R. and Connelly, L. B. 2015. “Does school type affect cognitive and non-cognitive development in children? Evidence from Australian primary schools”. _Labor Economics_ , 33, 55-65.

Onuk, E. G., Ogara, I. M., Yahaya, H., and Nannim, N. 2010. Economic Analysis of Maize Production in Mangu Local Government Area of Plateau State, Nigeria. PAT Journal, 6(1), 1-11 Ricker-Gilbert, J., C. Jumbe, and J. Chamberlain. 2014. “The Impact of Increasing Population Density on African Agriculture and Livelihoods: The Case of Malawi.” _Food Policy_ (Forthcoming).

Ricker-Gilbert, J., T. S. Jayne and E. Chirwa. 2011. “Subsidies and Crowding Out: A DoubleHurdle Model of Fertilizer Demand in Malawi.” _American Journal of Agricultural Economics_ 93(1): 26–42.

Sauer, J., Tchale, H., 2009. The economics of soil fertility management in Malawi. _Review of Agricultural Economics_ 31 (3), 535–560.

Sheahan, M., R. Black and T.S. Jayne. 2012. “Are Kenyan Farmers Under-utilizing Fertilizer? Implications for Input Intensification Strategies and Research.” _Food Policy_ 41:39-52.

Singh, I., L. Squire, and J. Strauss. 1986. _Agricultural Household Models_ . Baltimore: Johns Hopkins University Press.

Xu, Z. Z. Guan, T.S. Jayne and R. Black. 2009. “Factors Influencing the Profitability of Fertilizer Use on Maize in Zambia.” _Agricultural Economics_ 40: 437-446.

Mundlak, Y., 1978. “On the pooling of time series and cross section data.” _Econometrica_ , 46, 69– 85.

World Bank. 2007. “ _Malawi: Country assistance strategy FY2007-FY2010.”_ Washington, DC: The World Bank.

World Bank. 2015. _The cost of the gender gap in agricultural productivity in Malawi, Tanzania, and Uganda_ . Washington, D.C.: World Bank Group 31
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# **SUPPLEMENTARY APPENDIX**

_NOT FOR PULICATION_

**Resolving Puzzles of Monetary Policy Transmission in Emerging Markets**

Jongrim Ha, Dohan Kim, M. Ayhan Kose, and Eswar S. Prasad<sup>36</sup> November 2024

> * Ha: World Bank; jongrimha@worldbank.org. Kim: World Bank; dkim23@worldbank.org. Kose: World Bank; Brookings Institution, CEPR, and CAMA; akose@worldbank.org. Prasad: Cornell University, Brookings Institution, and NBER; eswar.prasad@cornell.edu. We would like to thank Evi Pappa, David Furceri, George Kouretas, Cristiano Cantore, and an anonymous referee for detailed suggestions. The authors are also thankful to Carlos Arteta, Jakob De Haan, and other participants at the ICMAIF 2024, the PIER-ADB International Conference 2024, and the World Bank internal seminars for helpful comments. We gratefully acknowledge support from World Bank RSB Funds. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the institutions they are affiliated with.

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sources show much more evidence of improvement in the former than in the latter. Unbundling administrative corruption, both sources exhibit reductions in bribe paying in utility connections, tax collection, and in importing and exporting, little change in the area of public procurement, and an increase in corruption in dealing with courts.

The various data sources disagree somewhat on which countries in the region have experienced the most progress. The BEEPS shows dramatic improvement in Georgia between 2002 and 2005, but no other source corroborates it. Expert assessments show more progress for other countries, particularly in the Balkans, and the WEF did not include Georgia in its 2002 surveys. There is some evidence of convergence in assessments of the various sources between 2002 and 2005, which can account in part for apparent inconsistencies across sources on which countries are experiencing improvements. To some extent, these discrepancies in changes – as with levels - may be explained by differences in how different sources define corruption. As Gray, Hellman and Ryterman (2000: 50) conclude from the BEEPS I and II data: “One cannot simply say that corruption is going up or down in individual countries, as we find a complex web of movements and mutations across different forms, features and dimensions of corruption. We need to be cautious and modest and to constantly recognize the full complexity of the measurement effort.” Gray, Hellman and Ryterman (2000: 40) attribute part of the decline in corruption measured in BEEPS I and II to optimism, perhaps associated with relatively strong economic performance. Continued favorable economic conditions may similarly play some role in the improvements measured by BEEPS II and III. Expert ratings can also be affected by recent economic performance: other things equal one might infer that 45
# Table 3: **Effects of Inpres Schools on Intergenerational Educational Mobility: Dependent Variable: Children’s Primary Completion**

||**Linea**|**r Model**|**Quadrat**|**ic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**<br>**(Relevant CEF)**|**Daughters**<br>**(Relevant CEF)**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|0.204***|0.216***|0.195***|0.197***|
||(0.041)|(0.044)|(0.042)|(0.047)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.022***|-0.014**|-0.038***|-0.017*|
||(0.006)|(0.006)|(0.011)|(0.010)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.002***<br>(0.001)|0.001*<br>(0.001)|
|R2|0.107|0.172|0.117|0.188|
|Observations|1199814|848350|1199814|848350|

Notes: The **Relevant CEFs** imply correct functional form, which are based on Table A.2 in the online appendix. Quadratic model is adopted following Becker et al. (2015). Robust standard errors are in parentheses, clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Sample corresponds to children born between 1957 and 1962 (comparison), or 1968 to 1972 (treatment). Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table. All coefficients are reported in Table A.3. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

40