rafmacalaba/datause-encoder-data huggingface dataset:
income of all census respondents is comparatively lower at 124 USD. Literacy among baseline respondents in Panel B is 64 percent, more than 10 percentage points higher than in the census. Further, 80 percent of baseline respondents report being self-employed, compared to only 32 percent in the census. The use of financial services is again higher among baseline respondents with 52 percent already having a savings account and virtually everyone reporting to save informally. In contrast, only 46 percent of census respondents have another savings account. The baseline sample in Panel B is thus richer on average, more educated, and more actively engaged with financial services than the full census sample in Panel A.

# _2.2 Treatments_

After the baseline data were collected, individuals in the treatment groups were offered the opportunity to open a Sukaliku account, which has a number of features designed to appeal to the poor: no opening or account maintenance fees, no minimum balance, and no limit to the number of deposits and withdrawals to and from the account (although there is a 500 CFA or 83 US cents minimum amount for deposits and withdrawals). Account balances earn interest at 4 percent per year and, importantly, there are no fees on deposits at either location or withdrawals at the branch. During the study period, a holder of a Sukaliku account could make up to 3 free withdrawals per month at an agent, and for additional withdrawals, they were charged a fee to withdraw money that depended on the amount of the withdrawal. Online Appendix OA1 provides more details about the fee structure that clients face during the study period.<sup>10</sup> All four treatment groups received information about the Sukaliku account features. The monetary incentive of 1,500 CFA (2.8 USD) that individuals in two of the four treatment groups were offered was only received if the savings account was opened before a certain date and was transferred as balance to the newly opened account. This monetary incentive was covered by Microcred. All individuals in the treatment groups were encouraged to open the Sukaliku account at an agreed day and time, but they had two weeks from the day of the visit to do it. They were handed a card with the address of the agent or branch, date, time and necessary documentation to open the account.<sup>11</sup> Online Appendix OA2 provides examples of

> 10 Account opening and minimum balance fees were also waived in Dupas and Robinson (2013), Dupas et al. (2016), Schaner (2016), Prina (2015) and Brune et al. (2016). Only Prina (2015) also waived withdrawal fees. In Dupas et al. (2016, 2018) or Banerjee and Duflo (2011), for example, transaction fees, and in particular those associated with withdrawals, have hindered the use of savings accounts. 11 The documentation required to open a Sukaliku account in both locations is the national ID card and a telephone number.

9
<!-- Start of picture text --> .2 .3 .4 .5 .6 .2 .3 .4 .5 .6<br>Gini Gini<br>1 1<br>.8 .8<br>.6 .6<br>resilience<br>.4 .4<br>social inclusion<br>.2 .2<br>0 0<br><!-- End of picture text --> Note: Fitted values and 95% confidence interval shown Source: Social Sustainability Global Database 2022 Social sustainability should therefore not be interpreted as just another strong correlate with high income and low poverty, whereby rich countries are inevitably more socially sustainable. Nor should we expect that poor economies inevitably fail in areas of inclusion, cohesion, resilience and legitimacy. Figure 6 illustrates this complexity. Rwanda, a low-income country, Lao PDR, a lower middle-income country, and Namibia, an upper middle-income country, all display middling levels of social inclusion. The level of social cohesion in Ethiopia is similar to that of Ecuador, despite the GDP per capita of Ecuador (US$ 5,600) exceeding that of Ethiopia (US$ 936) by more than five times in 2020.

Post-conflict countries can also display high scores in some surprising aspects of social sustainability. Differences are also wide within regions and despite similar socio-economic profiles. Process legitimacy in three countries that have recently undergone civil conflict—Sierra Leone, Philippines, and Lebanon—is well above that in Cambodia, which underwent a genocide several decades ago. Ghana and Senegal meanwhile have far higher levels of process legitimacy than Sierra Leone, despite also being West African countries.

In fact, top performers in our sample in terms of inclusion, cohesion, resilience and legitimacy vary in terms of income level, population size, country size, region and (recent) history of conflict. See table 3. By contrast, countries recently or currently afflicted by war, civil conflict and terrorism, recurrent natural disasters, and autocratic regimes are consistently among the worst performers.

17
Comparing insured losses to total losses that result from catastrophic events further evidences the "low" quantities of risk transfer at both the international level and that of the Caribbean region. Graph 2.3 shows that a small portion of total losses are effectively insured. As already mentioned, Hurricane Andrew and the Northridge Earthquake totaled over US$45 billion in 1997 dollars, with the insured component amounting to US$30 billion. In terms of insurance coverage, oily 3.8 percent of the damages from natural catastrophes between 1985 and 1999 in Latin America and the Caribbean were insured, ranking it last among the regions of the world.<sup>9</sup> Graph 2.3: Insured and Uninsured Losses from Weather-Related Natural Disasters <!-- Start of picture text --> 100)<br>so -<br>80t<br>70<br>60<br>Sgc Uninsured<br>40 4<br>30<br>20<br>sharofGPinteCrbenrgoiscmaaltotaofidsraid counsuriesdTal<br>10-<br>1980 1981 1982 198 1984 1985 1988 1987 1988 1989 199 1991 1992 1993 1994 1995 1996 1997 199 1999<br><!-- End of picture text --> <!-- Start of picture text --> Souge: Munich Re; Guy Carpenter; World Bank Reporte in Pollner 200 1, p 33.<br><!-- End of picture text --> Prices of catastrophe insurance are "high". Non-life insurance penetration (written premiums as a share of GDP) in the Caribbean region is comparable to that of industrialized countries (Table 2.1). In the Caribbean region, it is estimated that about half of non-life premiums (equivalent to 1.5 percent of GDP) corresponds to coverage against natural hazards (Box 2.3). However, as shown above, the quantity of risk transfer is "low" in the Caribbean region. High insurance penetration and "low" quantities of risk transfer indicate that the prices of catastrophe insurance in the Caribbean region are "high". The fact that catastrophe insurance premiums are estimated to represent about 1.5 percent of GDP while average (insured and uninsured) losses per annum accounted for only about 0.5 percent of GDP during the period 1970-99 (whereas they would be equal under "fair" pricing) further confirms this finding.

> 9 Insured losses in other regions during the same period were North America, 34.5 percent; Europe, 26.7 percent; Africa, 8.9 percent and Asia 4.3 percent. Data are from Swiss Re. Reported in Charveriat, 2000, p. 41.

13
35 labor. A major shift occurred in 1971 with the establishment of the New Economic Policy (NEP), which was designed to foster ethnic Malay ownership of businesses within Malaysia--a measure that had important consequences for FDI. A distinction was made in the amount of foreign ownership allowed, depending on whether a project was import-substituting or export oriented. Thus, new importsubstituting FDI projects were required to have majority ownership by ethnic Malays at the outset, and any forei&u ownership was required to be phased out by 1990. Majority foreign ownership was still permitted in export-orienLed projects, but foreign ownership overall had to be reduced to 30 percent of total FDI stock by 1990. In 1986 these NEP ownership restrictions were relaxed, at least temporarily.

The regulatory framework for FDI in Thailand is relatively liberal, except that there are significant sectoral restrictions on FDI. Majority foreign ownership is not allowed in some manufacturing sectors and most service businesses. in sectors that are freely open to foreign investors, however, there is freedom of profit remittance and capital repatriation. In addition, Thailand's industrial policy includes a generous package of incentives that are equally available to foreign investors (in permitted industries) and domestic investors.

In Korea a liberal FDI policy regime has prevailed since 1984. Under the system in effect since then, foreign investment is allowed in most sectors, the percentage of foreign ownership is generally not limited, and capital and profits can be freely transferred. Prior to the liberalization measures in the early 1980s, however, there were extensive restrictions on FDI.

To assess the effects of these policies on FDI it is helpful to isolate several major dimensions of the policies.<sup>18</sup> One obvious dimension is the degree to which the host country is hospitable to FDI--a dimension that varies considerably both across countries and over time for any one country. In table 5-3 the eleven countries are listed with the degree of hospitality of their FDI polices during the two-and-a-half decades covered by the country studies, and then separately for the period since the early 1980s.

As examples of extreme opposites, Brazil, which has generally adopted hospitable FDI policies, has experienced disproportionately large amounts of FDI flows, while India nas been the recipient of disproportionately low levels of FDI as a result of its relat:vely inhospitable policies. The relationship between a hospitable policy and FDI flows, however, is only approximate; the overall attractiveness of a host country's FDI policies depends on other dimensions as well, particularly their transparency and their steadiness (see Becsky, Lee, and Ordu 1990 and Frank 1980).
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15

# Box _5:_ ELEMIENTS OF ORGA TIZATION DESIGN

Development is a function of policy, institudtons and programs. Policy reflects the<sup>values of the</sup> society and the exigencies of the global economic clirnate. Isdtutions are shaped by<sup>history, culture and tradition.</sup> Programs (and projects) have technical, economic, financial and organizational dimensions which are routinely subrnited to review and appraisal by decision making bodies. In this<sup>context, organizational design</sup> feamres need to receive the same kind of rigorous scrutiny as other aspects. This<sup>is a multdisciplinary arL The</sup> matix below provides analytical elenents to assess organizational options.<sup>Since an effective organization requires</sup> compatibility between the overall cultural content and internal management<sup>and orgaiizational variables, consisteny</sup> in the timigular mix of instruments and characteristics should nonnally<sup>be soughL For example, a highly</sup> cencralized organization would not be suitable for the management of a<sup>creative organization operating in a highly</sup> competitive environmenL

# Graph: Elements of Organlzational Analysis

# INSTITUTIONAL ENVIRONMENT

VALUESf Liberty Equality Fraternity POLICY: Growth Equity Sustainability GOVERNANCE: Decentralized Federated Cenbalized ECONOMY: Market Mixed Planned

## ORGANIZATIONAL VARIABLES

FOCUS: Autonomy Cooperative Control TYPE: Private Ngo Public MODE: Independent Inter-dependent Dependent AT'RIBUTES Responsiveness Relevance Rigor DESIGN: Sruchude Process Culture ._______ INTERNAL MANAGEMENT TOOLS. Incentives Recognition Penalties STYLE: Motivation Persuasion Charisma SKILLSCreativity Flexibility Reiability ATITTUDESTolerance Understanding Trust FUNCTIONSDelegation Coordination Direction STANDARDS: Convenience Quality Coast
The Table also shows the very slow growth in exports of non-manufactures by Sub-Saharan African countries, explained in good part by weak prices for their main raw material exports. Other studies (Martin, 1999) have shown that a significant portion of the growth in developing countries expiorts of manufactures in Latin America and Asia is the result of expanding trade among the developing countries themselves.

# **HI. Developing Countries' Trade Policies and Institutions**

The Trade Policy Reviews document in detail the great progress most developing ccuntries members of tilh WTO have made in liberalizing their trade regimes during the last decade. The liberalization has had several dimensions: (a) applied tariffs have been lowered; (b) many countri!s have bound a significant number of tariff lines in the context of the URA; (c) the overall use of nen-tariff barriers to trade has decreased in practically all countries; and (d) in general, the incidence

**Table 3**

|**Developing Countries**|**Trade (1989-19**<br>Annual<br>(|**97): Manufactur**<br>growth rates<br>in %)|**es and Non Man**|**ufactures**|
|---|---|---|---|---|
||**Manufactures**<br>**exports**|**Non manufactures**<br>**exports**|**Manufactures**<br>**imports**|**Non manufactures**<br>**imports**|
|42 Developing countries|14.2%|5.5%|14.6%|9JI;|
|Latin America and the Caribbean|15.3%|6.7%|17.7%|10 1l|
|Asia and Pacific|12.6%|6.0%|12.4%|8.8 %|
|Suo-Sahara Africa|15.2%|1.7%|7.0%|7<br>l|
|Europe. Middle-East and North Africa|11.1%|6.9%|12.5%|9.: %|
|All Developing countries|14.2%|8.5%|14.0%|9.<br>l|

Explanations: For definitions see Tables I and 2.

_Source .7 WTO, 1998: World Bank, 1998._ 8
# Table of Contents

|1.<br>Introd|uction|1|
|---|---|---|
|2.<br>The M|odel|4|
|2.1<br>22<br>2.3|Mexican and Venezuelan Bonds<br>The Analytical Model<br>Estimation Techniques|4<br>6<br>8|
|3.<br>The R|esults|11|
|3.1|Raw Prices|11|
|3.2<br>3.3|Estimates of the State Variable Process<br>The Relationship between State Variables and Economic Variables|12<br>14|
|4.<br>Concl|usions|16|
|Appendix A||17|
|Appendix B||22|
|References||25|
|Tables||28|
|Figures||33|
Policy Research Working Paper 9389

# **Abstract**

The impact of drought on household welfare is the cumulative effect of crop losses and price changes in a local economy that are triggered by these initial losses. This paper combines data on monthly grain prices and wages in 82 retail markets over 17 years with data on district-level weather shocks to quantify the impact of drought on local prices and how this impact varies by month after harvest. The results show that price increases occur immediately after the completion of harvest and then dissipate so that inflationary effects are quite low during the lean season, contrary to commonly held views. The impact of shocks on prices is quite low now in Ethiopia—4 percent at its peak post-2005 compared with 12 percent before 2005. In areas of the country where infrastructure investments have been high, there is now almost no inflationary impact of drought on prices. It is not clear whether it is infrastructure investments or something else that has driven that, but it shows that it is possible for rainfall shocks to have no inflationary impacts in low income economies. Inflationary impacts were also reduced more in districts where the Productive Safety Net Program was introduced. Comparing inflationary effects in districts with food versus cash transfers suggests that cash transfers do not have inflationary effects on grain prices during times of drought.

This paper is a product of the Poverty and Equity 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/prwp. The authors may be contacted at RHill@worldbank.org and HFuje@worldbank.org or Habtamu.fuje@columbia.edu.

_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
**Table 7. Decomposition of changes in asset-based poverty between 1995 and 2005**

||Headco|unt|Decomp|osition acco|rdingto edu|cationgroups|Decomp|osition a|ccordingtog|endergroups|Decomp|osition ac|cordingto c|hildrengroups|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Poverty line<br>(perc. in|||(W<br>No or|ithin)<br>Secondary|(Between)<br>|(Interaction)<br>Crossed|(Wit<br>Female|hin)<br>Male|(Between)|(Interaction)<br>Crossed|(Wit<br>Without|hin)<br> <br>With|(Between)|(Interaction)<br>Crossed|
|1995)|19952005|Change|<br>primary|or more||effect|head|head||Effect|under5|under5||effect|
|20th|0.201 0.172|-0.028|-0.012|0.000|-0.017|0.001|0.001|-0.030|-0.003|0.003|-0.011|-0.014|-0.002|0.000|
|40th|0.400 0.339|-0.062|-0.029|-0.002|-0.032|0.002|0.000|-0.059|-0.008|0.005|-0.011|-0.044|-0.009|0.002|
|60th|0.600 0.513|-0.088|-0.040|-0.005|-0.043|0.001<br>|-0.013|-0.071|-0.009|0.005|-0.017|-0.062|-0.012|0.002|
|80th|0.800 0.711|-0.089|-0.025|-0.021|-0.034|-0.009<br>|-0.027|-0.057|-0.005|0.001|-0.027|-0.051|-0.010|0.000|

Source: Own computations using DHS 1995, 2005

**Table 8. Asset-poverty and vulnerability to asset-poverty**

|%|Number of<br>individuals<br>('00,000)|Number of<br>households<br>('00,000)|Fraction<br>poor|Mean<br>vulnerability<br>to poverty|Fraction<br>vulnerable<br>to poverty|Fraction<br>highly<br>vulnerable<br>to poverty|Fraction<br>extremely<br>poor|Mean<br>vulnerability<br>to extreme<br>poverty|<br>Fraction<br>vulnerable<br>to extreme<br>poverty|Fraction<br>highly<br>vulnerable<br>to extreme<br>poverty|
|---|---|---|---|---|---|---|---|---|---|---|
|Overall|79.3|17.0|71.5|70.7|71.5|64.8|34.5|34.7|34.7|24.9|
|Non poor|22.6|6.8|0.0|13.3|10.6|3.2|0.0|0.3|0.0|0.0|
|Poor|56.7|10.2|100.0|93.6|95.8|89.3|48.3|48.4|48.5|34.8|
|Extremely poor|27.4|5.1|100.0|99.7|100.0|99.6|100.0|81.1|86.1|67.8|
|Non vulnerable to poverty|22.6|8.1|10.6|8.8|0.0|0.0|0.0|0.0|0.0|0.0|
|Vulnerable to poverty|56.7|11.5|95.8|95.3|100.0|90.5|48.3|48.4|48.5|34.8|
|Vulnerable to extreme poverty|27.5|7.0|100.0|100.0|100.0|100.0|85.7|87.0|100.0|71.7|
|Highly vulnerable to poverty|51.3|10.7|98.6|98.4|100.0|100.0|53.1|53.4|53.6|38.4|
|Highlyvulnerable to extremepoverty|19.7|5.8|100.0|100.0|100.0|100.0|94.1|95.3|100.0|100.0|

Source: Own computations using DHS 19
_2_

# **I. Introduction**

There is overwhelming scientific evidence that climate change poses possibly the biggest risk to the world and demands an urgent global response. Greenhouse gas emissions trap heat and lead to global warming, contributing to a rise in temperature (Stern, 2008). According to the Intergovernmental Panel on Climate Change, a temperature rise of 1.0 degree Celsius (<sup>0</sup> _C_ ) above pre-industrial levels (1850–1900) has already materialized, and global warming of 1 _._ 5<sup>0</sup> _C_ and 2<sup>0</sup> _C_ will be exceeded during the 21 _st_ century unless deep reductions in carbon dioxide ( _CO_ 2) and other greenhouse gas emissions occur in the coming decades (see Figure 1).(Masson-Delmotte et al., 2021)<sup>1</sup> . Understanding the mechanisms through which climate change affects economies and estimating its impacts in various sectors are essential to implement mitigation and adaptation policies and actions at national and global scales. These mechanisms include climate finance and the implementation of the historic agreement at the 2022 United Nations Climate Change Conference to establish ”loss and damage” funds for vulnerable countries.

Fig ~~u~~ re 1. : Global Surface Temperature Anomalies <!-- Start of picture text --> 2.0<br>2024 2023<br>2024<br>1.5<br>2023<br>2020s<br>2010s<br>1.0 2000s<br>1990s<br>1980s<br>1970s<br>0.5<br>1960s<br>1950s<br>1940s<br>Reference for preindustrial level (1850-1900)<br>0.0<br>Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br>Temperature anomaly (°C)<br><!-- End of picture text --> _Source: Copernicus Climate Change Services (C3S) Data Store._ This study examines the uneven effects of exposure to temperature anomalies on firm-level productivity.<sup>2</sup> We use data from the World Bank Enterprise Survey (WBES), which has more than 190,000 observations covering 154 countries

> 1Some estimates put the increase in temperatures between 40 _C_ , and 60 _C_ (Houghton, 2004; World Bank, 2013)

> 2The US National Aeronautics and Space Administration defines temperature anomaly as “the difference in temperature from an average or baseline.” The baseline temperature is typically computed by averaging 30 or more years of temperature data.
83 different from the rest. The comparison shows, that economies with a high dependency on the export sector seem to have a lower budget deficit than the other group. However, there is very little difference between the two groups of countries in regard to the accuracy of deficit projections. The record on current account deficit is generally the same as on the budget deficit. Also, the group with high export dependency has done poorly in the growth of exports and GDP, in comparison to the rest of the group. Again, the difference between projected and actual values for export dependent countnes and the other group is similar - actual growth is over 2 percentage points less than what was projected. No difference is seen between the groups in regard to investment rates. Surprisingly, average savings rate of export-dominant economies has remained between 10 and 13 percent, relatively close to what was projected. In comparison, the rest of the group has had dismally poor savings ratios-zero to about 4 percent-that are totally divergent from projected rates of about _5_ percent.

# Lengt of Adjusmn

The length of adjustment progmms could be a factor in explaining the varying experience with PFP projections. ks noted before, since the projections are taken from the latest PFPs, the more PFPs a country has, the more reasonable the projections are expected to be. More importantly, the longer duration of adjustment programs would ensure that the economy can meet the targets as well as achieve the projected adjustnent outcomes. The paper first look, at the grouping RAL lm carried out - intensive adjustors and other adjustors. Intensive adjustors are those which have had at least 2 SALs or at least 3 SECALz. The rest are grouped as "other adjustors".