rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Percentage of IKP SHG participants benefiting from various IKP programs, 2008**

|**_Type of program_**|**_Program_**|**_Percentage_**<br>**_benefiting_**|
|---|---|---|
|_Financial_|Savings<br>Internal group lending+Community<br>Investment Fund<br>Bank loans ("bank linkages")<br>Pavala Vaddi (GoAP int. rate subsidy)<br>Bridge Loans<br>Total Financial Inclusion Program|96%<br>75%<br>66%<br>25%<br>7%<br>4%|
|_Skills_|Skills Training - Economic<br>Skills Training- Social|47%<br>41%|
|_Livelihoods_<br>_interventions_|Output Market Intervention<br>Grain Bank<br>Livelihood Advanc. Business School<br>Input Market Intervention<br>Livestock Insurance<br>Non Pesticide Management<br>Land Programme|2%<br>1%<br>1%<br>1%<br>1%<br>0.4%<br>0.4%|
|_Social programs_|IKP Insurance Program (Old age)<br>RCL/FSL/Aasara/RPS<br>Social sector govt. programs (other)<br>Nutrition Centres<br>Education programs<br>FamilyCouncil Centres|10%<br>6%<br>4%<br>1%<br>0.4%<br>0.3%|

Note: Data from the 2008 survey. N=2936.

# **_3. Data and descriptive statistics_**

The data for this study come from a panel survey conducted in 2004 and 2008 on a sample of about 4,250 households in 89 blocks in five districts of Andhra Pradesh.<sup>3</sup> Beyond district selection, which was done purposively to represent the state's five agro-climatic zones and average levels of economic, human, and infrastructure development, the sample was drawn randomly at the block, village, and household level.<sup>4</sup> In each village, 10 households were

> 3 The two surveys were respectively the baseline and end-line survey for the World Bank-supported Andhra Pradesh Rural Poverty Reduction Project (2003-2009, US$150 million).  Attrition in the panel was limited: about 300 households visited in 2004 could not be re-interviewed in 2008 (a 6% attrition rate over the original 4,800).  Another 250 households were discarded due to incomplete data, leaving us with about 4,250 households.  A mid-term survey was also conducted in 2006, but has not been used it in this study because of data cleaning issues.  The data were collected by the Centre for Economic and Social Studies in Hyderabad.

4 The five agro-climatic zones were North Coastal Andhra, South Coastal Andhra, Rayalseema, South Telangana and North Telangana. Levels of economic, human, and infrastructure development were assessed based on, respectively, percentage of gross irrigated area, per capita income and percentage of urban population; percentage of SC, ST population, female literacy, infant mortality rates and percentage of children out of children (5-14 years); total road length per 100 sq. km, number of banks per 10,000 people and number of hospital beds per 10,000 people. The five districts selected (Kadapa, Nalgonda, Nellore, Visakhapatnam, and Warangal) ranked average in a composite index based on the indicators above. After selecting districts, 12 sub-district administrative units or blocks (called "mandals" in AP) were randomly selected in each district, and eight villages in each block, for a total 4
44 file in BESD provides government<sup>expenditure data in a form suitable for</sup> cross-country and inter-regional analysis. Sources of data<sup>on social</sup> expenditures at the regional<sup>level include McGreevey (1990),38</sup> Grosh (1990),39 Sahn (1989),40 PAHO (1989).1

# The data

Public Social Sector Expenditure<sup>tables 1 through 6 are based on data</sup> from IMFGFS. The tables are<sup>useful for assessing trends in the shares of</sup> public spending on education,<sup>health and "other welfare" programs. *Other</sup> welfare" includes expenditures<sup>on social security and welfare; housing and</sup> community amenities; and recreational,<sup>cultural and religious affairs and</sup> services.

Note, the tables include only central<sup>government data. Ideally,</sup> consolidated general government<sup>statistics that include information on all</sup> levels of government, i.e.<sup>central, state, and local, should be used to</sup> construct public expenditure<sup>tables. However, IMFGFS provides expenditures by</sup> function are only available<sup>at the central government level. A major drawback</sup> 38Bill McGreevey, "Social Security in Latin America: Issues and Options for the World Bank," (Human Resources<sup>Division, TAC Technical Department,</sup> World Bank, 1990).

> 3914argaret Grosh, "Social Spending<sup>in Latin America: The Story of the</sup> 19809s, (Human Resources Department,<sup>LAC Technical Department,</sup> World Bank, 1990).

4<sup>1</sup> David Sahn, "Fiscal and Exchange<sup>Rate Reforms in Africa: Considering the</sup> Impact Upon the Poor." Cornell<sup>University. (Prepared for USAID African Bureau,</sup> 1989). (unpublished).

41Pan American Health Organization (PAHO), "Health and Development: Repercussions of the Economic Crisis," (PAHO: Washington, D.C.).
# **6. Conclusions**

Despite the growing policy relevance of climate migration and the acceleration of climate change impacts, our understanding of the causal relationship between climate shocks and human mobility outcomes remains limited. While this is in part due to the existence of severe data gaps, there is much that can be done to inform policy making, even with the current data. In this paper, we carried out an empirical test which shows that coupling rich household survey data and theoretical insights with recently developed data-driven techniques is a promising route that can shed new light on the key drivers underlying heterogeneous mobility responses in the face of climate risk and help target groups most at risk of resource-constrained immobility.

We find that pre-shock wealth, _in situ_ adaptive capacity, and repeated exposure are the most important drivers able to determine not only the magnitude but even the sign of the effects of weather anomalies on the (im)mobility outcomes of Nigerian farming households. While local adaptive capacity acts as a substitute for migration, the importance of climate-induced immobility is consistent with recent global evidence that many people will be trapped in impoverished and troubled regions and that climate-induced poverty is likely to be a real threat <mark>(Burzyński et al., 2022). T</mark> he inability to migrate in reaction to climate change is an important and neglected policy concern (Castells-Quintana et al., 2018) and stresses the need for policies that facilitate both local adaptation as well as more internal migration to lift vulnerable people out of poverty traps (Kraay & McKenzie, 2014). If there is an inflection point at which migration starts to decrease as households get substantially wealthier and, in turn, become able to adapt _in situ—_ i.e., a kind of 'climate migration Kuznets curve'—we do not observe it, most likely because even relatively richer Nigerian farming households are still poor.

In terms of policy prescriptions, our findings suggest that, in this and similar contexts, any onesize-fits-all approach for the management of climate migration is doomed to fail. The different groups we identify call for targeted measures and group-specific programs and adaptation policies able to tackle the complex challenges stemming from climate-mobility interactions at multiple levels. We propose a methodology for group decomposition and threshold identification which is based on public, nationally-representative microdata available to policy makers and that can be easily tailored to different contexts. As such, this type of empirical approach for detecting 28
subsidization are very common because differentiated tariffs, particularly across urban and rural areas, often pose challenges to rural electrification due to the higher cost of connections and lower income levels in rural areas. Kenya Power and Lighting Company has a uniform tariff for all domestic customers, despite rapid expansion of its network into lower-demand regions, with over a million new customers connected in both the 2015/2016 and 2016/2017 financial years.

**Figure 10: Cross-Subsidization Levels and Full-Cost Recovery**

<!-- Start of picture text --> 120%<br>Colombia Ukraine<br>Philippines India - Odisha<br>100% (MERALCO) (WESCO)<br>Vietnam Peru Uganda<br>India - Odisha<br>(CESCO)<br>Kenya<br>80%<br>Philippines Pakistan (LESCO)<br>Tanzania<br>(BENECO) Senegal<br>Pakistan (KE)<br>India - Andhra<br>60% India - Rajasthan Egypt, Arab Rep. Pradesh<br>(JVVN)<br>(APEPDCL)<br>India - Rajasthan R2 = 0.32<br>40%<br>(AVVN)<br>India - Rajasthan India - Andhra<br>(JDVVN) Pradesh<br>20% (APSPDCL)<br>0%<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 45%<br>Level of Cross-Subsidization<br>Full-Cost Recovery<br><!-- End of picture text --> _Source:_ World Bank staff.

_45._ On a related but more technical level, the qualitative evidence from the case studies also suggests that electricity tariffs structures must be carefully designed in order to avoid incentivizing ‘grid defection’ in view of recent developments in distributed energy. Most low-income countries rely on energy charges as the primary means of recovering costs. As a result, consumers can self-generate to save almost the entirety of their electricity bill, while still benefiting from the back-up services provided by the grid. If fixed costs are bundled into the per kWh energy charges, rather than reflected in a separate fixed charge, each grid defection shifts these costs onto a smaller group of customers and further incentivizes grid defection. Given that the costs of distributed generation may be even lower for non-residential customers consuming at larger scales, cross-subsidies further exacerbates the incentive for grid defection. Odisha provides an illustrative example of how cross-subsidies can lead to grid defection. After privatization, support to low-income residential customers in the form of free connections was reduced in favor of cross-subsidies from industrial customers, which led these industrial customers to seek out alternative sources to avoid paying higher tariffs. This sort of grid defection combined with residential tariffs that are kept artificially low, can put utilities in increasing financial distress as they are unable to recover their costs of service.

24
<!-- Start of picture text --> _ 250<br>> — Pessimistic scenario<br>S200} — Intermediate scenario<br>5 — Optimistic scenario<br>2 150<br>Fed<br>a)<br>© 100<br>fo)<br>6<br>2 50<br>He<br>D><br>a) —=<br>0 20 40 60 80 100<br>Water level (cm)<br><!-- End of picture text --> <!-- Start of picture text -->
1.0 ]<br>¥ 3 0.8 -— |<br>o i |<br>a: 0.6 |<br>fs —/ |<br>Be 0.4 |<br>o w | -— . . . .<br>vo 0.2 a — Pessimistic scenario<br>£9oar] , || || — Intermediate: age scenario.<br>0.0 | — Optimistic scenario<br>“0 20 40 60 80 100<br>Water level (cm)<br><!-- End of picture text -->
from about 31.7 percent in 1993 to 36.5 in 1996. In 1999, it fell back to the 1993 level.

It increased again in 2002 to 34.3 percent. This reveals that inequality increased both before and after the crisis, while it decreased during the crisis.

Table 3.3. Extended Gini Coefficients for Indonesia

|Focal Parameter|19931996|19992000 2002|
|---|---|---|
|1|0.00 0.00|0.00 0.00 0.00|
|2<br>|31.69<br>36.45|31.50<br>30.33<br>34.31|
|3<br>|41.90 46.44|41.07 39.68 44.04|
|4<br>|47.16 51.41|46.05 44.63 48.96|
|5<br>|50.42 54.47|49.20 47.79 52.02|
|6<br>|52.68 56.56|51.40 50.02 54.12|

Source: Computed This observation is confirmed by the shape of the GICs and the relative growth incidence curves<sup>14</sup> (see figures A.1 and A.2 in the annex). The results underlying the relative growth incidence curves reveal the following patterns of growth in relative expenditures. Over the 1993-1996 period, average expenditure grew 7.8 percent per year. Poverty declined even though the expenditure of the poorest 92 percentiles grew at a rate below the overall growth rate. This pattern reversed itself during the crisis. Between 1996 and 1999, mean expenditure declined by almost 9 per year. Poverty increased despite the fact that only the expenditure of the richest 19 percentiles fell faster than the expenditure of the poorest 81 percentiles. This pattern was also observed by Kraay (2004). The pattern of growth in the 1999-2002 period is similar to the one observed in the pre-crisis period. Average expenditure grew about 7 percent a year. Poverty declined, but the distributional shifts favored the non-poor (the corresponding relative

> 14 A relative growth incidence curve is a graphic representation of the pattern of growth in relative living standard (Kraay 2004).  Our curves are obtained by plotting changes in the slope of the Lorenz curve i.e. the second term in the definition of the GIC given by expression (2.5).  The relative growth incidence curve

> may thus be written as: _g r_ ( _p_ ) = ∆ ln _L_ ′( _p_ ) = _g_ ( _p_ ) −γ .  A downward sloping curve indicates a propoor pattern of relative growth.  When the curve is upward sloping, distributional shifts do not favor the poor.

22
_1. The teacher provides specific comments or prompts that help clarify students’_

- _misunderstandings_

_2. The teacher provides specific comments or prompts that help identify students’ successes_

# _1. The teacher provides specific comments or prompts that help clarify students’ misunderstandings_

Teacher feedback that encourages students to raise questions about the learning task or to further clarify their misunderstandings can enhance the overall learning environment of the classroom. Hattie, Biggs, & Purdie (1996) found that students are better able to develop self-regulatory learning skills and detect errors in their understanding when teachers create a trusting learning environment where feedback is welcomed. For teachers to create a trusting environment, teachers not only need to detect and highlight errors, as well as respond to them appropriately (i.e., never criticizing students), but also to identify how their feedback can be most constructive for students moving forward (Brophy, 1986; Fischer, Mazor, Baril, Alper, DeMarco, & Pugnaire, 2006; Seifried & Wuttke, 2010; Yerushalmi & Polingher, 2006).

The consensus in the literature is for teachers to avoid using general questions (i.e., “How do you do this?”) or giving general declarations that do not identify specific aspects of the problem or task that needs improving, as this would only serve to confuse students more (Salomon & Globerson, 1989; Webb, 2009). Particularly, when students provide an incorrect answer, the teacher should help them identify the root of their misconception before moving on to a new topic (Brophy, 1986; Lemov, 2015). Teachers’ reaction to students’ errors is a well-researched area also found to relate to student success. When students need clarification on content or have misunderstood a concept, it is important for teachers to address the errors to avoid negative transfer and future misconceptions (National Research Council 2001, 2007).

_2. The teacher provides specific comments or prompts that help identify students’ successes_ Just as identifying and clarifying students’ misunderstandings is important for learning, effective teachers highlight students’ successes, so students know they are on track. Classic work has shown the importance of teachers systematically offering positive reinforcement to students and building upon students’ responses to solidify these successes (Brophy & Good, 1985; Levin & Long, 1981; Wilen & Clegg, 1986). Effective teacher prompts and questions encourage students’ growth mindset by helping individual learners 24
**FIGURE 5: DETERIORATING AND INSUFFICIENT INFRASTRUCTURE CONTRIBUTES TO UNCOMPETITIVE INDUSTRIES, 2004**

<!-- Start of picture text --> 30<br>OECD<br>25 LAC<br>20<br>15<br>10<br>5<br>0<br>Losses to markets. Logistics cost as a Inventory levels<br>Proportion of proportion of value<br>goods not reaching of products<br>markets (Primary<br>sector)<br>Source: Guasch and Kogan, 2006<br>Percentage<br><!-- End of picture text --> In this section we present an assessment of the levels and determinants of inventory costs so as to facilitate appropriate government interventions to reduce them and, in doing so, improve competitiveness.

# **5.1 Inventories**

At present, the logistics industry is a global hub-and-spoke network designed to link hundreds of towns and cities with an overnight communication infrastructure that keeps the world’s ‘just-intime’ supply chain taut. In developed markets such as the US, the ability to guarantee overnight shipment of parts and finished goods has allowed companies to reduce average inventory levels by a fifth over the last decade and is thought to have played a significant role in improving productivity across the economy.

In developing markets, however, the evidence shows large inventory holdings. While it is well known from anecdotal evidence that inventories are higher in developing countries, there are no systematic studies that attempt to explain this phenomenon or even to quantify the difference. In their 2001 paper, Guasch and Kogan assembled data for 52 countries from the 1970s and 1980s to draw out some stylized facts about the pattern of inventory holdings. Recent data in Latin American countries, for the 1990s, shows that the problem persists.

US business typically hold inventories equal to 15 percent of GDP while inventory levels in many developing countries are often twice as large, and for raw materials three times as large. If the private sector interest rate for financing inventory holdings is 15 to 20 percent, a conservative estimate in most developing countries, then the cost to the economy of the additional inventory holdings is greater than 2 percent of GDP.

Suppose that firms in developing countries keep high levels of inventories in response to poor infrastructure and logistic services. Then, as an example, consider that the total transport 19
# **Box 1. Addressing unemployment among young men**

In Africa, young women are more disadvantaged than young men in the labor market due to norms, institutions, and differences in resource endowments necessary for accessing income-generating opportunities. However, in fragile and conflict-affected states, governments often attempt to promote stability by targeting at-risk young men and addressing the drivers of conflict and violence.<sup>a</sup> Crosscountry studies and within-country studies from outside Africa demonstrate the link between income shocks and crime, but no such link has been demonstrated for terrorism and the existence of conflict, and additional individual-level data are needed.

Though limited evidence is available on which kinds of violence can be addressed by income-oriented programs, some information is available on which programs are most effective at changing attitudes towards violence and reducing time spent on illicit activities. Skills development and microfinance programs have generally been ineffective on their own (Blattman and Ralston, 2015), though one study suggests that agricultural skills programs may be more effective at changing incentives in fragile agrarian states (Blattman and Annan, 2015). However, when combined with the promise of future capital (suggesting the importance of periodic payments) conditional on the male beneficiary’s location, training programs shift hours away from illicit activities. Income-oriented programs and psychosocial programs may reinforce each other to produce more long-term effects: while one study from Liberia found that training, inputs, and counseling did not produce a change in attitudes towards violence and social integration (Blattman and Annan, 2015), another found that cognitive behavior therapy (focused on self-control and building a noncriminal self-image) changed attitudes and reduced violence, when combined with cash transfers (Blattman, Jamison, Sheridan, 2015).

a Recognizing that engaging men is crucial in conflict and post-conflict settings, other teams within the World Bank have worked specifically on this topic. For example, the Learning on Gender and Conflict in Africa Program (LOGICA) was an initiative that aimed to generate knowledge and good practice on addressing gender and conflict issues, and funded several studies on at-risk young men in Sub-Saharan Africa.

6
**Figure 1.2  Key financial system indicators**

<!-- Start of picture text --> 80 Assets/GDP 10 Loans/GDP, Deposits/GDP, Loans/Deposits<br>50 120<br>70 9 45<br>8 100<br>60 40<br>7<br>35<br>50 6 80<br>30<br>40 5 25 60<br>30 4 20<br>3 15 40<br>20<br>2 10<br>20<br>10 1 5<br>0 - 0 -<br>Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03 Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03<br>Gross Loans/GDP Total Deposits/GDP<br>Total assets / GDP Asset/GDP growth Gross loans/deposits<br>Intermediation by type of institution Share of total assets in Colombian banking system<br>180% 100<br>160%140% 80 1818 1 1617 1 1518 1 1418 0 1416 0 1316 0<br>120% 60 11 12 12 11 12 12<br>100%<br>80% 40<br>60% 52 54 54 56 58 59<br>20<br>40%<br>20% 0<br>1998 1999 2000 2001 2002 2003 Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03<br>Private local banks Public banks<br>Foreign banks Financial coops Private Domestic Banks Public Banks<br>Others Private Foreign Banks Fin. cooperatives<br>Others<br>(*) Private local banks include mortgage banks<br>Gross Loans Total Deposits<br>100<br>80 1818 14171 14181 13170 12161 11171 10080 26 11181 10181 10170 1509 1508<br>60 12 9 7 7 8 8 151 11 11 11 12 11<br>% 60<br>% 11<br>40<br>60 59 60 62 63 65 40<br>20 47 60 60 62 64 65<br>20<br>0<br>Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03 0<br>Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03<br>Private Domestic Banks Public Banks<br>Private Foreign Banks Fin. coops Private Domestic Banks Public Banks<br>Others Private Foreign Banks Fin. coops<br>Others<br>%<br>Total assets/GDP % Gross loans/deposits %<br>Asset/GDP growth %<br>Gross loans/GDP, deposits/GDP<br>%<br>Gross loans/deposits %<br><!-- End of picture text --> _Source: World Bank calculations based on Superintendencia Bancaria data_ 11. At the same time, **financial intermediation in Colombia has been steadily declining since 1998** . Total financial sector loans as a percentage of total assets decreased from 65% in 1999 to 51% in 2003 and from 116% in 1998 to 85% in 2003 as a percentage of total deposits (see Figure 1.2). As a result, financial depth in Colombia stagnated around crisis levels with a ratio of credit to GDP of about 34%. This is significantly below the level of Chile, while at par with Mexico and above the levels of Peru and Venezuela (see Figure 1.1). The decline in intermediation by Colombian banks can be attributed to their riskadverse behavior following the banking crisis and a resulting shift in 20