rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7663

# **Abstract**

This paper presents a model to assess the socioeconomic resilience to natural disasters of an economy, defined as its capacity to mitigate the impact of disaster-related asset losses on welfare, and a tool to help decision makers identify the most promising policy options to reduce welfare losses due to floods. Calibrated with household surveys, the model suggests that welfare losses from the July 2005 floods in Mumbai were almost double the asset losses, because losses were concentrated on poor and vulnerable populations. Applied to river floods in 90 countries, the model provides estimates of country-level socioeconomic resilience. Because floods disproportionally affect poor people, each $1 of global flood asset loss is equivalent to a $1.6 reduction in the affected country’s national income, on average.

The model also assesses and ranks policy levers to reduce flood losses in each country. It shows that considering asset losses is insufficient to assess disaster risk management policies. The same reduction in asset losses results in different welfare gains depending on who benefits. And some policies, such as adaptive social protection, do not reduce asset losses, but still reduce welfare losses. Asset and welfare losses can even move in opposite directions: increasing by one percentage point the share of income of the bottom 20 percent in the 90 countries would increase asset losses by 0.6 percent, since more wealth would be at risk. But it would also reduce the impact of income losses on wellbeing, and ultimately reduce welfare losses by 3.4 percent.

This paper is a product of the Climate Change Cross-Cutting Solutions Area. 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 authors may be contacted at shallegatte@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
The experiment was implemented in the República Bolivariana de Venezuela 2006 Enterprise Survey (ES). The República Bolivariana de Venezuela ES was conducted between February and June 2006 covering 500 private sector firms.<sup>3</sup> All firms are formal (registered with a government entity) and include 5 or more employees. The sampling methodology was stratified random sampling. Levels of stratification were size, sector, and location. The República Bolivariana de Venezuela ES covered three regions: Caracas, Maracay, and Valencia. The sample is representative of the non-agricultural, non-mining private sector of the economy.<sup>4</sup> The respondents are typically business owners and managers; interviews are administered faceto-face. There were 33 enumerators and 5 supervisors that carried out the fieldwork.

The survey experiment was conducted as follows. The sample was randomly split into half – 250 firms in each group. The first group received the typical Enterprise Surveys where at the end of each section, a question is posed to the respondent to rate an element of the business environment related to the section. The question is structured as follows: “ _To what degree is XX an obstacle to the current operations of this establishment?_ ” where “XX” refers to the specific business environment element referenced at the end of the section. The respondent has 5 options: “no obstacle,” “minor obstacle,” “moderate obstacle,” “major obstacle,” and “very severe obstacle.” The business environment elements covered in the survey include: (1) Tax administration, (2) Access to land, (3) Access to finance (availability and cost), (4) Corruption, (5) Crime, theft and disorder, (6) Electricity, (7) Inadequately educated workforce, (8) Functioning of the courts, (9) Political instability, (10) Labor regulations, (11) Business licensing and permits, (12) Practices of competitors in the informal sector, (
the qualitative research (which, certainly in Vietnam, attracts greater skepticism). Even in these areas of overlap, there was a useful difference of focus between the two sets of information with the quantitative data providing robust, nationally representative trends and the qualitative studies helping to indicate some of the explanation for those trends.

Important areas where findings from the PPAs and the VLSS differed included:

- The socio-economic position of female-headed households, who were routinely described as being poor and vulnerable in the PPAs but which emerge from the data as being better off in per capita expenditure terms;

- The scope and nature of urban poverty, which does not appear to be a pressing problem from the VLSS but which the PPAs suggest could be an issue of importance.

# **_Policy responses: follow-up beyond the final report_**

Many would agree that most of these issues had been raised in various qualitative and/or quantitative studies over recent years. The difference here, arguably, was that the research was embedded in an influential process, a process that involved many of the important agencies in local and national policymaking for poverty reduction. Though it is still early to have generated dramatic policy change, there are several examples of the PPAs feeding into policy debate in a substantive way.

## Broadening the policy debate

A recent workshop of various stakeholders (including central and local policymakers) involved in the PPAs concluded that, for a number of reasons including the short time since the PPAs were implemented, the impact of the PPAs _on policymaking_ had been greater than the influence on _policy_ to date. The impact on policymaking has come about through:

- Shaping the public policy agenda, broadening the policy debate

- Elevating the status of qualitative research in national assessments of poverty and promoting bottom-up approaches to planning and policy-making

- Creating more places at the national policy-making table and ensuring that voices of all stakeholders are heard

- In addition, one group at the evaluation workshop reported that the PPA had given local levels of Government stronger grounds for identifying "demands
> <sup>8</sup>

The cost of capital is expressed as a rate, to be added to the pure premium rate, and is therefore equal to this aggregate cost to the insurer of acquiring risk capital for a given portfolio divided by the total sum insured for that portfolio.

Where the insurer must quote premiums for products before the entire portfolio is finalized, judgment must be applied in assessing the level of diversification in the forthcoming season’s mNAIS portfolio. The more diversified the portfolio, the less risk capital will be required, and the lower the cost of capital will be. If there is some uncertainty surrounding the level of diversification in the forthcoming portfolio the insurer may wish to estimate the cost of capital on a prudent, or cautious, basis.

The risk management team can support management decisions about the risk financing strategy by estimating the portfolio Probable Maximum Loss (PML) using historical data. See Chapters 4 and 5 and Annex G of World Bank (2011) for examples of such calculations for the WBCIS and NAIS portfolios.

# <u>Actuarial justification</u>

The cost of acquiring capital to allow for the risk of random variation from expected claims should be included in the commercial premium rate. The insurer must acquire capital in the form of reserves, reinsurance and contingent credit to ensure that it is able to pay all claims as they fall due. Most regulatory regimes require private insurance companies to demonstrate that they hold enough capital to be able to survive specified extreme events; for example the Europe Union’s forthcoming Solvency II will require each (re)insurer to demonstrate that it is be able to meet its obligations over the next 12 months with a probability of at least 99.5%.

# _Administrative Load_

# <u>Description of step</u>

The administrative load should be sufficient to cover all costs to the insurer of administering the mNAIS and is a decision for the insurer’s management team.

# <u>Actuarial justification</
.1|28|25|35.5|41.8|Fuel|Electricity|
|Pakistan|3.3|21|37|25.0|35.6|Electricity|Gas|
|Vietnam|2.3|11|46|70.3|52.5|Electricity|N/A|

Note: This table provides background information about energy subsidies in the 12 study countries. The size of subsidy is the weighted average discount provided across all types of energy due to subsidies. Sources: IEA 2023 (for cost of subsidy, subsidization rate, and subsidy per capita; World Bank 2023A (for internet access and internet population 18+); IMF 2023 (types of energy subsidized).
from Zhang, Lv & Lv, et al., 2008; c. 5 years after forestation, source from Zhang, Lv & Lv, et al., 2008; e. source from Xie, et al.2011 c. data from the newly 7<sup>th</sup> forest resource survey from 2004 to 2008 by State Forestry Administration (SFA, 2009); d. data from Wang (2011).

**Table 7: Land resources distribution and potential for developing oil-bearing trees**

|Province|Main Oil Species|Area of<br>Available<br>Land|Available<br>forest land|Biodiesel<br>potential on<br>marginal land|In which, potential<br>on available forest<br>land|
|---|---|---|---|---|---|
|U|nit|Milli|on ha.|Mill|ion tonne|
|South<br>China<br>and<br>Southwest|Jatropha,<br>euphorbia<br>tirucalli, etc.|2|1|1-4|0.5-2|
|North China, Central<br>China and East China|Chinese pisache, tung<br>tree,<br>varnish<br>tree,<br>sapium sebiferum,<br>Idesia,<br>euphorbia<br>tirucalli|4|3|2-8|1.5-6|
|Northwest, North of<br>North China and west<br>of Northeast|xanthoceras sorbifolia,<br>Chinese pisache, robur|30|10|15-60|5-20|
|Total||36|14|18-72|7-32|

Note: Calculated based on the assumed land productivity as 0.5-2 tonne biodiesel/ha.

Source: Lv, et al.2008 12
24 For all forty crisis episodes, Honohan and Klingebiel used interviews with country experts, IMF reports and other sources, to determine whether, for each crisis, government officials reacted to the emergence of insolvent banks with forbearance. Here the focus is on type III forbearance, the most lenient, in which the variable equals one when governments relaxed regulations or did not enforce regulations for at least a twelve month period after being informed about solvency problems in the financial sector, and zero otherwise. Twenty-four countries (26 crisis episodes) exercised this level of forbearance.

Result 5 indicates that the effect of the number of veto players on fiscal transfers and forbearance varies with the costs to voters of favors to special interests. In the case of the financial sector, favors to special interests create deadweight losses by encouraging the extension of credit to less creditworthy borrowers, by encouraging high risk projects at the expense of low risk projects, and by creating incorrect price signals that induce an inefficient transfer of human and physical capital from some sectors to others. All of these costs are likely to be proportionate to the size of the financial sector. That is, the more that economic actors rely on financial intermediation in the conduct of business, the larger should be the costs associated with failures in financial sector oversight. The size of the financial sector is therefore used as a proxy for the costs to voters of favors to special interests in the financial sector.

In particular, a commonly used measure of the size of the financial sector, M,/GDP, is employed in the regressions below, for the year prior to the first year of the crisis. The simple correlation between M,/GDP and the fiscal costs of crisis is negligible (0.018), confirming that there is no necessary relationship between the level of favors granted to special interests in the financial sector, as measured by fiscal costs of crisis, and the costs to the electorate of additional favors.
groups who are able to employ intimidatory tactics to evict them. Those with a greater degree of influence may be in a position to see that weaker groups are evicted, yet also manage to evade the law themselves through bribery to allow their encroachment on public and common land to go unnoticed, and eventually to have the encroached land registered in their own names. A lengthy process of de facto occupation _ofperomboke_ land and payment of annual fines for encroachment may thus eventually lead to a change in the revenue classification of the land and acquisition of legal title. Revenue records inevitably lag significantly behind the true extent of encroachment at any given moment.

From our field investigations, it appears that opportunities for further encroachment are now generally limited. Local revenue records and discussions with Rls and tehsildars reveal that most of the encroachment takes place on land designated as _peromboke,_ cultivable waste, permanent pastures and other grazing lands, and only to a very limited extent on land under the jurisdiction of the forest department. Focus group discussions with _50_
proportions are adjusted by the ratio of each sector’s profit rate to the average profit rate for the whole economy. This is similar to the method used by Thurlow (2004), the details are discussed in the Appendix A. Also, Autonomous Energy Efficiency Improvement (AEEI) in the CGE model is considered in this study, and is assumed to be 1% per year following the common assumptions in CGE. Since the available social accounting matrix (SAM) is for 2012, our base year is 2012. If the model has to adopt a projected growth rate of GDP (e.g., projected by the government), it is done through adjustments in total factor productivities (TFPs).

The BAU scenario is constructed using assumptions on labor supply growth and projected real GDP growth, as shown in Table 2.

**Table 2** The Labor supply growth rate and GDP growth rate from 2012 to 2030

|Year|Labor supply growth rate|Average annual GDP growth rate|
|---|---|---|
|2012-2015|2.70%|8.51%|
|2015-2020|4.60%|6.56%|
|2020-2025|0|5.49%|
|2025-2030|0|5.47%|

_Note: Values for 2012-2015 are actual or observed values. The projected GDP is set at a moderate level based on a review of relevant literature_<sup>_[6-8]_</sup> _. Labor supply growth rate are taken from China Statistic Year book 2017 and UNDESA_<sup>_[5]_</sup> _with very small modification based on our understanding about labor quality development tendency of China in the future._

# **2.3 Bottom-Up TIMES Model**

The bottom-up energy sector TIMES model is an optimization model. It is based on a reference energy system (RES) principle. An RES refers to an optimal system where useful energy demands
rates, particularly by young households, is quite plausible.

And vice versa, economies with small rental sectors are expected to face increased migration costs and thereby a reduced elasticity of the labor supply relative to spatial wage differences. The reason is that migrants, at least initially, face job tenure and income insecurities and thus seek to reduce housing search and transaction costs, which are lower in the rental sector. Analysis for the United States shows that because of these factors two-thirds of migrating homeowners become tenants first (Joint Center for Housing Studies 2003).

A recent report on rental housing by the United Nations Human Settlements Programme (UNHabitat) (2003) makes the point that there may be an inverse relationship between unemployment and homeownership.<sup>15</sup> This supports our contention that high levels of homeownership reduce residential and hence labor mobility, which is better served by rental housing - especially in transition countries. The role of the housing market in labor mobility is to

> 13  The two outliers, The Netherlands and Spain, can be partly explained by policy peculiarities. The Netherlands has traditionally emphasized social rental, which includes detached housing. The Spanish rental sector, on the other extreme, has been depressed by decades of rent control. Eastaway and Varo (2002) discuss the Spanish case.

> 14  In their survey of 13 countries, ownership rates at ages 50–59 vary 53 - 82% while at ages 30–39 they vary 33 - 70% and at ages 25–29 they vary 11 - 59%.

> 15  The cited study estimated that a 10 percentage point increase in homeownership could be equated with an extra 2.2 percentage points of unemployment.

10
# **1 Introduction**

The long-term trend of shrinking agriculture employment and expanding employment in the secondary or tertiary sectors has been associated with economic development. And, since moving out of agriculture and getting a job in the non farm segment normally means earning higher incomes, this type of intersectoral migration should also lead to lower poverty rates. In recent years, Brazil, as many other developing countries, has experienced a significant reduction of its agricultural employment and there is indirect evidence that this migration out of agriculture is poverty reducing. Establishing more direct evidence is difficult mainly because of lack of data reporting the characteristics of migrants at the time of moving, a limitation that affects (sometimes with intractable endogeneity issues) a lot of migration models. This paper, relying on a special section of the Brazilian household survey that chronicles individuals’ past employment, offers, for the case of Brazil, a direct assessment of how much poverty reduction can be attributed to these intersectoral labor movements. By estimating a mover-stayer model using Brazilian micro data, we can identify the main factors affecting the propensity of moving out of agriculture and rank individuals accordingly. The estimations show that individuals that are most likely to migrate are found among the poorer, often landless heads of households, but also among the better educated, and thus less poor, non-head household members. Overall though, we observe, and this is one key finding of the paper, that movers experience the highest poverty reduction when compared to both agricultural and non-agricultural stayers.

This confirms that changes in sectoral employment can significantly contribute to poverty reduction. Chronic poverty is often linked to the difficulty of finding employment in a better paid job in a growing sector. There is mounting evidence of the existence of poverty traps characterized by situations where occupational or technology choices are discrete, and where choosing a higher return occupation or technology implies large sunk or fixed costs (Barrett 2004). Moving out of agriculture, where poverty rates are often much higher than in other sectors, is one example of such
on households, including increasing the threshold rate of the Guaranteed Minimum Income program, the main national poverty-targeted program; and a self-targeted public works program for unemployed people. Our simulations indicate that these measures may have helped cushion crisis impacts for some of the hardest hit households. However, the scale of the crisis exceeded the ability of programs launched under the Emergency Social Safety Net to offset negative impacts. As a result, poor and near-poor people continued to suffer considerable hardship.

> 13 We have SILC data collected in 2009 but since SILC income data refer to the previous year’s earnings, the 2009 SILC income data cannot be used as post-crisis income. 14 Poverty numbers are based on HBS income data. A household is considered to live in poverty if total household disposable income is below LVL 90 per capita per month.

17
obstacles to emigration had been removed and emigration was no longer a crime punishable by law. Largely in response to this policy shift, between 1960 and 1974 over 1.5 million, predominantly low-skilled, Portuguese left the country in search of better economic and political conditions in more developed nations (Figure 1). Following the Carnation Revolution in 1974, which overthrew Estado Novo, emigration ‡ows fell sharply re‡ecting the combined e¤ect of the introduction of democracy and, initially, more restrictive policies towards immigration in several recipient countries. As a result, and also in light of limited return migration from more developed countries, stocks of emigrants have remained largely stable over the past three decades.<sup>5</sup> The motives and timing of Portuguese emigration ‡ows alleviate concerns of reverse causality in the relationship between migrant networks and export outcomes. In addition, the concentration of emigration ‡ows in time and space

> 5 Despite the limited emigration ‡ows observed in Portugal since 1975, the Global Migrant Origin Database (described below) recorded a total of 1.95 million Portuguese emigrants in the year 2000. This amounts to nearly 19% of the population living in Portugal, which has remained fairly stable in recent years.

6
Policy Research Working Paper 5487

# **Abstract**

How should Tajikistan adapt to ongoing and future climate change, in particular given the many pressing development challenges it currently faces? The paper argues that for developing countries like Tajikistan, faster economic and social development is the best possible defense against climate change. It presents some key findings from a recent nationally representative household survey to illustrate the strong public support for more climate change related spending on better management of water resources, disaster management, agriculture, and public health—four key sectors that the government’s latest poverty reduction strategy identifies as being especially important from a climate change perspective. Finally, the paper argues that, as important as project-based adaptation measures may be, it is imperative that they be supported by an overall policy framework that provides a truly enabling environment to facilitate faster climate change adaptation.

This paper is the product of the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at szaidi5@worldbank.org. Financial support from the Trust Fund for Environmentally and Socially Sustainable Development (TFESSD) is gratefully acknowledged in helping to carry out this study.

_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