rafmacalaba/datause-encoder-data huggingface dataset:
# Environmental Reliance, Climate Exposure, and Vulnerability: A Cross‐Section Analysis of Structural and Stochastic Poverty

By Arild Angelsen<sup>1,2</sup> and Therese Dokken<sup>1</sup> 1School of Economics and Business, Norwegian University of Life Sciences (NMBU), Ås, Norway; 2 Center for International Forestry Research (CIFOR), Bogor, Indonesia _<u>arild.angelsen@nmbu.no; theresedokken@gmail.com;</u>_ Keywords: environmental reliance, forests, poverty, climate vulnerability JEL: Q23, Q54, Q56, I32, O13

## **Acknowledgement**

The authors are grateful to Ulf Narloch for close collaboration and very extensive comments to early versions, which have improved the paper considerably. Comments from Stephane Hallegatte and Sven Wunder are also appreciated. We thank Fredrick Noack for providing the CRU climate data and Martin Herold for providing the forest cover data used in this paper.
9 definitions<sup>6</sup> with the other HEFPI bed net use data points rendered meaningful comparisons impossible.

The trend checks, dropping of bed net use data points from the second MICS round and additional ad hoc discovery of surveys which did not fulfill HEPFI requirements<sup>7</sup> led us to exclude a total of 338 data points from the 2019 HEFPI database which were included in the 2018 version.

# 3.2. <u>Changes in data points</u>

# 3.2.1. Changes in indicator values due to changes in sources

The HEFPI 2019 database changed the underlying data sources of 161 data points that were already included in the 2018 version.

## _From multiple to single sources_

For the 2018 database, we allowed multiple data sources for data points of a given year and indicator. In these cases, the HEFPI data point’s value was computed as the unweighted mean of the indicator values from each source. For instance, the HEFPI 2018 stunting rate for Rwanda in 2000 – 46.2% – was computed as the mean over the data points from the 2000 DHS and MICS surveys.

In the 2019 database we no longer use such averaging and instead rely on a single source per data point. When data is available from multiple sources, we select the point to include based on four criteria: (1) Consistency in data sources over time (e.g. prefer the Rwanda 2000 DHS stunting point

> 6 The other HEPFI insecticide-treated bed net points consider a bed net treated if it is a) a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pretreated net which was soaked in insecticides less than 12 months ago. MICS 2 data points by contrast consider bed nets treated if they were _ever_ treated.

> 7 For example, we dropped two STEPS surveys as their samples did not represent populations meaningful for HEFPI’s purposes, namely the 2015 STEPS in Turkey which focused on Syrian refugees and the 2004 Bhutan STEPS which was carried out among patients of one hospital’s outpatient ward.
<!-- Start of picture text --> Overall, Destination and Product Overall lockdown<br>100% 100%<br>Overall import survival No Lockown<br>Destination survival Lockdown<br>75% Product survival 75%<br>50% 50%<br>25% 25%<br>0% 0%<br>0 2 4 6 8 10 12 14 16 18 20 22 24 0 1 2 3 4 5 6 7 8 9 10 11 12<br> Survival time (months) Survival time (months)<br>Destination lockdown Product lockdown<br>100% 100%<br>No Lockown No Lockown<br>Lockdown Lockdown<br>75% 75%<br>50% 50%<br>25% 25%<br>0% 0%<br>0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12<br> Survival time (months) Survival time (months)<br>Estimated survival probability Estimated survival probability<br>Estimated survival probability Estimated survival probability<br><!-- End of picture text -->

**Figure 5: Philippine’s import survival before and under the lockdown** Source: Authors’ compilation using PSA data

rate across destinations, as observed in Figure 4, originated from the rest of the world. The mean survival period of exports to the top ten destinations of exports was 0.6 months higher than to the rest of the world during the lockdown.

The second part of Table 4 shows results for imports. Columns 2 and 3 indicate no major difference in survival rates of intermediate and Other Products. Roughly two-thirds of these goods survived beyond the first month of trading. The survival rates in the 6<sup>th</sup> and 12<sup>th</sup> months were 39% and 37% respectively. The mean and median duration of importing these products was around 4.5 months while the median period was 2 months. The last two columns show that the survival rate of imports from the top ten was 10% higher than that of the rest of the world for the entire period of the lockdown. The mean difference between the two categories of countries was 1 month.

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Policy ReseaRch WoRking PaPeR 4578

# **Abstract**

In many markets, industry and policymakers agree that there may be too many insurers. In others, the consensus is that there could be benefit from more competition. But this broad consensus is often supported by evidence that is more qualitative, anecdotal, or judgmental despite being unanimous. What is less clear, however, is how far consolidation or liberalization will go, how fast, and when it will end. This paper presents some initial observations from a cross-country data set and proposes that individual country results can be interpreted against this data set to inform expectations regarding trends in competition, concentration and consolidation, to inform analysis of the sector, for individual firm strategic planning and wider market risk assessments. A “natural level” for measures is suggested as a starting hypothesis. Further consideration is then made of the role of absolute market size, stage of market development, and differentials between life and non life segments. Analysis of the natural level, adjusted for market conditions, can then be used to develop preliminary views on current and expected market dynamics, strategic planning, and to inform policy, regulatory and supervisory priorities.

This paper—a product of the Finance and Private Sector Vice Presidency, Financial Systems Department—is part of a larger effort in the department to develop an understanding and diagnostic capacity to support country assistance and policy development in the financial sector. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at cthorburn@worldbank.org.

_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
|Computer and related activities|4.8|
|---|---|
|Supporting and auxiliary transport activities; activities of travel agencies|4.8|
|Air transport|4.7|
|Manufacture of chemicals, chemical products and man-made fibres|4.7|
|Post and telecommunications|4.7|
|Water transport|4.6|
|Research and development|4.6|
|Manufacture of tobacco products|4.5|
|Electricity, gas and water supply|4.5|
|Renting of machinery and equipment without operator and of personal and household goods|4.4|
|Extraction of crude petroleum and natural gas; service activities incidental to oil and gas extraction,<br>excluding surveying|4.3|
|Manufacture of coke, refined petroleum products and nuclear fuel|4.3|
|Real estate activities|3.9|

_Note: one standard deviation of regulatory quality indicator (0.38) is computed over the whole sample of countries for the 2003-08 period (see Table VI, Annex)._ Results from Table 4 show that openness to trade – measured at the country level – does not present a statistically significant effect on productivity convergence at sector level. When testing alternative measures for openness to (overall) trade at sector level - covering agriculture, fishing, mining and manufacturing sectors at 2 digit level of Nace 1.1 - estimation results in Table 6 suggest that trade openness has in fact a positive (and statistically significant) effect on productivity growth.<sup>27</sup>

> 27 Openness to (global) trade at sector level is defined as the sum of sector j’s global exports and imports divided by sector j’s production value. Data for global imports and exports by sector is from Comtrade, while production value information is from Eurostat.

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The aim of this paper is to investigate our proxy for the impact of China together with other FDI locational determinants within an analytical framework. With a panel data of 15 CEECs spanning the period from 1990 to 2004, this empirical study tries to shed some light on the relationship between the two regions in terms of FDI attraction and further economic cooperation in a broader scope. The paper is organized as follows. Section 2 introduces a background review on FDI development in the two regions. Section 3 describes our data and estimation methodology. Section 4 presents the estimation results. Some robustness checks are discussed in section 5. Section 6 concludes.

# **2. FDI in China and Central and Eastern Europe since 1990**

China has, after its first adoption of open the door policy in 1978, experienced several phases in inward FDI absorption from the rest of the world (Figure 1). In the first stage, from 1979 to mid 1980’s, only a small amount of FDI flowed to the country annually due to its insufficient infrastructure and nontransparent regulations.

Subsequently, FDI inflows to the country rose at a fairly steady pace until it made a slump in 1990 as a result of economic sanctions for political reasons. It was not until 1992, the year in which the then Chinese leader Deng Xiaoping toured Shanghai and Guangdong with further commitment to open the market that the value of annual FDI inflows jumped up again to a new high of $11.2 billion. This wave of rapid surge continued through the following years until it slowed again as a result of the Asian financial crisis in 1997. China regained its attraction for FDI since 2000. Its accession to the WTO in 2001 induced a new surge of FDI inflows to the country. Despite a 4
significantly. Following Uruguay’s loss of the investment grade, the spread on sovereign bonds as measured by the Uruguay Bond Index (UBI) jumped from less than 300 to almost 2,000 basis points by July 2002. Similarly, the interest rate spread between peso and dollar time deposits almost doubled over the period, going from 2,310 to 4,520 basis points. Deposit withdrawals throughout July 2002 also reflected the resignation of the minister of economy on July 22.

On July 30, 2002, the government declared a four-day bank holiday, during which it negotiated financial support from the multilateral agencies (for a total of 1.5 billion dollars) to fund a program designed to avoid a general deposit freeze, such as the one imposed in Argentina six months earlier. On August 4, 2002, a law created a special purpose fund (Fund for the Stabilization of the Banking System, or FESB in Spanish) to provide full backing for dollar sight and savings deposits at state-owned banks and those financial institutions that had been suspended. The same law extended the maturities of all dollar time deposits held at state-owned banks. An amendment to this law was approved in December 2002, with the purpose of further strengthening the banking system.<sup>11</sup>

# **3. Systemic Risk and Depositor Responses**

To evaluate depositor responses to systemic risk, we follow two distinct approaches, using two different types of data for the recent crises in Argentina and Uruguay. We begin by running regressions similar to those in the traditional market discipline literature discussed in the introduction. For each country, we conduct panel regressions using monthly bank-level

> 11 The amendment imposed reporting obligations on bank employees that acquire knowledge of irregularities, authorized bank regulators to fine state-owned banks, and created a public register for bank shareholders. The new law also provided the basis for the liquidation of the four private banks (whose operations had been suspended during the bank holiday), expanded the powers of the central bank in connection with the liquidation of financial institutions and the application of prudential regulations of state-owned banks, and mandated the creation of a deposit insurance program.

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household number BMGF/DigitalGlobe (DG) settlement outlines for polygons North Somalia UNFPA/PESS rural population estimates points Pre-war regions boundary polygons Waterbody (OSM) polygons

# **2.4 Data pre-processing**

# **2.4.1 Definition of urban and rural stratum within 18 pre-war regions**

Defining major strata is the first step in generating EAs as the maximum population size and areas constraints of EAs may need to vary in different strata. For example, Urban versus Rural strata typically need very different constraints to account for the differing population densities. In Somalia, urban strata were defined using the previous urban EAs from PESS 2014 (UNFPA 2014). The previous urban EAs were dissolved using the dissolve tool in ArcGIS. The remaining area outside of the urban strata was considered rural. To define and compute the urban and rural strata for each Somalia pre-war regions, urban and rural strata were intersected with the 18 pre-war regions administrative boundary. Based on World Bank recommendations the urban and rural strata in Banadir were merged and considered as urban.

# **2.4.2 Settlement boundaries**

Data on settlement locations and boundaries are needed to inform EA delineation in the ‘merge’ part of the algorithm and are also used to create our refined 100m population density estimates. Recent increased availability of high-resolution satellite imagery and high-power computing resources with adequate image processing algorithm have advanced the development of high-resolution human settlement layers (Vijayaraj et al. 2007; Florczyk et al. 2016; Roy Chowdhury et al. 2018). Examples of recently developed human settlement layers include Global Urban FootPrint (GUF) (Esch et al. 2017), Global Human Settlement Layer (GHSL) ( <mark>Pesaresi et al. 2013</mark> ) and LandScan Settlement Layer (LandScan SL) <mark>(Cheriyadat et al. 2007)</mark> . These data sets either are not available for Somalia or are incomplete for the country or they might not specifically be trained for Somalia since the country is facing a constant regional instability and severe drought that forced substantial migration within the region. In addition, during this work, several new data sets on settlements were collected from a variety of sources (table 1), each of which georeferencing settlements absent from the other existing settlement layers. This has motivated the current work to generate an improved high-resolution settlement map by combining the datasets listed in table 1. Appendix 1 gives more detail on the processing done on each data set. The resulting settlement map was used to improve population density predictions and EA delineation.

# **2.4.3 High-resolution population density estimates for Somalia**

A high spatial resolution map (100m x 100m) is necessary to estimate the population of each sub-region created during the automated EA delineation process. Multiple high to moderate resolution global modeled population datasets are freely accessible to download including WorldPop 7
estimate the 30A Program impact. The results are presented in Tables A.8 to A.11 in Appendix A. Overall, the estimated program effects on both individual-level and householdlevel outcomes remain similar to those reported in Tables 2 to 4. These findings suggest that our estimates are not biased by the 135 Program effects.

# **5.2. Further robustness analysis**

We conduct a number of additional robustness checks that support our estimation results. First, we perform several falsification (placebo) tests. In the first test, we examine the placebo program effects using the pre-program data, specifically the VHLSSs from 2004 to 2008. We consider 2008 as the post-program year and 2004 and 2006 as the pre-program years. We apply equation (3) to estimate the placebo effect on both individual-level and householdlevel outcomes in 2008. The results, presented in Tables A.12 and A.13 in the Appendix, indicate that the variable 'Program*Year 2008' is not statistically significant in nearly all regressions. It is only statistically significant at the 10% level in the regressions of 'wage job'.<sup>7</sup> In the second test, we exclude the 30A Program districts and allocate the control districts to a placebo treatment using cutoff points derived from the 2006 MOLISA poverty rate. Subsequently, we employ the same model specifications to estimate the impact of these placebo cutoffs on individual and household outcomes. Tables A.14 and A.15 report the effects on the individual-level outcomes at placebo cutoff points at 30% and 40%. It shows that there is only a marginally statistically significant effect of the cutoff point at 40% on health care utilization at the 10% level. We also conduct similar analysis for the household-

> 7 We also estimate the placebo effects when using 2006 and 2008 as the post-program years and 2004 as the pre-program year. The results are very similar, demonstrating that the placebo program has no statistically significant effects on almost all the outcome variables.

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phones by fishermen and wholesalers is associated with a dramatic reduction in price dispersion, and increased market efficiency, given the high degree of perishability of fish. As a result, both consumer and producer welfare increased. The overall net effect is positive on the producer side, with profit increases coming from the changes in the price and quantity sold in the market, partially offset by the costs associated with mobile phones and increased travel to take advantage of arbitrage. On the consumer side, prices decrease on average with the introduction of mobile phones.

Finally, mobile phone coverage also has the potential to trigger collective action as well, by enabling people to acquire and spread information about living conditions and to organize a civic response. Manacorda and Tesei (2020) test whether mobile phone technology has the potential to foster mass political mobilization and explore the underlying channels of impact in the context of Africa between 1998 and 2012. The results show that a fall in national GDP growth of 4 percentage points leads to a differential increase in protests per capita of between 8 and 23 percent, comparing areas with full 2G mobile phone coverage to those that have none. In a similar vein, Pierskalla and Hollenbach (2013) investigate the impact of cell phone technology on violent collective action in Africa. The availability of cell phone coverage significantly and substantially increases the probability of violent conflict by improving cooperation and coordination within rebel groups in the continent.

# **4. Development Impact of Energy Infrastructure**

There is a strong link between energy and economic development. Many studies on advanced economies focus on the historical impact of grid access on rural households. Electrification increases demand for labor in the rural areas in the United States (Kitchens and Fishback, 2015), provides workers with stronger bargaining power in Sweden (Molinder et al, 2021), and drives structural change through the decline of employment share in agriculture in the United States (Gaggl et al, 2021) and Norway (Leknes and Modalsli, 2020). Lewis and Severnini (2020) show that the rural U.S. counties with early access to electricity experienced increased economic growth compared to their counterparts. This impact persisted decades after the county was fully electrified. Electrification also has long-lasting effects on the productivity of firms. Using cityindustry level data from the United States for the years 1890 – 1940, Fiszbein et al (2020) show a rapid increase in the labor productivity rates in the manufacturing sector.

As in developed economies, electricity is a key input to producing goods and services and improving the quality of life in developing countries. Therefore, achieving universal access to electricity is an important policy goal for developing countries. Despite large investments, relatively little is known about the causal effects and channels of electrification on economic outcomes, such as on household welfare, firm productivity, or economic growth. This section 14
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# **Empirical examples**

In each of the four examples, we re-estimated the inefficiency scores allowing for multiple quality dimensions or multiple outputs. In the education examples, instead of averaging test scores across subjects we allowed for four quality dimensions in the California case and three in the PISA case. In the health systems example, we also allowed for multiple quality dimensions, allowing for six separate amenable causes of mortality. In the hospital example, we allowed for three outputs, outpatient cases, inpatient cases and surgery cases.

Figure 6 compares the distributions of inefficiency scores for the single- and multipleoutput exercises. In the education examples, the switch to multiple outputs/dimensions results in an appreciable leftward shift of the inefficiency distribution; many units that looked inefficient when outputs or quality are reduced to one dimension look less inefficient when we allow for different cost structures across different outputs or quality dimensions. This is also evident comparing the two columns in Table 2 for each study. In the California case, the shift is quite dramatic, reducing mean inefficiency from $3,997 to $1,931. The top two maps in Figure 9 show this graphically: the right-hand map, which allows for multiple outputs, is considerably lighter in shade than the left-hand one, which is based on average test scores across all subjects. Figure 7 sheds light on how much reranking goes on in the move from the single-outcome to multipleoutcome exercises. In the California case, the scatter plot is quite busy, implying a fair amount of reranking. The Vietnam scatter is much tidier.

# V. **ALLOWING FOR EXOGENOUS CONSTRAINTS ON EFFICIENCY**

A common response from policymakers and managers when confronted with estimated efficiency scores from DEA and SFA is that the analyst has made no allowance for factors that are beyond the policymaker‟s or manager‟s control that prevent them from operating on the frontier. A school may be operating in a catchment area with a very poor population and may not receive additional funding to compensate. A health facility may be operating in a sparsely populated or topologically challenging area, making outreach difficult.

In DEA and SFA, the response has typically been to „explain‟ inefficiency scores through regression analysis to such constraints and other influences on efficiency that are under the control of the decision-making unit, although surprisingly, such analysis does not, as one might