rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10822

# **Abstract**

The world is experiencing a severe loss of biodiversity, highlighting the need for a strong global conservation strategy. Effective conservation depends on accurate information about where endangered species live and the local threats they face. Using data from the Global Biodiversity Information Facility, this paper creates threat and protection indicators for more than 600,000 species, including animals, plants, and fungi. The indicators include habitat size, level of protection, nearby population density, and specific threats like population encroachment for land species and fishing activity for marine species. The paper then uses an ordered logit model to analyze the relationship between these indicators and the extinction risk categories assigned by the International Union for Conservation of Nature. The model is based on 87,731 species in the Global Biodiversity Information Facility database that have been assessed by the International Union for Conservation of Nature. The results are used to predict threat levels for 512,675 species without International Union for Conservation of Nature ratings, revealing many more potentially threatened species and changing the maps of “conservation hotspots.” The paper concludes by noting that its methods can support rapid updates of species maps and threat indicators as the Global Biodiversity Information Facility database continues to grow.

This paper is a product of the Development Research Group, the Development Data Group, Development Economics and the Environment, Natural Resources and Blue Economy 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 bblankespoor@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
# Table 9: Descriptive statistics on pay and tenure for YPs and non-YPs , May 1997

|YPs|Part I men|Part II men|Part I women|Part II women|
|---|---|---|---|---|
|Current salaries|100|99.5|83.3|81.2|
|Salaries at entry|100|97.8|103.6|105.0|
|Mean annual growth(%)|3.83|3.94|3.49|3.38|
|Mean tenure (years)|16.1|16.5|11.1|10.7|
|Non-YPs|Part I men|Part II men|Part I women|Part II women|
|Current salaries|100|92.6|87.2|82.4|
|Salaries at entry|100|90.5|80.1|72.7|
|Meanannualgrowth(%)|3.15|3.19|3.90|3.92|
|Mean tenure (years)|11.1|11.4|10.6|11.3|

Table 10: Descriptive statistics on current grades of YP sample

||22|23|24|25|26|27|28|29|30|Total|
|---|---|---|---|---|---|---|---|---|---|---|
|% at grade currently|14|6|23|23|23|4|7|3|--|100|
|Mean years in grade|1.8|1.9|7.9|4.7|8.3|4.2|6.4|4.6|--|5.7|
|N|30|14|50|50|49|9|15|6|--|217|
|**.**....<br>:TT<br>LEE:ii<br>E2.................**...**<br>; .. .<br>I<br>~.'<br>.:<br>.i ,<br>-<br>Pa I Wvomen::g;|;|.<br>:<br>:-.;;:-|i||.<br>:: : :|: : : : : : :: : : :|: i : :!~ ;iA: ;|:S E E<br>...... i..|S4i0ihE<br>ii,50EiSg<br>..........<br>.**.**.**.**ESi|||
|<br>% at grade currently|23|21|25|12|13|3|_5_|--||100|
|Meanyearsingrade|1.8|2.3|5.4|1.8|3.3|0.7|4.6|--|--|3.1|
|N<br>|25<br> <br>|23<br> <br>|28<br> <br>|13|14|3|_5_|--|-|111|
|..**..** **.**..**.**..**..**..**.**... ....<br>PrIIM-- ;:iXi d:|:;enE::<br>f;i;:!;|S;;tVS<br>;giE|.<br> <br>E:|**..**<br>. .....|...|||||. ......................................<br>.|
|% at grade currently|5|12|28|21|22|4|8|2|--|100|
|Mean years in grade|1.7|2.8|6.8|3.6|6.1|_5.1_|4.7|4.0|--|_5.0_|
|N|9|21|48|35|37|6|14|4|--|170|
|11 .. iE.._.._iE........-|||||||||||
|% at grade currently|22|18|35|13|8|0|3|--|--|100|
|Meanyears in grade|2.1|2.3|5.7|3.6|3.4|0|1.8|--||3.7|
|N<br> <br>|13<br>|11|21<br>|8<br><br>|5|0|2|--|--<br> <br>|60|
|~~~~~~~~~~~...<br>.-i- a|||d<br>.. ..|**...**..**...**..<br>.- .i**l**}?.||||..........|...**...**<br>R i**E.**<br>LiiE**.**<br>.....||
|% at grade currently|14|12|26|19|19|3|6|1|-|100|
|Mean years in grade|1.8|2.4|6.8|3.9|6.6|3.9<br>|5.2|4.4|--|4.8|
|N|77|69|147|106|105|18|36|6|-|564|

41
22

|**Independent variable**|**Guatemala**|**India**|**Indonesia**|**Kenya**|**Pakistan**|**Sri**<br>**Lanka**|
|---|---|---|---|---|---|---|
|Dummyfor engagingin agriculture<sup>d</sup>|–0.014|–0.22|–0.010|–0.039|–0.14|–0.22|
|Highest level of education for men|–0.001|0.021|0.001|0.006|–0.005|0.014|
|Highest level of education for women|0.007|0.009|0.002|0.009|0.046|–0.0002|
|Age of household head|—|0.001|0.0002|—|0.001|0.002|
|Dummyfor male household head|—|0.046|—|—|—|–0.0002|
|Dummy for indigenous household head|0.043|—|—|—|—|—|
|Dummyfor urban residence|0.029|0.17|—|0.033|0.33|0.23|
|Dummy for electricity connection|–0.069|0.048|0.040|0.086|0.18|0.38|
||**Unconditiona**|**l margi**|**nal effect**||||
|Log of household expenditure|0.26|0.74|0.32|0.28|0.73|0.59|
|Logof LPGprice|–0.41|–0.96|–0.79|–0.75|–1.0|–0.91|
|Log of firewood price|0.11|0.18|—|—|—|0.095|
|Logof charcoalprice|—|—|—|0.042|—|—|
|Log of kerosene price<sup>a</sup>|—|0.77|0.053|—|0.91|0.065|
|Logof keroseneprice,other<sup>b</sup>|—|0.073|—|—|—|—|
|Number of rooms<sup>c</sup>|—|—|0.0001|0.001|–0.010|0.026|
|Dummyfor house ownership|0.11|—|—|—|0.19|—|
|Dummy for car ownership|—|0.094|0.042|—|0.23|0.20|
|Household size|–0.042|–0.038|0.031|–0.017|–0.036|–0.029|
|Household size squared|—|0.000|–0.001|—|—|0.003|
|Dummyfor engagingin agriculture<sup>d</sup>|–0.29|–0.28|–0.016|–0.042|–0.14|–0.36|
|Highest level of education for men|0.005|0.046|0.002|0.006|–0.002|0.031|
|Highest level of education for women|0.028|0.042|0.003|0.009|0.048|0.021|
|Age of household head|—|0.001|0.0004|—|0.001|0.002|
|Dummyfor male household head|—|0.046|—|—|—|0.018|
|Dummy for indigenous household head|–0.18|—|—|—|—|—|
|Dummyfor urban residence|0.27|0.32|—|0.035|0.30|0.42|
|Dummy for electricity connection|0.25|0.25|0.055|0.096|0.18|0.54|

_Source_ : Authors’ calculations _Note_ : — = coefficient not significant in either equation.

a. For India, the price of subsidized and rationed kerosene distributed through the Public Distribution System.

b. Price of unsubsidized kerosene in India.

c. Number of bedrooms for Sri Lanka, floor area for Indonesia.

d. This variable is not available in Pakistan and is replaced by an alternative dummy that is 1 if the household owned, released, or rented agricultural land.

In certain cases variables are significant in the selection equation but not in the consumption equation. The conditional and unconditional marginal effects on consumption depend not only on the sign of the coefficient in the selection equation but also on the sign of the inter-equation error correlation (rho). As noted in appendix 3, a positive rho can reverse the sign of the variable identified in the selection equation in the calculation of the conditional marginal effect. The number of rooms and the highest level of male education in Pakistan yielded positive coefficients in the selection equation, but their conditional and unconditional effects, determined in part by the positive values of rho, are negative—the correlation of the variable omitted from
- 18 -

<u>Table 6. Regression Results for Bids in Water Project Procurements</u>

||OLS|OLS|OLS|
|---|---|---|---|
|Entrant bid dummy||-2.04|17.83|
|||(7.59)|(11.68)|
|Dummy for entrant facing with incumbent(s)|||-22.52 <sup>**</sup><br>(9.92)|
|Share of incumbent bidders in auction||34.12 <sup>**</sup>|71.09 <sup>***</sup>|
|||(14.11)|(25.94)|
|Number of contracts before 2002||-4.85|-8.36 <sup>**</sup>|
|||(3.09)|(3.81)|
|ln(_N_)|-11.82 <sup>**</sup>|-7.30 <sup>*</sup>|-5.57 <sup>*</sup>|
||(4.85)|(3.80)|(3.46)|
|Water|6.56|10.11|14.52 <sup>*</sup>|
||(6.68)|(7.38)|(8.44)|
|Treatment plant|-89.28 <sup>*</sup>|-78.63 <sup>*</sup>|-111.28 <sup>**</sup>|
||(47.95)|(45.97)|(55.28)|
|Network|13.65|11.21|16.25|
||(18.18)|(17.19)|(18.63)|
|ln(_Treatment capacity_)|9.42 <sup>**</sup>|8.51 <sup>*</sup>|11.29 <sup>**</sup>|
||(4.62)|(4.40)|(5.20)|
|ln(_Tunnel network length_)|0.24|0.52|0.30|
||(0.44)|(0.37)|(0.42)|
|ln(_Iron pipe network length_)|0.95 <sup>***</sup>|1.00 <sup>***</sup>|0.61 <sup>*</sup>|
||(0.27)|(0.23)|(0.37)|
|Contract duration|0.29 <sup>*</sup>|0.40 <sup>**</sup>|0.28 <sup>*</sup>|
||(0.18)|(0.19)|(0.16)|
|Donor 1|-2.41|-8.92|-7.54|
||(6.97)|(8.43)|(7.79)|
|Constant|160.02 <sup>***</sup>|141.89 <sup>**</sup>|158.52 <sup>***</sup>|
||(59.20)|(55.06)|(59.14)|
|Obs.|236|236|236|
|R-squared|0.360|0.400|0.422|
|_F_-statistics|15.39|14.05|11.25|

Note: The dependent variable is the bidding amount. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively. Source: Author’s calculation.
exceed 1 in absolute value. This sample is notably smaller than the main sample used in our analysis. However, the various negative coefficients on the TSC path in this sample suggest it is unlikely that TSC implementation was _positively_ correlated with pre-program trends in cognitive achievement.

# **7 Mechanism check: Effect by timing of exposure**

How plausible is it that the difference-in-differences effects we have estimated indeed reflect causal effects of sanitation on cognitive achievement? One further way to asses this is to analyze the apparent mechanisms of the effect, to assess whether they match what is known about the effects of early life health and disease on human capital from the literature. In this section, we examine the age at which exposure to poor sanitation shapes subsequent cognitive achievement. Section 4.5 reviewed evidence in the literature that health and net nutrition in the first year of life are particularly important for subsequent cognitive achievement. Therefore, we expect exposure to the TSC in a child’s first year to have the steepest gradient with learning outcomes.

We investigate this in two ways, using the same ASER test score data and TSC administrative records as in our main analysis. First, we continue to focus only on six-year-old children, but use the same identification strategy to identify an “effect” of exposure to the TSC in other years of life. Because we have more years of TSC implementation data than of ASER test score data, we can displace the TSC exposure in time without reducing the sample. Second, we replicate our estimation strategy for children of different ages, holding constant the years of TSC implementation used as the independent variable; this has the result of varying the year of the child’s life in which TSC exposure is being assessed.

Our finding that the sanitation-learning gradient is steepest for exposure in the first year of life has two implications, both of which support a causal interpretation of our result. First, and most importantly, they match the mechanism that we would expect, if the effect 25
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# **Table 1. Labor Force Characteristics for Cambodia and Sri Lanka**

||Panel A:<br>|Cambodia<br>|||||||
|---|---|---|---|---|---|---|---|---|
||1996|1999|2004|2007|2008|2009|2010|2011|
|_Employment and Education_|||||||||
|Labor force participation rate, all|33.3|56.6|17.0|27.0|21.2|20.8|25.5|27.6|
|Labor force participation rate, male|43.5|73.1|20.6|31.6|27.5|25.2|31.1|32.5|
|Labor force participation rate, female|24.1|41.0|13.9|22.3|15.5|16.8|20.4|23.3|
|Females in employment (percent)|52.3|51.5|50.1|49.2|48.6|49.7|50.8|51.2|
|Hours worked, all|39.3|45.7|37.1|39.7|39.2|36.0|39.5|39.4|
|Hours worked, male|41.3|46.1|38.0|41.0|40.6|37.3|40.8|40.9|
|Hours worked, female|37.5|45.4|36.1|38.3|37.7|34.7|38.3|38.0|
|Years of education|3.4|4.5|5.2|5.5|5.7|4.9|5.9|6.3|
|Years of education for females in T&G|4.5|5.5|6.1|6.1|5.7|5.9|6.3|6.9|
|Years of education for men in T&G|5|7.5|7.3|8.3|7.3|6.9|7.8|8.2|
|Average years of education in T&G|4.6|5.8|6.3|6.5|6.1|6.1|6.5|7.1|
|_Employment Share of the Industry_|||||||||
|Agriculture (percent)|74.3|63.5|60.7|51|45.2|57.4|46.8|48.4|
|T&G (percent)|1.6|4.2|4.3|4.7|5.5|5.3|6.5|7.8|
|Share of female in T&G (percent)|81.7|82.8|81.1|82.3|78.7|82.6|85.2|84.9|

_Source:_ Calculations based on the Cambodia Socio-Economic Survey (CSES). _Note:_ T&G = textiles and garments.
sources and definitions (Section 2.1), along with empirical methodology (Section 2.2). Section 3 discusses both the benchmark results as well as the robustness of these results to alternative specifications and measurements (Section 3.3) and more stringent endogeneity testing (Section 3.4). The section also attempts to tease out the manner by which interaction effects (Section 3.5) and subsamples (Section 3.6) be driving the key findings. A final section concludes with some reflections on policy implications.

# **2 Data and methodology**

## **2.1 Data sources and**

The dataset for the investment regressions is an unbalanced country-level panel, covering up to 129 economies over 5-year periods between 1980–2009.<sup>1</sup> Variables for the benchmark regressions were sourced from the World Bank’s World Development Indicators (WDI) as well as Financial Development and Structure (Beck, Demirg ̈u ̧c-Kunt & Levine 2000) databases, the International Country Risk Guide (ICRG), and Chinn & Ito (2008). Additional variables included in the robustness tests were drawn from the World Bank’s Global Economic Monitor (GEM) and Doing Business databases, Beck, Clarke, Groff, Keefer & Walsh (2001), and Laeven & Valencia (2012).

Full details of variable sources, definitions, and other summary statistics are given in Appendices A.1, A.3, and A.4. Two important statistical features are worth noting. First, the standard deviation in the institutional and structural variables, while small relative to the level of investment, are nevertheless larger than most of the economic controls, which supports the notion that variations in the former may be important for better understanding cross-country investment patterns.

Second, the correlation among the distinct families of institutional and structural variables considered is actually fairly small; the highest correlation is between institutional quality and financial development ( _ρ_ = 0 _._ 56), and even then the relationship is not particularly strong. This suggests that the various variables of interest are sufficiently distinct—statistically speaking—to warrant their inclusion as independent variables.

Given the centrality of structural factors in this paper, we briefly discuss here the definitions for the main institutional and structural variables of interest, along with the motivation behind their selection. To accommodate the host of variables that we consider, we organize them into various classes of determinants, as suggested by theory.

One important factor we consider is the level of maturity of the financial sector as well as its its structure, which are measured respectively by domestic credit to the private sector (as a share of GDP) and the ratio of the total value traded in the stock market to domestic credit. Constraints arising from limited access to finance have the potential to adversely affect

> 1In the preferred benchmark specifications, however, the sample coverage is 105 economies. These are listed in Appendix Table A.2.

4
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column 3, the first-stage regression results are consistent with Espey and Towfique (2004): population concentration has a negative and significant effect on treaty formation. The small F-statistic from the first stage is consistent with weak instruments in this just-identified model.<sup>25</sup> We then add five additional instruments to the model. In column 4, we include two variables describing a country’s membership in conventional international organizations. Strong trade ties and other such links are correlated with treaty formation (Espey and Towfique 2004, Zawahri and Mitchell 2011).<sup>26</sup> The instruments are counts of the number of global organizations and of multiregional organizations in which the country was a member during the period 1952-97. The Center for Systemic Peace (Marshall _et al._ 1999) classifies the organizations and provides data on membership. Another pair of instruments describes historical forms of governance and political participation. We use country-level historical averages (1940-2013) of the weighted autocracy index and the weighted democracy index from the Polity IV database of the Center for Systemic Peace (Marshall et al. 2013).<sup>27</sup> Although these characteristics may drive treaty formation, we do not have strong priors on the direction of this correlation: democratic regimes may be more likely to cooperate, as is often posited, but autocratic regimes may find it easier to conclude agreements without popular support. Finally, we add an the interaction between downstream country and a treaty, similar magnitudes for the two coefficients, and neither is significantly different from zero.

25 A cluster-robust Anderson-Rubin-Wald test fails to reject the null hypothesis that the overidentifying restrictions are valid.

26 If water treaties and membership in other international agreements both formalize an underlying propensity to cooperate, however, the exclusion restriction might be problematic for our equations.

27 Both indices capture competitiveness of political participation, openness/competitiveness of recruitment of the chief executive, and constraints on the chief executive. The autocracy index also captures regulation of political participation. Because distinct elements of autocratic and democratic authority may co-exist in a single regime, the two indices enter our model as separate instruments (Marshall _et al._ 2013).

19
any of the first five rounds of the phone survey preceding Round 6, and 0 otherwise.

# **_(ii) Contact with Their Teacher_**

To define the contact between students and their teacher, we use the question asking whether children or else in the household were in touch with their teacher - a anyone question that was asked only in Round 1, Round 2, and Round 5 of the Nigeria COVID-19 NLPS. We generate a binary variable equal to 1 for an individual residing in a household who answered "Yes" to this question in any of the survey Rounds for which the question was asked, and 0 otherwise. Table 1 shows that 52% of students have been in contact with teachers during the period of protracted schools’ closure.

# **2.2.7 COVID-19 and the Opportunity Cost of Schooling**

Central to this study is the hypothesis that lockdown measures induced by the outbreak of COVID-19 undermine children’s school resilience. Nigeria offers an excellent context for testing this hypothesis because according to UNICEF’s 2013 estimates, in absolute value, one in every five of the world’s out-of-school children lives in Nigeria. Moreover, various factors inherent to the country’s socioeconomic fabric drive education deprivation, including geography, poverty and socio-cultural norms and practices that discourage attendance in formal education, especially for girls.<sup>17</sup> This multiplicity of potential factors raises the challenge of identifying the impact of COVID-19 on school resilience.

Descriptive statistics presented in Table 8 show the impact of COVID-19 lockdown measures on proxies/determinants of the opportunity cost of schooling to parents. We built this table using survey respondents’ answers to various questions about household socioeconomic conditions contained in the 2020 COVID-19 NLPS phone surveys. For example, in the COVID-19 NLPS round 4 conducted in August 2020, households were asked to compare their current income from various sources to their income in August 2019, and to indicate whether these incomes had increased, decreased, or remained the same.

> 17See UNICEF Nigeria, 2013. Education. Available online at `https://www.unicef.org/nigeria/ education`

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Learning from Developing Country Power Market Experiences: The Case of Philippines Table 5‐3 Evolution of energy demand, peak power demand and generation capacity in Philippines (including Luzon, Visayas and Mindanao).

|**Year**|**System peak**<br>**demand**<br>**(GW)**|**Dependable**<br>**generation**<br>**capacity (GW)**|**Total Electricity**<br>**Consumption**<br>**(TWh)**|
|---|---|---|---|
|**2005**|8.6|13.6|45.2|
|**2006**|8.8|13.6|45.7|
|**2007**|9.0|13.5|48.0|
|**2008**|9.1|13.0|49.2|
|**2009**|9.5|13.3|50.9|
|**2010**|10.4|13.9|55.3|
|**2011**|10.4|14.5|56.1|
|**2012**|10.8|15.1|59.2|
|**2013**|11.3|15.4|61.6|
|**2014**|11.8|15.6|63.3|
|**2015**|12.2|16.4|67.8|
|**2016**|13.3|19.1|74.2|
|**2017**|13.8|20.5|77.8|
|**CAGR 2005‐17**|**4.0%**|**3.5%**|**4.6%**|

Source: Department of Energy.

<!-- Start of picture text --> GW<br> 20.0 35%<br> 18.0<br>30%<br> 16.0<br> 14.0 25%<br> 12.0<br>20%<br> 10.0<br>15%<br> 8.0<br> 6.0 10%<br> 4.0<br>5%<br> 2.0<br> ‐ 0%<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Peak demand + Reserves [MW] Dependable generation capacity [GW]<br>Reserve margin (%)<br><!-- End of picture text --> Figure 5‐5 Evolution of peak power demand, generation capacity and system reserve margin in the WESM*.

* Visayas included since 2010 due to its integration to WESM; Mindanao excluded. A reserve requirement of 23.4% was considered.

Source: Department of Energy.

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