rafmacalaba/datause-encoder-data huggingface dataset:
Before estimating whether the growth pattern is correlated with poverty reduction, we checked whether aggregate growth in per capita value added is correlated with poverty reduction. In all models, except for non upper middle income countries, we find that growth explains poverty reduction. For upper middle income countries, only changes in inequality affect growth. It is also interesting to note that in negative growth episodes both aggregate growth and changes in distribution have a reduced power in explaining poverty changes, but their coefficients are significantly larger than in periods of positive growth. This result highlights possible asymmetries in the way poverty and per capita value added growth are related. Table 2 shows the results.

**<u>Table 2: Percent Change in Headcount Ratio and Aggregate Growth</u>**

||(1)<br>Benchmark|(2)<br>Controls|(3)<br>Non Upper<br>Middle<br>Income|(4)<br>Non LAC|(5)<br>Positive<br>Growth|(6)<br>Negative<br>Growth|
|---|---|---|---|---|---|---|
|Change in Y/N|||||||
|(%)|-3.616***|-3.650***|-1.479|-4.848***|-3.280***|-9.604*|
||(-3.95)|(-4.19)|(-1.69)|(-3.39)|(-3.87)|(-1.90)|
|Change in Gini|||||||
|(%)||2.562***|1.565***|2.375***|1.729***|5.081*|
|||(2.82)|(3.94)|(3.03)|(3.33)|(2.03)|
|Constant|0.151*|0.158*|0.00409|0.310*|0.130|-0.0687|
||(1.79)|(1.92)|(0.06)|(1.75)|(1.41)|(-0.28)|
|Observations|104|104|63|41|73|31|
|Adjusted R-<br>squared|0.139|0.241|0.142|0.283|0.215|0.177|
|*p<.10,**p<.0|5,***p<.01.|T-statistics inp|arentheses.||||

Table 3 shows the results of estimating Equation 3. Both the aggregate productivity intensity of growth and the aggregate employment intensity are negatively correlated with poverty changes, with employment having a slightly smaller significance level. In periods of negative growth, however, only employment changes seem to matter. A test for the equality of the coefficients cannot be rejected in any of the models estimated. Therefore, it appears that the impact of productivity-intensive growth is not different from employment-intensive growth. If anything, we can conclude that it is productivityintensive growth that is more robustly correlated with poverty reduction, with the possible exception being during recessions.

To estimate whether growth patterns may matter for poverty alleviation we estimate Equation 4 using a three sector disaggregation (primary, secondary and tertiary) and a seven sector disaggregation (agriculture, mining and utilities, manufacturing, construction, commerce, services, and transport and communications). The results suggest that only the secondary and tertiary sectors and their sub-sectors have any impact on poverty. However, neither in the three sector disaggregation nor in the seven sector case could we reject equality of coefficients across 18
||**Table A**|**10: Interactio**|**n term results**|**forgender sp**|**ecific labor law**|**s**|||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Exports (%, cell average)*Women<br>can work the same night hours as<br>men Y:1 N:0|0.247***<br>(0.087)|0.250***<br>(0.087)|0.231***<br>(0.089)|0.265***<br>(0.097)|0.276***<br>(0.098)|0.318***<br>(0.091)|0.301***<br>(0.097)|0.305***<br>(0.095)|
|Exports (%, cell average)|-0.056|-0.047|-0.044|-0.066|-0.054|-0.128|-0.618|-0.626|
||(0.081)|(0.081)|(0.080)|(0.088)|(0.089)|(0.083)|(0.514)|(0.535)|
|Women can work the same night|-9.626***|-9.150***|-8.745***|-6.629**|-6.797**|0.503|0.482|-0.971|
|<br>hours as men Y:1 N:0|(2.700)|(2.767)|(2.760)|(2.872)|(2.889)|(2.945)|(3.945)|(3.954)|
|Exports (%, cell average)*GDP per|||||||0.056|0.065|
|<br>capita (logs)|||||||(0.059)|(0.078)|
|Exports (%, cell average)*WBL<br>Index||||||||-0.112<br>(0.392)|
|No. of Workers (logs)*Women can|||||||0.055|0.445|
|<br>work the same night hours as men|||||||(0.715)|(0.726)|
|<br>Y:1 N:0|||||||||
|No. of Workers (logs)*WBL Index||||||||-3.368*<br>(1.732)|
|No. of Workers (logs)||0.533**|0.699***|0.743***|0.903***|0.902***|0.867|2.887**|
|||(0.243)|(0.257)|(0.280)|(0.291)|(0.291)|(0.655)|(1.271)|
|GDP per capita (logs)||13.906***|14.101***|15.962***|15.592***|5.876|4.688|4.791|
|||(5.151)|(5.346)|(5.348)|(5.748)|(5.710)|(5.768)|(5.740)|
|Herfindahl-Hirschman Index|||<br>-6.321*|<br>-4.724|<br>-8.467**|<br>-5.197|<br>-5.364|<br>-5.022|
||||(3.499)|(3.528)|(4.117)|(3.662)|(3.714)|(3.709)|
|Age of Firm (logs)|||<br>-0.522|<br>-0.914***|<br>-0.689*|<br>-0.833**|<br>-0.842**|<br>-0.831**|
||||(0.357)|(0.339)|(0.362)|(0.358)|(0.359)|(0.359)|
|Foreign Ownership (proportion)|||<br>-0.904|<br>-0.156|<br>0.030|<br>0.103|<br>0.081|<br>0.130|
||||(0.844)|(0.879)|(0.970)|(0.961)|(0.962)|(0.962)|
|Female Ownership Y:1 N:0||||6.203***|6.013***|5.975***|5.968***|5.987***|
|||||(0.615)|(0.651)|(0.652)|(0.653)|(0.654)|

73
# **Figure 14: Comparison of maize SV at farm level and parity prices (USD/ton)**

<!-- Start of picture text --> Maize, Farm Level SV (USD per MT) Maize prices at the farm-gate, US$/ton<br>200<br>180<br>300<br>160<br>250<br>140<br>200<br>120<br>150<br>100<br>100<br>80<br>60 50<br>40 0<br>20<br>-<br>Malawi Moz ThaiBrazil Nigeria Zam Cameroon<br>Malawi 2005Malawi 2006Malawi 2007Malawi 2008Thailand 2006Mozambique 2006Brazil 2006Cameroon 2007Zambia 2005/06Nigeria 2006<br>OPV FAM(L) FAM(H) FAM(L) FAM(H) FAM LCF FAM ECF LCF FAM ECF LCF FAM(L) FAM(H) COM AGI<br><!-- End of picture text --> 63. As shown in Figure 14, compared to other countries in the region and elsewhere, Malawi is a high cost producer of maize. Compared with neighboring Mozambique, for example, it costs more than three times as much to produce a ton of maize in Malawi. Similarly, compared with family and emerging commercial farmers in Zambia, Malawi also has higher costs. Only in the case of Cameroon, where the models were based on production in a remote inland area with high fertilizer costs, are the costs for family sector farmers similar to those in Malawi. _There are minimal benefits that may be obtained from use of open pollinated maize varieties (OPV) and small-scale irrigation._ 65. The next two charts in Figure 15 show the build- up of total SV for FAM-low maize at import and export parity prices including farm-level and into-mill assembly costs. In this case, the same overall pattern applies to OPV, FAM-high, and irrigated maize and these data are not needed to illustrate the value chain cost build-up.

66. In the first chart, trader profits are based on an import parity price of USD 379 per MT, the total profits paid to the trader with an import parity price amount to USD 117 (MWK 16,380) per MT. In actual fact, however, these profits are likely to be shared between multiple traders and are sometimes captured by farmers themselves to the extent individual producers are able to play an assembly roll by selling maize. The analysis also shows relatively high maize producer prices at the farm-gate (see Figure 14, second graph). At MWK45/kg and above, Malawi’s maize producer price is higher than in most of the comparator countries and suggests that maize production may become a more profitable enterprise for the producer. But such high producer prices equally contribute to higher food costs for urban consumers, inflate the total SV for assembled grain, and ultimately dampen the prospects for trade competitiveness.

27
Policy Research Working Paper 7201

# **Abstract**

Inorganic fertilizer use across Sub-Saharan Africa is generally considered to be low. Yet, this belief is predicated on the assumption that it is profitable to use rates higher than currently observed. However, there is little rigorous empirical evidence to support this notion. Using a nationally representative panel data set, and with due recognition of the role of risk and uncertainty, this paper empirically estimates the profitability of fertilizer use for maize production in Nigeria. The analysis finds that inorganic fertilizer use in Nigeria is not as low as conventional wisdom suggests.

Low marginal physical product and high transportation costs significantly reduce the profitability of fertilizer use. The paper finds evidence that strategies to reduce transportation costs are likely to have a much larger effect on the profitability of fertilizer use than fertilizer subsidies. Apart from reduced transportation costs, other constraints such as timely access to the product; availability of complementary inputs such as improved seeds, irrigation, and credit; as well as good management practices are also necessary for sustained agricultural productivity improvements.

This paper is a product of the “Agriculture in Africa—Telling Facts from Myths” project managed by the Office of the Chief Economist, Africa Region of the World Bank, in collaboration with the African Development Bank, the Alliance for a Green Revolution of Africa, Cornell University, Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, and the University of Rome Tor Vergata. 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 author of the paper may be contacted at lliverp@msu.edu, the task team leader of the “Agriculture in Africa—Telling Facts from Myths” project at lchristiaensen@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 5_** _– Marginal effects after Probit - Remittances_

|**Dependent variable = 1 if the house**|**hold receive**<br>[1]|**d a remittanc**<br>[2]|**e in the las**<br>[3]|**t 12 months**<br>[4]|[5]|[6]|
|---|---|---|---|---|---|---|
|**treated**|**-0.033 *****|**-0.030 ****|**-0.027 ****|**-0.028 ****|**-0.023 ****|**-0.015 *****|
||[0.012]|[0.012]|[0.011]|[0.011]|[0.010]|[0.007]|
|**Village level shocks**|||||||
|Lower yields due to drought or floods||**-0.012**|**-0.013**|**-0.011**|**-0.010**|**-0.007 ****|
|||[0.012]|[0.011]|[0.011]|[0.009]|[0.005]|
|Rise in food prices||**-0.003**|**-0.002**|**-0.002**|**0.000**|**0.000**|
|||[0.012]|[0.011]|[0.010]|[0.009]|[0.004]|
|**HH level shocks**|||||||
|Crop disease|||**-0.003**|**-0.001**|**-0.002**|**-0.001**|
||||[0.011]|[0.011]|[0.010]|[0.004]|
|Livestock died or stolen|||**0.032 ****|**0.031 ****|**0.029 ****|**0.020 *****|
||||[0.019]|[0.019]|[0.017]|[0.012]|
|Theft|||**0.019**|**0.019**|**0.017**|**0.011**|
||||[0.023]|[0.023]|[0.022]|[0.013]|
|House damage|||**-0.006**|**-0.005**|**-0.004**|**-0.001**|
||||[0.010]|[0.010]|[0.009]|[0.004]|
|Safety net (food)|||**0.003**|**0.002**|**0.001**|**-0.001**|
||||[0.011]|[0.010]|[0.010]|[0.004]|
|Safety net (agr. inputs)|||**0.012**|**0.014**|**0.011**|**0.004**|
||||[0.011]|[0.011]|[0.010]|[0.005]|
|**HH assets**|||||||
|Furnitures (mattress, table, chairs)||||**-0.007**|**-0.007**|**-0.004**|
|||||[0.013]|[0.011]|[0.003]|
|Animal (chickens, pigs, etc)||||**0.007**|**0.006**|**0.002**|
|||||[0.017]|[0.015]|[0.006]|
|Agr tools (axe, sickle, hoe)||||**-0.035 ***|**-0.036 ****|**-0.023 ****|
|||||[0.026]|[0.026]|[0.018]|
|**HH characteristics**|||||||
|HH size|||||**0.001**|**0.001**|
||||||[0.002]|[0.001]|
|Chronically ill adults|||||**-0.005**|**-0.003**|
||||||[0.009]|[0.003]|
|Presence of orphans|||||**-0.024 ****|**-0.011 ****|
||||||[0.012]|[0.007]|
|Deaths in the past 5 years|||||**0.012**|**0.007**|
||||||[0.012]|[0.007]|
|Disable persons|||||**-0.014**|**-0.005**|
||||||[0.008]|[0.004]|
|**Head of HH's charact.**|||||||
|Female headed||||||**-0.003**|
|||||||[0.004]|
|Over 65 yrs old||||||**0.024** ***|
|||||||[0.009]|
|No education||||||**-0.013** ***|
|||||||[0.007]|
|Poor health status in the past year||||||**-0.005**|
|||||||[0.007]|
|Employment in past year||||||**0.003**|
|||||||[0.006]|
|**Nr. of obs.**|749|749|749|749|748|744|
|**R-sq**|0.03|0.04|0.08|0.1|0.14|0.27|

**Notes:** Table reports estimated marginal effects after probit.Significant at the *** 1% level, ** 5% level, * 10% level. Robust standard errors in squared brackets.

23
from similar analyses set in nations with a much higher rate of agricultural participation (and correspondingly, increased caloric requirements). As people in the Philippines have moved out of agricultural labor and into the industry and service sectors, poverty rates and the Gini coefficient have declined. But, despite these advances, individuals and families in the Philippines continue to struggle with undernutrition. There has been almost no reduction in the prevalence of undernutrition in the country in three decades (Mbuya et al., 2021).

The choice of using Philippines data is in large part due to the availability of individual-level consumption data, for all members of the household, as collected in the NNS in 2013. The NNS allows us to directly compare individuals’ within-household consumption, which is uncommon among household data, particularly in low-income countries. Despite the importance of food consumption and/or dietary data for understanding important questions of health and intrahousehold dynamics, there are very few examples of nationally representative individual-level dietary surveys outside North America and Europe. A notable exception to this is the Bangladesh Integrated Household Survey (BIHS), which has nationally representative data on dietary intake of surveyed household members (for work using these data, see Brown et al. (2021), D’Souza and Tandon (2019)). However, these sorts of data are still relatively rare. Coates et al. (2017) highlight a few of the challenges with collecting and using individual consumption surveys. These include the lack of infrastructure required to facilitate the collection of these data. This problem is compounded by the often-significant time and cost mandated in their collection. As a result, there are relatively few individual-level data sets which analyze consumption for all those within a household. For researchers, this lack of data can pose a serious challenge for assessing and monitoring individuals’ welfare across the world. Dang et al. (2019) create a typology of consumption data, which classifies household survey data as either entirely missing individual consumption data; partially missing such data, as in a case in which the data is only available for certain administrative areas; or missing such data over time. The NNS is thus an advantageous data set as it provides a unique opportunity to investigate questions of individual consumption and expenditures within a household, which is not possible in most other contexts.

Our findings extend the existing literature on intra-household dynamics and the value of individual welfare estimates. Standard welfare measures rely on household per-capita consumption metrics, which assume equal allocations among family members (World Bank, 2015). This is the case, 4
**Table 6: The impact of typhoons on total PCE and different components of PCE controlling for household unobserved heterogeneity**

||**6**|**.a: Random**|**Effects Spec**|**ification**||||
|---|---|---|---|---|---|---|---|
|PCE<br>Total<br>(1)|PCE<br>on Food<br>(2)|PCE<br>Non-Food<br>(3)|PCE<br>Protein<br>(4)|PCE<br>Fruit<br>(5)|PCE<br>Cereal<br>(6)|PCE<br>Education<br>(7)|<br>PCE<br>Medical<br>(8)|
|Usinga wind damage in|dex:|||||||
|f203_hy<br>-0.0139|-0.112**|0.0423|-0.286***|-0.0379|-0.0285|-0.130|-0.0241|
|(-0.38)|(-2.83)|(0.94)|(-4.66)|(-0.56)|(-0.84)|(-1.08)|(-0.19)|
|R2 overall<br>0.631|0.504|0.634|0.384|0.306|0.171|0.298|0.227|
|N<br>412286|412268|412286|410242|410149|411118|255586|355402|
|Usinga binaryvariable on|windspeed:|||||||
|W119_hy<br>-0.0251***|-0.0254**|-0.0305**|-0.0603***|-0.0138|-0.00293|-0.00810|-0.0201|
|(-3.38)|(-3.11)|(-3.25)|(-4.28)|(-0.89)|(-0.42)|(-0.30)|(-0.76)|
|R2 overall<br>0.631|0.504|0.634|0.384|0.306|0.171|0.298|0.227|
|N<br>412286|412268|412286|410242|410149|411118|255586|355402|
||**6**<br>|**b: Fixed ef**<br>|**fects speci**<br>|**fication**<br>||||
|PCE<br>Total<br>(1)|PCE<br>on Food<br>(2)|PCE<br>Non-Food<br>(3)|PCE<br>Protein<br>(4)|PCE<br>Fruit<br>(5)|PCE<br>Cereal<br>(6)|PCE<br>Education<br>(7)|<br>PCE<br>Medical<br>(8)|
|Usinga wind damage ind|ex:|||||||
|f203_hy<br>0.0633|0.00703|0.103*|-0.0718|0.0784|0.00225|-0.148|0.106|
|(1.61)|(0.16)|(2.12)|(-1.07)|(1.01)|(0.06)|(-1.18)|(0.72)|
|R2 overall<br>0.298|0.252|0.303|0.208|0.187|0.115|0.0686|0.298|
|N<br>412286|412268|412286|410242|410149|411118|255586|355402|
|Usinga binaryvariable on|windspeed:|||||||
|W119_hy<br>-0.000965|0.00131|-0.00146|-0.0104|0.00685|0.00804|0.0190|0.0171|
|(-0.13)|(0.15)|(-0.15)|(-0.70)|(0.41)|(1.04)|(0.65)|(0.57)|
|R2 overall<br>0.300|0.252|0.306|0.206|0.186|0.115|0.0719|0.107|
|N<br>412286<br>Notes: Additional ex<br>female, age of head,<br>type (single house v<br>floor area, type of to<br>region, along with a<br>errors are corrected|412268<br>planatory v<br>marital sta<br>s, apartme<br>ilet, water s<br>complete<br>for clusteri|412286<br>ariables inc<br>tus of head<br>nt etc.), typ<br>ource, and<br>set of inte<br>ng at the ba|410242<br>luded but<br>, education<br>e of mater<br>separate b<br>ractions am<br>rangay lev|410149<br>not repor<br>level of<br>ial for ro<br>inary var<br>ong the<br>el.|411118<br>ted here:<br>head, hea<br>of, type<br>iables for<br>se binary|255586<br>family siz<br>d has a job<br>of materia<br>semester,<br>variables.|355402<br>e, head is<br>, building<br>l for wall,<br>year, and<br>Standard|

Another potential source of bias in the panel estimates above may be the possible attrition of households across semesters in the FIES. In Table B in Annex 5 we present OLS estimates analogous to those presented in Table 1 based on the sub-sample of households appearing in both semesters (i.e. excluding households who appear only in one semester). If attrition bias were a serious problem, then the estimates should differ. The estimates suggest that attrition 21
- Ravallion, M., S. Chen, and Y. Wang (2008). _Does the Di Bao Program Guarantee a Minimum Income in China’s Cities?_ , Chapter 16, pp. 317–334. The World Bank.

- Rodilitz, S. and E. H. Kaplan (2021). Snapshot models of undocumented immigration. _Risk Analysis 41_ (9), 1643–1661.

- Semega, J., M. Kollar, J. Creamer, and A. Mohanty (2019). Income and poverty in the united states: 2018. _Current population reports_ (P60-266).

- US Census Bureau (2019). Industry and occupation classification. Technical report, US Census Bureau.

- Weinstein, E. (2002). Migration for the benefit of all: Towards a new pardigm for economic immigration. _International Labour Review 141_ , 225.

- Wooldridge, J. M. (2013). Introductory econometrics: a modern approach, fifth edit. _SouthWestern, Cengage Learning_ .

21
# 1 Introduction

Aggregate uctuations in emerging countries are dierent from those in developed countries. Table 1 illustrates this point. The rst and second rows of the table display summary statistics of the business cycle in two representative emerging and developed countries, Mexico and Canada respectively. The third and fourth rows of the table report the same statistics averaged across 13 emerging and 13 developed countries. Overall, the table shows that, while economic aggregates are substantially more volatile in emerging countries (except, perhaps, hours worked), there is a deeper dierence between these countries: consumption is more volatile than output in emerging countries but less volatile in developed countries; and the share of net exports on output is highly countercyclical in emerging countries but less so in developed countries. Moreover, these regularities hold across a large number of emerging and developed countries (Neumeyer and Perri, 2005; Aguiar and Gopinath, 2007).

Table 1: Business Cycles in Emerging and Developed Small Open Economies

|Country/Moment|_σ_(_y_)|_σ_(_l_)|_σ_(_m/y_)|_σ_(_c_)_/σ_(_y_)|_σ_(_x_)_/σ_(_y_)|_ρ_(_m/y, y_)|
|---|---|---|---|---|---|---|
|Mexico|2_._12 (_._45)|1_._4 (_._2)|1_._63 (_._43)|1_._28 (_._05)|4_._51 (_._43)|_−_0_._80 (_._11)|
|Canada|1_._44 (_._21)|1_._69 (_._24)|0_._91 (_._1)|0_._85 (_._08)|4_._6 (_._23)|_−_0_._05 (_._24)|
|Emerging Countries|2_._74|_−_|3_._22|1_._45|3_._91|_−_0_._51|
|Developed Countries|1_._34|_−_|1_._02|0_._94|3_._41|_−_0_._17|

This table reports second moments of HP-ltered quarterly aggregate data. The statistics _σ_ ( _x_ ) and _ρ_ ( _x, y_ ) measure, respectively, the standard deviation of the series _x_ and the correlation coecient between the series _x_ and _y_ . The variable _y_ denotes output; _l_ , hours worked; _m_ , net exports; _c_ , private consumption; and _x_ , investment. GMM-based standard errors are reported in parentheses. Sources: Rows 1 and 2, author's calculations. Rows 3 and 4 taken from Aguiar and Gopinath (2007).

While researchers agree on these dierences, they disagree on their causes: by introducing dierent frictions and shocks into their models, researchers provide alternative stories for why aggregate uctuations in emerging countries are dierent. (Christiano, Gust, and Roldós, 2004; Neumeyer and Perri, 2005; Mendoza, 2008; Uribe and Yue, 2006; Aguiar and Gopinath, 1
(Figure 6). WWF defines an ecoregion as “a large unit of land or water containing a geographically distinct assemblage of species, natural communities, and environmental conditions.”<sup>9</sup> Accordingly, we adopt the ecoregion as a general proxy for distinctive plant and inspect species, as well as animal species that are not represented in the range maps provided by IUCN and BirdLife International.

Our method for incorporating WWF ecoregions resembles our treatment of species endemicity. For this exercise, we identify all moist and dry forest ecoregions in Lao PDR. We compute the percent of total forest area in Lao PDR accounted for by each ecoregion. Then we compute its vulnerability index as the inverse of its area share and assign the appropriate index value to each cell. This accounting assigns high values to cells in smaller ecoregions, where clearing single cells may pose more significant threats to biome integrity.

# **6.4  A Composite Biodiversity Indicator**

The example in Table 4 suggests that alternative vulnerability indicators may yield significantly different risk metrics. It is important for a vulnerability indicator methodology to accommodate different risk-weighting schemes in a consistent and plausible way. From the World Bank database, we obtain 50 spatially-formatted indicators:

**Overall species indicators (8)** : total cell counts and total endemicity measures for four vertebrate classes: amphibians, mammals, reptiles and birds<sup>10</sup> ;

**Species indicators by IUCN status (40)** :  cell counts and endemicity measures for species within each vertebrate class in five IUCN risk status categories (per Table 3):  CR (critically endangered), EN (endangered), VU (vulnerable), NT (near-threatened); and LC (least concern).

> 9 Complete information about the WWF terrestrial ecoregions is available online at http://wwf.panda.org/about_our_earth/ecoregions/.

> 10 The total indicators include species in IUCN threat status class DD (data deficient).

23