rafmacalaba/datause-encoder-data huggingface dataset:
In this section, we increase spatial resolution to assess the distribution of endemic percentages using a global grid with 0.25 degree (25 km) resolution. We perform cellwise addition across all rasters in apparent. First, all three groups exhibit huge global variation, from 0% to 60% endemic for terrestrial variation within their borders (e.g., Brazil, United States, South Africa, India, China) while others do not very high endemic intensities over their entire areas. In Brazil, by contrast, all three groups exhibit progressively-declining endemic intensities from the Atlantic coast to the western frontier region. Similarly-broad patterns of variation across regions are visible in the United States, Mexico, Colombia, Argentina, South Africa, Morocco, Ethiopia, India, China, the Russian Federation and Indonesia. Countries have added protection responsibilities for endemic species, so large spatial variations in endemic intensity have significant implications for protected-area policies. Scale economies are important divert fewer hectares from economic use. For both reasons, spatial clusters with high endemic intensity offer the potential for cost-effective area protection. Figures 6 and 7 identify candidate clusters in many 5. The Global Distribution of Small-Occurrence Region Species Jenkins et al. (2015) note that “small range size is the best predictor of extinction risk and, thus, the first metric for conservation priority”. This factor has been studied extensively in the empirical literature (Kraus Brooks and Pimm 1999). It has particular significance because it is a widely-recognized indicator of extinction risk that is computable for any species that can be mapped. In contrast, expert assessments of extinction risks for individual species are limited by their resource intensity. The result is a gap between In this section, we explore the size and global distribution of small-occurrence region species in our GBIF maps database. Then we consider the implications for conservation policymakers, who must balance the of that territory. At the outset we should note that small-range status is not determinate; no single critical minimum habitat size exists, given the myriad interactions between species and habitat characteristics that affect extinction risks. Instead, we begin by exploring the implications of changing the species map These factors are considered as general propositions, . Other factors may intervene in particular
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- World Bank. 2006. Infrastructure in Europe and Central Asia Region: Approaches to sustainable services. Infrastructure Department, Europe and Central Asia Region, The World Bank.

- World Bank, 2008. _Deterring Corruption and Improving Governance in the Urban Water Supply and Sanitation Sector_ .  Water Working Notes No. 18, The World Bank.

- World Bank, 2009a. _Deterring Corruption and Improving Governance in the Electricity Sector_ . The World Bank.

- Yoo, Seung-Hoon. 2007. Estimation of household tap water demand function with correction for sample selection bias. _Applied Economics Letters_ , Vol. 14, pp. 1079-1082.
# **Appendix A:  Poverty and Inequality by Region**

Table A1 Regional Distribution of Poverty in 2001

||Headcount|Poverty Gap|Squared Poverty Gap|Watts|PopulationShare|
|---|---|---|---|---|---|
|Douala|10.89|2.07|0.72|2.61|9.70|
|Yaoundé|13.34|2.66|0.86|3.27|8.72|
|Adamaoua|48.38|15.39|6.38|20.31|4.47|
|Center|48.18|14.97|6.63|21.05|7.85|
|East|43.98|15.37|6.75|20.85|4.81|
|Far North|56.29|18.84|8.18|25.34|17.74|
|Coast|35.48|10.09|4.17|13.43|4.88|
|North|50.08|15.50|6.36|20.43|7.26|
|North West|52.48|20.90|10.70|30.83|11.52|
|West|40.33|11.10|4.19|14.19|12.06|
|South|31.55|7.35|2.43|9.04|3.45|
|SouthWest|33.83|10.50|4.51|14.13|7.53|
|Cameroon|40.18|12.79|5.55|17.38|100.00|

Source: Authors’ Calculations Table A2 Regional Distribution of Poverty in 2007

||Headcount|Poverty Gap|SquaredPoverty Gap|Watts|PopulationShare|
|---|---|---|---|---|---|
|Douala|5.50|0.87|0.21|1.01|9.96|
|Yaoundé|5.94|0.97|0.24|1.13|9.60|
|Adamaoua|52.95|14.49|5.41|18.46|5.18|
|Center|41.19|9.48|3.10|11.68|7.63|
|East|50.40|15.69|6.22|20.25|4.66|
|Far North|65.87|24.58|11.21|33.35|18.11|
|Coast|31.08|7.65|2.71|9.60|3.50|
|North|63.66|20.99|8.58|27.43|9.85|
|North West|51.00|16.61|6.83|21.78|10.14|
|West|28.95|6.64|2.27|8.24|10.58|
|South|29.25|7.37|2.65|9.31|3.24|
|South West|27.51|6.87|2.47|8.65|7.55|
|Cameroon|39.90|12.31|5.03|16.11|100.00|

Source: Authors’ Calculations 44
knowledge of who the poor are or where they are located. It is therefore unable to distribute its budget in any manner other than a lump-sum transfer to the entire population of size _N_ . We thus calculate the impact of transferring _S/N_ to the entire population.<sup>10</sup> The analysis in this paper is modeled closely on the approach laid out in Elbers, Fujii, Lanjouw, Özler and Yin (2007), in which the gains from geographic targeting at different levels of geographic disaggregation are assessed in Ecuador, Madagascar and Cambodia. Optimal use of geographically disaggregated information on poverty has been further investigated by Kanbur (1987), Ravallion and Chao (1988), Glewwe (1991), Ravallion (1993), and Baker & Grosh (1994). Kanbur (1987) formalized the theoretical problem of policy design under imperfect information, while Ravallion & Chao (1988) demonstrated how this general targeting problem can be solved in a computationally feasible way.<sup>11</sup> Kanbur (1987) shows that to minimize poverty summarized by the Foster-Greer-Thorbecke (FGT) class of poverty measures with parameter value _α>1,_ the group with the higher FGT( _α-1_ ) should be targeted on the margin.<sup>12</sup> Hence, to minimize the squared poverty gap (equal to a poverty measure from the FGT class with _α_ =2), target populations should be ranked by the poverty gap (FGT with _α_ =1) and lump-sum transfers made until the poverty gap of the poorest locality becomes equal

> estimates, over the _r_ replications, yields the estimated poverty rate in community _c_ , and the standard deviation yields the associated estimated standard error.

> 10 It could be argued that our benchmark scenario is not terribly realistic.  Perhaps more likely would be a situation where absence of detailed information on the extent and distribution of poverty, and absent any specific effort to target the poor, would result in a default situation of resources being distributed in a way that mirrored pre-existing inequality.  To the extent that this is true our estimates of the gains from targeting, once we assume some information on the distribution of poverty, might be seen as conservative estimates of the true benefits.

> 11 As we use predicted expenditures from census data unlike Ravallion and Chao (1988), who use observed income data from household surveys, we utilize a different algorithm to solve the optimization problem.  Applying their algorithm to our setting would yield the same results.

> 12 Following  Foster, Greer and Thorbecke (1984) the FGT class of poverty measures take the following form:

where xi is per capita expenditure for those individuals with weight wi who are below the poverty line and zero for those above, z is the poverty line and ∑<sup>_w_</sup> _i_ is total population size. α takes a value of 0 for the Headcount Index, 1 for the Poverty Gap and 2 for the Squared Poverty Gap. For further discussion, see Ravallion (1994).

8
# **IV. DETERMINANTS AND OUTCOMES OF SKILL DEFICITS AND EMPLOYER CHOICES**

In this section, we present a number of results from our skills demand model: (1) firms’ demand for types of education and occupational skills, (2) the skill deficits reported by employers, and (3) strategies used by employers to mitigate identified skill deficits. Taken together, these results help identify the factors shaping employers’ decisions regarding workforce education and skills. At the end of the section, we present the results of our regression analysis to identify (4) the impacts of employers’ actions to improve skill mix on the firm’s performance.

The regression analyses consider a broader set of choice or outcomes variables than the ones highlighted in the pair-wise correlations. The outcome variables used in each set of analyses are described first before presenting the regression results. Various regression models are used to explain these different outcomes: logistic models to estimate probabilistic binary outcomes and ordinary least squares models for continuous linear or log-linear outcomes. The regression models relate these outcomes to different sets of explanatory variables:

- _Broad enterprise characteristics_ . These include identifiers for two firm sizes, seven industrial sectors and four regions, measured relative to medium-size firms, firms in the furniture sector, and firms in the Dar es Salaam region.

- _Firm attributes_ . These variables are often associated in the literature with firm productivity differentials. They include education of top managers (reflecting higher quality managerial talent or management practices), foreign ownership (embodied technologies of FDI firms), multi-establishment firms (managerial talent and/or economies from integration), and female-led and young firms (in operation less than 10 years) with limited operational experience and technical capabilities.

- _Demand drivers_ . These are the exporter and innovator status indicator variables used to define the high-skill firms. They measure the degree of (and the firm’s response to) market competition from international trade and the diffusion of new technologies and services.

Depending on the specific models being estimated, additional sets of variables may also be included on skills composition and skill strategies:

- _Workforce skill characteristics_ . These are the shares of workers with higher-level education (upper secondary and tertiary), TVET qualifications, and high-skill MPT occupations.  They are included in models to estimate skill deficits, strategies and firm performance.

- _Skill deficit mitigating strategies_ . These include in-house and external training, hiring new workers, using foreign MPT workers, and outsourcing services.  They are included in models to estimate firm performance and the effects of skills on labor productivity.

25
**Table 6. Size, capital intensity, and innovation in Chinese Firms**

|**Panel A. Firm size and capital intensity of firms in China**|||
|---|---|---|
|Asset (10,000 Yuan)|No. of employees|Capital intensity (10k Yuan|
||(Person)|per worker)|
|**Private firms**|||
|1993<br>84.55|60.76|4.31|
|1997<br>126.06|78.25|1.74|
|2002<br>440.47|149.0|4.67|
|2006<br>463.65|153.2|5.00|
|2010<br>609.18|153.8|7.31|
|2012<br>798.17|163.4|8.72|
|**Listed firms**|||
|2000<br>94261.03|2737|101.17|
|2004<br>208019.64|3406|179.42|
|2008<br>1175581.40|5840|191.12|
|2012<br>1676041.67|6354|127.85|
|**Panel B. Innovation intensity of firms in China**<br>Year<br>Total R&D expenditure/sales|No. of IPRs/ employee|No. of Self-designed<br>products/ employee|
|**B1. Distribution over time**|||
|2002<br>0.054|0.008|0.035|
|2004<br>0.058|0.008|0.039|
|2006<br>0.019|0.010|0.031|
|2008<br>0.017|0.014|0.051|
|2010<br>0.018|.|.|
|2012<br>0.014|.|.|
|**B2. Distribution over firm size (Asset quantiles)**|||
|0-25%<br>0.136|0.008|0.041|
|26%-50%<br>0.250|0.011|0.042|
|51%-75%<br>0.346|0.011|0.038|
|76%-100%<br>0.614|0.011|0.032|
|**B3. Distribution over firm ownerships: Total R&D expen**|**diture/sales**||
|Year<br>Private firm|SOE|Foreign firms|
|2001|0.001|0.001|
|2002<br>0.067|.|.|
|2004<br>0.075|.|.|
|2006<br>0.021|0.003|0.001|
|2008<br>0.020|0.002|0.001|
|2010<br>0.018|0.002|0.001|
|2012<br>0.016|0.002|0.002|

_Data sources for Panel A: National Surveys of Privately-Owned Enterprises in China; CSMAR database on Chinese listed firms. The numbers is based on simple average of the sample. Firm asset and capital intensity are adjusted for inflation and expressed in 1990 RMB. Data sources for panel B: National Surveys of Privately Owned Enterprises in China (Panel B, B1 and B2); Chinese Ind. Survey data (Panel B, B3)_ 42
We estimate (3) using ordinary least squares (OLS), as implemented in Stata SE 9.2. Standard errors are robust to heteroskedasticity and clustering by country pair. It is not possible in this case to use alternative econometric methods such as Poisson pseudo-maximum likelihood (Santos Silva and Tenreyro, 2006) or Heckman sample selection (Helpman et al., 2007). Such methods address the presence of bilateral trade flows that are zero or missing from the dataset. However, they require that the independent variables be observed for those flows. In our dataset, it is in fact the extent of data availability for the independent variables that is the binding constraint for our estimation sample, not the presence of zero trade flows.

# **_4.2 Results_**

Estimation results for our preferred specification are reported in Table 7 column 1. Broadly speaking, we find that the model performs well. From its R<sup>2</sup> statistic, we can see that it accounts for around 64% of observed variation in bilateral trade within our sample. All estimated parameters have the expected signs: distance and tariffs impact bilateral trade negatively, while improvements in trade facilitation or closer historico-cultural ties have a positive impact. With the possible exception of air transport infrastructure, their magnitudes are sensible and broadly similar to the results of Wilson et al. (2005).<sup>14</sup> However, some estimated coefficients are not statistically significant at the standard 10% level. This is the case for applied tariffs, the quality of maritime ports, and the extent of irregular payments in import/export transactions, as well as the common colonizer and same country dummy variables. We expect this to be due to the relatively small (by gravity model standards) dataset we are using in this case, and the resulting correlations amongst the explanatory

> 14 While it is impossible to make a formal comparison between our Table 7 and Table 2 in Wilson et al. (2005) due to the different modeling approaches adopted, the two sets of coefficients are generally quite similar.

- 16 -
<!-- Start of picture text --> period period<br>(a) Average distance (b) Share contiguous<br>period period<br>(c) Average Herfindahl (d) Share wealthy OECD<br>2200 100<br>2000<br>90<br>1800<br>distance (km) 1600 share (%) 80<br>1400<br>70<br>1200<br>1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017 1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017<br>25<br>.6<br>20<br>.5<br>index 15<br>share (%)<br>.4 10<br>5<br>.3<br>1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017 1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017<br><!-- End of picture text --> Figure 3: Trends in refugee reach, 1987-2017 Note: This figure plots the average distance traveled by a refugee, the share of refugees finding themselves in a contiguous country, the average Herfindahl index of refugee shares by source country, and the share of refugees finding themselves in a wealthy OECD country.

a pronounced upward trend: the average distance traveled rises from around 1300km at the beginning of the sample to 2200km in the mid-2000s, before settling at around 1800km in the last decade. Panel (b) plots the share of refugees that find themselves in a country that shares a land border with their country of origin. At the beginning of the sample 95 percent of refugees were in a country contiguous with their home country. That share fell to 77 percent in the the period 2012-2017.

Another manifestation of the increasing geographical reach of refugees is the greater number of destination countries to which they go. Similar to the global destination Herfindahl index above, we construct a source-specific Herfindahl index that captures whether refugees from a given source country diversify their destinations over time. That is, for a specific source country _s_ and year _t_ , the source Herfindahl index is 9
- Hausmann, R. and Rodr ́ıguez, F. (2014). Why did Venezuelan growth collapse? _Venezuela Before Ch ́avez: Anatomy of an Economic Collapse_ , pages 30–56.

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- Ib ́anez, A. M., Moya, A., Ortega, M. A., Rozo, S. V., and Urbina, M. J. (2025). Life out of the shadows: The impacts of regularization program on the lives of forced migrants. _Journal of European Economic Association, Forthcoming_ .

- Insight Crime (2018). Colombia’s ELN, ex-FARC mafia recruiting hungry Venezuelan migrants. _Insight Crime_ .

- Insight Crime (2019). Colombia’s armed groups prey on Venezuelan migrants. _Insight Crime_ .

- Insight Crime (2021a). Human trafficking accompanies illegal mining in Venezuela’s Orinoco. _Insight Crime_ .

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47
the risk of that instruments are themselves endogenous. 7 Size is included because larger district can hold more people. Arable land area proxies for food production potential. Distance to the nearest river proxies for accessibility, as roads often follow river valleys. Elevation controls for climate - towns are less likely at higher elevations. The standard deviation of elevation is a measure of roughness in the terrain; towns are more likely in flat districts. The mountain dummy is included for the same reason. Regression results are presented in Appendix 1. The _R_<sup>_2_</sup> of the regression is 0.27. Regressors in general have the expected sign but are multicollinear (mean VIF of 8.7), which explains why regressors are not individually significant. Since we are only interested in predicting population, multicollinearity does not matter.

Travel time between a ward and a town is instrumented using foot travel time as well as physical characteristics of the ward and town: size of the district; arable land; mean elevation; distance from the nearest river (available only for the town); standard deviation of elevation; and regional dummies (East-West). Foot travel time is computed using iso-elevation curves to account for the mountainous nature of the terrain. The regression is estimated in log form. Results are presented in Appendix 2. As expected foot distance is the major determinant of travel time. The R<sup>2</sup> of the regression is 0.84. Population variables **_ph_** used in the subsequent analysis are constructed using predicted urban population and travel time instead of actual values.<sup>8</sup> As it turns out, results are virtually identical to those obtained with actual values, suggesting that the potential endogeneity of town population and road construction are not a source of bias in these data.

'We also experimented with a longer list of instruments, including area of irrigated land and the like. This results in a better fit for town population but the rest of the analysis is unaffected. Additional regressors are omitted in the analysis presented here to minimize the risk of endogeneity and overfitting.

8Standard errors are not corrected for the use of predicted regressors. Doing so would be extremely difficult given the way in which regressors are constructed. The fact that instrumented and unininstrumented results are very similar suggest that inference is very unlikely to be affected.

17
LICs.<sup>20</sup> Probit regression analysis performed on the relationship between a country’s CPIA debt management score and its probability of becoming a Highly Indebted Poor Country (HIPC), showed that an increase in the quality of debt management equivalent to a one‐point increase in the CPIA Debt Policy Indicator reduces the probability of a LIC having an unsustainable debt burden by 25% (see Appendix II from regression results).<sup>21</sup> These finding are reinforced by a wider body of empirical work that finds strong linkages between debt distress and institutional quality.<sup>22</sup> An important conclusion to be

**Figure 19: Avg. CPIA Debt‐Policy Indicator for LICs**

<!-- Start of picture text --> (#3)<br>3.320<br>3.310<br>3.300<br>3.290<br>3.280<br>3.270<br>2005 2006 2007<br><!-- End of picture text --> <!-- Start of picture text --> Source: World Bank<br><!-- End of picture text --> drawn from this analysis is that while the HIPC Initiative and the MDRI have reduced the debt burden indicators of heavily indebted poor countries (See Figures 20‐21), there is a continued need to improve DeM capacity and institutions in LICs in order to reduce the risk of debt distress.

**Figures 20‐21: HIPC Countries: Indicators in 2008 and at Time of Default (’76‐89)**<sup>**23**</sup>

<!-- Start of picture text --> 2,000 COG 1,600<br>1,800 At Default: '76‐'89 1,400 BOL At Default '76‐'89<br>At Completion Point 2008<br>1,600 At Completion Point 2008<br>1,400 BOL UGA At Decision Point 2008 1,200 CMR NIC At Decision Point 2008<br>1,200 CMR 1,000 ZMB CMRCIV<br>1,000 CIV CMR CGO 800 BOL<br>800 ZMB CIV<br>CIV BOL 600 ZMB<br>600400200 MDGMWISELMOZUGANERTGOMOZGMBGMB SEL TGOMDG ZMB MWI NER 400200 MOZSEL LIB NERMWIMDGSLE NERTGOGMBMDGMWINICGMB MOZ TGO<br>0 0<br>0 10 20 30 40 50 60 0% 50% 100% 150% 200% 250%<br>Total Debt Service as % of Exports PV of Debt to Exports<br>GNI per Capita ($US) GNI per Capita ($US)<br><!-- End of picture text --> Source: Author’s calculations; Decision Point countries benefit from conditional interim relief, Completion Point countries benefit from irrevocable debt relief This section introduces results from recently implemented DeMPA and MTDS analyses. The objective of these assessments is to improve DeM performance in developing countries, with the goal of lowering a country’s probability of becoming heavily indebted and reducing its vulnerability to external shocks.

# **a. The Debt Management Performance Assessment (DeMPA)**

The DeMPA<sup>24</sup> is a benchmarking exercise of a country’s DeM strengths and weaknesses. The tool is designed to examine the institutional underpinnings of government DeM practice and procedures

> 20 Highly indebted as measured by Heavily Indebted Poor Country Initiative (HIPC) criteria of 150 percent of the PV of debt to exports.

> 21 World Bank (2006)

> 22 See Kraay and Nehru (2007), and Reinhart, Rogoff , and Sevastano (2003)

> 23  Measuring a country’s external debt as a percent of exports is a way to gauge a countries capacity for repaying foreign currency denominated debt using foreign exchange earnings.

11