rafmacalaba/datause-encoder-data huggingface dataset:
We work with a balanced sample, requiring firms to be listed in 2013. Therefore, we exclude firms that had an initial public offering (IPO) after 2013 and focus on secondary equity offerings (SEOs) of already publicly listed firms. SEOs explain most Chinese equity issuance growth since 2013 (Appendix Figure 1). Moreover, SEOs allow us to compare firm performance before and after the capital raising activity, which we cannot do with IPOs as there is no issuance or balance sheet information for a firm before its IPO. 2013 also marks the beginning of most internationalization announcements and events we analyze.

We define _domestic listed firms_ as those that had only issued equity in the Shanghai or Shenzhen stock exchanges (A shares) up to 2013. We define _foreign listed firms_ as those that issued equity (at least once) in the Hong Kong Stock Exchange or other foreign stock exchanges (such as New York) before 2013.<sup>15</sup> Therefore, the foreign listed group includes Chinese firms that only issued equity in international markets and dual listed firms issuing equity in domestic and international markets.<sup>16</sup> Within domestic listed firms, we distinguish between connected and unconnected firms using information from the Hong Kong Stock Exchange. _Connected firms_ are domestic listed firms whose A shares became available to international investors through the Stock Connect program and were added to the MSCI Emerging Markets Index. _Unconnected firms_ are the remaining domestic listed firms that did not gain direct access to foreign capital through these events.<sup>17</sup> Our sample comprises 2,017 domestic listed firms (82 percent) and 438 foreign listed firms (18 percent). Among domestic listed firms, there are 1,289 connected firms and 728 unconnected

> 15 Chinese firms issuing American Depository Receipts (ADRs) are also categorized as foreign listed firms. Foreign listed firms include Chinese firms that raised capital in international markets through variable interest entities (VIEs). 16 Dual listed firms include mostly Chinese companies with stocks listed in both the mainland stock markets (Shanghai or Shenzhen) and the Hong Kong SAR, China stock market. Alternatively, we exclude the dual listed firms from the foreign listed group, restricting this group to firms that are exclusively listed abroad.

> 17 We focus on firms connected during 2014-18 and omit those connected afterward. A shares from connected and unconnected firms were available to foreign institutional investors through the QFII/RQFII programs.

10
**Figure 17. Deprivation in Three Indicators of Affordability and Access, 2010–15, Percent of Population**

<!-- Start of picture text --> Hot Running Water Healthy Heating Centralized Sanitation/Garbage<br>Disposal<br>90% 90% 90%<br>80% 80% 80% Other Urban<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>Rural<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>Yerevan<br>10% 10% 10%<br>0% 0% 0%<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> _Source:_ ILCS data _._

# **Regional Differences in Multidimensional Poverty**

Disaggregation by location offers a more nuanced picture of multidimensional poverty and describes geographic differences in the complexity, depth, and persistence of poverty. Regional disparities are largest on the housing dimension, although recent investments in physical assets have reduced the gaps between urban and rural areas. Most countries show differences in the availability of public infrastructure and housing conditions, related not only to disparities in climate and geography, but also to the much higher costs of providing public goods and services in rural areas (and even generally outside the capital city) and are often rationalized in terms of cost-benefit analysis. Yet unavailability or limited access, combined with unaffordability, heavily influences the experience of poverty in Armenia and illustrates how identification of the dimension of nonmonetary poverty complements the analysis of monetary poverty.

Between 2010 and 2015, the share of the population with hot running water and access to centralized sanitation and waste disposal consistently rose (see Figure 17). Recent infrastructure improvements seem to have had a positive influence on nonmonetary poverty. A similar situation can be observed for deprivation of centralized sanitation and waste disposal, where the share of the population deprived in this indicator is now lower for other urban areas than for Yerevan (Figure 18).

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broader range of programme population coverages from 10% to 50% can be found in appendix A.

The graphical results show the number of optional questions that are required for a given accuracy, measured by EER. This ordering implies that policymakers choose a minimum required accuracy with reference to a benchmark method, rather than assigning a question budget per household. Recall that the full questionnaire starting point and the core questionnaire-only end point are shared, as the predictive model is identical across sequences. A method that is closer to the origin is preferable as it offers a superior cost-accuracy trade-off. The complete questionnaire that mirrors Indonesia’s targeting survey of 37 optional questions results in a simulated EER of 26%<sup>10</sup> . At the other extreme, a gradient boosting model trained only on core questionnaire variables would achieve an EER of 37.78%. The range between core and full questionnaire spans almost half the EER’s lower end value, implying a major – and likely unacceptable – decline in targeting accuracy when minimizing question numbers.

# **4.1 Truncated questionnaires**

The first set of results is for questionnaires of uniform length administered to all households. We consider outcomes for sequences based on stepwise selection, the group lasso<sup>11</sup> , and variable importance for the generation of grouped variables sequences that underlie these questionnaires. For the treebased variable importance measures, we noted the option of using either split counts or loss function gains, as well as their computation via either a oneway ranking or a backwards selection-style recursive feature elimination. From the resulting four possible implementations, we show one-off gain as the best-performing one, which leaves recursive split-based elimination as a complement. Figure 5 shows the four methods’ relative performance.

Figure 5 reveals stepwise selection to be the most effective approach in this setting, followed by the group lasso and then the variable importance measures. Whereas the performance penalty of the group lasso is moderate, the variable importance measures underperform significantly and cannot be recommended for this use case. Whereas there is a steep rise in the required number of questions to achieve the ultimate, small reductions in EER (top left), the sequencing of variables by predictive power results in a strong accuracy impact for initial questions (bottom right). The second key result is that fewer than 5 questions raise the EER strongly, and beyond 15 questions the accuracy gain becomes imperceptible for the best-performing method of stepwise selection. An intermediate range provides a moderate

> 10The full questionnaire would be selected when a gradient boosting model is used in the PMT algorithm instead of OLS on nation-wide data.

11 We use the Group Lasso python library and the LightGBM framework [Ke et al., 2017] to implement gradient boosting.

20
**Figure 6: Quarterly US Import Growth Rates During Past Three Crises**

<!-- Start of picture text --> 30%<br>20%<br>10%<br>0%<br>‐10%<br>‐20%<br>‐30%<br>‐40%<br>‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5 6 7 8 9 10<br>Quarters from Peak<br>1990 recession 2001 recession 2007 recession<br><!-- End of picture text --> Source: Own illustration. Data US: Bureau of Economic Analysis, National Income and Product Accounts Table, Gross Domestic Product, seasonally adjusted at annual rates. 0 refers to the peak of the US business cycles recessions according to the definition of the NBER.

There are a number of possible explanations for the shift in the V-curve. For one, the recent downturn may involve a macroeconomic restructuring in many industrial countries, as debt burdens are worked off, household consumption is reduced and as government spending partly offsets declines in consumption and business investment demand. Consumption expenditure in the US in particular is recovering much more slowly than in previous recoveries.<sup>21</sup> A second, but related factor is China’s development trajectory and exchange rate policy, with the renminbi generally considered undervalued vis-à-vis the U.S. dollar. While the renminbi appreciated vis-àvis the dollar until 2006, it has remained constant since. This has been associated with expanding Chinese market share for many US and EU import products. But pressures for a revaluation remain, and a US recovery at a higher rate of private saving, a higher rate of public dissaving and a weakened dollar, may bring a very different world trade picture, with lower levels of trade and consolidated GVCs.<sup>22</sup> After reviewing the experience of individual countries and sector in the recent period, we turn to analysis of GVC consolidation.<sup>23</sup> We should add that the financial origins of this downturn resulted in a tightening of trade credit, which may contribute to the shift in the V-curve.

> 21 Kaplinsky  and  Farooki (2010), and  Ferrentino and Larsen (2009) make a similar argument. 22 See Kaplinsky and Farooki (2010). 23

> Freund (2009) in fact predicts some worsening of the US current account imbalance based on past experience.

14
likely to be in the labor force than similar women without remittances. While the rates of being a _nini_ for men are not as high as for women, the percent difference in _nini_ status associated with receiving remittances was higher for men than for women in each country in 2014. For men, remittances were associated with an increase in the likelihood of becoming a _nini_ by about a third in Honduras and El Salvador and by twothirds in Guatemala.

**Table 4.** **_Nini_ rates by gender**

|||El Sal<br>|<br>vador<br>|<br>Guate<br>|<br>mala<br>|Hon<br>|duras<br>|
|---|---|---|---|---|---|---|---|
|||2006|2014|2006|2014|2006|2014|
||Treated|0.188|0.204|0.077|0.125|0.122|0.171|
||Control|0.153|0.150|0.045|0.074|0.098|0.130|
|Men|Diff(p.p.)|0.035|0.054|0.032|0.051|0.024|0.041|
||Diff(%)|0.230|0.358|0.714|0.686|0.241|0.313|
||t-stat|2.51|4.38|2.69|2.89|2.30|1.73|
||Treated|0.492|0.503|0.522|0.546|0.429|0.514|
||Control|0.363|0.411|0.445|0.481|0.414|0.431|
|Women|Diff(p.p.)|0.129|0.092|0.077|0.065|0.015|0.083|
||Diff(%)|0.356|0.224|0.173|0.134|0.037|0.193|
||t-stat|7.74|6.16|3.68|2.52|1.09|2.74|

Source: Authors’ tabulations using SEDLAC (CEDLAS and the World Bank).

# _Employment outcomes_

The analysis shows that, conditional on being employed, individuals in El Salvador and Guatemala who live in households that receive remittances are more likely to have informal employment. Salvadorans and Guatemalans were 11 percent and 14 percent more likely, respectively, to be informal if they received remittances (Table 5). The analysis suggests that there is no statistically significant relationship between remittances and informality in Honduras. Looking at informality separately by gender reveals that the effect of remittances is larger for men than for women in El Salvador (15.7 percent and 6.5 percent in 2014). This was also the case in Guatemala in 2006, but the effect was about the same size for men and women in 2014 (13.7 percent and 14.6 percent, respectively).

**Table 5 Informality rates by gender, propensity score matching results**

|||<br>El Sa|<br>lvador|<br>Guat|<br>emala|<br>Hon|<br>duras|
|---|---|---|---|---|---|---|---|
|||2006|2014|2006|2014|2006|2014|
||Treated|0.598|0.652|0.729|0.705|0.525|0.555|
||Control|0.538|0.585|0.637|0.618|0.507|0.542|
|Overall|Diff(p.p.)|0.060|0.066|0.093|0.086|0.018|0.013|
||Diff(%)|0.112|0.113|0.145|0.140|0.035|0.024|
||t-stat|6.95|8.78|10.02|6.72|2.22|0.83|
||Treated|0.556|0.627|0.673|0.651|0.483|0.508|
||Control|0.482|0.542|0.571|0.573|0.488|0.516|
|Men|Diff(p.p.)|0.074|0.085|0.102|0.078|-0.004|-0.008|
||Diff(%)|0.154|0.157|0.179|0.137|-0.009|-0.016|
||t-stat|6.36|8.33|7.83|4.48|-0.41|-0.38|
|Women|Treated|0.650|0.685|0.793|0.780|0.572|0.619|
||Control|0.607|0.643|0.724|0.680|0.536|0.575|

11
13

# **Table 4. Elasticities of the Vehicle-Road Ratio with Respect to Per Capita Income, Population, and Population Density**

||El<br>|asticity Estimat|es<br>||
|---|---|---|---|---|
|Type of Elasticity Estimates|Per capita<br>income|Population|Population<br>density|R-squared|
|**National vehicles/total road km**|||||
|Constant|0.4 *|0.0|0.2*****|0.66|
|Varying: at the mean of low-income group**(1)**|0.8 *|0.0 *|0.4|0.73|
|Varying: at the mean of high-income group|0.3 *|0.1*|_0.5_|0.73|
|First difference|0.4|0.3|n.a.|0.18|
|**National vehciels/paved road km**|||||
|Constant|0.0|0.0|-0.1*****|0.20|
|Varying: at the mean of low-income group|0.4*****|0.0 *|-0.1|0.33|
|Varying: at the mean of high-income group|-0.2*****|0.1*****|-0.1|0.33|
|First difference|0.2|-0.9|n.a.|0.01|
|**Urban vehicles/road km**|||||
|Constant|-0.03|0.1|0.6*****|0.40|
|Varying: at the mean of low-income group|0.1*****|n.a.|n.a.|0.42|
|Varying: at the mean of high-income group|_-0.5_*****|n.a.|n.a.|0.42|
|First difference|0.9|0.5|_0.5_|0.28|

n.a. Not applicable.

* Statistically significant at the 0.05 level.

(1) The mean of per capita income for each income group is taken from Appendix 1, Table A-1.

The relation between the vehicle-total road ratio and income is reasonably similar in cross section and (fixed effect) time series specifications. The relations between the vehicle-paved road ratio and income differ for the cross section and first difference estimates, but both are not significantly different from zero. However, the quadratic loglog regression produces relatively significant non-linear results for both total and paved roads, and these non-linear relations are also confirmed by the piecewise regression analysis. In addition, the R-square statistic is notably higher for the nonlinear specifications--suggesting that the vehicle-road income elasticities are nonlinear.

The relation between the vehicle-road ratio and population is insignificantly different from zero in both the constant elasticity and first difference specifications at both the national and urban level. This is consistent with the constant scale effect of population in the vehicle ownership and road length equations. But the relation is significant, although numerically small, in the nonlinear specifications which indicates that population growth slightly increases the vehicle-road ratio at the national level. Population density is significant only in the constant elasticity specifications, where it is positive for the ratio of vehicles to total national roads and to urban roads, but negative for paved roads. This indicates that higher population densities increase the overall vehicle-road ratio, but also stimulate improvements in road quality via paving.
|Tabl|e S.8: Im|pacts on chil|dren’s ed|ucational ac|hievement -|Math: h|eterogeneity|(OLS)||
|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||||Math:|GPA z-scor|es||||
|_X_:|Age|Education|Male|Household<br>size|No. of<br>male<br>members|Head<br>age|Head<br>education|Head<br>male|Land<br>size|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|(1g)|(1h)|(1i)|
|**December in 2013**||||||||||
|All products|-0.981*|-0.126|-0.130|0.297|0.105|0.233|-0.030|0.055|-0.034|
||(0.556)|(0.198)|(0.084)|(0.222)|(0.144)|(0.370)|(0.062)|(0.222)|(0.066)|
|One product|-0.048|0.103|-0.063|-0.055|-0.026|-0.302|-0.050|-0.114|-0.026|
||(0.657)|(0.243)|(0.090)|(0.316)|(0.165)|(0.349)|(0.077)|(0.239)|(0.067)|
|All products _× X_|0.076*|0.018|0.212*|-0.068|-0.060|-0.007|-0.008|-0.105|-0.001|
||(0.045)|(0.041)|(0.121)|(0.044)|(0.051)|(0.008)|(0.028)|(0.227)|(0.001)|
|One product _× X_|-0.003|-0.041|-0.060|-0.006|-0.025|0.005|-0.030|0.029|-0.006|
||(0.053)|(0.051)|(0.133)|(0.064)|(0.067)|(0.008)|(0.028)|(0.263)|(0.004)|
|R-squared|0.392|0.390|0.391|0.390|0.390|0.390|0.390|0.389|0.391|
|No. of obs.|850|850|850|850|850|850|850|850|850|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|(2g)|(2h)|(2i)|
|**December in 2014**||||||||||
|All products|-0.633|-0.108|0.028|-0.697*|-0.083|-0.092|-0.138|-0.429|-0.132|
||(0.532)|(0.255)|(0.090)|(0.385)|(0.171)|(0.406)|(0.104)|(0.316)|(0.097)|
|One product|0.089|0.435|-0.023|-0.076|0.047|-0.362|0.054|-0.275|0.023|
||(0.675)|(0.292)|(0.093)|(0.368)|(0.226)|(0.522)|(0.095)|(0.358)|(0.097)|
|All products _× X_|0.043|0.001|-0.308*|0.120*|-0.008|-0.000|0.030|0.361|0.003|
||(0.041)|(0.047)|(0.156)|(0.067)|(0.056)|(0.010)|(0.031)|(0.320)|(0.002)|
|One product _× X_|-0.004|-0.086|0.152|0.023|-0.003|0.010|-0.016|0.343|0.002|
||(0.053)|(0.058)|(0.186)|(0.074)|(0.096)|(0.012)|(0.030)|(0.367)|(0.004)|
|R-squared|0.162|0.164|0.168|0.166|0.162|0.163|0.163|0.164|0.163|
|No. of obs.|846|846|846|846|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

53
been reflected as a separate category. Emerging segments such as bioinformatics solutions, food biotechnology and nanotechnology represent only 2% of approved projects to mid-2011.

In terms of type of innovation, about a third of funded ventures (27 of 86) are attempting new product development as their core stated objective, typically for a preventive or therapeutic solution in health care or a new transgenic crop in agriculture. The other funded ventures are seeking to develop new process approaches, devices or validation systems that can accelerate biotechnology development pathways.<sup>19</sup>

**Table 3: Main source of technology for approved SBIRI projects**

|**Area**|**Total projects**<br>**supported**|**In-house**<br>**technology**<br>**source**|**Technology from**<br>**domestic public**<br>**research entities**|**Technology from**<br>**foreign public**<br>**research entities**|**Developed thru**<br>**private-private**<br>**collaborations**|
|---|---|---|---|---|---|
|Health care|44|36|5|2|1|
|Agriculture and<br>allied areas|21|12|4|1|4|
|Industrialprocess|13|11|2|-|-|
|Instrumentation &<br>devices|5|5|-|-|-|
|Environment|1|1|-|-|-|
|Bio informatics<br>and others|2|2||||
|**Total**|**86**|**67**|**11**|**3**|**5**|

Source: DBT SBIRI database.

Table 3 describes the main source of technology for SBIRI funding recipients. Overall 78% of the funded projects relied on their own technologies for further advancement, validation and commercialization. Although in-house technology is by far the largest source of technology supported by SBIR to-date, this figure appears to be an overstatement, as evidenced by one of our detailed case studies (see Box 4), where the recipient company, SPAN Diagnostics, acquired and leveraged foreign intellectual assets through its prior purchase of a French company. Other companies may well have reported in-house technologies as the main source based on prior further development of existing technologies acquired from outside the firm, as well as access to technologies and research tools that are no longer protected due to expiration of the underlying patents. Of the 22% of projects reporting absorption of external technologies, the area of agriculture has experienced a far greater transfer of technology for further advancement (12 or 43% of agriculture projects) than in healthcare (18%) or industrial processes (15%). This could have been due to a relatively lesser engagement in translational research and technology transfer of the public research bodies in the area of bio-medical sciences. The agriculture universities and international agriculture research institutions as well as private collaborations appear to have supported technology transfer to private enterprises relatively more, by way of transfer of

> 19 A classification of all approved projects as either product development-oriented (D) or process-oriented (P) based on the hypotheses stated in the project titles is available from the authors upon request.

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