rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Effect of the interaction between treatment and trust on giving for youth and adults

|||A|ll|Young (18-29)|Adults (30+)|
|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|
|(A) Aggregate/all prop|osals|||||
|Mean giving|Treatment|0.08|0.07|0.19|0.02|
|||[0.054]<br>|[0.050]<br>|[0.093]**<br>|[0.050]<br>|
||Treatment*Trust|-0.05<br>**|-0.05<br>**|-0.10<br>**|-0.03<br>|
|||[0.020]|[0.020]|[0.047]|[0.031]|
||Trust|0.11|0.11|0.11|0.10|
|||[0.038]***|[0.039]***|[0.063]|[0.042]**|
||Young (18-29)||-0.10<br>|||
||||[0.047]**|||
|Frequent giver|Treatment|0.13|0.11|0.21|0.07|
|||[0.077]|[0.074]|[0.132]|[0.082]|
||Treatment*Trust|-0.12|-0.11|-0.20|-0.08|
|||[0.043]***|[0.042]***|[0.085]**|[0.050]*|
||Trust|0.14<br>|0.14<br>|0.16<br>|0.13<br>|
|||[0.050]***|[0.051]***|[0.094]|[0.061]**|
||Young (18-29)||-0.15<br>**|||
||||[0.063]|||
|(B) Individual proposa|ls|||||
|<br>|<br>Ttt|014|013|025|007|
|Unconditional cash|reamen|.|.|.|.|
|transfer||[0.075]*|[0.075]*|[0.134]*|[0.085]|
||Treatment*Trust|-0.12|-0.11|-0.16|-0.11|
|||[0.038]***|[0.037]***|[0.071]**|[0.050]**|
||Trust|0.18|0.18|0.17|0.18|
|||[0.000]***|[0.000]***|[0.088]*|[0.058]***|
||Y 1829||013|||
||oung (-)||-.<br>|||
||||[0.047]***|||
|Unconditional food|Treatment|0.07<br>|0.06<br>|0.21<br>|0.00<br>|
|transfer||[0.050]|[0.048]|[0.000]***|[0.042]|
||Treatment*Trust|-0.05|-0.05|-0.14|-0.02|
|||[0.027]*|[0.028]*|[0.057]**|[0.076]|
||Trust|0.05|0.05|0.06|0.03|
|||[0.036]|[0.037]|[0.053]|[0.068]|
||Young (18-29)||-0.03<br>|||
||||[0.050]|||
|Unconditional cash|Treatment|0.09|0.08|0.13|0.05|
|transfer and school||[0.072]|[0.068]|[0.141]|[0.076]|
||Treatment*Trust|-0.04|-0.03|-0.03|-0.03|
|||[0.027]|[0.027]|[0.038]|[0.039]|
||Trust|0.15|0.15|0.08|0.17|
|||[0.063]**|[0.063]**|[0.097]|[0.078]**|
||Young (18-29)||-0.12|||
||||[0.060]**|||
|Cash transfer|Treatment|0.03<br>|0.02<br>|0.19<br>|-0.05<br>|
|ditil||[0.064]|[0.058]|[0.170]|[0.090]|
|conona on<br>tii|Treatment*Trust|0.01|0.01|-0.09|0.06|
|ranng||[0.020]|[0.047]|[0.054]|[0.056]|
||Trust|0.05|0.06|0.11|0.02|
|||[0.055]|[0.059]|[0.084]|[3.495]|
||Young (18-29)||-012|||
||||.<br>[0.056]**|||
|Number of observation|s|411|411|116|295|

Notes: The estimation method is a linear probability model. All regressions include controls for gender, three education levels, location in the capital city, and number of cars in the household. The dependent variable is the giving rate. Standard errors clustered at the PSU level using wild bootstrap-t are reported in brackets. Trust is a dummy variable equal to one if the answer to the question “ How confident are you that the public funds allocated for social assistance reach the poor? ” is “ completely confident ” or “ somewhat confident ” , and zero otherwise. ***, **, * significant at 1%, 5%, and 10% level.

26
# **Appendix B – The TIMES Model**

## **B1. Introduction**

The TIMES model is an energy sector optimization model built on the Reference Energy System (RES). The model is designed to minimize the total cost of the entire energy system to meet the projected end-use energy demand satisfying technical, resource, environmental and any other constraints. It selects an optimal combination of energy production, transformation, transportation and utilization technologies accounting for their investment, operation and maintenance and any other associated costs. It is capable to simulate a range of scenarios related to energy and environmental policies. The TIMES modeling system has a series of modules, such as demand module, resource module, production module, transformation module, utilization module. The demand module projects end-use energy demand using demand drivers, such as GDP and population growth rates, urbanization rate, and evolutions in energy utilization technologies (light bulbs, roof/wall/window insulations, cooking stoves, refrigerators, heating and cooling devices, etc.). The demand module is simplistic. The relationship between energy demands and corresponding drivers are established through regression analysis using historical data. The resource module provides information on energy extraction costs, such as costs of coal mining, natural gas drilling and production, crude oil drilling and production considering resource potential, costs of technologies used. It also includes production of electricity from primary sources (hydro, nuclear, wind, solar, geothermal). Power generation costs from various primary sources are derived from investment costs, economic lives of generation technologies, resource availability profiles (availability factors). The energy conversion module does the same when primary energy resources (e.g., coal, natural gas) are converted to another form of energy, such as electricity from coal and gas; refinery petroleum products from crude oil, processed gas from natural gas, charcoal from fuel wood, production of ethanol and biodiesel. It also includes many physical conversions of energy commodities such as raw coal washing and dressing. The various modules are briefly discussed here as presenting all equations of the TIMES model system will be too long.<sup>8</sup> Instead of presenting detailed equations of the TIMES modeling system, below we present key characteristics of the Chinese energy system reflected in the TIMES model used for this study.

## **B1.1 Resource module**

> 8 Interested readers could refer to the technical manual of TIMES modeling system (Richard Loulou et al. 2005).

39
Table A17: Gender, soft skills, and employment

||**Obs**|**No controls**|**Gender gap**<br>**+ degree type**|**+ work status**|
|---|---|---|---|---|
|**Aggregate indices**|||||
|Aggregate SES Index|895|-0.08<sup>_∗∗_</sup><br>(0.03)|-0.10<sup>_∗∗∗_</sup><br>(0.03)|-0.09<sup>_∗∗_</sup><br>(0.03)|
|Intrapersonal|895|-0.06<sup>_∗_</sup>|-0.08<sup>_∗∗_</sup>|-0.07<sup>_∗∗_</sup>|
|||(0.03)|(0.03)|(0.03)|
|Interpersonal|895|-0.10<sup>_∗∗∗_</sup><br>(0.03)|-0.12<sup>_∗∗∗_</sup><br>(0.04)|-0.11<sup>_∗∗∗_</sup><br>(0.04)|
|**Intrapersonal skills**|||||
|Emotional Awareness|895|-0.01|-0.01|-0.01|
|||(0.04)|(0.05)|(0.05)|
|Self Awareness|895|-0.13<sup>_∗∗∗_</sup>|-0.14<sup>_∗∗∗_</sup>|-0.14<sup>_∗∗∗_</sup>|
|||(0.04)|(0.04)|(0.04)|
|Emotional Regulation|895|-0.05|-0.05|-0.05|
|||(0.04)|(0.04)|(0.04)|
|Self Control|895|-0.03|-0.07|-0.07|
|||(0.04)|(0.04)|(0.04)|
|Perseverance|895|-0.07|-0.08<sup>_∗_</sup>|-0.08|
|||(0.04)|(0.05)|(0.05)|
|Personal Initiative|895|-0.09<sup>_∗∗_</sup>|-0.11<sup>_∗∗_</sup>|-0.11<sup>_∗∗_</sup>|
|||(0.04)|(0.05)|(0.05)|
|Problem Solving|895|-0.04|-0.05|-0.05|
|||(0.04)|(0.04)|(0.04)|
|**Interpersonal skills**|||||
|<br>Listening|895|-0.08<sup>_∗_</sup>|-0.10<sup>_∗∗_</sup>|-0.11<sup>_∗∗_</sup>|
|||(0.04)|(0.04)|(0.04)|
|Empathy|895|-0.08<sup>_∗_</sup>|-0.10<sup>_∗∗_</sup>|-0.09<sup>_∗∗_</sup>|
|||(0.04)|(0.04)|(0.04)|
|Expressiveness|895|-0.16<sup>_∗∗∗_</sup>|-0.15<sup>_∗∗∗_</sup>|-0.16<sup>_∗∗∗_</sup>|
|||(0.05)|(0.05)|(0.05)|
|Relatedness|895|-0.11<sup>_∗∗_</sup>|-0.11<sup>_∗∗_</sup>|-0.11<sup>_∗∗_</sup>|
|||(0.04)|(0.05)|(0.05)|
|Infuence|895|-0.13<sup>_∗∗_</sup>|-0.13<sup>_∗∗_</sup>|-0.12<sup>_∗∗_</sup>|
|||(0.05)|(0.05)|(0.05)|
|Negotiation|895|-0.14<sup>_∗∗∗_</sup>|-0.15<sup>_∗∗∗_</sup>|-0.15<sup>_∗∗∗_</sup>|
|||(0.05)|(0.05)|(0.05)|
|Collaboration|895|-0.08<sup>_∗_</sup>|-0.09<sup>_∗∗_</sup>|-0.09<sup>_∗_</sup>|
|||(0.04)|(0.05)|(0.05)|

Note: Each coefficient is a result from a separate regression of a soft-skill index on an indicator variable for female. Robust standard errors reported in parenthesis. *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals.

38
|Food insecurity score (0-12)|2.41|2.37|2.48|0.04|-0.08|-0.12|
|---|---|---|---|---|---|---|
||(0.25)|(0.25)|(0.16)|(0.35)|(0.29)|(0.29)|
|Received support from social programs (1=yes)|0.16|0.20|0.18|-0.05|-0.02|0.02|
||(0.04)|(0.05)|(0.03)|(0.07)|(0.05)|(0.06)|
|Total land size (in ha)|1.35|1.28|1.32|0.06|0.03|-0.03|
||(0.14)|(0.10)|(0.06)|(0.17)|(0.15)|(0.11)|
|No. of TLU owned|3.20|3.32|3.30|-0.13|-0.10|0.03|
||(0.24)|(0.18)|(0.15)|(0.30)|(0.28)|(0.24)|
|No. of adopted quickwins (0-5)|2.25|2.20|2.24|0.05|0.01|-0.05|
||(0.13)|(0.11)|(0.09)|(0.17)|(0.16)|(0.14)|
|Grows main crop (1=yes)|0.95|0.96|0.97|-0.01|-0.02|-0.01|
||(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|(0.01)|
|N|416|387|803|803|1,219|1,190|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Total no. of observations N = 1,606. FREG stands for ‘Farmer research and extension group’. For variable definitions see notes of Table 1. Robust standard errors in parentheses, clustered at the mws level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

12
13 categories are included in the decomposition and the contributions are expressed as deviations from the grand mean following the procedure suggested by Jann (2008). The following analysis is based on the specifications shown in columns (6) of Tables 4, 5, and 6, which do not include the rural or province dummies.

The decomposition results are shown in Table 7. They suggest that 39 percent of the observed decline in postneonatal mortality, i.e. 9.7 out of a decline of 24.8 deaths per 1000 live births, can be explained statistically by the increase in ownership of ITNs in high malaria risk zones of Kenya. Changes in the remaining variables do not explain the decline in postneonatal mortality to a significant degree.

The separate decomposition analysis for infant mortality shows a similar result for ITN ownership in high malaria risk zones. The change in ITN ownership explains a drop of 12.1 deaths per 1000 live births out of the overall drop of 20.8, i.e. 58 percent of the overall decline. Unlike the postneonatal results, the infant mortality results imply a mild role for improved sanitation. Increased access to improved sanitation explains 6 percent of the decrease (1.2 deaths per 1000 live births).<sup>10</sup> Notably, the decomposition point estimate values also imply fairly substantial roles for changes in immunization rates, but the corresponding decomposition results are statistically insignificant. This reflects the fact that although vaccination rates increased substantially, from 53.2 percent to 68.4 percent between the two surveys, the correlation between vaccination and child survival is imprecisely estimated in the cross-sectional regressions. This imprecision may be due to the fact that vaccination rates are measured at the district level and thus only imperfectly indicate the likelihood that a given child was vaccinated.<sup>11</sup> We interpret these results with caution as evidence of a causal effect of ITNs. However, it is possible that the changes the decomposition attributes to the increase in ITN ownership in high malaria risk zones are actually due to other anti-malarial interventions that are correlated with

> 10 In the Oaxaca-Blinder decomposition, the explained part of the overall difference is a sum of positive and negative contributions of all explanatory variables. Thus, the sum of positive contributions alone adds up to more than 100 percent of the overall change.

> 11 Vaccination rates are measured this way becase vaccination status is not available for deceased children.
# **_b. Starting a Foreign Investment: The startup process for companies establishing subsidiaries abroad_**

# _i. Regional overview_

A recent study published by the World Bank’s Development Research Group found that one of the main reasons why firms choose to operate in the informal sector, even when granted financial incentives to formalize their business, is a burdensome registration process (De Mel et al., 2012). Our data finds that in almost every economy observed, establishing a local subsidiary of a foreign company takes longer and requires more steps than establishing a domestic enterprise. The only exceptions to this are Madagascar, Hong Kong SAR, China, Cyprus, Iraq<sup>9</sup> , Australia, Ireland, and the Netherlands. In these economies, we noticed no difference in treatment between domestic and foreign companies with regards to establishing local subsidiaries of foreign firms. For the remaining economies surveyed, it takes a foreign business on average an additional 13 days and 2 more procedures than a domestic enterprise in the same economy (Figures 9 and 10).

**Figure 9: Days required to establish a foreign-owned company**

<!-- Start of picture text --> Days required of domestic and foreign companies<br>Additional days required only of foreign companies (including international trade license)<br>High Income OECD 12 2<br>Eastern Europe and Central<br>14 2<br>Asia<br>Global average 25 14<br>Sub-Saharan Africa 23 17<br>East Asia and Pacific 37 18<br>Latin America and Caribbean 49 20<br>South Asia 18 18<br>Middle East and North Africa 25 19<br>0 20 40 60 80<br>Source: FDI Regulations database 2012<br><!-- End of picture text -->

> 9 Although Iraq does not impose additional procedures on foreign investors, the establishment process still takes 74 days to complete, longer than the average for the Middle East and North Africa region.

14
Policy Research Working Paper 10949

# **Abstract**

Using Business Pulse Survey data for 61 countries during the COVID-19 pandemic, this paper presents novel findings on remote work, enabled by digitalization, as a source of resilience for firms. The results suggest the following. First, firms in sectors with greater amenability to remote work experienced a smaller adverse impact of the pandemic in countries with better digital infrastructure. Second, these effects apply to both exporting and non-exporting firms. Third, there are differences across sectors. Among firms in the manufacturing sector, the benefits of remote work in countries with better digital infrastructure accrue more to exporters relative to non-exporters, thereby reflecting a premium to exporting. This exporting premium is not observed in the service sector, which largely comprises firms in non-knowledge intensive services in the sample. Fourth, the effects of the amenability to work remotely in countries with better digital infrastructure do not dissipate over time.

This paper is a product of the Office of the Chief Economist, Prosperity Practice Group, the Development Research Group, Development Economics, and the International Finance Corporation. It is part of a larger effort by the World Bank Group 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 agrover1@ifc.org; ineagu@worldbank.org; and gnayyar@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_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
16 subsidy and feeding programs will have similar costs. 75% of the costs after all are those of the food. Thus it is unlikely that the differentials are as wide as suggested by Mateus (1983). Programs which provide greater services are definitely more costly on a unit cost basis, although potentially more cost-effective. However data on cost-effectiveness, discussed in the next section, are not as yet adequate to address that issue.

# IV. Cost-effectiveness.

ection I has already described the methodological and practical difficulties involved in costeffectiveness measures for nutrition interventions. Table 9 summarizes the available estimates from program data either on the basis of cost per death averted, case of malnutrition averted and case of blindness prevented. Only 7 projects provided information of this type, of which 1 provided information on cost per death averted, 5 on cost per case of malnutrition averted (child removed from moderate or severe malnutrition), and 1 on cost per case of blindness prevented. The estimate of cost per case of blindness prevented is converted into cost per death averted, using estimates of casefatality rates. Similarly the cost per case of malnutrition averted could be converted to cost per death averted, using data such as in Ho (1985) or Burger et al (1990). (This is an upper bound since not only might children with moderate or severe malnutrition move into the mild malnutrition class, but some children might also move from mild malnutrition to the normal group, which also reduces the mortality rate).

The data tabulated here are direct estimates from individual projects. Other authors have calculated cost per death averted or cost per discounted healthy life year (DHLY) gained, making assumptions using "typical" project cost data and "typical" outcome data. Burger et al (1990) estimate cost per death averted from preschool food supplementation to be $1,236 ($40 per DHLY), and from prenatal maternal supplementation to be $724 ($24 per DHLY). McGuire (1990) also
To convert PTR changes into monetary terms, we focus on their inverse: teacher-pupil ratios (TPR), which measure the fraction of a teacher assigned to each pupil. We value TPRs by the average teacher salary for Kenyan public schools. While salaries vary by seniority and qualification, a nationwide survey conducted by the Kenya National Examination Council (Kenya National Examination Council 2010) found an average salary among civil service teachers of $262 per month over twelve months in 2009 (the nearest available year), which we use as the basis for our calculations.<sup>8</sup> Assigning these heterogenous changes in class size to different socio-economic groups is much more difficult. It is impossible to link household survey data and school-level administrative data on pupil-teacher ratios at the school or even the district level. Instead, we explore two alternative (extreme) assumptions about the relationship between class-size and socio-economic status before the introduction of FPE.

The first assumption we explore is that there was perfect socio-economic sorting of households into schools prior to FPE, such that the richest households (proxied in our data by the education of the household head) attended the schools with the lowest pupil-teacher ratios. For example, Table 5 shows that households whose head had some tertiary education constituted 3.6% of all households in 1997. Thus we assign these households to the 3.6% of pupils with the smallest initial class sizes. Those pupils saw a reduction of 3.1 teachers per one hundred pupils. Meanwhile the – 26.9% of pupils with the largest initial class sizes corresponding, by hypothesis, to the to 26.9% of households whose head had no schooling in 1997 – saw a reduction of just 0.16 teachers per one hundred pupils.<sup>9</sup> Scenario 1 in the table converts these changes to monetary terms. The poorest households saw a small decline in fees under FPE of $2.50 and a further small decline in per-pupil resources due to

> 8We lack data on teacher salaries for more than one point in time, however starting teacher salaries increased substantially during this period, likely pushing up the average. Thus our calculations are likely to overstate any reduction in resources under FPE.

> 9To see how these calculations are consistent with the changes in PTR depicted in Figure 2b, note that the 26.9% of pupils with the largest class sizes at baseline includes everyone in schools with PTR greater than 40.1. So even though PTR fell for schools that began with very large classes, averaging over this whole group above a PTR of

> 40.1 yields a slight increase in PTR.

25
of natural resource degradation? To provide a framework for analysis, this paper develops a model of optimal budget allocation that allows for variations in three factors: administrators' valuation of objectives; their willingness to accept tradeoffs among objectives and regional allotments; and regional administrative costs. We explore the model's implications using provincial data from Lao PDR. Our results suggest that under real-world conditions, single indicators of poverty or environmental degradation may provide very poor guidance for allocating program resources. In many cases, priorities suggested by the indicator-based approach have large, _negative_ correlations with priorities suggested by a more complete optimizing model. In light of these results, we believe that regional poverty-environment strategies should incorporate information on administrators' preferences and costs, as well as indicators of poverty and environmental damage.

The remainder of the paper is organized as follows. In Section 2, we use alternative poverty indicators to establish priorities for program budget allocation in Lao PDR. Unfortunately, we find that different indicators suggest very different priorities. In Section 3, we broaden the analysis with an optimizing model that incorporates administrators' preferences and administrative costs. We show that the indicators-guided approach reflects highlyrestrictive assumptions about the parameters of the model. Using Laotian data, we relax these assumptions in Sections 4 and 5, and explore the consequences for regional allocation. Section 6 provides a summary, conclusions, and suggestions for future research.

4
**<u>TABLE 5.  LEGAL CHARACTERISTICS</u>**

||Foundation|Form|Character|Patrimony|Creation|Type of<br>Autonomy|
|---|---|---|---|---|---|---|
||||_Africa_<br>||||
|**KRA**|KRA Act<br>(1995)|Corporate body<br>with perpetual<br>succession|Separate<br>legal<br>character|Can own<br>assets|Parliamentary<br>Act|Unspecified|
|**SARS**|SARS Act<br>(1997)|Revenue service<br>outside the public<br>service but part<br>of the public<br>administration|Separate<br>legal<br>character|Can own<br>assets|Parliamentary<br>Act|Administrative|
|**URA**|URA Act<br>(1991)|Corporate body<br>with perpetual|Separate<br>legal|Can own<br>assets|Parliamentary<br>Act|Unspecified|
|||<br>succession|<br>character||||
|||_La_<br>|_tin America_<br>||||
|**SAT**|SAT Law<br>(1995)|Deconcentrated<br>service|No separate<br>legal<br>character|Can own<br>assets|Legislative<br>Law|Management,<br>budgetary, and<br>technical|
|**SENIAT**|SENIAT<br>Decree<br>(1994)|Autonomous<br>service|No separate<br>legal<br>character|Can not<br>own assets|Presidential<br>Decree|Functional and<br>financial with<br>its own human<br>resource<br>system|
|**SUNAT**|SUNAT Law<br>(1988)|Decentralized<br>Superintendency|Separate<br>legal<br>character|Can own<br>assets|Legislative<br>Law|Functional,<br>economic,<br>technical,<br>financial, and<br>administrative|

_Source: ARA laws, acts, and decrees._ The legal foundation of a ARA defines three important dimensions of autonomy: its corporate nature, the ability to own assets, and the nature of its legal underpinnings. The corporate status of an ARA depends fundamentally on whether or not it has its own separate legal character. ARAs generally have their own separate corporate status (see Table 5). ARAs without their own corporate character are inherently less autonomous, as shown by the Mexican and Venezuelan cases. Though the impact of not having a separate corporate character may not necessarily be experienced as problematic by management on a day to day basis, the issue takes on great importance when disputes and ambiguities arise over the nature of the ARA’s autonomy. Agencies without their own corporate identity are ultimately subject to greater control by external actors.

Legal Foundation Issue 1: To bolster autonomy ARAs should have their own separate corporate character as defined by national law.

The right to own assets is also an important determinant of autonomy. ARAs that can own assets are more autonomous in that they are less dependent on the MOF for the 47