rafmacalaba/data-use-mentions-extended huggingface dataset:
and 32% in the manufacture of wearing apparel to 71% in computer . programming services and 72% in financial services (see Appendix Table A3) We measure internet penetration at the country level by the fraction of the population using the internet as well as by the number of secure internet servers per 1 million people for 2019 (or the latest year available before the pandemic).The share of individuals using the internet ranged from 15.0% in Madagascar and 16.5% in Mozambique to 89.9% in the Slovak Republic and 90.8 % in Cyprus. Similarly, the number of secure internet servers per 1 million people ranged from 3 in Burkina Faso and 8 in Madagascar to 83,313 in Estonia and 56,187 in Czechia (see Appendix Table A4). We measure the severity of the pandemic across countries and over time using data from Google mobility reports around transit stations (Google, 2021). For countries without available data, we impute the severity based on the Oxford Government Response Tracker index (Hale et al., 2021) following Apedo-Amah et al. (2020).

In countries among the top 5% in terms of digital infrastructure (% of the population using the internet), firms in sectors with greater amenability to remote work experienced a smaller decline in sales (Figure 1). However, in countries among the bottom 5% in terms of digital infrastructure, there is no clear relationship between greater amenability to remote work and the change in firms’ sales (Figure 2).

> 2The sample includes 43 _,_ 389 panel businesses, which were interviewed more than once in the 2-year span.

> 3The algorithm for allocating a 2-digit or 4-digit ISIC sector to each firm based on the firm’s main activity has been developed by Giesberts and Eapen (2022) and facilitates a fast, high-quality and, in great part, automatic ISIC assignation to text from multiple languages.

5
|Park et al. (2019)|A bottom-up approach is used to<br>estimate the energy consumption and<br>cost of small (50 L and 100 L)<br>refrigerators.|The refrigerator electricity use can be reduced by<br>about 50% and 70% using commercially available<br>energy-efficient components at an incremental cost<br>of about $45–$60 and $100–$120 per unit,<br>respectively.|
|---|---|---|
|Witt et al. (2019)|Based on a project of exchanging<br>LED bulbs for 181 households in<br>low-income areas in Salt Lake City,<br>Utah over the period of 8 months.|A saving of approximately US$18,219 is estimated<br>in electricity bills for the residents. This is an<br>equivalent of about US$100 savings per household<br>per year.|
|Alfattah et al.<br>(2017)|An econometric approach is used for<br>field survey of residential sector in<br>Amman and Zarqa, Jordan.|The net saving by implementing EE measures<br>(refrigerators) from 2011 to 2020 is estimated at<br>about 4451 GWh which translate into saving of<br>electricity bills by 320 million JD.|
|Krati and Dubey<br>(2017)|An optimization analysis is used to<br>evaluate economic and<br>environmental benefits of a wide<br>range of EE technologies for new<br>and existing buildings in Oman.|The implementation of a government funded large<br>scale energy retrofit program for the existing<br>residential building stock is highly cost-effective<br>and provide a reduction of 957 GWh in annual<br>electricity consumption.|
|Jacobsen et al.<br>(2012)|
in the kWh billed. In this study, the benchmark unit cost is based on 90 percent of kWh dispatched, which assumes aggregate T&D and collection losses of 10 percent.

- This study calculates the average tariff by dividing the amount billed by kWh billed. In the AICD, the average tariff is the effective residential tariff for monthly consumption of 100 kWh, which affects underpricing.

- The capex for LV distribution lines in this study is based on analysis of costs assumed for setting tariffs for the five-year period 2013–2017 in Peru—assumed to be a suitable reference country—in six prototype areas, ranging from high-density urban to low-density rural areas. The costs are increased by 25 percent to reflect higher costs in SSA.

- The AICD assumed that staff levels are entirely a function of the number of customers that distribution companies have, but such an assumption does not apply to power generation. This study instead estimates overstaffing by using benchmark employment in generation, transmission, and distribution separately. The most extensive analysis is carried out for generation, for which information on the type and size of each generation plant was collected. Efficient levels of staffing for generation were estimated based on the type and size of each generation plant. The benchmark number of employees for T&D is set by km of T&D lines with voltage greater than 1 kV and the number of customers. The total numbers of employees reported in the utilities’ annual reports are compared with the staff complement in utilities in three clusters of power utilities in Latin America with similar numbers of customers and km of T&D lines. Annex A1.4 provides additional details.

# A1.2 Financial statement data

Utility financial statements were the primary source of revenues, opex, and other financial data. The study assumes utilities follow IFRS and IAS unless otherwise stated by the utility. Table A1.1 provides a description of each element and the assumptions applied.

**Table A1.1: Description of financial data collected and assumptions applied*
- We report all psychosocial results from the weekly data (described in Section 5.1.1 of the PAP) in the Appendix, since the time series data does not offer additional evidence beyond the endline data.

- We report heterogeneity by past violence, baseline depression, and extroversion (sociability) in the Appendix given space constraints and the loss of their centrality to the main message of the paper.

- We report the impact of alleviating future uncertainty via schedule provision in the Appendix given space constraints and the loss of their centrality to the main message of the paper.

- We do not explore heterogeneity by mild and moderate depression. For simplicity of exposition, we only show results for mild depression, or _PHQ_ 9 _>_ 4. Results are similar for moderate depression.

- In our examination of differential effects by gender, we move time-use to the Appendix given space constraints and the loss of their centrality to the main message of the paper.

# **B.3 Sample**

- We planned to visit four different camps for this RCT: 5, 8W, 17,4 for a total sample size of 1,000 households (featured in the randomization sample). Upon entering the camps for the full survey, we found that Camp 4 was more difficult to travel to and the conditions were not conducive to collecting high-quality data. We decided not to proceed with including Camp 4 in our sample.

63
|-15|12|-12|-14|
|Fastest growth quartile|48|20|10|17|
|**High income**|**13**|**11**|**2**|**0**|
|Slowest growth quartile|-7|6|-5|-8|
|Fastest growth quartile|36|21|7|7|
|**All low- and middle-income**|**26**|**21**|**4**|**1**|
|**countries**|||||
|Slowest growth quartile|-8|15|-8|-16|
|Fastest growth quartile|63|26|17|20|

_Source:_ World Bank calculations based on UIS and IMF data. _Note:_ Slowest and fastest growth quartiles refer to the 25<sup>th</sup> and 75<sup>th</sup> percentile growth rates within an income category. Column 1 shows the total percentage change in education spending. Columns 2 – 4 breakdown the overall change into its component parts (see Box 5). Column 1 is equal to the sum of columns 2 – 4 with small differences due to rounding errors. In Figure 9, we used actual percentage changes in spending, whereas in this table we use the results from the decomposition using logs, which explains the slight differences in some of the variables.

Countries in the top quartile of public education spending growth almost universally derived growth from all three sources:<sup>12</sup> they enjoyed fast-growing economies, their government spending accounted for a growing share of GDP, and they increasingly prioritized education over other components of their government budgets. In contrast, for countries in the slowest growth quartile, reductions in education spending were due to two main factors: a drop in aggregate public education spending as
constrained by the current total value of its assets. Arrow _et al._ (2012) argue that this may not be the most comprehensive measure of total wealth, but it is arguably a comprehensive measure of the wealth which underpins the consumption of produced goods and services.

As Annex I makes clear, estimating total wealth from the top down depends crucially upon a number of assumptions and parameter choices. World Bank (2011, Chapter 5) and Ferreira and Hamilton (2010) provide some tests of the extent to which the total wealth estimates are reasonable. Figure 1 below shows the distribution of estimated rates of return on total wealth across countries – this builds on a result in Hamilton and Hartwick (2005) and Asheim (2000), which shows that net national income is the return on total wealth. As can be seen, 80% of the implicit rates of return on total wealth across countries fall between 4% and 6% – a reasonable result.

**Figure 1. Distribution of the implicit rate of return on total wealth, 2005**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Implicit rate of return on total wealth<br>Number of countries<br><!-- End of picture text --> Source: World Bank (2011) Ferreira and Hamilton (2010) carry out panel regressions with intangible wealth as the dependent variable to show that human capital, based on the Barro and Lee (2001) data set on educational attainment is a statistically significant component of intangible wealth. They also perform a development accounting test to show that intangible capital includes what might be termed the ‘stock equivalent of TFP’ (total factor productivity).

Table 1 displays one of the key findings from the World Bank wealth accounts. At least in cross section, there is a clear tendency for natural
# <u>4.4 Parenting Practices and Children’s Human Capital Outcomes</u>

The behavioral component seeks to induce behavioral changes among cash transfer beneficiaries in four main domains: nutrition, health, psycho-social stimulation and child protection practices. As such, we first test whether the interventions lead to changes in practices in these four domains (Section 5.1). They represent intermediary outcomes that capture behaviors directly promoted by the program. We then consider whether the interventions affect final (or downstream) outcomes capturing children’s human capital, including anthropometrics, cognitive development and socio-emotional development (Section 5.2).

To analyze whether the program triggered behavioral changes, we build indices by aggregating indicators for the practices it promoted in each domain. The consideration of these families of outcomes helps to address potential concerns about multiple hypothesis testing, besides providing exact p-values as discussed in Section 4.3. The indices are built as additive indices of z scores, standardized in the control group.<sup>22</sup> We provide disaggregated results for the individual components of the indices in the annex to facilitate the interpretation of the results.

_Nutrition practices (Intermediary outcome domain #1)_ . The BCC intervention promoted exclusive breastfeeding until 6 months of age and encouraged greater diversity in children’s diet after 6 months. Therefore, we construct an index of nutrition practices for children aged 12-23 months, which captures (i) whether infants were exclusively breastfed until 6 months, and (ii) the number of types of foods each child started to consume between 6 and 9 months old, which measures food supplementation. We then analyze the food consumption score for all children 6 -59 months old, which provides a measure of dietary diversity at the time of the follow-up survey (WFP, 2008).<sup>23</sup> _Health practices (Intermediary outcome domain #2)_ . The BCC intervention promoted preventive health behaviors to protect children against disease, as well as encouraged
Sivadasan (2013), Amiti and Khandelwal (2013), Caron, Fally, and Markusen (2014), Harrigan, Ma, and Shlychkov (2015), Fan, Li, and Yeaple (2015), Bas and Strauss-Khan (2015), Atkin, Khandelwal, and Osman (2017), Bastos, Silva, and Verhoogen (2018) and Bastos, Dias, and Timoshenko (2018). Second, we contribute to the literature on impacts of the effects of international trade agreements on trade flows. Our analysis follows the methodology of Hoekman, Ng, and Olarreaga (2002), who draw on a simple and transparent partial equilibrium model to estimate the impact of duty free access for developing countries to high-income markets (Canada, EU, US and Japan) and compares this with a non-discriminatory reduction of tariff peaks to 5 percent.

Partial equilibrium models are static in nature, allowing only for a comparative static comparison of pre/post policy change when all other variables are held constant. Thus, the dynamics which affect the change are not explicitly modeled, nor can complex variations in the setup be considered. Since these models look at the partial effects – i.e. for one set of markets – of a policy change, they do not capture important feedbacks between markets. Dynamic linkages and market feedbacks can be captured in Computable General Equilibrium (GE) models. However, CGE models are much more complex to set up and require more information than partial equilibrium models, e.g. up-to-date input-output tables, or social accounting matrices. They also provide more aggregate information than partial equilibrium models. Since we focus on a relatively small subset of imports (digital goods imports), for which we require disaggregated data by product and country, we rely only on estimates from a partial equilibrium model, leaving a more comprehensive examination of GE effects for further research.

The rest of the paper is organized as follows. Section 2 describes the sources of data used in the paper, as well as the
-0.691***|0.0008|431,436|
|Asset ownership|-0.010***|-0.026***|0|-0.187***|-0.204***|0|264,680|
|Literacy|0.001|-0.012***|0.0006|-0.220***|-0.233***|0.0006|694,463|
|Not employed in the<br>agricultural sector|-0.008***|-0.038***|0|-0.141***|-0.171***|0|450,521|
|Access to electricity|0.015***|0.021***|0.3081|-0.351***|-0.346***|0.3081|697,574|
|Piped drinking<br>water|0.000|0.023***|0.0002|-0.140***|-0.117***|0.0002|595,934|
|Improved sanitation|0.000|-0.017***|0|-0.224***|-0.241***|0|592,937|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: This table shows the results of regressing an indicator variable of being poor on (ranked) residuals of covariates</u> of poverty (see Section 3b in the paper for more details). The residuals are determined by conditioning out household size from the covariates of poverty, including years of schooling, asset ownership/index, literacy, access to electricity, etc.
|1.549*|
||(0.352)|
|Hh head is polygamous|1.074|
||(0.172)|
|Nb of children 0 – 4 y.o.|0.870**|
||(0.0556)|
|Nb of children 5 - 14 y.o.|0.969<br>|
||(0.0415)|
|Nb of women 15 - 65|0.893**|
||(0.0452)|
|Nb of elderly over 66 y.o.|0.867<br>|
||(0.111)|
|Constant|4.407***|
||(1.136)|
|Observations|1,762|

Source : « Pauvreté et Structure Familiale » (PSF) survey, authors’ calculations. Note : Dependent variable: dummy equals 1 if all meals are taken jointly by all household’s members. Logit estimates. Odd ratios reported. Coeff. standard errors in parentheses. Top consumption decile as a reference category. *** p<0.01, ** p<0.05, * p<0.

24
The intervention was successful in altering the intention of parental leave usage among fathers. However, we find no effect of the intervention on planning nor effective use among both women and men.<sup>15</sup> There is evidence of a significant increase in the probability of using it only himself for fathers in experiment 2 who received the role model message compared to the planning message.<sup>16</sup> The emphasis on the father's role as caregiver appears to have had a positive effect on the intention to use the parental leave by men.

Table 8. Effects on intention to use parental leave, experiment 2

||||**Panel A**<br>|**: Men**<br>||
|---|---|---|---|---|---|
||Planning|Use both|Use onlyme|Use only partner|Effective use|
|Role model (M2)|0.046|0.007|0.045|-0.046|0,003|
||(0.078)|(0.072)|(0.045)|(0.077)|0,016|
|Planning (M3)|-0.041|0.071|-0.039|-0.022|0,014|
||(0.081)|(0.077)|(0.039)|(0.080)|1,017|
|Observations|302|294|294|294|304|
|R-squared|0.105|0.192|0.136|0.149|0,544|
|Control mean|0.511|0.407|0.0659|0.440|0,011|
|Diff M2-M3|0.0872|-0.0641|0.0832
.689***|0.639***|
|||||(0.091)|(0.184)|(0.172)|
|Observations||||1095|1095|1095|
|Mother controls||||Yes|Yes|Yes|
|Child controls||||Yes|Yes|Yes|
|Household controls||||Yes|Yes|Yes|
|Weak identification F-Stats (Full IV set)||||13.61|13.65|11.75|

Table IV presents the effects of maternal mental health on children vocabulary at the age of 8 years. Dependent variable was measured using the standardized value of the PPVT test. Mental health indexes are standardized values of the SRQ-20 items using three different estimation approaches. Columns (1)-(3) present estimated coefficients using OLS and columns (4)-(6) show coefficients using IV. Both approaches were implemented using the option PDS-selected variables and full regressor available in the LASSO command. Selected instrument was suffering crop or livestock loss during pregnancy or within the first year of index child. Selected controls are mother's age, mother is indigenous, mother literacy, wealth index, living in urban area, consumption of durable goods index, and Height for age Z-score. * and ** indicate statistical significance at 10% and 5%, respectively. Robust standard errors in parentheses 29
statistical significance due to higher variance. This analysis confirms the fact that grouping transactions into FPDs is a useful way to filter out some of the noise inherent in the data and therefore produce robust, statistically significant and meaningful results.

# **5 CONCLUSIONS**

We have outlined in this paper the results of an export survival analysis based on a novel dataset from the Lao PDR Customs Department that contains firm-level transactions and covers a period _Exporting from a small landlocked economy_ _22_
state‟s responsiveness to citizen demands for public services would be measures of public finance. _Ceteris paribus_ , higher overall public spending and especially those with higher social spending are more likely to be a result of the central state‟s responsiveness to citizen demands for public services. In contract, the perennially low taxation and hence the level of public spending are likely to be reflective of the state‟s responsiveness to elite interests which depend less on public services for their welfare.<sup>4</sup>

# **_Central Government Influence:  Stewardship Capabilities_**

The desire to improve service delivery is one thing but the ability to do so is another. In a decentralized setting, it is not sufficient if the central government was itself institutionally capable of effective service delivery. It must also possess the ability to direct or induce sub-national governments to improve service delivery within their respective jurisdictions. We call this stewardship. The stewardship capabilities mean abilities to set clear and coherent policies including objectives and standards, to monitor local government performance, and enforce the policy intent, including by use of positive incentives and sanctions (e.g., fiscal incentive, legal sanctions, party discipline). Countries vary considerably in terms of their central governments‟ stewardship capabilities. Simple quantitative indicators of stewardship capabilities are not easily available. But, some qualitative measures that capture aspects of these capabilities include the presence of a coherent sector policy and regulatory standards, the existence and use of performance information systems with availability of statistical data that allow the central government to measure sub-national government performance (at least indirectly), and the availability of discretionary budget funds which the central

> 4 For a discussion in the case of Latin America see De Ferranti et al (2004)

14
(together with inventor information) are registered and so can be organized on a yearly basis—contrary to census data, which are collected only every 10 years. Second, the level of education attained may still differ markedly among tertiary educated workers. Tertiary education can include nonuniversity tertiary degrees, undergraduate university degrees, and postgraduate and doctorate degrees, which may not be fully comparable across different countries. Inventors on the other hand constitute a specific class of highly-skilled workers that is more homogeneous than the tertiary-educated workers as a whole. They are behind the production of new knowledge and innovation that encourage economic growth and well-being.

The seminal contribution using inventor data for migration research is due to Kerr (2007) and his successive papers, which analyze immigrants’ contribution to US invention. Kerr takes inventors’ names from USPTO applications and assigns them an ethnic affiliation using a commercial repository of names and surnames of US residents classified by likely country of origin<sup>6</sup> —for a more recent contribution along the same lines, see Breschi et al. (2014, 2015). Albeit extremely valuable, Kerr’s contribution comes with some limitations: first, it is focused entirely on US immigration, while migration is a multifaceted phenomenon including numerous receiving countries. Second, it comprises a limited number of countries of origin—though it suffices for his analysis on skilled immigration in the United States. Finally, ethnic methods cannot distinguish between first and second generation immigrants—for example, African immigrants in Europe—or across countries belonging to the same linguistic group—for

> 6. In particular, nine broad ethnic affiliations are identified: English, European, Russian, Hispanic-Filipino, Chinese, Indian, Japanese, Korean, and Vietnamese

11
|Own color television|26.1|24.5|
|---|---|---|
|Father’s schooling|8.4|7.4*|
|Mother’s schooling|9.1|8.1*|

*Mean is significantly different between program and non-program samples at the .05 level.

# **Varying implementation lags**

Discussions with the ECD project management office and field administrative data indicated that there was substantial variance in the timing of the implementation of the project across municipalities and barangays, where implementation means procurement and receipt of material inputs and provider training related to the program. This variance implies differences in the duration of exposure to the program across program areas, and thus differences in the amount of time that the interventions could have had an impact. If program effects are estimated as if the program began before its actual effectiveness at the barangay level, then those effects might be underestimated. Many evaluations do not take into account this variation in the duration of program implementation, often because the program start is presumed to be well-defined (though in reality official starting dates often do not reflect start-up delays on the ground) and also because data on timing are not available. One exception is the analysis of the Bolivian PIDI pre-school program mentioned above (Behrman, Cheng and Todd 2004), which explicitly takes into account the dates of enrollment of individual children into the program and find that impact is most clearly observed among children who have been exposed to the program for more than a year, compared with those exposed for less time. Another is the evaluation of PROGRESA by Gertler (2004) who found no program impact after only six months of program exposure, but with 24 months of program exposure the illness rate of the treatment group was 39.5 percent lower than the control group, a difference that is significant at the 1-percent level.

With the help of the central project implementation office, we are able to use administrative data