rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table A-3. RUSMOD uprating factors, 2017**

||2016|2017||
|---|---|---|---|
|C Pi Id Db|||Source:|
|onsumer rce nex, ecemer to<br>December (2007=100%)|213.9|219.2|http://www.gks.ru/free_doc/new_site/prices/potr/tab-<br>potr1.htm.|
|Federal Subsistence Minimum Level (IV Q), rubles<br>|9691|9786|Source: https://fedstat.ru/indicator/30957.|
|Accrued average monthly nominal wages per<br>employee (IV Q), rubles|47554|51197|Source: Short-term indicators digest by Rosstat.|
|Average awarded pension (IV Q), rubles|12428.6|12949.5|Source: Short-term indicators digest by Rosstat.|
|Average per capita money income (IV Q), rubles|45938.2|48674.9|Source: Short-term indicators digest by Rosstat.|
|Average per capita consumption expenditures (IV<br>Q), rubles|17735.8|17908|Source: Income, expenditures and consumption (HBS<br>data).|
|Average per capita expenditures on housing and<br>utilities (IV Q), rubles|2087|1896.2|Source: Income, expenditures and consumption (HBS<br>data).|
|Expenditures on social benefits and assistance, mln<br>rubles|2552247|2552247|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on unemployment benefits and<br>material aid to the unemployed, mln rubles|37590|37590|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on childcare allowances up to 1.5<br>years, mln rubles|157361|157361|Source
the environmental policy in N. Globalization thus increases PCINS in conformity with the PHH (some of the previously dirty production in N is now carried out in S probably under less-stringent environmental standards). If the dirty industry is capital intensive and N is relatively well-endowed in capital, then if N and S had the same environmental policies, we would only observe a Factor endowment (FE) effect as N would have a comparative advantage in dirty products. Taking the two effects together, under the plausible assumption that the FE effect reduces the PCI and the PH effect increases it, globalization will obey the PHH if PH is positive and dominates the FE effect.

In a Heckscher-Ohlin world, increasing Foreign Direct Investment (FDI) would lead to an increase in PCINS. World trade, however, is increasingly fragmented, in great part as a result of globalization that has reduced transaction costs. Taking into account outsourcing, the presence of FDI, could however, lead to a decline in PCINS. Consider a model with a continuum of intermediates as in Feenstra and Hanson (1996) where intermediates are ranked by decreasing pollution-intensity and the location of production between N and S depends on relative production costs (inclusive of pollution taxes). As shown by Dean and Lovely (2010), FDI to the South (often directed of CO2. The coefficient of correlation between CO2 and SO2 emissions is higher than 0.9 for UK industries for the average over 1990-2000.

> 14Using  data on SO2 emission intensities Grether et al. (2009) estimate that over the period 1990-2000 SO2 emissions fell by 9.8% with a technique effect contributing to a fall by 14% while the scale effect contributed to an increase in emissions of 9.5%. Between-country shifts contributed to a reduction of 2.4% and between-sector shifts within countries to another reduction of 3%. Dean and Lovely (2010) also decompose changes in the pollution intensity (air and water) of Chinese exports and imports during 1995-2004 and find a small composition effect
accuracy of the records. This survey found false reporting was below 5 percent (Health Development and Performance, 2008).

Quarterly payments go directly to facilities and are used at each facility’s discretion. In the sample of 10 treatment facilities in our study, the P4P payments amounted to 14 percent of overall expenditures in 2007. On average, facilities allocated 60 to 80 percent of the P4P funds to increase personnel compensation.

It is worth noting that the Rwanda P4P scheme was implemented in the context of a larger health sector reform and during a period in which HIV/AIDS services, including delivery of antiretroviral treatment, were extensively scaled-up. As of 2005, 83 health facilities were delivering ART to 19,058 persons living with HIV/AIDS (PLWHA), and 229 facilities were providing HTC services with 449,259 individuals ever tested (Center for Treatment and Research on AIDS Malaria Tuberculosis and Other Epidemics, 2007). By 2008, coverage of ART had increased more than threefold and more than doubled for HTC (Center for Treatment and Research on AIDS Malaria Tuberculosis and Other Epidemics, 2008).

# **3. Impact evaluation design, data and baseline characteristics**

# _3.1 Impact evaluation design_

The evaluation design with sample sizes is presented in Figure 1. The evaluation design took advantage of the phased implementation of the program at the district level. Administrative districts with pre-existing NGO P4P pilot schemes were excluded from the sampling frame. The remaining districts were then grouped into eight blocks based on similar characteristics for rainfall, population density, and predominant livelihoods using data from the 2002 Census blocks included between two and four districts depending on characteristics and size. Facilities within each district within each block were then randomly assigned into the treatment group and the other into the comparison group.

Prior to implementation of the baseline survey, the administrative district boundaries were redrawn in the context of a decentralization effort (MINALOC (Ministry of Local Government), 2006). As a result, some of the experimental areas were
F-stat|67.811<br>80.459|22.143|35.639|
|Cragg-Donald F-stat|235.768<br>305.882|46.495|68.561|
||**C. Male**|||
|SC vs. next-best|-0.237<sup>_∗_</sup><br>-0.243<sup>_∗∗_</sup>|-0.434<sup>_∗∗∗_</sup>|-0.228<sup>_∗∗_</sup>|
||(0.122)<br>(0.123)|(0.128)|(0.114)|
|N|151707<br>151707|124951|124951|
|Kleibergen-Paap F-stat|100.798<br>101.022|108.271|113.189|
|Cragg-Donald F-stat|373.543<br>426.592|300.575|358.374|

* ** *** Note: _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Standard errors in parentheses, clustered at the high school level. The next-best options refers to a combination of enrollment options different from short-cycle programs (that is, choosing a bachelor’s program or choosing not to enroll in higher education). The "No-interaction" columns present TSLS estimates without interacting the variables of supply of higher education ("HEI only offers SC" and "Other type of HEI"); the "Interaction [Other HEI=1]" columns present TSLS estimates interacting "HEI only offers SC" and "Other type of HEI" and evaluating the effects at "Other type of HEI=1".

29
# **1 Identifying Star Firms**

We use data from Compustat that provides detailed financial information on publicly traded firms in the US over an extended period of time. We drop cross listed ADRs and restrict the sample to firms incorporated in the US. We also drop firms in Utilities (SIC 49), Finance, Insurance and Real estate (SIC 60-69) and Public Administration (SIC 90-99), observations with missing SIC codes, negative values for employees, sales, total assets, current assets and current liabilities, fixed assets, cash, and goodwill and missing total assets or sales.

The advantage of using Compustat is that we have detailed balance sheet information that allows us to compute intangible capital. The caveat however, is that there are firm selection issues. First, it may be that listed firms, as a class, might not consistently represent star firms. Doidge, Kahle, Karolyi, and Stulz [2018] and Kahle and Stulz [2017] show that there are fewer US listed corporations today than 40 years ago. However, Grullon et al. [2017] argue that the void left by listed firms has not been filled by an increase in the number of private unlisted businesses. Using US Census data that includes both private and public firms, they show that even though more private firms have entered the economy, their marginal contribution to the aggregate product market activity has been relatively small. Public firms also account for one third of total US employment (Davis, Haltiwanger, Jarmin, Miranda, Foote, and Nagypal [2006]) and about 41% sales (Asker, Farre-Mensa, and Ljungqvist [2014]). Also using U.S. Census data, Maksimovic, Phillips, and Yang [2017] show that high initial firm quality at birth predicts subsequent listing decision. These findings suggest that while our sample will not be picking up small and young potential star firms in their private stages, we are targeting the sample of firms among which economically significant stars are highly likely
the IMF WEO, April 2011 edition<sup>4</sup> ‐ see Graph 1(b). In the following subsection we look at pre‐conflict trends and projections

> 1 WDI data for Syria are publicly available only up to 2007. We supplement these data for the last three years up to 2010 with data on GDP growth from the World Bank’s internal macroeconomic model (MFMod), November 2017 vintage, which is consistent with the IMF WEO October 2018 data.

> 2 WDI: GDP is at market prices, 2010 USD. IMF WEO: GDP is at market prices, 2000 local currency.

> 3 Source: http://documents.worldbank.org/curated/en/293851468162862428/Global‐economic‐prospects‐January‐2011‐ <u>navigating‐strong‐currents. GDP growth of 5.0% for 2010 is an estimate. No GEP projections for Syria are available beyond</u> the January 2011 publication. GDP is at market prices, 2005 USD.

> 4 Source: https://www.imf.org/external/pubs/ft/weo/2011/01/weodata/index.aspx.

4
# **6. Propensity Score Matching and Sample Balancing**

# **6.1. Propensity Score Matching**

This section reports the results of estimating equation (4) with Probit methodology using data for 2007.<sup>47</sup> The results of the estimation are reported in table 4. Using these results, we compute the propensity score for schools to join _Mais Educação_ . This propensity score is important because we do not know the true likelihood of schools taking up the program, and this likely varies depending on a number of school characteristics and will be correlated also with the educational outcomes at the school level in which we are ultimately interested.

Results show that schools with larger enrollments and lower IDEB Index have higher probabilities of participating in _Mais Educação_ . Meanwhile, even if one of the program’s selection criteria is that schools are located in cities with more than 200,000 inhabitants, _ceteris paribus_ , municipal population decreases schools’ propensity score. Although the estimated probability of a school joining _Mais Educação_ is negatively associated to duration of the school day, it increases with the percentage of full-time schooling classes. Also, schools with lower average mathematics test scores and higher dropout rates have higher propensity scores. Nevertheless, schools’ average Portuguese test scores are positively associated with the probability of adhesion to _Mais Educação_ . Changes in IDEB and in mathematics scores between 2005 and 2007 have positive effects on the propensity score, and changes in Portuguese test scores have a negative effect.

# **6.2. Sample Balancing**

Table 5 reports the differences in the characteristics of treatment and control schools as of 2007, after matching, weighted by propensity score matching weights. Student’s _t_ tests were performed to detect the mean differences of the variables between the two groups (control and treatment) after matching. If the quality of the matching is good, we expect a lack of statistically significant differences in these results. Indeed, compared to table 3, table 5 suggests a good
37]|[0.68]|[0.68]|[0.63]|
|Mean wage employed in-person||||||
|weekly|0.82|4.40|5.35|41.75|50.78|
|Phone effect self-employed|-0.23|-1.24|-0.15|-3.68|7.20|
|p|0.05|0.08|0.64|0.68|0.34|
|Number of observations|3,562|3,562|2,852|3,562|2,852|
|Adjusted R2<br>Note: *** p<0.01, ** p<0.05, * p<0.|0.077<br>10, standard er|0.168<br>rors cluster|0.272<br>ed by household a|0.247<br>re presente|0.330<br>d in parentheses|

Note: *** p<0.01, ** p<0.05, * p<0.10, standard errors clustered by household are presented in parentheses below the coefficient. Multiple testing adjusted FDR-adjusted q-values are presented in square brackets. The omitted category are individuals who were working at baseline and completed weekly in person interviews. Regressions are attrition weighted with weights corresponding to 1/number of weeks for which we have data for each individual. All regressions control for gender, age, marital status, education, days worked per week at baseline, hours worked per week at baseline, pay per week at baseline, self-employed at baseline, wageemployed at baseline, dummies for being employed in manufacturing, trade, services, the public sector, household dependency ratio, and log household assets, and week of interview dummies.

38
# **2. Setting and field experiment**

## **2.1. Setting and background**

The study took place from April 2019 to May 2020. The setting for our experiment was Dakar, Senegal. Dakar’s workforce is, on average, aged 30 years old, 49% have attended middle school, and 72% are employed in low-skilled occupations or are self-employed (Agence Nationale de la Statistique et de la Démographie Sénégal and ICF, 2020). We worked with a company that forms part of a joint venture involved in the construction of a new 36 km long railway express train connecting the city center of Dakar with Diamniadio, a new city established outside Dakar, and in a second phase with the new international airport. The express train is one of the flagship projects of the government’s five-year strategic plan, which highlighted the need for improving transport in the Greater Dakar Area. The first phase of construction started in 2017 and was completed in 2020.

## **2.2. Field experiment**

Workers employed at the company responsible for the construction of the new express train in Dakar were recruited for the study. Having a sample of workers employed by the same company allows us to hold unobservable determinants of conscientiousness constant, such as how motivating supervisors are, company work atmospheres and management styles. To select our sample, the company provided us with a list of workers in low-skilled positions that were working on different portions of the tracks of the express train. There were a total of 440 workers on these lists that worked during the day.<sup>11</sup> This formed our sample. Almost two-thirds of the sample were manual workers, barrier workers, and security agents. The remainder were other types of workers on the construction site that were classified by the company as having a low level of qualifications.<sup>12</sup> Following recruitment to the study, we administered a baseline survey in April 2019 to collect
>φ</u>_<sup>_i_</sup> _<u>R</u>_<sup>=59.</sup> The _φ_<sup>_i_</sup> _L_<sup>_wR_</sup> parameters _θ_ and _ν_ govern the trade elasticity and the elasticity of substitution between robots and workers, respectively. The Cobb-Douglas share parameters are calibrated using WIOD data.

68
# **Appendix: Construction of skills measures**

With the objective of constructing skills measures of occupational groups in the Indonesian labor market, we faced the problem of not having direct information on the skill content of occupations in this country. In this situation, the approach followed by many studies is to use the U.S. Department of Labor’s O*Net database, which collects occupation-specific attributes for detailed occupations. The disadvantage of using U.S. data is that these two countries, the United States and Indonesia, differ in their development levels and production technologies, which may impact both the levels and rankings of the skills used in each occupation. Another possibility is to use information from the STEP (Skills Towards Employment and Productivity) survey, which is collected by the World Bank in some East Asian countries, such as Vietnam and the Philippines. This is an individual-level survey conducted among working-age people (15-64) mostly residing in urban areas. This urban feature of the STEP survey is a disadvantage for a country like Indonesia where only half of the population is urban. We decided to proceed using the STEP survey of the Philippines for our main results, a country from the East Asian region with a development level similar to Indonesia, and the U.S. O*Net database as a robustness exercise.<sup>8</sup>

# **A) Skills measures using the Philippines STEP survey**

We use the 2015-2016 Philippines STEP survey to construct the skill content of occupations using the following categories: non-routine analytical, non-routine interpersonal, routine cognitive, routine manual, and non-routine manual. We work with a sample of 1,565 workers aged 15-64. We mapped the <mark>Philippines Standard Occupational Classification, that follows closely the International Standard Classification of Occupations (ISCO) 2008, to the Indonesian Classification of Occupation 1982 that is used in the Sakernas (the I</mark> ndonesian National Labor Force Survey). <mark>We work with six occupational
# **2.3 PMT algorithm**

PMT uses a ‘greedy’ approach to break the intractable variable selection problem down into a sequence of manageable tasks. At each step, it selects the best currently available option, until a stopping criterion is triggered. Such a myopic procedure can result in a selection that is very different to the optimal subset. However, the lack of theoretical guarantees is outweighed by its feasibility and results that tend to be credible, as evidenced by widespread policy adoption. A further benefit from the perspective of this paper is that a greedy algorithm produces a sequence of nested variable sets, which is a requirement for the adaptive approach laid out below. The original PMT algorithm, implicit in Grosh and Baker [1995], uses stepwise regression as the basic algorithm. It consists of the following key components:

- _Predictive model._ Ordinary least squares (OLS) regression is the workhorse of PMT. Starting with an intercept, one OLS model is trained for each un-queried variable.

- _Selection criterion._ The convention in stepwise regression tends to be the use of the Bayesian or Akaike information criterion for in-sample model selection [Friedman et al., 2001]. In forward selection, each variable is considered in turn, and that which provides the largest increase in the information criterion is added to the current variable set.

- _Stopping criterion._ Variable selection continues until the best available model in the current step offers no improvement in the chosen information criterion.

The PMT algorithm is described algorithm 1 below, where _X_ is a design matrix with partitions, _Xsel_ , _Xtmp_ , and _Xcand_ are column-wise partitions thereof, _y_ is the consumption level per household member that PMT estimates to determine eligibility, _f_ () is an OLS predictive model, _AIC_ is the Akaike information criterion [Akaike, 1998].

# **2.4 Data and background**

## **The Indonesia context**

Indonesia uses a social registry to implement its PMT for targeting multiple programmes. Much
dollar amounts across the distribution increase quickly as we move toward richer households, showing that the regressivity of these subsidies is steep and consistent across countries. Diesel is important only in Morocco and in Tunisia and only for the top quintile.

Figure 1: Distribution of Energy Subsidies, in US$ at PPP/capita/year <!-- Start of picture text --> Liquefied Petroleum Gas Electricity<br>400 1,000<br>350 900<br>300 800<br>250 700<br>200 600<br>150 500<br>100 400<br>300<br>50<br>200<br>0<br>Libya Iran, Islamic Djibouti Tunisia Morocco 100<br>Rep. 0<br>Iran, Islamic Rep. Libya Tunisia Morocco<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Gasoline Diesel<br>1,200 140<br>1,000 120<br>100<br>800<br>80<br>600<br>60<br>400<br>40<br>200 20<br>0 0<br>Iran, Islamic Rep. Libya Tunisia Morocco Tunisia Iran, Islamic Rep. Libya<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Energy subsidies, in US$ at PPP/capita/year Energy subsidies, in US$ at PPP/capita/year<br>PPP/capita/year PPP/capita/year<br>Energy subsidies, in US$ at Energy subsidies, in US$ at<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

The variety and amounts of food subsidies are much smaller than energy subsidies (Figure 2). They are below $40-PPP for
Our hope is that these data can be fruitfully used to further our understanding of how national economies innovate and learn, including how the structure of financing of R&D evolves over the course of development, and how innovation affects development outcomes. For example, by utilizing this database, Lederman and Maloney (2003) found that the social rates of return to R&D can be significantly higher in developing countries than in industrialized economies. But econometric analysis indicates that developing countries fail to raise their R&D effort due to financial underdevelopment, poor IPR protection, and other factors. Bosch et al. (2005) extended a literature on the link between R&D and patents that had previously been circumscribed to firm-level panel data (e.g., Hausman et al. 1984) that cannot assess the social returns of R&D at the national level, and utilized our database to estimate the impact of national R&D investment on patenting activity and investigated the determinants of the low patent elasticities with respect to R&D in various regions of the world. Surprisingly, there seem to be constant returns to scale in developed countries but slightly lower returns in developing countries. Furthermore, these authors found that factors such as the collaboration that exists between universities and the private sector, as well as the quality of the research institutions have a positive impact on patenting elasticities.<sup>6</sup> The data has also been used to conduct simulations of R&D-driven growth models with externalities (Klenow and Rodriguez-Clare 2004). Nevertheless, these modest contributions are admittedly the tip of iceberg of the research question on development and innovation that we can investigate with internationally comparable and consistent data from around the world. What is clear from the theoretical literature and from the empirical evidence, including the econometric evidence discussed herein, is that innovation should be placed at the top of the research agenda.

> 6 The data on the quality of research institutions and links between the private sector and universities used by Bosch
39 0.76<br>Note: To have comparable results for different countries, the same substet of questions was kept, using Big5 data. data.<br>from other countries, and the factorial analysis uses varimax rotation. The absolute value of correlations between<br>factor loads is used to calculate congruence.<br><!-- End of picture text --> Note: To have comparable results for different countries, the same substet of questions was kept, using Big5 data. data. from other countries, and the factorial analysis uses varimax rotation. The absolute value of correlations between factor loads is used to calculate congruence.