rafmacalaba/datause-encoder-data huggingface dataset:
utility patents granted to researchers in each country during 1963-2000 by the United States Patent and Trademark Office (USPTO) per person. The latter provides a measure of the density of innovative ideas available to firms operating in each country.

The literature on Market Structure studies how market concentration affects firms’ investments in innovation. We use data on “business density” from Djankov _et al._ (2007), which were recently added to the World Bank’s _Doing Business_ database. This variable is defined as the (log of the) number of firms per capita. Although this variable does not directly measure market concentration, together with the variable on the regulatory environment, this variable sheds light on the role of policy and economic factors that determine market structure as they measure variables that affect the ease of entry into existing product markets. The data limited our sample to 40 countries, but section 4 also discusses results based on a broader sample of 58 countries, which was derived by imputing the missing values of the business density variable by exploiting the observed correlations between this variable and the other country characteristics.

Some studies, such as Criscuolo et al. (2005), also treat explanatory variables measured at a higher level of aggregation than the firm level as exogenous factors, but these are measured with data from the firm surveys themselves. Our approach is different in this regard, as we use objective data from other sources. The use of aggregate variables derived from the same dataset as the firm data can be assumed to be exogenous only under certain conditions, namely firms’ deviations from the average must be orthogonal to the average and normally distributed with expected value of zero. We do not have to make any assumptions in this regard, because our data are objectively measured at the country level from data from other sources. The disadvantage of our approach is that we have fewer degrees of freedom to estimate the relevant coefficients of the variables measure at the national level, which is limited by the number of countries.

Some of the surveys did not include certain data. The average years of education of the labor force was not available in 9 surveys. Table 1 reports the average and the standard deviation of the log of the number of years of education variable for two samples. One is the original sample of more than 18,000 firms and the other is the sample of over 25,000, which corresponds to the one used in the estimations. The larger sample was constructed by imputing the education variable by using the observed correlations between education and the other firm-level characteristics, except the dependent variable (the dummy variable for product innovation). Table 1 shows the resulting sample average and dispersion, which are very similar to those of the original reduced sample. In the sub-samples of firms reporting a product innovation, the averages are 2.305 versus 2.303 and 0.564 versus 0.652 in the standard deviation.

8
# **3.2. Reporting**

<mark>Although 90 percent of the women who report having been a victim of intimate partner violence said they were aware of the Intrafamily Violence Law,</mark><sup>14</sup> <mark>only 22 percent filed a formal complaint. Disaggregated by type of violence, the data reveal that 34 percent of the victims of physical violence, 21 percent of the victims of psychological violence, and 15 percent of the victims of sexual violence said they formally reported the abuse.</mark><sup>15</sup> <mark>Additionally, for all types of violence, most women reported the abuse to the police force (C</mark> _<mark>arabineros</mark>_ <mark>). Ninety-seven percent who filed complaints of sexual violence, 92 percent who filed complaints of physical violence, and 82 percent who filed complaints of psychological violence did so to the police.</mark>

## Figure 3

_Distribution of victims of intimate partner violence in the past 12 months, by type of violence and complaint_

## Figure 4

_Distribution of victims who formally reported intimate partner violence in the past 12 months, by type of violence and institution where the complaint was filed_ <!-- Start of picture text --> Did not report Reported Police (Carabineros) Investigation Police (PDI)<br>Courts Prosecutor’s Office<br>100<br>15 Other<br>21<br>80 34<br>Phycological 82 7 5 4 3<br>60<br>40 79 85 Physical 92 8 1<br>66<br>20 Sexual 97 21<br>0<br>0 20 40 60 80 100<br>Phycological Physical Sexual<br>Percentage<br>Source: Own elaboration based on 2020 ENVIF-VCM. Source: Own elaboration based on 2020 ENVIF-VCM<br>Percentage<br><!-- End of picture text --> The percentages of informal complaints of the different types of violence, such as asking relatives, friends, or others for help when an episode of violence occurs, are relatively higher than the rates of formal complaints. Of the total number of women who report having been victims of physical violence perpetrated by their partner in the past 12 months, 50 percent state that they asked for help, either from their family, friends, neighbors, or others. This percentage is 30 percent for victims of psychological violence and 18 percent for victims of sexual violence. Additionally, of the total number of victims of intimate partner violence who report having asked for help, between 60 and 68 percent asked for help mainly from their family (Appendix C provides details).

The reasons victims of intimate partner violence do not file a formal complaint vary according to the type of violence. However, some reasons are common to all kinds of violence. Nearly 40 percent of the victims of psychological violence who did not file a formal complaint said that they did not consider it severe or did not believe it necessary. Among victims of physical violence who did not file a complaint,

> 14 In the 2020 ENVIF-VCM, all women who report having experienced any type of violence are asked if they are familiar with the Domestic Violence Law.

> 15 Economic violence is not included because the survey does not collect information on complaints of this type of violence.

8
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Table 13: Network regressions: log Average Wage Pooled

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||logAvWag|logAvWag|logAvWag|logAvWag|logAvWag|logAvWag|
|SOE|0.263***|0.263***|0.263***|0.263***|0.263***|0.263***|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|Connect|0.154***|0.154***|0.073***|0.077*|0.261***|0.176***|
||(0.028)|(0.028)|(0.027)|(0.041)|(0.017)|(0.024)|
|BigI|0.084***|0.008||-0.008|||
||(0.031)|(0.035)||(0.054)|||
|HasBetween||0.146***||||0.192***|
|||(0.028)||||(0.034)|
|logDegree|||0.097***|0.098***|||
||||(0.015)|(0.030)|||
|BigI_×_logDegree||||0.001<br>(0.035)|||
|Politician|||||-0.083***|-0.031|
||||||(0.025)|(0.033)|
|Politician_×_HasBetween||||||-0.104**<br>(0.051)|
|Constant|7.700***|7.700***|7.700***|7.700***|7.700***|7.700***|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Observations|7,465,385|7,465,385|7,463,907|7,463,907|7,465,385|7,465,385|
|R-squared|0.66|0.66|0.66|0.66|0.66|0.66|
|Sector Dummies|✓|✓|✓|✓|✓|✓|
|Size Dummies|✓|✓|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|✓|✓|

Robust standard errors in parentheses clustered at the level of firm *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 37
**Table 4 – Years of Life Lost, by Gender, 2016**

||**All**|**Women**|**Men**|
|---|---|---|---|
|30-34|120,406|21,587|98,819|
|35-39|185,642|36,704|148,938|
|40-44|300,647|52,518|248,129|
|45-49|415,610|65,885|349,726|
|50-54|773,506|104,615|668,891|
|55-59|1,109,687|141,986|967,701|
|60-64|1,221,584|169,004|1,052,579|
|65-69|917,030|125,746|791,284|
|70-74|347,145|47,219|299,926|
|75-79|416,913|51,640|365,273|
|80 plus|171,339|29,945|141,395|
|**Total**|**5,979,509**|**846,848**|**5,132,661**|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

# **Tobacco-related medical costs**

The total tobacco-related medical expenses in the case of Russia used in this analysis have been calculated using national data. The direct health care costs related to the treatment of tobacco-related diseases include the costs of medical care provided in outpatient facilities, hospitals, and day-care hospitals. It is calculated by disease using the information on the number of cases and the weighted average costs of the treatment for each case. The diseases considered are as follows:

- Diabetes

- Cardiovascular disease

- Respiratory disease

- Cancer

- Tuberculosis

The number of diseases associated with tobacco smoking is calculated through the use of the indicator generally accepted in the available literature on population attributable risk (PAR). The PAR is calculated based on data on smoking prevalence and on the indicator of the relative risk (RR) of disease in smokers vs. nonsmokers. For the purposes of this paper, the RR has been taken from previous studies. The details of the calculations and the sources of information are summarized in annex C. The total direct tobaccorelated medical costs in Russia in 2017 are estimated at the level of 418 billion rubles.<sup>11</sup>

> 11 Alternatively, as a robustness check, the simulations have been performed using data from a cross-country comparable database. The most complete and standard study estimating the economic cost of smoking in Russia is Goodchild, Nargis, and Tursan d’Espaignet (2018). The study uses a cost of illness approach to estimate the economic cost of smoking attributable-diseases in 152 countries. Annual direct health care costs attributable to tobacco use in Russia in 2012 amounted to 526 billion rubles.

8
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# **Household Responses to Shocks in Rural Ethiopia: Livestock as a Buffer Stock**<sup>**¶**</sup>

## **Daniel Ayalew Ali**

Development Research Group, The World Bank _JEL classification:_ D91; O16 _Keywords:_ Livestock; Consumption Smoothing; Savings; Risk

> ¶ The author would like to thank Stefan Dercon, Will Martin, Frans Spinnewyn, Bart Capéau and Marno Verbeek for their valuable comments and discussions.
Appendix Table A2: Individual Financial Awareness Indicators

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Financial<br>Budget|Savings<br>Account|Interest Rate|ATM|Debit Card|Bank<br>Transfer|Transaction<br>Fees|Pawnshop|Foreign<br>Currency|Exchange Rate|Insurance|TKI insurance|
|Migrant-Only Training (A)|0.034|0.053|0.046<br>|-0.001|0.054|0.022|0.094*|0.006<br>|0.014|0.046|0.039|0.067*|
||(0.047)|(0.051)|(0.052)<br>|(0.054)|(0.033)<br>|(0.052)|(0.053)|(0.042)<br>|(0.053)|(0.049)|(0.050)|(0.040)|
|Family-Only Training (B)|0.202***|0.128***|0.102**<br>|0.135**|0.053|0.075|0.120**|0.038<br>|0.124**|0.087*<br>|0.166***|0.248***|
||(0.048)|(0.047)|(0.049)<br>|(0.053)|(0.033)<br>|(0.050)|(0.051)|(0.041)<br>|(0.051)|(0.047)|(0.050)|(0.045)|
|Migrant and Family Training (C)|0.300***|0.178***|0.181***|0.164***|0.077**|0.206***|0.230***|0.077**|0.188***|0.150***|0.260***|0.291***|
||(0.048)|(0.047)|(0.050)<br>|(0.053)|(0.033)<br>|(0.050)|(0.053)|(0.039)<br>|(0.051)|(0.050)|(0.052)|(0.047)|
|Follow-up 2 Indicator|0.013<br>|0.126***|0.130***<br>|0.002<br>|0.073***|0.044|0.001<br>|0.084***<br>|0.027|0.078***|-0.023|0.112***|
||(0.029)|(0.032)|(0.032)<br>|(0.030)|(0.025)<br>|(0.031)|(0.030)|(0.029)<br>|(0.032)|(0.029)|(0.033)|(0.029)|
|Follow-up 3 Indicator|0.010<br>|0.053*|0.076**<br>|-0.042|-0.001|0.043|0.041|0.064**<br>|0.035|0.097***|-0.002|0.122***|
||(0.030)|(0.031)|(0.033)<br>|(0.031)|(0.022)<br>|(0.031)|(0.032)|(0.028)<br>|(0.034)|(0.032)|(0.032)|(0.027)|
|Constant|0.231***|0.524***|0.447***|0.511***|0.077***|0.520***|0.355***|0.713***|0.385***|0.214***|0.371***|0.106***|
||(0.036)|(0.038)|(0.039)<br>|(0.041)|(0.024)<br>|(0.038)|(0.040)|(0.033)<br>|(0.041)|(0.035)|(0.039)|(0.029)|
|R-squared|0.070|0.034|0.030<br>|0.028|0.016|0.031|0.029|0.016<br>|0.027|0.025|0.045|0.082|
|Sample Size|1133|1132|1132<br>|1132|1132|1132|1132|1132<br>|1132|1132|1132|1132|
|Number of Clusters|394|394|394|394|394|394|394|394|394|394|394|394|
|Mean of Dependent Variable in Control Group|0.230|0.577|0.510<br>|0.493|0.101|0.542|0.364|0.755<br>|0.399|0.262|0.357|0.175|
|Test: A-B = 0 (p-value)|0.001|0.121|0.271<br>|0.012|0.988|0.309|0.619|0.450<br>|0.037|0.409|0.012|0.000|
|Test: A-C = 0 (p-value)|0.000|0.011|0.010<br>|0.003|0.503|0.000|0.011|0.074<br>|0.001|0.048|0.000|0.000|
|Test: B-C = 0 (p-value)|0.051|0.268|0.110<br>|0.593|0.493|0.008|0.036|0.311<br>|0.200|0.211|0.071|0.419|
|Test: A = B = C = 0(p-value)|0.000|0.001|0.003<br>|0.001|0.094|0.000|0.000|0.172<br>|0.000|0.021|0.000|0.000|

This table presents pooled regression analysis across three follow-up survey rounds for Individual components of the aggregate measure "Financial Awareness", presented in Table 3. All regressions include survey round dummies. Standard errors in parentheses, clustered at the individual level. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% **significance level)** .
firms then repaying through royalties on incremental sales if the activities funded succeeded in increasing sales. The World Bank project was ultimately cancelled following low demand for the original matching grant.

Third, the most common reason for the inability to randomize was that program take-up was low: our randomization strategy was based on selecting among an excess supply of eligible applicants. Instead, all projects struggled to obtain enough eligible applicants. In one country where the project was half over when we started discussing the evaluation, the disbursement rate was only 23% and new efforts were under way to increase spending. World Bank operations have an incentive to ensure that the loans “disburse”, that is, that the money promised in the loan is actually spent, and similarly project implementation units and governments want to show that their project can spend the money allocated to it, to help support future requests for such money. Therefore, when the few applications were actually received, grants were given to all firms that passed the eligibility criteria making randomization impossible.

These problems are not unique to the matching grant programs that our studies were built around. Goldberg and Ortiz del Salto (2011) undertook a review of World Bank matching grant programs and found that of the 42 projects with an original closing date before 2012, 79 percent were extended at least one year, reflecting both delays in initial implementation, as well as in disbursing money. In a review of 15 completed projects, they report that on average only 70 percent of the allocated money was disbursed, although this average masks considerably heterogeneity across projects, ranging from only 6 percent being disbursed in one project in Zimbabwe, to 99 percent in a project in South Africa and 116 percent in a project in Mozambique.

# **4.2 Underlying Causes of a Randomized Evaluation Not Occurring**

It is easy enough to blame the inability to conduct an experiment on low program take-up, repeated implementation delays, or government unwillingness to randomize. But in order to understand how researchers and project teams can design projects and evaluations better in the future, it is important to delve deeper into some of the fundamental causes underlying these outcomes. These concern the incentives facing governments, firms, researchers, and project implementers. Again these are to some degree interrelated, but we discuss each issue in turn.

a) _Political economy and capture reasons:_ Matching grant programs consist of handing out subsidies to firms. As a result of this free money, there is a risk of capture, with those in charge of the 19
and ET scenarios. This is because, although the higher the biofuel target requires more lands for biofuel production, it would also replace more fossil fuel consumption.

**Figure 5: Marginal rate of deforestation due to expansion of biofuels**

<!-- Start of picture text --> 80%<br>70%<br>AT ET<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2010 2015 2020 2025 2030 2035 2040<br><!-- End of picture text --> Whereas GHG emissions from land-use change are mostly incurred at the time of land conversion (with the exception of emissions from soil that occur over the next 20 years), GHG savings through the replacement of fossil fuels with biofuels accumulate gradually overtime. Therefore, it would be more appropriate to present GHG emissions or mitigation in cumulative terms instead of annual terms. Moreover, different countries have different timeframes to meet their biofuel targets. For example, Australia, New Zealand and Canada have targets to meet by 2012. On the other hand, China, India, Indonesia and the EU have targets for 2020. The cumulative account of emissions also makes it easier to aggregate the climate change mitigation impacts of these time-varying targets at the global level. Figure 6 illustrates the relationship between biofuel penetration and cumulative CO2 emissions from the consumption of fossil fuels and from land-use change. The bottom panels of the figures show the number of 16
We start our analysis using income data for 1976 because this is when the CPS begins identifying individuals by state. Consistent with studies of the U.S. labor market, our main sample (a) includes prime-age (25-54) black and white civilians, (b) excludes people living in group quarters, those with missing observations on demographic characteristics, and a few individuals for which the CPS assigns a zero sampling weight, and (c) corrects for top-coding and translates income to constant 1990 dollars.<sup>2</sup> Table 1, Panel A provides details on the construction of the sample, while Panel B presents summary statistics on the individuals in the sample.

Table 1 Panel C presents descriptive statistics on the logarithm of the Gini coefficient of income inequality. These data are calculated on a state-year basis, so there are 1,433 observations. For total individual income, the average Gini across states and over time is 0.48, corresponding to an average of -0.74 for the log of Gini. The standard deviation of the log of Gini within states over years is 0.56.

# **1.3. Control variables**

To control for time-varying changes in a state’s economy, we use the U.S. Department of Commerce data to calculate the growth rate of per capita Gross State Product (GSP). We also control for the unemployment rate, obtained from the Bureau of Labor. We also condition on government taxes to personal income and government expenditures to personal income in order to account for governmental redistribution policies. These data were obtained from the U.S. Census Bureau. In further robustness tests, we test whether the impact of deregulation on income inequality varies in a predictable way with different state characteristics at the time of

> 2 Following the usual practice in analyses of income distribution using the CPS data, we inflate top-coded incomes by a factor of 1.5. For purposes of confidentiality the CPS does not report exact incomes above a certain threshold. Instead, these incomes are grouped into a single category called the “top-code”. The top-code changes across years and types of income. Furthermore, consistent with usual practices, we drop all allocated incomes, i.e., incomes that were originally missing but were assigned a non-missing value by the CPS based on demographic characteristics of the respondents.

10
# **4.5 Concentration of exports by product categories**

We now verify whether exports are equally concentrated by product as in terms of export destinations. Subsequently, we explore what type of products Rwandan manufacturing firms specialize in. In both cases, we continue to show separate results for small and large exporters as firm dynamics and comparative advantage could evolve differently for both types of firms.

The results in Figure 9 show the share of exports accounted for by the three (in blue) or ten (in red) most important product categories using the 2-digit Harmonized System (HS) classification. In stark contrast with the results for export destinations, we now see only a small difference for large and small exporters. In 2008, the top 10 (out of 90) product categories accounted for 98% of total exports of large firms and an even larger share for small exporters (shown, respectively by the solid and dashed red lines). Over time, this share declined monotonically, but very gradually. By 2016, the fraction was 89% for large exporters and 90% for small exporters. The only notable difference is that the fraction for small firms started its decline a bit later, as it was still over 99% in 2012, and its lowest value was also slightly lower at 83% in 2015.

The overall evolution in the share of exports accounted for by the top-3 product categories (represented by the blue lines) also looks similar for both types of firms, only the average level is lower. However, closer inspection does reveal some notable differences. For large exporters, the top-3 share was 70% in 2008 and declined to 54% by 2016. However, it already reached a low of 54% in 2009 and 59% in 2011. Ignoring the 2008 outlier, the top-3 concentration is very stable for large exporters barely showing any trend over time.

**Figure 9:  Evolution of product concentration in manufacturing**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Top 3 categories (large) Top 3 categories (small)<br>Top 10 categories (large) Top 10 categories (small)<br><!-- End of picture text --> Note: Product categories are defined at the Harmonized System (HS) 2-digit level. Small exporters are firms exporting at least 1,000 USD, but not more than 100,000 USD per year.

21
electricity subsidies in a variety of countries find that barely 10-35% of subsidy resources are captured by low-income households<sup>11</sup> .

> 11 These studies include (a) World Bank (2000) _Maintaining Utility Services for the Poor: Policies and Practices in Central and Eastern Europe and the Former Soviet Union_ , The World Bank Group, Washington DC; (b) _India Power Sector Reform and the Poor_ , Mimeo, South Asia Region, The World Bank Group, Washington DC ; (c) Foster, V. and Araujo, C. (2001) _Does Infrastructure Reform Work for the Poor? A Case Study from Guatemala,_ Mimeo, Latin America and Caribbean Region, The World Bank Group, Washington DC.

30