rafmacalaba/data-use-mentions-extended huggingface dataset:
# **II. Data: Definitions of Innovation Variables**

As mentioned, our key variables are patent counts and R&D investment across countries and over time. The definition and sources used for each variable are found in the Annex. The following paragraphs briefly discuss variable definitions and coverage.

# _A. Patents_

We use patents as indicators of the output of innovation. For this purpose we gathered data from the U.S. Patent and Trademark Office (USPTO) and from the European Patent Office (EPO). These countries presumably have the best existing intellectual property system and the high costs involved in the patent application process reflect the high potential economic returns to patenting in these markets (Maskus 2000). With data from these two sources, future research can address the question of whether the location of the patent request changes the empirical determinants and consequences of patents.

Data from the USPTO are available for the period 1963 to 2000 for 164 countries. Granted patents are assigned to countries based on the country of residence of the first inventor. The total number of observations was 8,372.<sup>1</sup> Patents granted by the EPO are available for the period 1980-2000. The country of origin of a granted patent is also classified by the country of residence of the first inventor. Granted patents data were obtained for 172 countries (including Puerto Rico). The total number of observations for patents granted is 3,423.

> 1  For Germany, patents granted to inventors residing in the former East and West Germany were combined together under “Germany" by the USPTO.  In the case of former Czechoslovakia, USSR, and former Yugoslavia, patents grants are registered after they stopped existing. This happens because patents are classified by the country of residence of the inventor at the time of the application.

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# Table 7: **Which Curriculum Is the Most Effective? - Social-Emotional Skills and Emotion Regulation** _Intention-to-Treat Estimates_

|||Estimated<br>Coeffcient<br>(1)|Standard<br>Error<br>(2)|_P-_value<br>(3)|_P_-value<br>Wild Bootstrap<br>(4)|Mean<br>Clubs Group<br>(5)|P-value<br>_Virtue_=_Mindful_<br>(6)|Observations<br>(7)|
|---|---|---|---|---|---|---|---|---|
|SES index|_Virtue_|-0.000|(0.014)|(0.996)|[0.995]|0.000|0.381|755|
||_Mindful_|0.007|(0.014)|(0.608)|[0.685]||||
|Short-term perseverance|_Virtue_|0.004|(0.010)|(0.736)|[0.725]|0.000|0.902|759|
||_Mindful_|0.005|(0.008)|(0.576)|[0.580]||||
|Self-control|_Virtue_|-0.008|(0.012)|(0.497)|[0.530]|0.000|0.469|776|
||_Mindful_|-0.013|(0.011)|(0.233)|[0.230]||||
|Risk aversion|_Virtue_|0.025**|(0.012)|(0.032)|[0.020]|0.000|0.066|
within the group of aspirational peers, which have a much higher income level though. But Ghana also does worse than over half of the countries within the group of comparably similar countries in those two variables (right panel). Finally, Ghana falls short of more than 50 percent of countries in both peer groups concerning trade openness and levels of credit to GDP, a measure for financial development.<sup>21</sup> Additional insights come from the results of the World Bank Enterprise Surveys. For the Ghana survey, business owners and top managers in 720 firms were interviewed from December 2012 through July 2014. Key data are presented in Figure 10 and compared to results from other SubSaharan African countries. Consistent with our benchmarking exercise, access to finance and key aspects of infrastructure are seen as major business constraint by firms—to an extent beyond the Sub-Sahara African average. This is especially true for electricity and water insufficiencies, while transportation seems to be less of an issue (consistent with our benchmarking exercise). The only deviation arises in the area of schooling, where our benchmarking exercise identifies a major gap whereas only 15.3 percent of surveyed firm managers see an inadequately educated labor force as a major business constraint. At the same time, Ghana ranks last in PISA scores for students’ mathematics and science performance among 76 countries analyzed. Note, however, that surveys were conducted with existing enterprises only. With a broader educational background of its labor force, new firms may possibly have developed in more productive sectors.

> 19 These criteria were whether a country is/has: a) commodity exporter (World Bank DEC definition), b) agrarian (above world average), c) population between 3 million and 55 million, and d) income level (USD 900 to 2,200). This methodology is based on a World Bank tool called “Find My Friends” that allows to compare development data to identify common characteristics of countries.

> 20 More precisely, we calculate quantiles by
explains 7.5 percent of the variation in heigh for age Z score when not considering the conditional random effect, and 11.3 percent when the conditional random effect is included. The model was estimated in a subsample of 6,379 children again of approximately 3.2 million children in the census.

Regarding the distribution of the residuals, the conditional random effect has a skewness of 0.144 while the error term has a skewness of 0.004. The estimated kurtosis, which is 2.4 for the random effect and 3.05 for the idiosyncratic error term, differs moderately from the benchmark value of 3 that characterizes a perfectly normal distribution.

**Table 1: SAE diagnostics for stunting**

|**Characteristic**|**Value**|
|---|---|
|Marginal R<sup>2</sup>|0.075|
|Conditional R<sup>2</sup>|0.113|
|Random effect||
|Skewness|0.144|
|Kurtosis|2.442|
|Idiosyncratic error term||
|Skewness|-0.004|
|Kurtosis|3.050|
|Number of sample observations|6,379|

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ondary (grade 9). We use, separately, math and Spanish results of ENLACE 9th grade of 2009–the year when the candidates applied for a scholarship–to divide our sample in terciles of the distribution of test scores. We ran two specifications within each of the terciles, with and without controls, to measure the effects of the scholarship on the probability of graduating and test scores at the end of EMS. The effects of PROBEMS on the probability of graduation, by tercile of the 9th grade math distribution, are summarized in Table 6.

The results show positive and statistically significant effects (p-value of 0.02) of the scholarship on the probability of graduation among those candidates in the top tercile of the 9th grade math test scores distribution (i.e. those with academic _readiness_ ). The inclusion of controls does not change the results. For candidates with academic _readiness_ , receiving a scholarship increases their probability of graduation by 4 percentage points (of a mean level of 76% among the control group). We found no effects within the lowest and middle terciles of the 9th grade test scores distribution. There are also no effects of the scholarships on learning outcomes by tercile of 9th grade math or Spanish test scores.<sup>13</sup> Candidates with academic _readiness_ , those at the top tercile of the 9th grade math test scores, have the largest probability of finishing EMS (76%), as oppose to those in the bottom tercile (57%). It seems that the scholarship is only effective as long as the candidates have academic _readiness_ , otherwise the requirements of EMS are such that a scholarship is irrelevant to address this constraint. This is consistent with other school interventions that seem to build on prior success (Machin, McNally, and Meghir 2010).

In Appendix Table A5, we cross academic readiness with socio-economic status measured by the IHA. We found that the positive effects of PROBEMS on EMS graduation rates among students with academic _readiness_ hold both for poor
security or pension beneficiaries. If the CPI does not reflect the true COL, some population groups are at risk of falling into poverty <u>(Colavecchio et al. 2011). Fritzer and Glatzer (2009)</u> demonstrated that the interaction of household composition and income level defines the impact of inflation. In Austria, families with two or more adults or a single person with low to medium income and single parents with high income experienced an inflation 0.2 percentage points above the headline inflation rate. Brachinger (2008) finds that in the period of high inflation in 2007 in Germany, the inflation rates faced by families with more than three children were significantly higher than the official CPI inflation. Michael (1979), using US data from 1967 and 1974, finds that the relationship between household characteristics and true COL is not stable over time. In some periods, households with specific characteristics may face higher inflation than the average, but in other periods, these same households may face lower inflation.

<u>Amble and Stuart (1994) argue that relatively higher inflation for the elderly is due to price</u> increases for medical care that represent a larger proportion of older households’ budgets. <u>Oldfield et al. (2018)</u> document that pensioners in the UK spend a larger share of their budget on food and fuel and less on housing than non-pensioners and thus are more affected by food and fuel price inflation. <u>Kalwij et al. (2018) report higher inflation faced by households of pensioners than</u> average households in the Netherlands. <u>Hobijn and Lagakos (2005)</u> find similar results for the US during 1987-2001. They show that inflation exceeds the official CPI inflation for the elderly, lowincome households, and households with children over 18 years old. In Ireland, inflation impacts more rural than urban households, followed by lower-income families and pensioners, because of differences in spending on
received by any other country in this year, but results are not sensitive to dropping these observations instead.

Our main tests do not include other control variables, because our interest is mostly in comparing selectivity across aid sources. We control in some tests for a fragile state dummy, because some donors in recent years have given them favored status in allocating aid. A very small share of IDA funds are set aside for a handful of “post‐conflict” countries that overlap considerably with fragile state status. We create a dummy variable for fragile country status from the official World Bank list of fragile and conflict situations, first produced in 2006 and updated annually. When we include the fragile dummy, we limit the sample to the fiscal years 2006‐2012, where the dummy equals one in those country‐years in which a country was on the official lists of fragile situations.<sup>18</sup> We create a separate dummy for countries eligible for special “post‐conflict” funding (World Bank 2014b). In some specifications, we also control for (logged) bilateral aid and development aid received from official donors through channels other than trust funds and IDA using OECD/DAC data (2015) and World Bank data for (logged) IDA flows. If other aid flows are to some extent policy‐ and poverty‐selective, these aid flows are collinear with our measures of policy quality and poverty for which the coefficients are then imprecisely estimated.

In robustness tests, we control for several donor‐interest variables, following the literature on allocation of bilateral aid (e.g., Alesina and Dollar 2000). First, we measure geopolitical interests

> 17 Full results using GDP per capita are available from the authors upon request. In reporting results below we note two instances in which significant results for one variable become insignificant when GDP is used instead of GNI. 18 The absence of a list prior to 2006 likely reflects the lack of any influential view among donors that “fragility”
influence we are trying to separate out. We therefore create a new index incorporating information on the percentage of the population living in urban areas, mortality rates, health infrastructure (number of nurses per capita, doctors per capita, hospitals per capita, beds) education (students per teacher in primary, secondary schools), and infrastructure (percentage and education of houses with electricity and without water drainage). We identified four significant factor loadings

> 6 The ENIGH gives information on expenses for all items, but only unit price and quantity consumed  for food items.  Nevertheless the survey also includes House Characteristics which allow us to estimate also Housing.

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. We use a newly available dataset to generate measures of migration ‡ows by education levels for all countries in our sample for the period 1990-2000. We analyze the wage e¤ects on the highly educated (college graduates) non-migrants and less educated (high school graduates or less) non-migrants separately to be able to assess the distributional impacts along with overall e¤ects.

In order to calculate these e¤ects at the national level, we adopt an aggregate production model which has been used in the evaluation of the impact of immigration at the national level (e.g. Borjas 2003, Manacorda et al., forthcoming) and in macroeconomic studies analyzing growth, productivity and skill premium in the US and other countries (e.g. Acemoglu and Zilibotti 2001, Caselli and Coleman 2006, Card and Lemieux 2001, Goldin and Katz 2008). We use this model to simulate the wage e¤ects of immigration and emigration, isolating this phenomenon from all other changes that happened in the same period. This simulation approach bypasses the issues of endogeneity and omitted variables encountered in the regression estimates of the wage e¤ects of immigration. The results, however, rely in important ways on the assumptions and on the parameter choice, which are therefore discussed and documented in detail. The model we use has four important components (with

> 1See, for example, the following recent articles from the Economist magazine on immigration to Spain ("Bad new Days", February 24 2010), to Italy ("Southern Misery" January 14th 2010) and to Europe in general ("No Boatloads but still troubles" August, 12th 2010).

> 2Even the academic literature has been mostly concerned with the impact of immigration on European Countries. Vis-a-vis the occasional study on the size of the "Brain drain" from Europe (such as Saint-Paul, 2008) there are scores of studies of the labor market impact of immigrants in Europe (see for instance Longhi et al 2005 for a summary).

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are two types of cross‐subsidization that takes place within Transnet: (i) cross‐subsidy within an unregulated sector between consumers of the same output (within a vertically integrated entity – e.g. TFR); and (ii) cross‐subsidy between regulated and unregulated sectors which have economically distinct outputs (within a horizontally integrated entity – Transnet) (Heald, 1997). The first type occurs within TFR between the heavy haul and the general freight businesses. In this case, a certain degree of cross‐subsidization is unavoidable: there are costs that are common to both business units. Even strict accounting rules for the separation of are unlikely to prevent this. The second type of cross‐ subsidy occurs, for example, from the profitable Pipelines and National Ports Authority divisions to the rest of Transnet. The National Ports Authority, which generated 22 percent of its EBITDA and 13 percent of its revenue, received only 9 percent of its capital investment. This is not a once‐off anomaly, using historic financial data it is clear that it reflects a trend: despite TNPA being one of Transnet’s most profitable divisions, its share of total investment has declined in recent years.

Profits from Transnet’s more profitable divisions are being diverted to the freight rail division to correct for historical underinvestment.<sup>21</sup> This diversion of profits may have the unintended consequence of undercapitalizing key components of the freight logistics network, such as the port system, in order to compensate for years of underinvestment in freight rail. Also, it is possible that cross‐subsidization delays Transnet’s response to the competitive pressure from road haulage: profits generated in the export commodity lines and other businesses to some extent permit continued underperformance of the general freight business. Cross‐subsidies continue to distort the incentives for efficient cost recovery which prevents a business from improving capacity allocation, responding to user needs and maximizing cost‐recovery for use of infrastructure.

> 20 Examples include the RBCT operated by
beneficial effect on precision than accuracy. More research can shed more light on the pros and cons of “sub-area models” that predict poverty rates with the sub-area as the unit of observation, vis-à-vis household level models with sub-area mean predictors, where repeated simulations are used to generate poverty estimates.

Finally, there are new developments applying tree-based machine learning techniques. These generally offer greater predictive power than linear models at the cost of parsimony and transparency (Efron, 2020). Tree-based machine learning models are more robust to outliers, and less susceptible to bias arising from informative sampling when predicting out of sample. Two important obstacles to more widespread adoption of machine learning methods have recently been surmounted. The first was the lack of asymptotic theory. Asymptotic theory has, however, recently provided for generalized random forests, a large class of methods that includes boosted regression forests, which is a type of gradient boosting (Athey et al, 2018). The second obstacle was the lack of an accepted method for uncertainty estimation. Although Athey et al. (2018) developed methods to estimate uncertainty for boosted regression forests, the random effect residual bootstrap developed by Chambers and Chandra (2013) and first applied by Krennmair and Schmid (2022) is also an attractive and simple option that appears to work well for wealth prediction using extreme gradient boosting in multiple contexts (Merfeld and Newhouse, 2023).

While the potential of regularly pairing survey data with geospatial data is clear, more work on research and tools is needed to further instill confidence in the estimates and facilitate use. Research could benefit from more comparative work on methods, ideally utilizing design-based simulations using georeferenced census data. These can examine several outstanding research questions, including the relative benefit of convolutional neural networks versus simpler estimation approaches, quantifying the benefits of including conditional random effects when using machine learning models, probing the robustness of methods for estimating the uncertainty associated with tree-based machine learning estimates, experimenting with different geospatial features, and determining the age threshold at which census-based estimates become less accurate
analysis will focus separately on the pre- and post-2000 subperiods.

# **3. Latin American growth during the second half of the twentieth century**

LatAm’s growth performance after WWII and until the rise of China in the early 2000s was rather dismal when compared to that of Southern Europe and South East Asia (Figure 2).<sup>6</sup> LatAm’s per capita income fell steadily from being 50 percent above that of the world in 1950 to around 10 percent below by 2000. Over the same period, Southern Europe’s per capita income, which was similar to that of LatAm in the early 1950s, rose to two-and-a-half times that of the world by 2000. As strikingly, South East Asia’s per capita income, which was barely 40 percent of LatAm’s in the 1950s, rose rapidly starting in the 1970s to a level 60 percent higher than that of the region by 2000.

The 1950-2000 period was marked by the rise of the US, Western Europe, and Japan as the three major engines of world growth, against the background of a powerful post-WWII economic reconstruction momentum. From a trade perspective, therefore, LatAm’s poor growth performance reflects an inability to take advantage of its links to the US economic locomotive, in sharp contrast

> 6 While cross-country data on GDP per capita start in 1950, comparable export data become available no earlier than 1960.

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1998.

- **Bown, Chad P.** 2014. “Temporary Trade Barriers Database.” Available at http://econ.worldbank.org/ttbd/.

- **Bown, Chad P., and Meredith A. Crowley.** 2013. “Self-Enforcing Trade Agreements: Evidence from Time-Varying Trade Policy.” _American Economic Review_ , 103(2): 1071–90.

- **Broda, Christian, Nuno Lim ̃ao, and David E. Weinstein.** 2008. “Optimal Tariffs and Market Power: The Evidence.” _American Economic Review_ , 98(5): 2032–65.

- **Estevadeordal, Antoni, Caroline Freund, and Emanuel Ornelas.** 2008. “Does Regionalism Affect Trade Liberalization toward NonMembers?” _Quarterly Journal of Economics_ , 123(4): 1531–1575.

- **Feenstra, Robert C., Robert E. Lipsey, Haiyan Deng, Alyson C. Ma, and Hengyong Mo.** 2005. “World Trade Flows: 1962-2000.” NBER Working Paper No. 11040.

- **Freund, Caroline, and Emanuel Ornelas.** 2010. “Regional Trade Agreements.” _Annual Review of Economics_ , 2: 139–166.

- **Gawande, Kishore, and Pravin Krishna.** 2003. “Political Economy of U.S. Trade Policy: Empirical Approaches.” In _Handbook of International Trade_ . , ed. James Harrigan and Eric K. Choi, 213–250. Blackwell.

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(complete freedom). Note that the values of the original Freedom House indices have been reversed. Source: Freedom House.

**_FREESTAB:_** interaction between **_FREEDOM_** and **_GOVCHANGE._** The variable is constructed as follows. First the original (i.e. not reversed) Freedom House index of freedom is multiplied by the nLmber of significant govermment changes. Second, this product is multiplied by (-1), se that higher values of **_FREESTAB_** are associated with countries with more freedom aind fewer significant government changes. Source: see above.

**_GOVCHANGE:_** number of significant government changes, i.e. changes that produce a transfer of power from one leading party to another. A government change is considered significant if the following conditions hold: for a parliamentary system if there is a change in the party of the prime minister and in the coalition of parties supporting the government; for a presidential system, if there is a change in the party of either the prime minister or the president. Also irregular transfers of governments (coups) are recorded as significant government changes. Source: see above.

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