rafmacalaba/data-use-mentions-extended huggingface dataset:
# US Historical Settlement Patterns

Columns 9 and 10 of Table S1a provide the largest cities for each country’s ancestry population. Due to their large overall size and high immigration shares, cities like New York, Los Angeles, and Miami appear frequently. Nonetheless, there is substantial heterogeneity. East and West Coast cities are more likely to link respectively to European and Asian ancestries, for example, while southern parts of the United States link more to Latin American ancestries. The migration from Nordic countries to the mid-west is evident (e.g., Finland’s presence in Minnesota).

Robustness checks use the spatial distributions present in the 1990 Census of Populations. These patterns are constructed from the 5% sample in a manner that parallels the 1980 distribution.

# US Patenting Data

The addresses listed on inventor records are used to group patents to the cities identified in the 1980 census. This procedure uses city lists collected from the Offi ce of Social and Economic Data Analysis at the University of Missouri, with a matching rate of 99%. Manual recoding further ensures that all patents with more than 100 citations and all city names with more than 100 patents are identified. Some smaller metropolitan areas identified in the patent data are excluded since they do not link to places identified in the 1980 census. Only patents with all inventors living in the United States at the time of their patent application are included, and multiple inventors are discounted so that each patent receives the same weight when measuring inventor populations.

Second, the USPTO issues patents by technology categories rather than by industries. The work of Johnson (1999), Silverman (1999), and Kerr (2008) develops concordances that link the USPTO classes to ISIC3 industries in which new inventions are manufactured or used. The main estimations focus on industry-of-use, affording a composite view of the technological opportunity developed for an industry. Studies of advanced economies find accounting for these inter-industry R&D flows important (e.g., Scherer 1984, Keller 2002b). Estimations
# **1. Introduction**

Agent banking holds the promise of facilitating financial inclusion for underrepresented and marginalized groups, including women, through improved accessibility, convenience, and reduced costs. As an alternative to bank branches, agents represent physical access points that enable customers to make deposits, withdrawals and money transfers, and to repay loans (Lyman et al., 2006; Siedek et al., 2008; Flaming et al., 2011). In contrast to personnel of a bank branch that likely change from visit to visit, the agent is usually the same contact for clients. Customers therefore choose agents based on location, or personal characteristics of the agent such as gender, religion, ethnicity or age.

In this paper we investigate whether gender plays a role in the choice that customers make about which agents to transact with. Research to date says little about matching between agents and clients, or the potential implications of agent gender on clients’ financial access and utilization. This is an important policy question since closing the gender gap in financial access is a goal of institutions such as the World Bank, the International Finance Corporation (IFC), and the United Nations. According to 2017 Global Findex data (Demirguc-Kunt et al., 2018), the gender gap in financial institutions account ownership was 9 percentage points (35% of males versus 26% of females had an account).

We study the role of gender in financial transaction decisions using data from FINCA DRC, one of the largest microfinance institutions (MFIs) in the Democratic Republic of Congo. The Democratic Republic of Congo is a country characterized by large gender inequalities–it was ranked 176th out of 189 countries in the 2018 Gender Inequality Index (United Nations Development Programme, 2018). These gender inequalities impede women’s full participation in social and economic life.

Females only represent 39% of FINCA DRC’s customers and 23% of all agents. In this context of maledominated agent networks, both genders transact predominantly with male agents
Mozambique) to be terminated.

47. Compared to the large number of privatization transactions there have been few instances of outright reversal or re-nationalization. Those that have taken place usually occurred when very large heavily indebted privatized firms failed, or in times of crisis and/or political change. Among the prominent examples: Rail Track in the UK (as a result of bankruptcy), Air New Zealand (a $300 million rescue package), La Paz and El Alto water concessions in Bolivia, and banks in Chile and Mexico that had been poorly privatized in the first rounds with protection from foreign competition, then renationalized, and subsequently reprivatized.

48. Though relatively few in number these and other recent transactions under stress have contributed to declining public support, worldwide, for privatization. Recent public opinion polls show the following:

- In Latin America, about 75 percent of the population in 1995 supported privatization (Estache 2004), but a more recent Latinobarómetro poll found that between 1998 and 2002 the percentage of people who say that the state should leave productive activity in private hands fell from 51 to 35 percent.  During the same period the percentage of those who believed that privatization has been beneficial for their country fell from 46 to 28 percent (Figure 12).  And when asked specifically whether the state or the private sector should be responsible for service delivery in the water and power sectors, 70 percent of respondents wanted the state to take charge;
br>t~~~~~~~~~~ .Traders 0..<br>Su ce Ta0a0ba F igur T 03 t n T 0<br>'fearsl<br>wMnirrum age earners wa earsi ooer r f i<br>I Food f arnrers 2 - Cocoa f amers I--<br>grops,it s iffcul tobeconidet -. . Traders aou th cors of rualura .__. incomedfeec<br><!-- End of picture text --> _Source:_ Tabatabai Figure 3. The data are presented in Tabatabai, Table 9.

groups, it is difficult to be confident about the course of rural-urban income difference over this period. But in so far as food producers are the most important part of the rural economy, and wage earners constitute a sizable section of the urban labor force, it is likely that real income per head, on the average, probably had declined in the urban areas relative to the rural, and indeed could have been lower in the former at the end of the period. Official Census data are consistent with this conclusion. They show that between the two Censuses of 1970 and 1984, urbanization had slowed down considerably, and the share of the three largest cities in total population had remained largely the same. (Tabatabai, table 7, p.719).

# 6.3 **The Rural-Urban Differential After the Recovery**

39
<!-- Start of picture text --> c. Progressivity of direct transfers and indirect subsidies<br>(Kakwani Index)<br><!-- End of picture text --> <!-- Start of picture text --> All indirect subsidies -0.08<br>Agriculture 0.44<br>Water -0.26<br>All direct transfers 1.03<br>NUSAF 1.23<br>UWEP 1.15<br>YLP 1.18<br>SAGE 0.40<br>-0.50 0.00 0.50 1.00 1.50<br>Kakwani Index<br><!-- End of picture text --> <!-- Start of picture text --> d. Redistributive and Poverty Reduction Effects direct<br>transfers and indirect subsidies<br>0.4<br>0.32<br>0.3 0.26<br>0.2<br>0.10<br>0.07 0.10 0.07<br>0.1 0.01 0.03 0.01 0.06 0.07<br>0.01 0.01 0.01<br>0.0<br>-0.03<br>-0.1<br>-0.10<br>-0.2<br>Redistributive Effect<br>Poverty Reduction Effect<br>SAGE YLP UWEP NUSAF All direct transfers Water Agriculture All indirect subsidies<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17._ _Note for d: A positive redistributive effect means inequality declines, while a negative redistributive effect means inequality increases (in Gini points). A positive poverty reducing effects means poverty is declining while a negative poverty reducing effect means poverty is
All SVAR models are estimated by using quarterly data on a country-by-country basis. The country-specific estimations are based on a Bayesian approach with the Minnesota priors proposed by Litterman (1986). For models with recursive identification, a total of 2,000 samples are drawn, and a burn-in sample of 1,000 draws is discarded. For models identified by sign restrictions, we search for 1,000 successful draws (satisfying our sign restrictions) of at least 2,000 iterations with 1,000 burn-ins. In all models, the remaining 1,000 draws are used to determine the cumulative impulse responses and forecast error variance decompositions for each country as well as for each country group. For example, when empirical results are presented for oil exporters as a group, 1,000 draws coming from all oil exporting countries are pooled together, and the corresponding measures (e.g., median or interquartile range) are calculated out of this pool. Following Fry and Pagan (2005, 2011), rather than directly calculating the corresponding measures (e.g., median, or interquartile range) across all draws for a particular horizon, we search for the draw that produces impulses closest to these measures by minimizing the distance between a measure and the selected draw for a horizon of five years.

# **3.1. Real oil price shocks and current account balances**

The purpose of this investigation is to estimate the effects of real oil price shocks on current account balances, where recursive identification is used. **Benchmark model.** In the benchmark model, consists of year-on-year change in log real crude oil prices (global variable), year-on-year change in log real GDP (country-specific variable), and current account tt −1 balance as percentage of GDP (country-specific variable). The recursive structure imposed on zz 0 requires an ordering of the variables used in the estimation. Accordingly, real crude oil prices growth is ordered first as oil prices are mainly determined in the global economy.
|Mozambique|11.1|0.13|0.74|0.27|0.24|
|Uganda|7.9|0.38|0.26|1.92|0.28|
|Rwanda|6.0|0.24|0.76|0.58|0.15|
|Tanzania|3.5|0.20|1.04|0.60|0.22|
|Ethiopia|2.7|0.14|0.28|0.54|0.23|

<mark>Source: Research ICT Africa surveys (various years). See World Bank (2016). Ratios refer to percentages, e.g., percent rural / percent urban.</mark> <mark>Second, studies reviewed in this paper suggest that basic price and market information systems can improve efficiency and welfare. The evidence, though strong, is still limited to certain countries and in certain contexts. A number of recent studies have cast doubt on the overall</mark> 23
. Blackwell Publishers, Ltd Oxford, pp 409–430.

Hall, Bronwyn and Josh Lerner. 2010. “The Financing of R&D and Innovation” in Handbook of the Economics of Innovation. Vol. 1, edited by Bronwyn Hall and Nathan Rosenberg. Elsevier.

Haltiwanger, John C., Ron S. Jarmin, and Javier Miranda. (2010). Who Creates Jobs? Small vs. Large vs. Young. NBER Working Paper 16300, NBER.

Hannan MT, Diane Burton M, Baron JN (1996) Inertia and Change in the Early Years: Employment Relations in Young, High Technology Firms. Ind Corp Chang 5(2):503–536. <u>https://doi.org/10.1093/icc/5.2.503</u> Harris, M.N., Rogers, M., Siouclis, A. (2003). Modelling firm innovation using panel data probit estimators. Applied Economic Letters 10(11): 683-686.

Hausman, Naomi. 2018. “University Innovation and Local Economic Growth.” https://drive.google.com/file/d/1dfYTyG2zzVvfbRBJQ7arOLt7d_7M29Fv/view.

38
**TABLE 8: Investment financial performance and capital market development.** The dependent variable is the investment-level PME vs. S&P 500. Explanatory variables are measured in the year of first cash flow, and have been normalized as Z-scores by subtracting off the sample mean and dividing by the sample standard deviation. All columns include country fixed effects (a version without fixed effecs is provided in Table A10). The de jure financial openness index is the first principle component of dummy variables that codify the restrictions on cross-border financial transactions reported in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER). Private sector credit is domestic private credit to the real sector by deposit money banks. Standard errors are clustered at the country-year. *** p<0.01, ** p<0.05, * p<0.1

||(1)|(2)|(3)<br>Qua|(4)<br>ntile Regr|(5)<br>ession|(6)|
|---|---|---|---|---|---|---|
||OLS|0.1|0.25|0.5|0.75|0.9|
||PME|PME|PME|PME|PME|PME|
|Panel A) Direct investments, vintage|pre-201|0|||||
|De-jure fnancial openness index|-0.10|0.00|0.02|0.06|0.14|-0.60***|
||(0.52)|(0.03)|(0.04)|(0.06)|(0.11)|(0.14)|
|Private sector credit (% of GDP)|-1.33|-0.00|-
nationally representative data. Measured male and female rural employment-to-population ratios drop by 18 percentage points for males and 12 percentage points for females in Nigeria, and by 12 and 19 percentage points, respectively, in Malawi (corresponding to relative declines of approximately 20 to 30 percent in both countries). In Malawi, the decline in rural employment is larger for females than for males, while it is the opposite pattern in Nigeria. The magnitude of the decline in rural employment-to-population ratios not only reflects the share of farmers re-classified under the revised standards but also the prevalence of farming in the total population and the extent to which those farmers are also engaged in non-farm household enterprises and/or wage work. In other words, many farmers who produce only or mainly for family consumption remain employed under the revised standards because they are engaged in these other, mostly non-agricultural, activities (for at least one hour during the

> 5 It is important to note that while rural employment in developing countries has always displayed some seasonal variation, this will most likely be amplified under the 19<sup>th</sup> ICLS standards. Under both the previous (1982) and the revised (2013) labor statistics standards employment is measured in relation to a short reference period (e.g. the past seven days). Hence, even the under previous standards, rural employment ratios (in Sub-Saharan Africa) often fluctuated over the course of the agricultural season, due to whether farmers were engaged in any agricultural activity over the past seven days or not (Fox and Pimhidzai 2013). Under the revised ICLS standards, however, rural employment ratios are affected by an additional margin of variation – i.e. changes in farmers’ reports on the intended use of their produce – which is expected to lead to even larger seasonal fluctuations.

4
online appendix District COVID-19 infections and given limited testing capacity during the early months of the COVID-19 pandemic. In this section, we leverage data on COVID-19 infections at the District level to test the validity of number of purchases of paracetamol as a proxy for COVID-19 infections. Specifically, we estimate the association between increases in number of purchases of paracetamol and COVID-19 infections, and test whether a 1% increase in number of purchases of paracetamol is associated with a 1% increase in COVID-19 infections. We fail to reject this null in our preferred specifications, although we have limited statistical power due to relatively limited testing during our study period.

**Data** We gather daily confirmed District-level COVID-19 case counts from the Rwanda Biomedical Centre’s COVID-19 operations dashboard.<sup>A14</sup> We aggregate confirmed COVID-19 case counts to the District-by-month level. For comparison, we aggregate our sub-District-by-month data on number of purchases of paracetamol (used as an outcome in Table 2) to the District-by-month level.

**Empirical strategy** To estimate the association between number of purchases of paracetamol and COVID-19 infections, we estimate by Poisson pseudo maximum likelihood

log **E** [COVID-19 cases _dt|_ log # paracetamol receipts _dt, Xdt_ ] =

_β_ log # paracetamol receipts _dt_ + _Xdt_<sup>_′δ_</sup> (A6) where _Xdt_ is a vector of control variables; we include either a constant, destination District fixed effects, month fixed effects, or destination District fixed effects, month

> A14 `https://gis.rbc.gov.rw/portal/apps/opsdashboard/index.html# /59872985985446bbaf8c394ad857c5cd`

A34
Table 4. Breakdown of built-up proportion by agro-ecological zones

|||S|oil quality|||So|il humidity||No info|
|---|---|---|---|---|---|---|---|---|---|
|Year||Poor|Moderate|Good|.|ep|Moist/dry|humid|Humid||
|1975|0.55%|0.30%|0.21%|0.41%|0.09%|0.21%|0.47%|0.15%|3.56%|
|1990|0.89%|0.70%|0.43%|0.81%|0.23%|0.37%|0.88%|0.42%|5.88%|
|2000|1.31%|1.08%|0.66%|1.07%|0.29%|0.50%|1.32%|0.66%|8.09%|
|2014|2.03%|1.69%|1.02%|2.71%|0.75%|0.69%|2.07%|1.34%|9.32%|
|N.obs.||1,447,084|1,816,564|282,295|64,568|189,485}|648,635|2,666,937|484,424|53,277|

Table 5. Breakdown of built-up proportion by presence of a road

|Year|Road in|1976|Road in|1986|Road in 2|008|Road in|2014|
|---|---|---|
to perform a second random reshuffling of all grid cells that belong to urban areas to generate a counterfactual population density distribution within those areas. Contiguous urban area grid cells that have an actual population that is above the 95<sup>th</sup> percentile of this second counterfactual distribution are “urban cores”. Any given urban area may have either zero or one or more cores. Urban areas that possess one or more cores are “cities”, while urban areas that lack a core are “towns”.

In applying the above methodology, the criteria used to designate grid cells as potentially habitable are important. Following de Bellefon et al. (2021), we define cells as potentially habitable if they do not: (i) intersect with a water body; (ii) are not at high altitude, defined here as below the 99<sup>th</sup> percentile of grid cells with at least one inhabitant in terms of altitude; and (iii) are not on a very steep slope, defined here as below the 99<sup>th</sup> percentile of grid cells with at least one inhabitant in terms of steepness. We also define as uninhabitable those grid cells which intersect with a desert. We do this for the 14 SSA countries in our sample which have more than 0.5 percent of their land area covered by desert.<sup>16</sup>

# **_2.2. Data_**

The main data that we use as input into the dartboard algorithm is constrained WorldPop gridded population data from the University of Southampton’s WorldPop group for the year 2015.<sup>17</sup> This data has an underlying resolution of approximately 100 m*100 m, but to avoid excessive compute times, we aggregate the data into 250 m*250 m grid cells. This aggregation also ensures compatibility with other spatial layers that we use to classify cells as potentially habitable. As with other “top down
ranked trainers in Table A.8. This us with additional reassurance Appendix provides about the validity of this empirical exercise. Table 7 examines the effect of trainer characteristics on the first-stage outcomes. Because the majority of individuals who attended a matching meeting started training, it is not surprising that we find only limited effects on any of these first-stage outcomes.

We examine the transmission of skills in Table 8. We measure craft skills using a short test designed in collaboration with industry experts. Innovation is measured using a series of self-reports such as the number of new designs created. Job skills are measured based on the skill content of a job following the World Bank STEP surveys. A higher value of this index reflects a shift away from jobs that are intensive in physical tasks. This index is only relevant for individuals who are working. Managerial skills are measured by the number of improved management practices adopted. These are relevant only for individuals who run their own business.

Apprentices assigned to trainers with the best math skills did not obtain better skills (Column 1). Nor did training with the most profitable trainers affect apprentice skills (Column 2). Training with the most experienced trainers (Column 3) improved test scores by about 0.25 _σ_ (p-value _<_ 0.1) compared with trainees assigned to less experienced providers. These trainers increased the skill content of jobs by a similar magnitude (p-value _<_ 0.1). Neither of these effects is robust to multiple testing adjustments, however. Apprentices paired with trainers with the highest wage bill (Column 4) scored almost 0.2 _σ_ higher on the skills test than those assigned to trainers with smaller wage bills, although this effect was just outside the standard thresholds for statistical significance. Training with these providers also increased innovation (or creativity) by 0.18 _σ_ (p-value _<_ 0.05).

Finally, we examine the effect of trainer characteristics on labor market outcomes for apprentices in Table 9. For brevity,
results of estimation of equation (1) – a strong country’s</u> focus on job protection policies is associated with a higher GDP in 2021, a higher employment rate, a lower inactivity rate, and a lower poverty headcount rate. While the sign of the effects is consistent with our main results, the coefficients on the share allocated to job protection are not statistically significant. The associated F-statistic has a value corresponding to a size distortion of

> 11 While we are _a priori_ agnostic on the direction of the effect, evidence from Latin America suggests that governments expand cash transfers in the run-up to elections (Bueno, 2021), therefore implying in our case that income protection policies would be a priori more present in countries where elections were scheduled after March 2020 than in countries that had a recent election before that date.

> 12 We exclude countries where political competition is suppressed according to the Polity5 project (a value of 1 in the scale 1-10 of the _POLCOMP_ variable) and also countries with interim non-democratic governments in March 2020 (Bolivia, Sudan) or that have little effective control of their territories (Central African Republic). Singapore and Tajikistan are also excluded as political competition in these countries is virtually suppressed as well despite having a value higher than 1 in _POLCOMP_ .

19
Changes in rates of violence per 100,000 women ages 15-49.

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