rafmacalaba/data-use-mentions-extended huggingface dataset:
neighborhood N2 has a consistently negative effect on subsequent buildup predictions. A similar negative coefficient is observed in some regressions for neighborhood N1 as well. These findings are again consistent with the existence of a substitution effect in buildup: if the road is in a neighboring cell but not in my cell, my cell is likely to be built-up a decade and a half later – an outcome we would expect if local inhabitants prefer to locate immediately near these smaller roads. When we estimate regression (6) with 2014 buildup _yit_ and the 2008 road data, we find that all the road coefficients but one are positive. The only exception is for the presence of a primary road in the cell itself, which by 2014 may have too much traffic to be an attractive location. The 2008 road data is completely different from the earlier data, however, with very little correlation between the two, thereby making comparisons problematic. The 2014 road data is strongly correlated with buildup in that year, but since it is contemporary to our last data year, it cannot be regarded as a useful predictor.

We also estimate a different version of regression (6) setting _t − s_ = 1975 for lagged buildup and _t−s_ = 1976 for the road data. By regressing each of the subsequent data years 1990, 2000 and 2014 on these initial buildup and road conditions, we examine the delayed predictive effect of buildup and roads over time. Figure 7 summarizes the buildup coefficients in a way similar to Figure 4, the main difference being that now all regressions use the same 1975 buildup data as regressors. The Figure shows clearly that, over time, the predictive effect of past buildup changes. For buildup in neighborhoods N1 and N2, we see a systematic increase in buildup predictions as time passes. This is consistent with some form or preferential attachment – the presence of buildings in the past attracts new buildings later – possibly due to agglomeration externalities. Past buildup in the cell itself varies depending on the original buildup intensity: for heavily built-up
number of firms pursuing them. Consequently, companies often source consulting services from South Africa, Portugal, and other more advanced markets.

Furthermore, Mozambique has a significant skills gap. Compared to other countries in sub-Saharan Africa, the educational attainment in Mozambique is low. In 2010, less than half of adults were estimated to be literate, the result of Mozambique’s long civil war which – in the affected regions - prevented almost an entire generation from attending school. Only one-fourth of those above 30 years living in urban areas attended post-primary education. Nevertheless, there is some reason for optimism: the younger generation is significantly more likely to be literate and to have attended education beyond primary school (Lachler & Walker 2018).

**Figure 4. Mozambique’s management practices scores**

<!-- Start of picture text --> Rationale for Lean (Modern) Manufacturing<br>Introduction to Lean (Modern)...<br>Low scores (1-2) mean<br>Instilling a Talent Mindset<br>practically no structured<br>Time Horizon management practices or very<br>Process Documentation weak management practices<br>Clarity of Goals and Measurement implemented<br>Consequence Management<br>Interconnection of Goals<br>Performance Tracking<br>Performance Dialogue Scores between 2<br>Type of Targets and 3 mean that<br>Performance Review some informal<br>Goals are Stretching practices are<br>Building a High-Performance Culture implemented, but<br>Creating a Distinctive EVP these practices<br>consist mostly of<br>Developing Talent<br>a reactive<br>Retaining Talent<br>approach to<br>Making Room for Talent<br>managing the<br>0 0.5 1 1.5 2 2.5 organization 3<br><!-- End of picture text --> Source: Authors based on World Management Survey.

# **3 Data and measurement**
<!-- Start of picture text --> 160<br>139<br>140<br>wa<br>= 120<br>S<br>r= 100 86<br>3 80<br>E<br><=<br>> 60<br>=<br>fa]<br>2 40 29<br>ow<br>70 12<br>0<br>Monthly Total<br><!-- End of picture text --> Bi Lifeline Consumers @ Non-Lifeline Consumers
12|0.11|0.12|0.13|0.14|0.10|0.11|0.10|0.12|0.15|
|No. ofobs. (farms)|2,251|1,059|336|464|124|268|619|790|501|341|

_Source:_ Own computation from MAPF/WB/EU small producer survey.

_Note:_ As discussed in the text, a farm is considered credit constrained in 2022 if it indicated that it would want to borrow but did not have access to credit in this year. Area cultivated at the farm level reported for 2022.

23
|N Treatment 2|4,990|2383|2,607|3,465|1,247|112|166|2,332|2,658|1,656|1,643|1,691|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_R2_|_0.929_|_0.928_|_0.923_|_0.923_|_0.926_|_0.916_|_0.954_|_0.922_|_0.939_|_0.706_|_0.556_|_0.444_|

* p < 0.05, ** p < 0.01, *** p < 0.001

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Own survey data, administrative data, school year 2015/2016, own calculations.

36
# **C Analyzing Cerutti, Classens and Rose: frequency, aggregation, estimation**

As noted in the main text, our results regarding the variance contribution of the estimated factors stand in contrast with those of Cerutti et al. (2017c), who report much smaller figures. From this they conclude that the ’global financial cycle’ (or, more broadly, common shocks) is a relatively minor force behind capital flows.

There are five main differences between our analysis and that of Cerutti et al. (2017c): the frequency of the data (yearly in this paper versus quarterly in theirs), the level of aggregation of capital flows (total flows versus inflows disaggregated into FDI, portfolio debt, portfolio equity and bank credit), the estimation method (principal components versus Bayesian), the normalization of the flows (by trend GDP<sup>35</sup> versus current GDP), and the estimation sample, both regarding country coverage as well as time frame (1979-2015 versus 1990Q1-2015Q4). In Tables C.1 and C.2 we show that the first two differences account for the bulk of the discrepancy. Using the data of Cerutti et al. (2017c)<sup>36</sup> we reestimate the two factors they consider (one derived from advanced non-central countries, the other from major emerging markets)<sup>37</sup> using the standard principal components approach, which estimates the factors as the eigenvalues of the sample covariance matrix. Since the data are not a balanced panel, we deal with missing values<sup>38</sup> by estimating the sample covariance matrix as:

> 35Trend GDP is calculated using an HP filter with parameter 100 (with data at yearly frequency).

36Downloaded from http://faculty.haas.berkeley.edu/arose/RecRes.htm#Reverse

> 37As detailed in table A1 of Cerutti et al. (2017c), the advanced non-central countries are Australia, Canada
<!-- Start of picture text --> a. Incidence: transfers<br>100<br>_f-)<br>=<br>8<br>£ 80<br>0)<br>PE”4<br>o<br>=> 60<br>-fo}<br>&<br>a 40<br>°<br>®<br>3<br>2 20<br>0<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>3 Pensions &% Cash Transfer Program (CTP) 19 Near-cash transfers<br>G8 Elec. subs. (direct eff.) | MEG) Fuel subs. (direct + indirect eff.) | HE Primary healthc.<br>[5 Hosp. healthe. 9 Basic educ. [3 Seco. educ.<br>Sonejotes: Authors' estimates based on PECS 2016/2017; LFS 2017.<br>[1] Incidence is a measure of size and distribution relative to a reference income.<br>(3{3] TransfersHouseholds areare shownranked as aover positiveMarket barincome and taxes are shown as a negative bar.<br><!-- End of picture text -->
# **2.2.2 Elasticity parameters**

Most of the elasticity parameters are taken from the literature. Since the results are sensitive to some elasticities, special attention was paid while choosing the values for elasticity of substitution between biofuels and competing fossil fuels. Birur et al. (2008) collect historical data allowing them to determine a default value of 2.0 for this elasticity parameter. Yet, they point out the lack of data availability for conducting econometric analysis and they acknowledge that the value could vary significantly across countries (especially if a country is already equipped with flex-fuel vehicles or not). After several sensitivity analyses varying this parameter, we decided to nearly triple its value overtime, between 2004 and 2020, from 1.2 to 3.0 for all countries. We think it is realistic with future expansion of flex-fuel vehicles and we prefer not to consider higher values as they would tend to accelerate biofuel penetration too rapidly.

In our CGE model, we split total land into 18 Agro-Ecological Zones (AEZs) depending on climate type and humidity levels of the land (Hertel et al. 2009). We use a CES functional form with a high value of 20 for substitution elasticity between AEZs. For a given AEZ, the land supply is constrained across the different land-specific uses with nested constant elasticity of transformation (CET) functional forms. Following Hertel et al. (2008) we select -0.2 for the first level of the structure but consider the same values as Choi (2004) with respectively -0.5 and -1 for the next second levels. We apply the same elasticities for all countries.

Values for other elasticities (as shown in Figures 1a-c), are taken from Burniaux and Chateau, OECD (2010); van der Werf (2008); Timilsina and Shrestha (2006); Ma et al. (2010); Jarrett and Torres (1987) and Narayanan and Walmsley (2008).

# **3. Key results from model
extracted as a 15-year monthly panel between 2006 and 2020.

# **3.4 Taliban Control**

Afghanistan’s war between the government and the Taliban for two decades led to the country’s changing profile of territorial control. Areas under the control of the Taliban increased since the reduced presence of U.S. forces in 2011, with the entire country under the Taliban government since August 2021. We extract data on district control in 2018 from FDD’s Long War Journal that maintains real-time data on district control in Afghanistan: contested, government-controlled, Taliban-controlled. A district is marked as contested if the district center is under the government, but the Taliban control most areas outside the district center. A district is marked under Taliban control if the district center is under the Taliban.<sup>13</sup> In our study sample, 134 districts (39 percent sampled households) in Afghanistan were contested in 2018, 124 districts (52.5 percent sampled households) were under the government, and 30 districts were under the Taliban control (8.5 percent households). Figure 1 maps data on district control in 2018.

# **3.5 Measure of Weather Shock**

We construct the measures of weather shock at the cluster level, the lowest administrative unit, from which ten households were randomly sampled. Although we combine the data for coordinates of every household in IE-LFS 2019-20, each household in a given cluster is almost surely going to experience the same weather conditions.<sup>14</sup> Further, weather variables in the last growing season are more likely to affect incomes and consumption in the current period. With this in mind, we construct measures of weather shock for the 2018 growing season to analyze its effects on 2019-20 consumption.<sup>15</sup> There are several ways to define the functional form of the weather variable. Most studies either focus on levels or level changes in weather indicators. Another common approach is to use
There are limits to how far cross-country comparisons, with limited degrees of freedom, can be considered conclusive about the determinants of the SPEC. However, by pooling crosscountry observations with time series data, we will have 2,481 observations for assessing the relative importance of these various factors. On average, we have 17.5 time-series observations per country with non-missing shares of SP expenditure. We focus mainly on total SP spending, though we note any important differences between pensions and other SP spending, since public pension schemes often appear to be serving a somewhat different role to other types of SP spending (notably the more poverty-focused category of “social assistance”). The sources allow us to distinguish pension from non-pension SP spending for more than 1,500 observations across 104 countries, although coverage is less complete for low-income countries.

The panel data are unbalanced. A saturated panel would have 3692 (=142 x 26) observations. So, we have about two-thirds (0.67=2481/3692) coverage for the basic SPEC (though this falls further when we introduce covariates). The number of country observations per year is increasing, from 75 in 1995 to a maximum of 112 in 2017. In no year do we have SP spending data for all 142 countries; 45 countries have 26 years of data, and 73 have 20 or more years. The Addendum provides a series of graphs (Figure A2) that show the missing observations are more likely to be lower-income countries. (A similar pattern is found for most other covariates discussed below.)

# **3.2 Descriptive statistics on SP spending**

Some simple descriptive statistics based on the (pre-pandemic) panel data set are instructive. On average, we find that SP spending has risen over time as a share of GDP, and that this has been more pronounced among initially low- and middle-income countries. This can be seen in Table 1, which tabulates the SP spending share by countries classified according to their initial (1995)
The main data source we use is firm-level survey data collected by the World Bank’s Enterprise Surveys (ES). We complement the ES with other data sets such as the World Development Indictors (WDI, World Bank), Worldwide Governance Indicators (WGI, World Bank), Doing Business (World Bank), and Economic Freedom of the World (Fraser Institute).

The ES are nationally representative surveys of the non-agricultural and non-financial private economy. The survey includes only the formal or registered firms with 5 or more employees. Thus, informal or unregistered firms and micro firms (registered with fewer than 5 employees) are not covered by the survey. A common sampling methodology – stratified random sampling – is followed in all the surveys along with a common questionnaire.<sup>1</sup> The sample for each country is stratified by industry, firm-size, and location within the country. Weights are provided in the survey and used in all our regressions so that the sample is representative of the target population.

The sample that we use consists of SMEs in the manufacturing sector of mostly developing countries. Based on the definition used by the ES for stratification purposes, we define SMEs as all firms with fewer than 100 full-time permanent workers employed at the firm three fiscal years ago (lagged employment).<sup>2</sup> From this sample, we exclude firms with 10 or fewer employees. We do so because R&D activity involves substantial fixed costs and is therefore of not much relevance for the very small firms. Large firms (100 or more workers) are excluded because these firms operate in very different product markets than the informal firms. Thus, spillovers from informal sector to the large formal sector firms are unlikely. Results for large firms are shown in separate

> 1 Details of the sampling methodology and other related information are available at www.enterprisesurveys.org.

> 2 Information is available in the ES on the number of workers at the end of the last fiscal year
technical and managerial skills to oversee an important expansion of the power infrastructure and achieve universal access to electricity. Importantly, the liberalization of generation gave ONEE the fiscal space to invest and focus on rural electrification.

# 2.3 Utility Efficiency and Financial Viability

## 2.3.1 Performance

The analysis of Morocco’s utilities here is based on, where available, time series data for technical and financial indicators on transmission and distribution losses and profitability. Given data limitations, we draw on data snapshots of ONEE and the 11 electricity distribution utilities. Since ONEE is also engaged in electricity distribution and provides water and sanitation services, these are combined in its consolidated financial reports. Though utilities are required to report technical and financial data, these are often aggregated and not publicly available.

Transmission and distribution losses<sup>62</sup> have generally increased alongside rising output over the years. In 2012, total losses stood at 13 percent, a three-point increase from 10 percent in 1990 (Figure 12). The efficiency of thermal generation plants has improved from

> 60 (Nygaard & Dafrallah, 2015)

> 61 (Blimpo & Cosgrove-Davies, 2019)

> 62 These losses can be defined as the difference between total electricity output (total gross generation + imports) and the total billed to and paid by end consumers (and exports).

27
**Figure 1. Total Mexican Exports and Imports Billions 2009 U.S. Dollars**

<!-- Start of picture text --> Total Exports Billions $09 Total Imports Billions $09<br>27<br>24<br>21<br>18<br>15<br>2001 2005 2010<br>Year<br>Billions 2009 US$<br><!-- End of picture text -->

**Notes: Exports and Imports are in billions of U.S. dollars deflated by the U.S. Consumer Price Index for all urban consumers (series CUUR0000AA0) in which 2009=100. Totals include petroleum trade.  Data are from** http://dgcnesyp.inegi.gob.mx/cgi-win/bdieintsi.exe/Consultar#.

35
in the same industry matters, but only for the quality of goods exported by Russian firms to developed country markets. The estimated coefficients are highly statistically significant. This is true both when the FDI proxy enters by itself as well as in the full specification. The result holds for both measures of export quality. A one-standard-deviation increase in the FDI presence in the same sector is associated with a 5% and 7% increase in the quality of exports when the quality is proxied with unit values and the Khandelwal et al. (2013) measure, respectively.

Table 3 distinguishes between exports to neighboring countries and other destinations.<sup>13</sup> When I consider unit values, the results are confirmed, suggesting that foreign presence in the input-supplying sector is positively related to the exports by Russian firms. The estimated coefficients are highly statistically significant in all four specifications, but the magnitudes are twice as large when exports to non-neighbors are considered. Regarding the alternative measure of export quality, the effect of interest is statistically significant only when non-neighboring countries are being considered.

The ORBIS database contains information on the nationality of investors, which allows me to classify FDI as originating from developed countries versus the rest.<sup>14</sup> Germany, the United States and the United Kingdom are the top three source countries of FDI accounting for about 226, 126 and 104 foreign affiliates, respectively. This translates into about 21%, 12% and 10% of foreign affiliates within the grouping, respectively. They are followed by the Netherlands and Finland. The presence of FDI originating from non-developed countries is much smaller. In this group, China, Turkey and Belarus are the largest investors each with about 30 affiliates. They are followed by the Democratic People’s Republic of Korea and Poland, with 25 affiliates each.

> 13The neighboring countries include: Azerbaijan, Belarus, China, Estonia, Finland, Georgia, Kazakhstan, the Democratic People’s Republic of Korea, Latvia, Lithuania, Mongolia,
|1.66|1.53|1.36|1.43|1.37|2.38|2.97|-|
|**Mean travel speed (km/h)**|24.7|-|24.2|26.7|24.8|21.9|24.8|27.3|23.9|-|
|**Median travel speed (km/h)**|18.5|-|22.3|17.0|22.0|17.0|17.7|19.3|22.8|-|
|**Max travel speed**<sup>6</sup> **(km/h)**|178.3|-|101.6|99.0|96.2|106.6|148.2|89.6|80.5|-|

# **3.3 Open Street Map (OSM) Road Network Data Set**

<mark>In addition to the five public transportation networks, we also added the drive as well as the pedestrian network leveraging the Open Street Map data set to account for commuters who walk or drive to work. The OSMnx Python package was used to create topological graph structures for the drive and pedestrian network layers (</mark> Boeing, 2017 <mark>). These two network layers were later integrated with the five public transport layers to create the final multi-modal network</mark><sup>7</sup> <mark>model for Kinshasa.</mark>

> 5 There are no GTFS records for Transco bus and Moto taxi mapping under flooded (wet) conditions.

> 6 Unreasonably high travel speeds can occur due to small inaccuracies in the GTFS files, but are clear outliers. If two stop points along a route are very close (in reality or due to
i) Elimination of non-existing HS codes: First, we combine all the codes existing under the three different HS classifications (HS1996, HS2002 and HS2007) and end up with a unique aggregated list of 6065 HS 6-digit categories.<sup>18</sup> Next, we eliminate the observations with an HS 6-digit code that is not included in that aggregated list. This elimination accounts for about 0.5 percent of total exports in our cross-country raw dataset.<sup>19</sup> ii) <u>Creation of a time-consistent HS classification: While the HS 6-digit classification</u> allows comparisons across countries in a given year, it has undergone transformations over time. The World Customs Organization (WCO) revises the HS classification on the basis of the value of trade realized under each product during the previous period. Three major revisions took place in years 1996, 2002, and 2007.<sup>20</sup> The modifications introduced in each of these revisions have taken two forms: i) two different codes with low trade volume were converted into a single code and ii) an existing code with an increasing trade volume was split into various codes. For example, code 030269 (other fish, fresh or chilled, excluding fish fillets or other fish meat) which included swordfish and toothfish in the HS2002 classification was split into codes 030267 (swordfish), 030268 (toothfish), and 030269 (for other fish) in the HS2007 classification. These modifications create problems for the tracking of trade volumes for certain products over time. In the example above, exports of swordfish under code 030267 would appear as a new export from 2007 onwards, while in reality they might have already been exported before but were recorded under the code 030269.

> 18 The number of HS categories included in the original classifications HS 1996, HS 2002, and HS 2007 are 5209, 5224, and 5053, respectively.

> 19 Most of this elimination results from eliminating observations with a product
Exchange Rate (dolars per peso)<br><!-- End of picture text --> _Source:_ Data from Central Bank of Mexico.

A subsequent rebound in external demand as of the second half of 2009 gave rise to a recovery, even though private consumption and investment are trailing behind and have not yet contributed significantly to the upturn of economic activity. Figure 2.2 depicts levels of economic activity and the main components of aggregate demand, showing that by the second quarter of 2010 GDP was still slightly below its pre-crisis level. A similar scenario occurred for private consumption and 3