rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure G.3: Willingness-to-pay for Database of Workseekers’ Assessment Results <!-- Start of picture text --> 0 2000 4000 6000 8000 10000<br>Willingness-to-pay (South Africa Rands)<br>30<br>20<br>Frequency<br>10<br>0<br><!-- End of picture text --> Notes: This figure shows the distribution of willingness-to-pay for access to the database of assessment results described in Section 5.1 and shown in Figures G.1 and G.2. Values are in South African rand, with 1 rand _≈_ USD 0.167 in purchasing power parity terms. The maximum possible bid is 10,000 South African rand.

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than the fourth or the seventh days (and none of the other days are significantly different from each other).<sup>21</sup> In other words, the reported consumption did not drop off in a worrisome pattern over time. Finally, while enumerators filled out diaries for household members in certain circumstances (due to illiteracy and lack of time), we do not believe that this happened often. In a follow-up discussion with enumerators, they generally agreed that the diaries were close to full completion in the second, third, and fourth visits.

# 9.2.2 Fieldwork implementation and coordination

Extensive coordination among teams and treatment arms was necessary to be able to collect the data in the planned timeline. The one-line recall surveys required one visit, the 7-day individual recall (T3) arm required one or more visits (or calls), the bounding/salience arm required two visits 7-days apart, and the diary required 4 separate visits across a period of 8 days<sup>22</sup> (see Figure 9 and Figure 10 for the field schedules for the diary and bounding and salience arms).

> 21 We did this analysis using reported consumption over days. For some households, certain days were set as missing. When we set these entries to zero rather than to missing, we see the same patterns of consumption over the course of the week as described, but the p-value from a joint orthogonality test of treatment arms is higher than 0.10.

> 22 More information on the implementation can be found in Section 3 Experimental design and context.

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utility drinking water and wastewater tariffs for a sample of over 80 countries in 2012 from the GWI. The authors estimate that water and sanitation subsidies provided through public utilities were about $456 billion, or 0.6 percent of global gross domestic product (GDP), in 2012. Across regions, subsidies range from between 0.3 percent and 1.8 percent of GDP. Note that these estimates include China and India, the former with an estimate of around $130 billion, or about 1.5 percent of its GDP in 2012. Without China and India, this estimate becomes 0.5 percent of global GDP, or, adjusted for general price inflation from 2012 to 2017, $347 billion. Both of these numbers fall within our estimation range.

While our estimates of subsidies for OPEX are relatively straightforward—they predominantly represent explicit expenditures required to sustain service provision at current levels of efficiency and quality—our estimates of subsidies for CAPEX, or that required for the major repair and/or replacement of existing infrastructure, require additional nuance.<sup>26</sup> Because of a lack of data on most countries’ direct expenditure on networked water and sewered sanitation, our model instead estimates the CAPEX required for the replacement of existing infrastructure. However, there have been several recent attempts to extrapolate direct expenditure from countries with more comprehensive and transparent expenditure data to regional, and even global, levels of expenditure.

Prior estimations of global and regional direct CAPEX on WSS services in low- and middle-income countries, making use of data available from a limited number of countries, are between 0.4 and 0.5 percent of GDP. Fay et al. (2017) have found that the WSS sector has traditionally received a small share of Latin America and the Caribbean’s investments in infrastructure, hovering between a quarter and a third of a percent of GDP in the period 2000–12. Data from Foster and Briceño-Garmendia (2010) suggest that Africa spends around 0.5
that a consistent experiment is conducted across tax instruments.

The economic relationships in the model can be represented by a rectangular social accounting matrix (SAM) such as Table 2. The entries in the SAM are expressed as percentages of GDP at market value. All rows and columns sum to zero, reflecting a Walrasian equilibrium in which incomes equal expenditures. In the consumer’s column positive entries are endowments or factor incomes, negative figures are expenditures on goods, including investment. In the production columns, positive entries are the receipt of sales revenue or investment, and nega-

> 10In Uganda, Reinikka & Svensson (2002) found that schools received only 13% of the money paid by the central government for schools’ non-wage expenditures. Pritchett (1996) observes that growth accounting data can be reinterpreted supposing that total factor productivity growth is the same across all regions. Actual public investment in Africa could then be as little as 8.4% of the amount spent on capital. Rajkumar & Swaroop (2002) find that the effect on outcomes of marginal public spending on health and education is not significantly different from zero in countries rated as very corrupt or with ineffective bureaucracies. These studies suggest, by different means, that only a small portion of public spending in Africa arrives in projects with positive social rates of return.

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total withdrawals, and net deposits into OIBM accounts in different time periods. The “preplanting” period, from March 2009 to October 2009, is the period when funds are accumulated from the previous season’s harvest in preparation for purchasing inputs for the 2009-2010 growing season. The “planting” period, from November 2009 to April 2010, includes the time of year when farmers purchase inputs and tend their crops prior to the 2010 harvest. This period includes the February to March 2010 “hungry” season when households may have depleted stocks of maize from the previous season’s harvest and have not yet harvested crops or received payments for the 2010 harvest. These data were obtained from OIBM administrative records.

Results from column 1 show that while none of the farmers in the control group transferred money via direct deposit into an OIBM account (since they were not offered direct deposit or account opening assistance), 16% of farmers in the ordinary account, no raffle treatment did transfer money. This percentage is somewhat larger at 21% for farmers in the commitment savings treatment without raffle. There are no statistically significant effects of either the public or private raffle on farmers assigned to either of the savings treatment conditions.

Figure 3 shows a histogram of commitment account release dates (when commitment account funds would be “unlocked” and transferred into ordinary accounts) that farmers chose during account opening. In accordance with their stated savings goals, 60% of farmers chose release dates in the months of October to December when most input purchases occur, immediately prior to or at the start of the planting season. Some farmers also chose to have access to the funds in January and February, during the lean or “hungry” season.

Turning to dependent variables related to deposits, both ordinary and commitment treatments led to higher total deposits as well as higher total withdrawals during the pre-planting period compared to the control group. Coefficients on both types of savings treatments are positive and statistically significantly different from zero for deposits (column 2), and
whether the firm has sufficient resources to “smooth things over.” Indeed, Chinese firms are able to reduce their effective tax burdens and obtain better government services when they spend more money entertaining government officials (Cai, Fang, and Xu 2011). The reported enforcement of various government regulations such as the number of days to obtain various permits differs greatly among firms in the same country, according to the Enterprise Survey, and a significant share of firms in countries tend to enjoy much better government services than what is warranted under de jure regulations and laws (Hallward-Driemeier and Pritchett 2015). Thus, since enforcement is better controlled for in the case of de facto BE indicators, the de facto indicator of the same theme of the BE (say, access to finance) should have stronger predictive power for firm performance than de jure indicators.

Furthermore, the effects of the de jure DB indicators on firm performance likely depend on contract enforcement. DB indicators reflect what the laws say about proper procedures and implied costs. A good BE aspect on paper (as reflected in _Doing Business_ ), without strong enforcement, would not translate into real benefits to firms in terms of performance. Indeed, the recent literature on state capacity emphasizes that the same rules and regulations have vastly different effects depending on state capacity, including the key element of contract enforcement (Besley and Perssons 2009, 2010). As an example, when obtaining a title for urban land, Chinese firms that do not expect strong enforcement (that is, firms without political connections) did not actually increase investment, Miao et al. (2019) find. The BE effects would thus likely be effective only when enforcement is also strong. Of the three sets of BE measures, the _Doing Business_ series are de jure, and their effectiveness thus depends crucially on contract enforcement. We expect that the _Doing Business_ indicators should have stronger effects when contract enforcement is better, especially for those BE elements that require strong government enforcement.

Thus, our _enforcement hypothesis_ is as
information from OpenStreetMaps (OSM 2021). We overlay the digital subway maps on our 25 km grid and compute the total length of subway lines in each grid cell and year. Both subway age and line length measures are highly right-skewed, so we apply the inverse hyperbolic sine transformation prior to estimation.<sup>6</sup>

# **2.3 Accounting for Wind Displacement of CO2 Emissions**

Space-based observations detect higher CO2 concentrations over emissions sources because atmospheric diffusion is not instantaneous. As the prevailing winds displace emissions from their sources, deviations from background concentrations persist for some time. City-level or plant-level estimates have commonly employed measures of wind direction to model these effects (Nassar et al. 2017; Wu et al. 2020; Ye et al. 2020). We replicate this exercise at global scale, using ERA5 monthly wind direction data for all grid cells in the database (Hersbach et al. 2019). For emissions from each grid cell, we determine the wind-directed path across neighboring cells. We compute monthly wind bearings at 0.25<sup>◦</sup> resolution from 10-m u and v components and then resample to our 25 km grid.<sup>7</sup> Wind paths are calculated in sequence. For each origin cell (A) in the sequence, the destination cell (B) is determined by the wind bearing in cell A. Using each grid cell as a source, we determine the sequential path across nearby cells through 20 iterations.

Theory provides no guidance on local atmospheric persistence as wind displacement proceeds, so we address the issue empirically. In preliminary regression experiments, we perform a grid search across two variables. The first is the duration decay function, modeled as the inverse of the iteration sequence number raised to a power that varies in increments of 0.1 between 0 and 2.0. The second is the number of iterations, which varies from 1 to 20. Our grid search yields best fits for decay and
A casual examination of the actual transactions data suggests that the overall pattern of cross-border M&A investment by emerging-market firms is consistent with the typical international growth strategy of individual corporations. When companies venture abroad, they often first establish a small foothold in new markets through branch or representative offices, small distribution networks, or maintenance centers. Such small greenfield investments can be the first step toward execution of a firm’s globalization strategy, allowing companies with limited international exposure to gain experience and local knowledge before making a major commitment to a particular market through an outright acquisition or large-scale investment via mergers.<sup>6</sup> In carrying out M&A transactions, companies often appear to seek more immediate access to local markets.

Firms may potentially capitalize on technological and informational advantages that may be gained by their foreign acquisitions, or when they can apply their unique expertise to the same industries abroad. In particular, emerging-market firms with expertise overcoming the difficult institutional environment in their home countries may be eager to apply this informational advantage to similar environments in other emerging markets. Some M&A may also be motivated by the desire to exploit factor cost differentials in target markets (relative to their own). Finally, international M&A activity may demonstrate some persistence, when initial investments lead to additional cross-border investments through the necessity of the restructuring or upgrading of acquired assets, or as part of acquiring other firms’ vertical- or horizontal-integration growth strategies.

# **3 Potential Factors Related to M&A Activity**

Economic models of bilateral trade flows have most successfully been modeled on the basis of an empirical gravity equation, which has more recently been contextualized in the form of a broad variety of theoretical models (Anderson & van Wincoop 2004). We therefore rely, as a point of departure, on a gravity equation, where cross-border M&A flows are positively related to the pair’s respective output and negatively related to the bilateral distance between them. Bilateral country distances capture not only explicit
discussed business practices, such as account keeping and use of bank accounts, the impact on which we examine in the following section.

Finally, we test whether the training had a differential effect on individuals who had a business at baseline and who did not.<sup>11</sup> The results in Column 3 of Table 7 indicate that the effect is slightly larger for individuals who owned a business at baseline than for individuals who did not, but this difference is not statistically significant.

# _V.V. Evaluation Results – Effects on Business Outcomes_

This section examines the effects of the training on business outcomes, including survival, practices, and performance.

# _<u>Business Creation and Survival</u>_

First, we study whether the training had an effect on business survival and business creation.

Table 8 includes our complete sample, i.e. all entrepreneurs who responded to the follow‐up survey, independent of whether they had a business at baseline or not. We find that the training had no significant effect on whether our study participants had a business at follow‐up (Column 1). This is true for individuals with below and above median financial literacy levels at

> 11 As mentioned above, we did not stratify by this variable in the randomization, but the variable is balanced across treatment and control groups.

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of picture text --> 100<br>1 Finance<br>80 X X Trade<br>60-<br>40 -1<br>40<br>1980 82 84 86 88 90 92 94 96<br>Financial markets include stocks and international bonds and loans outstanding<br>Trade figures are averages of imports and exports.<br>Sources: IFC, BIS, and IMF.<br><!-- End of picture text --> _5_
||||Table A1: Main variables|
|---|---|---|---|
|Database|Source|Variable|Description|
|||Dropout rates|Percentage of students who drop out of school in a given grade or school cycle|
|||Urban|Dummy variable, equals one if schools are in urban areas|
|||Basic sanitation|Dummy variable, equals one if schools have appropriate conditions of basic sanitation|
|||Library|Dummy variable, equals one if school has a library|
|||Sports court|Dummy variable, equals one if school has a sports court|
|||Number of classrooms|Number of classrooms available in the school|
|||Number of classrooms used|Number of classrooms used in the school|
|||Computers|Computers available in the school|
|||Science lab|Dummy variable, equals one if school has a science lab|
|||Computer lab|Dummy variable, equals one if school has a computer lab|
|||Teachers' room|Dummy variable, equals one if school has teachers rooms|
|School Census|INEP|DVD|Dummy variable, equals one if school has a DVD|
|||Overhead projector|Dummy variable, equals one if school has an overhead projector|
|||School meals|Dummy variable, equals one if school provides meals to its students|
|||Number of employees|Number of employees working at the school|
|||Morning lessons|Dummy variable, equals one if schools have morning lessons|
|||Child care|Dummy variable, equals one if schools have child care (for babies)|
|||Kindergarten|Dummy variable, equals one if schools have kindergartens|
|||High school|Dummy variable, equals one if schools have high school|
|||State schools|Dummy variable, equals one for state schools|
|||
the US of economic interdependence to force other countries to go along with its policies has been termed “weaponizing interdependence.”</mark><sup>8</sup> <mark>It suggests that no matter what other countries’ preferences and intentions are, ultimately it is the priorities of the largest economy of the world that may shape the future of the global economic system. Seen in this light, US’s turning inward may have important implications for the future of globalization.</mark>

# **I.C. Public Discourse**

Just as economic variables respond with a lag to policy, policy responds with a lag to public sentiment and attitudes. Therefore, we now turn to an investigation of such attitudes regarding trade and globalization. Data from the PEW Global Attitudes Surveys show that the

> 8 The term was coined by H. Farrell and A. Newman (2019).

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trade flows since 2018 up to 2021, when the most recent COMTRADE data are available. Values are reported as a multiple of the 2018 world import value in constant prices. There was a negligible drop in all goods trade during the pandemic during 2020, and a full recovery in 2021, as seen already in Figure 1. Imports of face masks, shown in Panel A of Figure 4, exhibit an interesting pattern: these imports increased sharply in 2020 reflecting the increasing use of face masks from China and Korea to meet the surge in domestic demand. Not only did international trade not let down the economy in this case, without imports, domestic demand for face masks would not have been met by domestic producers. Growth in use of imports to supply electric car batteries and semiconductor chips grew in real terms during the pandemic, with imports of batteries accelerating. Use of imports to 20
The impact of commodity _j_ on the differences between the two indexes is then determined by the difference in the price change of commodity _j_ and the average rate of inflation Ltt−1, and by the difference in the shares of commodity _j_ in the two baskets.

# **4 Data**

Two data sources were used to construct household-specific price indexes and analyze the impact of applying them in measuring poverty and inequality. The first source is price data on 12 consumption components corresponding to the two-digit Classification of Individual Consumption According to Purpose (COICOP) specification (UN 2018).<sup>4</sup> The time series of country-specific price indices by the COICOP two-digit groups covering the period from January 2010 to the latest month available for 2022 were obtained from the CPI data repository of the International Monetary Fund (IMF 2022a) for 47 countries in ECA (all except Kazakhstan, the Kyrgyz Republic [after January 2022], the Russian Federation [after February 2022], Tajikistan, Turkmenistan, and Uzbekistan [before January 2020]). The COICOP two-digit group price indices for Kazakhstan and the Kyrgyz Republic (after January 2022) were obtained from the countries’ national statistical offices; data for Russia, Tajikistan, Turkmenistan, and Uzbekistan (before January 2020) were not available.

The second set of data corresponds to household-level consumption expenditure. This information comes from microdata from the household budget surveys of 23 countries in ECA for the latest available year. These surveys are nationally representative and include detailed information on consumption expenditure for each household. Aggregates in these surveys are harmonized by extracting variables with the same definitions and employing standardized classification and aggregation methods to achieve maximum inter-country comparability. We partition the total household consumption expenditure into the 12 two-digit COICOP components and calculate the share of total consumption expenditure allocated to each component for each household. We also compute aggregate country-level shares. In the few cases in which consumption shares at the country level were missing, we imputed them from previous rounds of the survey.<sup>
# 1. Introduction

Inadequate infrastructure and low productivity of manufacturing firms tend to be closely related, especially in developing economies (Tybout 2000; World Bank, 1994; Bloom et al., 2010). A substantial literature has linked infrastructure quality to economic performance (Calderón et al., 2003; Canning, 1998; Jiemenex, 1995). However, fewer studies, typically region-specific, have established a link between infrastructure and the performance of the private sector. This is in sharp contrast to the large literature focusing on access to finance and firm performance (Davis et al. 2001). Furthermore, infrastructure encompasses roads, ports, power, communication facilities, and water, with the latter receiving less attention than the others (for example, Arnold et al., 2008 consider how communication and electricity infrastructure, and financial services affect firm performance in Sub-Saharan Africa, but do not consider the importance of water). Evidence of the lower prominence given to water infrastructure can also be seen in the World Bank’s Enterprise Surveys, which have the broadest and most consistent coverage of firm-level data globally. While the surveys do contain information on water outages, the questionnaire does not consider access to or reliability of water as a potential obstacle for operations. Specifically, when respondents are asked to rank the top obstacles they face – a legacy question that has existed since the inception of the surveys – the choices encompass power, communication, and transportation, while water is not presented as an option. Similarly access to water and water infrastructure does not feature in the World Bank’s _Ease of Doing Business Index_ while electricity is included. However, one would expect this to change in the near future, given the increased focus on climate change that has brought the issue of looming water challenges to the frontlines of policy.

The sidelining of water as an area of focus is surprising given its importance in driving the economy. Consider, for example, the prominent role water plays in the Sustainable Development Goals (SDGs). The
public works ( water storage and pump stations, broadcasters)), and small-and-medium industries. Also, all customers in the dataset use prepaid meters: Rwanda has a universal roll-out of prepaid meters, with large and heavy industries the only exception who are allowed to use postpaid meters. Our data exclude these customers (i.e. large and heavy industries). For each customer, we have monthly records on the amount (kWh) of electricity consumed (purchased), the monetary value in Rwandan Francs (RWF), location (community/district), and rural-urban status. In all, the data contains 21 million customer-year-month observations.

We complement the electricity data with monthly data on temperature and total precipitation from the Copernicus Climate Change Service.<sup>9</sup>

> 9https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset

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