rafmacalaba/data-use-mentions-extended huggingface dataset:
resulted in a sample with two important features: (1) baseline cluster characteristics are balanced across treatment and control districts, a condition for internal validity; (2) the average share of cluster population living in solid houses is around 50%, which allows the estimation of conditional treatment effects for both types of dwellings. The table also provides insights into the main metrics of our recruitment campaign, pointing out an average ad cost of 85 cents per final survey respondent.

> 21According to the India DHS, the distribution of housing types in urban areas is as follows: 85.8% solid, 13.3% semi-solid, and 0.9% non-solid houses. In rural areas, these percentages change to 48.7% solid, 44.8% semi-solid, and 6.5% non-solid houses, respectively.

> 22By leveraging real-time response data, Virtual Lab optimized the budget allocation across ad sets to get all the strata filled as quickly as possible.

13
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE this state of affairs may simply reflect small size (as for The Gambia and Guinea-Bissau), but more generally it reflects low traffic volumes and limited purchasing power in the domestic market. Both of these facts make it difficult for air transport to compete with surface transport alternatives, such as longdistance bus services. Nigeria has the second-largest domestic air market in Sub Saharan Africa, after South Africa, accounting for 11 percent of the overall scheduled domestic traffic.

By and large, each country has at least one daily connection to one of the main regional hubs. What appears to be low connectivity may not be a problem as long as each country has frequent connections to one or more of the main regional hubs. Whether this is so can be assessed by looking at the origindestination matrix for the ECOWAS countries (table 2.10), which shows that all of the countries in the region indeed have at least seven flights per week to one of the main regional hubs. However, only two countries (Mali and Togo) have as many as two daily flights to their respective hubs. Abidjan is the main hub for Benin, Burkina Faso, Guinea, and Togo. Dakar is the main hub for Cape Verde, Gambia, Guinea-Bissau, and Mali. Accra is the main hub for Liberia and Sierra Leone. While strongly connected to Accra, Lagos is not so strongly

**Table 2.9  International and domestic connectivity for 2007 (number of city pairs served)**

|Country|Domestic<br>city pairs|International<br>city pairs|Intercontinental<br>city pairs|
|---|---|---|---|
|Benin|0|19|1|
|Burkina Faso|1|11|1|
|Cape Verde|10|5|23|
|Central African Republic|0|2|1|
|Côte d'Ivoire|0|23|5|
|Gambia
Similarly, from the perspective of extending the financial incentives (intervention groups 3 and 4) to a larger scale, some people might argue with the fairness of such incentives. They would say that paying people to take up screenings is unfair to individuals who are already regularly screening: why would people who have never screened be paid an incentive for testing, when someone who regularly screens never received anything? If, for fairness reasons, the financial incentives have to be extended to a larger segment of the population, they might be less costeffective.

In the personal invitation containing statistics about screening among peers, we have used absolute numbers instead of percentages because those percentages were relatively low, and we were not sure whether communicating low rates of screenings would be perceived as encouraging. This feature of intervention group 2 is different from most behavioral economics interventions that convey social norms using percentages. This might have contributed to the absence of differences between intervention groups 1 and 2. This might be an interesting question to explore in further research.

While our study results contribute to the debate on the cost-effectiveness of conditional and unconditional financial incentives, they also shed interesting light on the contrast between financial and non-financial incentives (de Walque and Valente 2018). While the conditional voucher intervention in intervention group 4 is clearly the most effective intervention, our costeffectiveness analysis indicates that intervention groups 1 and 2 are equally cost-effective even though they lead to a smaller increase in screenings.

Finally, our study uses an existing e-health database to identify eligible study participants, measure the main study outcomes, and verify compliance for the conditional intervention. This considerably reduced the intervention and data collection costs. As such databases become more common across the world, they could be used more often for such purposes, provided that, like in this study, patient privacy and confidentiality are guaranteed.

25
or close to zero, this would suggest that the new CPS did not fundamentally improve the available provision of education.

To identify the subLATE parameters, we would ideally need an extra source of variation that affects the share of _a-compliers_ (within the compliers) without directly affecting the CPS treatment effects. These additional instruments (which we refer to as **X** ) should affect the treatment effects only through the share of _a-compliers_ and not because they capture other forms of heterogeneity (e.g. heterogeneity driven by gender). This assumption is called constant treatment effect (Kline and Walters, 2016) or homogeneous LATE (Hull, 2018). We refer to this approach as conditional sub-LATE. Kline and Walters (2016) (see also Hull (2018) and Feller et al. (2016)) show that sub-LATEs can be identified by interacting the random assignment with observed covariates. The structural equation takes the following form: where _Yi_ is a follow-up outcome, 1 _Di_ = _a_ a variable taking one when the child i is enrolled in an ASP and zero otherwise, **_X_** a set of additional variables orthogonal to the treatment, and **_W_** the preferred set of control variables used for the reduced form estimation. _γ_ 1 captures the _LATEhc_ , and _γ_ 2 captures the effect of going to ASP. To derive the _LATEac_ , we subtract _γ_ 2 from _γ_ 1. 1 _Di_ = _c_ and 1 _Di_ = _a_ are both endogenous and instrumented using following first stage equations: The identification of _γ_ 1 and _γ_ 2 relies on the independence of _Z_ and _Z ∗_ **_X_** and on the assumption that the h and a compliers have a constant return to preschool on **_X_** . Since the X variables are selected from the baseline survey, their independence is guaranteed by randomization. However, these extra instruments could capture sources of variation that are not related to the variation in counterfactual enrollment, therefore causing a violation of the constant treatment
The data used in this paper suggest that total on-chain transaction volumes reached US$2.8 trillion in the first half of 2021 alone. In comparison, industry data indicate that total volumes, which include “off-chain” transactions, were approximately US$16 trillion during the same period (CryptoCompare (2021)). Off-chain transactions are facilitated by intermediaries such as cryptoexchanges and are only registered on private ledgers and order books rather than the distributed ledgers that underpin crypto-assets. As such, it is important to keep in mind that the on-chain activity we study in this paper is not fully representative of all crypto-assets activity.

A limited, but growing number of institutions such as corporations, asset managers, and pension funds around the world have started to invest in crypto-assets, bitcoin in particular.<sup>9</sup> Crypto-asset derivatives and futures markets have grown rapidly, and spot- and futures-based exchange-traded products are already active in various countries. Several large international banks, payment card companies, and payment processors have started to offer crypto-asset wallets and related services. And new services have appeared such as crypto-asset-based lending (i.e., on a collateralized basis), borrowing, trading, asset management, and custody solutions, including by asset management companies and large banks which have expressed a desire to become more involved in the cryptoassets space (GFMA, et al (2021)).

The ascent of crypto-assets has put a welcome spotlight on various well-known weaknesses in the traditional financial and monetary system. Some of these deficiencies are related to: financial inclusion, since 1.7 billion people around the world remain “unbanked” and have limited or no access to financial services (Demirgüç-Kunt et al (2017)); (cross-border) payments and remittances, which can be slow, costly, and opaque; citizen’s trust in and efficiency of traditional financial intermediaries, as in some countries competition is limited and memories of banking sector stress are still fresh; and macroeconomic policies, given that some countries experience regular
among developed economies (e.g., La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1998).

Another important aspect of the information environment is the sharing of information. Commercial and consumer credit bureaus provide formal organizational mechanisms for the exchange of payment performance data. Credit bureaus have also been found to reduce the cost and time to process loans and the

> 12 Some studies also found that higher measured market power is associated with higher SME loan interest rates (e.g., Hannan 1991, Berger, Rosen, and Udell forthcoming).  Although this finding may appear to support the SCP hypothesis, it may also be consistent with the alternative hypothesis of an expansion of relationship lending if relationship loans tend to have higher interest rates on average than transactions loans (which may or may not be the case).  Again, much of the difficulty in interpreting the results arises because the lending technologies are not identified.

14
55 realistic cost parameters the increase in enforcement by imposing restrictions on the use of bonds actually improves matters.

The benefits from regulations may be substantial even when inflation tax is relatively unimportant in terms of revenues. For example, when the inflation tax is equivalent to a 4% consumption tax and most agents spend 2% of their income on tax evasion, the benefit from regulation is close to 2% of GDP but the revenues from the inflation tax are small. This type of example can also explain why a steady state inflation of say 16% is considered a disaster while a consumption tax of 4% (that is equivalent to an inflation of 12% when the length of the period is a quarter) seems acceptable. To understand the difference in the attitude towards the two seemingly similar taxes, we may want to think of the case in which labor supply is inelastic. Under the consumption tax regime agents do not spend resources on tax evasion and get services or lump sum payments from the government in an amount that is equal to the tax they pay. The inflation tax regime is akin to a situation in which the consumption tax is not perfectly enforced and as a result agents spend resources on tax evasion and get a relatively small transfer payment or services.

In our model the welfare cost of inflation is equal to the area under the demand for money curve only in the special case in which there are no direct taxes and no proportional intermediation costs. To estimate the welfare cost and the gains from regulation, we use the 2011 income distribution in the US. We focus on the range of the fixed cost  that leads to the use of both assets (bonds and money) when the interest rate is between 1.5% and 8.5%. The choice   0.001 is in the middle of this range.

> When   0.001 and the nominal interest is 1.5% per period (or 4.5% annual rate if the length of the period
The definition of the upper threshold is not the focus of this paper and it is less relevant than the lower threshold because of three main factors. First, because moving the upper threshold up (down) the income distribution includes (excludes) a small percentage of individuals. As shown by Birdsall et al. (2011), varying the upper threshold from $50 to $100 a day would move the percentile of the LAC‘s elite from the top 2.2 percent to the top 0.5 percent. In contrast, moving the lower threshold would move dramatically the percentage of excluded/included population. Second, an income threshold above $50 would also limit the representativeness of the upper class in some countries. Finally, and related to the second factor, an upper threshold of $50 seems reasonable as it has been evidenced that household surveys do not collect realistic information of the richest population of a country (Alvaredo and Piketty, 2010), so that it is likely that a number of households in the surveys‘ top percentiles would be considered, actually, as part of the middle class. Overall, we propose that the middle class is thus defined as those households living with a per capita income on $10-50 a day at 2005 PPP.

# **Table 4: Robustness to different models specifications**

_Daily per capita income for a ten percent probability of falling into poverty_

|**Model Specification**|**Chile**|**Mexico**|**Peru**|
|---|---|---|---|
|**Complete specification (see annex)**|**8.5**<br>**$**|**9.7**<br>**$**|**9.6**<br>**$**|
|Excluding shocks|8.5<br>$|9
<br>19%<br>10% 9%<br>19%<br>16%<br>18%<br>8% 8%<br>14% 18%<br>6% 7% 17%<br>12%<br>17%<br>4% 6%<br>10% 16%<br>2% Unemployment rate (left)Private Investment - Nonresidential (in% of GDP, right) 8% 5% Unemployment rate (left) 16%15%<br>0% Private Investment - Total (in % of GDP, right) 6% 4% Private Investment - Total (in % of GDP, right) 15%<br>20002001200220032004200520062007200820092010<br>Source: Authors' calculations based on data from BEA and BLS. Source: Authors' calculations based on data from Eurostat.<br>00q1 00q4 01q3 02q2 03q1 03q4 04q3 05q2 06q1 06q4 07q3 08q2 09q1 09q4 10q3 11q2<br><!-- End of picture text --> 5
The WCO<sup>42</sup> serves as a medium for international cooperation and exchange of information. Its technical assistance program encompasses subject-specific assistance (origin, valuation, procedures etc.), seminars, workshops, expert missions etc. aimed at an effective modernization of customs (capacity building diagnostic framework). The WCO has developed several international instruments that guarantee an effective customs administration, the most being the Revised Kyoto Convention, whose principles have been incorporated in the national legislation by several countries.

Other WCO instruments relating to TF include a risk management guide for implementing risk management; WCO Data Model to establish an international, harmonized data set for cross-border trade, Immediate Release Guidelines for expeditious but controlled release small, low value goods across borders. The time release study, that measures the average time taken between the arrival and release of goods, helps customs identify bottlenecks in release of goods. Some examples of custom capacity building include E-customs, customs smart cards and WCO Asia Pacific Regional Office for CB in Bangkok. As mentioned earlier, the work of the WTO and WCO is complementary and not contradictory in terms of developing instruments to support WTO rules. WCO also provides TA and training to support those rules.

The World Bank has established a trade logistics group, driven the GFP for trade and transport (created in 1999), produced several toolkits and handbooks, and organized TF seminars. The World Bank has had 120 projects globally in the past decade with TF or Customs component. E.g. TTFSE, Tunisia, Vietnam etc. It has completed Transport Trade Facilitation (TTF) Audits in several countries that review the situation pertaining to international transport and trade in terms of transaction and transport costs. Its Customs Modernization Handbook is a resources manual that provides operational guidelines for a successful customs modernization strategy, drawing on several customs reform projects undertaken by the Bank. Performance Indicators related to Customs can employ a simple but universal methodology that includes benchmarking, targets and a process of monitoring and review<sup>43<
as well as investment costs associated with the debt portfolio, which may be lower than for the equity portfolio. Because it is not possible to accurately apportion fixed costs to each investment, and because the IFC engages in significant non-investment activities like research, we assess individual investment performance on a gross basis, without subtracting operating expenses. When assessing the performance of the complete portfolio, we report additional results which reduce returns by subtracting a hypothetical “two and twenty” management fee to direct investments.

# **3.2 Private Equity Investment Data**

**Cash Flows.** Our primary data are the complete set of cash flows to and from all 2,509 equity investments (in companies or funds) from the IFC’s founding in 1956 until June 30th, 2019.<sup>7</sup> The data include the month of each cash flow, the exact value in U.S. dollars, and the most recent mark-to-market valuation of investments still held in the portfolio. Each investment’s “vintage 7Because we focus on cash flows exclusively related to equity investments, we do not include investments that include both equity and debt components (e.g., convertible loans). We leave analysis of the IFC’s debt investments to future research.

8
rates between 15 - 25%, including an exemption threshold: any income below that level is not subject to PIT and also does not trigger withholding requirements. The exemption threshold throughout the period we study was typically set at around one and a half times the corresponding annual minimum wage, and this mostly defines the population for whom we observe labor and mixed income in this data-source.<sup>5</sup> Unlike in other countries, individuals whose income is entirely withheld at source, such as capital income and wages, are not required to file the yearly PIT declaration. Income tax declarations are only required when individuals earn income that is not withheld at source, such as some forms of service provision and income from nonincorporated commercial enterprises. In order to assign income to individuals, we use both self-declared information on PIT declarations as well as third-party information through withholding mechanisms.

We use datasets at the taxpayer level for each year in the period 2003-2019, including all possible income sources observed by the tax authority. Recovering information from several different data sources within the tax administration is possible since taxpayers are uniquely identified in all datasets using a personal identification number (RTN, for _Registro Tributario Nacional_ in Spanish).

We present a summary of taxpayer-level data availability in Table 1 where we highlight the following facts. First, the maximum number of individuals observed in the tax data is approximately 650,000 in 2019, representing less than 15 percent of the estimated adult population in that year. That is a direct result of the high levels of informality and of the high exemption rate for PIT, among other things.<sup>6</sup> As discussed below, even in those years we only use a fraction of the administrative data to complement survey observations, since many of these observations in the tax data have very low incomes (e.g., only small declared amounts from interest in bank accounts). Second, information from withholding sources is very important: in 2019, for example, only 116
18 a negative effect of distance between cities on spatial spillovers. Thang, Pham, and Barnes (2016) estimate productivity spillovers in 61 provinces in Vietnam and find that although there is evidence of spillovers between provinces, spillovers within provinces are much larger, reflecting the positive effect of spatial proximity between MNC affiliates and local suppliers. For India, similar findings of a positive effect of proximity between the two types of firm are presented by Song and Son (2019). Merlevede and Purice (2016) report for Romania that spillovers from backward linkages within regions are larger and occur faster compared to productivity spillovers that materialize between regions.

# **3.4. Evidence from Rich Data Sets**

The evidence discussed in the previous sections is characterized by certain limitations. In particular, studies tend to examine only parts of the processes depicted in figure 1. Studies that provide detailed information on the extent of backward linkages and technology dissemination use mostly small-scale, firm-level surveys (often only containing MNC affiliates) and do not examine whether inter-firm linkages materialize as productivity spillovers among local suppliers. Quantitative studies that estimate productivity spillovers usually do not have information on the actual size and nature of linkages that exist between MNC affiliates and domestic firms and rely on national input-output tables to calculate indirect indicators of the magnitude of these linkages. Some recent studies attempt to address these shortcomings by combining various firm-level data sets and/or by using large-scale firm-level surveys that contain information on both the level and nature of linkages between MNC affiliates and local suppliers and information that allows for the estimation of productivity effects of these linkages.

A good example is the detailed study on productivity spillovers in Costa Rica by Alfaro-Urena, Manelici, and Vasquez (2019). They combine an administrative data set that tracks all firm-to-firm transactions in this host economy for the 2008–16 period with other firm-level data sets containing information on profits, revenues and costs, inputs, human capital, and exports and imports. To identify spillovers, they conduct event study estimations, comparing productivity changes
**Table 3: International Comparison of exit rates and changes in sales and employment during the global finance crisis**

|Indicators|Cambodia|Bulgaria|Hungary|Latvia|Lithuania|Romania|Turkey|
|---|---|---|---|---|---|---|---|
|**Exit Rates**||||||||
|% of firms closed|3.9|0.7|0.0|4.5|1.0|3.3|5.8|
|% of firms closed, insolvent, or|9.0|16.4|11.9|7.2|4.4|17.6|15.6|
|impossible to locate||||||||
|**Sales**||||||||
|% of firms with decreased sales|85.4|66.3|63.1|88.5|86.8|73.3|71.7|
|Change in sales (%)|-30.7|-20.5|-15.4|-41.1|-40.7|-25.2|-23.8|
|**Labor**||||||||
|% of firms that decreased their|16.6|48.6|51.9|66.1|62.5|49.8|60.2|
|number of permanent workers||||||||
|Change in permanent employment<br>(%)|-0.8|5.4|28.6|-12.1|6.4|47.8|1.3|

Note: Data from East Europe has a slightly different methodology, see Correa