rafmacalaba/data-use-mentions-extended huggingface dataset:
interest rates. In the period following the GFC, incentives for carry trade were strong as declining interest rates in developed economies widened the gap between these interest rates and the ones in emerging economies. In this context, emerging market firms could profit by issuing international bonds at low interest rates and investing the proceeds in local bank deposits, shadow banking products, and commercial paper (or other instruments) at relatively higher rates (Bruno and Shin, 2016).

Consistent with the carry trade idea, there is evidence that emerging market firms held a substantial amount of the proceeds from international bond issuances in cash through bank deposits, which in turn could have encouraged banks to increase lending (Shin and Zhao, 2013; Chung et al., 2015; Acharya and Vij, 2016; Bruno and Shin, 2016). In fact, there is a positive correlation between emerging market firms’ international bond issuances and domestic bank lending in emerging economies during the post-GFC period (Avdjiev et al., 2014; Powell, 2014; Caballero et al., 2016). A survey by the BIS suggests that firms’ deposits contributed to about a third of the debt liability growth of banks in emerging economies during 2009-13 (BIS, 2015). Moreover, there is some evidence that emerging market firms directly acted as financial intermediaries themselves. For example, some firms issued debt abroad and then lent the proceeds by granting more trade credit in domestic currency to their suppliers (Hardy and Saffie, 2019).

Whereas foreign investors seem to have been a key driver behind emerging economies’ bond issuance activity since 2008, the rise in nonfinancial corporate bond financing in China occurred amid low levels of foreign investor participation. The share of foreign participation in Chinese bond markets remained stable at about 1.6 percent of the total value of bonds outstanding during 2014-17 (Cerutti and Obstfeld, 2019). Continuing efforts to liberalize the Chinese bond market and the inclusion of Chinese bonds in benchmark indices (which institutional investors tend to follow to make investment decisions
(of more than 2,422) reported to have been displaced from their original governorate the month before. And this is despite the fact that there was significant violence in October 2017, including 6 governorates that had more than 100 fatalities each.

# **Impact of Port Blockages and the Tightening of the Ports**

Although we find little correlation between the local effects of violence and food access, we find further evidence of the supply chain blockages having an immediate and significant effect. The monthly mobile phone surveys began after imports through the ports supplying the north of the country significantly dropped (e.g., WFP 2017), and it is difficult to estimate how food access changed in response. However, we analyze the effects on food access of a sharp event that caused this port blockage to get significantly worse.

Specifically, we use the complete sea and air blockade of _all_ of Yemen’s ports (not just the ports that supply the north),<sup>20</sup> and compare food access of those surveyed immediately before to those surveyed immediately after the event. Importantly, there was an ongoing mobile phone survey conducted by the World Food Programme (WFP) that interviewed 2,424 respondents. Of these interviews, approximately 900 occurred in the five days before the blockade was implemented; approximately 900 occurred during the full blockade on the entire country (November 6‐13); and approximately 600 occurred during the time period during which only northern ports were blockaded (November 13‐November 18). The ongoing survey and the distribution of responses over time allows one to gauge how households adapted to the economic difficulties, and whether this affected their access to food.

> 20 For example, see (accessed April 2017) <u>https://reliefweb.int/report/yemen/escalating‐humanitarian‐crisis.</u>

15
saved more when they received their wages into their own account through direct deposit (Breza, et al., 2020). Likewise, workers in Afghanistan who automatically deposited part of their salary into a mobile savings account had higher savings and financial security than workers who did not (Blumenstock, et al., 2018).

In the realm of payments, receiving them directly into an account brings several benefits over receiving them in cash, including reduced transaction costs and increased safety. Teachers in Liberia, for example, saw the cost of collecting their money (including bus fare) reduced by 84 percent—from US$25 per paycheck to US$2—when they received their salaries as digital deposits. Because they no longer had to take time off to collect their wages, they were able to spend more time in the classroom (USAID, 2016). The teachers also experienced a safety benefit, as they were able to avoid traveling with cash in their pockets.

This safety connection between digital payments and crime has also been documented in highincome economies. For example, an analysis of 20 years of crime data from the US state of Missouri and nearby areas found that the introduction of digital safety net payments reduced burglary, assault, and larceny, lowering overall crime rates by about 9 percent (Wright, et al, 2017).

# **_3.1 Government payments as a driver of broader financial usage_**

In developing economies, many payments from the government—such as social transfer and wage payments—are still made in cash. However, in high-income economies they are typically paid into an account.<sup>4</sup> There is broad evidence showing that digitalizing these payments benefits both the government and the citizens receiving them. In India, for example, internal fraud and leakage from pension payments dropped by 47 percent when the country transitioned from cash to smart cards. Recipients spent less time collecting payments and received more money because of the fraud reductions. The government saved millions
meeting startups. As part of Vilcap’s normal training program, trainee investors then continue to evaluate startups three more times, using standardized criteria.

In the second stage, we leveraged the panel dataset of trainee investors’ investment decisions and tested the effect of an additional treatment, added to the first treatment. We prompted treated investors to inquire about startups’ progress during the selection period. Using this panel dataset allowed assessment of whether gender disparities appeared at specific stages of the selection process (e.g., Botelho and Abraham 2017, Bohren et al. 2019).

To conduct the two-stage field experiment, we worked with Vilcap in eight of its investment training programs (two each in four regions – Africa, India, Middle East, and Latin America – allowing for one treatment and one control group in each region). Trainee and professional investors evaluated startups in these eight Vilcap programs. This resulted in a dataset of 31,714 evaluation scores. Our first stage leveraged the cross-section sample of 1,341 dyadic investor-startup decisions by investors made after the investor met a startup founder. This sample included both professional investors as well as trainee investors who were trained by Vilcap to allocate $320,000 to 16 out of 87 startups. The second stage leveraged the panel nature of the trainee investor dataset. We randomized investors into treatment and control groups, with a panel dataset of 1,530 decisions (from 510 investor-startup dyads over three time periods after the initial analysis we observed in the first stage). Both stages of the field experiment were pre-registered.

# **<mark>5. First Stage: S</mark> ystematizing Inquiry about Risk and Reward**

**5.1. Setting and Design.** We systematized how Vilcap prompted investors to inquire about

risk and reward in their evaluation template and assessed the impact on reducing gender disparities in continuing due diligence (H1), and the consistency of investor inquiry across startups (H2). Trainee investors met startups in a
studies have used foreign affiliate statistics for other purposes, including Tørsløv, Wier and Zucman (2018) to assess the extent of profit shifting by multinationals (for 2017), and by Fukui and Lakatos (2012) and Ramondo, Rodr ́ıguez-Clare and Tintelnot (2015) to study the pattern of multinational production activities. For US-based multinationals, the Bureau of Economic Analysis (BEA) foreign affiliates data has been widely used (one notable instance being Desai, Foley and Hines Jr (2004)), as it will be below. The OECD’s Activities of Multinational Enterprises (AMNE) Database has also compiled inward and outward foreign affiliate statistics for 31 OECD countries between 2008 and 2016, as described in Cadestin et al. (2018).

12
_1VA97._ ln( V ) _=,60_ + _Al 1 ln(IVA821 )_ + _Ih2_ ln(edu;) + _j3_ ln(I _/ GDP,)_ + _/ 4PRSJ_ + e, (3) _IVA82,_ where _ln(I/GDP)_ is the (natural logarithm of the) ratio of aggregate gross fixed investment to GDP. Estimates based on the above equation are reported in Table 2. Specifications 1 through 6 contain PRS variables 1 through 5 and a composite measure of those indexes in each respective equation<sup>4</sup> . With the exception of Institutional Quality indicators, all other variables used in the analysis are from the Statistical Information Management and Analysis (SIMA) database of the World Bank.

The coefficients of primary interest are all positive and comfortably significant. The coefficients on the (logarithm of the) investment rate are positive and significant, rejecting the hypothesis that the rate of capital accumulation are identical across countries. The choice of measure of human capital (or education) turned out to be a crucial one here. We hypothesized that unlike the case of economic growth in general, higher education is more important for industrial growth than secondary education. The results support our proposition. In most regressions the average Secondary School Enrollment ratio failed to provide the sings (and significance) of coefficients predicted by theory<sup>5</sup> . Owing to the lack of reliable data on High Education we included the (logarithm of the) Tertiary Enrollment rate as an explanatory variable to capture the effect of human 4 The composite measure of PRS variables included in the **6th** regression is a simple average of PRS variables lthrough 5. Note that including the PRS Composite measure in an equation is identical to including all 5 PRS variables into the regression and restricting their coefficients to be the same for all 5 variables.

_5_ In another attempt to improve the "performance" of educational indicators, we included Government Educational Expenses (
|U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(prelim.)|
|Sweden|Sep 80|8320 J|UNPVSR7/86 (official<br>est.)|Based on INED10/87|Based on UNPVSR<br>4/87|
|Switzerland|Dec 80|6366 J|Bank projection fromofficiaL<br>1984 est., UNPVSR<br>7/86|<br>Based on INED10/87|Based on UNPVSR<br>4/87|
|Syrian Arab Re|p. Sep 81|9053 F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Taiwan, China|Dec 80|17969 F|Bank projection from census|Based on USBOC<br>1985|Based on USBOC1985|
|Tanzania|Aug 78|17513 F|Bank projection from census|Bank est.|Bank est.|
]|.|(.806)|(.812)|(1.41)|(1.42)|
|**_D. Index_**|||||||
|**Disruption Index**|0|320|-.219|-.227|-.036|.127|
||[1]|.|(.146)|(.140)|(.309)|(.320)|
|**Disruption Dummy**|.250|215|-.066|-.084|-.133|-.079|
|[=1 if disruption index greater than 75th percentile]|[.434]|.|(.062)|(.062)|(.124)|(.129)|
|**N (villages)**|200|200|200|200|200|200|

**Notes:** All data comes from the Village Leaders Survey, collected in October 2015. Both PHUs and Daily Markets are considered as disrupted or closed if reported as such by village leaders for at least one month for the period between July 2014 and September 2015. For those villages that do not have a daily market (n=62), both dummies for disruption and closure are assumed as equal to one. The between group variation reported in Column 2 is computed through a one-way ANOVA analysis of the dependent variable across districts. For each measure of disruptions, Columns 3 through to 6 report estimated coefficients on assignment to treatment from regressions that use incrementally larger sets of village-level covariates. All regressions include district fixed effects and calculate robust standard errors. Column 3 reports the coefficient on ELA from a regression of the Ebola measure of interest on treatment assignment. The coefficient in Column 4 is obtained controlling for: number of dwellings, whether the village is a political stronghold (i.e. it is the residence of a
_GLOBAL FOOD INSECURITY OUTLOOK_ _25_ with an R<sup>2</sup> of 0 _._ 97 using historical data only. Even though prediction errors may possibly be even smaller when making the vast number of possible subtleties explicit in the model through high-dimensional data, it will be more difficult to understand the types of errors and biases that are likely to emerge from the more complex methods and make the model less easy to sustainably support.

# _B. Possible missing data dimensions_

While the model produces statistically good results, it is still useful to consider what data dimensions may possibly be missing as this helps understand what factors may typically drive prediction errors.

Ideally, food price inflation would be made explicit in the model. This is mostly complicated by data availability. Historically, the food component of CPI is available for many countries, and it would make sense to add it to the model as a description of food price developments compared to overall price developments would provide a vastly superior interpretation of food insecurity pressures stemming from price development compared to what can be inferred from CPI inflation alone. However, in highly food insecure countries and in fragile and conflict-affected countries particularly, the availability of detailed sub-indexes has remained rather limited. This is pointed out by (Andr ́ee, 2021a,b) whose efforts have recently sought to produce food price inflation data from alternative sources to fill important data gaps. Such approaches are however not yet fully mainstreamed and would introduce a complex dependency into the model’s data pipeline. More importantly, since the application is interested in projecting future food insecurity, there would also be the need to have an outlook for food price developments. The WEO, which is central to the empirical application here, only provides forward guidance on CPI inflation rates and not food price inflation rates, which complicates the issue further.<sup>28</sup> There is also some scope to argue that CPI inflation rates alone should provide a reasonable predictive signal. The earlier references already highlighted that a
30 see Annex Table A1). We noted and investigated discrepancies, and corrected any coding mistakes. No datapoints were excluded – we are sure any discrepancies are due to definition differences.

However, even when indicator definitions are identical, for many country-year-surveyindicator combinations, we do not have a published number to compare ours with: STATcompiler does not tabulate all indicators by wealth; older MICS surveys do not tabulate any indicators by wealth; and some surveys are not (yet) included on the DHS STATcompiler and MICS reports. For surveys other than the DHS and MICS, there are no equivalents of DHS STATcompiler and MICS reports. In these cases, and DHS and MICS surveys without published data population and quintile rates, we ran some basic checks, the most important of which involved making sure that population rates were within a reasonable interval, e.g. proportion indicators should be in the interval [0, 1], under-five mortality should not be above 400 per 1,000, systolic blood pressure should be in the interval (90, 250), cholesterol (in mmol per L) should be in the interval (3, 8), women’s weight (in kg) should be in the interval (40, 120), and men’s height (in meters) should be in the interval (1.3, 2.8). None of our datapoints failed these basic checks; therefore, no datapoints were excluded.

# <u>Illustrations using the health equity data</u>

Figure7 shows inequalities across wealth quintiles in immunization for selected African countries in the 1990s. The gaps are much larger in some countries than others. But the figure also illustrates how even the better off in some countries do poorly. For example, children in the richest 20% of the population in Ghana were being immunized at the same rate (65%) as the poorest 20% of children in Kenya.
female-owned informal businesses are more likely to be operated from within the household. The concentration of females into sectors with limited technology and economies of scale, such as retail, likely partly contributes to the differences in the performance of female vs. male-owned businesses (World Bank 2012; Essers et al., 2021).

Informal businesses constitute an important source of livelihood for their owners. In the existing literature, these businesses are often viewed as side activities run to supplement income from employment elsewhere. However, this does not appear to be the case in the Informal Sector Enterprise Survey data. Only about 12% of informal business owners have employment with a contract elsewhere, and about a third of them were unemployed before starting the business. In formal micro-enterprises, by contrast, 33% of owners currently work elsewhere while 13% were unemployed prior to starting or acquiring the business. About 66% of informal owners note that they started their businesses primarily because they could not find a better source of livelihood.<sup>12</sup> Aga et al. (2020) analyze the income of households and main owners that comes from informal business activities and shed light on the reliance on such sources in an economic crisis, such as the one induced by the coronavirus. Importantly, for about 70% of the informal businesses, the owners are the main income earners for their household, ranging from as high as 86% in Zimbabwe to 53% in Lao PDR. Female owners are much less likely to be the primary earners: 50% compared with 85% for male informal business owners. By comparison, 83% of formal micro-enterprises are owned by the household’s primary income earner. The average informal business owner lives in a household with five members, ranging from an average of 4.6 in Zimbabwe to 6 in Zambia.

Informal Sector Enterprise Surveys also enable the study of the link between the informal sector and migration that has been extensively investigated (e.g., Harris and Todaro, 1970
<u>Panel 2</u>

|||G<br>|eorgia<br>|K<br>|enya<br>|
|---|---|---|---|---|---|
|||Men|Women|Men|Women|
|Openness|Mean|2.92|2.98|3.02|2.93|
||SD|0.57|0.50|0.58|0.56|
|Conscientiousness|Mean|3.09|3.19|3.26|3.21|
||SD|0.57|0.51|0.50|0.50|
|Extraversion|Mean|2.47|2.54|2.85|2.83|
||SD|0.51|0.52|0.61|0.58|
|Agreeableness|Mean|3.13|3.18|2.88|2.87|
||SD|0.55|0.52|0.57|0.54|
|Emotional|Mean|2.63|2.51|2.71|2.68|
|stability|SD|0.65|0.70|0.49|0.47|
|Grit|Mean|2.78|2.79|2.74|2.67|
||SD|0.62|0.59|0.61|0.58|
|Decision-making|Mean|3.27|3.39|3.16|3.14|
||SD|0.50|0.45|0.52|0.51|

<u>Panel 3</u>

|||Uk<br>|raine<br>|Vie<br>|tnam
generating processes. Martin and Pham (2020) found Monte Carlo conclusions about gravity model estimators to be very sensitive to key parameters such as the distribution of predicted trade.

Unfortunately, no Monte Carlo simulations on data with properties matching those of the relevant underlying data generating processes appear to be available. The important SST (2006) Monte Carlo experiments involved: (i) trade predictions assumed log standard normal before adding a dummy variable and (ii) model disturbances distributed log standard normal before adding arbitrary patterns of heteroscedasticity (SST 2006, Case 3). Head and Mayer’s Handbook chapter (2014, p181) used a Monte Carlo design that yields the right share of zero trade observations, but considered only two arbitrary types of heteroscedasticity—a constant variance to mean ratio consistent with the PPML model and a constant variance in logs model consistent with estimation in logarithms.

The goals of this paper are to reduce the uncertainty about the best estimator for the gravity model with zeros resulting from sample selection and missing data, and to identify good, practical estimators that allow for potential heteroscedasticity. To foreshadow the key results, the standard truncated OLS estimator was strongly biased downwards—by between 38 and 48 percent. The PPML estimator was biased downwards by an average of 18 percent and threshold Tobit models by 8 to 15 percent. A maximum likelihood estimator following Eaton and Kortum (2001) in dealing with the limited-dependent variable problem but also allowing for heteroscedasticity (the EK-H estimator) provided unbiased parameter estimates. Including missing trade data mapped to zeros had small impacts on most parameter estimates but increased some by around 20 percent.

4
comparatively less strong than would be expected, though more in-depth analysis of comparative performance of transport may be warranted to develop a more nuanced picture, especially across different modes of transport. The main policy implication is that there remain significant needs to scale up infrastructure investments––especially in the yet unserved areas––and improve efficiency in all four major infrastructure sectors if South Africa’s infrastructure performance is to catch up with its group of upper middle-income countries. In a separate paper, we provide panel-data based estimates of South Africa’s investment needs in electricity and telecommunications sectors (Bogetić and Fedderke 2006).

More specifically, compared to the benchmark and other country groupings, South Africa’s utilities provide solid service at reasonable quality and, in some cases, very competitive prices. But there remain significant shortfalls relative to benchmarks in all infrastructure sectors, largely related to limited access and less than expected quality, especially in rural areas, where large numbers of South Africa’s poor live with limited or no access to service. To _catch up_ with its income group in terms of these indicators, South Africa will need to improve access to and quality of electricity, and water and sanitation services in presently unserved areas. Indicators of transport performance, while less definitive, potentially also point in the same direction. Even in telecom, the most globalized and “modern” infrastructure sector, access in rural areas can be improved and pricing of local services remains uncompetitive. Moreover, to put it simply, if South Africa is to strengthen its overall infrastructure performance, it will need to improve these dimensions’ performance in the sectors _faster_ than the average country in the upper middle-income group while maintaining or improving its position in the already strong areas of performance. This will not be easy in competition within a group of countries all aspiring to move to much higher levels of income and with ambitious poverty reduction targets. But catching up with these _relative_ shortfalls may also help South Africa reduce its economic inequities and poverty rates and, therefore,