rafmacalaba/data-use-mentions-extended huggingface dataset:
supply-side effect.The measures are developed using the 2001 population census, and Table 1 provides descriptive statistics.

Our population control captures the size of the local consumer market, which can be especially important for services businesses, and the overall level of surrounding economic activity (e.g., general availability of workers).Ghani et al. (2011c) find higher entry levels partially correlate with greater population, but we do not have a theoretical reason to suspect population influences the gender balance after controlling for other district attributes. Population density again reflects some measure of local market size, but it also goes beyond to consider the competition for local resources (e.g., higher land rents). Ghani et al. (2011c) find population density reduces organized manufacturing entry, as has been shown in other contexts, but that it is not systematically related to entrepreneurship in the unorganized sectors that we study here. With respect to gender ratios for entrepreneurs, the work of Rosenthal and Strange (2011) on sorting in the United States suggests that the female entrepreneurial share may be declining in density.

We model the district’s age structure asthe ratio of working age population to nonworking age population. This ratio is sometimes called the demographic dividend in the Indian context.While some work finds age structure matters for entry rates (e.g., Evans and Leighton 1989, Bönte et al. 2009, Glaeser and Kerr 2009), this fact has not been established for India. The general education of the workforce has been linked to higher entry in India (e.g., Ghani et al. 2011c) and the United States (e.g., Doms et al. 2010, Glaeser et al. 2010), and Amin and Mattoo (2008) further study human capital in the Indian context. It is not clear, however, if general education should influence the gender balance of entrepreneurs. We measure the general education level of a district by the percentage of adults with a graduate
Iraq, Jordan, Kuwait, Lebanon, Morocco, Oman, and Sudan and minor protests in Djibouti, Mauritania, the West Bank and Gaza, and Saudi Arabia.

While the rather blooming economic context in which the Arab Spring broke out prompted some development economists to label it a puzzle or a paradox (Devarajan, 2015), examining these events through the lens of the SC, which places emphasis on the political context as well, allows to greatly improve the understanding of their root causes. In the first decade of the 2000s, the standard indicators of economic well-being (growth, poverty, and inequality) and human development (child mortality and school attainment) all had encouraging levels and trends. Yet, as shown by World Values Surveys and Gallup surveys, there existed significant dissatisfaction of citizens in MENA and much of it was about political factors such as voice and accountability and freedom of speech to which the middle class and the youth were aspiring. The authoritarianism of governments was also of great concern to these populations.

With no doubt the Arab Spring has given rise to a magnificent wave of hope among young people in the MENA region who have raised their voices strongly against corrupt practices and called for more jobs, freedom and dignity. But recent events in the region suggest the possibility that this hope may have evaporated.<sup>23</sup> Even in the few cases where protest movements have taken place in an extremely peaceful manner, for various reasons that certainly deserve to be explored but go beyond the scope of this document, what has happened is, instead of the advent of a new SC responding to the aspirations of the frustrated population, at best, governments attempted to return to a governance of the authoritarian bargain-type discussed earlier.<sup>24</sup> Thus, although the SC of the post WWII in the MENA region has been seriously challenged by the difficulties of the countries of this region in the 1980s and 1990s and that the Arab Spring 2010 has severely shaken their social structures
_q_ = 0 _._ 01) greater than the cash arm (Column 1 of Table 6. Notably, this is a combination of a 0.11 SD increase in ranking among the work group and a 0.06 SD _decrease_ (though imprecise) among the cash group.

The large impact of employment on family status, paired with the potential disempowering effect of cash, hints at these improvements being gendered: because males are the traditional breadwinners in our context, deprivation of that role and receipt of a ‘handout’ may be particularly costly for them.<sup>15</sup> Indeed, we find in Table 7 (Columns 5 and 6) that, while women exhibit improvements in their family ranking under both employment and cash arms, males exhibit a substantial increase in their family ranking from employment and a reduction from cash. The non-pecuniary impact of employment on self-worth appears to be driven almost entirely by men, who exhibit greater [self-perceived] sense of self-worth within their family when employed, but reduced family status from cash alone.

> 15This is despite the fact that we framed the cash transfer as ‘earned’ for the completion of weekly surveys in efforts to limit the potential disempowering effects of cash.

20
CDR-based method only improves marginally on random targeting.<sup>10</sup>

# **3.5 Additional Tests and Simulations**

Our main analysis considers the household head to be the unit of analysis. As described in Section 2.3, this analysis is based on matching survey-based indices to phone data from the household head, which is consistent with the design of the TUP program and the TUP survey

> 9These 1,807 households include households that report owning a phone on a different network (this network is estimated to have around 30% market share in Afghanistan), as well as phones on our network that were not active during the six-month period of CDR that we analyze.

> 10A key nuance in this analysis is that for the CDR-based and combined methods where households without phones are targeted first, the precision and recall measures in Table 2 correspond to programs that only target households without phones (at random), as the number of households without phones exceeds the budget constraint of the program. The AUC score, on the other hand, is a summary statistic that represents targeting accuracy at all counterfactual targeting thresholds, and thus is not sensitive to the budget constraint — which explains the contrast between AUC and precision and recall in Table 2 Panels B and C. The ROC curves (Figure 1) and Precision-Recall curves (Figure S5) highlight how budget constraint affects precision and recall.

14
and it increases juvenile catch inside closed areas after the end of closure periods (temporal spillover). These areas and time periods are not targeted by the policy; the regulator only intends to change juvenile catch inside closed areas during closure periods. Summing the direct, temporal spillover, and spatial spillover effects, I estimate that the policy increases total juvenile catch by 48% despite a second regulation which sets binding limits on total catch (juveniles and adults). The closures policy backfires—worsening its target outcome on net—because closures cause vessels to reallocate fishing to areas where the share of juveniles is higher.

Why do closures change where vessels fish? Due to contracts and other regulations in the fishery, reducing search costs is the primary margin by which fishers can increase profits. Closures might help fishers reduce search costs because closures implicitly signal high-productivity fishing locations: the regulator implements closures in response to realtime anchoveta catch data from all vessels, and there is only anchoveta catch in an area if anchoveta are sufficiently abundant. This information is potentially valuable because there is zero anchoveta catch in most areas,<sup>2</sup> but strong correlations in anchoveta catch over time and space.<sup>3</sup> For these reasons I posit that closures implicitly provide information about the value of fishing near closures (before, just outside, and after closures). I confirm that one way in which vessels respond to closures is by fishing closer to them. Then I provide evidence that absent vessels’ responses to the information, fishing near closures is more productive than fishing elsewhere.

If information is a mechanism underlying the policy’s spillover effects, then vessels that receive stronger information shocks from closure announcements should have larger treat-

> 2I observe the universe of fishing events. Zero anchoveta catch includes instances in which there is no fishing in an area as well as instances in which vessels set their nets but catch zero tons.

> 3For example, the daily probability any vessel catches anchoveta in a given
availability of regular and relevant market prices would reduce the risk of mispricing portfolios or collateral held by mutual funds through repos.

# **Egypt: money market mutual funds as product diversification**

**In Egypt, only money market mutual funds have developed, but their extraordinary growth to LE 50 billion over a five year period (2003-2008) has been the result of arbitrage in the market.** The trigger was the combination of high yield on the Central Bank’s overnight deposit facility, aiming to drain excess liquidity, and the strategy of the major banks to offer higher remunerated substitutes of cash deposits to corporations and high net worth individuals. As banks stated at that time, the higher Central Bank yields were “shared” with mutual fund clients.

**The current money market mutual fund business model in Egypt leads them to invest a large proportion of their assets in bank deposits and, hence their role in diversifying the investor base for public debt is negligible.** According to April 2008 figures, mutual funds could potentially buy 25 percent of outstanding public debt. With appropriate regulations money market mutual funds could shift a large proportion of their holdings from bank deposits into the Treasury bills and the repo market and become significant participants in the public debt market.

**Money market mutual funds could become the first step to develop a more diversified investor base including fixed income mutual funds** . Recently some market participants have started to develop fixed income mutual funds after two regulatory changes that may be acting as triggers for the industry. The first one limits the average maturity of money market mutual funds from thirteen to six months. Thus, longer maturity funds should take the form of fixed income funds. The second regulation bans insurance companies from investing in banking products, including term deposits, thus fixed income mutual funds are currently seen as a substitute.

# **Tunisia: banks and mutual funds**

**Tunisia ranks first in terms of mutual funds holdings of public debt at around 28.
**MONGOLIA'S INFORMAL SECTOR** -

**Notes**

_58_ borrowed to supplement the household budget. It should be noted that the categories used for the analysis of trends were chosen according to the breakdowns presented by the SSO, and are different from the groupings of informal and formal income used later in the report.

36.There is no doubt that the increase in the importance of individual activities is attributable in part to the privatization of herds and the income generation that animals have brought to rural households. But since the herds were largely privatized by 1993, the trend apparent in Figure 3 is more than just an artifact of reclassification of types of income. Household survey data, disaggregated to the level of Ulaanbaatar, are not available prior to 1995. During 1995 and 1996, there is no discemable trend in the imnportance of any category of income. However, for Mongolia as a whole, virtually all of the decline in the importance of wages and salaries transpired before 1995, so the lack of a recent trend for Ulaanbaatar implies nothing about the overall trends during the transition period. See Centre for Social Development, 1996, for a discussion of micro enterprise in rural areas.

37.The results in this paragraph are nearly identical for both October (22% and 32%) and November (23% and 32%), 1996.

38.See Porter and Bayer (1984), Gutmann (1977, 1979), and Feige (1979). This approach has also been employed by Suarez and Agastin, as cited in Alonzo (1991).

39."On Some Measures of Currency Regulation and Payment Discipline Enhancement," Resolution by the Ministry of Finance and Mongolbank, October 1, 1993.

40.The survey included 15 street informals representing the relative low-end of the informal spectrum, and 15 kiosks representing the relative high end of the informal spectrum. See also note 66.

41.See, especially, IMF (1996), pages 31-32, which argues that the
whether the remote sensing data is rainfall or temperature. Figure 9 presents the share of significant coefficient estimates for three standard _p_ -values: for _p >_ 0 _._ 90 _, p >_ 0 _._ 95 or _p >_ 0 _._ 99. To these bars we add the 95% confidence interval on the mean number of significant coefficients. The north panel presents results from precipitation products while the south panel presents results from temperature products. Each bar and confidence interval in the rainfall panel is based on 6,048 regressions while each bar and confidence interval in the temperature panel is based on 1,728 regressions. To facilitate comparison, we draw red lines to designate the top and bottom of the confidence interval on the mean for Household bilinear, the preferred method.

A quick visual inspection of the results in the top panel of Figure 9 does not reveal many, if any, differences across extraction method. Comparing numerical values for the share of significant coefficients from one extraction to the 95% confidence interval on the mean of any other extraction does reveal some small differences. The share of significant coefficients produced by regressions using data from modified EA simple and Administrative area bilinear are weakly different from EA bilinear. The only other pairwise comparison that is weakly difference is between Household bilinear and Household simple. There are no comparisons that are strongly different from each other. The results in the lower panel on temperature look similar to those with rainfall. No obvious differences exist, though a numerical comparison reveals that Household bilinear is weakly different from Administrative area bilinear. No other pairwise comparisons are weakly different and no comparisons are strongly different. As with our examination of adjusted R<sup>2</sup> values, the preponderance of evidence here implies that different extraction methods do not introduce substantial mismeasurement into the analysis. We fail to reject Hypothesis 1. However, there is the possibility of heterogeneity across or within countries. As such, we next consider this same metric, disaggregated by
ucation with financial investment in the education production function of children. This complementarity may reflect peer and role model effects in addition to more efficient investment by educated parents, and can give rise to a convex CEF. Unlike the standard linear model, relative mobility in our model depends on parent’s schooling level, and the urban and rural relative mobility curves can cross. The approach developed in this paper is of wider interest as it can be applied to household survey data from other developing countries.

For the empirical analysis, we take advantage of the rich household panel data from the Indonesia Family Life Survey (IFLS) to make progress on two major issues highlighted in the literature: (i) truncation bias due to coresident sample, and (ii) omitted variables bias due to ability heterogeneity. An important data constraint on the analysis of intergenerational mobility in developing countries is truncation bias caused by coresidency restrictions in defining household membership in surveys (see Emran et al. (2018)). The IFLS data do not suffer from any significant truncation as the survey collected information on nonresident parents of any household member older than 15.<sup>9</sup> A second important advantage of the IFLS 2014 data used in this paper is that good quality measures of cognitive ability are available which allows us to control for ability heterogeneity across children in the regressions. Our main analysis of intergenerational mobility is based on the 18-40 years old children in the 2014 wave of IFLS who went to school in the 1990s and 2000s. We use the earlier rounds of the panel data (1993, 1997, 2000, 2007) to understand the effects of parent’s education on financial investment in children’s schooling. Another important part of our empirical analysis is the estimates of returns to education at the household level in rural vs. urban areas.<sup>10</sup> The availability of 5 rounds of panel data on household consumption expenditure allows us to provide estimates for both the parental and children’s generations
**Notes:** For each regional sub-figure the upper part plots the number of active trade policy measures and the absolute number of COVID-19 cases over time while the lower part plots the weekly variation of COVID-19 cases.

**Source:** COVID-19 data from John Hopkins University CSSE database.

As noted previously, there is a more marked increase in the number of measures pertaining to medical products/PPE than actions targeting food products, and, for both sectors we observe a greater number of liberalization-cum-facilitation measures than trade restrictions. The latter pattern is stronger after May 2020, reflected in a gradual decline in the overall number of export control measures for medical products. Starting in August-September we also see a decline in the number of import-facilitating measures, reflecting a partial rollback of temporary reductions in import barriers (tariffs, taxes) for medical goods. The number of measures affecting food trade rise steadily throughout the period, with trade facilitating measures only showing a slight decline in late September/early October. The correlation between trade policies and the weekly increase in global COVID-19 cases is high, as expected, and appears to be stronger for food-related measures than for trade policies towards medical products (Table 4). Correlations that also consider cross-country variation reveal greater similarity between food and medical product-related trade policy activism.

**Table 4: Correlation between trade measures and number of global COVID cases**

||Weekly|Variation|Tota|l Cases|
|---|---|---|---|---|
||Food|Medical|Food|Medical|
|Restrictions|.867*|.666*|.743*|.515*|
|Liberalization|.926*|.806*|.827*|.657*|

Source: COVID-19 data from John Hopkins University CSSE database.

Regional patterns (see Appendix Figure A1) confirm to some extent the timing of the aggregate (global) response, but also reveal significant heterogeneity. Countries
an alternative training modality aimed at strengthening the original mentoring program.<sup>3</sup> The API _Plus_ modality embeds all the features of the _Original_ modality, with two significant changes in the training module. First, it entails two weeks rather than one week of initial training. The extra week is focused on hands-on strategies to improve students’ reading and math competencies. Second, mentors attend an additional day during each one of the bimonthly meetings throughout the school year. The schedule of the extra day is organized around peer-to-peer sessions in which mentors share experiences and design common strategies to better address the most pressing issues. Among the large array of possible improvements in the design of the program, enhanced training was ex-ante scalable and yet a promising alternative from the perspective of the government. The _Plus_ modality entails a cost per child of US $332 as opposed to US $285 per child for the _Original_ modality. These cost figures are very much in line with another recent government-run intervention that targeted both children and parents through home visits in Colombia (Attanasio et al., 2022a).

We randomly selected 230 schools in rural Chiapas from a set of schools that were not previously part of the API program. Assignment of the program was carried out using a randomized block design at the school level, with the strata represented by the deciles of the 2012 school-average in a national standardized achievement score in the Spanish test (see Appendix A.1). As a result, 60 schools were assigned to the API _Plus_ , 70 schools were assigned to the API _Original_ modality, and the remaining 100 schools were in the control group with no API intervention.

We rely on both administrative and survey data sources as well as qualitative interviews for the second field experiment. Most of these variables are shown in Table B-2, and they are balanced with respect to treatment assignment. The data collection took place by the end of the second school year after the inception of the API program in the evaluation sample. By that time
<!-- Start of picture text --> Internet Users in the World<br>3,000,000,000 Ml Internet Users<br>2,250,000,000<br>1,500,000,000<br>750,000,000<br>0<br>1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br>1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Year (as of July 1)<br><!-- End of picture text -->
with an inevitable cost, or had to substitute fishery hours with working secondary jobs.

# **4.2 Impact on satellite-detected fishing activities**

Having shown a significant decline in average fishery earnings during the immediate period after _Formosa_ took place, we next provide corroborating evidence on the damage caused to fishing intensity in the contaminated zone. We rely on VIIRS’ Satellite Boat-Detection data, which provide a direct measure on the prevalence of night-time fishing boats. Specifically, we build upon the initial visual inspection discussed in Figures 2 and 4, and empirically estimate the causal impact of _Formosa_ under a DiD setting. We use a balance panel of monthly marine grids from 2013 to 2016, with each cell resampled to a 10-mile-square resolution. We focus on – two key measures of fishing intensity, including (1) boat detection prevalence the average 14
authors. In this manner, they score the regulatory regimes of the 11 sectors in the 103 countries according to the Australian authors’ criteria.

# **4.2.2 Estimates of AVEs of Barriers to Cross-Border Trade in Services.** Several

authors have used gravity models to estimate AVEs of barriers to cross-border trade in services. The most comprehensive of these efforts is by Francois, Hoekman and Woerz (2007). Using data from the International Monetary Fund, they estimated the AVEs of barriers in four categories of cross-border services for 178 countries, including the Philippines. We use their estimates for the Philippines in their table A2 and map their four services categories into our eight services sectors as follows: (i) transportation: our three transportation services sectors; (ii) producer services: communication, insurance and other financial services; (iii) other business services: professional services; and (iv) other non-traded services: trade and various mechanical repairs. The results for the AVEs are in table 3.

# **4.3 Estimates of the AVEs of the Costs of Time in Exporting and Importing.**

In order to estimate the impact of improved trade facilitation, in this paper we apply the time cost of trade dataset of Hummels and Schaur (2013) and Hummels _et al_ . (2007). Using the estimates of Hummels and his co-authors, Peter Minor (2013) provided estimates for the regions and products in the GTAP database on a bilateral basis. There are separate estimates for the time cost of exports and imports. We use estimates from Peter Minor, which we aggregate to the sectors and regions of our model, yielding the time cost of trade for exports and imports by product and country on a bilateral trade basis.<sup>21</sup> The results are displayed in table 3.

# **4.4 Estimates of the AVEs for Non-Tariff Measures (NTMs) for the Regions of our Model**

Cadot and Gourdon (2014) have shown