rafmacalaba/data-use-mentions-extended huggingface dataset:
access to a helpline through phone, text messages (SMS) or WhatsApp to answer general questions throughout the application process.

**_Treatment Group 2 (T2)_** localities received the same intervention as T1 (T0 + simplification and additional communication channels), and in addition they were exposed to: (i) modified posters from T1 with messages designed to leverage insights from behavioral sciences literature,<sup>5</sup> such as mentioning aspects of their identity as women and role as mothers when arguing the importance of participating; messages and images normalizing the participation of women in activities outside their home, setting an example of accepted behavior and challenging a social norm; and posters and text messages using loss aversion framing by telling potential participants not to miss out on the opportunity to participate; (ii) up to 37 SMS/WhatsApp messages with reminders and encouragement about the call for proposals, using some of the insights mentioned above and trying to address the intention-action gap with the reminders; and (iii) a checklist to help interested individuals complete the application process and close the intention-action gap.

We can interpret the differences in outcomes between T1 versus T0 localities as the added effect of simplification and expanded communication channels (e.g. local meetings, helpline, etc.), and T2

> 3 It is important to highlight that, as detailed in the sampling section, the call for proposals and World Bank program were promoted and implemented in many localities across five Mexican states; however, because of budgetary reasons, we restricted our sample to two of the five states: Yucatán and Oaxaca. Morover, we only ran the RCT and collected data in a subset of the available localities in these states (116, of which only 36 were part of T0).

> 4 Inefficient or unnecessary steps that might delay or prevent people from performing an intended behavior.

> 5 For a summary of these behavioral insights and how they impact these behaviors, see World Bank (2018), Closing the
<br>Gini/poverty, ne<br>The change is th<br>contribution bef<br>The budget chan<br>percentage chan<br>administrative b<br>**Approximate b<br>Source: HEIS 201|bution is the<br>nt is reduced<br>me; a positiv<br>gative contri<br>e difference<br>ore the refor<br>ge in JOD is<br>ge in the ‘sur<br>udget.<br>read subsidy<br>7-18 and Wo|points which t<br>between mar<br>e contribution<br>bution is an incr<br>with respect to<br>m.<br>estimated by a<br>vey-budget’ to<br>compensation<br>rld Bank calcul|he Gini Index/<br>ket income and<br>is a reduction in<br>ease in Gini/poverty.<br>the marginal<br>pplying the<br>the actual<br>budget for 2020.<br>ations.|

# 6. Conclusions

In this paper we analysed the impact of Jordan’s main fiscal policies on poverty and inequality applying the CEQ methodology to data from the 2018 HEIS. We covered the key fiscal policies: personal income taxes, GST and SST (indirect taxes), direct transfers from NAF, the bread subsidy compensation scheme and the recent Takaful expansion, indirect subsidies to electricity and water and in-kind benefits for education and health. Our results show that the fiscal system in Jordan is only modestly progressive, but more could be achieved to further its poverty reducing and equalising impact.

In the middle of 2020, Jordan faced extraordinary challenges. While swift action by the government avoided the health crisis, the pandemic was wreaking global economic havoc and Jordan was no different. Moreover, Jordan, like many other countries, was under significant fiscal pressure before the
# **II. Data Description**

We focus on U.S. equity mutual funds that are established to purchase assets around the world. Funds that focus on both debt and equity are excluded from our sample, even though they do invest a significant share of their portfolios in foreign stocks. We use two types of data in our empirical analysis: data on mutual fund holdings and data on firm attributes.

Mutual fund holdings are available from Morningstar, a company that collects mutual fund data. We analyze monthly reports from March 1992 (when they became available) until June 2004. While some mutual funds provide monthly reports, most do so quarterly or semiannually (depending on the reporting SEC rules at the time). Given this heterogeneity in the release of new information, we construct our database with the last reported portfolio information for each fund in any given year. For example, our sample of mutual fund holdings for 2003 contains portfolio data for the Fidelity Worldwide fund, with portfolio holdings as of October 2003, and portfolio data for Scudder global fund, with portfolio holdings as of December 2003. We collect detailed information on portfolio holdings: stock names, amount invested in each stock by each fund, and country of origin of these holdings on a yearly basis.<sup>14,15</sup> The U.S. mutual fund industry is organized by splitting funds according to their investment scope. In particular, funds are classified into five distinct categories: world funds,

> 14 Some families sell the same portfolio to investors under different names depending on their fee structure and minimum investment requirements. We consider these different funds (with identical portfolios) only once; i.e., we do not treat them as separate funds as Morningstar does. For example, Fidelity Advisors Funds contain the following Latin America funds with the same portfolio: Fidelity Advisors Latin America A, Fidelity Advisors Latin America B, and Fidelity Latin America T.

> 15 We classify holdings according to the country of
<sup>26</sup>

# **Stylized fact 3: Attitudes towards gender roles in South Asia have remained conservative for men and women born across half a century.**

In parallel to the outcome variables, we examine the long-term evolution of attitudes towards gender roles and find no progress in the four attitudes studied. Using various waves of the World Values Surveys, we construct pseudo-panels for three countries in the region, Bangladesh, India and Pakistan, and follow the evolution of attitudes from cohorts born in the late 1940s to cohorts born in the late 1990s. Like Table 3, Table 4 offers a bird’s-eye view of the decadal changes of key beliefs, while Figure 11 shows the detailed trends.

The share of people agreeing with conservative attitudes – such as the priority for becoming breadwinners should be given to men, or women’s preeminent goal is maternity – are remarkably high in the most

> 26 Detailed graphs and comments for all these norms-sensitive outcomes are available in Annex Figures A7 to A26.

22
Figure 2: Regional contributions to EMDE investment and investment growth A. Share of EMDE investment B. Contribution to EMDE investment growth <!-- Start of picture text --> Percent EAP ECA LAC MNA SAR SSA Percentage points<br>100 10 EAP ECA LAC MNA SAR SSA<br>8<br>80<br>6<br>60<br>4<br>40<br>2<br>20<br>0<br>0 -2<br>2000-10 2011-21 2022-23 2000-10 2011-21 2022-23<br><!-- End of picture text --> Sources: Haver Analytics; World Bank, World Development Indicators database; World Bank. Note: EMDEs = emerging market and developing economies; EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MNA = Middle East and North Africa; SAR = South Asia; SSA = SubSaharan Africa. 2022-23 indicates forecast.

A.B. Investment growth rates are estimates for 2022 and forecasts for 2023. Investment is real annual fixed investment in constant U.S. dollars as weights. Shares for 2000-10, 2011-21, and 2022-23 are simple averages of regional annual investment growth. Sample includes 11 EAP, 13 ECA, 20 LAC, 11 MNA, 5 SAR, and 38 SSA economies.
during the crisis is smaller than during the period of calm. Generally, for most countries the difference in coefficients between the two periods under study is not statistically significant. Thus, no definitive conclusions should be drawn from the results in Table 4.

To gain more conclusive insights on the effect of growth on employment generation in times of crisis, the analysis was refined to focus on s sub-set of the sample: the Philippines, Korea, and Taiwan, China. These are the countries with particularly weak OLC estimates in Table 4. They are also countries for which more fine-grained data are available for a more thorough analysis.

One factor that may conceal the overall effect of growth on employment is that jobs in agriculture may respond to growth differently. For one, to the extent that agriculture is not commercial, overall economic activity in a country affects farm employment less. Moreover, accordingly to the Lewis model, farmers move away from their land for job opportunities and higher wages in the ‘modern’ sectors (Lewis 1954). Yet when the economy slows or contracts, the opposite may happen where workers move back to the rural areas when they are laid off in the modern sector.

In all of East Asia, the percent of the labor force employed in agriculture has fallen. Yet there still exists considerable variation across the region. In the Philippines, around 40 percent of employment is still in the primary sector, while it is less than ten percent in Korea and Taiwan, China (Figure 5). Just inspecting growth and employment data – separated by employment in agriculture and non-agriculture visually, reveals an interesting relationship. Indeed, non-agricultural employment tends to be more in sync with growth than agricultural employment (Figure 5 through 8). There also appears evidence that agricultural employment is countercyclical, especially in times of crisis.

**Figure 5:** Employment in agriculture as a percentage of total employment

<!-- Start of picture text --> 60 Philippines<br>Korea, Rep.<br
of the Matadi-Kinshasa-Brazzaville route over the Pointe NoireBrazzaville-Kinshasa route suggested by the official data (see Section 2.2). One explanation could be that this result is too imprecise to be afforded much weight. Indeed, in one specification, the relevant coefficient is not statistically significant, and in the other specification it is borderline significant (at the 90 percent confidence level). However, in view of the high costs of crossing Malebo Pool, prices for import goods would be expected to be higher in Brazzaville than in Kinshasa if Matadi indeed were the main entry point. That we find the reverse pattern, even though somewhat imprecisely estimated, is quite remarkable and another pointer towards large undocumented imports to Brazzaville via Pointe Noire and Gabon/Cameroon, and attendant unexploited scope for transit trade to Kinshasa via Brazzaville.

**Overall, our results suggest that trade facilitation across Malebo Pool would lower prices in both cities, with particular scope for falls in import prices in Kinshasa and for falls in prices of local produce in Brazzaville.** The observed price differentials imply significant trade barriers between the two cities but not within them. Given the proximity of the two markets, this implies considerable potential for intensified arbitrage through cross-Pool trade.

> 19 Details on the random effects estimations can be obtained from the authors.

> 20 Due to space limitations, the estimations with market fixed effects are not shown in Table 7.

18
0.1

Note: the difference in predicted values from (mean -.5*SD) to (mean + .5*SD) of (log) monthly sales, an increase of about $483 in sales per month. The difference in harassment exposure is the nearby (donut) harassment rate is (mean -.5*SD) to (mean + .5*SD), or 1% to 20%. Marginal effects produced using margins and mlincom commands in Stata, using vce(unconditional)option as it is survey data.

In line with expectations, there are positive, significant signs for coefficients for NECESSITY and NOWAGE; the marginal effect of the former is a 10% increased likelihood of being exposed to (22)
# **II. LITERATURE REVIEW**

The central research question proposed above sits at the intersection of a wide range of theoretical and empirical literature relevant to modern public-sector management and international development. In particular, this research touches on previous work related to the delineation of “Big Data,” of which “Open Data” and “Open Government Data” are extensions. Taken further, OGD as a concept can be viewed as a more specific element of a larger issue area related to government transparency and accountability initiatives (TAIs), which may take a myriad of manifestations including, among others, open budgeting, asset disclosure, and declassification initiatives, as well as freedom of information (FOI) and access to information (A2I) legislation. For its part, the literature on transparency is often tightly linked with literature on government accountability, including key theoretical concepts such as democratic accountability, social accountability, and social capital as well as those related to citizen voice, participation, and engagement. Underpinning these conceptions of accountability is a large body of literature on public management and administration, including that related to new public management, public choice theory, and welfare economics, which provides a theoretical foundation for analyzing the public provision of social services.

Given the diversity of themes surveyed, the literature review that follows is presented in two parts. The first element is a theoretical section, which provides an overview of the key concepts, including Open Government Data, transparency, accountability, and service delivery. Given that there is significant conjecture among authors on the exact meaning of these terms, this first section seeks to provide a plurality of perspectives, as well as to specify the definitions that will be used throughout this analysis, including the conceptual framework put forth in the following section. The second part of the literature review presents the available qualitative and quantitative empirical literature, which highlights the current understanding of the relationships between OGD, transparency, accountability, and service delivery.

# **_Theoretical Perspectives, Definitions, and Differentiations_**
of households from their villages. Moreover, comparison with a hypothetical pure-PMT demonstrates the two methods to be roughly equivalent in their ability to identify poorer households within a village.

Further, we shed light on what information village heads use to assess the welfare status of households. We asked village heads to rank households by different welfare criteria and then compared these data to household survey data. We find that village heads seem to base their assessments of specific criteria like household land ownership, likelihood of being hungry and likelihood of needing assistance on a holistic impression of household welfare, rather than specific indicators like actual land ownership, nutrition or economic shocks. Using random forest analysis, we find that their rankings are best explained by easily observable indicators of welfare like household characteristics and asset ownership.

In this program, updating poverty ranks by village heads provided a much quicker and less expensive alternative to a full PMT plus community-ranking and validation process, without compromising the targeting efficacy of the program and produced outcomes acceptable for the community. The results suggest that the performance of village heads compared to alternative methods may be particularly strong in remote areas with varying poverty levels. There, the village heads’ familiarity with residents and local circumstances can help them distinguish among community members’ relative welfare statuses. In these cases, a flexible targeting led by village heads may be successful at achieving pro-poor targeting objectives.

There are caveats about the applicability of these findings in other contexts. First, village heads only updated prior, community-supported poverty rankings in this program. This reduced the reliance on village heads for determination of eligibility and may have also improved community acceptability, since applicants may have perceived the updated rankings as a revision of a system they had already endorsed. Other populations without a comparable experience may view village head targeting as less legitimate. Second, the selection for the RMG program also relied on self-selection into the program by the neediest households. This may explain some of the targeting success of the program. Third, our analysis suggests that village heads hold a holistic
the same approach for the other two more conservative scenarios, we can calculate the unofficial economy index and shares, respectively, and present all three scenarios together in Tables 6 and 7. The data of Table 7 is depicted graphically in Figure 2.

Table 6: Ukraing Unofficial Economy Index: three scenarios.

|||Uno|fficial Ec|onomy_I_|_ndex_||
|---|---|---|---|---|---|---|
||1989|1299|1991|1292|1993|1994|
|Unitary Elasticity Scenario|12.0|16.3|25.5|32.2|34.0|36.6|
|Conservative Elasticity Scenario|12.0|16.5|25.2|30.9|31.5|32.7|
|Overall Conservative Scenario|12.0|14.8|22.5|28.0|28.8|30.9|
first three years of the 1990s based a comparison of Call Report data from 1989:Q4 with 1992:Q4 (Berger, Kashyap, and Scalise, 1995). Although some of the decline may be due to reduced credit demand, the massive size of the drop and the fact that it corresponds with a period of significant bank distress means that there was almost surely a credit crunch, or significant reduction in credit supply.

The extent to which the credit crunch harmed the real economy depends in large part on alternative opportunities of the potential borrowers. Large businesses more often have alternative sources of external finance, while small businesses are more often bank-dependent and face financial constraints. Call Reports at that time reported no information on loan or firm size, but one study combines the Call Report data with information from the Federal Reserve's Survey of Terms of Bank Lending to Business (STBL) to try to differentiate the effects by firm size. The authors’ estimates suggest much more severe credit cuts for small businesses – reductions of 38.5% in loans to firms with less than $1 million in bank credit, and 45.2% for small business borrowers with credit below $250,000 (Berger, Kashyap, and Scalise, 1995, Tables 8, A2, A8, A10). It seems very likely from these results that the credit crunch had significant negative real economic consequences and contributed to the recession.

A significant amount of banking research focuses on whether the toughening of bank prudential policies helped cause the credit crunch. Some test the effects of implementation of the Basel I risk-based capital requirements (e.g., Haubrich and Wachtel 1993, Berger and Udell 1994, Hancock and Wilcox 1994, Wagster 1999), and some test the effects of higher explicit (regulatory) or implicit (supervisory) capital standards based on leverage ratios (e.g., Berger and Udell 1994, Peek and Rosengren 1994, 1995, Hancock and Wilcox 1994, Hancock, Laing, and Wilcox 1995, Shrieves
45 account inflation in the country between the survey date and 2011, for which we need the country’s consumer price index (CPI). Thus, for a survey conducted in year _t_ , we compute the poverty line in LCUs using the formula: where PL������� is the poverty line expressed in $US (either $1.90 or $3.20 in the 2018 HEFPI data set), PPP� is the country’s implied PPP for year _t_ , PPP���� is the country’s PPP for 2011, CPI� is the country’s CPI in year t, and CPI���� is the country’s CPI in 2011. We thus convert prices in the survey year in the country in question to 2011 prices in that country, and then apply the 2011 PPP conversion rate to convert 2011 LCUs of that country into international dollars.

In the 2018 HEFPI database, we use the 2014 version of the 2011 PPP factors produced by the International Comparison Program (ICP). These PPP factors cover 199 countries and are expressed in terms of 2011 prices. We extract the PPP conversion factor series PA.NUS.PRVT.PP from the World Development Indicator (WDI) database. This conversion factor is for private consumption, i.e., household final consumption expenditure. Whenever possible, we rely on the CPI series used by PovcalNet.<sup>47</sup> When the country’s CPI series are not available, we rely on the International Monetary Fund’s World Economic Outlook (WEO) data or on World Bank’s WDI CPI series.

# _Computing the incidence of catastrophic and impoverishing expenditures_

The incidence rates of catastrophic and impoverishing health expenditures are computed using (the code underlying) the Stata module FPRO (Eozenou and Wagstaff 2018). Where health expenditure data are individual-level, the data are aggregated to the household level. A household is defined as incurring catastrophic expenses if its out-of-pocket health
# **6. Comparison with other asset classes**

In this section, we compare the returns on external sovereign bonds from our new database with those of other major asset classes traded on UK and US capital markets. As in the previous analysis, we use a global portfolio time series of returns on all active foreign-currency sovereign bonds in the sample, weighted by debt amounts.

# 6.1 Data preamble

For comparison, we start with “risk-free” assets in financial centers. Specifically, we use returns on US and UK three-month Treasury _bills_ as well as US and UK long-term government _bonds_ . The time series of US and UK sovereign bonds and UK bills are gathered for the full 200-year sample. For bills, the data for the 19th century are approximated using commercial bills and related rates (US Treasury bill rates data start in 1919, and for the UK in 1900; see Appendix A). For stocks, we use a spliced total return index for the UK (FTSE) and for the US (S&P index), both from 1815 onward. To measure total returns on US corporate bonds, we use the S&P AAA Corporate Bond Price Index, which is available over 1900– 84 and combine this with the Bank of America US Corporate AAA Bond Index from 1985 onward. More details on the data sources are shown in Appendix A.

In addition to the US and UK series, we draw on the cross-country return database by Jordà et al. (2017), which covers 16 advanced countries over 1870–2015. We use their data to compute advanced country returns on domestic sovereign bonds as well as equities. Specifically, we first convert their local currency return series on bonds and equities into US dollars, using their exchange rate data. In a second step, we compute yearly average returns using equal weights for the 16 countries since 1870.

To compute excess returns, we use two approaches. First, we benchmark against monthly UK or US Treasury _bills_ , depending on