rafmacalaba/data-use-mentions-extended huggingface dataset:
Chart 5 **Chile: Base Money** Ratio to GDP <!-- Start of picture text --> 0.42<br>0.40<br>0.38<br>0.36<br>0.34<br>0.32<br>0.30<br>0.28 ._______l______,__________<br>88 89 90 91 92 93 94 95<br>- CHHGDPI<br><!-- End of picture text --> Source: IMF, International Financial Statistics Even though Chile has experienced a downward trend in **k,** its value is still quite high, translating into high revenues from seignorage. For example, if we merely consider the - inflation tax, multiplying the 1995 real stock of base which was about 30 percent of GDPby the inflation rate of the same year (about 8 percent), the result is an inflation tax of roughly 2.4 percent of GDP. This is without considering income growth.

# **Germany**

For the sake of comparison with a developed country, we consider **Germany,** ndassume a zero primary balance and the following parameters: gP=.04; r=.04; gy=.02; k=.10 Equation (11) yields in this case a sustainable b = .30 23
One issue in the literature is that there are no good measures of the contribution of R&D to technological improvements as they are reflected by the fluctuations of aggregate production. Patents might be an indicator of the inventive activity but they are not very explicit about the degree of the e↵ect of R&D on macroeconomic fluctuations. Griliches (2000) argues that patent applications are usually taken early during research processes in expectation of long run gains. As a result, there is lag between granting a patent and actual innovation. Lach and Schankerman (1989) point out that advancements in science and technology have a direct impact on R&D spending.<sup>6</sup> We argue that potential shocks identified from fluctuations of R&D expenditures (investment) reflect precicely technological innovations resulting from R&D activities. Griliches (1979) proposes the introduction of the stock of knowledge, approximated by past R&D expenditures, as an input in the production function. This idea is also implemented by Doraszelski and Jaumandreu (2007), who assume a linear accumulation equation for the stock of knowledge in order to estimate production functions and retrieve productivity and its relation with R&D at the firm level.

Throughout our analysis, we use US data on investment in R&D, adjusted GDP and employment for R&D activities. The data on R&D investment and adjusted GDP is provided in the satellite account which is developed jointly by the Bureau of Economic Analysis (BEA) and the National Science Foundation (NSF). Data on domestic employment of R&Dperforming companies is provided by NSF.<sup>7</sup> As shown in figure 1, R&D investment is on average 2.7 percent of nominal GDP and is characterized by the peaks of the mid 1960s,

> 6Work by Rosenberg (1969, 1974), Pakes and Schankerman (1984) and Grilinches, Hall and Pakes (1988) also stresses the importance of past technological improvements as
projects delivering perceptible benefits and the concerned population assigning credit to the government for those benefits.

One of the main challenges in identifying the impact of projects on counterinsurgency outcomes is the non-random assignment of projects, which leads to spurious correlations if project placement is dependent on local security conditions, as is often the case. Two recent studies which address the question use different empirical strategies for inference and arrive at different results. Berman, Shapiro, and Felter (2011) examine development projects undertaken by the U.S. military in Iraq and, after controlling for region-specific characteristics and pre-existing trends, find that projects reduced violence, although only after a significant increase in troop strength in 2007. Crost, Felter, and Johnston (2011) employ a regression discontinuity design to examine the effect of development projects in the Philippines and find that projects exacerbated violence in the short run and had no effect in the long run.

Our study differs from these existing works in two important ways. First, we use randomized assignment of projects across villages to eliminate selection bias. Of 500 villages in our sample, half were randomly assigned to receive a development program in 2007, with the other half not receiving it until after 2011. Second, in addition to events data on security incidents, we use survey measures of household-level economic outcomes and of individual perceptions and attitudes. This allows us to test the specific mechanisms through which projects affect security.

We find that projects improve villagers’ perceptions of economic well-being and attitudes towards central and sub-national government, NGOs, and U.S. military forces. Projects also improve villagers’ perceptions of the local security situation and cause a reduction in the number of security incidents recorded by the International Security Assistance Force (ISAF) a year or more after project implementation. However, there are no effects on incidents occurring within a year of project implementation and no effects on the number of incidents reported by villagers in surveys.

In areas facing high levels of violence, no effects are observed on attitudes to government or on
Government, National Government, and State Enterprises.<sup>2</sup> Bussolo, Commander, and Poupakis (2018) provide a detailed discussion on the PEP data set.

Second, we source firm-level data from the Orbis data set by Bureau van Dijk. Orbis reports corporate ownership and shareholder information, along with balance sheet and financial data for listed and unlisted firms. While Orbis provides an extensive resource for firm-level empirical studies, there are several challenges related to using this data set. We address these challenges by using the three steps approach first proposed by Kalemli-Ozcan,<sup> ̈</sup> Sorensen, Villegas-Sanchez, Volosovych, and Yesiltas (2015) and Kalemli-Ozcan,<sup> ̈</sup> Laeven, and Moreno (2018). First, we drop firms with inconsistent information on generic variables such as date of establishment, type of company, and suspect of inconsistent units.<sup>3</sup> Second, we drop firms for which total assets, fixed assets, sales, number of employees, wages, or cost of goods sold is negative in any year. We also drop firms that report having more than 2 million employees. Third, we drop observations for firm-years with zero or missing values for total assets.

Our main firm-level variables are Investment, Return on Assets (ROA), Leverage, Capitalization, Cash Flow, Sales Growth, and Marginal Product of Capital. Investment is defined as change in fixed assets scaled by total assets. ROA is the percentage share of financial and operational profits (before taxes) to total assets. Leverage is the ratio of total debt to total assets. Capitalization is the ratio of total assets to total funds from shareholders. Cash Flow is taken from balance sheet data directly and is scaled by total assets. Sales Growth is calculated as the annual change in sales over total assets. We follow Hsieh and Song (2015) and use average return to capital (measured as value added over fixed assets) as a
Whether agricultural PAC money influenced electoral outcomes is left as an open issue deserving further research.

Returning to Figure 5.1 it should be noted that there are candidates on this top 20 Ag money list who are not members of Ag committee (Fazio, Edmondson, Bliley, Bonior and Anthony). However, they were all (with the possible exception of Anthony) from congressional districts with influential agricultural constituents (CA, OK, VA, MI). For example, Fazio’s district (CA, district 3) was among the 30 leading congressional districts by market value of agricultural products sold in 1997 (1997 Census of Agriculture), and Edmondson’s district was among the thirty leading Cattle and Calves districts Finally, contribution to Ag committee members, and membership on Ag committees are not independent of each other. The received wisdom in the literature on the political economy of agriculture policy is that representatives from districts with agriculturally influential constituencies seek out membership on agriculture committees and lobby hard for positions of influence on its subcommittees. Consequently, they are in position to influence agriculture policy especially during the five-yearly Farm Bill legislations. Their influential positions endear them to PAC influence. Ag PAC contributions and the ability to influence agriculture policy are therefore simultaneously determined.

Figures 5.2–5.5 depict the top-20 Ag PAC list from the four election cycles between 1993 and 2000. They reinforce the foregoing observations. In each of these cycles, giving to members of the House agriculture committee is highlighted. And candidates who appear in the list but are not members of the agriculture committee belong to districts with agriculturally influential constituencies.

An interesting pattern emerges when comparing the combination of party with committee membership across the five election cycles. A natural experiment that the cycles provide is the switch from Democratic to Republican majority in 1995 and thereafter. Whereas, the top-20 recipients who were agriculture committee members comprise largely Democrats during the 1991-92 and 1993-94 congresses, they were mainly Republicans in the three later congresses. This pattern reinforces the view that Ag
, Jimenez and Yanez (2020) for Bolivia; Lara Ibarra et al (2019) for Egypt and World Bank (2023B) for Angola. The estimates for the following countries were produced by Bank staff: Ecuador (using Encuesta Nacional de Ingresos y Gastos de los Hogares Urbanos y Rurales 2011 (ENIGHUR)), Ghana (using the 2017 Ghana Living Standard Survey-7), Kazakhstan (using the 2021 Household Income and Expenditure Survey), Nigeria (using the 2015/16 Nigeria General Household Survey), and Vietnam (using the 2018 Vietnam Household Living Standards Survey (VHLSS)). In countries where fuel (or electricity) is the predominant type of subsidized energy, the distributional incidence was estimated for fuel (or electricity) subsidies.

15
the other debts actually decreased as a percent of GDP, being responsible for the remaining

# 5.0% of the federal bonded debt growth.<sup>20</sup>

We now turn to the decomposition of each of these three factors: the federal deficit, the assets accumulation, and the repayment of other debts.

# **_111.1_** _Financing of thefederal government (+ Central Bank) deficit_

In order to make the net debt variation of the period (R$165,422 or 13.1% of the GDP) compatible with the nominal deficits registered during the same period, it is necessary to make three adjustments. The first is to add the states', municipalities' and state-owned enterprises' net debt variation.

The second adjustment recognizes the privatization revenues. Since privatization revenues occur only once, they are not included as current revenue in the public deficit computation. Nevertheless, they are financial inflows public revenues which, _ceteris paribus,_ would lower the net debt (the state-owned enterprises that were sold were not previously included in the public sector assets). Under the hypothesis that everything else stayed constant, and assuming that all the privatization revenues were used for public debt redemption, the gross debt would diminish by the exact amount of these privatization revenues. Therefore, we have to add these revenues to the variation of the total net debt in order to obtain the debt variation concept that best conforms to the public deficit statistics.21 20 In Table 111.3, all figures are in R$, while in Table 111.4, the figures are in percent of GDP. Therefore, given that nominal GDP grew during the four-year period, the fact that "Other Debts" increased in nominal terms, while they decreased as percent of GDP is an indication that the increases occurred more to the end of the period relatively to the decreases.

**21** In reality, the relation between privatization and public debt is much more complex for at least two
of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Togo Guinea‐Bissau Liberia Senegal Mali Nigeria Burkina Faso Ghana Sierra Leone Niger Gambia, The Cote d'Ivoire Cape Verde Cape Verde Gambia, The Cote d'Ivoire Nigeria Ghana Niger Liberia Senegal Sierra Leone Mali Benin Guinea‐Bissau Burkina Faso Togo<br><!-- End of picture text --> _Note:_ The first (second) group of bars is for the highest (lowest) 10%. Data points were from different years for different countries but all within the period 2001‐2007. Togo data is from 2006. _Source:_ DDP data, World Bank.

## **5.1 Demographic Dynamics**

**Togo has a high fertility rate.** However, 4.8 births per woman is not higher than expected in a country with the same per capita income level (see Figure 19).

> 27 Most of the analysis in this section is based on the Core Welfare Indicators Questionnaire (QUIBB/CWIQ) 2006. The poverty line applied is FCFA 164842, using the FAO/ WHO equivalence scales (see World Bank (2007a)).

22
# **1. Introduction**

At least 64 countries around the world are implementing or planning national strategies for financial education (OECD 2015). Many of these strategies involve teaching financial education in schools. Proponents of this approach argue that school financial education guarantees broad coverage and reaches individuals at a stage in life where they are particularly receptive to learning and forming behavioral patterns (e.g., Bruhn et al. 2014; Lusardi et al. 2010; Frisancho 2021; Lührmann et al. 2018). On the other hand, opponents claim that “requiring schools to spend time and money teaching financial literacy is a worse financial decision than any that those highschoolers are likely to make anytime soon,” pointing out that effects on financial behavior seem to be small and are uncertain in the long run (Ogden 2019).

While the literature on the impact of financial education in schools has expanded in recent years, we still know very little about the effects of these programs on long-term financial behavior. A meta-analysis of 37 studies measuring the effect of school financial education programs for children and youth recently concluded that the programs have strong effects on financial knowledge, and weaker but still statistically significant effects on financial behavior (Kaiser and Menkoff 2020). However, most studies measure only short-term effects of financial education, less than a year following the intervention. In fact, the meta-analysis suggests that the effects of school financial education decline over time (in a timeframe of up to 80 weeks), which may be because people forget what they were taught (see also Fernandes et al. 2014).<sup>1</sup> Another literature review likewise concludes that financial education for students has “a positive effect on short-term financial knowledge and awareness of the young, but there is no proven evidence on long-term behavior” (Entorf and Hou 2018).

This paper addresses this evidence gap by studying the long-term effects of a comprehensive highschool financial education program in Brazil. The program was implemented during a 17-month period
0.43<sup>***</sup>|-0.28<sup>**</sup>|-0.08|
||(0.07)|(0.08)|(0.22)|(0.11)|(0.13)|(0.09)|
|Field FE||X|X|X|X|X|
|Mean of Dependent Variable|0.00|0.00|0.00|-0.01|-0.03|0.01|
|Adjusted R-squared|0.12|0.19|0.22|0.18|0.15|0.14|
|Observations|851|851|529|322|223|628|

_Source_ : Authors’ calculations using administrative data. See Appendix 1 for the definitions of the variables. _Notes_ : This table reports the associations between program-level contribution to wages (in log) and quality determinants and local market conditions. See Figure A4, Panel B for an illustration of the coefficients’ relative size. A program is the unit of observation. All variables are dummies except when the unit of measure is indicated in parentheses. Indices can take a value between 0 and 1. Regressions are weighted by 1/v, where v is the standard deviation of the estimated program-level contribution. Specifications control for field fixed effects (FE), except in Column (1). Standard errors clustered at the institution level are in parentheses. Significance levels: * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01. HHI=Herfindahl-Hirschman Index.

45
into 9 categories of policy measures, namely Sector openness, Forms of entry, Licensing, Operations, Regulatory Environment, Conditions of provision of service, Restrictions on service providers, Type of entry and Entry condition.

The OECD developed a regulatory database as a basis for its own STRI, which was publicly released in 2014 (OECD, 2015, 2017).<sup>15</sup> The database currently includes information on the 36 OECD members, as well as 9 non-OECD economies (Brazil, China, Colombia, Costa Rica (since 2017), India, Indonesia, Malaysia (since 2018), the Russian Federation and South Africa, respectively). Measures for 22 sectors are presented, organized according to five policy areas (Restrictions on foreign ownership and other market entry conditions, Restrictions on the movement of people, Other discriminatory measures and international standards, Barriers to competition and public ownership, and Regulatory transparency and administrative requirements). The database has been updated annually since 2014.

In 2013, the World Bank and the WTO Secretariat started the development of a WTO-World Bank Services Trade Policy Database (STPD), which is disseminated through the I-TIP services portal.<sup>16</sup> For further information on the actual database and its functionalities, please refer to section 4 below.<sup>17</sup>

# **3  MEASURING SERVICES TRADE POLICY RESTRICTIVENESS**

It is notoriously difficult to measure policies affecting services trade because of their variety and complexity (see, for example, Hoekman (1996) and the overview by Deardorff and Stern (2008)).

> 13 See https://www.usitc.gov/, <u>http://www.servicestradeforum.org/, http://madb.europa.eu/madb/barriers_crossTables.htm, https://investmentpolicyhub.unctad.org/</u> and <u>http://www.oecd.org/investment/fdiindex.htm.
of the province, and its geographical classification (mountainous, delta, etc). The ministries place different weights on different criteria and are known to disagree in their recommendations.

In addition to providing subsidies to the target groups, regular relief transfers from the central budget can be used to cover the costs of building, maintaining, administering, and equipping the centers, and of the rehabilitation, training and job placement of invalids. Whether the transferred funds make allowance for these expenditures or are based only on numbers of qualifying individuals times the subsidy allotted for each is not clear.

Labor departments at the district and commune levels are in charge of distributing transferred funds from provincial budgets to individuals. Central transfers do not appear to be sufficient to cover all those on the lists. Officials emphasize the need to be strict about who qualifies and to rely on contributions from the community. A fair amount of doubt was expressed in interviews about the reliability of provincial lists. MOLISA has supervision missions to the provinces to check lists, and try to reduce them. By their criteria, more people are placed on the lists than deserve to be. Communities are not usually able to mobilize much and so either the lists or the amounts must be reduced. It is not known exactly what happens then at the district and commune levels.

In reality, coverage of the target groups is far from complete. MOLISA figures for 1993 indicated that, nationwide, only 10 percent of orphans, 21 percent of the "lonely" elderly, and 5 percent of the disabled population actually received program benefits. 1<sup>7</sup> In addition, tremendous variance is evident across regions both in numbers and in the share of the eligible who are covered. How these figures, and in particular the denominator, were defined _15_
loans in fiscal 1995/96. Interestingly, this figure is significantly higher than in the previous year, indicating that robust growth in savings continued even when lending growth slowed. Voluntary savings kept at the group level accounts for about one-third of total savings (Table 30).

_Outreach Depth._ P4K is the only one of the programs studied that explicitly targets low-income households. The program carefully screens applicants to ensure that all participating households have annual per capita incomes below the monetary equivalent of the price of 320 kilograms of rice (approximately Rp. 320.000 or LJS$137 in 1996 prices). Program documents claim that only about 15 percent of households nationwide have incomes at this level or below. When adjusted to 1993 Rupiah prices and compared to Indonesia's household expenditure survey, this is equivalent to virtually all program recipients having incomes in the bottom 25 percent of the income distribution for Central Java. It also implies that almost all participants have incomes as low or lower than the poorest one-third of PHBK program beneficiaries.

The program's average loan size is Rp. 155,000 (US$66). This is equivalent to about 7 percent of GDP per capita. Women's groups account for 38 percent of all P4K groups, and women account for about 50 percent of P4K beneficiaries. Approximately 60 percent of clients use loans for off-farm agricultural activities, 20 percent for home industries; and 20 percent for trading.

_Productivity._ The P4K program is relatively cost-effective as reflected by its unit costs and the reasonable interest rate required to eliminate all subsidies. Further, the subsidy figures detailed above include the start-up costs of expanding the program to new regions of the country and to first-time borrowers. It is likely that once the program has been rolled out nationwide and most target beneficiaries have received their first loan, required subsidy levels will decline significantly.

Nevertheless, it is possible that this program has a hidden cost for rural development. Ministry of Agriculture extension workers function as credit officers in this program.
(or program of study), we can see from Table 4 that the effect of FPE on student achievement is bounded between zero effect to 19.36 percentage points increase on students achievement. The upper bound slightly increases in this case, but not by a significant margin. More importantly, there are no noticeable heterogeneities by gender. This implies that FPE policy has affected academic performance of females and males equally. However, the effect of FPE varies by program of study. For instance, looking at column (2) results, we notice that, post-FPE, academic performance of students doing science and technology programs is 4 percentage points (i.e. 19 _._ 36 _−_ 15 _._ 13) lower than that of a typical student, this effect is significant at 5 percent. Further, we can see from column (4) results that the program decreased academic performance of those in the faculty of agriculture.

The faculties of agriculture, health sciences, and science and technology have exactly the same entry requirements, the most important being a good COSC pass in mathematics and sciences. Therefore, while these results seem to suggest that FPE-treated students are less prepared on mathematics and science subjects, they likely reflect the effect of discontinuing the bridging program for students into these faculties (or programs), known as the pre-entry science program (PESP), in 2012.<sup>15</sup> In the next section, I test the sensitivity of the results against this potential confounder.

# **6.3 Robustness checks**

In this section, I present a number of robustness checks’ results. First, I show that the results are less likely to have been driven by selection bias. Second, I present the placebo test results, using the pre-FPE data, assuming that FPE was introduced in 1999, such that the first FPE-treated cohort entered university in 2011. Third, I narrow the age window to include only students aged between 18 and 22 years old