rafmacalaba/datause-encoder-data huggingface dataset:
compliance and expands this literature in two ways.<sup>7</sup> First, unlike many experiments that give a generic nudge or reminder to taxpayers, this paper (specifically, experiment one) leverages a significant real investment in the identification capacity of the government. Second, whereas most papers in the literature can only study the immediate impact of a notice, this paper is able to show that the impact of identification and penalty information on compliance persists over three years.<sup>8</sup> The rest of the paper proceeds as follows. Section 2 provides background information on Liberia and the administration of property taxes, while section 3 outlines a simple conceptual framework for thinking about these three aspects of tax capacity on tax compliance. Sections 4 and 5 describe the design of the two experiments and present the corresponding results. Section 6 concludes and provides policy implications.

# **2 Context**

Liberia has low compliance rates for real estate taxes (as well as other tax categories). Prolonged civil conflict in the 1990s and early 2000s led to the erosion of state capacity across several sectors, including tax administration. In 2018, the real estate tax register contained fewer than 7,000 properties in Greater Monrovia, the largest urban metropolis in Liberia, with over 1 million residents. Even among properties on the tax register, timely tax collection remains a challenge. After taxpayers enter the tax base in one year, over 60 percent of them do not pay taxes in the subsequent year (according to the author’s analysis of administrative tax data from 2015 to 2018). Many property owners who pay are motivated by the desire to obtain a tax clearance needed for administrative purposes in another sector, such as to bid for a contract with the government or to import goods at the port.

Several factors related to weak identification and collection capacity contribute to the low real estate tax compliance. In the absence of a cadaster that outlines all the properties in the city, the LRA adds properties to the tax register on a case-by-case basis, resulting in very low coverage, with about 5 percent of taxable properties on the tax register. Further,

> 7 Mascagni (2018), Pomeranz and Vila-Belda (2019), and Slemrod (2019) provide reviews of this literature.

> 8 Important exceptions are Brockmeyer et al. (2019) in Costa Rica and De Neve et al. (2021) in Belgium, who find sustained impacts of messages on tax compliance over two to three years.

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Rodriguez, F. and D. Rodrik. (1999) “Trade Policy and Economic Growth: a Skeptic’s Guide to the Cross-National Evidence”, NBER Working Papers 7081, National Bureau of Economic Research, Inc Singh, I., L. Squire and J. Strauss, eds. (1986). _Agricultural Household Models, Extensions, Applications and Policy_ . The World Bank and The Johns Hopkins University Press.

Taylor, J.E. and I. Adelman. (1996). Village Economies: The Design, Estimation, and Use of Villagewide Economic Models. Cambridge University Press.

Topel R. H. (1986). “ _Local labor markets_ ” Journal of Political Economy; 94, Part 2:S111-S143 Valdés, A. (2002). “Agricultural price distortions and price transmission in developing and transition countries.” OECD Global Forum On Agriculture: Agricultural Trade Reform, Adjustment And Poverty, Paris, 23-24 (May), CCNM/GF/AGR(2002)7.

Winters, L. A. (2002). “Trade liberalisation and poverty: what are the links?” _World Economy (U.K.);_ 25, No. 9:1339-67.

Yang J. (1997) “Exchange rate pass-through in U.S. manufacturing industries” _Review of Economics and Statistics_ ; 79:95-104 Yunez-Naude A. (2002). “Lessons from NAFTA: The Case of Mexico’s Agricultural Sector”. World Bank Report.

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# **4. Cost of Issuances and Role of Investors**

In this section, we first study whether the supply side of financing (investors) was a force behind the observed acceleration in domestic bonds issuances by East Asian firms. We do so by analyzing the cost of issuances. We focus on the period after 2008 because, during those years, domestic bond issuances grew the most, domestic markets became the main source of bond financing for firms, and a significant number of new and smaller firms started to borrow bonds.

Movements in the cost of issuances are informative because a decline in the cost of capital suggests that the liquidity provided by investors grew faster than firms’ demand for new financing.<sup>16</sup> We also compare trends in the cost of issuance in domestic debt markets with those in other debt markets. In particular, we use international bond markets and (domestic and international) syndicated loan markets. This comparison can provide insights on whether the observed patterns were economywide or specific to some financial markets.

To measure the change in issuance costs across debt markets after the GFC, we run regressions estimating the change in the yield to maturity for bonds and in the spread over LIBOR for syndicated loans between 2000-07 and 2008-16. The regressions include a dummy for the period 2008-16 and several controls: public or private issuance, ownership of issuer, type of coupon, currency, maturity fixed effects, and economy-industry fixed effects.<sup>17</sup> The estimates show that East Asian firms issued corporate bonds in 2008-16 at yields about 39 percent lower than in 2000-07 (Table 5). Moreover, these regressions show that yields declined more for domestic than for international issuances. In particular, yields of domestic and international bonds declined by about 42 and 29 percent, respectively, between 2008-16 and 2000-07, at the same

> 16 An increase in demand (albeit slower than the increase in supply) could still have occurred (di Giovanni et al., 2019). 17 We do not collapse these regressions at the economy-industry level given the large number of controls. These regressions exclude China because of its large weight in the issuance data. But including China does not change the results.

17
SYS-REVIEW/HI-LMIC SHI has also been mandated for formal sector workers in a number of developing countries. To achieve universal health care coverage, an institutional structure that emphasizes payment to providers for services delivered has been offered to those beyond the formal workforce in Vietnam, Nigeria, Tanzania, Ghana, India, and China over the last 15 years. Although schemes for the poor may share the same administrative structure as SHI for the formal sector, the former usually offer a reduced package or restrictions on providers. Alternatively, we find freestanding schemes (separate from SHI) that offer financial protection to the poor through subsidized, usually voluntary household enrollment into a defined benefits arrangement (Wagstaff 2009). Insurance is offered at premiums that are considerably below the actuarially fair price. Given that most employment in low- and middle-income countries is informal, governments manage compulsory insurance in the formal sector with limited avenues to crosssubsidize the informal sector. Thus, health insurance is tax financed, although funding for it may be ring-fenced.

Starting from a point of low utilization by the formal sector, both free care and prepayment of financing for care are implemented to encourage use for illness or to increase contact with health workers to facilitate better delivery of preventive care. From the perspective of the user, prepayment insurance schemes, whether highly subsidized or zero-entry fee, should be understood differently than free care. First, even if the entry fee is small, some households may not be able to afford the fee. For example, Sparrow, Suryahadi, and Widayanti (2008) report that in Indonesia, it may be costly to provide photos of household members for insurance cards. Second, at the point of care, there may be copayments to limit frivolous care-seeking behavior because of the extremely low marginal cost of seeking care if zero copayments prevail.

4
sections we rely more heavily on the ITT analysis, as the cleanest estimate of the impact of being assigned as a treatment school for receiving textbooks.

# **7. Overall Impacts**

At the school level, we find no impact of the program on student enrollment. We do find, however, that the likelihood of parents complaining to head teachers about the lack of textbooks is substantially lower in treatment schools (Table 4).

Information on teaching practices was collected through interviews with teachers for Grades 4 and 5 at endline. These impacts are summarized in Table 5, wherein each teacher interview is a distinct observation and standard errors are clustered at the school level. We find that the program had no impact on the likelihood of a teacher assigning homework from textbooks (Table 5, Columns 1 and 2). This is most likely because the reported propensity to assign homework from textbooks is fairly high even in control schools (93 percent). One may question how teachers are able to assign homework from textbooks if very few students have access to the textbooks. Anecdotal evidence suggests that this is very common: Teachers copy textbook questions onto the blackboard and ask students to copy them down and answer them at home as a part of their homework. Likewise, the program did not influence the likelihood of physical punishment for students found mistreating textbooks (Columns 3 and 4).

We also find that the program did not impact the likelihood of a teacher producing pamphlets (informal notes or study guides) for students (Columns 7 and 8). However, we do find significant positive impacts of the program on the likelihood of a teacher having a lesson plan (Columns 5 and 6).

Student attendance data were collected through unannounced visits to classrooms (Grades 4 and 5) at endline. We see no pattern of improved attendance, although we do observe a positive significant effect on girls’ attendance in grade 5. No other discernible program impacts on student attendance by grade or by student gender are found (Table 6).

These unannounced classroom visits also yielded data on teacher activity (Grades 4 and 5) in class. The program did not affect the likelihood of finding a teacher in class but did lead to an increased likelihood of finding a teacher in class _teaching_ (Table 7). Note that in the control group about 18 percent of the teachers were absent from the classroom at the time of the visit; however, of the teachers in class, only 57 percent were found to 12
Figure 5: LIC policy frameworks (A) Countries meeting DeMPA minimum requirements, select categories

- (B)  Countries meeting DeMPA minimum requirements, select categories

<!-- Start of picture text --> Number of countries Number of countries<br>12 Most recent DeMPA 12<br>Most recent DeMPA<br>10 1st DeMPA 10<br>1st DeMPA<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>(C) Central bank transparency index (D) Exchange rate regimes<br>Index Number of LICs<br>6 LICs Other EMDEs 35 Flexible Pegged<br>5 30<br>25<br>4<br>20<br>3<br>15<br>2<br>10<br>1<br>5<br>0 0<br>Legal Structure DMS Audit with FP with MP Domestic Borrowing External Borrowing LGLD CFF and CBM SD, SC and BCP Debt Records<br> framework Managerial DA and DS<br>Evaluation and Reporting Coordination Coordination<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015<br><!-- End of picture text --> Source: Bloomberg, Debt Management Performance Assessments (DeMPA), Dincer and Eichengreen (2014), International Monetary Fund, Shambaugh (2004), World Bank.

A.B. BCP=Business Continuity Planning; CBM=Cash Balance Management; CFF=Cash Flow Forecasting; DA=Debt Administration; DMS=Debt Management Strategy; DS=Data Security; FP=Fiscal Policy; GLD=Loan Guarantees, On lending Derivatives; MP=Monetary Policy; SD=Segregation of Duties; SC=Staff Capacity. Sample covers 17 low-income countries.

C. Unweighted averages. The range of the index is from 0-15, 0=least transparent and 15=most transparent. D. De facto exchange rate regime from the Exchange Rate Regime Classification of Shambaugh (2004) is used to determine whether a country has a pegged or flexible exchange rate. The original classification has four categories: “1” reflects no fluctuation at all, “2” indicates movements within 1 percent bands, “3” indicates movements within 2 percent bands, and “4” indicates a one-time devaluation with 0 change in the remaining 11 months of the year. Shambaugh (2004) assesses these movements against relevant base currencies. The constructed dummy variable indicating a pegged exchange rate regime was defined to equal 1 for countries classified as 1, 2, 3, or 4. A value of 0 is assigned to flexible exchange rates—i.e., exchange rates that routinely fluctuate outside a 2 percent band. Based on 31 LICs.

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This macro picture is also supported by micro data. The Moroccan High Commission for the Plan (HCP) estimates that household consumption in real terms has increased at an average rate of 2.8 percent a year between 1999 and 2007 as compared to -1.8 percent for the previous decade. The same institution estimates that the poverty rate has declined at an annual rate of -7.3 percent as compared to a rate of +2.8 percent for the previous decade.<sup>6</sup> Comparing the 2001 and 2007 household consumption surveys shows that the poverty rate has declined from 15.3% to 8.9% and this has resulted in an effective reduction in the number of the poor from 3.4 million in 2001 to 2.7 million in 2007. In sum, one could argue that growth over the past decade has been pro-poor as well as pro-rural areas vis-à-vis the previous decade (Figure 4).

**Figure 4 – Official Poverty Rates Estimates 1991-2007**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>Poverty rate (%)<br>8<br>Number of poor (m.)<br>6<br>4<br>2<br>0<br>1991 1994 1999 2001 2004 2007<br><!-- End of picture text --> Source: HCP (2012). 1994 and 2004 are population census estimates made with poverty mapping techniques.

This change in fortunes for the poor can be traced back to a combination of public policies that have been implemented over the years. Starting from the mid-1990s, the government has launched ambitious macroeconomic reforms coupled with stabilization measures. The macroeconomic policies include regulatory and institutional improvements to attract foreign investments, price liberalizations, acceleration of the privatization process, better competition laws, a better framework for SME development and a progressive opening of the economy to global trade. The country joined the WTO and signed bilateral trade agreements with the US, the EU, and several non-EU Mediterranean countries. Stabilization policies aimed at consolidating public finance, controlling inflation, and reducing the debt/GDP ratio. All of these objectives have been largely achieved by 2010. A large program of

> 6 The poverty line in 2007 was set at 3,834 MAD per person per year for urban areas and at 3,569 MAD per person per year for rural areas. In 2007, this was equivalent to 1.25 USD per person per day in urban areas and 1.16 USD per person per day in rural areas (non-PPP).

7
US market (of some 4 percent). The latter is driven by the fact that nondiscriminatory liberalization by the US more than offsets any erosion losses. Cameroon, Cote d'Ivoire, Kenya, Senegal and Zimbabwe account for 85 percent of the total predicted loss. Yeats predicts that sub-Saharan Africa will lose more exports through the erosion of preferential treatment than it will gain from MFN trade liberalization. Page and Davenport (1994) come to similar conclusions, with predicted reductions in ACP and LDC exports on the order of 1-2 percent, with Ethiopia (5.9 percent), Malawi (5.3 percent), Guyana (4.8 percent), Mozambique (4.6 percent), and Jamaica (3.2 percent) experiencing larger impacts (OECD, 1997).

The limited number and small size of most of the economies concerned imply that measures to help mitigate the impact of preference erosion should be closely targeted at the countries at risk. The detailed assessment by Alexandraki and Lankes (2004) indicates that many of the countries concerned are not among the poorest, but are middleincome economies. They include sugar, textiles and banana programs in their analysis. Indeed, the IMF analysis and Steven and Kennan (2004) suggest that the potential erosion problem is heavily concentrated in (small island) economies that are dependent on quotatype preferences and the associated rents in these sectors.<sup>35</sup> The problem is therefore also commodity-specific since they are concentrated in areas where OECD protection and preference margins are the highest. As stressed by Tangermann (2002), insofar as preferences have had the result of “sucking” investment and production into the wrong direction (i.e., areas where they do not have a comparative advantage), the effect has been to make the beneficiaries dependent on preferential exports. As the sugar and banana regimes are reformed in the EU, such countries will be confronted with major adjustment costs.

> The solution to this problem is not to maintain (or expand) the existing preferences but to ensure that the countries concerned are assisted. An appropriate response would be for the OECD countries to convert the (implicit) “preference

> 35 Preference dependent or sensitive countries include Mauritius, Malawi, Mauritania, Cambodia, Maldives, Haiti, Cape Verde, Sao Tome, Tanzania and the Comoros (Stevens and Kennan, 2004). The only large country expected to suffer from preference erosion is Bangladesh, which has benefited significantly from the quota restrictions imposed on other large competitive developing countries such as China. It can be noted that the potential impact on non-quota controlled textile and clothing exporters of removal of quotas has been on the agenda since the negotiation to end the MFA in the Uruguay Round (Page and Davenport 1994).

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# **Annex 1: Collection of Data on Urban PPI**

Unless otherwise noted, figures quoted on investment in infrastructure projects with private participation have been taken from the World Bank’s Private Participation in Infrastructure Project Database (http://ppi.worldbank.org/). Investment figures are investment in facilities (as provided in the database) and thus do not include divestiture transactions or any other transfer of assets (all figures are in units us$ M) . Figures are gross of government investment contributions. While these are not quantified consistently in the database, some studies indicate the 10-15% of total investment captured in the database is actually provided by the government. (PPIAF:2005:8) Projects are considered to have private participation if a private company or investor bears a share of the project's operating risk. ) The information in the database is updated yearly through a comprehensive review of activity in each of the low- and middle-income economies using commercial news databases, specialized and industry publications and internet resources such as web sites of developers, sponsors, and regulatory agencies. If necessary, information is also requested from or verified with project companies, developers and sponsors and regulatory agencies.

# **Method for Identifying Urban PPI within the PPI database:**

We created an urban subset that consisted of all the projects in the PPI database that cover urban infrastructure, viz. water and sanitation, urban transport, municipal solid waste, street lighting, etc. Excluded are sectors such as telecomm and power, which while used widely in urban areas, are not local city-based infrastructure services. Unfortunately, there is no coverage of municipal solid waste in the database, and while this omission could be material, it is beyond the scope of this paper to address it.

We examined sectors such as water and transportation in more detail, because these sectors may include a mix of both urban and other infrastructure. To do this, we obtained detailed descriptions for each project in the water and transport sectors. Within transport, we excluded ports and airports, and focused on the road and rail subsectors. For water projects, we searched for rural projects, and found very few references to rural water, and no self-standing rural projects. Hence all water projects were included as urban. For urban transport, we excluded any projects that appeared to be inter-city or long haul rail. We also excluded inter-city highway projects, and focused only on roads such as expressways and ring roads that served urban areas. We included city bypasses, even when these were managed by a higher level national or provincial authority, on the grounds that these substantively serviced urban traffic, even if inter-city traffic also benefited. When the information was not complete enough to determine whether roads projects fell into one or the other category, we assumed that highway projects with capacity (provided in kilometers) less than 50 kilometers would be classified as urban. Some of the transactions recorded in the database involve two different sectors, such as 24
following scoring guidelines. Score 1: Output information is absent or so vague it is impossible to assess completion; Score 3: Output information is given but there is some ambiguity over whether the target was met; Score 5: Output information is clear and unambiguous.

**Completion Status** _How did actual achievement compare to the target?_ Coders had to answer on a 1-5 scale (where integers and half values were permitted) using the following scoring guidelines. Score 1: No action was taken towards achieving the target; Score 3: Some substantive progress was made towards achieving the target. The output is partially complete and/or important intermediate steps have been completed; Score 5: The target for the output has been reached or surpassed.

**Completion Remarks** _Were any challenges/ obstacles mentioned? Coders could select all that applied from the following:_ (i) awaiting action from another division, organization or stakeholder; (ii) 2 = Procurement/sourcing delay or problem; (iii) Sequencing issue (can’t start until another output has been completed); (iv) Lack of technical knowledge to complete activity; (v) Delayed/ non-release of funds; (vi) Unexpected event; (vii) Activity not due.

There are at least two coders per project. Given the tendency for averaging scores to reduce the measured variation, we use the maximum and minimum scores to code whether projects are fully complete/never initiated respectively. We show robustness of our main result to alternative methods by which to combine codings.

# **A.3.1 Nigeria: Measuring Projects and Project Completion**

The output data for the RR study based on the Federal Civil Service in Nigeria exploited the following historical event. In 2006/7 the Nigerian Government undertook the Overview of Public Expenditure in NEEDS (the ‘OPEN initiative’), in which it traced, by project, the use and impact of a representative sample of 10% of federal social sector expenditures approved in 2006/7. Under the OPEN initiative, expert teams visited projects to record their implementation. This monitoring process was independent of civil servants: projects were evaluated by teams of independent engineers and civil society.<sup>25</sup> Monitoring teams visited project sites 18 months after projects were approved. The projects studied in RR had 12 month completion schedules. RR hand-coded the material from all projects recorded in OPEN initiative reports from 63 federal civil service organizations, covering 4721 projects. 11 project types are covered (road, borehole, training etc.) with boreholes being the modal project type, and 75% of projects relating to small-scale infrastructure.

> 25A system of checks and balances were further put in place to underpin the credibility of the initiative. First, a centralized team of technocrats monitored the evaluation teams, providing them with training and opportunities for standardization of their methods. Second, evaluators were asked to provide material/photographic/video evidence to support their reports. Third, random checks were performed on evaluated sites.

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