rafmacalaba/datause-encoder-data huggingface dataset:
a CV of 55.76, 96.05, and -23.10 in Tables 2A, 2B, and 2C, respectively. By contrast, the corresponding CV for Profits2 is 0.85, 1.77, and 1.12, respectively; and for Profits3 is 1.13, 2.68, and 1.20, respectively. Not surprisingly, the correlation between Profits1 and either Profits2 or Profits3 is low, likely due to the substantial difference and adjustments between the measures. These findings on precision demonstrate that the AAA approach can meaningfully reduce noise in traditional profit measurement.

Finally, we compare the autocorrelation of AAA performance measures to traditional performance measures over time. Previous studies have found that traditional microenterprise sales and profit measures are quite noisy not only in the cross-section but also over time. To examine how our tool compares in this regard, we leverage two panel data sets of small firms in Rwanda and Uganda.<sup>7</sup> Table 3 presents the autocorrelation coefficients of the AAA and traditional sales and profits measures. Across both settings, we find that the AAA measures have higher autocorrelation over time. For sales, we observe small improvements in autocorrelation coefficients from 0.55 to 0.62 in Uganda, and from 0.53 to 059 in Rwanda. The improvements are particularly large for profits, from -0.05 (Profits1) to 0.34 (Profits2) to 0.41 (Profits3) in Uganda, and from 0.05 (Profits1) to 0.28 (Profits2) to 0.37 (Profits3) in Rwanda. Hence, for both sales and profits, we find consistently higher autocorrelation in measurement using the AAA tool over traditional measures.

# **3.3. Comparison with Self-Reported Profits**

As outlined in Section 2.4, the AAA tool does not record unaided self-reported profits. The first recording of profits is _after_ the respondent has gone through the detailed _Money In_ section of the survey. This aided recall measure (Profits2) is not directly comparable to the typical self-reported profits question that is commonly asked in surveys and which is predominantly unaided.

To draw a meaningful comparison between the AAA tool and self-reported profits, we explicitly collect and record this latter measure at the very beginning of the AAA tool in a sample of 59

> 7 These data sets were collected as part of baseline and endline data collections for evaluations of business interventions in Rwanda and Uganda. Specifically, in Uganda we use the baseline and endline data collected between 2015 and 2017 for our control group of entrepreneurs to remove any experimental treatment effect. In Rwanda, due to intervention delays, we collect baseline data twice for our group of entrepreneurs between 2016 and 2017 so no experimental treatment effect had been introduced at this stage. Table 1 includes summary statistics for the baseline sample in Uganda and for the second (most recent) baseline sample in Rwanda.

21
and restricts the expansion of public services. Figure 1 displays a cross-country comparison of Indonesia’s general revenue and government expenditure. In both cases Indonesia is below the average of its peers. According to the World Bank, 2020 " _The main reason for the low level of spending is the structurally low level of revenue collections. Indonesia’s revenue-to-GDP ratio is low at I4.6 percent in_ 2018 _, compared with the emerging economy average of 27.8 percent_ .

Revenue mobilization relies heavily on tax revenues, which have historically accounted for over 75% of the government’s total revenues. However, the country has experienced a decline in tax performance over the last two decades, evidenced by a significant drop in the tax-to-GDP ratio from 13.3% in 2008 to 9.1% in 2021. This decrease, even after a recovery post-pandemic, indicates a persistently low tax-to-GDP ratio compared to peers.<sup>5</sup> The World Bank identifies the compliance gap as the primary issue for the decline of revenue mobilization performance. It accounts for about 85% of the total revenue gap, or roughly 6.1% of GDP. The compliance gap arises from taxpayer non-compliance, including evasion, fraud, and avoidance, rather than policy decisions like rate reductions or exemptions. For instance, Indonesia’s audit performance is weaker than that of its peers, with fewer audits per capita between 2018 and 2020 and less tax liability generated from these audits when compared to peer countries. The system’s vulnerability to evasion and avoidance is exacerbated by an ineffective risk-based audit system, which has shown diminishing returns over time.

Improving audit performance, particularly in risk-based audits, is crucial for Indonesia to bolster its revenue mobilization. Following the deterrence model by Allingham and Sandmo, 1972, enhancing the probability of detection through more effective special and risk-based audits is key to inducing compliance and addressing the country’s revenue challenges.

5The figures presented in the last two paragraphs are from the central government revenue authority, which is different from subnational taxation, which is what we focus on in this paper. However, they reflect Indonesia’s structural problems with revenue mobilization that also occur at the local level.

12
26 years later than in other major world regions. The effects of high, relatively uncontrolled fertility on the health of mothers and children are increasingly well documented. The total fertility rate is over 6 for the region as a whole. A decline to 4 would not only slow the pace of population growth, but reduce maternal mortality by about one-third. The effect of reductions in family size on the health of children is not so easily quantified, but spacing children at least two years aIart would reduce infant mortality in Africa by an estimated 5% to 20%. In a region where one child in five does not live to see his or her fifth birthday, this would save many lives.

72. In this context, the lending pipeline for Africa is disappointing. A January 1988 memo from Mr. Jaycox to Mr. Conable on Bank population activities in Sub-Saharan Africa, mentioned seven PHN projects in FY89, four of which were to have population components. Only one of these projects (Benin) is still in the lending program. One (Tanzania) has now slipped to FY90, one (Zambia) has changed to reserve status, and one (Mali) has been dropped. On the other hand, one project with the population component (Nigeria -- Imo Health), has since been added to the Region's FY89 lending program. The situation is similar for FY90. Of the 12 projects with a population component, six have either slipped or been dropped.

73. Difficulties in the lending program arise from both external and internal constraints. In Botswana, for example, an IBRD country, project preparatio- **i** n all human resource areas is not receiving priority as the governmen, is unwilling to borrow at IBRD rates.

74. Many Bank staff are of the opinion that, despite external constraints, high-level Bank management could do more to initiate and support efforts in Africa -- particularly francophone Africa -- to impress upon (generally unwilling) governments, the importance of population. Policy dialogue takes a great deal of time and effort. In Ethiopia, for example, it was three years and over 240 staff weeks from project identification to approval. Initially, the authorities were extremely reluctant to discuss population, but in the end agreed to formulate a population policy.

75. Resource constraints affect our ability to develop substantial population components. Health projects usually do not require the prolonged and delicate policy dialogue that family planning-oriented projects require. Until this year, nearly all projects in Africa were joint health/population projects. In FY88, two of the five projects in Africa had no, or minimal, population components. Several of the projects in the five-year lending program are also special-purpose health projects -- e.g., national essential drugs (see para. 9). These health projects are vitally needed, but to the extent that projects are not "population-driven," the argument that we are 8 Senegal - 5%; Ghana - 17%; Kenya - 20%. World Fertility Survey data cited in 'Family Planning Saves Lives," 1986, Washington, D.C., The Population Reference Bureau.
Turnovsky, eds. Inequality and growth. Theory and Policy Implications. Cambridge: The MIT Press.

- Bourguignon, F., F.H.G. Ferreira, P.G. Leite (2002). _Ex-ante Evaluation of Conditional Cash Transfer Programs: The Case of Bolsa Escola_ . World Bank Policy Research Working Paper No. 2916.

- F. Bourguignon, F.H.G. Ferreira, N. Lustig (2005). A Synthesis of the Results. In: F. Bourguignon, F.H.G. Ferreira, N. Lustig, eds. (2005). _The Microeconomics of Income Distribution Dynamics_ . World Bank and Oxford University Press.

- Bussolo, M., J. Lay (2003). _Globalization and Poverty Changes: A Case Study on Colombia._ Working Paper No. 226, OECD Development Centre, Paris.

- Dias, G.L.S., C.M. Amaral (2002). Structural Change in Brazilian Agriculture, 1980-98. In: Baumann, R. (2002). _Brazil in the 1990s – An Economy in Transition_ . Palgrave.

- Elbers, C., J.O. Lanjouw, P. Lanjouw, P.G. Leite (2004). _Poverty and Inequality in Brazil: New Estimates from Combined PPV-PNAD Data_ . The Word Bank. Mimeo. Draft April 2004.

- Ferreira, H.G., P. Lanjouw, M. Neri (2001). _A Robust Poverty Profile for Brazil Using Multiple Data Sources_ . Paper Presented at the LACEA 2000 and ANPEC 2001 meetings.

- Ferreira, H.G.F., R. Paes de Barros (2005). The Slippery Slope: Explaining the Increase in Extreme Poverty in Urban Brazil, 1976-96. In: F. Bourguignon, F.H.G. Ferreira, N. Lustig, eds. (2005). _The Microeconomics of Income Distribution Dynamics_ . World Bank and Oxford University Press.

- Fiess, N.M., D. Verner (2003). _Migration and Human Capital in Brazil During the 1990s_ . World Bank Policy Research Working Paper 3093.

- Golan, A., G. Judge, D. Miller (1996). _Maximum Entropy Econometrics. Robust Estimation with Limited Data_ . John Wiley and Sons.

- Harrison, G.W., T.F. Rutherford, D. Tarr, and A. Gurgel (2003 _). Regional, Multilateral, and Unilateral Trade Policies of MERCOSUR for Growth and Poverty Reduction in Brazil_ . World Bank Working Paper 3051.

- Helfand, S.M. and G.C. De Rezende (2004). The Impact of Sector-Specific and Ecoonomy-Wide Reforms on the Agricultural Sector in Brazil: 1980-98. _Contemporary Economic Policy_ 22 (2).

- IBGE (Instituto Brasileiro de Geografia e Estatística) (1997). Anuário Estatístico do Brasil 57.

- Johnston, J., J. Di Nardo (1997). _Econometric Methods_ . 4<sup>th</sup> ed., McGraw-Hill.

- Kappel, R., J. Lay, S. Steiner (2005), Uganda: No More Pro-Poor Growth? _Development Policy Review_ , 23 (1).

- Long J.S., J. Freese (2001). _Regression Models for Categorical Dependent Variables Using STATA_ . STATA Press.

54
The economic viability analysis report provides the user with information on each particular constraint. The output explains why the project does not pass certain requirements. Figure 11 shows an example of the viability output.

Figure **11:** _INFRISK_ Economic Viability Output

|**Test Name**|**Test Result**|<br>**Maximum**<br>**Probability of**<br>**Being Below**<br>**Acceptable Level**|**Year of**<br>**First**<br>**Failure**|
|---|---|---|---|
|Dividend|failed|0.978|1996|
|IRR test|NA|||
|NPV|passed|0.000||
|Debt Service coverage ratio|failed|1.000|<br>1996|
|Interest coverage ratio|failed|0.924|<br>1996|
|Social Benefits|not selected|||

# **_Box 6: Computation of NPV and IRR in INFRISK_**

1. NPV. _INFRISK_ calculates NPV in according to the following equation:

<!-- Start of picture text --> NPV=Ec(_1) ES, + LS, +BS, + NCFj<br>(1 + r)N =' (1 + r)i+c<br><!-- End of picture text --> Where c and o are the respective number of construction and operation periods, and _ES;,=_ Equity allocation during 14h construction period _ES,_ = Loan allocation during _i_ construction period _BSj =_ Bond allocation during _i_ construction period _NCFi =_ Net cash flow associated with the project in **_ii"_** operating period _NCF =_ TOR-TOE-TAX+DEP (see also ANNEX 1) _r_ = a specified annual discount rate 2. IRR. The IRR function is closely related to NPV. It is the rate that equates NPV to a value of zero (from the point of view of the investor). However, the cash flow used for NPV and IRR in _INFRISK_ is not the same. For IRR we use the Equity on the negative side and the Dividend on the positive side of the equation. The solution is found using the Excel built-in function which employs an iterative method that clearly depends on starting values. Theoretically, there may be as many solutions as the power of the respective polynomial; however, we solve for a local solution close to the assumed discount rate. If no solution is returned by the Excel IRR function, we indicate this situation by printing N/A in the corresponding cell.

26
|United Kingdom|20.1|22.1|17.3|0.933|–|0.940|0.958|
|---|---|---|---|---|---|---|---|
|**ECA (country average)**|**18.0**|**21.1**|**25.9**|**0.904**|**90.3**|**0.932**|**0.945**|
|**ECA (population-weighted average)**|**18.4**|**22.5**|**25.6**|**0.894**|**91.4**|**0.927**|**0.938**|

_Source:_ Data on obesity, smoking, and alcohol consumption are from the European Health Interview Survey, <mark>Health Equity and Financial Protection Indicators,</mark> and the World Health Organization. The ECA average for the share of children not stunted is calculated based on countries for which data are available only.

_Note:_ HCI = Human Capital Index; ECA-HCI = Europe and Central Asia extension of the HCI.

- Not available.

- a. Includes consumption of smokeless tobacco.

# **_5. Estimation of the ECA-HCI_**

The ECA-HCI is the product of three components: The three components are defined as follows: The estimates of the ECA-HCI in table 8 show that countries in the region can achieve large increases in their long-run productivity if they reduce the distance between the expected educational attainment and adult health status of children born today and the benchmarks of complete education and full health. The average country for which the ECA-HCI is calculated has a value of 0.481, meaning that children born today in the average country in the region will be almost half as productive as they would have had they reached the benchmark of complete education and full health (14 years of basic education; 3.5 years of higher education; no obesity, tobacco smoking, or heavy drinking; no statistically significant child stunting; and 100 percent adult survival rate to age 60). The correlation between income levels and the ECA-HCI is positive, as it is for the original HCI (figure 7).

## **Figure 7. Estimates of ECA-HCI and country income levels**

22
Chilean parties, the Mexican PAN and PRD, the Brazilian Worker’s Party, and APRA in Peru. Moreover, the DPI tends not to code as programmatic those parties that are not organized to support a programmatic agenda, even though party leaders pursue one. Hugo Chávez’ _Partido Socialista Unido de Venezuela_ (PSUV), is _not_ coded as programmatic in the DPI, because it is organized around the personality of the leader, with little institutional basis of its own.

Other parties with a more uncertain programmatic content and that rely more on clientelism to mobilize support, such as the Peronists in Argentina or the National People’s Party in Ghana, are coded as programmatic. Again, however, this is consistent with the earlier discussion. In contrast to other clientelist parties, particularly in their regions, these parties have more sophisticated organizational arrangements to limit member free-riding. Compared to more loosely-organized clientelist parties, which are more reliant on the ability of individual politicians to mobilize votes, they also give members greater sway over leader selection – visible, not least, in the fact that these parties have experienced leadership transitions even when incumbents were still alive.

The DPI data on programmatic parties are not, however, sufficiently nuanced to classify parties according to the underlying strength of their organizational arrangements to enforce the party program. All purely programmatic parties that exhibit a high level of mutual control by members and leaders of each other’s potential shirking are certainly classified as programmatic. Similarly, all loosely organized associations of clientelist politicians are classified as non-programmatic. The differences between these two allow for the effects of programmatic parties to be identified from the data despite the presence of a noisier group of countries, such as those with machine parties that are coded as being 22
**English Knowledge Score:** This is measured the knowledge score of each student in the English test for each school.

**Total Knowledge Score:** This is measured by the knowledge score of each student averaged across both subjects for each school.

We also measure intermediate impacts variables, combined into Anderson indices: _resource utilization_ , including the ratio of PTRs between lower and upper primary, lower primary PTRs, and lower primary pupil-textbook ratios; _teacher management_ , including frequency of observation of teachers by head teachers and of use of rewards for teacher performance; _teacher effort_ , including teacher attendance and the share of teachers at a school who were found in the classroom on MLSS visit; assigned or marked homework during observed lessons; reported satisfaction with their job; or were above the median in terms of the amount of time they spent teaching on a typical day; _teaching practices_ , including whether teachers asked students questions, employed teaching aids, and employed local materials to help deliver lessons; _remedial classes_ , including the frequency of such classes and the share of grades in which they are offered; and _school cultures_ , including the existence of clear rules on bullying and the incidence of bullying by teachers and students. For more detail on the construction of these indexes and the underlying variables, see section A.0.2.

# 2.8 Data

We draw on data from the Malawi Longitudinal School Survey (MLSS). The MLSS is an independent, nationally representative survey that provides data on conditions, practices and outcomes in Malawi’s primary schools. The MLSS was designed to align with the MESIP project cycle to both provide information to the government on implementation and outcomes of the project and to support a number of impact evaluations of pilot interventions under the project, following rigorous experimental designs; as well as to inform general education policymaking. Visits are unannounced.<sup>12</sup> Data is collected through observations and interviews – for example, observations of school and classroom facilities; observation of lessons and teaching practices; interviews with head teachers, teachers, and members of community committees; and interviews and testing of Grade 4 students, as well as a longitudinal sample of students in later rounds.

The MLSS has its own randomly selected, nationally representative sample of 924 schools

> 12 The first visit to each school is unannounced to help capture the real situation of the school in terms of infrastructure, school performance, school and classroom management practices, student and teacher absenteeism and student learning outcomes. If required to complete all instruments, a second visit is made on an announced or pre-scheduled basis.

16
# **5. Summary**

## **_Exposure to natural hazard risk in urban areas is large and increasing, largely driven by economic opportunities in cities._**

The population in large cities exposed to cyclones is estimated to rise from 310 to 680 million between 2000 and 2050. And the urban population exposed in areas with significant probability of major earthquakes will increase from 370 million to 870 million over the same time period.<sup>15</sup> Even in the most hazard-prone cities, disaster risk is unlikely to reduce population growth, because the economic premium due to agglomeration economies and the amenity value of large cities dominate the location decisions of firms and people (World Bank 2008). Public policies aimed at slowing down the growth of hazard-prone cities are unlikely to succeed. On the other hand, reducing urban hazard risk through large scale mitigation infrastructure must carefully consider the dynamics of city demand. Building large protective infrastructure may make sense in rapidly urbanizing places that are attracting skilled workers and private investment, where land is scarce, and fiscal capacity is sufficient. They may not be justified where they protect stagnant or declining areas, as some have suggested is the case in New Orleans (Glaeser 2005). This applies to ex-ante investments as much as to the decision to rebuild. Sometimes, rather than “build back better”<sup>16</sup> the preferred strategy is “better build elsewhere.”

## **_The cope-mitigate-transfer framework of risk management also applies to different types and sizes of cities in a country’s urban system._**

All cities are not equal. We expect that city characteristics will influence the nature of response to disaster risk. So the priority policies will also be different for “successful”, large and rapidly growing cities compared to smaller towns and cities in lagging regions.

> 15 No estimates are currently available for flood risk. Global hazard distributions data focus on large rural floods, while a large share of flood damage in cities comes from localized floods often caused by inadequate drainage infrastructure.

> 16 See, e.g., Kennedy et al. 2008.

41
# **References**

- Alatas, V., Banerjee, A., Hanna, R., Olken, B. A., & Tobias, J. (2012). Targeting the Poor: Evidence from a Field Experiment in Indonesia. _American Economic Review_ , _102_ (4), 1206–1240. 5

- Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. _Proceedings of the National Academy of Sciences_ , _113_ (27), 7353–7360. Publisher: National Academy of Sciences Section: Colloquium Paper. 3, 17, 18

- Bergolo, M., Ceni, R., Cruces, G., Giaccobasso, M., & Perez-Truglia, R. (2019). Tax Audits as Scarecrows: Evidence from a Large-Scale Field Experiment. SSRN Scholarly Paper ID 2979096, Social Science Research Network, Rochester, NY. URL `https://papers.ssrn.com/abstract=2979096` 3, 53

- Bertrand, M., Crépon, B., Marguerie, A., & Premand, P. (2017). _Contemporaneous and Post-Program Impacts of a Public Works Program_ . Other papers. World Bank. URL `https://elibrary.worldbank.org/doi/abs/10.1596/28460` 5

- Brockmeyer, A., Smith, S., Hernandez, M., & Kettle, S. (2019). Casting a Wider Tax Net: Experimental Evidence from Costa Rica. _American Economic Journal: Economic Policy_ , _11_ (3), 55–87. 4, 7, 14, 15, 16

- Bruhn, M., & McKenzie, D. (2009). In Pursuit of Balance: Randomization in Practice in Development Field Experiments. _American Economic Journal: Applied Economics_ , _1_ (4), 200–232. 10

- Carrillo, P., Pomeranz, D., & Singhal, M. (2017). Dodging the Taxman: Firm Misreporting and Limits to Tax Enforcement. _American Economic Journal: Applied Economics_ , _9_ (2), 144–164. 4, 7

- Castro, L., & Scartascini, C. (2015). Tax compliance and enforcement in the pampas evidence from a field experiment. _Journal of Economic Behavior & Organization_ , _116_ (C), 65–82. Publisher: Elsevier. 2, 5

- Cohen, I. (2020). Low-Cost Tax Capacity: A Randomized Evaluation with the Uganda Revenue Authority. _Working Paper_ . 4

- Collin, M., Di Maro, V., Evans, D. K., & Manang, F. (2021). Property Tax Compliance in Tanzania. _Working Paper_ . 4

- Dal Bó, E., Finan, F., Li, N., & Schechter, L. (2021). Information Technology and Government Decentralization: Experimental Evidence From Paraguay. _Econometrica_ , _89_ , 677–701. 5, 53

- Dalton, A. G., Manning, L. A., Jamison, J. C., Sen, I. K., Karver, J. G., Castaneda Nunez, J. L., Guedes, L. P., & Mujica Estevez, S. B. (2019). Behavioral Insights for Tax Compliance. Tech. Rep. 144398, The World Bank. 7

- Davis, J., & Heller, S. (2017). Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs. _American Economic Review_ , _107_ , 546–550. 5, 18

21