rafmacalaba/datause-encoder-data huggingface dataset:
in the sense that they had never paid the tax, this sample differs in that they had paid tax at least once, but had overdue taxes for one year or more. Their prior compliance suggests their ability to overcome barriers to compliance and thus makes them a more practical focus for targeted enforcement action. Similar to experiment one, this sample also excluded property owners with the highest tax liabilities (above US$1,000) as the LRA prioritized them for enforcement given their revenue potential. In practice, taxpayers below this threshold often effectively fall out of the radar of the tax authority and do not receive significant enforcement attention.

To locate these delinquent properties, a verification team was dispatched with the name of each owner and the property address on file. The task of the team was to go to the broad location indicated on the property registration record (e.g., “18th Street” or “Catholic Hospital community”) and ask around in the community to identify the property. Once the property was verified, the team was to capture the GPS coordinates of the property and a photograph using a form on a mobile device. Of the 1,382 properties the team attempted to locate, 728 were identified.

Property owners were randomized into two groups. The control group received a notice requesting the payment of delinquent tax liability. The notice was comparable to the combined identification and penalty notice in experiment one: it was personalized to include the name of the property owner and a photograph of the property and also stated the penalties for noncompliance. The treatment group received a similar notice (the _enforcement notice_ ) with one addition: it included a sentence informing property owners that their property had been included in the current batch of properties designated for intensive follow-up and, if they did not respond by the deadline, that the LRA would proceed to the next stage of enforcement actions. Figure A2 provides an image of the notice.

The randomization was stratified by two variables: location (communities across the city were divided into three broad areas) and payment history (whether the individual had only one year or more than one year of unpaid taxes). These variables were considered to be potential dimensions of heterogeneity in the responses of taxpayers to the intervention.

The properties were located in October and November 2018, and the treatment notices 18
# **S1 Data**

## **S1.1 Harmonized Household Data Sets**

### **Sources and Geographic Coverage**

The main data source in sections 2 to 6 of this paper is the Socioeconomic Database for Latin America and the Caribbean (SEDLAC), a harmonized data set of Latin American and Caribbean household surveys compiled by the World Bank and the Center for Distributive, Labor and Social Studies (CEDLAS, for its acronym in Spanish) at the Universidad Nacional de La Plata in Argentina. The analysis includes 16 Latin American countries for 1995-2015. Table S1.1 provides descriptions of the household surveys used for the analysis. For 2015, there are 14,908,680 observations, representing 87.54 percent of the Latin American workingage population.

In some countries, the geographic coverage of the household surveys has changed over time. To ensure comparability, the data were restricted to a common geographical coverage per country. In the case of Argentina, the analysis refers to the following areas: Gran Buenos Aires, Ciudad de Buenos Aires, Partidos del GBA, Gran La Plata, Gran Santa F ́e, Gran Paran ́a, Cdro. Rivadavia – Rada Tilly, Gran C ́ordoba, Neuqu ́en – Plottier, S.del Estero - La Banda, Jujuy - Palpal ́a, R ́ıo Gallegos, Salta, San Luis - El Chorrillo, Gran San Juan, Santa Rosa - Toay, and Ushuaia - R ́ıo Grande. For Brazil, the rural areas of Randonia, Acre, Amazonas, Roraira, Par ́a, and Amap ́a were excluded for 2002-15, since they were not surveyed pre-2002. For Colombia, between 2001 and 2005, the survey covered 13 regions: Antioquia, Atl ́antico, Bogot ́a, Bol ́ıvar, Caldas, C ́ordoba, Meta, Nari ̃no, Norte de Santander, Risaralda, Santander, Tolima, and Valle del Cauca. The sample has been restricted to these areas for 2008 to 2015. Finally, for Uruguay, the 2006-15 sample was restricted to Montevideo and all localities with more than 5,000 inhabitants, which were the only areas covered before 2006.

### **Circa Concepts**

The analysis often draws comparisons between 1995, 2002, and 2015. However, survey data are not available for all the countries for these three years. A circa concept was applied when these years were not available. Data for 1995 were approximated by data for 1996 for Chile and Mexico; 1997 for Bolivia, Paraguay, and Peru; and 1998 for Nicaragua. Data for 2002 were approximated by data for 2004 for Bolivia and Mexico, and 2005 for Nicaragua. Data for 2015 were approximated by 2014 in Argentina, Guatemala, Mexico, and Nicaragua. In addition, for analysis that required harmonized information for occupation categories, data for 1995 were approximated by data for 1999 for Bolivia, and data for 2015 were approximated 45
- Fay, Marianne, Hyoung Il Lee, Massimo Mastruzzi, Sungmin Han, and Moonkyoung Cho. 2019. “Hitting the Trillion Mark: A Look at How Much Countries Are Spending on Infrastructure.” Policy Research Working Paper, WPS 8730, World Bank.

- Grandes, Martin, Damien Tupac Panigo, and Ricardo Aníbal Pasquini. 2017. “Corporate Credit Spreads and the Sovereign Ceiling in Latin America.” _Emerging Markets Finance and Trade_ 53 (5): 1217–1240.

- Grimsey, Darrin, and Mervyn K. Lewis. 2017. _Global Developments in Public Infrastructure Procurement: Evaluating Public-Private Partnerships and Other Procurement Options._ Cheltenham: Edward Elgar.

- Guasch, J. Luis. 2004. _Granting and Renegotiating Infrastructure Concessions: Doing It Right._ WBI Development Studies, Washington, DC: World Bank.

- Hamilton, James D. 2018. “Why You Should Never Use the Hodrick-Prescott Filter.” _Review of Economics and Statistics_ 100 (5): 831-843.

- Harris, Clive, and Kumar V Pratap. 2009. _What Drives Private Sector Exit from Infrastructure? Economic Crises and Other Factors in the Cancellation of Private Infrastructure Projects in Developing Countries._ Gridlines, No. 46, Washington, DC: World Bank.

- Hart, Oliver. 2003. “Incomplete Contracts and Public Ownership: Remarks, and an Application to Public-Private Partnerships.” _Economic Journal_ 113 (486): C69–C76.

- Herrera Dappe, Matias, Martin Melecky, and Burak Turkgulu. 2022. _PPP Distress and Fiscal Contingent Liabilities in South Asia._ Background paper for Hidden Debt, Washington, DC: World Bank.

- IMF (International Monetary Fund) and World Bank. 2019. “PPP Fiscal Risk Assessment Model: PFRAM 2.0 User Manual.”

https://library.pppknowledgelab.org/documents/5783/download.

- Iossa, Elisabetta, and David Martimort. 2012. “Risk Allocation and the Cost of Public-Private Partnerships.” _RAND Journal of Economics_ 43 (3): 442–474.

- ———. 2015. “The Simple Microeconomics of Public-Private Partnerships.” _Journal of Public Economic Theory_ 17 (1): 4–48.

- Irwin, Timothy C. 2007. _Government Guarantees: Allocating and Valuing Risk in Privately Financed Infrastructure Projects._ Directions in Development: Infrastructure, Washington, DC: World Bank.

- Laeven, Luc, and Fabian Valencia. 2020. “Systematic Banking Crises Database II.” _IMF Economic Review_ 68: 307-361.

- Lambert, Paul C., and Patrick Royston. 2009. “Further Development of Flexible Parametric Models for Survival Analysis.” _Stata Journal_ 9 (2): 265–290.

32
||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|||Height-for-a|ge z-scores|||
|_Mother's Education_|||||||
|Did not finish Primary School|0.026|0.024|0.027|0.019|0.026|0.024|
||(0.114)|(0.115)|(0.115)|(0.114)|(0.115)|(0.115)|
|Finished Primary School|0.116|0.117|0.116|0.109|0.113|0.099|
||(0.254)|(0.255)|(0.255)|(0.254)|(0.256)|(0.256)|
|Did not finish Secondary|||||||
|<br>School|-0.052|-0.049|-0.053|-0.063|-0.048|-0.082|
||(0.222)|(0.222)|(0.224)|(0.222)|(0.222)|(0.222)|
|Finished Secondary School|-0.195|-0.199|-0.194|-0.215|-0.194|-0.198|
||(0.418)|(0.414)|(0.418)|(0.419)|(0.415)|(0.414)|
|Higher than Secondary School|0.901***|0.904***|0.905***|0.850**|0.909***|0.878**|
||(0.339)|(0.339)|(0.339)|(0.343)|(0.339)|(0.343)|
|Piped water|0.059|0.062|0.059|0.054|0.066|0.027|
||(0.247)|(0.245)|(0.248)|(0.247)|(0.247)|(0.248)|
|Latrine|-0.063|-0.063|-0.057|-0.044|-0.064|-0.052|
||(0.193)|(0.195)|(0.197)|(0.197)|(0.196)|(0.195)|
|No toilet|-0.143|-0.137|-0.145|-0.114|-0.154|-0.124|
||(0.242)|(0.244)|(0.245)|(0.246)|(0.245)|(0.244)|
|Asset index 2|0.267***|0.269***|0.263**|0.234**|0.270***|0.250**|
||(0.102)|(0.102)|(0.102)|(0.105)|(0.103)|(0.103)|
|Asset index 3|0.487***|0.489***|0.487***|0.429**|0.498***|0.460***|
||(0.178)|(0.175)|(0.175)|(0.182)|(0.177)|(0.177)|
|Rural|-0.344**|-0.349**|-0.351**|-0.341**|-0.358**|-0.332**|
||(0.161)|(0.161)|(0.162)|(0.160)|(0.162)|(0.162)|
|Dodoma|-0.199|-0.186|-0.201|-0.170|-0.180|-0.226|
||(0.596)|(0.598)|(0.599)|(0.591)|(0.598)|(0.595)|
|Arusha|0.018|0.011|0.050|0.016|0.068|0.016|
||(0.620)|(0.620)|(0.622)|(0.616)|(0.623)|(0.620)|
|Kilimanjaro|0.338|0.309|0.335|0.329|0.355|0.282|
||(0.612)|(0.613)|(0.610)|(0.609)|(0.614)|(0.610)|
|Tanga|0.207|0.196|0.208|0.169|0.242|0.167|
||(0.605)|(0.595)|(0.597)|(0.599)|(0.602)|(0.593)|
|Morogoro|0.206|0.186|0.205|0.199|0.237|0.171|
||(0.604)|(0.603)|(0.603)|(0.599)|(0.604)|(0.600)|
|Pwani|0.174|0.157|0.173|0.176|0.193|0.124|
||(0.681)|(0.673)|(0.670)|(0.666)|(0.672)|(0.670)|
|Dar es salaam|-0.025|-0.029|-0.030|-0.042|0.011|-0.096|
||(0.576)|(0.572)|(0.575)|(0.570)|(0.580)|(0.571)|
|Lindi|0.064|0.061|0.063|0.093|0.084|0.032|
||(0.599)|(0.595)|(0.595)|(0.590)|(0.597)|(0.593)|
|Mtwara|<br>-0.171|<br>-0.181|<br>-0.173|<br>-0.164|<br>-0.139|<br>-0.194|

95
# **<mark>Table 1 - Summary Statistics</mark>**

Panel A exhibits macro level data of states and municipalities. Panel B presents statistics on the fiscal balance components of local governments. Panel C presents a series of statistics of bank lending to local governments. See Table A1 in the Appendix for variable definitions.

|governments. See Table A1 in t|he Appendi<br>Obs.|x for varia<br>Mean|ble definition<br>p10|s.<br>Median|p90|Std. Dev.|
|---|---|---|---|---|---|---|
|**Panel A. Economic Indicators**<br>|**of States/M**|**unicipali**|**ties**||||
|DebtStates,16Q1|30|0.86|0.20|0.64|2.25|0.70|
|Maturitys,16Q1|30|14.6|8.3|14.2|19.3|3.9|
|DebtBankMunim,Mar16|984|0.24|0.02|0.34|0.37|0.15|
|DebtMunicipalitym,16Q1|1,083|0.03|0.00|0.00|0.08|0.05|
|Employment|434|0.006|-0.021|0.006|0.033|0.022|
|- Primary|434|0.004|-0.13|0.003|0.141|0.109|
|- Secondary|434|0.011|-0.049|0.009|0.074|0.05|
|- Tertiary|434|0.006|-0.028|0.006|0.039|0.028|
|GDPs,q|496|0.008|-0.05|0.009|0.062|0.039|
|Povertys,y|155|0.43|0.28|0.42|0.64|0.14|
|Poverty – Moderates,y|155|0.34|0.25|0.34|0.44|0.08|
|Poverty – Extremes,y|155|0.09|0.02|0.06|0.19|0.08|
|Ginis,y|155|0.47|0.42|0.47|0.51|0.04|
|**Panel B. Fiscal Balance Comp**<br>|**onents of S**|**tate Gove**|**rnments**||||
|Total Expenditures,y|174|0.158|0.090|0.149|0.258|0.060|
|Infrastructures,y|174|0.007|0.002|0.006|0.016|0.005|
|Social Servicess,y|180|0.066|0.029|0.057|0.125|0.038|
|Debt Servicings,y|174|0.007|0.001|0.004|0.018|0.008|
|Transferss,y|174|0.135|0.074|0.127|0.214|0.058|
|Tax Rates,y|150|0.490|0.289|0.465|0.670|0.168|
|Non-Infrastructures,2015|180|0.045|0.017|0.044|0.082|0.028|
|**Panel C. Bank Lending to Sta**|**te Governm**|**ents**|||||
|Loans – Govts,m|1,080|22.66|21.28|22.51|24.24|1.07|
|Credit Line – Govts,m|891|19.40|16.61|19.88|21.65|2.40|
|Term Loan – Govts,m|1,080|22.57|21.17|22.48|24.09|1.10|
|Interest Rates,m|1,080|6.81|5.51|6.38|8.74|1.31|
|Collaterals,m|1,080|0.01|0.00|0.01|0.01|0.00|
|Maturitys,m|1,080|5.00|4.59|5.07|5.32|0.34|

43
# **8. References**

Albornoz, F., H. Calvo Pardo, G. Corcos and E. Ornelas (2009). “Sequential Exporting.” mimeo.

Baldwin, J. and W. Gu (2004). “Trade Liberalization: Export-market Participation, Productivity Growth, and Innovation” Oxford Review of Economic Policy, Oxford University Press, vol. 20(3), pages 372392, Autumn.

Baldwin, R. (1988). “Hysteresis in Import Prices: The Beachhead effect.” The American Economic Review, vol.78, Issue 4, pp.773-785.

Baldwin, R. (1990). “Hysteresis in Trade.” Empirical Economics, vol. 15(2), pages 127-42.

Baldwin, R. and P. Krugman (1989). “Persistent Trade Effects of Large Exchange Rate Shocks.” The Quarterly Journal of Economics, Vol. 104, No. 4, pp. 635-654.

Balassa, B. (1974). “Trade Creation and Trade Diversion in the European Common Market: An Appraisal of the Evidence.” The Manchester School of Economic & Social Studies, Blackwell Publishing, vol. 42(2), pages 93-135.

Bernard A., S. J. Redding and P. Schott (2009). "Multi-Product Firms and Trade Liberalization," Working Papers 09-21, Center for Economic Studies, U.S. Census Bureau.

Bernard, A., J. B. Jensen, S. J. Redding and P. Schott (2009). “Trade margins of U.S. trade.” American Economic Review, American Economic Association, vol. 99(2), pages 487-93, May.

Bernard, A., J. B. Jensen and P. Schott (2003). “Falling Trade Costs, Heterogeneous Firms, and Industry Dynamics.” NBER Working Paper No. 9639.

Besedes, T. and T. Prusa (2007). “The Role of Extensive and Intensive Margins and Export Growth.” NBER Working Papers 13628, National Bureau of Economic Research.

Cameron, A.C. and P. Trivedi (2005). Microeconometrics: Methods and Applications, Cambridge University Press.

Besedes, T. and T. J. Prusa (2006). “Ins, Outs, and the Duration of Trade.” Canadian Journal of Economics, 39(1): 266-295.

Debaere, P. and Mostashari S. (2005). “Do Tariffs Matter for the Extensive Margin of International Trade? An Empirical Analysis.” CEPR Discussion Papers 5260.

Dixit, A. (1989). “Entry and Exit Decisions under Uncertainty.” The Journal of Political Economy, Vol. 97, No. 3, pp. 620-638.

Freund, C. and E. Ornelas (2009). “Regional Trade Agreements.” CEP Discussion Papers dp0961, Centre for Economic Performance, LSE.

Eaton, J., M. Eslava, M. Kugler, and J. Tybout (2007). “Export dynamics in Colombia: Firm-level evidence.” NBER Working Papers 13531, National Bureau of Economic Research.

24
- 57 -

determining financial structures.<sup>35</sup> This leads<sup>one to the so-called</sup><sup>_It cash flow or it pecking order_</sup> theory of finance whereby firms use up<sup>the least cost, source of finance first. Again, by concentrating</sup> solely on tax explanations as determinants<sup>of the financial structure, tax policy arguments about how to</sup> avoid distortions of the financing decision can most<sup>readily be seen.</sup> Perhaps more importantly, by taking the<sup>financial structure and the costs of various sources of</sup> finance **_(tL,_** p _and_ a) as given to the firm,<sup>one may not be capturing important differences between the</sup> marginal and average costs of various sources<sup>of finance. If firms are optimizing their financial</sup> structures, one might expect that the marginal<sup>cost of various sorts of finance will be the same. In this</sup> case, it might seem inappropriate to attribute<sup>different costs of finance to debt and equity in MEMR</sup> studies.<sup>3 6</sup> However, as has been argued in<sup>Boadway, Bruce and Mintz (1987), if financial decisions</sup> are separable from investment decisions, the<sup>optimizing choice of financial structure will give rise to</sup> different equilibrium values of the costs of various<sup>sources of finance which are appropriately used in</sup> MEIR calculations.

Consider, for example, the cost of funds given<sup>by equation (32) above. In it,</sup><sup>_i(a_-u) and</sup><sup>_pg_</sup> represent the _average_ costs of debt and equity<sup>finance to the firm, but note the</sup><sup>_marginal_costs. The</sup> marginal cost of funds will take into account<sup>the effect of an extra dollar of financing of either sort on</sup> the amount the firm must pay its creditors as<sup>a result of a marginal change in the firm's financial</sup> structure. Suppose that the rate of interest<sup>the firm must pay increases with the debt-equity ratio. The</sup> marginal cost of raising a dollar of debt exceeds<sup>_i(1_-u) because an extra dollar of debt raises the</sup> debt-equity ratio and the interest rate the firm<sup>must pay on all its debt. Similarly, the marginal cost of</sup> an increment in equity financing is less than<sup>p, because this extra equity financing lowers the</sup> debt-equity ratio and reduces the cist of debt.<sup>Thus, the firm will hold a diversified stock of financial</sup> liabilities even though the average costs of the<sup>two sorts of finance differ. It can be shown that when the</sup> cost of finance depends only upon the debt-equity<sup>ratio, the firm's financial structure will be determined</sup> independendy of investment, and the marginal<sup>cost of funds will exactly equal the weighted average of</sup> 35 Examples include Stiglitz (1976), Auerbach (1983).<sup>Poterba and Summen (1983) and Boadway and Bruce (1992).</sup> 36 This point has been made by Hansson and Stuart (1985).
Turning to components included in project design, computerization and related procedural, organizational and manpower reforms were most popular, the former figuring in all technical assistance and related (TLSR) loans. On the other hand, besides the limited presence of anticorruption and institutions for accountability and budgetary resources, the conclusions for structural and sectoral adjustment loans (SALs/SECALs) in a 1997 Operations Evaluation Department (OED) study,' that a greater focus is needed on "conducive legal environment, taxpayer education, information systems (used by tax departments), audits, withholding and presumptive taxation and improvements in VAT administration" are confirmed for structural adjustment operations reviewed here and, excluding VAT administration and taxpayer audits, technical assistance operations as well.

The specification and use of performance indicators, especially quantitative indicators, was limited, especially in SALs/SECALs and there was little uniformity across loans. Furthermore, few operations established pre-project benchmarks for indicators, and there was limited correspondence between indicators used in pre-project and post-project appraisals. Insufficient infornation on the rationale for sequencing of different project components prevents any firm conclusion from being drawn here. Nevertheless, instances of both effective and poor sequencing are reported in post-project evaluation documents.

Only a handful of projects undertook quantitative project appraisal and risk assessment. Where these were done, methods used were not uniform across projects and appear capable of improvement.

Since many operations are still active, a relatively small set of post-project Implementation Completion and (OED) Project Audit reports were available. This sample, however, confirms the importance of Bank and borrower performance and quality at entry for project success found in earlier work in the Bank. In addition, lessons distilled from projects largely pertained to project implementation rather than to improving the content of projects at the design stage.

_Suggestions for future operations:_ Upstream work to draw up a strategy for Bank operations in the area of revenue administration, including the content and the process of such operations, is a major suggestion made here. Among process factors, outsourcing, strengthening of external partnerships and strengthening the skill mix of Bank staff are important components of the strategy.

The need for a standard diagnostic tool<sup>2</sup> , a greater focus on and integration of governance components in project design, the development and use of a standard set of administration performance indicators and also a standard appraisal methodology are other important suggestions made here. In addition, a formal hypothesis-testing framework to distill lessons about best practices in revenue administration is needed to strengthen the content of future Bank operations.

'Datta-Mitra (1997).

2 example, the tool developed within the Tax Policy and Administrative Group by Gill (1998).

ii
-53 -

# ANNx 3. **BASIC** EARNNGS FUNCTIONS

## **ChIle** 1989

|||Gen|der|Sec|tor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.120|0.121|0.132|0.114|0.112|
|Experience (EX)|0.042|0.048|0.029|0.041|0.029|
|Experience-squared<br>(EX<sup>2</sup>)|-0.0004|-0.0005|-0.0002|-0.0004|-0.0002|
|Log Hours (H) Worked|0.885|0.768|0.753|0.918|0.660|
|Constant Term|5.189|5.652|5.487|5.104|6.418|
|R<sup>2</sup>|0.40|0.39|0.45|0.37|0.29|
|N|37,679|26,823|10,856|33,794|3,885|
|Means||||||
|Earnings (Y)|40,275|43,878|31,373|38,232|58,044|
|S|8.5|8.2|9.4|8.1|12.3|
|EX|21|22|20|22|20|
|H|47|48|44|48|43|
|Var (Ln Y)|0.638|0.591|0.692|0.629|0.423|

Source: _Programa Integrado de Encuestas de Hogares (PIDEH) Encuesta Nacional del Empleo, IV Trimestre, 1989_ Notes: The earnings variable (Y) is in pesos per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better.
PC is the measure of financial development, or private credit over GDP. I also use the share of non-performing loans in lending values as another measure of financial sector development. T is the transparency indicator of interest, and X represents other variables affecting financial development. In determining both the choice of the financial development indicator and the variables to be put in X, I follow Barth, Caprio and Levine (BCL, 2004, 2006). The transparency indicator is taken from Islam (2006), and represents the availability of timely economic information in countries.<sup>8</sup> Legal origin has been shown to be an important factor determining financial development (see Beck, DemirgucKunt and Levine, 2003) and is therefore used as an explanatory variable. Legal origin is divided into the following categories following La Porta et al (1999) - French, English, Scandinavian, Socialist and German.

I also include an index constructed by BCL (pmindex and pmindex pc) which reflects the amount of information available on banks in order to facilitate private monitoring (see Appendix 2). For example, if international audits are required then banks need to supply information to auditors. In addition, I have constructed a slightly adjusted private monitoring index (apmindex) in order to focus solely on the availability of information rather than the incentive to monitor. I have done this so that the private monitoring index is more closely analogous to the economic transparency index, T.<sup>9</sup> I have adjusted the index slightly by taking out the question on deposit insurance but leaving all the other subindices that refer to actual disclosure of information.<sup>10</sup>

> 8 This index measures how timely or up-to-date is the economic information published by government on 11 economic variables: inflation, unemployment, output, exchange rates, exports, imports, foreign direct investment, government revenue, government expenditure, money supply and the 6 month deposit interest rate.

> 9 The conclusions regarding T do not change depending whether pmindex or apmindex is used. 10 BCL give an higher rating to countries with no deposit insurance. They contend that deposit insurance reduces the incentives for private monitoring. My goal is to assess the impact of having information

15