rafmacalaba/datause-encoder-data huggingface dataset:
9 Table 3: Potential Explanatory Variables for Direction of Balance-of-Payments Causation.

|_RESC_|_-_|Article XIV status|
|---|---|---|
|_RESD_|-|Special rates for some or all capital transactions and/or some or all invisibles|
|_RESE_|-|More than one exchange rate for imports|
|_RESF_|-|More than one exchange rate for exports|
|_RESG_|-|Restrictions on current account payments|
|_RESH_|-|Restrictions on capital account payments|
|_RESI_|-|Import surcharges|
|_RESJ_|-|Advance import deposits|
|_RESK_|-|Surrender of export proceeds required|
|_PCY_|-|Natural logarithm of per capita income in U.S. dollars|
|_YG_|-|Rate of growth in realGNP|
|_INF_|-|Inflation (continuously compounded rate of change inGNPdeflator)|
|_OPEN_|-|Exports plus imports as a proportion ofGNP|
|_FLY_|-|Cumulated current account deficit as proportion ofGNP|
|_DETY_|-|Foreign debt as a proportion ofGNP|
|_GBLY_|-|Government deficit (-) as a proportion of_GNP_|
|_DCGR_|-|Net domestic credit to the government as a proportion of total domestic<br>credit|
|_DCGY_|-|Net domestic credit to the government as a proportion ofGNP|
|_BLA_|-|Black market exchange rate premium|
|_RR_|-|Square of real interest rate|
|_DCPY_|-|Domestic credit to the private sector as a proportion ofGNP|

account, and _FDIX_ for FDI being independent or **exogenous. These** variables take a value of 1 when a country exhibits the particular **Granger-causation specified** and 0 when it does not. Since there were only four countries with bidirectional Granger-causation, these were treated as positive for both _CAUSKC_ and _CAUSCK._ The potential explanatory variables **are listed** in **Table** 3. The **first** set of explanatory variables consist of balance-of-payments restriction variables reported annually at the back of the International Monetary Fund's Balance of **_Payments_** _Yearbook._ The variables _RESC_
based on the median value of the variable in question. The sample-split approach is similar to interacting each of the factors with the responsiveness of private credit to foreign liabilities.<sup>8</sup> First, we test whether LDR is driving the response differences. We calculate the pre-crisis median LDR ratio in our full sample using data from the fourth quarter of 2007. We then split the full sample into high and low LDR subsamples of equal size based on the median and rerun our baseline model for each sample. The impulse-response results are presented in Figure 4 and the variance decompositions are presented in Table 7, Panel B. We find that the difference between the response of PRIVATE CREDIT to a FOREIGN LIABILITIES shock in both high LDR and low LDR samples is positive and statistically significant.<sup>9</sup> In addition, the responses differ significantly in magnitude. The variance decomposition shows that in the high LDR subsample, a FOREIGN LIABILITIES shock explains 14.1% of the variation in PRIVATE CREDIT, while this is only 5.6% in the low LDR sample. This finding suggests that high LDRs are associated with a stronger response of PRIVATE CREDIT to FOREIGN FUNDING shocks and consistent with the notion that high LDRs are at least partially responsible for the ECAs weak crisis and recovery experience while the rest of the world proved more resilient.

Next, we perform another sample split and run new PVARs to test whether foreign funding dependence is driving the difference. In doing so, we use pre-crisis FOREIGN FUNDING values for all countries from the fourth quarter of 2007 and split the sample into high and low FOREIGN FUNDING subsamples. The impulse-response results are presented in Figure 5 and the variance decompositions are presented in Table 7, Panel B. We find that the difference between the response of PRIVATE CREDIT to a FOREIGN LIABILITIES shock in high and low FOREIGN FUNDING samples is positive and statistically significant. Again, we find a strong difference in the magnitude of response to shocks. The variance decompositions show that in the high FOREIGN FUNDING subsample a FOREIGN LIABILITIES shock explains 12.9% of the variation in PRIVATE CREDIT, while it only explains 6.9% in the low FOREIGN

> 8 Note that such interactions cannot be modeled directly in a VAR setting.

> 9 The significance is at about 5% in period 1 because the bottom 5th percentile line is touching the zero line, while it is stronger in periods 0 and period 2 and 3.

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that Bogotá had already started a district road-building plan, the program was also justified as a mechanism to counteract future congestion.

PYP has been enforced through the imposition of fines and vehicle immobilization. Since the moderate phase of the program, the annual number of fines has decreased by, on average, 40 percent, and hence this infraction has moved from second to fifth place on the list of the most frequent traffic fines imposed by the Metropolitan Transit Police (see Secretaría de Movilidad 2010). These facts have been used by the authorities to argue drivers’ progressive voluntary acceptance of and compliance with PYP.<sup>9</sup>

# II. SELECTION OF CAR USE AND POLLUTION INDICATOR

Traffic counts may be considered a direct measure of vehicle use for evaluating driving restriction programs such as PYP. However, as such data is only available for the drastic phase of PYP and then only for certain parts of the city and days of the year, it is not possible to construct a continuous time series pre- and postimplementation based on traffic counts.<sup>10</sup> Thus, instead of traffic counts, CO concentration is used as a proxy for car use. CO has several advantages compared with other possible proxies (Gallego, Montero, and Salas 2013). First, CO is mainly emitted by traffic (85–98 percent of total CO

> 9. The decline in fines may have other potential explanations as well, e.g., weak monitoring or drivers gradually learning how to circumvent the policy. Yet several years into PYP, weak monitoring appears unlikely since the police had reasonably improved their control procedures. Paying bribes to police officers is also unlikely since a driver may have to pay several bribes in a single trip, making it very costly.

> 10. See Gallego, Montero, and Salas (2013) for a brief discussion on the use of traffic counts to evaluate car use. Certainly, vehicle speed is also an alternative measure to analyze congestion. However, there is no comprehensive data in Bogotá to conduct a reliable PYP assessment using car speeds. Constructing a vehicle speed database would be relevant for policy-making given that congestion is such that Bogotá is considered the second worst metropolitan area to drive in according to Waze (2015).

7
**Figure 1:  Share of all GVC Trade in Total Trade, 1988‐2014**

<!-- Start of picture text --> 45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Share of all GVC Exports in Total Exports (256 countries)<br>Share of all GVC Imports in Total Imports (256 countries)<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> <u>Notes: Values in percentage shares from original variables expressed in thousand current US$ and calculated on the total number</u> of 256 countries/geographies with available data on GVC Exports in at least some years of the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits, mirror data). All trade data is drawn from Comtrade on WITS, and countries' GDP data comes from WB WDI.

<u>Sources: Trade: World Bank MC‐GVC database</u> <u>(http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator). World GDP:</u> World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators).

**Figure 2:  Shares of Each GVC category in Total GVC Exports, 1988‐2014**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Apparel ‐ Intermediate Good Apparel ‐ Final Good Motor Vehicles ‐ Intermediate Good<br>Motor Vehicles ‐ Final Good Electronics ‐ Intermediate Good Electronics ‐ Final Good<br>Footwear ‐ Intermediate Good Footwear ‐ Final Good Textiles ‐ Final Good<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> <u>Notes: Values in percentage shares from original variables expressed in thousand current US$ and calculated on the total number</u> of 256 countries/geographies with available data on GVC Exports in at least some years of the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits, mirror data). All trade data is drawn from Comtrade on WITS, and countries' GDP data comes from WB WDI.

<u>Sources: Trade: World Bank MC‐GVC database</u> <u>(http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator). World GDP: World</u> Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators).

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# **3 Administrative Data**

We use administrative data from January 2018 through December 2023, compiled through an ongoing collaboration with the local revenue agency in Tres de Febrero.

**Local property tax data.** The primary database consists of monthly property tax records constructed from the monthly bills issued to account holders. The unit of observation is an account that coincides with a dwelling unit. The data contain the following billing details: account number (unique property identifier), address, name of locality (neighborhood), year and month of the bill (12 yearly bills), the monthly fee (in pesos), a payment indicator, due date, date of payment, days overdue, means of payment (cash or electronic), type of account (residential, retailer, manufacturer), and also information about property size.

**Property ownership, assessed value, and gender.** Tres de Febrero receives a yearly data file from the Province of Buenos Aires containing the assessed values of local properties as well as individual tax identifiers of up to two owners. The authorities merge this register with their tax data using the cadastral nomenclature.<sup>6</sup> Crucially, there is a way to infer the sex assigned at birth and the age of the owners based on the first two digits and the last digit of the individual tax identifier (as well as from owners’ names). Appendix B provides a detailed description.

**RCT data.** We have access to treatment assignments from a large-scale randomized communication campaign conducted by Cruces et al. (2023), who estimate direct and spillover effects on property tax compliance. The campaign consisted of sending 25,000 personalized letters to randomly selected properties in October 2020 with reminders about due taxes, information about the status of the account, due dates, past due debt, and payment methods. We combine this database with the previous two sources to investigate heterogeneous behavioral responses to nudges between men and women and across the distribution of property values.

**Summary Statistics.** Table 2 provides an overview of property characteristics and taxation trends spanning the years 2018, 2019, and 2020. In Panel A, we show that residential properties constitute 83% of all properties in Tres de Febrero, with a concentration in the town of Caseros, where approximately 30% of all properties are situated. Panel B sheds light on the taxation dynamics and payment behavior within Tres de Febrero. In 2019, the mean assessed property value stood at 1.2 million pesos (about USD 30,000), with an average tax liability of 10,101 pesos (about USD 250). Between 2018 and 2020, the variable rate increased from 0.73% to 1.25%, while the empirical Effective Tax Rate (ETR), defined as total property taxes paid (including variable and fixed

> 6. The cadastral nomenclature is the number assigned to any property based on its location in the city, urbanization, street, and position in the street-block. This is an essential document issued by the Cadastre Office of the municipality of each locality.

7
# **B Details on the measures technology**

Appendix B describes remaining sector-specific business functions and related technologies. This section also explains the construction of technology measures in detail, which includes EXT index, aggregation of technology measures, normalization, and regional-level analysis. The FAT survey collects granular level of technology information at the business function level. In the main text, we explained how we constructed MOST, EXT, and GAP indices. In this section, we describe the technology structure and provide specific formula to construct the EXT index depending on four different types of technology structures. We also explain how we aggregate function-level technology measures at the firm-level or higher level (e.g., region-level).

## **B.1 Sector-specific business functions**

Modules B and C of the FAT survey collect information on the technologies used in key general and sector-specific business functions. In Section 2.1, we displayed two figures (Figure 1 and Figure 2) that portray the general business functions with related technologies for each business function, and the sector specific business functions in food processing sector with related technologies.

Here, we represent the business functions and associated technologies for the remaining sectors: crops (Figure B.1), livestock (Figure B.2), wearing apparel (Figure B.3), automotive (Figure B.4), pharmaceutical (Figure B.5), wholesale and retail (Figure B.6), transportation (Figure B.7), financial services (Figure B.8), health services (Figure B.9), and other manufacturing(Figure B.10). To identify business functions and technologies, we applied the same procedures. We began with reviewing the journal articles and technical reports to understand the consensus on definition of business functions and specific technologies. Based on our research, we had several internal review process with sector specialists at the World Bank Group to specify business functions and technologies for each sector. Additionally, we engaged in an external review process with private sector experts outside of the World Bank. These experts had at least 15 years experience in a specific sector.

58
- **Vitamin A Supplementation status** – Based on the WHO guidelines that recommendation children 6 through 59 months  should receive a vitamin A supplement in areas where vitamin A deficiency is a known public health problem.

- **Presence of unpenned animals** – Measures the likelihood that a child maybe in contact with non‐human sourced fecal matter.

- **ORS use for treatment of diarrhea** – Measure of use of oral rehydration solutions in treating diarrhea in young children.

- **Antibiotic treatment for pneumonia** – A national measure capturing the percentage of children aged 0–59 months with suspected pneumonia receiving antibiotics.

The surveys typically contain more information to construct a comprehensive measure of environment and health than comprehensive measures of food or care. In our measure, we consider access to safe water, improved sanitation, and require that more than 75% of child’s community have access to improved sanitation. In terms of prenatal health services, a mother must have had at least four prenatal visits. For post‐natal health services we require the child to have their immunizations up to date and that the child has received a vitamin A supplementation (as drops or tablets) since birth. Although some of the surveys collected information on ORS use or antibiotics, the information was only available for those children who had recently experienced diarrhea or a bacterial infection and not for all children. Table 5 lists the ideal and the available components to be considered for adequate environment and health.

**Table 5: Components of Environment and Health**

|**IDEAL**|**AVAILABLE**|
|---|---|
|Access to safe water|Yes|
|Access to improved sanitation|Yes (except Bolivia)|
|Community level sanitation|Yes (except Bolivia, Ethiopia,<br>Indonesia, and HK)*|
|Use of prenatal services|Yes (except Indonesia)|
|Age appropriate immunization status|Yes (except HK)|
|Vitamin A supplementation status (typically for children 6<br>months and older)|Yes (except Indonesia,<br>Bolivia)|
|ORS use for treatment of diarrhea|No|
|* Community level sanitation was not included for Bolivia as no<br>collected. Community level sanitation was not included in the a<br>as we did not have access to the village identifiers. Community<br>analyses in Ethiopia and Bangladesh HK as the resulting numbe<br>adequate environment becomenegligible.|sanitation information was<br>dequacy measures for Indonesia<br>sanitation was dropped from the<br>r of children with access to|

- **2.4 Other data issues and related considerations**

16
where there is a risk that the randomization may not get implemented in practice or a risk that take-up of a program may be lower than anticipated.

However, when the outcome has low autocorrelation, the baseline does not reveal very much about likely future outcomes, and thus can be an expensive undertaking. Researchers attempting to implement large-scale evaluations with governments or NGOs, where there is reasonable uncertainty as to whether randomization will actually be carried out and whether they will have sufficient take-up. For example, consider a micro-savings intervention, where a government or NGO agree to randomly choose villages in which to introduce a new savings product. There is a risk that after doing baseline surveys in the treatment and control villages that the government changes its mind, and decides to introduce the product in all villages (or decides not to introduce the product at all). There is also a risk that no one uses the new product, in which case there is no possibility of measuring the impact of savings accounts on household outcomes. In such a case, practical considerations may suggest it is optimal to not carry out a baseline, and then wait and only carry out post-treatment surveys if the product is indeed randomly introduced and take-up is high.

# **5. Conclusions**

This paper has shown that when the autocorrelation in outcome data is low, as is common with outcomes of interest like business profits, expenditure, and income, the standard paradigm of single baseline and follow-up, followed by difference-in-differences analysis, is unlikely to be optimal. Large improvements in power can be obtained from multiple post-treatment measures in experiments with fixed treatment and control group sizes, and from using ANCOVA instead of difference-in-differences. Researchers choosing how to allocate a fixed budget over multiple surveys may find they can obtain more power by not conducting a baseline at all, and if they use a baseline, will often get more power doing two follow-up waves with a smaller cross-sectional sample size than a single follow-up with a larger cross-sectional sample.

These findings are particularly likely to be of interest and use to researchers conducting experiments with interventions to help the poor, since the profits, incomes, and expenditures of 25
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