rafmacalaba/data-use-mentions-extended huggingface dataset:
consideration of Chile’s transport policy goals. Road density in Chile lags behind other Latin America and OECD countries, partly due to low overall population density but high concentration in Santiago and Valparaiso. The road network’s size (approximately 78,000 kms in 2010) has not significantly changed since 1990, though the share of paved roads has increased from 13.8 to 23.3 percent. This pilot application of IPF considers inter-urban road projects intended to extend the road network and link urban areas. Specifically, the pilot was applied to 50 projects in the BIP that met basic requirements (mentioned below) to be included in the IPF pilot, including HDM-3 data and the existence of a CBA.<sup>15</sup> Road repositioning projects were excluded from the pilot.

## Road Transport Project Sample

The sample of 50 interurban road transport projects to which IPF was applied includes only a set that has reached Execution status in the SNI. Additionally, this set has passed the basic profitability test of having IRRs of at least 6% and have been judged to align with transport policy goals.

> 14 The Organic Law on Financial Administration of the State, Article 19, requires only that a project’s profitability be analyzed, but does not require project profitability. The Organic Constitutional Law on Government and Regional Administration (19175), Article 75 states that projects financed with regional funds require an RS RATE.

> 15 Special classifications exempt some projects from requiring CBA (e.g., projects in extreme zones, projects with expected volumes of less than 400 vehicles per day).

36
household consumption/income survey, CRVS availability, and the IMF SDDS status of the country. Using these indicators, the extended SPI series contains 35 indicators with data back to 2004. These include 5 indicators covering data use, 1 covering data services, 16 covering data products, 8 covering data sources, and 4 covering data infrastructure. The correlation between the extended SPI and the official SPI overall score is around 0.90.

20
)_ and it can be ignored, unless there are distortions such as export taxes.

To make the approach easy to apply, we write the expenditure and revenue functions using a Constant-Elasticity of Substitution functional form for the expenditure function and a tariff revenue function based on the import demand functions derived from the first derivatives of this function with respect to prices. In this situation, for a single economy, the relevant elements of the distorted trade expenditure can be written: where P = [Σj βj(pj*(1+tj))<sup>(1-σ)</sup> ]<sup>1/(1-σ)</sup> , σ is the elasticity of substitution, and the βj’s are the distribution parameters of the CES expenditure function.

We calibrate this function using data on imports from the UNCTAD TRAINS database, and data on consumption of domestically-produced goods from the World Bank’s World Development Indicators. In line with standard practice in the CGE literature, all domestic prices were initially set to unity to allow decomposition of value data into prices and quantities. This allowed the β coefficients to be determined from the value share data at domestic prices. A σ value of 4 was assigned, raising the elasticity of substitution above that in most empirical estimates of import demand

> 6 A better longer-term solution to this problem would be to utilize a two-stage aggregation approach like that proposed by Bach and Martin (2001).

14
). We test, for example, whether the effect of ethnicity on trust in local government officials varies across EAs. Taking into account the multilevel nature of our data, we estimate random intercepts for the households and enumeration areas and treat our dependent variable as a binary outcome.

# **5 Results**

In this section, we present the results of our model on perceived trustworthiness of local government officials. We discuss regression results of trust in local government officials using predicted probabilities and first differences (King, 1989). In a multilevel logit equation,

> 10We are not completely satisfied with this control. According to Casey (2010), in Sierra Leone voters tend to cross the party lines for local elections. We therefore believe that a variable indicating whether respondents voted for the winning party/candidate in the 2004 and/or 2008 elections will be more relevant for our purposes. We are in the process of collecting this information, and will include this variable in the next version of the paper.

11A complication in our data is that 80 percent of wards were redistricted between the 2007 and 2008 rounds of the NPS. There does not appear to be any systematic differences between wards that were re-districted and wards that were not. Nonetheless, we estimated separate models using the 2008 ward boundaries and the 2004 boundaries. Unfortunately, we had to re-create the latter boundaries from other data sources and we could not determine the boundaries for 23 percent of the wards. Because the results were similar for both estimation strategies, we focus our discussion on the results that use the 2008 ward boundaries.

15
# **1. Introduction**

Banks have become increasingly international through the ownership of foreign subsidiary networks and the provision of cross-border loans. The Bank for International Settlements (BIS) reports that international banks’ foreign claims, which include claims through foreign subsidiaries as well as cross-border loans, stood at 15.9 trillion dollars in 2012.<sup>2</sup> While banks have become more international, bank regulation and supervision remain mostly national. This implies that international bank flows to some extent could be driven by international regulatory differences as banks seek to avoid burdensome regulation in their home countries.

Using aggregate data on international bank flows from the BIS, Houston, Lin, and Ma (2012) find that banks tend to have more claims on countries with fewer regulations, which they interpret as evidence of regulatory arbitrage by international banks. The provision of loans to countries with less stringent regulations is indeed consistent with regulatory arbitrage, if these loans are originated by subsidiaries located in these countries, as then the more lenient regulatory regime applies. However, the greater claims on countries with fewer regulations could reflect more lending by international banks’ establishments located in other countries with relatively stringent regulation, in which case there is no regulatory arbitrage. The aggregate BIS data do not give details on where international loans have been originated, and hence cannot provide conclusive evidence of whether international banks engage in regulatory arbitrage.

In this paper, we examine regulatory arbitrage using micro data on cross-border syndicated loans.<sup>3</sup> For each loan, we know where a bank has originated the loan. In

> 2 <mark>Based on BIS Locational Banking Statistics data.</mark>

> 3 <mark>Cross-border syndicated loans are an important component of international banking flows with total new commitments of $1.8 trillion in 2012 (BIS Quarterly Review, September 2013, Table 10). 2012 is the last full year for which the BIS reports aggregate cross-border syndicated loan volumes.</mark
# **4.2 Impacts on Firm Growth**

Better business practices are strongly correlated with greater firm performance in both the crosssection and over time (McKenzie and Woodruff 2017). We would therefore expect improvements in practices to start leading to firm growth for the insourcing, outsourcing and consulting treatments. However, the heterogeneity among firms in our sample, coupled with a smaller than anticipated sample size, makes measuring impacts on firm growth more challenging. Our preanalysis plan specified that our primary measures would be the IHS transformations of firm sales and profits for two reasons. The first is an economic theory rationale: we believe the treatments are more likely to have a constant proportional treatment effect than a constant level treatment effect. That is, better business practices are more likely to help firms of different initial sizes grow by the same proportion, than by the same absolute amount. Second, these transformations help improve statistical power, by reducing the influence of large outliers. Table 5 shows the estimated treatment effects on firm sales, profits and employment for all four treatments.

<u>Outsourcing. In the first follow-up survey, outsourcing has a positive and significant impact on</u> yearly sales. This treatment also has a positive, but statistically non-significant, effect on the other measures of firm growth. The magnitudes of these impacts increase in the second year, and are statistically significant at the 5 percent level for outsourcing on four out of the five sales and profits measures. This includes our overall Sales/Profit Index: outsourcing increases sales and profits by 0.23 standard deviations.<sup>10</sup> Outsourcing also has a positive impact on firm employment, which is significant at the 10 percent level when using the IHS measure.

<u>Insourcing. The impacts are less clear for the insourcing treatment in both survey rounds. The point</u> estimates are all positive, but none are statistically significant from zero – although, at the same time, we can not reject that
project on the content of trade agreements. These data are described in Mattoo _et al._ (2020) with labor market provisions in particular being discussed in detail in Raess and Sari (2020). We focus in particular on trade agreements that include reference to the “abolition of child labor.” We also consider whether such provisions are legally binding. The trade agreement content coded in Raess and Sari (2020) does not differentiate between non-derogation child labor provisions—provisions that reinforce existing child labor bans under national laws—from child labor bans introduced via issue linkage in the trade agreement. Regardless of whether such provisions reinforce existing national laws or introduce new legal commitments via the trade agreement, issue linkage under the RTA exposes non-compliant countries to potential trade retaliation under the RTA, regardless of national legislation. Additionally, our trade agreement data does not record how child labor provisions are implemented at the national level. As a result, we cannot differentiate child labor supply shocks (labor supply bans) from child labor demand shocks (employer-based fines) and, hence, we cannot directly test for heterogeneous impacts of different implementation schemes as predicted in our theoretical model. Our estimates of the effects of child labor provisions will therefore reflect average effects across different unobserved implementation schemes. Table A.1 provides the list of RTAs notified to the WTO and currently in force that have included child labor provisions and the legal enforceability of such clauses.

# **3.1 Methodology**

We identify both the effects of trade liberalization on our target labor market outcomes as well as the impact of child labor provisions in RTAs with the following triple-difference model on our stacked country panel: where _i_ indexes countries, _t_ indexes time, and _g_ indexes RTAs with _yigt_ as the outcome of interest. _RTAigt_ is an indicator equal to 1 if year _t_ is later than the year that RTA _g_ enters into force and zero prior, and _Childig_ is in indicator equal to 1 if RTA _g_ includes the child labor provision. We include 17
2. _Infrastructure_ (4). Indicators cover electricity, connectivity, irrigation and housing. The connectivity measure is the density of railway and metro stations. Data on road density, based on Open Street Maps (a crowdsourcing platform) is also available, but it is too recent to be reliable.

3. _Market access_ (2). We compute the weighted average of economic activity across all neighboring districts, discounted by distance, for up to 400 kilometers. We do so using GDP and nighttime light intensity as alternative measures of district‐level economic activity.

4. _Economic structure_ (7). We include traditional measures on the sectoral composition of the economy (except for agriculture, to avoid collinearity), a measure of output diversification, an indicator on mineral production capacity, and two measures of the distribution of firms by size.

5. _Employment structure_ (2). Because the employment or unemployment rate is not very relevant in India’s case, we use the shares of self‐employment and wage employment relative to the working‐ age population to provide a snapshot of the local employment structure.

6. _Urbanization_ (2). To mitigate biases that may arise from the administrative definition of urban areas, we use population density instead of population size to measure city size. We use the population share of the state capital in a district to proxy for city governance.

7. _Human capital_ (3). We use three measures of educational attainment – primary, secondary and tertiary – in line with the city growth literature. By differentiating between the three levels of education we allow nonlinearity in the impact of schooling on local growth.

8. _Social inclusion_ (4). We consider the fraction of households with a bank account, the gap in secondary educational attainment between men and women, the shares of scheduled castes, and the share of scheduled tribes in the total population.

14
annual turnover. They were only interviewed if their turnover ranged between Ksh 500,000 and Ksh 5 million (i.e., they were meant to be covered by the TOT regime or slightly below the minimum threshold). However, the registered small businesses did not necessarily need to be registered for TOT (only 57 percent were). The survey was part of a diagnostic exercise performed by the World Bank at the request of the KRA to help improve the design of the TOT regime.

The second survey (hereafter the “SMS survey”) covered 645 TOT payers in Kenya and was carried out in May and June 2024. Around 6,000 TOT payers were invited to participate in a very brief survey about their experience paying tax, via an SMS message that was sent from the KRA (refer to Appendix Figure A.1 for an example of the SMS message that was sent). The response rate was around 10 percent of taxpayers who were invited to participate in the survey, which is consistent with other studies that involve surveying taxpayers (e.g., Cruces et al. 2024). The sample frame was the database of TOT payers with a valid phone number registered with the KRA, who were randomly selected to participate in a randomized experiment (details in the following section).

Both surveys include three key questions of interest that examined taxpayers’ knowledge of the TOT regime (refer to Appendix for the exact wording). The first question relates to the minimum threshold, particularly whether it exists and what level it is set at. The second question relates to the percentage of turnover that taxpayers are expected to pay. The third question provides respondents with a scenario where they need to calculate the amount of tax paid based on the annual turnover of a hypothetical business. We refer to the results of both of these surveys throughout the following section to help shed light on how a lack of knowledge among taxpayers may have influenced their behavior.

18
stratification by firms’ location, sector, and size.<sup>5,6</sup> Since the onset of the pandemic, two rounds of the COVID-19 Follow-up Surveys (COV-ES) were conducted in both countries, re-contacting all of the ES respondent firms and asking questions on a wide range of topics, including whether they have received any government support during the pandemic. We use these three sets of data in both countries, and we merge them through the unique firm identifier.

Table 1 shows the timing of all three surveys. The latest ES in El Salvador was conducted between March and August 2016, interviewing 719 firms, whereas the Georgia ES was implemented between March 2019 and January 2020, covering 581 firms. The first round of the COVID-19 Follow-up Surveys (COV-ES, R1) started in June 2020 in both countries. It took almost two months to complete it in El Salvador, but Georgia COV-ES, R1 was completed within the same month. The second round of the COVID-19 Follow-up Surveys (COV-ES, R2) was completed in October 2020 through January 2021 in El Salvador and October-November in Georgia. Importantly, the reference period for many questions appearing in ES is the last completed fiscal year prior to the survey implementation. This means that the reference year in ES is 2015 and 2018 for El Salvador and Georgia, respectively. The reference period for many questions appearing in COV-ES is the last completed month.

Both ES and COV-ES contain a rich set of information on firm characteristics, covering topics of sales, employment, finance, among others. As per the standard methodology, the ES data is obtained through face-to-face interviews with firm owners or top managers, lasting on average one hour, while COV-ES interviews were conducted by phone through much shorter interviews, lasting on average 25 minutes. All three datasets of each country contain sampling weights. COVES data combines the information contained in the ES sampling weights with the information about firm closures obtained during the implementation of COV-ES survey and provides updated sampling weights. Since the
19 A more effective mechanism should be devised to protect the poor from user fee increases; the current mechanism in place in China to exempt the indigent from fees is not working. As household expenditure data showed there is a weak relationship between household income and the average price paid for treatment, that is, households in the bottom income quintile spend a disproportionately high share (13 percent) of their total health expenditure on one annual visit. They also spend more on drugs, which are generally not subsidized. Of total private health spending, the poorest households pay 77 percent on drugs compared with 68 percent by the richest quintile. Thus, the burden of paying for service charge and drugs is higher among the poor than the better off. Since this disproportionate burden prevents the poor from using public health facilities, then improving access for them may require selective price reductions for essential services; the poorest hard-to-reach group should be exempt from paying fees, which may be compensated for by _increases_ in public spending and other insurance fees. This needs an appropriate pricing policy that differentiates prices by the income of users Finally, improving the quality of public health services would enhance utilization. One indicator of the quality gap is the variation across income groups in the amount of household expenditure on public health, but another indicator, available from the 1993 NHS, is that 15 percent of households do not use public health facilities due to the lack of beds (4.1%) and the absence of proper services and treatment (11.3%). Interprovincial variation is even wider. Another indicator is that the poor are less likely than the better-off to receive treatment by qualified and trained doctors when they are sick. Spending on medical equipment, supplies, drugs, and physical infrastructure in township health centers and the redeployment of trained personnel in needy areas would considerably improve the quality of services available to the poor.

# **Addressing Emerging Problems:**

_Strengthening the Information. Education and Communication (IEC):_ The disease burden in
br>|-0.010<br>|||
||||||(0.010)|(0.020)|(0.018)|(0.022)|||
|Real net ODA received,log|||||<br>-0.056|<br>-0.361*|<br>-0.054|<br>-0.118|||
||||||(0.069)|(0.204)|(0.073)|(0.202)|||
|Chinn-Ito KA index|||||||-0.001|0.022|||
||||||||(0.070)|(0.115)|||
|Trade (percent of GDP)|||||||0.003|0.020***|||
||||||||(0.004)|(0.006)|||
|FDI, net inflows (percent of GDP)|||||||<br>-0.007|<br>-0.051**|||
||||||||(0.026)|(0.024)|||
|Constant|10.485***|28.481***|8.357***|31.289***|<br>11.152***|<br>32.576***|<br>10.244***|<br>25.868***|<br>9.971***|27.689***|
||(1.745)|(2.503)|(2.019)|
**Figure 1**

**Convergence among EM?** This figure presents the relationship between 5-year average GDP growth forecast (2011-2015) and per capita GDP in USD in 2009 for the sample of EM used in this paper. (*) denotes significance at the 10% level. Source: IMF WEO April 2011.

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>y = -6E-05x + 5,5093<br>1 (*)<br>R! = 0,1534<br>0<br>0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000<br>GDP per capita in USD (2009)<br>5-year average growth forecast (2011-2015) in %<br><!-- End of picture text -->
Pauline Allemand, Doris Wiesmann, Terri J. Ballard, Megan Deitchler, Marie Claude Dop, et al. 2017. “Development of a Dichotomous Indicator for Population-Level Assessment of Dietary Diversity in Women of Reproductive Age.” _Current Developments in Nutrition_ 1 (12): cdn.117.001701. https://doi.org/10.3945/CDN.117.001701.

Miller, Victoria, Gitanjali M Singh, Jennifer Onopa, Julia Reedy, Peilin Shi, Jianyi Zhang, Adeem Tahira, et al. 2021. “Global Dietary Database 2017: Data Availability and Gaps on 54 Major Foods, Beverages and Nutrients among 5.6 Million Children and Adults from 1220 Surveys Worldwide.” _BMJ Global Health_ 6 (2): e003585. https://doi.org/10.1136/bmjgh-2020-003585.

Miller, Victoria, Patrick Webb, Frederick Cudhea, Peilin Shi, Jianyi Zhang, Julia Reedy, Josh Erndt-Marino, et al. 2022. “Global Dietary Quality in 185 Countries from 1990 to 2018 Show Wide Differences by Nation, Age, Education, and Urbanicity.” _Nature Food_ 3 (9): 694–702. https://doi.org/10.1038/s43016-02200594-9.

Miller, Victoria, Patrick Webb, Renata Micha, and Dariush Mozaffarian. 2020. “Defining Diet Quality: A Synthesis of Dietary Quality Metrics and Their Validity for the Double Burden of Malnutrition.” _The Lancet Planetary Health_ 4 (8): e352–70. https://doi.org/10.1016/S2542-5196(20)30162-5.

Molani-Gol, Roghayeh, Sorayya Kheirouri, and Mohammad Alizadeh. 2023. “Does the High Dietary Diversity Score Predict Dietary Micronutrients Adequacy in Children under 5 Years Old? A Systematic Review.” _Journal of Health, Population, and Nutrition_ 42 (1). https://doi.org/10.1186/S41043-022-00337-3.

Molla, Abebaw, Gudina Egata, Firehiwot Mesfin, Mikyas Arega, and Lemma Getacher
new light on these dynamics. LSMS-ISA panel data also enable study of the economic contribution of trees on farms over time so as to understand the extent to which such trees can provide a means for farmers to escape poverty or achieve more enduring prosperity. Finally, we see significant scope for future research to collect and analyze information on the economic contribution not only of trees on farms, but also from forests and other wildlands to gain a more complete picture of the dynamics of rural livelihoods in Africa over time at the national scale. This would require adding some specific questions to the LSMS-ISA questionnaires.

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0.077|0.040|0.474|-0.338|<br>-0.471|0.337|
|Strength of insolvency framework index (0-16)|145|-0.173|-0.337|0.309|0.083|0.034|0.778|-0.265|<br>-0.382|0.454|
|Credit: Strength of legal rights index (0=weak to 10=strong)|156|-0.391|-0.307|0.558|-0.003|0.045|0.770|-0.443|<br>-0.419|0.855|
|Depth of credit information index (0=low to 6=high)|156|-0.368|-0.329|0.786|-0.055|0.066|0.463|-0.402|<br>-0.436|0.800|
|Strength ofgovernance structure index(0-10.5)|156|-0.456|-0.237|0.126|-0.278|0.213|**0.002**|-0.427|<br>-0.409|0.895|

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