rafmacalaba/data-use-mentions-extended huggingface dataset:
liquidity because of uncertainty) could have constrained lending to SMEs in the treated and untreated countries in the period after the global financial crisis.

In our sample of countries, we observe that Basel III was implemented dominantly by larger countries. Although they add to the explanatory power of the (first-stage) selection regression, other characteristics, such as foreign ownership of banks and membership in the G20 group of developing countries, do not appear significant at common statistical levels.

Table 14 shows the estimation results for our panel of firms. Here, we also find a negative effect of Basel III implementation on SME access to financing. In general, the panel results largely correspond to the cross-sectional results. The Basel III coefficient is negative but insignificant in columns (1) and (2), regression specifications that may still carry too much residual variation that is cleaned up by including country context and bank characteristics. In columns (3) and (4), which do so, the estimated effect is significantly negative—about half the size of the coefficient estimated for the cross-section data set. The panel regressions use matched, firm-bank data as well as firm– banking system data for firms that did not have credit or did not report the name of the bank.

Initially less financially included SMEs—those with bank accounts but without a bank loan (column (6) in table 14)—felt that their access to finance constraints worsened more than those of SMEs that were initially more financially included—those that had bank accounts and credit with a bank (column (5)). Interestingly, SMEs that were initially using bank credit (and having bank accounts) did not perceive a significant worsening in access to finance after Basel III implementation—although the estimated coefficient still bears a negative sign. We examine this result further by estimating a supplementary regression on a smaller sample of firms that initially used credit and can be matched to banks. The estimation results, reported in table A.13, in the appendix, provide some
of the welfare impacts, we look at the incidence, the level and the relative budget shares of gas, electricity and fuelwood consumption in Armenia.<sup>7</sup>

> 4 Household consumption expenditures, rather than income, are used, as income suffers from widespread underreporting in household survey instruments. Consumption is also commonly used in poverty analysis.

> 5Ideally, the ILCS data would be matched with household-level data from the utility companies on gas and other alternative energy consumption and payment to allow a more accurate analysis of the residential demand and the distributional consequences of price changes. Unfortunately such data is not available for this exercise.

> 6 This assumes household don’t resort to other and cheaper sources of energy.

> 7 All data on energy access, use and expenditures come from the 2008 ILCS.

9
, and productivity in Africa. Using a similar approach, Houngbonon et al. (2022) show a positive effect of access to high-speed internet on innovation at the firm level. Combining information on the GPS location of firms with the location of the internet backbone in Senegal, Cirera et al. (2022) explore the contribution of digital infrastructure to the adoption of technologies through the effects of the proximity to better quality internet service.

> 4 As shown in the following section, the level of digital adoption in Zambia is notably higher in formal firms than in informal businesses. Given the differences, it is assumed that technological spillovers pass from formal firms to informal businesses.

3
made between direct inputs ("materias primas y auxiliares consumidas") and other costs ("otros insumos"). The first category is smaller than US or Brazilian. census inputs so the Mexican census concept of value added is different from that in the USA and Brazil (see chapter II). However, there is enough detail in the Mexican census to permit construction of a measure of value added conceptually equivalent to that in the US census (which we use here) or alternatively to measure Mexican value added in a national accounts sense (which we do in chapter IV).

For the detailed value added comparisons in this chapter for our 17 industries, we use the "US census concept" of value added. However, in the final section where we make estimates at branch levels and for manufacturing as a whole we were able to make estimates on a national accounts basis. Paige and Bombach discussed the possibilities of making value added (or to use their terminology "net output") comparisons (see also chapter I). One possible approach is the "double deflation" method, which makes separate measurements for output and inputs.
relative price distortions to agriculture – that is of particular relevance in the context of estimating the growth effects in largely agrarian Sub-Saharan African countries. Second, we control in the panel regressions for country and year fixed effects, identifying the effects that distortions to trade have on economic growth from the within-country variation of the data. Third, we provide an instrumental variables estimate of the response of policy induced price distortions to economic growth, thereby informing the political economy debate on the extent to which trade and price policies are endogenous to economic growth.

The remainder of our paper is organized as follows. Section 2 describes the data. Section 3 discusses the estimation strategy. Sections 4 and 5 present the empirical results. Section 6 concludes.

# **2. Data**

**_Relative Agricultural Price Distortions._** The World Bank recently completed a major global empirical study that estimated annual policy induced price distortions to agricultural incentives since 1955.<sup>4</sup> The study estimates nominal rates of assistance (NRAs) to agricultural industries as well as nominal rates of assistance to producers of nonagricultural tradables. The NRA is defined as the percentage by which government policies directly raise the gross return to producers of a product above what it would be without the government’s intervention (or lowered it, if NRA<0). For agriculture this was carefully estimated by comparing domestic and border prices of like products (at similar points in the value chain) for each of the covered farm industries, drawing on national statistical sources supplemented where necessary by producer prices and unit values of exports and imports from FAO (2011). To obtain the weighted average NRA for the agricultural sector as a whole, the World Bank study’s contributors calculated the weighted average of product

> 4 The study is summarized in four regional volumes, including one on Africa that also describes the myriad policies adopted and their drivers (Anderson and Masters, 2009), and a global overview volume (Anderson 2009). The panel dataset
the trans-African scale. It (1) conducts a detailed empirical stocktake of Africa’s present road network; (2) characterizes global economic optimality in network investments from a market access and welfare perspective; (3) investigates the effects of cross-border frictions on optimal spatial allocations; and (4) presents cost-benefit analyses and evidence on the macroeconomic feasibility of large scale (optimal) investments. This agenda mirrors questions of international policymakers who, in theory, can invest in roads anywhere on the continent and may ask: (1) is it still important to invest in African roads?; (2) where exactly should we invest in African roads?; (3) are investments sensible with/without trade facilitation?; and (4) are they macroeconomically feasible? The paper engages this ambitious agenda by building realistic economic topographies informed by routing engines, geospatial big data, and surveys, and analyzing investments on them using tools from graph theory and spatial economic modeling.

> * Kiel Institute for the World Economy and World Bank

> _E-mail:_ sebastian.krantz@kielinstitut.de or skrantz@worldbank.org

Latest version at sebastiankrantz.com/research, data and code at github.com/SebKrantz/OptimalAfricanRoads
1 The Role of Justice in Development: Empirical Evidence 7 respect to potential extra jurisdiction have 28.3% less congested courts and a 5% larger increase in secured loans per firm. In other words, firms with lower potential extra jurisdiction experienced higher increase in secured loans to manufacturing firms, and higher increase in firm investment and output after the introduction of the reform. Given these results, the authors make the case that an efficient judiciary is a necessary precondition for firms to benefit from financial reforms.

In India, Chemin (2009) examines the effects of judicial trial duration on credit markets, agricultural development, and manufacturing performance, using measures of procedural complexity of judicial institutions as instruments. The author finds that slower judiciaries reduce access to credit markets in the agriculture sector, leading to depressed agricultural outputs. In addition, contractintensive sectors of the economy, such as registered manufacturing, are negatively affected by weak judiciaries. Thus, these papers suggest that improvements in legal enforcement of contracts would result in a significant increase in the availability of credit and, as a result, spur economic outcomes.

Judicial efficiency also affects interest rate spread across countries, as shown by Laeven and Majnoni (2005). The paper uses a measure of the ex-ante cost of bank credit to study the relationship between judicial efficiency and the cost of financial intermediation. The authors find that judicial efficiency is an important determinant of interest rate spreads across countries. They suggest that improvements in judicial efficiency and judicial enforcement of debt contracts are critical to lowering ness to lend. Shvets (2013) studies whether the creditor’s willingness to lend to a firm is affected by the quality of courts that protect the creditor’s rights. The author analyzes loans to 11,000 Russian firms between 1996 and 2002. Court quality is measured through the share of the court’s decisions appealed. Using panel data regressions and matching firms and courts, the authors find that creditors make more loans when their rights are protected by courts with lower appeal rates.

Visaria
that lead to substantial differences in skill levels. Since we do not have information on cognitive abilities, we use only years of education here. As the estimates on the negative impact of the pandemic on enrollment in private education included in Section 3 suggest, not considering the quality of education makes our estimates to be a lower bound.

> 7 Monroy-Gómez-Franco _et al_ . (2022) provide an extension of the model that follows the framework of NLT and considers also cumulative learning losses. Their application to Mexican data indicates that a learning loss equivalent to one third of a school year could translate into a long-run learning loss between one and two years. Of course, there could be dynamic effects of the interruption in human capital accumulation. Disruptions earlier on in life may have longer lasting and more severe effects. This implies that younger children may be more heavily impacted than older ones.

11
beliefs about groups who are generally believed to exploit the tax system would be most effective, even if the subject is not a member of these groups.

The obvious group to use to distinguish these two effects is rich people. There is substantial reason for citizens to believe that the rich in society are not paying their full tax burden. The Panama Papers revealed the extent to which wealthy citizens around the world have been using various legal and extralegal methods to avoid paying tax in their home countries (Harding 2016). Similarly, companies are able to use various accounting mechanisms and legal structures to pay rates well below the formal rates (for instance the average corporate tax rate actually paid by US companies was 12.1% of profits in 2011 compared to a headline rate of 39.1% (Paletta 2011)).

The mechanism behind social norms treatments is important to understand because it affects what tax authorities should prioritize in terms of messaging and enforcement. If descriptive norms are the relevant mechanism, then focusing enforcement efforts on average taxpayers will be most effective because it will change the descriptive norms that are relevant to the most people. However, if the free-rider mechanism is the important one, it suggests that tax authorities should instead focus their enforcement efforts on visibly cracking down on tax evasion by groups who are most seen as exploiting the system, such as the richest taxpayers (Motel 2015).

We compare three different social norm treatments focusing on the tax paying behavior of people generally, rich people and companies. On the reference group account, only the people treatment should be effective, while the avoiding being a sucker account would predict that all treatments could be effective but especially the rich people and companies treatments.

We ran this survey in five English speaking countries covering a variety of development and tax compliance levels: Kenya, Australia, the United States, the Philippines, and South Africa. Our results support the free-rider interpretation of social norm interventions. We find large effects of priming rich people’s tax compliance (
scholarship. Out of the 13 baseline characteristics, only one is statically different for treatment and control institutes, namely the admission score obtained in the application process. This variable is only available for one-third of the students, as many institutes did not provide the score. Nevertheless, it deserves careful consideration since the variable correlates with student outcomes and displays a large and significant imbalance (p < 0.01). For this reason, we include it in our baseline specification.

The average number of professors per institute is relatively high (40) compared to the number of students (168). Furthermore, the SENESCYT disposes of information on professors’ cognitive and non-cognitive skills (collected using the DESCAES standardized test), a proxy for their human capital. These variables' information does not provide evidence of a significant imbalance between treatment and control schools.<sup>6</sup>

# _4.3. Intervention’s Take-up_

After randomization, 6,069 students in the 39 TTCs were assigned to receive ALEKS. However, only 84 percent (or 5,077 students) used the license. One possible explanation for the initial drop in the sample is that some students only confirmed their enrollment in the course after their license was issued or failed to follow through with their intention to enroll. Of the 5,077 participants, 97 percent used the platform at least once, and 74 percent used it for 360 minutes or more per month during at least one of the five intervention months. McGraw Hill recommends using the platform for at least 90 minutes per week or 360 minutes per month. Although teachers encouraged using the platform, doing so was not compulsory. Using ALEKS did not affect student grades, which may have decreased students' incentives to use it during the mandated times.

**Figure 2** shows that the take-up of ALEKS fluctuated from month to month, starting a little above 50 percent in January and achieving its peak in February and March, when 86 percent of the students with licenses
45.3%|-0.6|
|AfroBrazilian male head|30.4|37.9%|37.0%|-0.9|

Source: authors’ estimations based on PNAD-C 2019. Pre-fiscal income considers the household-level market income plus pensions, while the post-fiscal income considers the household-level consumable income. Afro-Brazilians are those declared to be either Black or Pardos. Proportion of households indicate the number of households in each group as a share of the total number of households in PNAD-C. Categories that account for less than 1 percent of the population not shown.

**Table 9. Moderate poverty headcount rates considering both pre‐ and post‐fiscal income aggregates, separately for distinct groups of individuals in terms of race and age**

|**Race/age of the survey respondent**|**Prop. of**<br>**individuals**<br>**(%)**|**Pre-fiscal**<br>**poverty**<br>**rate**|**Post-**<br>**fiscal**<br>**poverty**<br>**rate**|**Change in**<br>**poverty**<br>**(p.p.)**|
|---|---|---|---|---|
|White aged 0-15|9.2|31.6%|34.6%|3.0|
|AfroBrazilian aged 0-15|13.2|56.0%|59.2%|3.1|
|White aged 16-17|1.1|28.4%|31.2%|2.8|
|AfroBrazilian aged 16-17|1.9|52.1%|54.5%|2.4|
|White aged 18-59|
much as 47% to 57% in 1997.

Table 1 suggests that geographic targeting using any of the indicators under consideration appears to approximate the distribution of poverty reasonably well. When any of these indicators is used for geographic targeting, about one-half of all resources are transferred to rural areas, and about two-thirds of the remaining resources to urban areas outside Lima. Comparing the three indicators, the FONCODES index transfers more to Lima than the measures of infant mortality and imputed poverty, while the measure of infant mortality makes the largest transfers to rural areas.

irrespective of the district in which these households live. If a number of samples were drawn, on average, the total budget would be the same across indicators, but this does not hold for any one sample. I have corrected for this problem by normalizing the budget-in effect, summing equation (2) above only over the sample of districts in the LSMS. Note that this is not an issue with the concentration curve approach because concentration curves graph out the _proportion_ of a given budget that is allocated to each household

**10**
# **B Coding sex assigned at birth**

Determining the assigned sex at birth in administrative tax data is usually a difficult task. Gender is typically not reported on most tax forms. A solution used by scant literature is to infer gender using names and text analysis (e.g., see Scot et al., 2023; López-Luzuriaga and Scartascini, 2023, for recent applications). In this paper, we rely instead on personal tax identifiers known as CUIT (an acronym for _Clave Única de Identificación Tributaria_ , “unique tax identification”), which allows us to infer the gender assigned at birth from the first two digits and the last digit. The CUIT is a national tax identification number in Argentina, assigned by the Federal Administration of Public Revenue (AFIP) to people and businesses.

The CUIT number has a **YY-XXXXXXXX-Z** structure, with the following meaning:

- **YY** is the type: 20, 23, 24, 27 (individuals), 30, 33, 34 (businesses).

- **XXXXXXXX** is the national ID number (DNI) or business number.

- **Z** is a verifying digit.

In the case of individuals, the following administrative rules can be used to determine sex:

- **Female:** YY=27, or YY=23 and Z=4.

- **Male:** YY=20, or YY=23 and Z=9.

- **Unknown:** YY=24. This applies to a few cases for which we will have missing gender.

Following methodological guidance from our team, the municipality’s staff utilized this structural feature of the CUIT in the property register, to infer the gender assigned at birth and the owners’ ages for over 100,000 properties annually. We then linked the gender indicator to the main property tax database to generate a sex-disaggregated analysis.

Following the strategy described above, we were able
|12|19|19|<.001|<.001|
|Sample size|-|34|34|51|51|<.001|<.001|
|Aggregated to bias level||||||||
|Selection||9|12|39|40|<.001|<.001|
|Attrition||15|16|46|46|<.001|<.001|
|Performance||38|38|41|41|.515|.515|
|Detection||37|35|53|51|<.001|<.001|
|Reporting||0|0|17|17|<.001|<.001|
|Sample size||1|1|52|52|<.001|<.001|

Source: Authors’ RCT assessment data.

48
.0368)||(0.0383)||
|Constant|0.410***|‐0.224|0.151|‐0.251|‐0.00133|‐0.421|0.225|
||(0.120)|(0.135)|(0.302)|(0.245)|(0.314)|(0.264)|(0.502)|
|Observations|7,812|7,812|5,398|5,398|5,295|5,295|5,295|
|Countries|52|52|36|36|35|35|35|
|R‐squared|0.680|0.693|0.710|0.719|0.714|0.723|0.714|
|All variables in logs. "Sovereign EMBI spre<br>Total Government includes central and lo<br>Monthly data January 2000 to December<br>Robust standard errors in parentheses. *|ad" is the m<br>cal governme<br>2017. Countr<br>** p<0.01, **|aximum value<br>nts. PNFEs: pu<br>y and year fixe<br>p<0.05, * p<0|of Sovereign<br>blic non‐fina<br>d effects incl<br>.1|EMBI spread o<br>ncial entities.<br>uded.|bserved in eac|h month.||
|Source: National University of Singapore‐|Credit Resear|ch Initiative, I|MF‐Internatio|nal Financial S|tatistics, Bloo|mberg.||

50
check, while continuing to honor judicial independence (Rosselli 2020). Consistent with this finding, a large group of studies find mixed evidence of positive effects on judicial effectiveness from increased court budgets (for example, Voigt and El Bialy (2013) for European courts).

# **2b. Access**

Access to justice measures the ability of the justice system to deliver outcomes that are accessible to all, irrespective of wealth, status, and gender. This includes eliminating barriers that prevent people from understanding and exercising their rights; and delivering fair and just outcomes for all citizens, including those facing financial and other disadvantages (Cappelletti 1981; Peysner 2014).

Access is associated with higher rates of entrepreneurship (Lichand and Soares 2014). Evidence from debt recovery tribunals in India shows that the speed and affordability of justice greatly increases the use of formal courts (Visaria 2009; Lilienfeld-Toal et al 2012). Lower court fees in the resolution of commercial disputes are also associated with a smaller size of the informal sector (Djankov et al 2003), another proxy for access. Empirical evidence on access to justice on GDP per capita growth in a panel of 83 countries from 1970 to 2014 shows that increasing access to justice by 1% increases the five-year growth rate of GDP per capita by 0.86 p.p. (Deseau et al. 2019). Cross-country and within-country evidence shows that increased court access, in the form of higher speed and lower procedural formalism, is a strong correlate of economic development (Chemin 2009a).

Although laws may be equal, prevailing discriminatory social norms, deeply rooted stereotypes, unconscious bias, and even ignorance or reluctance by institutions responsible for enforcing rights can be a major stumbling block to the implementation of legislation (Hyland et al 2020). In Pakistan, for example, Holden and Chaudhary (2013) and Ahmad, Batool and Dzueguelewski (2016) find that despite a legal change, few women were able to access justice due to factors such as lack of education
college completion rates among low-income students: Evidence from a randomized controlled trial evaluation of a case management intervention (No. w24150). National Bureau of Economic Research.

- [32] Ferreyra, M., Dinarte, L., Urzua, S., & Bassi, M. (2021). _The fast track to new skills: short-cycle higher education programs in Latin America and the Caribbean_ . The World Bank.

- [33] Ferreyra, M., Avitabile, C., Botero Alvarez, J., Haimovich Paz, F., & Urzua, S. (2017). _At a crossroads: higher education in Latin America and the Caribbean_ . The World Bank.

- [34] Garriga, C. & M.P. Keightley (2007). A general equilibrium theory of college with education subsidies, in-school labor supply, and borrowing constraints. Federal Reserve Bank of St. Louis Working Paper 2007-051A.

- [35] Gurantz, O. (2020). What does free community college buy? Early impacts from the Oregon Promise. _Journal of Policy Analysis and Management, 39(1),_ pp. 11-35.

- [36] Hai, R., & J.J. Heckman (2017). Inequality in human capital and endogenous credit constraints. NBER Working Paper No. 22999.

- [37] Heckman, J. J., Lochner, L. & Taber, C. (1998). General equilibrium treatment effects: A study of tuition policy. _The American Economic Review, 88(2)_ , pp. 381-386.

- [38] Hendricks, L. & Leukhina, O. (2017). How risky is college investment? _Review of Economic Dynamics, 26,_ pp. 140-163.

- [39] Hendricks, L., & Leukhina, O. (2018). The return to college: selection and dropout risk. _International Economic Review, 59(3),_ pp. 1077-1102