rafmacalaba/data-use-mentions-extended huggingface dataset:
are nearly all concentrated in big cities, with not a single tier 1 private college in any small city. Meanwhile, the lowest quality heis are disproportionately located in small cities, with nine out of the ten bottom tier public colleges located in small cities. We anticipate that given this spatial distribution in quantity and quality of hei institutions in Colombia, individuals in small cities would seek to attend college in big cities while for the most part, only the most talented or with sufficient means would be able to move for college.

## **_Our sample vs. Colombian population_**

We compare demographics in our sample to those in the Socio-Economic Database for Latin America and the Caribbean (sedlac), a nationally representative household survey of Colombia. We restrict the sedlac data to workers with a bachelor’s degree employed in the formal sector who are aged 20-35. Our sample is remarkably similar to the sedlac sample. For instance, in the sedlac sample 55.5% are female (vs. 57% in our sample), aged 29 on average (vs. 26.6 in our sample), and earn raw average annual wages of 1,692,957 Colombian pesos (vs. 1,647,482 in our sample). Most importantly, the spatial distribution of the sedlac sample is similar to ours: 62.5% are in a big city for work (vs. 64.5% in our sample), 22% are in a medium city (vs. 17.2% in our sample), and 15.4% are in a small city (vs. 18.2% in our sample).

We also use sedlac data to examine labor force participation and incidence of work in the formal sector among young college graduates. The majority (78%) of those aged 20-35 with a bachelor’s degree in Colombia are employed in the formal sector, only 2.5% are in the informal sector, while 20% are not employed
is not collected in the household survey. Table A.1 in the appendix shows Custom Duty, Sales Tax, and Green Tax Rates in 2017 for select items identified in the household survey.

# **_Social insurance contributions_**

There are broadly pension-related contributions and non-pension related contributions. The former includes the National Pension and Provident Fund for civil servants, and the Provident Fund for private sector employees. Various life insurance schemes exist that fall under non-pension related contributions.

- National Pension and Provident Fund<sup>7</sup> : mandatory savings plan of the civil servants, state-owned corporations, and the armed forces. Civil servants contribute a minimum of 11 percent of the monthly salary, while members of the Armed Forces and the Royal Bhutan Police contribute 15 percent and 12 percent, respectively. The employer contributes an equal amount.

- Provident Fund<sup>8</sup> : covers employees of private sector and non-government entities. The contribution is determined by the employment contract and is no less than 5 percent of the basic salary.

- Rural Life Insurance: voluntary life insurance for people above eight years old residing in rural areas. In 2017 the annual premium was 87 Nu per person.

> 7 Rules and regulations of the NPPF are enacted in the “National Pension and Provident Fund Plan (Amendment of 2010), Rules and Regulations of the Kingdom”.

> 8 Rules and regulations of the provident fund scheme are contained in the Private Fund Scheme of Royal Insurance of Bhutan Limited, of the Kingdom of Bhutan 2011.

5
that participate in it.

We merge these data sets using unique tax identifications of workers and firms that are common across sources. To secure the privacy of workers and firms, the Central Bank of Chile mandates that the development, extraction, and publication of the results should not allow the identification, directly or indirectly, of natural or legal persons. Officials of the Central Bank of Chile processed the disaggregated data and merged them with financial records from the Financial Market Commission. The authors implemented all the analysis and neither involved nor compromised the Central Bank of Chile, the Financial Market Commission, or the Chilean tax authority.<sup>23</sup> The merged data set allows us to study the real and financial aspects of both the credit and the employment programs covering the universe of firms. For most of the analysis, we use the 2018–2020 period of these data sets.<sup>24</sup>

# **4 Credit Distribution across Firms**

## **4.1 Measuring Firm Risk**

To assess a firm’s _ex ante_ credit risk, we estimate a default probability model, which we then use in our selection models of government programs. We estimate the following cross-sectional probit model to predict default during 2019, based on attributes during 2018:<sup>25</sup> Pr( _Defaulti_ = 1) = Φ( _βCharacteristicsi,−_ 1 + _αs_ + _αm_ + _ui_ ) _._ (1)

> _Defaulti_ is a dummy equal to one if the firm defaults on a loan during 2019 (i.e., has a loan past due more than 90 days) and equal to zero otherwise. _Characteristicsi,−_ 1 is a vector of _ex ante_ firm-level attributes during 2018 that the literature uses to predict default rates (Glennon and Nigro, 2005; Crawford et al., 2018).<sup>26</sup> This vector contains five real economic variables reported to the tax authorities: net worth
characteristics, the urban and rural shares of consumption can be used as weights to proportionately allocate fixed costs across households.

More specifically, 27.8% and 72.2% of the utility’s total annual fixed cost requirements (after adjusting for lump-sum transfer of subsidies) are divided equally across all rural and urban households respectively. Figure 20 shows the results of this exercise. In contrast to Figure 19, the consumer welfare goes up in all quartiles but change in surplus relative to the current schedule persists to be negative.

To further improve the targeting of fixed costs, and especially to defray the fixed costs borne by households in the lowest quintile, the next counterfactual explores the use of historical householdlevel consumption data as a proxy for household’s willingness to pay for electricity. Historical consumption data is indicative of the level of investment made by households in buying electrical appliances. Households with greater ownership of these appliances (and therefore greater consumption in the past periods) will have a higher demand for energy and may potentially be willing to pay more for electricity. Following this rationale, fixed costs across households are allocated using the following formula: 2 2⁄ ) + 1 nn2⁄ ii � ~~�~~ ii − (∑QQ NN FFCC = (FFCC−1) ∗�1 +<sup>QQii</sup> where, denotes the fixed cost allocated to household �∑QQnn for each day in the billing cycle, ii NN is the per household per day fixed cost requirement of the utility,<sup>29</sup> 2 is the square of historical consumption ii FFCC uu FFCC across all billing cycles in a year and is the total number of residential consumers served by the utility. ii 2 QQ The term ⁄ is the mean of the square of annualized consumption across all residential NN consumers. The deviation of household’s annual consumption from the mean is used as a weighting nn ii factor to allocate fixed costs. Households with low levels of yearly consumption have
particular, an international bank can provide a loan through an entity located in its parent country, or alternatively through a foreign subsidiary located either in the borrower’s country or in a third country. In our sample for the years 1995-2016, we find that the shares of the loan volume provided through foreign subsidiaries located in the borrower’s country and in a third country were 8.9% and 3.1%, respectively. For 12.7% of loans provided through borrower-country subsidiaries, the subsidiary country had less stringent capital regulation than the parent country, while this was the case for 30.6% of loans provided through a thirdcountry subsidiary. These data are consistent with some regulatory arbitrage by international banks via loan origination through subsidiaries located in countries with relatively lenient capital regulations.

Analogously to Houston, Lin and Ma (2012), we estimate the impact of bank regulation and institutional quality on the aggregate volume of syndicated loans to borrower countries, and also on syndicated loan volumes bilaterally at the borrower country, lender country level. In addition, we examine how regulation and institutions affect the share of loans provided through foreign subsidiaries.

We find that loan inflows into borrower countries are positively related to private monitoring incentives related to financial institutions in these countries, consistent with a positive impact of institutional quality on loan inflows. Further, the foreign subsidiary share of loans at the borrower country level is negatively related to borrower country capital regulation stringency consistent with regulatory arbitrage, while it is positively related to the strength of creditor rights and the speed of contract enforcement in borrower countries, reflecting a role for institutional quality to affect the location of loan origination.

When considering syndicated loan data at the bilateral level, we find that loan volumes negatively reflect capital regulation stringency in lender countries consistent with regulatory arbitrage, while they positively reflect several aspects of institutional quality in both borrower 3
column 7 we show that 32% of the control group were perhaps confused in public. They give an incorrect answer in public but it was not what the leader and deputy said. Similarly, in column 10 we show that 24% of the control group switch to conform and answer the primed response (A) in the public game. For both these outcomes we find no evidence of a _Choices_ treatment effect. The coefficients on the variable _Any response in positions before correct answer C_ for the _confused_ and _conform_ outcomes in columns 7-12 are negative and significant. This suggests that the adolescents (especially for girls in columns 9 and 12) were less likely to give an incorrect response in public when an adolescent before them in the line-up gave a correct response.

Overall, the results in Tables 4 and 5 provide evidence of conformity among adolescent peers as was found in the Asch experiments. However, _Choices_ participation does not affect how adolescents play the line match games and therefore _Choices_ does not appear to affect general levels of conformity.

We also find no _Choices_ treatment effect in the response to the survey statement: “There have been times when I felt like rebelling against people in authority even though I knew they were right.” We measure results at midline and endline (not shown here). Therefore, put together our results suggest no indication of increased rebellion among _Choices_ adolescents. The _Choices_ program did affect private gender attitudes and it did affect public reports of gender attitudes in ways that suggest that adolescents’ perceptions of gender norms (or acceptable responses) may have shifted in interesting ways that could theoretically be linked directly to the program content on gender equality.

> 66Note - the line match confederates game was played at the very end of the session so that the leader and deputy could be primed outside before playing the final game in public.

> 67Asch (1952) showed that conformity increases when other members of the group are of a higher social status so we may expect conformity by the adolescents to be higher after the
education (Streatfield, Singarimbun, and Diamond, 1990). The authors argue that this is possibly due to higher social compliance with local authorities among women with no education, and greater awareness of the protective function of vaccines among women with higher education.

In addition to their socio-economic characteristics, individuals’ _beliefs and experiences_ are linked with their probability of being vaccinated. Perceptions of the risk of infection influence the decision to get vaccinated. Using data collected at the onset of the pandemic in the United States, Bundorf et al. (2020) find that individual beliefs about COVID-19 risks were related to actual preventive behavior. Those who considered themselves to be at high risk to infection significantly reduced activities which may expose them to the virus. Personal past experiences of disease positively affect the likelihood of getting vaccinated in the future. Jin and Koch (2018) show that this “learning by suffering” effect works both ways and find a negative impact on vaccination rate for individuals who got sick in the previous year despite having taken the shot.

The second strand of research that our paper contributes to deals with socio-cultural factors that influence individual health behavior. Social norms have increasingly been shown to play a role in vaccine acceptance and other preventive health measures (Moehring et al., 2021; Allen et al. 2021).<sup>2</sup> The direction of the effect is, however, dependent on the context. Social norms can have a conformity or peer effect such that individuals align themselves with others in adopting or rejecting preventive health measures (Goldberg et al., 2021). On the other hand, due to the positive externalities associated with vaccines, norms can generate a free-riding effect so that individuals benefit from others’ immunity without incurring the associated costs themselves. Using a study in Mozambique, Allen et al. (2021) show that the dominating effect depends on the level of local infection rates.

> 2 Norms can be descriptive (what people actually do) or injunctive (what people approve of
use of savings accounts.

There is less evidence from experiments involving African credit products than for savings products. But some evidence from micro-credit experiments in Africa suggests that beneficial effects are harder to identify than for savings. For example, Tarozzi, Desai and Johnson (2015) use data from an RCT conducted in rural Ethiopia to study the impact of access to microcredit on income from agriculture, animal husbandry, nonfarm self-employment, labor supply, schooling and indicators of women’s empowerment. The study does not find clear evidence of widespread improvement in socioeconomic indicators in treated areas.

Another key impediment to broader extension of credit in Africa is the lack of reliable methods for personal identification. Individuals who lack collateral and credit histories, which characterizes a large share of the African population, struggle to overcome informational asymmetries that make it almost impossible to access credit from formal sources. Establishing collateral and credit registries could help, but these can only function if people can be accurately identified. A field experiment among paprika farmers in Malawi tested whether biometric identification methods can improve the functioning of credit markets in a country where identity theft is common (Giné et al. 2013). Applicants for agricultural input loans from a state-owned bank were randomly assigned to a treatment group in which a fingerprint was collected from each member as part of the loan application, or to a control group in which no fingerprint was taken. Both treatment and control groups attended a training session on the importance of a credit history in ensuring future access to credit. For the sub-group of farmers identified as having high ex-ante default risk, fingerprinting led to a 40% increase in repayment rates.

30
**Table 2. Empirical Evidence on Selected Anti-corruption Programs**

|**Program**|**Empirical Evidence**|
|---|---|
|Anti-Corruption<br>Agencies|Anti-corruption agencies have been successful in Chile, Hong Kong, New South<br>Wales, Australia and Singapore (Allan; 1992, Clark; 1987, Holm; 2000; Doig;<br>1995; Klitgaard; 1998; Segal; 1999 and World Bank, 1999). Developing country<br>officials however do not see these as effective anti-corruption tools in countries<br>with endemic corruption (see Kaufmnann, 1997).|
|Public Opinion<br>Surveys|Public opinion surveys have served as a useful tool in articulating more precisely<br>citizens' concerns (e.g. Bangalore scorecard and a "corruptometer" by an<br>Argentine NGO). International surveys, such as those compiled by<br>Transparency International, highlight countries in which corruption is perceived<br>to be endemic.|
|Raising Public<br>Sector Wages|Rijckeghem and Weder (1997) find no short run impact (as the income from<br>bribery dominates total income). Gurgur and Shah (1999, 2000) find negative yet<br>insignificant effect. Treisman (1999) and Swamy et al. (1999) find no<br>relationship. The SDC experience in the forestry sector in Pakistan also<br>confirms this. In corrupt societies public positions are often purchased by<br>borrowing money from family and friends. Raising public sector wages simply<br>raises the purchase price and subsequent corruption efforts to repay loans. Of<br>course raising public sector wages which do not allow the employee to satisfy<br>basic needs of his/her family, are likely to reduce petty corruption.|
|Reducing Public<br>Sector Size|<br>Tanzi and Davoodi (1998
|
|Inflation targeting regime<br>(1995-2016)|<br>A dummy variable equal to 1 for inflation targeting regime and 0 for no<br>inflation targeting regime.|IMF, Annual Report on<br>Exchange Arrangements and<br>Exchange Restrictions<br>database|
|Central bank<br>transparency<br>(1995-2014)|Index calculated from responses to 15 questions. To expand the sample,<br>the index was extrapolated to 2015-16 using 2014 data.|Dincer and Eichengreen (2014)|
|Exchange rate regime<br>(1995-2014)|The exchange rate regime classification developed in Shambaugh (2004)<br>is used to determine whether a country has a pegged or flexible exchange<br>rate. To expand the sample, the index was extrapolated to 2015-16 using<br>2014 data.|<br> <br>Shambaugh (2004)|
|Financial openness<br>(1995-2015)|Index of de jure capital account openness. To expand the sample, the<br>index was extrapolated to 2016 using 2015 data.|Chinn and Ito (2017)|
|Trade openness<br>(1995-2016)|Imports divided by domestic demand (domestic demand = GDP +<br>imports - exports).|IMF, International Financial<br>Statistics database|
|Gross public debt (1995-<br>2016)|Gross public debt divided by nominal GDP.|IMF, World Economic<br>Outlook database|
|Oil prices<br>(1990H1-2017H2)|Index is in nominal U.S. dollars.|World Bank, Commodity Price<br>Data (the Pink Sheet)|
|Food prices<br>(1990H1-2017H2)|Index is in nominal U.S. dollars.|World Bank, Commodity Price<br>Data (the Pink Sheet)|
|Global liquidity<br>(1990H1-2017H2)|International claims on all sectors (annual
**Table 5. Means and Percentages for Selected ECD Indicators by Survey Round and** **<u>Treatment Status, Philippines ECD Study</u>**

|||Round 1|||Round 3||
|---|---|---|---|---|---|---|
|Indicator/Region|Program|Non-|Total|Program|Non-|Total|
|||program|||program||
|**1. Cognitive, Social,**|**Motor and**|**Language De**|**velopment**||||
|Gross motor skills|–.197|–.296|–.223|.091|–.329*|–.021|
||[.043]|[.053]|[.034]|[.029]|[.081]|[.029]|
|Fine motor skills|–.237|–.134|–.217|–.491|–.629|–.526|
||[.041]|[.080]|[.034]|[.052]|[.052]|[.040]|
|Expressive|–.255|–.277|–.260|–.012|–.416*|–.116|
|language|||||||
||[.034]|[.055]|[.029]|[.024]|[.061]|[.024]|
|Receptive|–.209|–.133|–.192|–.046|–.444*|–.149|
|language|||||||
||[.040]|[.085]|[.031]
15% of allocated land is operated, a share that decreases from 88% in the group below 5 ha to 11% in the above 500 ha group (table 5). While smallholders below 1 ha cultivate more than 95% of their land, mean land use intensity for the few (154) smallholder farmers with more than 10 ha is only 22%.

Recent digitization of all the country’s estate leases by a World Bank supported project supports these general conclusions. It puts the number of leases at 58,733 (of which 35,140 are in agriculture) and suggests that the majority is expired. Even if 40% of the land leased to estates were non-existent or unsuitable for agriculture, collecting lease fees equivalent to the median value of rent paid (or self-declared willingness to pay) in the 2010/11 IHS for the remainder would generate a total of close to US$50 million per year, in lease fees. Further analysis to eliminate overlaps and so-called ‘ghost estates’,<sup>14</sup> align registered estate boundaries with actual land use, trace changes in ownership or occupation, and renew expired leases can help further refine this. Analysis also suggests that not all estate land may be effectively used.<sup>15</sup> Comparing production structure between smallholders and estates reveals differences in cropping patterns: for sampled estates, 42% of area is devoted to tobacco, followed by maize (39%), groundnuts (7%), and other crops (table 5). While differences in terms of crop composition somewhat limit the scope for comparing yields, mean values suggest that for all crops with the exception of sunflower and tea, yields by smallholders are significantly above those by estates. As estates use consistently more inputs than smallholders, this suggests a strong negative relationship between farm size and productivity on the land area actually cultivated. Non-parametric regressions of maize, rice, tobacco and coffee yields for the pooled sample plotted against the log of
), then productivity is likely correlated with higher employment growth. From the perspective of this paper, both employment and revenue metrics are critical, although, given that the focus of developing country policy makers on jobs, HGFs computed using employment metrics is more relevant.

# **_Agglomeration and Growth Entrepreneurship_**

There is a paucity of research linking clustering to high growth even in the context of advanced economies. Clustering matters because firms located in cities may benefit from thicker markets for capital, labor and material inputs (Fujita et al., 1999), knowledge spillovers and reduced transaction costs (Audretsch and Feldman, 1996). Clustering of economic activity can also yield dynamic benefits, such as increased innovation (Matsuyama, 1991) and competition, which may also reduce the scope for opportunistic behavior (Collier and Venables, 2008). Studies suggest that firms located in geographic regions with industry clustering exhibit higher rates of growth due to enhanced demand for their products and a higher ability to harness reputational advantage (Gilbert et al., 2006, 2008). Clustering can generate high firm growth by providing greater access to both customers and prospective suppliers (Lechner and Dowling, 2003).<sup>14</sup>

# **_Manufacturing and Industrial Policy Reforms in Ethiopia_**

Ethiopian manufacturing has been the focal point of several studies that document the performance, productivity as well as the challenges for the sector. Ethiopian manufacturing grew at an average rate of 10.5 percent over 1996–2009, which coincided with employment growth of 4.1 percent. During this period, manufacturing was dominated by smaller firms, with a highly-skewed size distribution. Overall, less than 5 percent of manufacturing firms exported, with the total value of their exports being less than 8 percent of

> 13 This would be true for young firms relative to incumbents (Du et al., 2013).

> 14 On specific works pertaining to geography and HGFs, Anyadike-Danes, Bonner, and Hart (2013) focus on the type
the Jump$tart surveys administered to high school seniors. Beal and Delpachitra (2003), a study of Australian college students, also found low levels of financial literacy.

Various factors may account for different levels of financial literacy among youths. Valentine and Khayum (2005) find that working 10-20 hours a week, having a savings account, and plans to pursue post-secondary education are associated with high levels of financial literacy for high school students. Chen et al. (1996) find that college students’ knowledge of personal investment is low; business majors are more knowledgeable than non-business majors, and within business majors, finance/accounting majors are most knowledgeable. Chen and Volpe 11