rafmacalaba/datause-encoder-data huggingface dataset:
In the default model, we decompose total investment into private and public investment (these have identical effects in the LTGM-NR, but have different determinants). Private investment as a percentage of GDP is assumed to remain at approximately 17.7%, which is the historical average between 2004 and 2019 according to data from the Central Bank of Peru. Public investment is set at 4.7% using data from the IMFFAD, which represents the average of the last 10 years of available data.

Total factor productivity growth of the non-resource sector is generated using the LTGM-TFP extension (Kim and Loayza 2019) assuming that the determinants of TFP - innovation, education, market efficiency, infrastructure, and institutions – increase at their historical rate over the past decade.<sup>9</sup> This results in an almost-constant path of TFP growth over the simulation period of 0.5%-0.6%, which is similar to the historical average over 2004-19.

The human capital growth path for the baseline is generated using the LTGM-HC extension, assuming that the current quality and quantity schooling and health of today’s children persists throughout the simulation period. This generates human capital growth of 0.3% in 2022 and declines to about 0.1% in 2050. We assume the average expected years of schooling in the future is the same as that of today’s children, 12.7 years, taken from the World Bank Human Capital Index. Because today’s new workers are better educated than older workers moving to retirement, the average human capital of the workforce grows over time – despite no increase in the schooling of children. The rate of human capital growth slows over time due to rising average education quantity of the workforce, which reduces the educational gap between new workers and incumbents. Human capital also includes the quality of education, as well as health. The health of the population is measured by adult survival rates (ASRs), which is the probability that a 15-year-old will reach their 60th birthday, and stunting rates, defined as the fraction of 5 years old that are not stunted (see equation below).<sup>10</sup> In the baseline, we assume that schooling quality remains at its original level of 0.65, ASRs stay at 0.88 and the not stunted rate stays at 0.87. Data on the health and education variables are taken from the World Bank’s Human Capital Project. Due to a lack of historical data, we also assume that those rates apply to the whole working age population, and so schooling quality and health make no contribution to human capital growth in the baseline.

> 9 Specifically, we applied the average annual change in the overall determinant index over 2010-2019 to the period of 20212050.

> 10 hH = S hf × H Y ×Q −14 ) <u>� StS t</u> 2t <u>ASA t �</u> . The return to education is γγ t(N ttSSttS ttNNdd t−1)+γγ A(AASSAA t−1) assumed to be 12%. HH φφ(Y YYho lltt QQlliittyytt tt tt tt ooiioogg HHootth = HH × HH

9
thus expect that the impact of CB reform to be higher for firms in countries with high contract enforcement costs.

The Enforcing Contract module of the Doing Business report ranks countries on the time, cost and number of procedures involved from the moment the lender files the lawsuit until payment is eventually received. We combine the three components into a single standardized index using weights derived from principal component factor (PCF) analysis.<sup>7</sup> We also use the ICRG Law and Order risk rating<sup>8</sup> as a more general measure of the contractual environment. The Law and Order rating assesses strength and impartiality of the legal system (Law), and of popular observance of the law (Order). The variable ranges from two to six, and higher values correspond to better institutional environment.

Table 7 shows the differential effect of CB reform when one considers the heterogeneity in enforcement environment across countries. We are able to verify the hypothesis that the impact of CB reform on access to finance is greater in countries with a less developed contract enforcement environment.<sup>9</sup> For all dependent variables except interest rate, we find that the positive impact of CB reform is differentially greater for countries that score lower on the Doing Business contract enforcement index. In the case of the more general Law and Order ICRG variable, we find statistically significant and qualitatively consistent effects on the interest rate and on fraction of working capital and fixed assets financed by banks.

> 7 The weights assigned to the standardized (with mean 0 and standard deviation 1) subcomponents cost (% of claims), time (days) and number of procedures are 0.74, 0.53 and 0.75 respectively. Higher values of the index correspond with better enforcement.

> 8 PRS group publishes the International Country Risk Guide (ICRG) annually with information on 22 variables with risk ratings for political, financial, and economic factors. See < www.prsgroup.com> for more details. 9 We consider the interaction of CB reform with the average of DB contract enforcement index. The index has very low variability across time in our sample and is not available for earlier years. Since our aim with this interaction is to study variability across countries, we feel this approach of interacting with the country average overcomes data non-availability without compromising our analysis. The averaged index itself is subsumed into the country fixed effects and does not enter on its own. For consistency, we do the same for the ICRG Law and Order index.

16
ν is a cohort fixed effect that is time-invariant, and τ is a time effect. We estimate two labor market indicators of interest –the log hourly wage and the employment status –for the population between 35 and 40 years of age. The estimation with our pooled data also includes cohort fixed effects with which the coefficient α1 represents the scarring effect of the Osow youth status at early stages in the life cycle on future labor market outcomes. Following McKenzie (2004), since cohort sizes are quite large, we estimate equation (13) by OLS including cohort fixed effects, and since the independent variable for log wages is only computed for employed individuals, we do not expect significant biases from sample selection.

Table 3 summarizes our results for the female and male estimates, respectively, by presenting the coefficient of interest.<sup>30</sup> As can be seen, in the case of wages we find evidence of strong scarring effects of higher proportions of individuals in the Osow youth group at 15-20 years of age, being associated with lower wages both, for males and for females, later in the life cycle at ages 35-40. For employment levels, significant scarring effects from being Osow at ages 15-20 are also observed in the case of females, although not for males.

Thus, our results support the view that the prevalence of the Osow youth problem is of interest for reasons that have to do with contemporary concerns including the exposure to risks, and lower productivity due to loss of human capital acquired through education and experience, but there are also grounds for worries about future effects over labor market outcomes.

# **5. Conclusions**

This paper examines the Osow youth phenomenon through a dynamic approach by constructing a household survey database that allows tracing the life cycle trajectories of different cohorts over time. By following life-cycle trajectories of synthetic cohorts we are able to identify whether what is observed in other studies at snapshots during the 15-18 or 19-24 age spells are due to contemporaneous factors, or if they are also associated to dynamics at previous ages including for instance early school dropout, and whether the status of Osow youth remains or not thereafter. Furthermore, we estimate the influence of some of the variables associated with being Osow, as well as possible effects observed through labor market outcomes at later stages of the life cycle.

By smoothing out the age, time, and cohort effects inherent in life cycle profiles we find that for Latin Americans there is an average pattern of life cycle trajectories of high school attendance early from ages 6 to 12, with a considerable drop that starts smoothing out at age 20. Similarly, there is a trend of increased labor force participation from early ages and up to the same turning point observed when cohorts are 20 years old, with continuous increases thereafter that start smoothing out before age 30. Given that the drop in school dropout is faster than the rate of labor

> 30  Coefficients for the control variables are omitted for clarity in exposition.

30
nomic characteristics, sexual attitudes and behaviors, and knowledge about HIV/AIDS and other sexually transmitted infections.

To measure preference for risk, we implemented a standard hypothetical risk aversion question in the baseline questionnaire. The question was based on Holt and Laury (2002) and consisted of a set of where the could choose between re- sub-questions respondent ceiving a fixed amount of money for certain or participate in a lottery with 50% chance of winning 500 maloti. Using these data we construct an indicator variable “Risk loving” which takes the value 0 for respondents who preferred a fixed amount of money below the expected value (of 250 maloti) instead of a lottery with 50% chance of winning 500 maloti, and 1 for respondents who needed to be compensated with an amount equal or above (250 maloti) instead of a lottery with 50% chance of winning 500 maloti.

# **2.6 Power calculations**

Power calculations were based on a comparison of HIV incidence between two, equalsized study arms assuming a two-sided alternative hypothesis. No prior data were available on HIV incidence in the study communities. HIV prevalence data by age group from the 2009 DHS, however, suggested a high incidence rate in the study population (Ministry of Health and Social Welfare and ICF Macro, 2010). According to the 2009 DHS data, HIV prevalence among women was 4.1% in the 15-19 age group and 24.1% in the 20-24 age group, which is consistent with an annual HIV incidence rate of 4.6 percent over five years. HIV prevalence among women in the 25-29 age group was 35.4%, which is consistent with an annual HIV incidence rate of 3.9 percent over 10 years. HIV prevalence among men in the 15-19 age group and the 25-29 age group were 2.9% and 18.4%, respectively. With the assumption of a 4% annual incidence rate in the study population (which is consistent with a 4.6% annual HIV incidence rate for women and a 50% lower rate for men, and a study population with 75% women), a total sample size of 2,500 HIV negative individuals would be sufficient to provide at least 80% power to detect a 26% intervention-related reduction in annual HIV incidence (significant at the 5% level) over two years in each intervention arm.

# **2.7 Timing**

Recruitment and baseline data collection started in February, 2010. The lottery trial was stopped after two years, following the protocol. A follow-up study was implemented one year after the intervention ended (February-May, 2013).

8
the nearby city of Moradabad, bakeries in the neighboring town of Chandausi, a liquor bottling plant, various marble and steel polish shops in Moradabad, and brick kilns in the surrounding areas. Lanjouw and Stern (1998) noted that the growth of non-farm jobs in Palanpur was associated with commuting of some household members out of the village and a shift in the balance of activities in the household.

Data on employment patterns in Palanpur over the period between 1993 and 2008-09 have recently been subjected to detailed scrutiny in Mukhopadhyay (2011). Between 1993 and 2008, the number of non-farm jobs (primary and secondary combined) continued to grow significantly (Table 3). In 2008-09, 200 non-farm jobs were held by villagers, up from 107 in 1993 (and 125 in 1983/84), while the population of the village grew only from 1133 in 1993 to 1270 in 2008-9. An important change in employment trends, however, and one that echoes the NSS-based findings discussed above, is that non-farm employment expansion between 1993 and 2008 occurred mainly as a result of expansion of self-employment activities and casual wage labor outside of agriculture. The number of self-employment activities tripled (from 23 to 71), and casual wage jobs more than doubled (from 35 to 78). But regular (and semi-regular) jobs increased marginally from only 49 to 51. Mukhopadhyay points to two explanations for the slow growth of regular non-farm employment. First, it appears that closure in the late 1980s of the cloth mills in the vicinity of Palanpur, was not reversed in the years after 1993. Second, his analysis reveals that an important number of households and individuals who reported regular non-farm employment in 1983-84 were no longer residing in the village by 2008-09. Regular employment in the 1980s had been concentrated amongst villagers belonging to the Passi caste in the earlier survey years. By 2008-09, as a result of selective migration, no Passi villagers reported any regular non-farm employment, and indeed, the size of the Passi community had declined significantly as well.

The range and radius of non-farm jobs has continued to increase progressively. Palanpur villagers’ involvement in the labor market of Moradabad has become particularly noteworthy. For example, the Moradabad Railway Yard currently provides employment to anywhere between 10-50 villagers, with the number fluctuating in accordance with labor requirements of the agricultural cycle, and the availability of other non-farm jobs. Villagers join groups of laborers that unload rakes of cement and fertilizer bags, receiving payment on a per-sack-unloaded basis. On an average day, earnings for the members of the group come to around Rs 200 each. The work is very difficult and tiring, and not everyone can do it. But on average, the work is much more rewarding than agricultural labor (where the daily wage in 2008 was Rs. 100) and, importantly, is also considered to be much less demeaning than working as an agricultural laborer.

14
**Table 3. Individual- and household- level correlates of international migrant status (census survey)**

||Prob<br>|it equation<br>|Conditional Logi<br>|t equation<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||Dependent<br>variable<br>equals 1 if<br>individual<br>is either<br>current<br>and past<br>|Dependent variable<br>equals 1 if individual<br>is current migrant|Dependent variable<br>equals 1 if individual<br>is either current and<br>past migrant|Dependent variable<br>equals 1 if individual<br>is current migrant|
||migrant||||
|Age|0.158|0.194|0.367|0.457|
||(26.98)**|(27.56)**|(19.19)**|(18.08)**|
|Age square|-0.002|-0.002|-0.004|-0.006|
||(26.09)**|(26.48)**|(19.17)**|(17.64)**|
|Education level (years)|0.144|0.151|0.227|0.238|
||(15.45)**|(14.17)**|(7.26)**|(6.74)**|
|Square of education level|-0.008|-0.009|-0.014|-0.015|
||(11.88)**|(11.67)**|(6.65)**|(6.16)**|
|Dummy variable =1 if individual<br>married|0.152|-0.002|0.348|-0.043|
||(3.87)**|(0.04)|(3.09)**|(0.36)|
|Dummy variable = 1 if individual is<br>sibling of household head|-0.059|-0.311|0.244|-0.152|
|<br>|(0.79)|(4.04)**|(1.18)|(0.69)|
|Dummy variable = 1 if individual is<br>son/daughter of household head|-0.262|-0.453|0.332|-0.131|
||(4.45)**|(7.32)**|(1.79)*|(0.68)|
|Dummy variable = 1 if individual is<br>spouse of household head|-0.371|-0.380|-0.763|-0.837|
||(6.24)**|(6.09)**|(4.01)**|(4.14)**|
|Dummy variable = 1 if individual is<br>household head|-1.118|-1.567|-1.140|-1.912|
||(18.66)**|(23.61)**|(6.28)**|(9.19)**|
|Dummy variable = 1 if individual is<br>male|1.655|1.691|3.256|3.144|
||(32.37)**|(30.95)**|(22.26)**|(19.73)**|
|Amount of land owned by household<br>10 years before the survey date|.0004|0.0003|||
||(8.22)**|(5.96)**|||
|Dummy variable = 1 if household is<br>muslim|0.847|0.817|||
||(8.31)**|(7.07)**|||
|Dummy variable = 1 household owned|||||
|non-farm business 10 years before the|-0.249|-0.317|||
|survey|||||
||(6.53)**|(7.32)**|||
|Constant|-6.344|-6.714|||
||(40.37)**|(37.70)**|||
|Observations|23305|23305|8378|7030|

Asterisk (*) and double asterisk (**) denote variables significant at 5% and 10% respectively. Only population aged 15 and above were considered.

26
# Appendix Table 1: Analysis of Attrition in Follow-Up Survey OLS

||Depe|ndent vari|able:|
|---|---|---|---|
||Binary=1|if enterpris|was not|
||<br>interview<br>clos<br>|<br>ed or not c<br>ed at follow<br>|<br>onfirmed<br>-up<br>|
||(1)|(2)|(3)|
|Treatment|-0.020|-0.017|0.141|
||(0.029)|(0.030)|(0.157)|
|Commerce sector dummy||0.021|0.047|
|||(0.039)|(0.049)|
|Services sector dummy||0.041|0.072*|
|||(0.033)|(0.042)|
|Full-time paid employees||0.001|0.001|
|||(0.001)|(0.001)|
|Age of principal decision maker (years)||0.001|0.001|
|||(0.001)|(0.002)|
|Male principal decision maker dummy||-0.043|-0.020|
|||(0.036)|(0.045)|
|Business age (years)||0.000|0.000|
|||(0.000)|(0.000)|
|Log (Avg. sales Jul, Aug and Sep 2007 in 1000s USD)||0.002|0.000|
|||(0.008)|(0.011)|
|Profits (Sep 2007 sales minus costs, 1000s USD)||-0.000|-0.000|
|||(0.000)|(0.000)|
|Return on assets (ROA)||0.002|-0.021|
|||(0.025)|(0.038)|
|Commerce sector dummy*Treatment|||-0.071|
||||(0.080)|
|Services sector dummy*Treatment|||-0.094|
||||(0.073)|
|Full-time paid employees*Treatment|||0.000|
||||(0.002)|
|Age of principal decision maker (years)*Treatment|||-0.001|
||||(0.003)|
|Male principal decision maker dummy*Treatment|||-0.078|
||||(0.074)|
|Business age (years)*Treatment|||-0.000|
||||(0.003)|
|Log (Avg. sales)*Treatment|||0.006|
||||(0.017)|
|Profits*Treatment|||-0.000|

31
MENA is a puzzling case. It has the highest inequality and second highest level of corruption, and it is, on average, not procyclical despite its resource wealth. The level of credit to the private sector is the second highest, suggesting that financial depth may be compensating the negative impact of the other two factors. Maybe the extreme cases of the United Arab Emirates, Saudi Arabia, and Kuwait, that are strongly countercyclical drive the results for the region. This result needs further analysis, probably by introducing the level of international reserves as a control variable, which provides a cushion that allows the policy to be managed countercyclically.

# **Across countries**

We compute individual coefficients, and individual standard errors for the 38 SSA countries, which are available upon request. Using such a methodology, we find five to six countries which are countercyclical over the whole period: Guinea, Lesotho and Democratic Republic of Congo, Mauritius, South Africa, and Botswana.<sup>17</sup> Based on the individual country-coefficients, we examine whether procyclicality is different in the sample of SSA countries for resource-dependent economies.<sup>18</sup> We find that procyclical bias is higher in resource-rich or resource-dependent economies. The average coefficient for SSA economies is .31; for resource-dependent economies, it is .33; and for the other SSA economies, it is .27 (Figure D.1). The comparison between resource-dependent (and resource-rich) countries in SSA and resourcedependent countries in other regions shows that the procyclicality bias is bigger in SSA. While the SSA resource-dependent economies have a procyclicality coefficient of .33 the analogous category in other regions has .23. However, the resource-rich countries show an even larger contrast: the SSA resourcerich show a procyclicality coefficient of .33 while those of other regions register .08. This is due to the one-to-one mapping between the two groups of countries (resource-rich and resource-dependent), while in other regions the correspondence is not as clear (Figure D.2). In other regions there are resourcerich countries, such as Chile and Mexico, which are not resource-dependent; both show countercyclical behavior.<sup>19</sup>

# **[insert Tables D.1 and D.2; Figures D.1 and D.2]**

> 17 Cyclicality coefficients for the entire sample of countries in both subperiods are available upon request.

> 18 Table 1 shows that the variable is significant for the entire sample, assuming homogeneous coefficients and FE. 19 The evolution of the coefficient of procyclicality is available upon request.

23
1). The importance of the SME segment has increased to the point that more than 80% of the banks interviewed have created a separate unit to serve it.<sup>11</sup> In both countries there appears to be an integral relation with SMEs. Moreover, bank exposure to SMEs appears not to be limited to specific economic sectors or geographic regions.<sup>12</sup> Banks have a significant level of exposure to the SME segment in terms of loans, and this exposure is higher in Argentina than in Chile (Figure 2). The exposure to SMEs is measured as the ratio of SME loans to total outstanding private sector loans (including retail). In 2006, SMEs represented 37% of total bank loans to the private sector in Argentina and 13% in Chile.<sup>13</sup> These ratios are very similar for 2005. The level of exposure of the _most involved_ and _medium involved_ banks is unexpectedly high, representing on average 62% and 28% of the banks’ loan portfolios respectively (Figure 3.A).<sup>14</sup> On average, private domestic banks are the most exposed to the segment in both countries, with a level of exposure of 56% in Argentina and 16% in Chile. In Argentina, private domestic banks are followed by public banks (31%) and foreign banks (27%), while in Chile they are followed by foreign banks (12%) (Figure 3.B).<sup>15</sup> ,<sup>16</sup> The type of bank lending offered to SMEs is mainly short term.<sup>17</sup> The most important lending products are short-term loans and overdrafts both in Argentina and

> 11 In Argentina 77% and in Chile 87% of banks interviewed have separate SME, or SE and ME units. 12 See de la Torre, Martínez Pería, Politi, Schmukler, and Vanasco (2008) for an analysis of the business model and risk management practices banks use to serve SMEs.

> 13 The banks that provided this information and that are considered in this average account for 64% of total private sector loans in Argentina and 80% of private sector loans in Chile.

> 14 These are simple averages of the ratio of SME loans to private sector loans for the banks that belong to each category: _most involved_ and _medium involved_ banks. The _most involved_ banks are the top third of banks with the highest share of SME loans as percentage of total loans. The _least involved_ banks are the bottom third of banks with the lowest share of SME loans as percentage of total loans. The _medium involved_ banks are the ones that do not fall in either category.

> 15 These ratios are calculated as the sum of SME loans over the sum of private sector loans considering the banks belonging to each category of bank type (public, private domestic and foreign).

> 16 Despite the increasing importance of SMEs, most banks are not yet able to measure their exposure to the segment in terms of income, costs, or risk.

> 17 For 111 institutions of Latin America and the Caribbean, a survey conducted by FELABAN (2007) shows that 52% of the banks offer short-term commercial loans for working capital investments, 14% of these banks include long-terms loans, and 18% do not have an active credit policy.

8