rafmacalaba/datause-encoder-data huggingface dataset:
in the lower panel of Table 1, with standard errors clustered at the primary sampling unit (village in REDS data, and county in CFPS data). The evidence from both IGRC and IRC estimates shows that, in rural China, the null hypothesis of separability cannot be rejected at the 10 percent significance level; the F statistic for IGRC estimates is 0.014 with a P-value of 0.90, and the corresponding numbers for IRC are 0.13 (F statistic) and 0.72 (P-value). In contrast, in rural India, the null hypothesis of separability is rejected at the 10 percent level for IGRC (F=3.80, P-value=0.052), and at the 5 percent level for IRC (F=6.42, P-value=0.012). Since the estimated effect of parental schooling is larger in the nonfarm households in rural India, the evidence suggests complementarity between nonfarm occupation and father’s education in determining a son’s schooling.

# **Relative Mobility and Long-Term Variance in Schooling**

When interpreted as a dynastic model of the evolution of schooling across generations, a higher IGRC implies a higher long-term variance in schooling.<sup>31</sup> To see this, note that for the IGRC equation (12), we can write the long-term variance of education as: where _σs_<sup>2isthelong-termvarianceofeducationand</sup><sup>_σ_</sup> _ε_<sup>2isthelong-termvarianceof</sup> the error term capturing all other factors unrelated to father’s schooling such as market 1 luck, and macro and trade shocks.<sup>calledthe‘familybackgroundmultiplier’</sup> (1 _− ψ_ 1<sup>2)is</sup> by Emran and Shilpi (2019), which amplifies the impact of the shocks to education. Using equation (16) and the estimates of _ψ_ 1<sup>_f_and</sup><sup>_ψ_</sup> 1<sup>_n_reportedinTable1,wehavethefollowing</sup> estimates for sons in farm and nonfarm households in rural India: The subscripts _I_ and _s_ denote India and schooling, respectively, and as before, _n_ =nonfarm and _f_ =farm. The long-term variance of education of sons in the farming sample is 31 percent higher than the variance due to idiosyncratic factors alone (i.e., _σε_<sup>2),and is 45 percent</sup> higher in the nonfarm sample. Thus the contribution of family factors to the long-term variance is 14 percentage points higher in the nonfarm households.

> 31For a discussion on the dynastic interpretation of the model and the implications for long-term variance, see Acemoglu and Autor (undated).

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Table 16. Program impact on monitoring of upper-level authorities and dismissals and transfers of sta¤ at the health facility

|Dependent variable|Sub-county<br>o¢ cial|Parish<br>o¢ cials|Dismissals|Transferred|Left|
|---|---|---|---|---|---|
|Speci...cation|(1)|(2)|(3)|(4)|(5)|
|Program impact|0.12|0.06|-0.054|-0.083|0.079|
||(0.12)|(0.11)|(0.07)|(0.23)|(0.15)|
|District ...xed e¤ects|Yes|Yes|Yes|Yes|Yes|
|Observations|50|50|50|50|50|
|R<sup>2</sup>|0.43|0.45|0.22|0.16|0.17|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Dependent variable in speci...cation (1) dummy indicating if the facility has received a monitoring/support visit from any Sub-county o¢ cials in 2005; (2) dummy indicating if the facility has received a monitoring/support visit from any Parish o¢ cials in 2005; (3) number of sta¤ that has been dismissed in 2005; (4) number of sta¤ that has been transferred from the facility in 2005; (5) number of sta¤ that voluntarily left the facility in 2005. c. Robust standard errors in parenthesis.

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# **I. Introduction**

Latin American and Caribbean (LAC) governments and societies are giving more attention to youth than ever before. In response, several studies at the country and the regional level have been published recently to better understand the situation of young people in the LAC Region and to provide policy directions (CEPAL 2004, National Research Council and Institute of Medicine 2005). A shortcoming of many of these studies is that they do not identify the reasons that young people engage in negative behaviors, thus limiting the ability to design policies and programs that address the underlying factors driving choices that young people make.

Young people’s preferences are formed early in life through the influences of family, community, local institutions, and their macro environment (Bronfenbrenner 1979), which, together with constraints faced during the youth period (World Bank 2006a), are the basis of the decisions that young people make. The challenge, therefore, is to identify those factors underlying positive preference formation that can be encouraged via public policy and those factors behind negative preference formation that could be prevented so that young people arrive at their youth years with a healthy preference set to guide them through their experimental path toward adulthood.

The objective of this study is to identify key factors behind positive decisionmaking by young people in two Latin American countries. It uses unique data sets that contain information about youth behaviors related to school, the labor market, sexual health, alcohol, and social participation and information on potential causal factors such as mental health, family relationships, and local institutions. We use stepwise regression analysis to identify key explanatory factors behind seven behaviors.

This study contributes to the literature in two ways. First, most of the research to identify factors underlying youth behaviors uses US data; this study uses data from Mexico and Chile. These countries were selected based on the absence of research of the factors behind risky behaviors in these countries and the availability of data sets that include behavioral and causal factor information. Second, the results from Mexico and 2
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and, consequently, giving rise to “regulatory arbitrage.” Fifth, taxes on financial transactions in Argentina and Chile (check tax and stamp tax, respectively) have a negative effect on SME lending because they deter financial intermediation of SMEs. This reduces the ability of banks to learn about some clients by looking at their history of banking operations and it also makes cross-selling harder. Although the stamp tax is only mentioned by Chilean banks, some Argentine provinces are also affected by a stamp tax. In neither country do public banks consider regulations to be a significant obstacle, while domestic private banks in both countries claim that regulations are an impediment. However, despite these specific issues, banks consider that regulatory requirements are either appropriate and beneficial or inconsequential. Many banks acknowledge that in absence of these regulations they would ask SMEs for the same information.

Fifth, lack of adequate demand is thought to be a significant obstacle by half of the Argentine banks interviewed, while none of the Chilean banks see it as relevant. Although some banks consider demand to be strong, they point out that many SMEs are not creditworthy (or not as creditworthy as they could be) due to the high levels of informality. In Argentina, anecdotal evidence from the interviewed banks suggests that around 30% of the SMEs are served by financial entities. Most banks believe that there are plenty of worthy enterprises among the 70% that are underserved and that for unknown reasons do not approach banks.<sup>24</sup> Hence, in an attempt to increase their involvement with the segment, there is strong competition over the high-quality SMEs that are already in the market and a significant outreach

> 24 Another study (Fundación Capital, 2006) presents alternative reasons to explain the lack of demand for credit from SMEs in Argentina. Among them we can find that 45.3% of SMEs do not apply because they are using other sources or do not need financing, 13.4% consider that interest rates are too high, 4.3% think that banks ask for too many requirements like balances, cash flows, fiscal situation of the firm and years as a bank’s client, 3.4% of SMEs do not have a collateral and 2.6% do not trust in banks.

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Table 2. Descriptive Statistics: Bosnia and Herzegovina, 2001-2004 (In units as indicated)

||In<br>of the|percent<br>labor forc|e|In<br>of theworki|percent<br>ngagepo|pulation|
|---|---|---|---|---|---|---|
||2001|2002|2004|2001|2002|2004|
|Men|65|64|63|49|49|50|
|Age Groups|||||||
|Age 15-24|14|15|15|22|23|19|
|Age 25-44|52|51|50|41|40|41|
|Age 45-54|26|25|26|21|21|23|
|Age 55+|8|9|9|17|17|17|
|Educational Attainment|||||||
|Unfinished Elementary|7|7|6|13|13|12|
|Elementary|21|22|22|31|31|28|
|Vocational|42|40|38|32|31|31|
|High School|20|21|24|18|19|23|
|College (2+ years)|5|4|4|3|3|3|
|University|5|5|5|3|3|3|
|Unemployment Rate|16|22|22||||
|Labor Force Participation||||48|53|59|

# _Source_ : LSMS and authors' calculations.

Table 2 reports selected descriptive statistics of the sample used in our empirical analysis. The distributions of age, gender and skills are presented for each year (2001, 2002 and 2004). The labor force participation rate increased from 40 percent in 2001 to 59 percent in 2004. Despite this increase, the participation rate in Bosnia and Herzegovina is low compared to other ECA countries, mostly due to the very low labor force participation rate among women. Average unemployment rate is high, and increased from 16 percent in 2001 to 22 percent in 2004. The characteristics of the work force (and the working population) are fairly stable, where youth represent around 22 percent of the working population (the decline from 2001 to 2004 is due to the ageing for the panel). The educational attainment is relatively high, with over 70 percent with vocational education, secondary education or more. Interestingly the level of secondary education has increased significantly over the sample years, offset by a corresponding decrease in vocational education.

What do we know of youth labor market transitions in the ECA region? Appendix Table 1 reports summary data on labor market transition from selected countries in the region, including Bulgaria, Estonia, Poland, Russia and Turkey where panel data are available and allow us a systematic examination of the labor market experience of 8
A lingering question that emerges from our findings is why classroom improvements did not translate into improvements in child development outcomes in this study. In the Chilean study mentioned above, the authors speculate that there were no child-level effects due to the low intensity of the training program, and insufficient exposure of the children to the program, both of which could also be true for us. Another explanation could be that our study was not sufficiently powered to detect findings with a small magnitude. For example, in Ecuador where children were assigned to higher quality classrooms in kindergarten and showed increases in math, language, and executive function test scores, the reported effect sizes were 0.1 SD (Araujo et al., 2016). Finally, the lack of an improvement in classroom quality for activities related to _numeracy, literacy, and problem solving_ and _fine and gross motor activities_ may partly explain the lack of impacts on child assessments in language and fine motor skills in T2.

The ITT effects in the parenting arm at the 18-month follow-up were only slightly smaller than those found in the study of a home-visiting program combined with a CCT program in Colombia (0.26 SD) (Attanasio et al., 2014) but much smaller than the scaled-up, home-visiting program in Pakistan (0.60 SD for cognition) (Yousafzai et al., 2014). It is not surprising that our effect sizes are smaller because the parenting intervention was group-based and low-intensity; it should be noted, however, that the effects we report were quite large among children with educated primary caregivers (greater than 0.5 SD for MDAT language skills). A meta-analysis of home-visiting programs in the United States, found a lower average effect on cognitive outcomes of 0.18 SD (Sweet & Appelbaum, 2004). The intensive Jamaican homevisiting efficacy study found a very large effect size of 0.88 SD (Grantham-McGregor et al., 1991), but effect sizes from other efficacy studies of home-visiting programs in low- and middle-income countries have been much smaller (Grantham-McGregor et al., 2014). Finally, our effect sizes are larger than what was reported for older children in India who were exposed to a scaled-up, parenting literacy and learning 31
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Table 12: What matters for Basel III implementation?

|VARIABLES|(1)|(2)|(3)|(4)|(5)<br>Basel|(6)<br>III implemen|(7)<br>tation|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Sample|CS|Panel|CS|Panel|CS|Panel|CS|Panel|CS|Panel|CS|Panel|
|Log of GDP per capita (constant 2005 US$)|0.552<br>(0.603)|2.163**<br>(0.907)|||||||||||
|Log of GDP (current US$)|||1.854***<br>(0.533)|1.131**<br>(0.516)|||||||||
|Financial Crisis Dummy|||||-1.542*<br>(0.936)|-0.990<br>(0.902)|||||||
|Ease of Doing Business|||||||-0.001<br>(0.072)|0.089<br>(0.068)|||||
|G20 Developing Dummy|||||||||2.894**<br>(1.219)|2.521**<br>(1.170)|||
|Foreign bank assets to total bank assets (%)|||||||||||-0.044**<br>(0.020)|-0.051***<br>(0.019)|
|Constant|-3.525|-16.953**|-46.689***|-<br>27.933*<br>*|1.174*|0.803|0.853|-4.767|-0.331|-0.653|1.863**|2.579***|
||(5.034)|(7.393)|(13.768)|<br>(13.129)|(0.698)|(0.595)|(4.695)|(4.178)|(0.652)|(0.792)|(0.899)|(0.940)|
|Observations|16,120|2,989|16,120|2,989|16,120|2,989|16,120|2,989|16,120|2,989|16,120|2,989|
|Countries|22|18|22|18|22|18|22|18|22|18|22|18|
|PseudoR-squared|0.028|0.207|0.590|0.341|0.110|0.049|0.000|0.055|0.266|0.233|0.207|0.258|

_Note:_ The table reports the coefficients estimated with logit regression for Basel III implementation (0/1). “CS” stands for the sample of repeated cross-sections and “Panel” stands for the sample of countries with available firm panel data. GDP = gross domestic product.

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that female entrepreneurs, in particular lower educated ones and those running smaller businesses, are under-represented among KUR borrowers. If female entrepreneurs had similar access to KUR as male entrepreneurs, one would expect to see similar trends among KUR borrowers as in the population of Indonesian microentrepreneurs. However, female entrepreneurs are less prevalent among KUR borrowers than in the population of Indonesian microentrepreneurs (Figure 11). Whereas on average female entrepreneurs in Indonesia have lower education levels and smaller businesses than male entrepreneurs (World Bank 2022), female KUR debtors have higher education levels and seek larger loans than their male counterparts (Figure 12).

Figure 11: Female entrepreneurs are under-represented among KUR borrowers **Share of KUR borrowers and microentrepreneurs in Indonesia who are female** <!-- Start of picture text --> 49.4%<br>40% 36.7%<br>20%<br>0%<br>KUR borrowers Microentrepreneurs<br><!-- End of picture text --> Source: KUR borrowers: SIKP database; Microenterprises: World Bank 2022 Figure 12: Female KUR borrowers have higher education levels and larger loans than male KUR borrowers **Characteristics of male and female KUR borrowers** <!-- Start of picture text --> 11.7<br>Small KUR<br>8.3<br>88.3<br>Micro KUR<br>91.7<br>5.6<br>Diploma/Bachelor<br>3.7<br>30.1<br>Senior High<br>26.7<br>14.3<br>Junior High<br>14.2<br>46.3<br>Elementary<br>51.5<br>18.3<br>Not married<br>12.4<br>81.7<br>Married<br>87.6<br>0 10 20 30 40 50 60 70 80 90 100<br>% of KUR borrowers<br>Women Men<br>KUR<br>Type of<br>Education<br>Marital Status<br><!-- End of picture text --> Source: Author calculations using SIKP database Note: Gender differences are statistically significant at the 1% level

**21**
# **5.2 Calculating the Returns to Better Governance in Education**

The results in Table 5-7 suggest that of all the investments made in improving school quality in the period from 2003 to 2010, the only one that had a significant impact on reducing teacher absence was increased administrative monitoring and supervision. In this section, we calculate the returns to a marginal increase in the probability of a school being inspected. We make the following assumptions: (a) enough supervisory staff are hired to increase the probability of a school being inspected in the past 3 months by 10 percentage points (relative to a current probability of 56 percent); (b) increasing inspection probability by 10 percentage points would reduce mean teacher absence across the schools in a village by 0.64 percentage points (the most conservative estimate of the correlation between increased inspection probability and reduced teacher absence from Table 5); (c) the full cost (salary and travel) of a supervisor is 2.8 times that of a teacher; (d) a supervisor works 200 days per year and can cover 2 schools per day.<sup>28</sup> The results of this estimation are presented in Table 10 and we see in column 3 that the cost of hiring enough supervisors to increase the probability of a school being inspected by 10 percentage points is Rs.448 million/year. However, the reduction in wasted salary from this investment in terms of reduced teacher absence amounts to Rs.4.5 billion/year, suggesting that the returns to investing in better governance are ten times greater than the cost. Thus, improving school governance by hiring enough staff to increase the frequency of monitoring could be a highly cost-effective investment (on the current margin).

# **5.3 Hiring More Supervisors vs. More Teachers**

Column 5 of Table 10 shows the extent to which the effective STR (ESTR) can be reduced by hiring enough supervisors to increase the probability that a school was inspected in the past three months by 10 percentage points (the same magnitude used in the calculations in the previous section). To compare the relative cost effectiveness of hiring more supervisors versus more teachers, we calculate the salary cost of hiring more teachers to achieve the same reduction in ESTR and report these numbers in column 6. Comparing columns 3 and 6, we see that hiring more supervisors would be 12.8 times more cost effective at reducing ESTR than doing so by hiring more teachers. The difference between the estimates in columns 4 and 6 stems from accounting for the fact that hiring more teachers will increase the absence rates

> 28We use DISE data on the number of schools in each state to calculate the number of supervisors who will be required to increase the probability of inspections in a 3-month interval by 10 percentage points. The cost estimates are conservative and assume that the salary costs are double that of a teacher and that the travel costs are equal to 80 percent of a full months’ salary (which is higher than the typical travel and daily allowance provided to education department employees to travel to/from a village to district headquarters).

19
arises due to the presence of multiple donors who are motivated by self-interest, the mere coordination of aid, such as general budget supports, will not automatically guarantee the sub-optimality of aid provisions unless there is a fundamental change in the incentives for aid provisions.

In the spirit of the Holmstrom (1982) model, the principal can eliminate moral hazards by administering incentive schemes that do not balance the budget. For example, a contract under which all donors receive _g_ ( _a_ *) if _g_ = _g_ ( _a_ *), and receive nothing if _g_ < _g_ ( _a_ *). A feasible scheme should be based on a recipient-specific evaluation of the donors’ performance. The operationalization of such a contract would be an important task for the future.

# **References**

Acharya, Arnab, Ana Teresa Fuzzo de Lima, and Mick Moore (2006), “Proliferation and Fragmentation: Transaction Costs and the Value of Aid.” Journal of Development Studies, Vol. 42, No.1, pp.1–21.

Arimoto, Yutaka and Hsiaki Kono (2007), “Foreign Aid and Recurrent Cost: Donor Competition, Aid Proliferation and Conditionality,” forthcoming, Review of Development Economics.

Burnside, Craig and David Dollar (2000), “Aid, Policies, and Growth.” American Economic Review, 90(4), pp. 847–868.

Cassen, Robert and Associates (1986), Does Aid Work?, 2<sup>nd</sup> ed., Oxford: Clarendon Press.

Heston, Alan, Robert Summers and Bettina Aten (2006), Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September 2006.

7
China’s low labor productivity may possibly be attributable to small farm size (0.6 hectare on average), land fragmentation (Jia and Petrick, 2013), and to the labor-intensive nature of family based farming. In addition, several scholars report that the labor productivity gap between farm and non-farm sectors remains high in China (e.g., Fan, Zhang and Robinson, 2003; Kujis and Wang, 2006). Labor is likely to move out of agriculture and this shift is an inherent part of the process of economic development (World Bank and the Development Research Centre (DRC) of State Council, 2014).

Figure 8 and its attached table show the evolution of agricultural land endowments for selected countries. Following Rask and Rask (2011), hectares of land per capita are computed as a summation of arable land, land in permanent crops, and one-third of land in permanent pastures using FAOSTAT data. Since 1980, the worldwide per capita amount of agricultural land has decreased by about one third from 0.54 ha in 1980 to 0.39 ha in 2009, revealing a trend that agricultural land per capita has been becoming increasingly scarce worldwide. China appears to be a relatively land-scarce country, with its land endowment only about half the world average. Not surprisingly, relatively large net exporters of food, such as Brazil and the United States, are much better-endowed with agricultural land, having agricultural land endowments that are roughly four and five times China’s. However, the United States has reduced its per capita agricultural land by about a third over the same period. In comparison with neighboring net food importing countries such as the Republic of Korea and Japan, China has nearly four times the agricultural land endowment per person as the Republic of Korea and almost five times as much as Japan.

18