rafmacalaba/datause-encoder-data huggingface dataset:
Notice that SOFES prefers low-risk students to safeguard repayment of the loans. Students whose parents own a real estate property and sign a collateral agreement are eligible for a higher loan amount than students without such a guarantee. The additional loan amount equals at maximum an additional a 10%-points of the tuition cost. Secondly, students from middle or higher-income families can receive a larger loan than students from low-income families. SOFES has to balance between its equity goal (access independent of family income), and its goal to operate as a financially viable institution. In addition, the credit level depends on the study field, categorized into three areas (Humanities and Social Sciences, Economics and Administration, and Engineering and Natural Sciences).<sup>4</sup> Finally, SOFES can recommend universities to give grants to students from relatively needy families (i.e. with an ENF lower than 0.15).<sup>5</sup> Students eligible for the SOFES loan reapply every semester. Renewal of the loan is contingent upon student performance: students only receive the renewal when they are promoted to the next term. Renewal is also contingent upon payment of interest: insolvent students do not receive the next part of the SOFES-loan.

Figure 1 shows the development of the number of SOFES recipients over time. The data for 2003 and 2004 are forecasts. Currently there are almost 20,000 students with a credit from SOFES. This is a relatively small number, compared to a total higher education enrollment of almost 2 million students. Although SOFES is only 7 years old, the student loan scheme clearly appears to be financially sustainable. As of October 2003, default rates are exceptionally low for a student loan scheme in a developing country, 7 percent. Additionally, operation costs amount to only 1 % of the value of the portfolio. The student loans carry a 2 % real interest rate, which is adjusted monthly with the rate of inflation.

**Figure 1: Growth in the number of SOFES Beneficiaries**

<!-- Start of picture text --> 25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>SOFES Beneficiaries<br><!-- End of picture text --> <!-- Start of picture text --> Source: SOFES<br><!-- End of picture text -->

> 4 It should be noted that there is substantial variation in incomes per career (cf. Montano Román, 2003).

> 5 Private universities are required to spend some minimum fraction of their budget on student grants.

6
21

- Rojas, E. 1991. "Factores que explican los niveles de escolaridad media de los hijos en Guatemala." Santiago: UNESCO-OREALC. Cited in Schiefelbein 1992.

- Schiefelbein, E. 1992. "Repetition in Latin America's Primary Schools: Magnitudes, Causes, and Possible Solutions", The World Bank, LATHR (mimeo).

- Schiefelbein, E. 1989. "Repetition: The Constraint for Reaching Universal Primary Education in Latin America." Bulletin: The Major Project in the Field of Education in the Latin American and Caribbean Region 18: 7-28.

- Schiefelbein, E. 1975. "Repeating: An Overlooked Problem of Latin American Education." Comparative Education Review 19,3: 468-487.

- Schwille, J. et al. 1991. "Is Grade Repetition Always Wasteful? New Data and Unanswered Questions." Bridges Research Report Series 7 (January).

- Stephen, D. and P. Wearne. 1984. Centrl America's ndians with a summary by R. Stavenhagen. London: Minority Rights Group.

- Townsend, J. and B. Newman. 1985. "Bilingual Education Project in Guatemala: Preliminary Results on the Test of Principal Hypothesis during 1983." INCAP.

- Unesco, Office of Statistics. 1984. "Wastage in Primary Education from 1970 to 1980." ProV= 14, 3: 347-367.

- Varese, S. 1990. "Challenges and Prospects for Indian Education in Mexico." Prospcts 20,3: 345-356.

- Verhine, R.E. and A.M.P de Melo. 1988. "Causes of School Failure: The Case of the State of Bahia in Brazil." Pro[pects 18,4: 557-568.

- World Bank. 1986. Brazil: Finance of Primary Education. A World Bank Country Study. Washington, D.C.: The World Bank.

- Zuniga, M. 1990. "Educational Policies and Experiments among Indigenous Populations in Peru." Pros=ts 20,3: 365-376.
Brazilian President Dilma Rousseff announced a new program, Bolsa Verde that pays poor, rural households for not engaging in deforestation or forest degradation beginning December 2011. Households qualify if they currently make BR$70 (US$ 40) or less per month, and under the program they will receive BR$300 (US$180) every three months. The program is an expansion of Bolsa Floresta, which started in 2007 and is Brazil’s first PES program for avoided deforestation (Pereira 2010), with payments to 7,239 families. Bolsa Verde is expected to benefit an estimated 18,000 families in the first stage, and more than 200,000 families may eventually qualify for benefits. Monitoring for compliance will be done via satellite imagery and families found in violation will lose their benefits (Moukaddem, 2011). As of August 2010, a number of other carbon related REDD projects in the Amazon have been identified as well (see May et al., 2010).

Thompson et al. (2013) review six REDD+ projects in the Peruvian Amazon focusing on transaction costs associated with setup, implementation, and monitoring. The projects’ time periods ranged from 20 to 45 years, and the types of land enrolled were primarily national reserves and conservation concessions. Participant activities included patrolling the forest area, fire management, and promoting alternative income generation activities. They estimate that the cost of implementing REDD+ in the Peruvian Amazon based on these projects is as much as US$ 68 million per year. However, their results indicate that costs vary substantially depending on the project and have a high degree of uncertainty.

# **III. Implementing REDD: Issues of Concern**

15
and entry costs. Columns (v-viii) show that our results are robust to including legal origins in La Porta, et al. (1998) as an instrument for entry regulation.

Finally, in column (ix), the dependent variable is the share of incumbent firms in the industry in overall value added for the country in 1999. We find a negative coefficient estimate, but the effect is not statistically significant. One possible explanation is that entry barriers persist in countries where high-entry industries are large and thus have the political power to lobby for the barriers. When this is set against the fact that the barriers retard growth in these industries, we may find an insignificant coefficient of the interaction on average industry share.

Taken together, these results suggest that entry regulations seem to adversely affect the growth (and to a lesser extent the size) of those industries that might be presumed to most benefit by the added selectivity that such regulation might bring. This strongly suggests that such regulations are not intended in the public interest.

# **3.5  Selection Issues**

The last findings, however, suggest potential selection problems. One way to test the direction of causality is to use instruments. It has been generally found that the origin of a country’s legal system seems to be strongly associated with the regulatory system in place today (see, for example, La Porta et al. (1999)). While there has been some debate about the precise mechanism by which this association exists, a country’s legal origin offers a proxy for predetermined components of regulation. When we instrument entry regulation with legal origin, we find that the coefficient estimate for the interaction term is highly significant, the same sign and approximately the same magnitude as shown earlier in Tables 6 and 6A (see Table 8 column (i-v)).<sup>34</sup> A second concern is that countries with more untrustworthy populations may erect higher bureaucratic barriers so as to screen would be entrepreneurs more carefully. If this were true,

> 34 The legal origin variables explain 59 percent of the variation in the entry cost variable. Entry costs tend to be lowest in countries with Anglo-Saxon and Scandinavian legal origin and highest in countries with French legal origin.

24
Given that we do not have the resources to conduct population surveys in all countries necessary to provide a reasonably complete global picture of WTP for preserving the Amazon rainforest, in order to supplement our planned population survey work, we also are conducting regional “Delphi surveys.” In this approach, experts in environmental valuation and in SP in particular are surveyed concerning their beliefs about how random population samples, in their respective countries, would respond to CV surveys were they to be undertaken. These Delphi surveys can provide not only an order of magnitude estimate of global non-use values for a wider set of countries than those covered by our planned population surveys, but also preliminary estimates of WTP in the countries selected for the combined CV/CE surveys. For countries where both types of surveys are carried out, our results will permit comparisons, and calibrations, of the Delphi results in relation to the population surveys.

This paper reports the results of the first Delphi expert survey under this project umbrella, conducted in the spring and summer of 2012.<sup>8</sup> Average WTP among European households for particular programs to protect Amazon rainforest were assessed by asking experts on environmental valuation, from 20 different European countries, to provide their best subjective assessments of such values, for their own countries and for Europe as a whole.

In section 2 we first present, and offer a justification for, the Delphi CV method. Section 3 describes our European Delphi expert survey. Section 4 reports the WTP results from two rounds in the European survey, income elasticities of these WTP estimates, and the factors that were found to most significantly affect experts ́ WTP estimates. Section 5 presents and discusses aggregated WTP results for Europe, and offers concluding. The Round 1 and 2 Delphi CV instruments are reproduced in Appendices 1 and 2, respectively; and a theoretical note on the income elasticity of WTP can be found in Appendix 3.

# **2. The Delphi Method**

The background, history, and justification for the Delphi method are different from those for the SP method. The Delphi method was developed largely as a management decision-making tool, to ensure mutual consistency of preferences among a set of participants (managers), and to elicit experts ́ preferences rather than individual preferences that the SP methods seeks to elicit.<sup>9</sup> Underlying the method is a belief that these experts have considerable useful knowledge about such population values that is not embodied in already available valuation studies. In our case, a sample of European environmental economists acquainted with SP survey methods, were asked to predict

> 8 Delphi expert surveys also are being done for North America, Australia and New Zealand, and for a range of Asian countries.

> 9 See e g Sackman (1975).

4
12

# **Chapter 2. Rural Credit Requirement and Credit Delivery**

The demand for rural credit stems from agricultural production (investment and production credit), consumption smoothing especially by poor households (consumption credit) and investment needs of rural households engaged in livestock rearing and microenterprises. This demand is then met by institutional and non-institutional sources. This chapter estimates, in part A, the annual agricultural credit requirement of farmers from institutional sources, and then discusses how this is met. The delivery of credit by the non-institutional sources is discussed in Part B. The credit requirement of poor households (for consumption smoothing as well as for agricultural production), rural women and households engaged in microenterprises are discussed in Chapter 3.

# A: **Institutional Credit**

# **Demand for Institutional Credit and Credit Availability**

# Investment Credit

The requirement for institutional credit for agriculture is calculated as the difference between credit needed to support a given level of agricultural growth and other available sources of financing. Although it would **be** desirable to distinguish between farners' own savings and informal sources of credit, data do not permit such a distinction. However, the important differences between informal and formal sources will be pointed out based on the findings of various rural credit surveys.

The demand for agricultural investment credit<sup>4</sup> is associated with growth in agricultural GDP. Between 1979/80 and 1993/94 agricultural GDP grew at a steady 3.6 percent per year. The ratio of annual investment to GDP in the agricultural sector averaged 6.8 percent in 1979 - 88, and to 6.9 percent in 1988 - 93.

Using these figures, it is calculated that 1 percent agricultural growth in recent years has been associated with agricultural investment of about 2 percent of agricultural GDP. This investment demand was met partly through farmers' own savings and partly by borrowing from institutional sources. (Informal sources of credit lend only small amounts for investment purposes.) The analysis of savings presented in the Appendix shows that 60 to 70 percent of farm investments are financed by farmers through their own savings.

_4_ Given the price (interest rate) demand for credit is determined by rate of return from the use of credit. Requirements, in contrast, are estimated by indirect methods such as from the gap between total finance needed for optimal use of inputs and funds available from our resources
11 in-person sessions.<sup>7</sup> Due to the COVID-19 pandemic, the remaining 259 observations were collected using the online platform nodeGame.<sup>8</sup> Subjects earned tokens, which were converted to US dollars in the ratio 50 tokens to $1 USD.

The experiment consisted of three rounds. In Rounds 1 and 2, subjects completed risk preference elicitation activities and a real effort task, respectively. The treatment was administered between Rounds 2 and 3. For Round 3, we gave subjects a second round of earnings from the real effort task, but this time half of the subjects had earnings based on effort alone (same as Round 2) and the other half received earnings determined by both effort and zip code at birth. Before the final round, subjects reported their perceived income rank. Half were then told their actual rank. Round 3 was the investment game. The basic design is outlined in Table 1. We now describe each round and the treatment in more detail.

# **_Round 1_**

The risk preference elicitation tasks in Round 1 were Eckel and Grossman style tasks. For each, subjects chose a preferred prospect from a collection of risky prospects. Each prospect entailed different high and low pay-outs, with a 50% chance to win either pay-out. For the first task, all high and low pay-outs were positive. In the second task, the low pay-out for some prospects was negative, such that subjects could lose tokens with a 50% probability.<sup>9</sup>

# **_Round 2_**

In Round 2, subjects completed a computerized ball catching task (Ga ̈chter et al. 2016). In this task, subjects moved a basket across the bottom of the screen to catch balls that fell from the top of the screen. Catching a ball was worth 20 tokens, while clicking the mouse to move the basket one step over cost 10 tokens. Total earnings was equal to the balls caught times the earnings per ball, minus the number of clicks times the cost per click. The task involved 6 periods, with the first three periods used for practice. Each task period lasted one minute. Clicks and catches in each task period were displayed at the top of the computer screen (Figure 1).

The real-effort task serves three purposes. First, we needed to generate an endogenous distribution of earnings. Second, we wanted to elicit perceptions of lab-specific effort (in terms

> 7Power calculations indicate that a total sample size of 500 is required to achieve 80% power.

> 8Results are not sensitive to whether the experiment was completed in the laboratory or online, though the drop in power from smaller sub-samples does reduce significance.

> 9We use the measures of risk aversion and loss aversion from these tasks to explore treatment effect heterogeneity. We do not find evidence of such effects. Results are available upon request.

9
can accordingly reap the monopoly rents associated with this advantage. This can in turn initiate entry into high-risk environments.

**Table 6 Heckman estimation of the effect of Political Conflict on firm level greenfield FDI flows**

||Dependen<br>(<br>|t Variable: Log<br>1)<br>|greenfield FD<br>(<br>|I (in USD milli<br>2)<br>|ons), Heckman<br>(<br>|estimation<br>3)<br>|
|---|---|---|---|---|---|---|
||Tota <br>|FDI<br>|Resource-<br>|elatedFDI<br>|Non-resourc<br>|e-relatedFDI<br>|
||Intensive<br>Margin|Extensive<br>Margin|Intensive<br>Margin|Extensive<br>Margin|Intensive<br>Margin|Extensive<br>Margin|
|BRDt-1(ln)<br>BRD (ln) * localizedt-1|-0.247*<br>(0.109)<br>0.301**|-0.009<br>(0.022)<br>0.000|-0.093<br>(0.200)<br>0.142|-0.018<br>(0.041)<br>0.021|-0.276*<br>(0.108)<br>0.308**|0.002<br>(0.023)<br>-0.010|
||(0.106)|(0.022)|(0.191)|(0.041)|(0.106)|(0.023)|
|BRD(ln)*Diversficationt-1|-0.018<br>|0.007+<br>|0.010<br>|0.004<br>|-0.007<br>|0.007*<br>|
||(0.012)|(0.003)|(0.027)|(0.007)|(0.012)|(0.004)|
|||Bilateral C<br>|ontrols||||
|Distance|0.196+<br>(0.113)|-0.228***<br>(0.016)|-0.908<br>(0.571)|-0.337***<br>(0.039)|-0.058<br>(0.215)|-0.254***<br>(0.017)|
|Common Border|-0.961**|0.424***|0.925|0.349*|-0.523|0.496***|
|Common Language<br>Colonial History|(0.371)<br>-0.538**<br>(0.196)|(0.079)<br>0.307***<br>(0.040)<br>0.126**|(0.953)<br>1.729*<br>(0.767)|(0.172)<br>0.337***<br>(0.080)<br>0.145+|(0.535)<br>-0.152<br>(0.379)|(0.080)<br>0.339***<br>(0.042)<br>0.181***|
|||(0.046)||(0.087)||(0.049)|
|||FirmCo|ntrols||||
|Diversificationt-1(std) <br>FDIt-1(ln)|-0.155*<br>(0.062)<br>0.009<br>(0.018)|0.096***<br>(0.014)<br>0.061***<br>(0.002)|0.516<br>(0.381)<br>0.057***<br>(0.008)|0.216***<br>(0.036)<br>0.068***<br>(0.003)|-0.065<br>(0.113)<br>0.001<br>(0.018)|0.128***<br>(0.015)<br>0.059***<br>(0.002)|
|Aget-1(ln)|-0.105**|-0.019*|-0.209+|-0.029|-0.095**|-0.004|
||(0.035)|(0.009)|(0.118)|(0.025)|(0.033)|(0.010)|
|ROEt-1|-0.001|0.000+|-0.001|-0.002*|0.000|0.001*|
||(0.001)|(0.000)|(0.005)|(0.001)|(0.001)|(0.000)|
|Employeest-1(ln)|-0.059|0.118***|0.247|0.137***|0.106|0.170***|
||(0.051)|(0.006)|(0.239)|(0.014)|(0.137)|(0.006)|
|Constant|177.034***|-57.929***|39.617|-50.043**|201.298***|-62.014***|
||(46.194)|(9.001)|(77.702)|(19.365)|(48.315)|(9.271)|
|Inverse Mills Ratio|<br>-1.427**||<br>0.300||<br>-1.550***||
||(0.434)||(0.511)||(0.454)||
|Obti|519|030|58|528|49|123|
|servaons||,|,|||,|
|Economic Controls|Y|es|Y|es|Y|es|
|Destination FE|Y|es|Y|es|Y|es|
|Oii FE|Y||Y||||
|rgn||es||es||es|
|Year FE|Y|es|Y|es|Y|es|
||St|andard errors i|n parenthese|s|||
||*** p<0.|001, ** p<0.01|, * p<0.05, +|p<0.10|||

Concerning the control variables, distance positively affects the intensive margin, but has a negative effect on the extensive margin. A common border and a shared language negatively affect the intensive margin, but positively affect the extensive margin. In addition, a shared colonial history has a positive effect on the extensive margin. Hence, the bilateral controls indicate that an increase in psychic 34
# **Labor Market Transitions and Social Security in Colombia**

# **1. Introduction**

Despite the growing evidence of the impacts of the global financial crisis on labor,<sup>2</sup> analyses on the volume and determinants of labor transitions are few and not recent. This paper addresses this knowledge gap focusing on Colombia, a country with one of the largest rates of unemployment and informality in Latin America, even before the global financial meltdown. According to the National Department of Statistics Administration ( _Departamento Administrativo Nacional de Estadística_ —DANE), there are more informal workers than formal salaried workers (excluding public workers). Also, the country ranks first in the region in terms of minimum wages and non-wage costs. Since the social security reform in 2002,<sup>3</sup> labor market and social protection policies have been increasingly intertwined (Levy 2007 and 2008, Perry et al. 2007, Cunningham 2007 and Bernal et al. 2009), with important fiscal, productivity and growth repercussions. As a result, informality is believed by many researchers to be a preferred voluntary option for workers (as convincingly argued by Maloney 2004 and Perry et al. 2007 in the Latin American context), after comparing a social protection non-contributive system providing free health services and a contributive system with high levels of contribution (Cuesta and Olivera 2010). Others, such as Pagés and

> 2 Khanna, Newhouse and Pacci (2010) show that for a sample of middle income countries (between 14 and 40, depending on the indicator), declines in employment rates between 2008 and 2009 were larger than declines in GDP growth rates, even though labor adjustments took place mainly through earnings. Specific to Latin America, these adjustments add to a grim picture of persistently high informality levels and structurally low labor earnings mobility in the region (Cuesta, Ñopo and Pizzolitto 2010). ILO (2009) estimates a total of 17 million unemployed in Latin America, surpassing the 13.7 million estimated for that year in the US (IADB 2010). ILO (2009) also reports large return inflows back into traditionally labor-sending countries such as India, Pakistan, Ecuador, El Salvador and East European countries. Furthermore, substantive internal migration within China from urban to rural areas of about 20 million people is estimated in 2009 alone.

> 3 Colombia has reduced significantly its historical gap with the region in terms of social spending, mainly due to sizeable increases in social security, which increased from 3 percent to 7 percent of GDP between the early 1990s and mid-2000s (CEPAL 2010).

2