rafmacalaba/datause-encoder-data huggingface dataset:
However, these costs will be taken into account when calculating the Subsidy Dependency Index discussed below.

The P4K program achieved very impressive declines in cost per funds lent from 1991 to 1993 (Table 27). In 1991. total program costs were equal to 176 percent of funds lent. Only 2 years later in 1993, total program costs were equal to 18 percent of funds lent (or 9 percent if the Ministry of Agriculture's in-kind costs of salaries for extension workers are excluded from the calculation). In 1993 the program's total spending was about US$105 per group receiving a loan (or about US$ 10 per individual). Given the program s tight targeting of the very poor (see below), these figures are impressive. In recent years. program cost effectiveness has declined. but remains strong. In 1995, total program costs were equal to 30 percent of funds lent (or 23 percent if the Ministry of Agriculture's in-kind costs are excluded), and the program was spending about US$213 per group receiving a loan (about USS20 per individual).

Thus, in 1995, P4K was more cost effective than PHBK (as measured by total program cost per volume of funds lent, and total and cash-only costs per group receiving credit). One reason that P4K is able to operate at a lower cost than PHBK is that extension agents earn lower salaries than Bank of Indonesia staff. Also, because P4K has an annual volume almost double that of PHBK. it is able to take fuller advantage of economies of scale. Finally, P4K's simpler structure may contribute to lower costs.

However, P4K's cost effectiveness ratios have detefiorated over the last two years, whereas PHBK's have improved. This shift is probably driven by PHBK's rapid expansion, and P4K's much slower growth. In addition, P4K managers attribute their recent productivity decline to the shift in P4K responsibility within the Ministry of Agriculture, which necessitated a great deal of additional training, and to the program's expansion on a pilot basis to 12 additional provinces.

**Table** 27 **Estimated Financial Services Costs for P4K Program in Comparison to Program Outputs: For Calendar Years 1991 to 1995a**

|Total C|ost Per**Pr**|gram**Out**|ut||
|---|---|---|---|---|
||1991|1992|<br>1993<br>**1994**|**1995**|
|Annual cost (US$)|1.683.723|1.202.411|1,121.263 2,776,900|3,279.289|
|Cost percent of annual loan volume|<br>176|<br>53|<br>18<br>23|<br>30|
|Cost per grou credit (US$)|697|238|<br>_105_<br>146|<br>213|
|Cost per new group credit (US$)|813|320|<br>137<br>197|<br>383|
|**Cost per Program**Output**Exclud**|**ing Ministr**|**of Agric**|**ulture In-Kind Expe**|**nditures**|
||**1991**|**1992**|<br>**1993**<br>**1994**|<br>**1995**|
|Annual cash cost (US$)|1.118,709|642.641|576,804 2,036,908|2,503.066|
|Cost percent of annual loan volume|<br>117|<br>28|<br>9<br>17|<br>23|
|Cost per group credit (US$)|463|127|<br>54<br>107|<br>163|
|Cost per new group credit (US$)|540|171|<br>70<br>145|<br>292|

a/ Program costs were provided for fiscal years. Costs by calendar year are estimates based on fiscal year data.

69
# **_2.2 Descriptive Statistics on Student Achievement_**

The mean reading performance of the eight considered countries in PIRLS, reported in Table 1, clearly reveals the division into the two groups. The group of relatively “low performing countries” (LPC), Argentina, Colombia, Turkey and Macedonia, shows a mean performance between 418 and 449 achievement points (AP), while the group of relatively “high performing countries” (HPC), Germany, Greece, Italy and England, performs between 525 and 553 AP.<sup>7</sup> As the lowest performer among the selected countries, Argentina scores 4 AP lower than Colombia, 24 AP lower than Macedonia, 31 AP lower than Turkey and 122 AP lower than the HPC group. The size of these performance gaps can be illustrated by the unconditional difference of 42 AP between third graders and fourth graders in Sweden, which was the only country in PIRLS that tested third graders in addition to fourth graders. This difference can be interpreted as the additional value-added of one school year in Sweden, since both grades are tested in representative samples. Thus, the Argentine and Colombian performance is the equivalent of one-half to two-thirds of a (Swedish) school year behind the Macedonian and Turkish performance, and roughly three years behind the HPC group.

The mean performance hides substantial differences in the performance distribution between countries, though. The performance variation is much larger in Argentina than in all the countries of the HPC group. This is independent of the measure of performance variation, which include the standard deviation in national test scores (either absolute or relative to the national mean) and the performance difference between percentiles such as the 90<sup>th</sup> and 10<sup>th</sup> or the 75<sup>th</sup> and 25<sup>th</sup> . Only Macedonia shows a spread in performance that is similarly large as in Argentina, while depending on the measure, Colombia and Turkey show a variation that is slightly larger than in the HPC group, sometimes surpassed by England, but substantially lower than in Argentina and Macedonia. This pattern is also evident in the graphical depiction of Figure 1. The descriptive pattern shows that the Argentine education system has a shortcoming with respect to ensuring educational equity and providing support for the relatively weak students.

Another feature emerging from the descriptive data is that the performance of the 75<sup>th</sup> and 90<sup>th</sup> percentile students in Argentina is actually similar to (or even slightly better than in) Colombia. The lower mean performance and larger variation in Argentina arise from the

> 7 The fact that England is the highest performing among the considered countries may be related to the fact that it is the only country where most students start school at age five, so that England tested fifth-grade rather than fourth-grade students.

8
4 Lloyd index 5 to estimate the growth of trade due to fragnentation (Kierzkowski 1998). While the IIT clearly includes fragmentation-related trade, it also captures a large portion of trade that may have little to do with production sharing or fragmentation. Consider the following: IIT tends to be high among countries with higher similarity in terms of their endowments (incomes) and smaller thresholds of the minimum efficient scale of production (more firms and greater variety supported by the market).6 Consumer preferences rather than globalization of production shape trade in, for instance, motor vehicles between the EU and the United States. This trade allows realization of economies of scale thanks to greater product specialization in differentiated products.

Undoubtedly, fragmentation-driven trade accounts for some portion of ITT. The empirically observed positive correlation between multinational activity and IIT would clearly point in this direction. But it would be difficult to estimate the share of this trade in IIT. The distinction between horizontal and vertical intra-industry trade-the latter involves exchange of similar goods of different quality, whereas the former comprises exchange of similar goods that are not differentiated in terms of quality-does little to solve the problem. Products subject to mutual exchanges often enter different market niches, although in some cases lower quality products may be imported for processing.7 It seems that the index of horizontal trade specialization (HTS), which is a slightly modified version of the Grubel -Lloyd index, applied to selected product groups offers a better measure of trade due to production sharing and fragmentation. We shall use this index to assess the existence of links within narrowly defined production networks (see Section 5).

Other authors suggest the use of the 'end-use' categories of the US Bureau of Economic Analysis, which allow identification of products by their use by buyers rather than in terms of their positions in production process (Feenstra 1998, Irwin, 1996). They identify the following five categories: foods, feeds and beverages; industrial supplies and materials; capital goods

> 5 The GL index of intra-industry trade between two partners is usually expressed as: GL = 1 - [ Xi -M / E (Xi + Mi)], where X and M are exports of a country and imports by a partner correspondingly of product i. The index suffers from two problems: aggregation and aggregate trade imbalances-see Greenway and Milner (1987).

> 6 Technological factors may lower the minimum efficient scale of production and thus allow the market support more firms and greater variety (Hufbauer, 1970).

> 7For instance, Aturupane, Djankov, and Hoekman (1997) find that vertical intra-industry trade accounted for 80-90 percent of total IIT of CEEC with the EU. It would be impossible to estimate what proportion was further processed in the EU.
Table A.11: Effects of mining on marital status.

|VARIABLES|(1)<br>Divorced/separated|(2)<br>Partner|(3)<br>Single|(4)<br>Widow|
|---|---|---|---|---|
|active (20 km)|0.002|-0.002|-0.006|0.006**|
||(0.003)|(0.007)|(0.006)|(0.002)|
|inactive (20 km)|-0.004|0.004|0.000|-0.001|
||(0.004)|(0.013)|(0.012)|(0.004)|
|Observations|512,534|512,534|512,534|512,534|
|R-squared|0.032|0.219|0.359|0.058|
|F test: active-inactive=0|1.236|0.149|0.242|2.073|
|p value|0.266|0.699|0.623|0.150|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. *** p<0.01, ** p<0.05, * p<0.1 Table A.13: Effects of mining for young women (15-20 years old).

|VARIABLES|(1)<br>Working|(2)<br>Service|(3)<br>Sales|(4)<br>Agriculture|
|---|---|---|---|---|
|active (20 km)|0.004<br>|0.007<br>|-0.014*<br>|-0.008<br>|
||(0.014)|(0.007)|(0.009)|(0.013)|
|inactive (20 km)|0.056**|-0.006|-0.012|0.057**|
||(0.027)|(0.005)|(0.017)|(0.028)|
|Observations|138,606|138,606|138,606|138,606|
|R-squared|0.194|0.070|0.109|0.290|
|F test: active-inactive=0|3.069|2.085|0.0149|4.463|
|p value|0.0798|0.149|0.903|0.0346|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. *** p<0.01, ** p<0.05, * p<0.1 38
Table 4 also includes impacts based on the gender and location (rural or urban) to test for heterogeneous effects of the program. The analysis by gender uncovers clear differences in the magnitude of impacts for participant boys and girls. On one hand, results for the sample of girls based on OLS and matching estimates always yield positive and statistically significant effects on high school graduation rates. The magnitude of the effects ranges from 4.6-6.5 percentage points in the OLS framework to 5.2-8.9 percentage points in the matching analysis. For all these cases, results are strongly significant in statistical terms at the 1 percent level. On the other hand, the pattern in impact estimates for boys is less obvious. While OLS point estimates indicate that there are no program effects, two of the three point estimates obtained from matching models (Model 2 and Model 3) appear to be just marginally significant at the 10 percent level. Results from the sample used in the RD analysis, presented in Table 5, also seem to suggest that program effects on high school completion are a little larger among girls.

The distribution of impacts on high school completion varies with the location of program beneficiaries as well. However, there is a clear difference between the results from the matching and RDD approaches. Evidence from the PSM models suggests that the effects on high school graduation rates accrue mostly to participant children whose families resided in rural areas at the baseline (most likely girls based on the previous results). Whereas the effects on the samples of children in rural areas are positive and strongly significant in both economic and statistical sense in all models, analogous point estimates obtained for beneficiaries in urban areas are mostly insignificant – except for a marginally significant impact from the second matching model. Even if the discussion is limited to the parameter estimates of this particular model, the findings show that the size of the effect in urban centers is still half of that for children in rural municipalities - about 5 percentage points compared to 10.4 percentage points. Even though there are no data to empirically identify the channels that may explain these differences in terms of location and gender, a possible explanation is that the marginal effects of the transfer and the conditionality are larger for girls in rural settings for whom the opportunity costs of

> from high school. In Pakistan, the Female School Stipend program, a CCT targeting girls, appears to improve the chance of completing grade 9 in high school for girls aged 15-16 years old by 5 percentage points (Angrist and others, 2004; Barrera-Osorio and others, 2008; Alam and others, 2010).

22
FIGURE A4 FRANC ZONE: PRICES <!-- Start of picture text --> 3.<br>23<br>27<br>26<br>25<br>24<br>23<br>22<br>1.7<br>1.6<br>I .5<br>1.4<br>1.3<br>25 7 to 75 00 as<br>a CM 4 F O CAM<br><!-- End of picture text --> <!-- Start of picture text --> NIGERIA: PRICES AND EXCHANGE RATE<br>1975.1<br>1.0<br>17<br>1.3 -<br>1.1<br>aob s 7 ao 8S<br>75 so a<br>0 Femx 4- CPI<br>81<br><!-- End of picture text -->
# **1. Introduction**

Paralel foreign exchange markets-which are extremely common in developing countries- are those in which a market-determined exchange rate coexists with one or more pegged exchange rates. Although there is an extensive theoretical literature on the macroeconomic implications of parallel foreign exchange markets, evidence on how these systems work in practice is limited.' This paper reports the main lessons from a Worid Bank research project aimed at filling this gap.<sup>2</sup> The core of the project is a set of case studies of parallel foreign cxchange rate market episodes in Argentina, Ghana, Mexico, Sudan, Tanzania, Turkey, Venezuela, and Zambia. These countries span a wide range of development levels, degrees of sophistication of financial markets, apd degrees of legality of parallel markets. The case studies are supplemented by a pair of cross-country studies of parallel market experience in developed and developing countries. Taken together, the papers in the project provide an important body of evidence on the macroeconomic characteristics of parallel foreign exchange systems.

There is great variety in experiences with multiple exchange rate systems (as discussed in section 2). Some countries adopted a parallel foreign exchange market on a temporary basis to deal with balance of payments of crisis (mainly in Latin America) while in others the parallel market is more permanent and developed in response to widespread foreign exchange controls (especially in Africa). There are also differences regarding the spread (as shown in table 1). Based on data for 38 countries we find that there is wide dispersion in the size of average the premium for the period 197090, ranging from low levels (less than 10%) in most Asian countries and in the few industrialized countries that adopted the system, to very large (exceeding 50%) in many African and Latin lizondo (1990) provides a stwvey of the theoreticai literature.

**2** The study is "The macroeconomic implication of multiple exchange rate systems,' RPO 675-30.
Economy-wide macroeconomic models, particularly computable general equilibrium (CGE) models, are used to assess the economy-wide impacts of a policy. However, only a limited number of studies are available in the literature that assess renewable energy policies using a CGE framework (Timilsina and Landis, 2014; Böhringer and Löschel, 2006; Rana, 2003). The main obstacle to the use of CGE modeling approach to assess renewable energy is that the share of renewable energy in the total energy mix is very small. Therefore, renewable energy technologies are not treated as a separate economic activity or sector in input-output tables or social accounting matrix, the main database for a CGE modeling exercise. Moreover, most CGE models represent electricity generation technologies as a single technology thereby ignoring the heterogeneity among various technologies to generate electricity.<sup>3</sup> The existing literature also diverges on techniques for representing renewable energy policy instruments in a CGE framework, particularly modeling renewable energy mandates. Some studies represent a renewable energy mandate, such as a biofuel blending mandate (a regulatory policy), through an equivalent fiscal policy, such as a subsidy to biofuels to the level that increases its consumption to satisfy a mandate or target (see. Hertel et al. 2010; Sorda and Banse, 2011 and Timilsina et al. 2012b). This approach is easier to incorporate in a CGE model. However, the general equilibrium effects derived through this approach might be different from the actual effects of a mandate because a mandate has a significant, direct effect on energy prices and thus the behavior of consumers, whereas consumer behavior is more indirectly affected by the energy price impacts of a renewable subsidy financed by the government through increases in other taxes.

The remainder of the paper is structured as follows. In section 2, a brief description of the CGE model is given. In section 3, the results of our simulations are presented. Finally, section 4 contains a summary and the conclusions of this study.

# **2 Model Description**

We model Brazil’s efforts to promote wind power generation in a perfect foresight intertemporal computable general equilibrium model. Base year data about the economy are

> 3However, recognizing the role of power sector on climate change mitigation policies, CGE models developed for climate change mitigation polices started to represent different electricity generation technologies separately instead of lumping them in a single technology (see e.g., Rana 2003; Paltsev et al. 2005; Timilsina and Shrestha, 2006).

3
and second tercile, with a lower rainfall variance, we find that migrant networks take longer to build up after the introduction of ID cards; the slope of the relationship between changes in migration and years-since-IDs is not as steep as for the third tercile, for which the village migrant network responds rapidly after the introduction of ID cards. These patterns suggest that, once we have controlled directly for riskiness of the local economy through a fixed effect, then interactions of rainfall variance with the quartic in years since IDs were issued will allow us to pick up additional differences across villages in the effect of the existing village migrant network on subsequent migration.

The observed lowess plots in Figures 4 through 6 still do not rule out the possibility that local village level effects, such as shocks to the village economy, may affect both household incentives to migrate and ID distribution decisions. To directly address this possibility, we estimate a discrete time duration model for ID distribution and test whether exogenous rainfall shocks, which make migration more attractive, are also significantly related to the distribution of IDs. Rainfall shocks affect local agricultural productivity and returns to labor in both local agricultural and non-agricultural sectors. Large shocks will be positively associated with household decisions to supply labor to the migrant labor market, and if these decisions drive distribution of IDs, then we should observe an impact of rainfall shocks on ID distribution.<sup>24</sup> To implement this test, we estimate a logit hazard model using village level data in which the dependent variable is equal to one in the year that IDs are distributed and zero prior to distribution. After IDs are distributed, the village drops from the sample for subsequent years. Regressors include province dummies and rainfall shocks for year t − 1 and t − 2 (Appendix Table A.1). We find no significant relationship between exogenous shocks to the local economy and distribution of IDs, and thus we have some confidence that household desire to supply labor to migrant destinations is not driving the timing of ID distribution.<sup>25</sup>

> 24 Note that in this test we use the actual value of lagged shocks, rather than variance, which is a proxy for risk. In a Appendix, Giles and Yoo (2007) show the t − 1 July-November rainfall shock, calculated as either an absolute or squared deviation from mean, is systematically related to negative shocks to earnings from the winter wheat crop harvested in year t. They also show that this shock is strongly related to increased participation in migrant labor markets, increases in the number of days in migrant employment and increased migrant remittances.

> 25 Neither the t − 1 nor t − 2 rainfall shocks have a statistically significant independent effect on ID distribution. Moreover, the p-value on a chi-square statistic of the joint significance of rainfall shocks for years t − 1 and t − 2 is 0.26.

16
||**Table 10 P**<br>**(1)**<br>**Expensive**|**roximity to min**<br>**(2)**<br>**Manuals**|**ing and quality**<br>**(3)**<br>**Learning**|**of public educat**<br>**(4)**<br>**Absenteeism **|**ion services**<br>**(5)**<br>**Crowded**|**(6)**<br>**Equipment**|**(7)**<br>**Corruption **|
|---|---|---|---|---|---|---|---|
|Cum. gold within 20 km|-0.012***|-0.010**|-0.005***|-0.030|0.000|-0.003***|0.197**|
||(0.004)|(0.005)|(0.002)|(0.033)|(0.001)|(0.001)|(0.080)|
|Education|0.013|0.047|0.211***|0.181***|0.106**|0.087*|0.003|
||(0.043)|(0.042)|(0.040)|(0.045)|(0.046)|(0.045)|(0.086)|
|Age|0.001|0.004|-0.004|-0.000|0.004|0.001|-0.022***|
||(0.004)|(0.003)|(0.003)|(0.004)|(0.003)|(0.003)|(0.008)|
|Male|0.172*|0.165**|0.416***|0.387***|0.332***|0.335***|0.207|
||(0.096)|(0.082)|(0.094)|(0.099)|(0.083)|(0.088)|(0.181)|
|Poverty|-0.136***|-0.137***|-0.169***|-0.063*|-0.065*|-0.093**|0.018|
||(0.038)|(0.039)|(0.040)|(0.037)|(0.038)|(0.041)|(0.079)|
|Rural|0.130|-0.057|-0.021|-0.163|-0.278*|-0.031|-0.346|
||(0.178)|(0.146)|(0.168)|(0.179)|(0.150)|(0.183)|(0.262)|
|Observations|2,193|2,199|2,186|2,196|2,192|2,179|565|

_Note:_ Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

31
For each phase, the underlying index variable used to calculate payouts is accumulated rainfall between the start date and end date of the phase, measured at a given reference weather station.<sup>5</sup> The start of the first phase, rather than being a fixed calendar date, is set based on the monsoon rains. Namely, it begins on the first date on which accumulated rain since June 1 exceeds 50 millimeters or on July 1 if accumulated rain since June 1 is below 50 millimeters.

As an example, consider the contract linked to rainfall in phase 1 of the 2006 monsoon, measured at the Mahabubnagar Indian Meteorological Department (IMD) weather station. The structure of this contract is presented in Figure 6.3, below (source: Giné et. al. 2007). Although contracts differ, the basic structure shown in Figure 6.3 is broadly representative of the contracts underwritten by ICICI Lombard.

[Figure 6.3 here] As the figure shows, the policy pays zero if accumulated rainfall during the phase exceeds an upper threshold, or “strike,” which in this case is 70 millimeters. Otherwise, the policy pays 10 rupees for each millimeter of rainfall deficiency relative to the strike, until the lower threshold, or “exit,” is reached. If rainfall is below the exit value, which in this case is 10 millimeters, the policy pays a fixed, higher indemnity of 1,000 rupees. This exit level is meant to approximately correspond to crop failure. The choice of this nonlinear payout structure was in part made based on the use of crop models, in an attempt to maximize the correlation between rainfall deficiency and loss of crop yield.

This example is for insurance on a single segment of the monsoon; in this case the first phase, corresponding to sowing. In general, households may choose to purchase policies for an individual phase of the monsoon or a single policy covering all three phases.