rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Price Level (Adjusted Ten Year Average) and Relative Price Dispersion Within Income Categories see equation 4 and Dollar (1990) for a description of adjustment Middle Income C'ountries

||Relative Price Dispe<br>|rsion<br>||
|---|---|---|---|
|Price Level<br>|Low<br>|Medium<br>|H:gh<br>|
|Low<br>|Pakistan<br>|Philippians<br> <br>Ecuador<br>Chile<br>Colunmbia<br>|I truguay <sup>2</sup>'<br>Peru-1l<br>Sri Lanka-' <sup>9</sup>|
|Mlediuimi<br>|Costa Rica<br>Paraguay<br>Argentina<sup>49</sup><br>|Brazil- <sup>14</sup><br>El Salvador|Indonesia<br>Guatemala<br>Bolivia|
|High<br>|Ireland' <sup>2</sup><br>V'enezuela<br>Dominican Rep.-lo<br>Panama<br>|Greece<br>Korea|Honduras<br>Portugal|
|UTpper incom<br> <br>|e Countries<br>|||
|Low<br>l<br>|JUnited<br> <br>|____________|Kingdom'7<br>Israel<br>Canada -18<br>Spain|
|Mediumi<br>|Austria<br>France<br>Italy<br>|Norway<br>Luxembourg||
|High<br>|Belgium<br>|Denmark<br>Germany<br>Netherlands|Japan<br>Finland|
|Lower Income<br>|Countriet<br>|||
|Low<br>|Mali<sup>2 2</sup><br>Madagascar<br>|Malawi<br>Ethiopia-<br>26<br>_|India|
|Medium|Kenya<br>Zimbabwe|Zambia<br>Senegal<br>_|Tanzania<br>|
|High|Ivory Coast|Cameroon<br>-Nigeria|1 8|

The percent deviation of 1980 price level from the ten year average is given in the superscript 25
2 This paper collects and compares a broad array of stock market development indicators. Specifically, using data on 41 countries from 1986-1993, we examine different measures of stock market size, market liquidity, market concentration, market volatility, institutional development, and international integration. Since each indicator suffers from statistical and conceptual shortcomings, using a variety of indicators should provide a more accurate depiction of stock market development across countries. Furthermore, stock market development - like the level of economic development - is a complex and multi-faceted concept and no single measure will capture all aspects of stock market development. Thus, our goal is to produce a set of stylized facts about stock market development indicators that facilitates and stimulates research into the links between stock markets, economic development and corporate financing decisions.

After describing each of the stock market development indicators, we examine the relationship among the stock market development indicators. We find enormous cross-country variation in the level of stock market development as measured by each indicator. For example, five countries have market capitalization to GDP ratios of greater than one, while five countries have market capitalization to GDP ratios of less than 0. 10. We also find attractive correlations among the indicators. For example, large stock markets are more liquid, less volatile, and more internationally integrated than smaller markets; countries with strong information disclosure laws, internationally accepted accounting standards, and unrestricted international capital flows tend to have larger and more liquid markets; countries with markets concentrated in a few stocks tend to have smaller, less liquid. and less internationally integrated markets; and internationally integrated markets are less volatile.

Although many stock market development indicators are significantly correlated in an
from the same socioeconomic group (figure 1). Children from the wealthiest group have, on average, three times as many peers from the same upper group than those from the lowest group. And the reverse is also true; pupils from the lower social background share the classroom with almost three times as many classmates from the lower group than those in the upper group. It is precisely the effect of this observed class composition on pupils’ test scores that this paper estimates.

Figure 1: Proportion of classmates in each family background group, according to own family background <!-- Start of picture text --> Lower middle<br>Lower<br><!-- End of picture text --> Source: ONE 2000, Argentina The following graphs (figure 2) present the unconditional relationship between economic segregation and average test scores (Spanish), on the left panel, and gap in test scores between the top and bottom socioeconomic groups, in the right panel. Socioeconomic segregation is measured here as the proportion of the total variance in family background that is ‘explained’ by the variance between schools. At the national level, a third of the overall disparities in family background are due to differences across schools. Within provinces, the degree of segregation varies between 14 percent in Santa Cruz to 32 percent in Salta (upper panel). Within localities, the proportion of the variance explained by the variation between schools fluctuates from 0 to 84 percent (lower panel).

Provinces and localities that are highly segregated report not only a larger achievement gap between rich and poor pupils, but they also post higher average test scores overall. The relation appears to be stronger be9
34

# 11. **Investment funds and privatization - Conclusions**

Surely the lesson to be learned from the analysis is that investment funds in the Czech Republic will be the predominant institutional players in equity control of privatized firms. The survey in the Czech Republic shows that investment funds developed important steps to become external monitors of firms.

Looking at investment funds by different fund category, one can observe that foreign bank sponsored funds and non-bank funds tend to have more concentrated portfolios. Monitoring of firms by these two fund types should therefore be relatively stringent compared to domestic bank sponsored funds. In addition, one can see that shares owned by foreign bank sponsored and non-bank funds performed better than shares owned by domestic bank sponsored funds.

However., it would be highly misleading to conclude that domestic bank sponsored funds have a less important role to play in the post-privatization period. Rather, the opposite conclusion should be drawn because domestic bank sponsored funds are represented on the boards of considerably more companies than foreign bank sponsored and non-bank funds. Banks tried to establish closer customer relationships by being represented in as many companies of affiliated investment funds. The creation of bank sponsored investment funds can be regarded as a spontaneous institutional arrangement that emerged in an economy of increasing uncertainty since the state was withdrawing more and more from the economy **43** The survey of the top ten funds in the Czech Republic shows a widespread use of bank staff to serve as directors on boards of companies owned by domestic bank sponsored funds. Continuous bank monitoring on the boards of enterprises generally results in decreasing information asymmetries between banks and enterprises. Therefore, the Czech system of corporate governance is to a large extent characterized by a bank control-oriented monitoring process over enterprise restructuring. Banks control the equity of enterprises of 43 This situation is sometimes referred to as a 'corporate governance vacuum'; see Berglof (1995).
Table 8: Impact on secondary outcomes by marital status at baseline

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Violence<br>(15 yo+)|Decision-<br>making<br>(individual)|Finance|Nutrition|Mental<br>health|
|T1 (safe spaces)|-0.050|-0.028|0.038|-0.412**|0.302**|
||(0.102)|(0.167)|(0.155)|(0.181)|(0.124)|
|T1 x Married|0.162|0.151|-0.014|0.021|-0.035|
||(0.121)|(0.163)|(0.153)|(0.152)|(0.125)|
|T1 + Leaders|0.131|0.153|0.174|-0.161|-0.131|
||(0.120)|(0.153)|(0.126)|(0.160)|(0.150)|
|T1 + Leaders x Married|-0.261|0.148|-0.226|-0.110|0.218|
||(0.163)|(0.182)|(0.145)|(0.143)|(0.162)|
|T2 (safe spaces + IGA)|-0.029|0.206|0.034|-0.344**|0.260**|
||(0.112)|(0.160)|(0.097)|(0.169)|(0.128)|
|T2 x Married|0.020|-0.076|0.026|-0.017|0.014|
||(0.140)|(0.144)|(0.114)|(0.159)|(0.127)|
|T2 + Leaders|-0.027|0.121|0.032|-0.088|0.011|
||(0.111)|(0.149)|(0.097)|(0.206)|(0.148)|
|T2 + Leaders x Married|0.168|-0.185|0.185|-0.100|-0.018|
||(0.149)|(0.142)|(0.129)|(0.221)|(0.161)|
|T3 (safe spaces + husbands’ clubs)|0.090|0.208|0.113|-0.626***|0.097|
||(0.098)|(0.130)|(0.188)|(0.209)|(0.151)|
|T3 x Married|0.079|-0.019|-0.260|-0.083|0.020|
||(0.120)|(0.125)|(0.170)|(0.127)|(0.115)|
|T3 + Leaders|-0.182|0.389**|0.251*|0.061|0.295*|
||(0.130)|(0.156)|(0.142)|(0.154)|(0.176)|
|T3 + Leaders x Married|0.341**|0.003|-0.107|-0.187|-0.042|
||(0.138)|(0.164)|(0.171)|(0.147)|(0.175)|
|T4 (control spillover)|-0.048|0.095|-0.087|-0.277**|0.063|
||(0.101)|(0.144)|(0.086)|(0.135)|(0.112)|
|T4 (control spillover) x Married|0.157|-0.035|-0.063|0.056|-0.034|
||(0.109)|(0.129)|(0.092)|(0.113)|(0.120)|
|T1 + T1 x Mar = 0|.299|.312|.865|.0267|.0123|
|(T1 + L) + (T1 + L) x Mar = 0|.369|.0718|.655|.085|.455|
|T2 + T2 x Mar = 0|.927|.343|.602|.0407|.0204|
|(T2 + L) + (T2 + L) x Mar = 0|.147|.599|.0648|.285|.954|
|T3 + T3 x Mar = 0|.128|.0911|.154|.00106|.409|
|(T3 + L) + (T3 + L) x Mar = 0|.103|.00857|.319|.418|.0256|
|N|2,935|2,923|2,999|3,007|3,007|
|Control mean|0|0|0|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. IGA = income-generating activity; mar = married; yo = years old. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group, for the unmarried then the married sample. All regressions control for the baseline value of the outcome variable, stratification fixed effects, an indicator for being married at baseline, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.

27
# <u>Analysis of PISA 2006 “national option” samples</u>

Estimates of mean achievement by PISA cycle, grade and type of school program are presented in Table 6. First, 16-year-old students in the 10<sup>th</sup> grade score on average higher than do 15-year-olds in the 9<sup>th</sup> grade, and 17-year-old in the 11<sup>th</sup> grade score higher than 16-year-olds. This is in line with intuition that older students are more able to solve PISA tests. However, when we look at the type of school programs, it is clear that mainly students in ISCED 3A schools improved, whereas 17-year-old students in vocational schools have even lower scores. This seems to be counterintuitive but there are two possible, highly likely explanations. Firstly it has to be noted that students change tracks, mostly in the 10<sup>th</sup> grade, and these are mostly low performing students who are forced to move to the vocational or mixed general-vocational track. Because of such changes in the population, student achievement in mixed general-vocational or vocational upper secondary schools could be lower in higher grades. Second, students in ISCED 3C tracks devote more time for vocational training in higher grades. Therefore, their general skills tested in PISA could be diminished. Consequently, slightly lower achievement in ISCED 3C is not that surprising.

|Table 6: Mean achievemen<br>|t by PISA<br>|wave, grad<br>|e and type of s|chool progra<br>|m|
|---|---|---|---|---|---|
|PISA wave:|2000|2003||2006||
||||ittil|til|til|
|Type of school program:|9<sup>th</sup>grade|9<sup>th</sup>grade|nernaona<br>9<sup>th</sup>grade|naona<br>10<sup>th</sup>grade|naona<br>11<sup>th</sup>grade|
|Mean achievement|479.1|501.9|513.5|520.1|528.3|
|ISCED 2A_lower secondary_<br>_school_|-|501.9|513.5|-|-|
|ISCED 3A general secondary|543.4|-|-|580.8|592.6|
|ISCED 3A/B general, profiled<br>secondary|-|-|-|494.9|494.6|
|ISCED 3B vocational secondary|478.4|-|-|505.9|508.8|
|ISCED 3C vocational (basic)|357.6|-|-|388.8|384.1|

Box plots presented below summarizes score distribution for the categories presented in Table 6 (Figure 6). This time data for vocational upper-secondary schools and general profiled (mixed) upper-secondary schools were collapsed into one category, ISCED 3B. A slight improvement is visible from 2000 to 2006 and for the 10<sup>th</sup> and 11<sup>th</sup> grades. However, it is also evident that mean scores increased because of the improvement at the top of the achievement distribution. Looking at the vocational ISCED 3C schools it is clear that while some students caught up with their colleagues in other tracks, students performing at the lowest proficiency levels are still numerous.

22
deployment, some observers think that even higher rates are plausible (for instance as high as 30 percent for PV solar (Neij 2008)).

Applying these learning rates to the baseline estimates requires an additional step. Since learning rates refer to a doubling of capacity (“learning by doing” is the most important factor), we need estimates of future growth in globally-deployed renewable energy resources. We assume that the recent past gives some guidance for future trajectories. Table 6 shows estimates of growth rates over the last five to ten years. Annual capacity growth rates imply doubling times from less than two years to almost three years for solar, wind and biofuels. These, in turn, suggest the number of times the learning rate needs to be applied to current costs to yield an estimate of future costs. We implement a twenty year scenario of pure technological learning only—i.e., we apply learning rates only to the capital cost portion of levelized costs, not to O&M or other nontechnical cost components.

Learning rates lower costs for all electricity supply options. But comparisons change significantly only for those technologies where the learning rates are higher than those for technologies used to generate grid supplied electricity. Solar PV-generated electricity costs drop from a range of 66-122 cents per kWh to between 19 and 35 cents. While cost differences narrow everywhere in Ethiopa, costs are lower than grid for only about 8 percent (1.15 million) of households. Both household-level wind and wind minigrid energy become lowest cost where available at 12-15 cents and 8-9 cents per kWh respectively, covering around one third of households in Ethiopia—about 5 million households in supply areas where sufficient wind resources are present. Finally, a combined solar and wind minigrid option is expected to generate power at 10 to 44 cents per kWh, cheaper than the grid for about 21 percent of households. Diesel and biodiesel comparisons remain unchanged, because cost reduction potential is similar to that of grid technologies. In each case they could supply electricity more cheaply than the grid for about 8 percent of households.

To illustrate an alternative way of assessing technical change impacts, Figure 11 plots the learning rate for each BSP demand area that is required to achieve grid parity 19
18

# Measuring the Performance **of** ATP1

We will apply several measurements that utilize available data in order to demonstrate various aspects of ATP I's performances and impact. We start with applying a simple farm-level analysis to data available in the _Primera_ 1996 season in order to estimate the gross incremental benefits associated with ATP I technical assistance. Although this measurement can be the result of many other factors, it is an indicator that ATP 1 has had a positive impact on producers' incremental incomes.

Obviously the highly positive result in B3 and the highly negative result in A2 dominate everything else in Table 6. As a result, the "average" incremental gross margin of Cd 74,344 is not as robust as it could be. Our main objective in using these one-season-specific perfonnances is to demonstrate the use of one particular indicator in evaluating paid-extension activities.

Table 6: Performance of ATP I producers in various regions in Primera 1996 (Cordobas)

|Region|Incremental**income**|**Incremental cost**|**Incremental gross margin**|
|---|---|---|---|
|Al|8843|2460|6383|
|A2|-455159|8613|-463772|
|B3|699010|157178|541832|
|B5|-8485|1614|-10099|
|Totala|244209|169865|74344|

Source: Computed from Garcia, 1996 aNot including region C6 for lack of data.

One indirect measure of the performance of extension is the rate of stability of the producer groups in the program. It is expected that a higher rate of instability (measured by the share of farmers/groups discontinuing their participation) is a reflection of a lower rate of satisfaction and a lower rate of extension performance. However, many random events such as unfavorable weather conditions may also contribute to low performances. Table 7 presents data which compares the results between 1995 and 1996.
Household characteristics should not be included as regressors because these are only observed at the time of the survey, not at the time of exposure to conflict.

- b. <u>Specification 2: Comparison of cohorts experiencing the same total exposure to conflict, but at different periods in their lives</u>

An alternative identification strategy is to compare the outcomes of individuals, who despite having been exposed, during their potential primary schooling life, to the same degree of violent conflict, have experienced a different intensity of conflict at a given age. The timing of exposure is likely to matter: the existing microeconometric evidence on the impact of conflict on education indeed indicates that the effect of exposure to conflict varies with age at exposure. I therefore estimate the following equation: _Yijrt = θ1 districtj + θ2 regionr*year t + θ3 casualties_6_10jt + θ4 casualties_11_14jt_ + _ε ijrt (2)_ Where _Yijrt_ , _districtj_ and _regionr*yeart_ are defined as in Equation (1), _casualties_age_ **-_** _age_<sup>**_+_**</sup> _jt_ is the number of conflict-related casualties per 1000 inhabitants in district _j_ during the years included in a moving window defined by _age_ **-** and _age_<sup>**_+_**</sup> in the life of an individual born in year _t_ .

# c. <u>Specification 3: Comparison of outcomes in 2001 and 2006 for a given age</u>

An alternative way of considering the effect of exposure to conflict is to compare the change in outcomes (e.g., completed years of education) between two surveys for a given age at interview, for districts with a more or less pronounced intensification of conflict between the two surveys. By using two surveys relatively close in time, such as the 2001 and 2006 DHS’s, this approach also has the advantage of comparing cohorts that are only five years apart, which reduces concerns over potential confounders. Furthermore, when the sample is restricted to children under 14 years old, the concern over selection bias due to voluntary migration vanishes. More specifically, I estimate models of the form: _Yijrt = γ1 districtj + γ2 aget + γ3 regionr*aget*dhs2006_ 16
## **Table 11: Comparison of farm-level costs – subsidized and unsubsidized maize**

|**FARM PRODUCTION**<br>**Irrigated Maize (FAM-high)**|**Per He**|**ctare**|**Per T**|**on**|
|---|---|---|---|---|
||**MWK**|**USD**|**MWK**|**USD**|
|**Gross revenue (yield* price)**|**98,000**|**700.00**|**28,000**|**200.00**|
|**Production costs**|||||
|Variable costs|57,900|413.57|16,543|118.16|
|Investment costs (ex. irrigation scheme)|6,747|48.19|1,928|13.77|
|**Total costs**|**64,647**|**461.77**|**18,471**|**131.93**|
|**Farmer income**|||||
|**Gross margin (revenue - var costs)**|**40,100**|**286.43**|**11,457**|**81.84**|
|**Netprofit(gross margin - invest costs)**|**33,353**|**238.23**|**9,529**|**68.07**|
|**FARM PRODUCTION**|**Per He**|**tare**|**Per T**|**on**|
|**Hybrid Maize (FAM-high) - subsidy**|||||
||**MWK**|**USD**|**MWK**|**USD**|
|**Gross revenue (yield* price)**|**70,000**|**500.00**|**28,000**|**200.00**|
|**Production costs**|||||
|Variable costs|22,200|158.57|8,880|63.43|
|Investment costs|6,747|48.19|2,699|19.28|
|**Total costs**|**28,947**|**206.77**|**11,579**|**82.71**|
|**Farmer income**|||||
|**Gross margin (revenue - var costs)**|**47,800**|**341.43**|**19,120**|**136.57**|
|**Netprofit(gross margin - invest costs)**|**41,053**|**293.23**|**16,421**|**117.29**|

# **B. Maize Competitiveness**

70. Next, Table 12 compares the accumulated SV for rain fed and irrigated maize at the assembly level with the most relevant import and export parity prices for Malawi. Based on 2007 prices, these data show that Malawi does enjoy a competitive advantage in maize as an import substitute, but not for export.. Given the much higher import parity price, Malawi would rather focus on domestic maize production for import substitution and should perhaps only target export markets where some specific opportunities emerge to exploit seasonal niche for regional trade. Improving the long-term competitiveness of maize as an export would likely require investments that reduce the underlying cost of fertilizer to the Malawi economy and not just financial prices to farmers as the input subsidy program set out to do.

30
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