rafmacalaba/datause-encoder-data huggingface dataset:
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deliver. This methodology produces what is known as an _option-adjusted hedge ratio_ (and an option-adjusted fuitures duration).

Hedging examples. We treat here two examples of the use of bond futures as hedge instruments: (a) hedging a long position in the cheapest-to-deliver bond, and (b) a fixed-income anticipatory hedge.

# (a) Heding the cheapest-do-deliver bond

Nature of the problem. Suppose we wanted to hedge a long position of $100,000 par value in a particular bond that happens to be the cheapest-to-deliver bond for a particular bond contract How many futures contracts need to be sold against the bond position to eliminate its sensitivity to price risk?

Hedge ratn. The scale factor is one since the futures contract size is equal to the cash bond position to be hedged. The volatility factor is also one since the underlying for the futures price is basically the cheapest-to-deliver, which is the bond that needs to be hedged. The basis point value factor is equal to the bond's conversion factor. This is because, as explained above, the dollar duration of a bond futures contract can be approximated by the duration of the cheapest-to-deliver divided by its conversion factor. This implies that the ratio of the bonds to the futures' dollar duration is ,ust the conversion factor. To sum up, then, the hedge ratio is given by the conversion factor -- this is the hedge ratio used (implicitly) in the constuction of basis trades as described in the previous section.

(b) Fixed income anticipatory hedge example (Excerpted from Treasury Futures for Institutional Investor, Chicago Board of Trade, 1990.).

Nature of the vroblem. On July 1, an insurance company enters into a commitment to sell a 20 million Guaranteed Investment Contract ("GIC") with a five year duration to a corporation. The GIC will be effective on October 1 and will yield a return of 9%. How can the insurance company protect itself from a drop in interest rates (rise in price) between the time of comrrmitment (July 1) and the time of funding (October 1) using futures?

Hedge ratio. The scale ratio is given by $20,000,000 (the value of the GIC) divided by $100,000 (the 5-year futures contract size), or 200. Suppose that the modified duration of the corporate bond portfolio to be hedged is 4.92 and its market value (including any accrued interest) is $11.77 million. Then the basis point value of the corporate bond portfolio (per $100,000 of face value) is $11.77x4.92 = ',.918. Suppose, further, that the basis point value of
the formal and informal sectors affect the earnings paid in each sector and make it unclear what the magnitude or sign of the differential should be even in an unsegmented market. Informal earnings should exceed formal sector wages by the expected value of unreceived benefits, and fall below them by the taxation that is often evaded. Earnings both in contract and self-employment may reflect a premium for risk, lifestyle, and in the latter case, the implicit costs of capital invested, and the value of unpaid work by family members that the MES suggests comprise 34% of micro-firm employees. Informal salaried workers are among the youngest (table 1) and the MES reports that roughly 30% are related to their employer. Their reported earnings may therefore incorporate training costs (see Hemmer and Mannel, 1989 and Roberts, 1989) or unobserved payments in kind.

# _i. Estimates of earnings differentials_

Using the vast but undetailed NTJES, the first columns of tables 2a and 2b show the percentage change in hourly real earnings generated by movements of individuals among sectors. This holds worker characteristics constant and leaves the variations in the characteristics ofthe work itself as the residual explanatory factor. The next columns account for these characteristics more than has been possible previously and present changes in real wages net of taxes,' and real wages net of taxes per hour among the four sectors.<sup>9</sup> For all three- transitions into self-employment, the s Payments were calculated based on the Mexican tax tables. It was assumed that all informal workers avoided paying taxes and all formal workers paid.

9 The non-normality of the distributions makes the sample mean an inadequate measure of the central tendency of the data and its significance. Two alternate measures are presented in each column: the mean as determined by a robust estimation technique, and the median. The median is largely unaffected by the nonnormality of the distribution and is robust to outliers. The robust mean attempts to recover the information in the tails while compensating to some extent for outliers and non-normality. All calculations are done in STATA.

6
affecting household well-being. For the elasticity of demand, we used -0.15 in this analysis,<sup>3</sup> which influences the estimated changes in quantity of electricity consumed (and in the next section, the fiscal effect on government revenues and spending).

As discussed, Iranians spend around 2.1 percent of their budgets on gasoline at pre-reform prices, with the middle of the distribution spending the largest share. On average, people will lose about 2.8 percent of their expenditure due to higher gasoline prices (Figure 6). Consistent with this, the increase in gasoline prices will affect the middle of the distribution. The poor will also experience a non-negligible loss in expenditure. The poorest 10 percent will lose about 2.0 percent of expenditure and the second poorest decile about 2.3 percent of expenditure.

Figure 6. The direct impact on well-being from increase in gasoline prices by deciles, % of total expenditure <!-- Start of picture text --> 0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>-3.0<br>-3.5<br>%<br><!-- End of picture text --> Source: Authors’ calculation using forecasted HEIS 2018/2019.

In terms of poverty and inequality, Table 5 shows pre-reform and post-reform estimates. Without mitigation, poverty would be expected to increase by 1 percentage point due to the direct impact of higher gasoline prices. Inequality, measured by the Gini Index, is also expected to increase modestly by 0.1 point.

Table 5. The direct impact on poverty and inequality after increase in gasoline prices

||Pre-Ref|Post-Ref|Change|
|---|---|---|---|
|Welfare (per capita), (IRR)|310,157|301,373|-8,785|
|Poverty (%)|11.2|12.3|1.0|
|Inequality (%)|39.7|39.8|0.1|
|Transferper capita,daily (IRR)||3,109|3,109|
|Source: Authors’ calculation using fore|casted HEIS 2018/2019.|||
|Note: Inequalityis based on welfare ag|gregate,not spatiallyadjusted.|||

4.2 Indirect and overall impact on poverty and inequality without mitigation efforts The indirect impacts of the reform consist of the effect of the rise in gasoline prices on other goods and services that households consume. When the price of gasoline increases, the production costs of many other goods may also increase if gasoline is used in their production or transportation. Therefore, there is

> 3 The short-term price elasticity of gasoline was estimated to be around 0.15 in Taghvaee and Hajiani (2014).

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# Effective Processes

Three other classroom organization and teaching process variables that have been found effective in industrialized countries but have been studied only minimally in developing countries are: (a) teacher monitoring and evaluating, including testing, (b) cooperative group work, including peer tutoring, and (c) use of practical activities for science instruction Evaluation and Testing. Frequent monitoring and evaluation of student performance has been identified as one of the characteristics of effective schools (Purkey and Smith, 1983). The interest in monitoring and evaluation is not new, however. A recent review of the effects of timing of feedback on student learning (Kulik and Kulik, 1988) notes that the first systematic studies of the effects of feedback on student learning was conducted over sixty years ago by Sidney Pressey (1926), who believed that students would learn more quickly if they received immediate feedback on the correctness of their test answers, rather than waiting up to months for their results. Few studies have actually compared immediate feedback with such long delays; most research has compared immediate feedback with delays ranging from a few seconds to a week.

The effects of feedback immediacy on achievement has recently been examined in a review of 53 studies, covering both classroom applied research and experiments (Kulik and Kulik, 1988). In nine of the 11 applied studies reviewed, stuxdents achieved more in classrooms where they 8
of covariates by attrition status is not significant.

At the time of the endline survey, approximately half of the WEDP-X loans had fully matured and the other half had just a few months of repayment left. Wasasa also shared administrative data on loan characteristics and monthly loan repayment for the entire period of the study from June 2018-March 2022 (i.e. covering the months after the endline when some loans were still active) for all 62 women in the treatment group.

# **5 Empirical Strategy**

Our main impact estimations use the following ANCOVA specification: where _Yib,t_ =1 is the outcome variable measured at endline. _Ti_ is a dummy variable taking the value of one if the individual was in the treatment group. _β_ 1 gives the intent-to-treat effect of being assigned to the treatment group (i.e. randomly assigned to be offered an uncollateralized loan), compared to the control group who passed the cut-off score, documentation check and business appraisal but were not assigned to be offered a loan. _Yi,t_ =0 is the baseline value of the outcome variable, set to zero for missing values. If a similar but non-identical question was asked at baseline (e.g. the same outcome but for a different recall period), we control for the similar variable as measured at baseline instead. _Mi,t_ =0 is a dummy variable indicating whether the baseline value is missing. _testi,t_ =0 is an indicator for whether the individual’s psychometric score at baseline was above the median within the RCT sample. _Xi,t_<sup>_′_</sup> =0<sup>isavectorofanyunbalancedbaselinecontrolvariablesfromTable1.10</sup>

> 10We pre-specified a version of Equation 1 indexed by branch _b_ and including a branch fixed effect. However, in the final sample only eight observations are from one of the two branches; so we omit these fixed effects due to power concerns. Branch fixed effects are not needed for inference, as randomization was not stratified by branch. We also pre-specified inclusion of the continuous test score. However, given the distribution observed at baseline, our preferred specification uses a median split of the score to improve power to identify _β_ 4. All reported treatment effects _β_ 1 are virtually identical if we include the continuous test score instead.

10
Despite the lack of modern asset registries in the region, banks do not seem to be particularly worried about the registration of security interests in property, especially when it comes to registering mortgages on immovable assets. Only 16% of banks consider the registration of mortgages problematic, while almost 50% of banks consider the registration of security interests in movable property a key constraint (see Table 8). One would expect that given the lack of modern registries in the region, the percentages for registering movable property should be higher than 50%. Possible explanations for this are: (i) the relatively modest use by financial institutions in the region of movable property registries (given the fact that movable property is only taken as complementary or secondary collateral), and; (ii) the general lack of knowledge from financial institutions (specially domestic banks) on how good functioning registry systems work elsewhere.

**Table 8: How Problematic is it for Banks in the MENA Region to Register Interests in Property?**

|**Region**|**Percentage of Banks Reporting Problems Registering Rights on Assets**<br>**Movable**<br>**Immovable**|
|---|---|
|**MENA Average **|49%<br>16%|
|**GCC Countries**|56%<br>19%|
|**Non GCC Countries**|43%<br>14%|

_Source:_ World Bank MENA SME Lending Survey, 2010 To conclude with what the situation is in MENA with regard to technology infrastructure, there is no reason to believe that the infrastructure limitations in MENA preclude the development of effective registries. In MENA’s middle income countries, internet use is widespread enough to suggest that the infrastructure necessary for an electronic registry sharing data across multiple locations is feasible. Internet penetration is reported to be 28.3% overall in the Middle East, with variation among countries: Bahrain at 55.3%, Iran at 48.5%, Qatar at 52.3% and UAE at 60.9% having the highest penetration, and Yemen the lowest at 1.6%, with others in-between.<sup>26</sup> This penetration rate is higher than the penetration rate in Africa (8.7%) and Asia (20.1%) and is above the average world rate (26.6%).

As previously stated, currently in MENA, the understanding of the modern collateral registry function is not widespread. Public education will be needed to change the view of registration as a two-party contract validation. Banks, leasing companies and other lenders will need to express a demand for and willingness to use the collateral registry in order for the government to be motivated to create modern ones, and for it to continue in operation so that it is a reliable longterm record of secured interests. They will also need to understand the nature of the registry, the

> 26 <u>http://www.internetworldstats.com/links10.htm; statistics on internet penetration.</u>

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**Table 1. Summary statistics, international transactions, firm level, 1997**

||all|all|estimation|
|---|---|---|---|
||exporters<br>(1)|importers<br>(2)|sample<br>(3)|
|exports per frm|1.34||5.06|
||(0.13)||(0.72)|
|share of exports to richer nations|0.61||0.79|
||(0.00)||(0.01)|
|number of export destinations|3.78||7.71|
||(0.05)||(0.15)|
|number of export categories|8.47||10.03|
||(0.19)||(0.28)|
|imports per frm||1.29|3.44|
|||(0.06)|(0.37)|
|share of imports from richer nations||0.88|0.90|
|||(0.00)|(0.00)|
|number of import source countries||3.66|5.63|
|||(0.02)|(0.08)|
|number of import categories||16.92|21.74|
|||(0.23)|(0.65)|
|fraction exporter|||0.45|
|fraction importer|||0.49|
|fraction exporter and importer|||0.35|
|N (frms)|12660|20280|6585|

Notes: Table reports averages across firms, weighting firms equally. First four rows are conditional on being an exporter (i.e. having positive exports), and second four rows are conditional on being an importer (i.e. having positive imports). Values of exports and imports in millions of 2000 euros. Petroleum exports and imports excluded in calculations. Standard errors of means in parentheses.
a farm experience a cool climate today versus a warm climate in the future. If there are important missing variables in our analysis that are correlated with climate, the predictions will be biased. Second, we assume that other changes in future conditions will not affect our climate predictions. For example, changes in technological advances, growth, and land use will not alter climate impacts. In practice, these future changes are both likely to occur and likely to have an effect on climate impacts. Future analyses should take these changes into account, but this is beyond the scope of this paper. We consequently limit ourselves to examine the impact of climate change on the current agricultural system. Third, we assume that prices will not change in any of these future scenarios even if supply changes dramatically. Partially, this can be justified because prices are determined in a world market and regional changes are not a good predictor of global changes. However, if prices change, this will tend to reduce the welfare impacts predicted in this analysis. Fourth, the analysis does not consider carbon fertilization effects. The increase in carbon dioxide is expected to be beneficial to plants in general and to specific plants in particular. Carbon fertilization is not taken into account in these forecasts although it will clearly increase productivity.

In order to see what impact future climates might have on Latin American agriculture, we examine three climate scenarios generated by Atmospheric Oceanic General Circulation Models (AOGCM’s). The three models we rely upon provide a broad array of outcomes from a mild wet scenario to a very hot and dry scenario. Specifically, the three models are the Parallel Climate Model (PCM) (Washington et al. 2000), the Center for Climate System Research (CCSR) (Emori et al. 1999), and the Canadian Climate Centre (CCC) (Boer et al. 2000). The climate projections of these three models for Latin America are presented in Table 8. The PCM is the mildest 16
# **Figure 1**

Marginal Effect of Years of Schooling by Intensity of COVID <!-- Start of picture text --> 2 3 4 5 6 7 8 9 10 11 12 131415<br>COVID Growth Shock (2020)<br>2<br>1.5<br>1<br>.5<br>Change in Schooling<br>0<br>Change in Digital Payments from<br>-.5<br><!-- End of picture text --> Notes. Figure derived from Table 5, model 1. Horizontal scale is logarithmic. The line reflects average marginal effects, the whisker endpoints mark the 90% confidence intervals.

40
and Southern Africa. In Uganda, for example, only 18 percent of households had income from this source in 1992, but by 2005/6 over 40 percent reported this source of income. Similarly, the number of households with income from NFEs increased by 20 percentage points over five years in Tanzania and Mozambique, and 12 percentage points in Rwanda.

As early as Adam Smith, it was noticed that in the process of moving out of a subsistence mode, households usually add activities to their portfolio, but as they get more established, specialization and commercialization are likely to occur. This age-old process is now at work in SSA. Of all the countries in our data, Ghanaian households are the only ones that appear to be on the path to specialization. Households are specializing in farm or non-farm earnings, and the total number of households reporting only one type of income (wages, farming or NFE) increased to over 50 percent while the number of household reporting three sources of income fell from 11 % to 6 %. The other countries in the sample appear to be in the diversification stage. In Uganda over 15 years and in Tanzania, Mozambique and Rwanda over five years the number of sources of income per household increased, as most households continued to report income (in cash or in kind) from farming even as they moved into the non-farm sector. Households added HEs as a source of income while maintaining farm activity.

**Figure 12 Share of households with income source**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100 Wage-public<br>80 Wage-private<br>60 Wage-Agriculture<br>40 NFE<br>20<br>Family farm<br>0<br>1991 2005 2001 2005 2000 2005 1992 2005<br>Ghana Tanzania Rwanda Uganda<br>(%)<br>Households with source of Income<br><!-- End of picture text --> _Source: Annex table 1 and authors‟ calculations_

# **Livelihood strategies and household welfare**

The reason agriculture households add NFEs is that HE owners‟ earnings are usually higher than in agriculture. However, Figure 13 panel (i) using data from Rwanda shows that they also have a wider variance. Similar data from Uganda shows a very high variance in average daily earnings for HE owners and household members working in the business, compared with other income sources (Fox and Pimhidzai, 2011). In urban areas, the differences are not so large. Data from Dar es Salaam, (Figure 13, panel (ii) non-agricultural earnings only), shows that in this capital city where opportunities for both wage employment and HE earnings are highest, median HE earnings are slightly lower than median wage earnings. Not surprising given the higher education levels that wage and salary employees have, the upper tails of the wage earnings distributions are fatter than the HE ones. But still, given the substantially lower level of education in HE owners compared with wage earners in Dar es Salaam, (Kweka and Fox, 2011), it is hard to argue that HE owners do poorly with the education that they have - indeed, as noted 24
Table 2: Correlation of Inflows and Outflows, by Instrument

||Advanc|d|||Inf|ows|||||O|utfow|s|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||economi|es|Pu|blic|Ba|nk|C|orp||Public||Ba|nk|C|orp|
||||PD|OID|PD|OID|PD|OID|PD|OID|Res.|PD|OID|PD|OID|
|nfows|Public<br>Bank|PD<br>OID<br>PD<br>OID||||||||||||||
|I|Corp|PD<br>OID||||||||||||||
|ows|Public|PD<br>OID<br>Res.||||||||||||||
|Outf|Bank|PD<br>OID||||||||||||||
||Corp|PD<br>OID||||||||||||||
||Emergi|g|||Inf|ows|||||O|utfow|s|||
||Market|s|Pu<br>|blic<br>|Ba<br>|nk<br>|C<br>|orp<br>||Public<br>||Ba<br>|nk<br>|C<br>|orp<br>|
||||PD|OID|PD|OID|PD|OID|PD|OID|Res.|PD|OID|PD|OID|
|s|Public|PD<br>OID||||||||||||||
|nfow|Bank|PD<br>OID||||||||||||||
|I|Corp|PD<br>OID||||||||||||||
|ows|Public|PD<br>OID<br>Res.||||||||||||||
|Outf|Bank|PD<br>OID||||||||||||||
||Corp|PD<br>OID||||||||||||||

Sample consists of 31 countries (15 advanced, 16 emerging) over 2004q1-2014q4, and is described in Appendix A.4. Correlations are conditional on country fixed effects, lagged log VIX, and lagged GDP growth. Blue indicates a positive correlation, red indicates a negative correlation, with darker shadings indicating stronger correlations.
# **Box 1. The synthetic panel methodology: Description and application to this regional work**

**Economic mobility has gained relevance in academic and wider policy discussion over the last years.** Despite significant reductions of economic poverty during the early 2000s in most countries in Europe and Central Asia, few studies currently exist on this process. Did poverty rates decrease as poverty reduction was a one-way street with households predominantly escaping poverty? Or is there significant churning with large numbers of households falling back into poverty offsetting transitions out of poverty? From a policy perspective it makes a large difference if a country which reduced poverty by 5 percent experiences either of these scenarios since each requires very different policy responses. Here, estimates of economic mobility allow for a better design of policies that can support economic development in a sustainable and inclusive way.

**Data requirements to analyze patterns of economic mobility are not trivial and require the use of innovative methodologies.** A proper study requires household-level information for one and the same household for at least two periods, not only for income or consumption, but also for other variables that can affect changes in income or consumption. Unfortunately, the availability of panel surveys that contain this type of information is quite limited, and even when existent, many times they suffer from high attrition rates and relatively short survey periods.

**The synthetic panel methodology overcomes these shortcomings and builds on an imputation methodology to construct synthetic panel data with predicted consumption using two different rounds of cross sections (Figure 12).** This way a new data set is created which provides information on consumption for one and the same household in two different time periods. The approach relies on timeinvariant individual and household characteristics. Consumption in each period is modeled as the sum of two components: a first one associated with **time-invariant characteristics** , and a second one capturing nonobservable factors. To create the predicted consumption in the second round ( _Ĉ2_ ) for households whose consumption were only observed in the first round ( _C1_ ), we generate a new component based on how their time-invariant characteristics are associated with consumption, but in the second round ( _F2(X1)_ ). Adding up this new component to the non-observable factors ( _ê2_ ), we obtain the predicted consumption in round two. With these two consumption measures ( _C1 and Ĉ2_ ), we construct transition matrices to analyze patterns of economic mobility between two rounds.

13