rafmacalaba/datause-encoder-data huggingface dataset:
|Observations|486|486|486|
|---|---|---|---|
|Prob > F|0.00539|0.0223|0.000834|

_Table 36: Has carried out enterprise investment in the last 12 months by flood exposure_

||Affected||||
|---|---|---|---|---|
||No|Yes|Difference|Significance level|
|Enterprise investment|27.0%|18.6%|-8.3%||
|Observations|375|212|||

Table 37 includes results from a multinomial logit regression to assess further the relationship between investments and risk. In this regression model the base outcome has been set to households that invest solely in their house to ease interpretation for our purposes. As such, being affected has a negative impact on enterprise investment compared to the base outcome of sole housing investment. Meaning, affected business-owning households are less likely to invest in their enterprise and more likely to invest in their house than business-owning households that were not affected. Furthermore, the regression table below shows other interesting findings, namely the fact that when a business-owning household has been affected by the flood it chooses housing investment not only over enterprise investment but also over inactivity (no investment) and investment in both house and enterprise. This highlights the urge affected households feel to improve and/or repair their house after the flooding event. Finally, the role of expenditure is noteworthy as it positively impacts the choice to invest/repair in the house rather than not conducting any type of investment. This is in line with our previous findings concerning the role of expenditure in foregoing housing investments (cf. supra and regression results in Table 34). However, we must be careful in interpreting these results as numbers are low for the concerned outcomes.

_Table 37 Multinomial logit regression results for trade-off between housing and enterprise investments for affected business-owning households_

||No investment|Investment in house<br>only (base outcome)|Investment in en-<br>terprise only|Invest in both house<br>and enterprise|
|---|---|---|---|---|
|Unaffected|||||
|Affected|-0.892***|ref.|-1.750***|-0.544|
||(0.23)|ref.|(0.605)|(0.391)|
|Expenditure|-4.93E-05|ref.|-1.73E-05|1.41E-05|
||(0.0000335)|ref.|(0.0000386)|(0.0000288)|

60
2009a) as one type of encouragement. Other types could include an informational brochure about a service which is available or subsidizing the service-fee or sign-up costs of a service for a limited period.

# 4.2. Adaptation and flexibility throughout the evaluation process

The unpredictability of the situation in which many peace building and conflict prevention impact evaluations take place sometimes calls for flexibility in the design and implementation of the evaluation.

Despite serious challenges to data collection in conflict-affected environments, all but one of the impact evaluations summarized by Samii et al. (2012) involved collecting primary data. It appears that the dearth of useful administrative data in these settings leaves little option but to collect primary data. The Samii et al. review did report that relative to the comparison group of impact evaluations carried out in other sectors, the impact evaluations appeared to be based on smaller average sample sizes than the comparison group of impact evaluations (2.5 – 4 times smaller), which may limit the analysis, for example of differential effects on sub-groups.

We asked the researchers of the conflict prevention and peacebuilding studies whether and how the research teams adapted the data collection methods for the conflict-affected settings. Of the survey-responses that reported some adaptations, the types of modifications can be roughly divided into four categories: 1) adaptations to the sample; 2) timing; 3) question formulation and focus group composition; and 4) the enumerators’ experience and training.

First, adaptation of sample size, either by design or due to unforeseen events, was a recurring response. In the evaluation of the Community Development Fund in Sudan, the researchers reported that they lost 60% of the sample communities due to the (re)outbreak of war (Paluck, 2009b), whereas in the impact evaluation of Afghanistan’s National Solidarity Programme (NSP), the districts in which the security of the enumerators and participants was at risk were excluded from the intervention and evaluation (Beath et al., 2010). The fact that the research was not being done in the hostile Pashtun communities clearly affects and limits the generalizability of the findings, and so it is important to be careful about how one reads the evidence. As described above, in the evaluation of ex-combatant agriculture and psychological training program in Liberia, the team decided to exclude high ranking commanders from the evaluation in order to avoid potential conflict caused by randomizing them into both treatment and control. The program was concerned that commanders who were randomized into the control group may cause problems for the overall program. Therefore, all commanders were 8
# **Security of Supply**

A system’s installed generating capacity can be considered adequate when peak demand for electricity is covered by an adequate reserve margin--the difference between the peak load and the portion of electric resources that are expected to be able to operate during the peak load. If planned maintenance can be performed outside the seasonal peak, the reserve margin must allow for forced (unplanned) outages of generating plant— the system’s operating reserve must be sufficient to withstand the loss of the largest plant.

As figure 20 indicates, by 2002 South Africa’s high spare capacity and more than adequate reserve margin came to an end. The system’s five-year forward committed plant reserve margin has been declining to below the active plant reserve margin since 2000 when the last generating unit was placed in operation.<sup>56</sup> This forward reserve margin declined from about 30 percent in 1998, to 13.6 percent in 2002, and just over 10 percent in 2003, indicating a very tight demand/supply balance (figure 20).

**Figure 20   South African reserve margin**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1998 1999 2000 2001 2002 2003<br>year<br> SA RM 5 year forward SA RM<br>%<br><!-- End of picture text --> _Source:_ Vundule _et al_ (2003).

# **Environmental Performance**

The generation of electricity from fossil fuels, notably coal and natural gas, is a major and growing contributor to the emission of carbon dioxide—a greenhouse gas that

> 56 Vundule, C, Boeije, W. and E. Viljoen. 2003. “Proposed Key Performance Indicators for the Electricity Industry in South Africa”. _NER Quarterly Journal_ 4: 14-29.

35
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Basis trading: sumrnmarv. It is basis traders who anchor the bond futures price on the basis of the cash bond market. The challenge in basis trading is in understanding what determines the cheapest-to-deliver bond among the delivery basket. Sophisticated basis traders apply fancy option valuation techniques to understand just how positive the net basis needs to be. The net basis changes with time: time weakens the short's option value and so the net basis converges to zero.

An alternative viewpoint: the imRlied repo rate. The net basis tells you by how much it is cheaper (in 32nds) to obtain bonds at the futures delivery date by buying a futures contract and taking delivery of the bond rather than by buying it directly in the cash market today and financing it at the prevailing repo rate to the futures delivery date. Alternatively, one could look at the same data by asking: what would be the break-even financing rate that would make me indifferent between owning a bond future and owning the actual bond? We define the implied repo rate (IRP), then, as the repo rate that sets the net basis equal to zero. Simple manipulation of the above relationships with the constraint of net basis = 0 produces the following formula for the IRP (assuming no coupon payments prior to the futures delivery date): IRP = (futures invoice price/bond purchase price - l)x360/(days to futures delivery) IRP and the determination of the cheapest to deliver. To repeat, the IRP is the theoretical return one would obtain from buying the cash bond, selling fitures short against it, and then delivering the cash bond into the futures. Obviously, then, of all the bonds in the deliverable basket, the one with the highest IRP will be the one the short will select as the cheapest-todeliver. Note that the cheapest bond to deliver need not be the bond with the lowest market price because one needs to adjust for the conversion factors. The cheapest-to-deliver bond is the one with the lowest purchase price _relative_ to its futures invoice price (notice that the inverse of this ratio is what drives the IRP in the formula above).

Basis calculation: an example. Suppose that the June 1993 U.S. T-bond futures contract is trading at a price of 111-00 (i.e., Ill and zero 32nds) as of March _25,_ 1993. Suppose, further that on that date the cheapest-to-deliver bond is the 7.125% of February 2023, which is trading at a clean price of 104-16.8 (equal too 104.525 in decimal terms) and which has a conversion factor of 0.9014. The bond pays semiannual coupons each February 15 and August 15, and the futures is expected to be settled on June 30, the last delivery date. The term repo rate to June 30 is 3. 10%p.a.
The data show significant gender differences in average earnings (Figure 4). Differentials are three times more prominent in the self-employed group than in other labor types. Male workers earn, on average, about 60 percent more than female workers in informal labor. Wage differentials are smallest for formal wage workers, where male workers earn, on average, about 20 percent more than female workers. Although income differences due to gender or education are not distinguished explicitly in the CGE model, they are captured in the earnings or wage functions of the micro-simulation.

**_Relative complementarity or low substitution among factors of production –_** is a key assumption in the model. Each economic activity can use nine labor categories plus capital in production. For reporting purposes, all skill levels of the self-employed are aggregated into a single input, self-employed labor; likewise for informal labor. In the production technology, it is assumed that substitution possibilities among inputs differ and the following structure is used: (1) it is difficult to substitute low-skilled labor for high-skilled labor in any of the three labor categories; (2) it is easy to substitute across labor categories for the same skill (i.e. a high-skilled formal worker is a good substitute for a high-skilled informal worker or a high-skilled selfemployed worker);  and (3) as the skill level of labor increases, it is more difficult to substitute capital for labor. In the CGE model, this behavior is represented using a translog production function.<sup>13</sup> The degree of substitution among labor inputs in production is important when measuring what impact a wage subsidy for low- and medium-skilled formal workers will have on unemployment.

**Table 7: Translog Elasticity Multipliers**

||Capital|High-<br>skilled<br>formal|Med-<br>skilled<br>formal|Low-<br>skilled<br>formal|High-<br>skilled<br>self|Med-<br>skilled<br>self|Low-<br>skilled<br>self|High-<br>skilled<br>informal|Med-<br>skilled<br>informal|Low-<br>skilled<br>informal|
|---|---|---|---|---|---|---|---|---|---|---|
|Capital|0|0.5|1|1.5|0.5|1|1.5|0.5|1|1.5|
|High-skilled formal||0|1|0.25|1.5|1|0.25|1.5|1|0.25|
|Med-skilled formal|||0|1|1|1.5|1|1|1.5|1|
|Low-skilled formal||||0|0.25|1|1.5|0.25|1|1.5|
|High-skilled self|||||0|1|0.25|1.5|1|0.25|
|Med-skilled self||||||0|1|1|1.5|1|
|Low-skilled self<br>High-skilled<br>informal<br>Med-skilled<br>informal<br>Low-skilled<br>informal|||||||0|0.25<br>0|1<br>1<br>0|1.5<br>0.25<br>1<br>0|

Source: CGE model database.

> 13 All activities except coal, gold, other mining, and refined petroleum use a translog production function; coal, gold, other mining, and refined petroleum use a constant elasticity of substitution (CES) production function with the assumption that it is difficult to substitute among inputs so the elasticity of substitution is low (0.2).

- 13 -
nitrogen equivalent mass of anhydrous ammonia using conversion factor of<sup><u>17</u></sup> 28<sup>_._</sup> We then use IPCC (2006b) Tier 1 estimates to compute the amount of nitrogen released to the atmosphere from ammonia application. We then convert the amount of nitrogen released to the atmosphere to nitrogen dioxide (NO 2 ) using conversion factor of<sup><u>44</u>Finally,wendthecarbondioxideequivalentofthe</sup> 28<sup>_._</sup> nitrogen dioxide using global warming potential of NO 2 _._ The GHG emissions coecients from livestock are taken from FAOSTAT database. These include the emissions from enteric fermentation, and manure management (per ton of livestock) and the manure left on pasture land (per hectare of land). The GHG emissions factor per hectare of converted nonreserved natural land is based on the estimates of Hertel, Golub, Jones, O'Hare, Plevin and Kammen (2010) using methodology from Searchinger et al. (2008). The non-land-use emissions of biofuels' production are taken from GREET lifecycle model (Searchinger et al. 2008, Dunn et al. 2011). We do not impose any regulation for land-use emissions in the baseline scenario, and consider it in the following sections of this study.

Following the literature on forest carbon sequestration in economic analysis of land-use (Sohngen and Mendelsohn 2007, Sohngen et al. 2009a) the carbon stock per hectare of harvested forest vintage _v_ , _μ_<sup>_h_</sup> _v_<sup>,isgivenby:</sup> In equation (C.19) the parameter _<u>μ</u>_<sup>_~~w~~_</sup> is the carbon conversion factor, that accounts for the stocking density of specic timber types, whole tree factors, and forest oor carbon, and _ψ_ 1 and _ψ_ 2 are the parameters dening merchantable timber yield function from equation (C.13).<sup>8</sup> Then the amount of GHG sequestered by a hectare of forest land of tree vintage _v_ is We obtain the carbon conversion factor and yield function (C.13) parameters from GTAP Global Forestry Data Base (Sohngen et al. 2009b). The share of permanently stored carbon in harvested forest products is from Sohngen and Mendelsohn (2007).

> 8Note that the minimum age parameter, _<u>v</u>_ , is not included in equation (C.20). This is because at young ages, stands may have substantial carbon, but little merchantable timber Sohngen et al. (2009b).

23
Martin, M., I. Mullis, & P.Foy,(with J.F. Olson, E.bErberber, C. Preuschoff, & J. Galia), (2008). _TIMSS 2007 International Science Report: Findings from IEA’s Trends in International Mathematics and Science Study at the Fourth and Eighth Grades_ , Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

- Martin, M., I. Mullis, P. Foy & G. Stanco (2012). _Trends in International Mathematics and Science Study 2011: International Results in Science_ , Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

- Martin, M.O., Mullis, I.V.S., Gonzalez, E.J., & Chrostowski, S.J. (2004) _TIMSS 2003 International Report: Findings From IEA’s Trends in International Mathematics and Science Study at the Fourth and Eighth Grades_ , Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

Martin, M., I. Mullis, E. Gonzalez, K. Gregory, T. Smith, S. Chrostowski, R. Garden and K. O’Connor (2000), _TIMSS 1999 International Science Report: Findings from IEA's Repeat of the Third International Mathematics and Science Study at the Eighth Grade_ , International Study Center, Boston College, Chestnut Hill, MA.

- Meyer, H.-D. and A. Benavot (eds.) (2013), _PISA, Power and Policy: The Emergence of Global Educational Governance_ , Symposium Books, Ltd, Oxford, UK.

Mullis, I., M. Martin, A. Beaton, E. Gonzales, D. Kelly and T. Smith (1997), _Mathematics Achievement in the Primary School Years: IEA’s Third International Mathematics and Science Study,_ Center for the Study of Testing, Evaluation and Education Policy, Boston College, Boston.

Mullis, I.V.S., Martin, M.O., & Foy, P. (with J.F.Olson, C. Preuschoff, E. Erberber, A. Arora & J. Galia,), (2008). _TIMSS 2007 International Mathematics Report: Findings from IEA’s Trends in International Mathematics and Science Study at the Fourth and Eighth Grades_ , Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

Mullis, I., M. Martin, P. Foy & A. Arora (2012). _Trends in International Mathematics and Science Study 2011: International Results in Mathematics_ , Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

Mullis, I., M. Martin, E. Gonzalez, K. Gregory, R. Garden, K. O’Connor, S. Chrostowski and T. Smith (2000), _TIMSS 1999 International Mathematics Report Findings from IEA’s Repeat of the Third International Mathematics and Science Study at the Eighth Grade._ International Study Center, Boston College, Chestnut Hill, MA.

- OECD (2015 forthcoming). _The Experience of Middle-Income Countries Participating in PISA, 2000-2015._ OECD Publishing, Paris.

OECD (2014), _PISA 2012 Results: What Students Know and Can Do – Student Performance in Mathematics, Reading and Science (Volume I, Revised edition, February 2014)_ , OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264208780-en.

OECD (2010), _PISA 2009 Results: What Students Know and Can Do – Student Performance in Reading, Mathematics and Science (Volume I)_ , PISA, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264091450-en 19
include tariffs on final goods. This variable captures foreign competition pressures. Finally, we also include a Herfindahl index at the sectoral level to control for domestic competition. Note that in order to keep the theoretical framework simple and rationalize the effects of input-tariff on foreign technology adoption, the model abstracts from these competition channels. They should be, however, included in the empirical estimation to avoid omitted variable concerns.

Next, we also explicitly take into account changes in observable firm characteristics that could affect firms’ import patterns. Using the same dataset, Bas and Berthou (2012) have found evidence on a positive correlation between firms’ decision to import capital goods and firms’ capital intensity. We therefore expect that non-importing Indian firms which experienced significant growth in their capital intensity during the period under analysis were more likely to import capital goods. _Xi,t-1_ is a set of firm-level controls such as firms’ capital intensity and the age of the firm. The Prowess dataset contains the year of creation of the firm that allows computing the age of the firm.<sup>29</sup> Table 3 shows the estimation results for equation (I) using a within-firm estimator. These results show the impact of lower input tariffs on the decision to import capital goods. In column (1) the coefficient on the input tariffs is negative and significant at the 1% confidence level, indicating that the drop in input tariffs between 1989 and 1997 increased the probability of importing capital goods. The estimated input tariff coefficient is robust to the inclusion of MFN tariffs for final goods set India (column 2). We also introduce tariffs on capital goods to be sure that the input tariffs are not just capturing the effect of changes in direct tariffs of imported capital equipment products (column 2). Not surprisingly, reductions in tariffs on capital goods enhance the probability of upgrading foreign technology embodied in imported capital goods. More interesting, the inclusion of capital goods tariffs does not pick up the indirect effect of reductions of tariffs on intermediate inputs. We next include additional industry and firm-level variables to control for industry and firm observable characteristics that vary over time and which could be related to input tariffs. The coefficient of interest on input tariff is robust and stable

> 29The Prowess dataset does not report consistent information on number of employees.

31
As the global population ages in the future, younger and better-educated cohorts will enter the workforce while older, less educated, ones leave. The average schooling of the working age population will tend to increase, even without any additional efforts to improve educational attainment rates – a pipeline effect or natural progression as students move up from one educational grade to the next over time. The larger intergenerational education gap in developing countries, combined with the large size and growing pool of younger cohorts relative to the older ones in those countries, forms the key and significant dynamics of the second wave that is analyzed in this paper. In the next section, we describe the methodology to quantify the effects of this and other related factors on global inequality.

# 3. Methodology

## 3.1 The global economic models

The global distributional impacts of the education wave depend on changes in per capita incomes between countries and changes in inequality within countries. To capture the full – between and within countries – distributional change, one needs a framework that captures both convergences at the macro level (country averages) and the evolution of factor markets at the micro level (dispersion). This paper adopts a dynamic global microsimulation approach which allows taking into account both these average and dispersion effects.

The origin of dynamic microsimulation can be traced back to the 1950s seminal work of Orcutt (1957) whose contributions aimed at overcoming the limitations of models available at that time. These models could be used to predict the aggregate impact, but could not describe the distributional impact of policy reforms or the effects on inequality of long-term trends, such as demographic change. Data availability and modeling have significantly advanced since then, but dynamic microsimulations remain the main tool to study distributional change, and still, provide the unique perspective of projecting samples of population forward in time (simulating different scenarios).

This paper uses the World Bank’s global microsimulation framework labeled Global Income Distribution Dynamics (GIDD) model in combination with the global computable general equilibrium (CGE) model called LINKAGE. Both tools have been described in detail in other papers (e.g. Bourguignon et. al. 2008 and Bussolo et. al. 2010; the full technical documentation of the LINKAGE is provided by van der Mensbrugghe, 2011), but it is still useful to briefly describe their structure in this section. The ultimate focus of analysis is household welfare, using household per capita income as its indicator. The distribution D of household per capita income y in year t can be expressed as the product of the joint distribution of all relevant household or individual characteristics X and the distribution of income conditional on these characteristics: 10
# **Fiscal Redistribution and Income Inequality in Latin America**<sup>*****</sup>

Edwin Goñi J. Humberto López Luis Servén The World Bank

## JEL classification codes: D31, H23, H20

Keywords: fiscal redistribution, income inequality, fiscal reform.

> * An earlier version of this paper was prepared for the conference “Políticas Económicas para un Nuevo Pacto Social en América Latina”, Barcelona, October 2006. We are grateful to David Taguas, José de Gregorio, Jean-Jacques Dethier and conference participants for helpful comments and suggestions. We thank Iamele Rigolini and Emmanuel Skoufias for sharing with us their data files on the incidence of taxes and transfers, and Karla Breceda for her assistance with the processing of the tax incidence data.  The views expressed here are ours only and do not necessarily reflect those of the World Bank, its executive directors, or the countries they represent.