rafmacalaba/datause-encoder-data huggingface dataset:
**engaging investors beyond the financial dimension of the transactions, as partners and stakeholders in development.** While these new financing products can be purchased by investors that are not new to the Bank, their product “content” is radically different. Along with their safe investment return, World Bank bonds offered through La Poste, Westpac, and DekaBank, as well as those issued for the World Supporter Fund, provide investors an opportunity to help with broader development goals. Proceeds from these SI bonds, launched over the 2005–8 period, totaled US$1.3 billion.

4.7 **SI bonds with climate change-related themes have enabled investors to support a specific cause and raised a total of US$771.5 million through 2008 (Box 4.1).** The proceeds from the World Bank Eco notes and World Bank Cool Bonds support the general country-based operations of IBRD. Proceeds from World Bank Green Bonds aim to provide some degree of earmarking in order to demonstrate the linkage between resources mobilized and specific World Bank projects that support climate change mitigation and adaptation. World Bank Cool Bonds offer the additional benefit of paying a portion of its returns based on the fast-growing market for CERs in China and Malaysia.

4.8 **Several development banks—and in particular regional ones—have also sought to issue** **_local currency bonds_ to fund country operations. Between 1995 and 2008, development banks raised US$52 billion through local currency bond issues.** In addition to the financial rationale for issuing bonds in certain local currencies, these debt offerings fund local currency lending (discussed in Section V). An ancillary benefit of issuing bonds in developing countries’ currencies is that they open potential markets for these countries’ governments and corporations to raise funds in their own currencies on international markets (thereby reducing currency risks). This benefit may be pronounced for local currency bonds issued on local markets if they help develop local capital markets. Table 4.2 indicates that key issuers of local currency bonds were the European Bank for Reconstruction and Development (EBRD) (26 percent of all such issues), IBRD (23 percent), followed by KfW (14 percent), and the Asian Development Bank (ADB) and Inter-American Development Bank (IDB) (8 percent each).<sup>26</sup>

|**Table 4.2**<br>**: Fund**<br>**Denominated in Devel**<br>**Organization**|**s Raised by D**<br>**oping Countri**<br>**First Issue**<br>**(Year)**|**evelopment Ban**<br>**es’ Currencies,**<br>**Amount**<br>**Raised**|**ks through Bonds**<br>**1995–2008 (US$ million)**<br>**Amount Raised**<br>**Local Market**|
|---|---|---|---|
|EIB|1996|13,701|2,335|
|IBRD|1996|11,853|1,773|
|KfW|2004|7,125||
|IDB|1996|4,280|2,159|
|ADB|1998|4,033|1,254|
|IFC|1996|3,904|861|
|EBRD|1995|3,346|325|
|AfDB|1997|1,279|264|
|IIC|2001|44||
|Others||2,812||
|**Total**<br>_Source:_Dealogic.||**52,377**|**8,973**|

> 26 Variations can be explained in part by the different mandates and policies of development banks.

18
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**Table A1 (Continued).** Elasticities Used in the Model

|#|Industry|||||||||
|---|---|---|---|---|---|---|---|---|---|
|i01|Other agriculture|0.0|0.0||||0.1|||
|i02|Sugar cane industry|0.0|0.0||||0.1|||
|i03|Soy beans industry|0.0|0.0||||0.1|||
|i04|Forest sector|0.0|0.0||||0.1|||
|i05|Livestock sector|0.0|0.0||||0.1|||
|i06|Food and beverage<br>|0.0|0.0||||0.1|||
|i07|Crude oil & natural<br>gas|0.0|0.0||||0.1|||
|i08|Metal & mineral<br>mining|0.0|0.0||||0.1|||
|i09|Coal mining|0.0|0.0||||0.1|||
|i10|Textile & leather|0.0|0.0||||0.1|||
|i11|Wood production<br>|0.0|0.0||||0.1|||
|i12|Pulp paper &<br>furniture|0.0|0.0||||0.1|||
|i13|Petroleum refinery|0.0|0.0||||0.1|||
|i14|Biofuels sector|0.0|0.0||||0.1|||
|i15|Chemical industry<br>|0.0|0.0||||0.1|||
|i16|Non metallic<br>industry|0.0|0.0||||0.1|||
|i17|Metal industry<br>|0.0|0.0||||0.1|||
|i18|Machinery<br>equipment|0.0|0.0||||0.1|||
|i19|Other manufacturing|0.0|0.0||||0.1|||
|i20|Electricity generation||||||0.3|0.4|0.6|
|ei01|electricity: hydro|0.0|0.0||||0.1|||
|ei02|electricity: nuclear|0.0|0.0||||0.1|||
|ei03|electricity: coal|0.0|0.0||||0.1|||
|ei04|electricity: gas|0.0|0.0||||0.1|||
|ei05|electricity: oil|0.0|0.0||||0.1|||
|ei06|electricity: wind|0.0|0.0||||0.1|||
|ei07|electricity: biomass|0.0|0.0||||0.1|||
|i21|Processed gas|0.0|0.0||||0.1|||
|i22|Construction sector|0.0|0.0||||0.1|||
|i23|Commercial sector|0.0|0.0||||0.1|||
|i24|Transportation sector|0.0|0.0||||0.1|||
|i25|Other service sector|0.0|0.0||||0.1|||
||Household||0.0|0.8|0.6|0.25||||

For each sector _i_ , the Armington elasticity for aggregating imported and domestic goods is denoted by . For all other elasticity notations, please see Figure 1 for details. Sources: Paltsev et al. (2005); Timilsina and Shrestha (2007).

# **REFERENCES**

Böhringer, C. and Rutherford, T. (1997). “Carbon Taxes with Exemptions in an Open Economy: A General Equilibrium Analysis of the German Tax Initiative.” _Journal of Environmental Economics and Management_ , 32: 189-203.

Chen, Y. H., Reilly, J., and Paltsev, S. (2011). “The Prospects for Coal-to-Liquid Conversion: A General Equilibrium Analysis.” _Energy Policy_ , 39(9): 4713-4725.

26
What type of jobs are the unemployed favoring? According to available surveys, more than 80 percent of the unemployed are searching for a salaried occupation. In fact, only a marginal portion of them is willing to start an independent activity. Skilled workers usually prefer to remain unemployed rather than accept a job in the informal sector (mostly due to the impact of the family environment on luxury unemployment) or go into self-employment. Thus, the fact that around 36 percent of the urban poor job seekers are ready to accept any occupation, against less than 15 percent of their non-poor counterparts, is a sign of their vulnerability.

> In urban areas, the rate of unemployment for the poor is about 50 percent higher than the overall rate of unemployment. Overall, the insertion on the

> labor market is a key determinant of the living standards. The dependency ratio-calculated as the ratio of the unemployed to the employed members

> of a household-is 50 percent higher for poor families. Along the same line,

> econometric estimates indicate that, ceteris paribus, unemployment significantly increases the probability of being poor (see El Aynaoui (1998)). The

> link between labor market functioning and poverty is a strong argument in favor of labor market reform to address segmentation and unemployment.

# **2.2.4 Returns on Education**

Globally, the few available studies assessing the impact of educational attainments on earnings are showing the usual positive influence. Experience also shapes the educational returns-there is a quadratic effect. Estimations of earning functions for wage earners at the national and regional levels indicate that private marginal returns of investment in education is around 10 percent per each additional year of education (see Touhami (2003), and Cherkaoui et al. (2002)). Returns of education are higher for females and in urban areas. However, the returns of educational achievements are rather different when controlling for the segmentation hypothesis (El Aynaoui (1998)). In fact, an actual effect is only observed in the formal sector while the private returns of education are not significant in the informal sector. This outcome might preclude investment incentives in human capital, specifically within particular categories of individual who, as evoked earlier, may not have access to the formal sector.
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9

# Non SENA-Trained-Workers:

While the SENA program is an important source of training in Colombia, the overwhelming majority of workers have never participated in its programs.<sup>6</sup> Our data suggest that of the workers sampled who received their job preparation without SENA, many obtained training from more than one source. Yet, surprisingly, the large majority of workers (about two thirds) received no structured training of any kind. Moreover, over three-fourths of (female) seamstresses were working in the trade without structured training. From this we infer that a considerable proportion of these workers, especially male workers, manage to acquire their skills informally, perhaps picking them up from co-workers while on the job.<sup>7</sup> One implication of this finding is that the major source of job preparation, and the primary alternative to SENA training, is not structured or institutionalized training outside the SENA framework, but informal on-the-job skills acquisition. Again we find that many of those that do benefit from structured training programs do so from multiple sources of training (Table 5); 2.3 to over 13 percent of the non SENA-trained workers received training from more than one source, be it secondary vocational training, practical training, or institutional training.

Gender Differences in Training Patterns. Table 4 reveals clear differences in the training patterns of women and men who are not SENA-trained. First, with the exception of seamstresses, most women receive some type of training (66.4 percent of secretaries and 60.1 percent of bookkeepers): approximately twice the proportion of men who receive "any type of training." Second, and again excepting seamstresses, women are much more likely to receive related secondary vocational training (about three times more so) than men; they are two to three times as likely to receive multiple types of training than 6 Household surveys suggest that less than 10 percent of (male) workers in the country's largest urban centers have ever participated in SENA (Horn, **1987).**

> 7 **This** finding is in conformity with the results from research for the US, listed earlier.
# **8.2 Payment Behavior**

We find a precisely estimated null effect of the engagement intervention for all primary payment outcomes and time periods measured in Table 4. The control group payments increase steadily over the nine-month period from 30.1% of property owners having made payments one month after the intervention to 55.8% having made at least one payment by nine months (cumulative). However, compounds being exposed to the engagement intervention track almost the exact same trajectory as their control comparison. The total amount paid, and proportion of balance paid off are similarly indistinguishable across treatment and control group.

In contrast to the engagement intervention, we find a sharp increase in payment behavior among compounds exposed to the disconnection notices (Table 5). The likelihood of payment within one month almost quadruples - increasing by 30 percentage points from 11 percentage points (p-value < 0.001). This difference in payment likelihood sustains through the nine-month period, although with a slight decline relative to the control group. A similar pattern is found for the total payments after one month, which increases by US$ 8.80 (p-value < 0.001) from a base of US$ 5.02. After this sharp initial increase, the difference remains roughly constant between treatment and control groups while both increase over time. Treatment compounds have paid off 11.3 percentage points more of their balance than control compounds after the first month of intervention (p-value < 0.001). Control compounds begin to catch up gradually over the nine months, closing this gap to 7.8 percentage = points (p-value 0.005).

Figure 5 presents the full time series data available from the daily payment information extracted from the utility billing database. The visualization strengthens the main messages identified through the regression results. First, we find strong evidence of balanced payment practices across treatment and control groups from 2014 when property owners first started connecting to October 2018 just before the enforcement intervention. Second, we see that the payment trajectories continue to overlap after November 2018 when comparing the engagement intervention group to the control. Third, we see a sharp jump in the enforcement intervention group immediately after the intervention was delivered, which then stabilizes over time, suggesting that most of the impact identified in the regressions is driven by the behavior change in the first month after the intervention.

# **8.3 Spillovers**

To test for spillovers on the payment behavior of disconnection-eligible property owners we compare control compounds in treatment clusters to the equivalent disconnection-eligible 17
international market for migrant labor via analysis of migrant flows and contracted wages in a unique data resource: the Philippine government’s database of contracted migrant worker jobs.

We estimate the impact of economic shocks in Filipino migrant destination countries on migrant flows to and the wages that migrants are paid in those destinations, from 1992-2009. We find that percent changes in destination country GDP have a large (roughly one-to-one) impact on percent changes in Filipino migrant flows, but, by contrast, essentially zero impact on migrant wages. This pattern is consistent with the existence of a particular type of distortion in the market for international migrant labor: binding minimum wages. These minimum wages appear to be occupation-specific; we cannot reject that the effect of GDP fluctuations is similar across higherand lower-skilled migrant occupational categories. We also provide direct evidence of the existence and impact of binding minimum wages for an important occupational category (domestic helpers), via analysis of a natural experiment that raised the mandated minimum wage for Filipino domestic helpers. This minimum wage increase led to increases in wages and reductions in migrant flows in this occupational category.

Direct evidence on the nature of distortions in the market for international migrant labor is important, because it clarifies the nature and interconnectedness of the welfare gains and losses associated with international migration. Wage floors for international migrant work mean that the wage gains for migrants that are able to secure work overseas are magnified. But at the same time, the total quantity of migrant labor is smaller than the market-clearing level. Furthermore, these same wage floors also lead migrant flows to be more sensitive to economic shocks in destination countries than they would be if markets cleared, since they lead all labor market adjustment to occur via quantities rather than wages.

Second, our evidence reveals important welfare consequences of policies instituted by destination countries as well as by the migrant-source countries that set wage floors for international migrant work. On the destination country side, the policies in question include the U.S. federally-mandated minimum wage as well as H1-B rules requiring immigrant workers be paid the prevailing wage for the worker’s occupation. On the migrant-source country side, the key policy relevant for our analysis is the Philippine government’s regulation of labor contracts to ensure wages paid are above occupation-specific minimums. Our results reveal that these 21
maximum or else) lead to higher efficiency levels oil decreases leave efficiency levels unaffected (i.e. there are no reversals in efficiency once prices decline).

The rest of the paper is structured as follows. In section II we review the trends in the evolution of oil intensities, introduce the econometric methodology to measure the impact of oil prices on oil intensities, and present the empirical results. In section III we explore why Latin American oil intensities do not react to changes in oil prices. Finally, section IV closes with some conclusions.

# **II. Oil prices and oil efficiency**

# _II.1 Oil intensities across countries_

Oil intensities (measured as barrels of oil consumed per day over annual GDP), have evolved in a very different fashion in different regions of the world. This is evident from figure 2 where we present oil intensities<sup>6</sup> for a selected number of countries over the period 1971-2004. In panel A, the figure shows countries belonging to the Latin American region; in Panel B, to the OECD group and in Panel C, to the non-Latin American middle income countries. Inspection of this figure suggests three main elements.

First, over the past three and half decades oil intensities have declined only moderately in Latin America. In fact, median intensity for Latin America declined from 1.6 barrels per day per million dollar produced (bdmd) in the early 1970s to 1.3 in the early 2000s The only exception would be Panama where intensities declined from 2.8 to close to 1.7 bdmd. In the rest of the Latin American countries one can observe a few modest declines and some increases. This would be in contrast to what we observe in the OECD where oil intensities have declined much more markedly. For example, over the period under consideration, the US (which is the biggest oil consumer) reduced its oil intensity from about 4.2 bdmd to about 2.1. Similarly in Japan and France: in the case of Japan, oil intensities declined from 3.6 bdmd to around 2, whereas in the case of France the decline was from about 3.1 to also 2. The median intensity for the OECD countries in this sample declined from 2.9 bdmd in the early 1970s to 1.7 in the early 2000s On the other hand, the evolution of intensities in Latin America does not seem very different from what we observe in other middle income countries.

Second, even though if the most important gains can be observed in the OECD countries, today oil intensities appear very similar in the OECD, in Latin America, and in the middle income countries and range between 1.1 and 2.1 bdmd. In fact, countries like Brazil or Turkey would have little to show in terms of inter-temporal gains, but since in the 1970s they already had very low intensities, today also fare very well on this front.

Third, about two-thirds of the reduction in oil intensity in OECD countries was achieved by 1985 (i.e. by the end of the second oil crisis). For example, Japan cut oil

> 6 Oil data are in thousand barrels per day and comes from OECD website, whereas the GDP series is from the PWT6.2 and is in million US dollar.

4
**Table 1: Stages of Export Diversification**

<u>(Country FEs estimation; data from 1992-2003)</u>

||`Herfindahl`<br>`Index`|
|---|---|
|`GDPper capita`|`-.0000117`|
||`(2.41)**`|
|`GDPper capita`<sup>`2`</sup>|`2.60e-10`|
||`(2.46)**`|
|`Diversification/Specialization`<br>`Transition Point`|`$22,500`|
|`Observations`|`1140`|
|`Number of Countries`|`130`|

`Absolute value of t statistics in parentheses` ** significant at 5%

**<u>Table 2: The Changing Nature of Innovation</u>**

||`Inside-the-Frontier:`<br>`Discoveries`|`On-the-Frontier:`<br>`Patents`|
|---|---|---|
|`ln(GDPper capita)`|`8.667`|`-16.237`|
||`(6.53)***`|`(2.96)***`|
|`ln(GDPper capita)`<sup>`2`</sup>|`-0.514`|`1.059`|
||`(6.67)***`|`(3.43)***`|
|`Constant`|`-32.289`|`65.872`|
||`(5.71)***`|`(2.74)***`|
|`Observations`|`73`|`68`|

``` Absolute value of z statistics in parentheses
*** significant at 1%
```

**Table 3: Discovery & GDP by Leamer (1984) Commodity Groups**

|**Leamer**|**Number of**|**Discovery Curve**<br>**Maximum Point (GDP**|
|---|---|---|
|**Commodity Group**|**Discoveries**<sup>**N1**</sup>|**per capita)**|
|Petroleum|1.17|2052**|
|Raw Materials|0.85|4901***|
|Forest Products|0.73|4416***|
|Tropical Agriculture|0.49|4486***|
|Animal Products|0.73|4109***|
|Cereals, etc.|0.72|4055***|
|Labor Intensitve|0.38|3626***|
|Capital Intensive|0.56|4546***|
|Machinery|0.61|4578***|
|Chemical|0.78|6838***|

N1: normalized by the number of lines in the HS 1989/1992 nomenclature composing that category. ** significant at 5%; *** at 1%. Source: Author’s Calculations.

16
**19**

observations this data would also add a great deal of noise relative to real information about the relationship of interest between long-rnm population and economic performance and hence is unlikely to improve the estimates. That said, we produce and report results using ten year data in a section below and in appendix tables.

# IV) Basic Results: Labor force erowt and out;ut per wre

Tlable 3 presents the correlationms of population and growth over the entire period. The three columns report three partial correlawion coefficients; the bivariate partial correlation and the multivariate partial correlation, controlling for just initial income in one instance and for other established policy growth correlates in addition to lagged income in anotber. The bivariate results are similar to the bulk of the previous literature of cross county regressions: a small and statistically insignfant conrelation. Over the whole sample the correlation is slightly negative (-.10), but insignificant, the p-value is *3721. Both sets of partal correlations are nely the same as the bivariate correlations. Adding our set of control variables (chosen from a review of the literature searching for robust growth correlates) does not change substantially the estimated impact of labor force growth, if anything, it reduces the estimated impac.

# A) Results by itujon

The first question is whether or not the effect of population growth varies across groups of countries, defined either by region or income class. Surprisingly, when the effect is allowed 21 Tkriughout the paper the tables present p-values, which is the si aclevel of the te that the coefficient is different from zero. A p-value of 10 (.05 (.01)) for the partial corrlation for instance is refered to as rejecting the null hypothesis of zero u the 10 (5 (1)) pe:cent significance **level.** 22 This is not suprising if the policy regimes are uncorrelaed (or only weakly corlated) with labor force growth as the excluded variable bias on the labor force growth coefficient would be zero.