rafmacalaba/datause-encoder-data huggingface dataset:
Figure A17: Specification Charts for z-Score of GDD <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.6 1<br>.4 .5<br>.2<br>* Weather Product * 0 * Weather Product * 0<br>MERRA-2 -.2 MERRA-2 -.5<br>ERA5 ERA5 -1<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.4 .2<br>.2 .1<br>0<br>-.2 0<br>* Weather Product * -.4 * Weather Product * -.1<br>MERRA-2 -.6 MERRA-2 -.2<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.4 .2<br>.2 0<br>-.2<br>0 -.4<br>* Weather Product * -.2 * Weather Product * -.6<br>MERRA-2 MERRA-2 -.8<br>ERA5 -.4 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 18 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

65
savings and the net externality from foreign savings. The tax gain is the result of the multiplication of the savings rate of 8.3 percent times the assumed average tax on savings of 10 percent for a negative tax externality (or a tax gain) of −0.8 percent. The tax externality on foreign savings has a tax gain of 8.2 percent times a 10 percent assumed withholding tax rate ( _tw_ ) or −0.82 percent and a positive tax externality of _i_ **m** *(1 − _tw_ )*k/Efs. With k = 0.90 (share of foreign savings responsive to changes in the market interest rate), tw = 10 percent (withholding tax), and Efs = 3.0 (price elasticity of supply of foreign savings), this results in a positive tax externality of 2.22 percent. Considering both the positive and the negative tax externalities yields a total of 1.4 percent (2.22 – 0.82 percent). Based on these estimations, the different tax distortions—such as the tax distortion per unit of capital invested, the tax distortions on savings (e.g. the interest rate times the tax on savings income, with a negative value or negative externality), and the tax distortion on foreign savings—have to be compounded using the same weights as the traditional EOCK. Using the same weights as in the previous exercise (f1 to f3 from Table 7), the weighted externality per unit of capital used in Mexico would be a relatively low 1.4 percent per unit of capital (Table 10).

## **Table 10 Externality** **<u>per unit of capital</u>**

|Domestic investment|Domestic savings|Foreign savings|
|---|---|---|
|_f1_|_f2_|_f3_|
|**Average weights**<br>**65.8%**|**24.7%**|**9.6%**|
|Return|8.3%|8.2%|
|Tax rate|10%|10%|
|Distortions/unit of capital<br>2.3%|−0.8%|1.4%|
|**EOCK externalities/unit of capital**||**1.4%**|

**The results for the EOCK are robust when compared to those obtained through the alternative approaches considered.** If we add a weighted cost of private funds ( _im_ ) of 8.9 percent (Table 11) and the EOCK externality per unit of capital of 1.4 percent (Table 10), we obtain an EOCK for Mexico of 10.3 percent, similar in magnitude to the value of 10.4 percent that was estimated using the top-down approach from national accounts data. Some differences can be explained by the average values for both the prices of debt and equity, as well as the ratios in the weights, which are an approximation.

# **4.2 Consistency of the results and interpretation**

**As we can observe, the results for the top-down and bottom-up approaches are very consistent—both methodologies point to a new recommended EOCK for Mexico of about 10 percent.** In this study we used the approximations from market information about Mexico (corporate bonds 30 percent, stock exchange 40 percent, and housing investments 30 percent of market shares representing the private sector in Mexico) and the rates of return from Table 5 and Annex III. We obtain 10.4 percent EOCK from the top-down approach and 10.3 percent from the bottom-up approach. These results, in addition to those obtained from reviewing the existing

24
|**Contribution Country**|**Liberia**|**Ethiopia**|**Rwanda**|**Nigeria**|**Zambia**|
|---|---|---|---|---|---|
|Localjob creation?|√|√|√|√|√|
|Local industrialization?|X|√|√|√|X|
|Technology transfer?|X|X|X|X|X|
|Overall|n/a|√|√|√|√|

_Source: Shen and Zai._ Several host countries provided specific data on jobs generated by Chinese investment projects. In Nigeria, 600 or so Chinese businesses generated approximately 69,000 jobs by 2011; in Ethiopia, 290 Chinese firms provided 35,000 regular jobs and nearly 40,000 seasonal jobs by the same time. In Liberia and Rwanda, where employment data are also available, over 3,000 jobs were reported in each case.

Responding host governments also gave their specific concerns regarding Chinese investment. As seen in Table 2, there are a number of concerns shared by host countries, with “increased competition hurting local industries” at the top. “Poor labor standards” are also commonly mentioned. Further, three out of the five countries found it difficult to communicate with Chinese firms due to “language and cultural barriers.” Finally, two countries expressed concerns about “poor environment standards” and one expressed concerns about “immigration.”

**Table 2: Concerns of African Governments about Chinese Investment**

|**Concern Country**|**Liberia**|**Ethiopia**|**Rwanda**|**Nigeria**|**Zambia**|
|---|---|---|---|---|---|
|**Competition hurting local industries**|**√**||**√**|**√**|**√**|
|**Low labor standards**|**√**||**√**|**√**|**√**|
|**Low environment standards**||**√**||**√**||
|**Language and cultural barriers**|**√**|||**√**|**√**|
|**Immigration issue**||**√**||||

_Source: Shen and Zhai._ 17
regional poverty levels are fully determined by average real income and inequality in its distribution. Therefore, the Shapley contributions now indicate the influence of deviations of mean (real) income and inequality from the national level. This decomposition allows us to uncover the dominant factor between these two.

Our results for some important members of the class of additively decomposable poverty measures are presented in table 2.5 (a&b for 2001 and 2007 respectively<sup>15</sup> ). There are six regions (the two major cities, and the coastal, western, southern and southwestern provinces where poverty is generally below the national average in both 2001 and 2007. Poverty is above the national average for the other six regions. The overall pattern that emerges from these results is that, except for the western, southern and southwestern provinces, the real income (scale) effect dominates (in magnitude) the inequality effect in 9 regions. Thus regions (among these 9) with lower poverty rates than the national average tend to have average real income higher than the national average. Similarly, average real income tends to be lower than the national average for those regions (out of 9) with higher poverty rates than the national average. Poverty levels in the West, South and South-West tend to be lower than the national average due to lower inequality.

The above results suggest that regional disparity in Cameroon is mostly due to differences in average real income, an indication of significant between-group inequality. The results of similar analysis applied to rural-urban differences for 1996, 2001 and 2007 are presented in table A5-A7 in the appendix. These results confirm the urban bias noted earlier to the extent that urban poverty is consistently below the national average while rural poverty is consistently above. A close look at the Shapley contributions reveals that rural poverty would be much higher than the national average if rural inequality were not lower than the national average. For instance in 2007, the incidence of rural poverty would have been about 21 percentage points higher than the national average if rural inequality had been at the same level as overall inequality. The observed difference stood at 15 points because the inequality effect was -6 percentage points.

> 15 Here we focus on these two years because the data from the 1996 survey are organized around 4 regions only in addition to the 2 major cities.

12
-16-

- -

- (6b) log(YKA) log(YKA(-l)

0.016 + 0.45 (INFT(-l)/Y(-l))

- (0.20) (1.20)

- 0.06 log(YKA(-l))

- (1.70)

In the actual model used for the simulations, (6b) was used, with the coefficient for log(YKA(-l) on the right-hand-side set equal to zero.

III. FISCAL POLICY, PRIVATE SAVINGS AND INVESTMENT AND OUTPUT GROWTH A. Th Lole of Fiscal Policy: an Outline The introduction argued that Turkey has adopted a growth-oriented debt strategy rather than rely on sustained high surpluses on the non-interest current account to keep the debt-output ratio in check. The key factor determining success or failure of such a strategy is an internal adjustment program that relies sufficiently on reduced consumption rather than reduced investment to generate the internal surplus that is required. If consumption does not fall, either external targets or output growth will need to be sacrificed; the former if investment is not reduced and the latter if it is. In this section, it is shown how Turkey has by and large succeeded in doing so, and how fiscal policy has contributed to this achievement. However, the analysis also brings out that continued success of this strategy is being jeopardized by a deterioration in fiscal deficits and the ensuing reliance on the issue of high cost internal debt.

Any internal adjustment program designed to complement external balance targets has two components. The first issue concerns the extent to which the external transfer will be matched by a reduction in the fiscal deficit rather than an increase in the private sector savings surplus. The ChhibbgrDisk/chhibber-Turkey _Papr/ac_ :07-28-88:lc
<!-- Start of picture text --> Differences in Values in Different Categories of Bribes<br>*<br>&<br>=<br>o= ao©8*.<br>=i<br>Cu<br>=<br>oOo oO<br>a=<br>a<br>Ea<br>wi<br>wi<br>Eo<br>i TM<br>3<br>#<br>oS<br>[SSS[SS GetGet anElectricityImport LicenseConnection [EG[I GetGetana ConstructionOperating LicensePermit<br><!-- End of picture text -->
**<u>Appendix Table 9: The Kitagawa-Oaxaca-Blinder Decomposition of Crop Sales by Age Group</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Age 15-29|||Age 30-64||
|VARIABLES|Overall|Endow.|Coeff.|Overall|Endow.|Coeff.|
|Male|11.632***|||11.803***|||
||(0.065)|||(0.029)|||
|Female|10.757***|||10.837***|||
||(0.092)|||(0.052)|||
|Raw Difference|0.875***|||0.966***|||
||(0.113)|||(0.060)|||
|Difference due to3|0.298***|||0.629***|||
|Endowments|(0.106)|||(0.057)|||
|Difference due to|0.577***|||0.338***|||
|Coefficients|(0.146)|||(0.070)|||
|Individual Age||0.046|2.129||0.013|2.088|
|||(0.104)|(8.699)||(0.025)|(2.361)|
|Individual Age Squared||-0.032|-0.766||-0.023|-0.861|
|||(0.098)|(4.630)||(0.027)|(1.164)|
|Number of Adults in HH||0.030**|0.209||0.018***|0.075|
|||(0.015)|(0.145)||(0.006)|(0.094)|
|Number of Children Aged 0-5||-0.122***|0.406***||0.001|0.043|
|In HH||(0.034)|(0.134)||(0.008)|(0.048)|
|Individual owns a Financial||0.048|-0.085*||0.031*|-0.052***|
|Account||(0.031)|(0.049)||(0.016)|(0.014)|
|Individual is Married||-0.010|0.766**||0.076**|0.355|
|||(0.013)|(0.316)||(0.035)|(0.243)|
|Individual is Divorced/Married||0.015|0.046*||-0.034|0.073|
|/Separated||(0.019)|(0.028)||(0.040)|(0.056)|
|Individual Completed||0.000|-0.006||0.004***|0.000|
|Secondary Schooling||(0.004)|(0.007)||(0.001)|(0.000)|
|Individual has a Vehicle||-0.101**|-0.030||-0.013|-0.132|
|(bicycle, motorcycle, car)||(0.048)|(0.166)||(0.030)|(0.103)|
|Household owns Mechanized||0.076***|-0.015||0.132***|0.009|
|Agriculture Equipment||(0.027)|(0.087)||(0.016)|(0.051)|
|Area of Plot of Land||-0.007|0.226*||0.034|-0.087|
|||(0.021)|(0.132)||(0.022)|(0.063)|
|Area of Plot of Land Squared||0.008|-0.088||-0.012|0.024|
|||(0.028)|(0.070)||(0.009)|(0.018)|
|Land Distance from House||0.009|-0.103||-0.000|-0.086***|
|||(0.009)|(0.076)||(0.000)|(0.030)|
|Crop Grown for Export||0.071***|-0.012||0.112***|-0.005|
|||(0.023)|(0.012)||(0.013)|(0.006)|
|Manager of Farm is the Owner||-0.002|0.044||-0.030*|-0.051|
|||(0.023)|(0.087)||(0.017)|(0.050)|

54
There are a number of caveats to be noted. Since our data on informal charging and moonlighting is self-reported the results in the subsequent analyses may simply reflect the willingness to acknowledge corrupt behavior not its _true_ incidence.<sup>12</sup> This is even more so because Tajik health workers operate in a risk-free environment and probably take advantage of all opportunities to take a bribe or moonlight whereas only a fraction of them truthfully report that behavior. Second, it is possible that health workers who did admit to informally charging patients still under-report the amount they extract. Therefore, a scenario where women are more honest in acknowledging informal charges but less candid in reporting the amount of their intake is thus possible.

# _Summary Statistics_

The following tables show the variables of interest for the different types of positions health workers hold and across the regions.

<u>Table 1: Women Presence in Tajikistan’s Health Sector (%)</u>

||**% FemaleHead**|**Doctor**|**Nurse/Feldsher**|**Administrator**|**Hospital**<br>**Attendant**|**All**|
|---|---|---|---|---|---|---|
|**Dushanbe**|40.0|42.9|95.8|75.0|100.0|67.2|
|**Sogd**|27.9|34.0|85.4|53.2|90.0|66.3|
|**Khatlon**|29.4|21.3|80.5|50.0|98.8|64.3|
|**RRS**|33.3|38.7|76.5|46.9|100.0|66.1|
|**GBAO**|61.3|66.7|100.0|60.0|100.0|86.2|
|**Tajikistan**|33.2|33.7|83.9|51.9|97.8|67.3|

**Source** : Authors’ calculations from Tajikistan Health PETS 2006 data **Note** : Gorno-Badakhshan Autonomous Oblast (GBAO) and Rayon under Republican Subordination (RRS) are regions

Table1 presents summary statistics about the presence of women in the health sector. The sector is dominated by women who constitute 67.3 percent of the overall health workforce. Women, however, make up only 33.2 percent of the heads of facility and 33.7 percent of the doctors. GBAO has both the highest proportion of female doctors (2 out of three doctors are women) and the highest proportion of women-headed facilities with 61.3 percent. Women

> 12 Swamy et al. (2001) face a similar issue.

11
# **2 The Model**

Consider the relational contract model in which two purely profit-maximizing risk-neutral parties, a buyer and a seller, have the opportunity to trade carbon emissions offsets at dates _t_ = 0 _,_ 1 _,_ 2 _,_ 3 _. . ._ . The seller possesses the exploitation rights for forested land and is interested in adopting land use and management practices that maximize her economic returns. She has the option to conserve the forest and maintain the carbon stocks or she can change the land use to a non-forest activity such as farming and timber harvesting, which would result in carbon emissions.

The buyer is interested in reducing greenhouse gas emissions from the forest clearing. Thus, he is willing to pay the seller to avoid changing the current land use and to maintain the carbon stock captured in the forest for a given period of time. Because carbon stocks only have value if they stay for a sufficient period of time, date _t_ is the period of time that the buyer wants the seller to keep the current land use.

At the beginning of period _t_ , the buyer and the seller agree on an initial baseline of tones of carbon stocked in the forested land exploited by the seller. Once the initial carbon stock baseline is established, the buyer proposes a compensation scheme that the seller receives if she does not change the land use and delivers the quantity of carbon initially agreed, _q_<sup>_∗_</sup> . Compensation consists of a fixed payment _pt_ and a contingent payment _bt_ : _Q →R_ , where _Q_ is the observed tones of carbon. Carbon stocks are observable by both parties but they are not enforceable because of weak court systems and weak governance in the developing country. Consequently, the desired carbon, _q_<sup>_∗_</sup> , may differ from the delivered quantity, _qt_ . We focus here on self-enforcing agreements, without adding the additional layer of analytical complication that would come about from assuming imperfect observability of results. The importance of self-enforcing agreements arises from uncertainty about how actual enforcement mechanisms might work under REDD. If the buyer were a national government (which in turn received payment from an international authority based on certified reductions in deforestation or forest degradation), the seller would have limited remedies if the buyer breached an agreement. There could also be imperfect enforcement against breach by politically powerful sellers who threaten to hold up delivery of certified reductions unless a larger payment is provided. Let _qt ∈ Q_ = [ _<u>q, q</u>_ ] denote the set of carbon delivered in period _t_ , where _<u>q</u>_ represents the tones of carbon dioxide sequestered at the beginning of the period given the initial land use. _<u>q</u>_ represents the quantity of carbon sequestered when the land use is completely changed to a profit-maximizing non-forest activity.<sup>1</sup>

> 1 In this model, I analyze REDD as a tool for limiting deforestation but not for increased afforestation; as

5
<!-- Start of picture text --> Ministry of Energy and Mineral Development<br>(MEMD) 4<br>Rural electrification<br>| Sectoral disputes<br>| Uganda Electricity Generation Uganda Electricity Generation<br>| Company Ltd (UEGCL) Company Ltd (UEDCL)<br>| Public GenCo Public DistCo<br>: Eskom Uganda Uganda Electricity Transmission Umeme Ltd.<br>- Public TransCo i<br>Independent power producers Other DistCos<br>lsolated/Rural concessions<br><!-- End of picture text -->
set of circumstances considered in the analysis as they may reflect the effects of omitted and correlated individual and environmental characteristics. Individual elements of the vector thus suffer from these omitted variable biases, and cannot be interpreted as causal estimates of the individual impact of a particular circumstance on test scores.

Another attractive feature of as a measure of inequality of educational opportunity is that, unlike any measure of the level of inequality, it is a parametric estimator of a ratio (equation <u>(3)) that is cardinally invariant in the standardization of test scores. To see this, note that any sub-</u> group mean is affected by standardization in a manner analogous to equation (1), so that: Given (7) and equation (2), it follows that: Finally, is decomposable into components for each individual variable in the vector C. Equation (6) can be rewritten as the sum over all elements (denoted by j) of the C vector:<sup>9</sup> Beyond the specific measures to be used, an additional challenge is that the TIMSS sample is representative of eighth-graders and not of the overall population of lower-secondary school age children. An estimate of the share of preparatory-level graduates among these cohorts can be obtained from the SYPE survey: 78 percent of youth between 16 and 21 complete preparatory schooling. Thus 22 percent drop out before and their achievements are not reflected in a basic analysis of TIMSS test-scores.<sup>10</sup> This selection is problematic for an analysis of educational

> 9This is an example of a Shapley-Shorrocks decomposition: it corresponds to the average between two alternative paths for estimating the contribution of a particular circumstance CJ to the overall variance. In the first (direct) path, all _Cj, j ≠ J_ are held constant. In the second (residual) path, Cj is itself held constant, and its contribution is taken as the difference between the total variance and the ensuing variance. Either path is conceptually valid, and the Shapley-Shorrocks averaging procedure yields (8) as the path-independent additive decomposition. See Shorrocks (1999) for the original application of the Shapley value to distributional decompositions. Ferreira and Gignoux (2011a) provide a formal proof that (8) is the Shapley-Shorrocks decomposition of the variance into the effects of individual circumstances.

> 10Among those, 6.5 percent have never been to school, 4.5 dropped out before completing primary, and 11 percent

17
facilities. While the analysis shows an increase in childhood leukemia near nuclear facilities, it does not support a hypothesis to explain the excess; the majority of studies find elevated childhood leukemia rates, but not statistically significant.

# **5. Air Pollution Impacts from the Energy Supply Chain**

As illustrated in the next section, air pollution is the largest source of human health risks as compared to accidents and occupational impacts linked to energy supply and consumption activities.

# **5.1 Health Impacts of Ambient Air Pollution**

Air pollution is one of the major environmental risks to human health. Exposure to elevated concentrations of particulate matters, specifically with size of 10 microns (PM10) or less (PM2.5) in diameter, are the main contributors to these risks as they cause cardiovascular and respiratory disease, and cancers.

There are hundreds of studies examining the linkage between ambient air pollution and human health. Anderson et al. (2012) reviews such studies published over 30 years between 1990 and 2010 to determine the linkage between PM and human health. The study concludes that the effects of PM on cardiovascular diseases are most pronounced. Similar relationship exists, but at lower scale, between PM and respiratory diseases. It suggests further studies for the relationship between PM and cerebrovascular disease.

With evaluation of 79 risk factors to human health in 195 countries from 1990 to 2015, the 2015 Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 2015) finds that air pollution is the fifth largest contributor to global mortality and morbidity, particularly in low- and middle-income countries after high blood pressure, smoking, diabetes and high cholesterol. Cohen et al. (2017) estimates that long-term exposure to ambient PM2·5 caused 4·2 million deaths in 2015 representing 7·6% of total global mortality.<sup>11</sup> The study also estimates that 103·1 million

> 11 In order to produce these estimates, Cohen at al. (2017) used (i) spatially and temporally resolved estimates of population-weighted exposure, (ii) specification of a theoretical minimum risk exposure level (TMREL), (iii) estimation of relative risks across the exposure distribution, and (iv) estimates of the deaths and disability-adjusted life-years (DALYs) for diseases linked causally to air pollution to attribute the deaths and DALYs to ambient air

15