rafmacalaba/datause-encoder-data huggingface dataset:
Figure 8a: Transmission to Non-Oil Developing Countries of U.S. Deficit Reduction: Permanent versus Tempoarary versus Anticipated <!-- Start of picture text --> LDC Trode Balonce<br>0.10<br>: a 0.06 '' - a-<br>E 0.02 E 3F<br>C -0.02<br>-0.06<br>o4Y<br>IL-01<br>0C14 5<br>- 0.12 Pemn_<br>S 0.22<br>_0.20 _3 _- - Temporary<br>-a..'. 1994 1996 1990 2000 2002 2004 2006 2005 2010 201 _ A<br>Yeor<br>U.S. Spending Cut (1.0% of G.D.P.)<br><!-- End of picture text --> <!-- Start of picture text --> e .6 Perrnonent<br>-0.05 . LDC Exports . | . X . . . * . s . . . , , . -Se- ~~AnticiprJted<br>0.35--<br>a 0.30 -<br>0I.2<br>CL 0.25 /<br>LA.1<br>C -~ ~ ~~~~3-1--E<br>-. 0.15<br>o 1/~~~~~~~~~~~~~~~1<br><!-- End of picture text -->
The aggregate gains from trade in Vietnam are 1.99 percent. These gains are mostly explained by gains from lower tariffs on Other Processed Food (0.92 percent), Bananas (0.88 percent) and Cigarettes (0.61 percent). These are mostly consumption gains (with minor or even absent income gains). Fish contributes 0.37 percentage points to the aggregate gains, with a consumption gain of 0.50 percent and a lower income loss of 0.13 percent. The case of Rice is interesting because lower tariffs (and lower prices) create large income losses of –1.57 percent. Other important products are Corn (with income losses of –0.13 percent), Alcohol (with consumption gains of 0.17 percent), Pork (0.28 percent) and Tea (0.13 percent).

The pro-poor index in Vietnam is –1.70. This anti-poor bias of agricultural liberalization is due to the fact that the gains for the rich (2.74 percent) are higher than the gains for the poor (1.04 percent). Tariff liberalization in products such as Bananas and Cigarettes yields the largest pro-poor biases, but the tariff cuts in Corn, Other Processed Food and Rice are distinctly anti-poor. Note in particular the role of the rice income effect: because of lower prices and because the poor are major producers and sellers of rice, the net rice income loss for the poor is –2.35 percent, much higher than the net rice income loss for the rich (of only –0.58 percent).

To end, we report in Table 6 the results from scenarios where tariffs are eliminated only in cereals (namely, corn, wheat, rice and other cereals—sorghum, barley—in the templates). This scenario is interesting because it combines a simulation for a set of related goods that accounts for more than 50% of global calorie intake,<sup>11</sup> across all countries.<sup>12</sup> Tariff liberalization in cereals would bring aggregate developing world gains of 0.42 percent, but there is a lot of variation in the gains from trade not only among the winners but also among the losers. For example, the gains from cereals liberalization can be as high as 1.92 percent in Guinea Bissau or 1.83 percent in Bhutan to as low as being almost negligible in Armenia, Georgia, and South Africa. There are also countries that would incur large losses, such as Vietnam and the Central African Republic (–1.70 and –0.99 percent, respectively), as well as countries in which losses are almost negligible (e.g., Ghana, the Arab Republic of Egypt,

> 11See World Health Organization (2003).

> 12Note that we are running country-by-country unilateral tariff reductions rather than multilateral liberalization scenarios. In particular, we ignore spillovers of tariff reductions in one country on another country, because the low-income countries in our sample only account for a small share of world trade.

12
**<u>Table 11: Sensitivity of the effects of a tax on carbon to the target emission reduction</u>**

||**Emissio**|**n Reductio**|**n**||||
|---|---|---|---|---|---|---|
||Base|5%|10%|15%|20%|25%|
|Welfare|786.32|-0.04|-0.14|-0.33|-0.65|-1.15|
|Carbon tax as a percent of total tax revenue|0.00|2.40|5.40|9.13|13.81|19.79|
|Carbon tax per metric ton (USD)|0.00|2.95|7.05|12.72|20.65|31.91|
|Carbontaxper metric ton(Rand)|0.00|22.29|53.32|96.25|156.21|241.38|

In relation to South Africa’s Long Term Mitigation Scenario (LTMS), its “Use the Market” scenario uses a starting carbon tax of R100 per ton, which is remarkably close to the calculated tax in the reference case (although the numbers in our study are in 2003 rand and US dollars). Hence, the emission target of 15 percent in this study could in effect be interpreted as an independent estimate of the emissions reduction possible for the LTMS, with the different simulations testing for the sensitivity of the carbon tax to various assumptions.<sup>22</sup>

# **IV. Conclusions**

Political, social and economic considerations will affect South Africa’s policies to reduce carbon emissions. In this paper, we have focused on one dimension and one instrument, namely the economic costs of the distortions created by different taxes applied to reduce carbon emissions. We find that the welfare costs of achieving significant reductions in CO2 emissions are fairly small. In general, the more targeted the tax to carbon emissions, the better the welfare results. If a carbon tax is feasible, it will have the least marginal cost of abatement by a substantial amount when compared to alternative tax instruments. Furthermore, the welfare losses from a tax on carbon are small regardless of the elasticities of substitution in production. If the revenue generated can be used to reduce pre-existing tax distortions, the net welfare cost becomes negligible. If a carbon tax is not feasible, a sales tax on energy inputs is the next best option, which has a better equity impact and a better welfare result if the substitution elasticities are really low, given the existing distortions in the economy.

There are several points worth emphasizing. The simulations do not account for the social gains of emissions reduction and there are no dynamic or productivity gains including from benefits from clean technologies. Our approach is therefore conservative, by leaving out welfare gains. Even then, we find that the welfare cost is still small and manageable.

Moreover, a carbon tax may be relatively simple to implement among alternative policy instruments because the carbon content of individual fuels is fixed

> 22 However, Pauw’s “use the market” scenario also includes projections about the change in the structure of energy use and changes in investment and savings needed to achieve the higher investment levels.  We do not include those changes.

27
firms in Blantyre own or share generators and on average 11% of overall electricity use comes from those private generators. In comparison, transportation is a relatively more important constraint in Lilongwe. Electricity shortages are less severe considering Lilongwe’s specialization in low-tech domestic market oriented industries. Only 28% of Lilongwe firms install or share generators, and provides only 4% of electricity used by the city’s manufacturers.

**Table 10: Varying industrial specialization and bottlenecks in Blantyre and Lilongwe**

||Ma|nufacturing fi|rms<br>National|relative t<br>total/av|o national<br>erage,%|
|---|---|---|---|---|---|
|Cities|Blantyre<br>(a)|Lilongwe<br>(b)|<br>total/ average<br>(c)|Blantyre<br>(a/c)|Lilongwe<br>(b/c)|
|Number of firms|97|32|159|61.0|20.1|
|Byindustry||||||
|Food & Beverage|25|11|56|44.6|19.6|
|Textiles|10|1|11|90.9|9.1|
|Apparel|3|1|4|75.0|25.0|
|Wood & woodproducts|5|2|10|50.0|20.0|
|Paper|3|0|3|100.0|0.0|
|Chemical & Chemicalproducts|14|2|17|82.4|11.8|
|Rubber &plasticproducts|12|5|17|70.6|29.4|
|Metalproducts|10|3|17|58.8|17.6|
|Machineryand equipment|3|0|3|100.0|0.0|
|Furniture|8|5|14|57.1|35.7|
|Construction Material|2|1|3|66.7|33.3|
|(exports/imports)||||||
|proportion of sales for direct exports,%|44.7|8.8|41.6|107.5|21.2|
|Mode of export transportation,%||||||
|maritime only|2.4|0.0|3.2|74.0|0.0|
|air only|4.1|16.7|5.5|74.7|305.3|
|land only|70.8|43.3|64.1|110.5|67.6|
|inter-modal|22.7|40.0|27.2|83.3|147.0|
|intermediate inputpurchase,%||||||
|from domestic sources|33.3|56.3|45.2|73.6|124.5|
|through imports|66.7|43.7|54.8|121.7|79.8|
|infrastructure problem for doing business||||||
|1(no obstacle),...,5(severe obstacle)||||||
|Telecommunications|2.3|1.9|2.2|103.1|86.8|
|Transportation|2.6|2.7|2.7|99.0|99.4|
|Electricity|3.6|3.3|3.6|100.3|93.2|
|proportion of firms withgenerators,%|55.7|28.1|48.8|114.2|57.7|
|electricityuse fromgenerators,%|11.3|3.7|10.2|111.4|36.4|
|production cost, per revenues||||||
|electricitycost|1.6%|1.3%|1.7%|92.6|77.3|
|Fuel cost|2.2%|1.1%|2.1%|101.3|49.6|
|water cost|0.3%|0.2%|0.2%|110.0|94.3|
|transport cost|5.6%|6.7%|5.5%|102.6|122.7|
|telecommunication cost|0.5%|0.5%|0.5%|98.5|107.4|
|marketing/advertisingcost|1.8%|0.3%|1.7%|106.0|20.3|
|Data Source: ICA 2004||||||

38. In summary, industrial specialization in Blantyre and Lilongwe is a response to differences in product market structures and their accessibility. As Blantyre is geographically closer to international markets, specifically to exporting ports in Mozambique – firms specialize in activities benefiting from access to international markets. As Lilongwe is located

- 17 -
hold spent a non-zero amount on items from that category, and 0 otherwise. The items represent goods that are more likely to be dropped from (included in) a household’s consumption basket when the household is subjected to negative (positive) income shocks, thereby improving the model’s ability to capture temporal changes in economic conditions. Figure 6 in Appendix 2.1 examines the evolution of premium good consumption in CPHS overtime.

# _Implementation_

The consumption model is estimated using data from the NSS and then applied to impute NSS-type consumption into the CPHS. Success of this approach is contingent on: (a) model stability (i.e., the model estimated in 2011 continues to apply in the years for which the CPHS is available), (b) sufficient predictive power of the model (i.e., the predictors are sufficiently correlated with household consumption), and that (c) the predictors are consistently measured between the two surveys. The analysis presented in section 3 confirms that the levels and trends in demographics, education and asset ownership observed in the (reweighted) CPHS are consistent with those observed in the nationally representative benchmark surveys.<sup>22</sup> The regression model, estimated separately for urban and rural India, is shown in Table 2 (the coefficients related to principal industry of occupation is suppressed for formatting purposes). The urban model fits the data better when compared to the rural model, which is consistent with consumption models estimated to data from other countries (e.g. Douidich, et al., 2016). Overall, families with higher share of dependents (members below the ages of 18 and above the age of 61) are associated with lower consumption per capita, while households with more educated members and greater ownership of assets are associated with higher per capita consumption.

|**Dependent variable:** Log consumption per capita|(1)<br>Rural|(1)<br>Urban|
|---|---|---|
|1-member household|0.74***|0.99***|
||(38.10)|(56.03)|
|2-member household|0.53***|0.64***|
||(46.90)|(47.17)|

> 22Figure 7 of the Appendix 2.2 shows the share of principal industry codes of households are also consistent across NSS-2011 and CPHS. Share of households with agriculture as the principal industry code are excluded in the graph for ease of representation: 39.1 percent of households in NSS-2011 and 33.1 percent of households (averaged across years) in CPHS belong to this category. In NSS-2011, principal industry code refers to the industry from which the households obtained their maximum income. In CPHS, we construct this variable based on the industry code of the household head. Households with missing principal industry code (due to head of household being unemployed or no member of the household being active in the labor market) are set to zero.

28
Table 1: Direct and indirect trade policy measures introduced in the 9th and 10th FYPs, 2001–2010<sup>_†_</sup>

|Policy dimension|Related programs|Timing|Plan|
|---|---|---|---|
|Export diversifcation|Allow private sector export of most public<br>industrial products; delink exports from imports.<br>Creation of export promotion agency and<br>quality control agencies; expand production base<br>and improve competitiveness of frms.|2001–2003<br>2006–2007|9th<br>10th|
|Enhance position as<br>regional trade center|Improve physical and legal infrastructure;<br>build and develop fnancial institutions.|2006–2010|10th|
|Improve trade<br>facilitation|Eliminate preapprovals for most exports;<br>eliminate government-mandated trade intermediary.<br>Improve transport and communications<br>infrastructure; create export guarantee fund;<br>simplify investment procedures and laws;<br>simplify import/export licensing.|2001–2004<br>2006–2007|9th<br>10th|
|Trade barriers|Full liberalization of GAFTA imports.<br>Tarif reduction and consolidation; tarifcation of<br>nontarif barriers.|2001–2004<br>2006–2007|9th<br>10th|
|Macroeconomic factors<br>afecting trade|Exempt agricultural exports from proft tax;<br>permit repatriation of export proceeds.<br>Unifcation of multiple exchange rates to<br>fxed regime.|2001–2004<br>2006–?|10th<br>10th|

> _†_ Source: Adapted from State Planning Commission (2005).

However, Syria’s participation in the Arab Free Trade Area Agreement<sup>3</sup> spurred the continued elimination of tariffs on products. GAFTA committed Syria to maintaining its pace of trade liberalization, and policy moves to promote intra-regional liberalization served as a complement to broader multilateral efforts. These reforms were largely realized in the 10th FYP.<sup>4</sup> In addition to the primary goal of export diversification, the plan included reforms aimed at export promotion, tariff and nontariff barrier reduction, and trade facilitation through revisions in the legal framework. The policies were also aimed at meeting specific quantitative targets; for example, reform objectives included reducing

> 3The agreement formed the Greater Arab Free Trade Area (GAFTA), which is also alternatively referred to as the Pan-Arab Free Trade Area, or PAFTA, in the non-English-language literature.

> 4Although a systematic assessment of the 10th FYP has yet to be performed, the government’s midterm review of progress in the area of foreign trade points to a reasonable degree of success in meeting the internal benchmarks. Many of the more straightforward legislative initiatives—such as the drafting and passing of trade-related laws—have been implemented, as have the more administrative elements (such as the formation of an export promotion agency). However, the record with regard to policy-related components is a little more mixed. While overall progress has been fairly good, several areas have seen more lackluster efforts. These include little progress in the tarrification and elimination of nontariff trade barriers, and some initiatives, such as the establishment of an export guarantee fund, have fallen behind schedule.

5
Actual data on the share of each sector in wage employment is combined with GNP per capita data as reported in the _1994 World Development Indicators_ [World Bank 1994] as well as with population data from the same source. Predicted values from simple OLS regressions of the share of each sector in wage employment on the log of GNP per capita, its square, log of the size of the population, its square, as well as a set of regional dummies are used as estimates of the shares. In order to capture the reliability of the GNP and population data, a dummy equal to one if the data come from the Table la of the World Development indicators is also included. Results from these regressions are presented in Table 4a.

The share of each sector in wage employment is then averaged by income group and by region using the 1995 working age population as the weight. The source data for these aggregations are given in Table 4b.

# 3. The world at work

## **3.1. A** note of caution

In interpreting the results of this estimation exercise, one must use a certain degree of caution. The group statistics are only valid up to the representativeness and accuracy of the underlying data. Given that the primary purpose of this exercise is to estimate the numbers for the world and for the income groups, it is these groups that are focused on. The aggregate income group statistics are driven by a few very large countries: The high-income group is dominated (in terms of population) by the United States, Japan and Germany which account for 58% of the group's working age population. The middle-income group is dominated by Indonesia, the Russian Federation, and Brazil which account for 40% of the group's working 7
services, health conditions (reasons for hospitalization and health problems that did not result in hospitalization), use of medicines and health services, and consumption expenditures.

Consumption expenditures represent the wealth of individuals. It is the monetary value of goods (including durable goods) and services and includes (1) purchased goods and services, (2) gifts, and (3) self-consumption. Health expenditures in the LSMS included only purchased goods and services. Health expenditure data were available for (1) outpatient drugs (referred to as medicines in the remainder of this paper), (2) ambulatory care, (3) hospitalizations, and (4) other health services and tools. For outpatient care expenditures, data were available for visits to general practitioners, specialists, dentists, and traditional healers as well as for outpatient medical examinations and care. For hospitalization expenditures, there was no disaggregation. For other health services and tools, data were available on corrective lenses, therapeutic and orthopedic devices, vaccinations, circumcisions, annual check-ups, and contraceptives. Health expenditures were available at the individual level except for contraceptives, which were at the household level. Total consumption expenditures were also available at the household level. We therefore estimated contraceptive expenditures and total <mark>consumption e</mark> xpenditures for each individual by dividing contraceptive expenditures and total consumption expenditures at the household level by household size. We defined socioeconomic quintiles using the individual-level total consumption expenditures we calculated.

# **Estimation of total health expenditures and their drivers**

We accounted for annual THE by including all health-related data that were available in the LSMS;<sup>[9]</sup> that is, data on drugs, ambulatory care, hospitalizations, and other health services and tools. For drugs and ambulatory care, the expenditure data collection covered the three months prior to the survey. We therefore multiplied these expenditures by four to obtain annual estimates. For hospitalizations, only the last health problem in the 12 months prior to the survey and the related cost for each individual were reported, although individuals could report more than one hospitalization. Nevertheless, 99% of individuals who were hospitalized reported three or fewer hospitalizations; 86% reported one hospitalization, 10% reported two hospitalizations, and 3% reported three hospitalizations. We therefore multiplied hospitalization expenditures by the number of hospitalizations to obtain annual estimates. For other health services and tools, expenditure data covered the 12 months prior to the survey. Therefore, for each individual, we used the following formula to calculate annual THE: (drug expenditures x 4) + 5
OZIER - REPLICATION REDUX – p. 15 of 41 30 percent absenteeism rate.<sup>12</sup> This finding was notable, since some previous studies had not found effects on student absenteeism, though perhaps previous non‐findings had been due to study design issues of the kind described above.

Because MK had randomly varied which schools were dewormed, there was not only random variation in whether a child’s school was dewormed, there was also random variation in how many nearby schools’ students were also dewormed. This variation allowed MK to estimate the spillovers themselves, up to some distance limit: with a large enough distance, there would be no such variation since all schools in the study area would eventually be included.

In the original paper, there seemed to be enough variation to precisely and separately estimate several kinds of reinfection‐related spillovers: spillovers within schools (from students who were present to take the medication on the day of deworming to those who were ineligible or absent that day); spillovers from dewormed schools within 3 kilometers; and spillovers from schools between 3 and 6 kilometers away. Taking just the 3‐kilometer spillovers into account (calculated by adding the direct and indirect effects together, weighting the spillovers by the number of students in the area), the overall effect of deworming was about an 8.1 percentage point improvement in school attendance. The spillovers from 3‐6 kilometers away seemed to be beneficial for health outcomes but were not significantly different from zero for school attendance. In fact, though statistically indistinguishable from zero, the estimate of these long‐distance spillovers on school attendance was negative, so incorporating them into the overall calculation reduced the overall calculated effect of deworming to a 7.5 percentage point improvement in attendance. This figure, however, was still clearly statistically nonzero, so despite the imprecision in the calculation, reporting the smaller of these precisely measured effects could be seen as a cautious choice. Thus was born the oft‐cited 7.5‐percentage‐point 12 See Miguel and Kremer (2004), Table IX, Column 1.
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