rafmacalaba/datause-encoder-data huggingface dataset:
third term is the changes in per capita expenditure due to induced changes in average monthly income, while the fourth term measures the effect due to induced changes in monthly share of income. We argue that a government stimulus affects both monthly share as well as monthly income and thus is expected to help smooth income and consumption during the crisis and its aftermath. It is not that households did not receive any such support before the crisis, but as Figure 8 shows, the extent of program coverage and its volume (in particular welfare programs) were increased following the crisis due to fiscal measures adopted as part of the government stimulus programs.

Estimating (1) to distinguish the role of government stimulus is problematic for a number of reasons. Because of lack of a proper counterfactual, we do not know what would have happened to expenditure had there been no crisis and consequently no government interventions adopted, especially to counter the negative consequences of the crisis. In this case, what we can infer is possible effects of government stimulus by estimating and comparing the effects of crisis and government programs on the changes in household outcomes (such as per capita expenditure and income) before and after the crisis of 2008.

Yet the effects of global economic crisis measured in this way may not be an accurate picture unless we control for the unobserved inherent ability of households and communities to cope with any shock that is independent of the observed coping mechanisms adopted by households, communities, and governments following a crisis. Hence, what we observe in the descriptive and trend analysis is an outcome of the interactions of all observed and unobserved forces. How to disentangle them in terms of the relative strengths of government responses versus household coping strategies is an empirical challenge. What we follow here is a three-step procedure to discern the role of household and government forces in averting the negative consequences of such a global crisis affecting the Thai economy.

The first step involves a structural model of income-consumption smoothing where monthly variations in per capita expenditure are explained by, among other factors, average monthly income and its monthly volatility (the ratio of last month‘s income, which was recorded in the SES, to 15
**Table 5:  Household PM10 Concentrations (ug/m**<sup>**3**</sup> **) by Fuel**

|Fuel|Sample<br>Households|Mean|Minimum|1<sup>~~st~~</sup><br>Quartile|Median|3<sup>~~rd~~</sup><br>Quartile|Maximum|
|---|---|---|---|---|---|---|---|
|Dung|95|291|60|172|231|380|755|
|Firewood|159|263|84|161|201|323|1165|
|Jute|68|190|72|136|165|219|727|

using “dirty fuels” such as firewood, dung and jute have PM10 exposures resembling those for natural gas, while others face concentrations at extremely high levels.

Of course, part of the variation across households is also determined by fuel use. Table 6 summarizes the MiniVol data for cooking areas across all households, by fuel type. While these descriptive statistics provide some insight, they can be misleading because fuel choices may be correlated with other variables in the sample: cooking practices, structural characteristics and ventilation practices. In Table 6, the cleanest fuels are natural gas (101 ug/m<sup>3</sup> ) and kerosene (134). As we have previously noted, these concentrations are not far above urban ambient pollution levels. Among biomass fuels, dung seems to be the dirtiest fuel (291 ug/m<sup>3</sup> ), followed by firewood (263), sawdust (237), straw (197), jute (190), and twigs and branches (173). In our sample, the relatively few households using LPG/LNG have an average PM10 count (206) above the level for several biomass fuels. We attribute the elevated PM10 concentrations in these households to several factors: Ambient pollution from biomass cooking in the same locality, ambient pollution from other sources (e.g., motor vehicles, industrial sources), underreporting of complementary biomass fuel use (particularly for LPG/LNG), and particulate pollution from the fuels themselves. Recent monitoring research has suggested that kerosene is a significant PM10 source when burned indoors (Leaderer, et al., 1999), and even natural gas produces some PM10 (Beer, 2000).

19
Figure 2.1. Distribution of Social Weights<sup>as aFunction of the AversionParameter</sup> <!-- Start of picture text --> 7<br>6-<br>5 4<br>4-<br>3<br>2<br>-<br>1<br>-<br>0<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br><!-- End of picture text --> Table 2.1 shows cut-off ranks<sup>computedaccordingto(2.10).</sup> These results suggest that the cut-off rank is a<sup>decreasingfunctionof theaversionparameter.As this</sup> parameter increases, the focus is shifted<sup>tothe lowerendof thedistribution.Thussocial</sup> evaluation will attach more weight<sup>to thesituationof thepoorestgroup.For instance,</sup> when the aversion parameter is<sup>equalto100,socialevaluationattachesmore weightto</sup> the lowest 5 percent of the distribution.<sup>When theaversionlevel reaches200,thefocus</sup> shifts to the poorest 3 percent of<sup>thepopulation.</sup> According to this table, the social evaluation function based on the<sup>ordinaryGinicoefficient assignsmore weightto every</sup> body up to the median. The weights<sup>then declineat a constantrate of 0.5from2to 0.</sup> Table 2.1. Cut-off Rank as a Function<sup>of the Aversion Parameter.</sup>

|o|1.1|1.2|_1.5_|2.0|3|6<br>|10<br>|20<br>|30<br>|50<br>|100<br>|200<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|p*|0.62|0.60|0.56|0.50|0.42|0.30|0.23|0.15|0.11|0.08|0.05|0.03|
|Sourc|e:Com|puteda|ccording|to(2.1|0)||||||||

The empirical implementation of<sup>theframework encapsulatedinexpression(2.5)</sup> requires an estimate of the cumulative<sup>distributionfunctionandthecomputationofa</sup> covariance. The cumulative distribution function<sup>**p=F(x)**givestheprobabilityof</sup> observing an individual with a<sup>living standardatmostequaltox.Theslopeofthis</sup> 7
reducing.

# **_5.  Conclusions_**

This paper provides econometric evidence suggesting that domestic climate change policies may have adverse effects on international trade. We focus on two policies -carbon taxes that target those industries that use energy intensively, and energy efficiency standards that affect those industries that produce outputs that use energy intensively. Through a panel of industry data of the OECD countries from 1988 to 2005, this paper shows that both carbon taxes and energy efficiency standards have a statistically significant negative effect on competitiveness through impacts on bilateral trade flows (depending on the model specification). This is particularly true when the focus is on those industries that are subject to higher energy efficiency standards and are not subsidized by governments.

This adverse effect is missing when the focus is on energy-intensive industries that usually receive some degree of protection from their governments. In some cases the subsidies are so generous that trade actually increases as a result. However, when we focus on other industries that use energy, but often not directly supported by the governments, such as the transport equipment industry and the metal industry, the negative effect of the carbon tax on trade is clear.

On the other hand, energy efficiency standards require that firms produce goods that are more environmentally friendly, which affects a broader range of industries. In this case, the adverse effects of such policy are also quite evident.

11
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Also within urban and rural areas, poorer households tend to live in communes with higher environmental risks. In rural areas they are more exposed to air pollution, tree cover loss, steep slopes, rainfall and temperature variability, but less exposed to flood hazards (Figure 3). The latter finding could reflect that many of the rural poor live in the mountainous areas where flood hazards are generally lower than in low‐lying, but wealthier river deltas and coastal zones. In urban areas, poorer households are more concentrated in areas with high tree cover loss, slope, flood and drought hazards, while living in places with lower air pollution and temperature variability. Possibly, wealthier households live in rapidly developing urban areas with heavy traffic and industrial congestion, thereby being more exposed to air pollution.

Some remarkable differences in risk exposure of poor people appear between urban and rural areas (Figure 3). First of all, poor rural households are much more concentrated on steep slopes and areas with high air pollution and temperature variability than their urban counterparts indicating that in rural areas poor people could have a higher exposure to multiple, interlinked risk factors. For floods, however, a different picture emerges, as flood hazards seem to be more a problem for poor urban households than poor rural households. This finding is consistent with a recent global analysis of flood exposure and poverty in 52 countries, which reports ambivalent results for flood exposure at the country level, but shows a strong signal of over‐exposure of the poor when only focusing in urban areas (Winsemius et al., 2015).

While most of these relationships hold over time, some differences can be observed in 2010 and 2012 in rural areas (Figure A.2). In 2014 there is no considerable relationship between consumption and land degradation, while in in 2012 and even more so in 2010, poorer households have a higher exposure to land degradation. This change over time could imply that people in highly degraded areas have become wealthier over time, a finding that is also reflected in the results of the next section. The U‐form relationship between consumption and air pollution and temperature variability in 2010 changes to a strongly negative relationship in 2014 suggesting that over time wealthier households could become more concentrated in low risk areas.

11
comply with other obligations of the agreements and are immune from trade disputes in the WTO Dispute Settlement Body. China and other WTO members can decide not to base their domestic standards on international standards. In case a member implements standards considered highly protectionists, it must provide specific justifications for the deviation according to the SPS agreement<sup>5</sup> (Du 2010). Thus, TBT and SPS Agreement do not mandate the use of international standards but provide strong incentives for China and other WTO members to harmonize their domestic standards.

Despite the incentives to adopt international standards, the number of Chinese standards based on international norms remains low compared to other countries. From the total number of standards in our database only 14 percent are based on international norms. In addition, China tends to adopt only parts of international standards, as 70 percent of all harmonized standards in our database are modified versions of international standards. Only one-third of standards in our database are identical to international standards. In the next section, we analyze empirically the impact of standardization and harmonization on Chinese food exports.

# **4. Empirical Model and Results**

## **_Data Sources and Descriptive Statistics_**

In this section, we describe our dataset and provide descriptive statistics based on the data. Export volumes are compiled from the COMTRADE database at the Harmonized System (HS 1992) and tariffs are compiled from the TRAINS data base. Consumption variables are computed from the Food and Agriculture Organization of the United Nations‟ statistical database FAOSTAT.<sup>6</sup> We deflated our data US Bureau of Labor Statistic's HS Import Price Indices. The indices are available for the whole period under consideration (1992-2008). The US Bureau of Labor Statistic has no import price deflator for sugar and cereals. We deflate import data for the two product groups with the import price indicator for foodstuffs.

Our dataset covers seven product groups meat (HS-02), fish (HS-03), vegetables (HS-07), cereals (HS-10)), milk (HS-0401 and HS 0402), tea (HS-0902), and sugar (HS-1701 and HS 1702). The seven product categories we cover amount to 40 percent of China‟s exports in agricultural and food products. There are other important Chinese exports, such as HS chapter 16 “Preparations of meat, of fish or of crustaceans, molluscs or other aquatic invertebrates” and chapter 20 “Preparations of vegetables, fruit, nuts or other parts of plants.” However, we could not unambiguously link these two chapters to an ICS code since there is only one ICS code for all prepackaged and prepared foods (ICS 67.230 Prepackaged and prepared foods). Moreover, the seven product groups are frequently a target of strict food

> 5 Article 2.4 of the TBT Agreement and Article 3.3 of the SPS Agreement.

> 6 <u>http://faostat.fao.org/</u>

6
# **Table C.8. (continued)**

|**Dependent variable:**||**Specific**|**ation 3**|||**Specifi**|**cation 4**||
|---|---|---|---|---|---|---|---|---|
|**Poor status**|**2008**|**2010**|**2012**|**2014**|**2008**|**2010**|**2012**|**2014**|
||**(9)**|**(10)**|**(11)**|**(12)**|**(13)**|**(14)**|**(15)**|**(16)**|
|Male|-0.2017***|-0.0255|-0.1058*|-0.1589**|-0.2877***|-0.0763|-0.1502**|-0.2204***|
||(0.0611)|(0.0662)|(0.0627)|(0.0636)|(0.0604)|(0.0663)|(0.0629)|(0.0632)|
|Primary Education|-0.4046***<br>(0.0537)|-0.2119***<br>(0.0597)|-0.2816***<br>(0.0523)|-0.3369***<br>(0.0527)|-0.4231***<br>(0.0527)|-0.2166***<br>(0.0596)|-0.2884***<br>(0.0520)|-0.3527***<br>(0.0522)|
|Middle School|-0.5211***|-0.4151***|-0.5145***|-0.4837***|-0.5515***|-0.4284***|-0.5260***|-0.5070***|
||(0.0580)|(0.0632)|(0.0565)|(0.0591)|(0.0570)|(0.0631)|(0.0563)|(0.0587)|
|Secondary Education|-0.6815***|-0.4752***|-0.5484***|-0.5130***|-0.7187***|-0.4914***|-0.5660***|-0.5416***|
||(0.0572)|(0.0623)|(0.0555)|(0.0562)|(0.0562)|(0.0622)|(0.0555)|(0.0558)|
|Tertiary Education|-1.1738***|-0.9848***|-1.0286***|-0.8800***|-1.2232***|-1.0136***|-1.0535***|-0.9275***|
||(0.0629)|(0.0726)|(0.0634)|(0.0612)|(0.0621)|(0.0729)|(0.0638)|(0.0609)|
|Male x Primary Education|0.1208*|-0.1116|-0.0050|0.1188*|0.1475**|-0.1049|0.0052|0.1392**|
||(0.0653)|(0.0699)|(0.0661)|(0.0660)|(0.0644)|(0.0699)|(0.0660)|(0.0656)|
|Male x Middle School|0.0858|-0.0424|0.0727|0.0826|0.1251*|-0.0258|0.0886|0.1086|
||(0.0688)|(0.0746)|(0.0706)|(0.0750)|(0.0680)|(0.0745)|(0.0705)|(0.0746)|
|Male x Secondary Education|0.1003|-0.1400*|-0.0321|-0.0129|0.1527**|-0.1186|-0.0072|0.0207|
||(0.0674)|(0.0722)|(0.0691)|(0.0702)|(0.0666)|(0.0721)|(0.0692)|(0.0700)|
|Male x Tertiary Education|0.0048|-0.1573*|-0.0003|-0.0404|0.0626|-0.1279|0.0299|0.0028|
||(0.0732)|(0.0833)|(0.0796)|(0.0762)|(0.0725)|(0.0835)|(0.0801)|(0.0761)|
|Year of birth fixed effect|||||||||
|Labor market controls|||||||||
|Household Characteristics|||||||||
|Exposure to shocks|||||||||

Standard errors in parenthesis. *p<0.1 **p<0.05 ***p<0.01

**Note:** Results are constrained to the sample of households whose heads were born between 1948 and 1973. Labor market controls included the employment status, sector of the economy and type of employment. Household characteristics includes household size and access to basic services as water, electricity and sewage. Exposure to shocks includes losing the job and the cause.

**Source:** Own estimations based on GEIH from 2008 to 2016.

40
<!-- Start of picture text --> Figure 5: Teledensity<br>1600 (total telephone subscribers/1000 people)<br>1393<br>1200<br>800<br>635<br>501<br>468<br>408<br>400<br>99<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text -->

**Figure 6:  Phone Faults (reported faults/100 mainlines)**

<!-- Start of picture text --> 60<br>57<br>48<br>40<br>37<br>25<br>20 18<br>11<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text -->

# **Transport**

**Overall assessment.** The transport sector’s overall performance indicators show South Africa lagging behind its comparator country groups and several other regions. This finding must be taken with caution though, reflecting at least two factors at play. First, it may simply reflect a more idiosyncratic territorial distribution of population and economic activity in a large, coastal, resource rich country such as South Africa than is the case for the average country group. Second, it may reflect some peculiarities in the type of road network that is appropriate for a country with semi-arid climate, and with a large proportion of its land surface carrying low population densities. However, it also reflects the legacy of the Apartheid era and wide inequalities in infrastructure service delivery between the privileged group and the majority of the population.  Perceptions of the quality of service delivery are relatively favorable (Table 4 and Figures 7-8).

11
27 characteristics of the regions involved, the other potential problems are associated with the existing economic and social structures of the regional activities. Therefore, these institutions can be generalized conditions prevailing elsewhere. The interested reader may find additional insights to the latter issues in Holden and Thobani (1996).

**Table 1: Volumes and Values of Water Transfer Transactions in New South Wals (19841991)**

|Year|1984|1985|1986|1987|1988|1989|1990|1991|
|---|---|---|---|---|---|---|---|---|
|Volumes Trans-|5.2|34.9|50.7|80.6|342.4|87.6|110.0|140.0|
|ferred (106m)|||||||||
|Values of|0.7|1.8|4.8|4.7|17.0|1.1|10.5|8.0|
|Transfer ($106)|||||||||
|Value per Unit<br>of Water ($/m<sup>3</sup>)|0.135|0.051|0.094|0.058|0.049|0.012|0.095|0.057|

Source: Calculated from Pigram et al. (1992, pg 27, 31)

**Table 2: Relative Importance of Potential Problems** in **Agency Decisions on Transferability Application**

||Numbe|r of Agencie<br>Problem W|s Indicating<br>as|
|---|---|---|---|
|Potential Problem|Very<br>Importa|Important<br>nt|Not<br>Important|
|Increased load on existing delivery or drainage facilities|2|1|1|
|Increased soil salinity levels in the area receiving transferred water|3|1|-|
|Increased water salinity levels in the receiving area|3|1|-|
|Reduced reliability of irrigation water supply in the area of origin|-|1|3|
|Reduced irrigated crop production in the area of origin|1|-|3|
|Reduced real estate values of irrigation farms transferring water|-|1|3|
|Discounting of securities on loans to irrigation farms|-|2|2|
|Dominance of big operators in any market for transferability|-|2|2|
|Source: Pigram et al. (1992, pg 20)||||
|Enrollment trends|0.50|0.50|0.47|0.50|0.48|
|---|---|---|---|---|---|
|Student feedback|0.85|0.36|0.83|0.38|0.53|
|More than once per year:||||||
|Analyze student performance to solve problems|0.85|0.36|0.89|0.31|0.13|
|Collect student satisfaction data|0.71|0.45|0.67|0.47|0.30|
|Time assigned to practical training (%)|44.79|16.38|46.13|15.86|0.34|
|Internships outside institution are mandatory|0.25|0.44|0.30|0.46|0.20|
|Mandatory internships at the end of the program|0.10|0.30|0.11|0.31|0.77|
|_Costs_||||||
|Funding options used by some students||||||
|HEI scholarships|0.78|0.42|0.69|0.46|**0.02**|
|Government scholarships|0.80|0.40|0.74|0.44|**0.09**|
|Loans by HEI|0.26|0.44|0.27|0.45|0.70|
|Loans by banks|0.18|0.38|0.19|0.39|0.75|
|Other loans|0.24|0.43|0.31|0.46|**0.08**|
|HEI has received funding from||||||
|Government|0.30|0.46|0.31|0.46|0.91|
|Private sector|0.14|0.35|0.19|0.39|0.21|
|Annual tuition (2019 PPP USD)|2,735.20|1,748.13|2,728.69|2,061.10|0.97|
|_Link with productive sector_||||||
|Engagement with firms||||||
|Collect employment data for graduates more than once per year|0.51|0.50|0.50|0.50|0.76|
|Communicate with local firms about their needs more than once per year|0.55|0.50|0.59|0.49|0.30|
|Industry helps with student evaluation or curriculum design|0.45|0.50|0.49|0.50|0.27|
|Industry has internship agreements with HEI|0.82|0.38|0.83|0.38|0.80|
|Industry has agreements with HEI to hire program grads|0.36|0.48|0.37|0.48|0.79|
|Industry has agreements to train faculty|0.27|0.44|0.26|0.44|0.84|
|Industry lends or provides equipment to program for student training|0.40|0.49|0.41|0.49|0.81|
|Somebody in charge of industry relations|0.83|0.38|0.80|0.40|0.45|
|Program has staff assigned to collect grads' employment data|0.42|0.49|0.39|0.49|0.45|

73