rafmacalaba/datause-encoder-data huggingface dataset:
been out of work and school for the last six months, and have been residing in the National Capital District (NCD)/Port Moresby for at least one year. To verify eligibility, representatives of the survey firm also conducted interviews with community leaders, and declared applicants ineligible if a consensus emerged that they have misrepresented any of these three criteria.<sup>9</sup> As the program has become better known over time, greater numbers of youths began to apply to the program. To balance the interests of competing groups, each neighborhood group was allocated a certain number of places, and the program strived for a 50:50 ratio of men to women applicants. Participants were selected randomly from the pool of eligible applicants, separately by neighborhood group and gender.

- (iv) Successful applicants were given the Eligibility Baseline Survey (EBS) that collects a richer set of information compared to ESS (see Annex 2 for a more detailed description of various surveys). They were then offered a 40‐hour Basic Life Skills for Employment Training (BLST) administered over the course of one week. Each BLST participant took a literacy and numeracy test, which was used to determine the quarter of each intake that is then streamed into the SDES component. Usually, many more than a quarter of the participants are literate, so only those with top scores and strong participation in the BLST were allowed to proceed to the next stage of the SDES component.

- (v) Participants were enrolled in either Component 1 (YJC) or Component 2 (OJT) of the Project. Participants in Component 1 (the YJC) were offered to undertake two months (40 days) of public works, such as cleaning the sides of roads. When youths completed the two‐month employment, they completed the program. This component of the Project was offered to approximately 12,500 eligible urban youths over the course of seven years, equivalent to approximately 80 percent of total program participants.

- (vi) Meanwhile, participants in Component 2 were provided with: i) one month (equivalent to 160 hours) of pre‐employment training (PET) that aims to prepare them for employment in the private, public, or civil society sectors, and: ii) five months of on‐the‐job training (OJT) with a private or public company. The PET has two program tracks that participants can choose from ‐‐ the first focuses on trade‐, industrial‐ and commerce‐related jobs, while the second focuses on basic book keeping, data entry, business practices, and information technology skills. Each PET program track is expected to train about 2,000 eligible youths over the life of the Project.

The UYEP enrollment process is summarized in Table 1.

> 9 In general, about 90% of youth screened through ESS are deemed eligible as the eligibility criteria are generally communicated by the Project and are known in the community before the enlisting takes place.

7
observe a discontinuous variation in the pre-treatment variable at the cutoff. Table 3 shows that there is no evidence of a difference in the share of votes for the incumbent in the 2000 elections for President. The coefficient for the below-cutoff variable is not statistically significant in the specification with the full set of controls. Therefore, estimates in Table 3 support the causal interpretation of an electoral response to government disaster spending suggested by the coefficient of 7.69 in Table 2.

An important assumption underlying the RD design is that producers are not able to manipulate the forcing variable. In our particular case, potential manipulation would have to be on the measurement of rainfall at the local weather stations, which seems extremely unlikely. Location and operation of weather stations were set many years before the specific insurance program we are analyzing and are independent of it. In 2005 and 2006, a total of 3,363 weather stations operated in the Mexican territory from which, 1200 under the coordination and supervision of the National Water Commission (CONAGUA). Furthermore, before paying any indemnities, CONAGUA is required to certify the weather data, which are sent to the international reinsurers. The Weather Index Insurance scheme is based on the fact that there is little reason to believe that the individual producer has better information than the insurer about the underlying index, and therefore little potential for adverse selection. One of the advantages of using the Mexican WII to test voter response to disaster spending is precisely the fact that under this scheme information asymmetries are minimized, as the producer cannot influence the realization of the weather index.

Another crucial assumption under regression discontinuity analysis is that the function of rainfall --which is the variable determining the disbursement of a government drought assistance-- has been correctly specified. Our primary specification is a linear model in rainfall estimated using OLS. Alternative polynomial functions are also estimated for robustness as shown in Table 4. From visual examination of the relationship plotted in Figure 4 we are able to determine a discernable discontinuity at the cutoff. The non-parametric graph suggests a linear relationship in the vicinity of the cutoff. Nonetheless, given the number of inflexion points in the plot, we test for higher-order polynomial functions including quadratic, cubic and fourth power terms in our specification. Table 4 shows that the coefficient of interest remains stable and the interactions are Page **12** of **29**
# **Table 4. Services in the Domestic Economy, 2011**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|**(11)**|**(12)**|**(13)**|**(14)**|**(15)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Primary|4.3|0.1|2.3|1.2|0.0|0.3|0.2|0.0|0.0|0.0|0.0|0.0|0.1|0.5|7.9|
|Energy|0.3|7.0|1.9|2.9|0.1|0.7|0.4|0.7|0.0|0.0|0.0|0.1|0.1|0.8|12.0|
|Manufacturing|0.2|0.4|8.2|4.5|0.0|1.9|0.6|0.3|0.0|0.0|0.0|0.2|0.1|1.2|13.3|
|Services|**0.8**|**2.9**|**4.8**|**58.3**|**0.7**|**11.7**|**14.9**|**4.3**|**0.7**|**0.3**|**0.3**|**4.4**|**1.0**|**20.1**|**66.8**|
|Electricity, gas, & water|0.0|0.0|0.1|0.9|0.5|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.3|1.0|
|Construction|0.0|0.2|0.3|9.2|0.0|8.5|0.1|0.1|0.0|0.0|0.0|0.1|0.0|0.3|9.7|
|Trade, distribution, &<br>hotels|0.4|1.4|2.5|17.3|0.1|1.7|12.9|0.8|0.0|0.0|0.0|0.2|0.2|1.4|21.6|
|Transport|0.1|0.5|0.7|5.3|0.0|0.7|0.8|3.0|0.0|0.0|0.0|0.1|0.1|0.6|6.7|
|Communication|0.0|0.0|0.1|0.9|0.0|0.0|0.1|0.0|0.5|0.0|0.0|0.0|0.0|0.2|1.0|
|Finance|0.1|0.3|0.4|1.0|0.0|0.2|0.2|0.1|0.0|0.2|0.0|0.1|0.0|0.2|1.8|
|Insurance|0.0|0.0|0.0|0.4|0.0|0.0|0.0|0.0|0.0|0.0|0.2|0.0|0.0|0.1|0.4|
|Other business services|0.1|0.4|0.7|6.8|0.0|0.4|0.8|0.3|0.0|0.0|0.0|3.9|0.1|1.2|8.0|
|Other consumer services|0.0|0.0|0.0|0.5|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.4|0.0|0.5|
|Other services|0.0|0.0|0.0|16.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|15.9|16.0|
|Backward/ Demand|5.6|10.3|17.2|66.9|0.8|14.5|16.2|5.4|0.7|0.3|0.3|4.8|1.3|22.5|100.0|

Source: World Bank Trade Value Added Database and Francois et al, 2013. Note: Column headings are: (1) Primary; (2) Energy; (3) Manufacturing; (4) Services; (5) Electricity, Gas, and Water; (6) Construction; (7) Trade, Distribution, and Hotels; (8) Transport; (9) Communication; (10) Finance; (11) Insurance; (12) Other Business Services; (13) Other Consumer Services; (14) Other Services; (15) Forward/Supply **.**
casualties in columns 3 and 4 of Table 9, with the magnitudes very similar to the baseline results in Table 8. This suggests that although nationalist sentiment associated with Japanese invasion may help to explain the CCP’s mobilization ability, it alone could not fully explain peasants’ support of the communist military forces.

**6.6. Famines, Land Ownership, and Communism**

Land has been the key asset in agrarian societies, and inequality in land ownership has always been a key trigger of violence and rebellion in ancient times (Scheidel 2017). In the Late Roman Republic, Tiberius Gracchus gained popular support by proposing land reforms which attempted to “stem the numerical decline of the free peasantry from which the armies of the Republic were traditionally recruited” (De Ligt and Northwood, 2008, page 1). His assassination marked the beginning of a long period of chaos which ended the Republic. Likewise, the CCP’s advocation for redistribution of land served as a powerful catalyst to recruit peasants with little or no land (Hsiao 1969). Indeed, ever since its military insurrection, the CCP was keen to carry out land reforms to attract the support of peasants in areas under its control by redistributing land from landowners to peasants, and would later call its revolution the Land Revolution. Fully aware that their distributed land would be gone if the CPP failed, a large number of peasants joined the CCP army, providing the CPP with a large fighting force.

To further our understanding of the underlying mechanism of our previous findings, we collect land ownership data from the Agri-business Statistics of the Republic of China (19181921). This data set was compiled from land surveys in 152 prefectures conducted by the then Ministry of Agri-business. Because of the unstable political environment during the warlord period, some prefectures dropped out of the survey, resulting in an unbalanced panel. The data set contains information on the fraction of rural households being tenants, that is, being landless, which is a reasonable measure of rural inequality. Tenant households account for 35% of all sample rural households. We first try to see if famines forced more peasants to sell their land and exacerbated inequality in land ownership: F 1F F F 2F F F (12) ii,tt ii,tt ii,tt−1 ii tt ii SShFFDDFF PPoo PPF PP hPPRRPPFFhPPMMRRPP = αα + αα + TT + θθ + εε 29
25

|Coumtry,<br>Last m ils<br>economy,<br>or<br>P<br>territory<br>Date|hed censu<br>opulati<br>(1000) *|s<br>on<br>Source<br>for population<br>(mid-1985)|Source<br>for fertility<br>(1985-90,<br>unlessIndicated)|Source<br>for mortality<br> (1985-90,<br>unlessindicated)|
|---|---|---|---|---|
|Yeen, People'sKay73<br>Dem.Rep. of|1590 F|U.N. 1988revision<br>(prelim.)|<br>Basedon U.N. 1984<br>assessment|Based on UNPVSR<br>4/87|
|Yugoslavi<br>Mar 81<br>Zaire<br>Jut 84<br>Zambia<br>Sep 80|22425 J<br>29671F<br>**5680**F|WIPVSR 7/86 (officiat<br>est.)<br>U.N. 1988 revision (prelim.)<br>Bank projection from census|Based on UNPVSR<br>4/87<br>U.N. 1988 revision (prelim.)<br>1980-85, Bank sector report;<br>1985-90, Bank projection|Based on UNPVSR<br>4/87<br>U.N. 1988 revision (prelim.)<br>1975-80, Bank sector report;<br>1985-90, Bank projection|
|Zimbabwe<br>Aug 82|7550 F|Bank projection<br>from<br>census|Bank est.<br>based<br>on officiat<br>contraceptive prevalence<br>rate<br>of 26X|U.N. 1988 revision<br>(prelir.)|

Notes: When a source for an estimate is directly cited (e.g.. UNPVSR 4/87), the estimate is taken as is from that publication. When an estimate Is "based on" a source, the estimate is not reported in but derived using data in that publication. When a source is given for a fertility or mortality estimate prior to 1985-90, the source for t e corresponding 1985-90 estimate is a projection usirg the earlier estimate. Citations for the main documents referred to here (usually by abbreviations) arein the list of references.

Nature of population estimate: F a de facto; J = de jure.

No census, or no fertility or mortality estimates and no projections made (the latter for small countries).

a Census was for entire peninsula. b 55 people.

CBR Crude birth rate. CDR Crude death rate.

Celade Centro Latino Americano de Demograffa (1987). DNS Demographic and Health Survey.

INED Institut National d'&tudes D6mographiques (Momier 1987).

TFR Total fertility rate.

UNPVSR United Nations, Population and Vital Statistics Report. USBOC United States, Bureau of the Census (1985).
with a more diversified portfolio will find that fluctuations in the prices of two or more products will have a smoother impact on total earnings. The more diversified and unrelated are a country’s exports, the less volatile its earnings will be.

**Table 2. Sensitivity (Elasticity) of DRC’s Exports with Respect to GDP of Foreign Markets and Impact on the Share of Exports in GDP, 2000-2012**

||**DRC’s Elasti**<br>**w.r.t. for**|**city of exports**<br>**eign GDP**|**Effec**<br>**on DRC Ex**|**t of a 1% chan**<br>**ports to GDP R**|**ge in foreign**<br>**atio (percent**|**GDP**<br>**age points)**|
|---|---|---|---|---|---|---|
||**All Products**|**Commodities**|**All Pr**|**oducts**|**Comm**|**odities.**|
||||**2000-2012**|**2008-2012**|**2000-2012**|**2008-2012**|
|**All Markets**|**1.363**|**1.357**|**0.4044**|**0.5012**|**0.4026**|**0.4990**|
|**Old Growth Poles**|**1.01**|**1.01**|**0.1592**|**0.0918**|**0.1527**|**0.0848**|
|United States|0.97|0.88|0.0298|0.0274|0.0265|0.0245|
|Japan|1.04|1.03|0.0015|0.0007|0.0014|0.0007|
|EU27|0.99|0.99|0.1242|0.0613|0.1185|0.0549|
|**Newgrowthpoles**|**1.807**|**1.802**|**0.1617**|**0.319**|**0.1600**|**0.3168**|
|Brazil|1.09|1.09|0.0052|0.0019|0.0050|0.0014|
|Russian Federation|1.14|1.15|0.0003|0.0003|0.0002|0.0002|
|India|1.60|1.60|0.0047|0.0091|0.0046|0.0091|
|China|1.89|1.89|0.1495|0.3077|0.1490|0.3074|
|Korea, Rep.|1.77|1.77|0.0043|0.0106|0.0043|0.0105|

w.r.t. ≡ with respect to. All ≡ total merchandise exports. Comm. ≡ commodity exports only. All estimated elasticities are statistically different from zero. Source: Calculated using data from UNCTAD database

# 13. **In addition to the sensitivity to international price changes, DRC’s exports appear to**

**be also sensitive to foreign income fluctuations in general.** The sensitivity of the exports to changes in GDP is not surprising, since mineral products which are in general income elastic commodities (Rankin, 2011), heavily dominate DRC’s exports. The supply and demand of these type of commodities are generally elastic to the change in the income of client countries. The first two columns of Table 2 present the estimates of the GDP elasticity of DRC’s exports for each specific market destination (see Annex 1 for a brief note on estimation of elasticity). There is no significant difference between the total merchandise exports elasticity to foreign GDP and of commodity exports elasticity. Indeed, while the former is 1.363, the latter is at 1.357. This is due to the very high share of minerals and commodities in exports, reaching above 90 percent of the

> (uranium); 4001-4006 (rubber); 41 (hides); 4401-4412 (timber, excluding 4402, 4404, 4406); 5101-2110 (wool); 5201 (cotton).

9
**<u>Table 3: Plot Characteristics for Entire Sample</u>**

||||**All owner-c**|**um-tenants**|||
|---|---|---|---|---|---|---|
||**All plots**|**Own**<br>**plots**|**Leased**<br>**plots**|**Output**<br>**sharing**|**Out-& input**<br>**sharing**|**Fixed**<br>**Rent**|
|**Relation with partner**|||||||
|TP relative|0.25||0.66|0.68|0.64|0.39|
|TP in-law|0.05||0.13|0.14|0.10|0.10|
|TP friend or neighbor|0.04||0.11|0.11|0.10|0.19|
|<br>TP unrelated|0.09||0.23|0.21|0.26|0.42|
|Female rental partner|0.47||0.47|0.49|0.42|0.28|
|<br>**Output and input use:**|||||||
|Crop output/ha (birr)|3476.47|3693.07|3127.07|3125.90|2952.21|3561.46|
|<br>Plot size (ha)|0.30|0.28|0.33|0.32|0.39|0.36|
|Used fertilizer|0.40|0.38|0.43|0.41|0.52|0.59|
|Fertilized used per ha (kg)|54.91|55.40|54.12|51.26|61.69|86.02|
|<br>Manure used|0.19|0.26|0.06|0.06|0.08|0.07|
|Manure used per ha (kg)|278.00|393.03|92.68|84.30|90.59|246.10|
|<br>Used pesticides|0.08|0.07|0.08|0.07|0.10|0.07|
|Pesticides used per ha (kg)|0.40|0.35|0.47|0.55|0.09|0.08|
|Pair of oxen days per ha|24.99|26.71|22.22|22.13|21.17|24.44|
|<br>Male family labor/ha (days)|111.94|127.32|87.11|87.82|84.11|82.76|
|Female family labor/ha (days)|56.56|65.54|42.06|42.80|39.41|37.31|
|<br>Total family labor/ha (days)|167.28|190.89|129.17|130.61|123.52|120.07|
|Used hired labor|0.84|0.86|0.81|0.81|0.81|0.71|
|Hired labor/ha (days)|8.08|9.85|5.24|5.36|4.23|6.10|
|**Plot characteristics:**|||||||
|Years of possession|13.97|19.75|4.66|4.81|4.28|3.16|
|Good soil quality|0.46|0.46|0.45|0.45|0.50|0.37|
|Medium soil quality|0.36|0.36|0.37|0.37|0.34|0.41|
|<br>Flat land|0.71|0.70|0.72|0.73|0.69|0.73|
|Gently sloped land|0.24|0.24|0.23|0.23|0.23|0.25|
|Irrigated land|0.05|0.05|0.04|0.04|0.03|0.01|
|<br>**Crop choice:**|||||||
|Teff|0.32|0.29|0.36|0.35|0.48|0.29|
|Wheat/barely|0.21|0.20|0.22|0.21|0.24|0.26|
|<br>Sorghum/maize|0.30|0.34|0.23|0.23|0.20|0.31|
|<br>Pulses|0.13|0.12|0.14|0.16|0.04|0.10|
|Oilseeds|0.05|0.05|0.04|0.04|0.04|0.03|
|Number of observations|5443|3360|2083|1728|238|102|

_Source:_ Own computation from AU/WB/UG land tenure survey. _Note:_ Only plots with cereals, pulses and oilseeds included.

19
# US Prestige Press." Global Environmental Change Part A 14:125-136.

The authors conduct a content analysis of coverage of global warming from 1988 to 2002 in the New York Times, the Washington Post, the Los Angeles Times, and the Wall Street Journal. Similar to other articles by Maxwell Boykoff, the authors describe how the press's adherence to “balance” produces “biased” coverage of both anthropogenic contributions to global warming by amplifying the perception of uncertainty in climate science in the public mind. The authors describe how this “significant divergence of popular discourse from scientific discourse” is the result of tactical media responses and practices guided by widely accepted journalistic norms.

Boykoff, Maxwell 2008 “The Cultural Politics of Climate Change Discourse in UK Tabloids” <u>Political Geography</u> 27: 549-569.

Boykoff examines cultural issues of identity and discourse through claims and frames on climate change in four daily ‘working class’ tabloid newspapers in U.K.: The Sun (and News of the World), Daily Mail (and Mail on Sunday), the Daily Express (and Sunday Express), and the Mirror (and Sunday Mirror). Data show that news articles on climate change were predominantly framed through weather events, charismatic megafauna and the movements of political actors and rhetoric, while few stories focused on climate justice and risk. Headlines with tones of fear, misery and doom were most prevalent.

Boykoff, Maxwell 2008 Lost in Translation? The United States television news coverage of anthropogenic climate change 1995-2004 Climate Change 86 1-11.

The author illustrates the significant difference between this television coverage and scientific consensus regarding anthropogenic climate change from 1996 through 2004. Boykoff does this by conducing a content analysis of televison news coverage of climate change in United States network television news (ABC World, News Tonight, CBS Evening News and NBC Nightly News) and cable news (CNN WorldView, CNN Wolf Blitzer Reports and CNN NewsNight) from 1995 through 2004. Results show that 70% of U.S. television news segments have provided ‘balanced’ coverage regarding anthropogenic contributions to climate change. Boykoff concludes that as a result of the journalistic norm of balanced reporting, United States television news coverage has “perpetrated an informational bias by significantly diverging from the consensus view in climate science that humans contribute to climate change.” As a result, the scientific consensus regarding climate change is not translated to the public, instead the media has produced an appearance of uncertainty and debate. This perception of uncertainty has then moved from the media into both public and policy discourse.

Boykoff, Maxwell “Flogging a dead norm? Newspaper coverage of anthropogenic climate change in the United States and United Kingdom from 2003 to 2006” Area 39(4): 470-481.

64
keep increasing in our modeled data, possibly suggesting that the 2008-2014 successes may have partly been driven by a combination of slow economies and improvements due to ongoing urbanization that will likely halt as rural areas become less populated. Improved economic conditions in the top income countries may result in renewed upward momentum, albeit the increase is much slower than what is seen in the lower income countries. The slowdown resulting from modeled data indicates that stabilized levels may possibly be reached soon in high income level countries. Disregard of the decoupling discussion in high income countries, that evidently must result from policy intervention, we see that carbon output in the fourth income quintile, and China in particular, dominates the global result. In total, 57% of average global 2030 emissions, or 35.8 GT, is produced in these countries. Without improved policies, the fourth income quintile produces more than the 2014 global output.

# **6 Discussion and Conclusion**

Through this paper, we have discussed penalized non-parametric modeling of environmental output across economic development. This type of modeling works well for nonlinear processes given that they do not result in overly complex dynamics. We deployed the framework to study environmental data in a panel of 95 countries. We specifically modeled satellitederived deforestation, satellite-derived air pollution, and reported carbon emissions. To deal with heteroskedastic variance, we transformed the data to logarithmic degradation intensity of per capita GDP. Our out-of-sample shrinkage of fixed error components did not support time fixed effects.

Our results suggest that production gradually favors conserving the earth’s finite resources as GDP increases, but that this alone is not sufficient to offset the effects of scale growth. Instead, structural change in the economy shapes environmental output curves. This process shares similarities between sovereigns, but remains largely heterogeneous. These results do not support a single Environmental Kuznets rule. Instead, the results emphasize the importance of local economic conditions on environmental results. Across all data levels, some effects hold unambiguously. Poverty and income inequality correlate with higher pollution, higher deforestation, and lower carbon emissions; agricultural GDP correlates with deforestation; population densities correlate with pollution; and higher manufacturing shares correlate with increased carbon emissions. We find various tipping points in other variables, notably across urbanization rates. While local conditions may 30