rafmacalaba/datause-encoder-data huggingface dataset:
billion in 2004 to RMB 29,160 billion in 2017, a compound annual growth rate of 30 percent.<sup>5</sup> Express mail service exceeded 30 billion pieces in 2016,<sup>6</sup> of which some 60 percent is related to e-commerce.<sup>7</sup> According to a 2017 McKinsey report, China’s worldwide e-commerce transaction value grew from less than 1 percent a decade ago to over 40 percent now, exceeding that of France, Germany, Japan, the United Kingdom, and the United States combined (Woetzel et al. 2017). Online retail sales have grown even faster, from RMB 125.7 billion in 2008 to RMB 5,155.6 billion in 2016 – only 1 percent of total retail sales of consumer goods was purchased online in China in 2008, compared with 16 percent in 2016. The number of packages sent through online sales increased tenfold from one billion in 2006 to 10 billion in 2014 (Goldman Sachs 2018). According to the State Post Office, Chinese express firms delivered 40 billion packages in 2017, the majority related to e-commerce.<sup>8</sup> The development of online retailing, however, remained uneven across Chinese provinces. In 2015, in Beijing, 45 percent of the total retail sales of consumer goods was purchased online, followed by nearly 40 percent in Shanghai, 35 percent in Zhejiang, and 28 percent in Guangdong. However, this share was much lower (less than 2 percent) in nine inland provinces (Figure 1), as measured by the Online Business Index (OBI) and the Online Shopping Index (OSI) developed by AliResearch (map 1 and map 2).<sup>9</sup>

> 5 Ministry of Commerce of China. E-Commerce in China (2015, 2016, and 2017).

> 6 Source: National Bureau of Statistics of the People’s Republic of China, <u>http://www.stats.gov.cn/tjsj/sjjd/201706/t20170626_1506952.html</u> 7 Source: State Post Bureau of the People’s Republic of China, <u>http://www.spb.gov.cn/zy/xxgg/201706/t20170624_1196398.html</u>

> 8http://www.spb.gov.cn/xw/dtxx_15079/201806/t20180604_1581131.html

> 9 The OBI and OSI data are obtained from the AliResearch team. OBI is a constructed index measuring the density of online stores and the percent of online stores with annual online sales above RMB 240,000. The value of the index ranges from 0 to 100. The higher the value, the more developed the online sales. OSI is a constructed index measuring the density of online buyers and the percent of online buyers with annual online consumption above RMB 10,000. The value of the index ranges from 0 to 100. The higher the value, the more developed the online purchases. The online transaction numbers are from the Alibaba platform, which accounts for the majority of online transactions in China. The values of the OBI and OSI are not comparable over time due to changes in the methodologies.

·· 5
Matin, Kazi and Duc, Minh Pham. 2000. "Vietnam Trade<sup>Liberalization:Followingthe</sup> AFTA Program." Social Development. (Summer 2000).

Nomura Research Institute, 2002. "Vietnam Development, Trade<sup>Facilitationandthe Impact</sup> on Poverty Reduction." Prepared for Ministry of Transport,<sup>Vietnam andtheWorldBank.</sup> (June 2002).

Ministry of Planning and Investment. 2002. "Public Investment<sup>Programme2001-2005".</sup> Social Republic of Vietnam. (July 2002).

Pham, Van Thuyet. 2002. "Vietnam: Regulatory Environment and<sup>SupportServicefor Export</sup> Development" (2002). Background Paper to the World Bank study<sup>"VietnamExports: Policy</sup> and Prospects".

Prime Minister's Research Commission, Vietnam Institute for<sup>TradeandGerman Technical</sup> Cooperation. (2001). "Inter-Ministerial Coordination during<sup>WTO Accession: Experiences</sup> of Selected (transition) Economies and some Considerations<sup>forVietnam."(September</sup> 2001).

Wang Yong. 1999. "Why China Went for WTO". China Business Review.<sup>Washington.</sup> July-August, pp. 4245.

World Bank. 2002. "Vietnam' Export Policies and Performance: Challenges and Opportunities." Draft.

Web sites: Classification of Vietnam as a non-market economy by the<sup>USDepartmentof Commerce:</sup> http://www.ia.ita.doc.gov/downloadlvietnam-nme-status/vietnam-nme-status.htm 16
Pastureland has a significant negative impact on wages, with a 10% increase linked to an annualized wage decrease of about 2.57% (coefficient of -0.219, significant at the 1% level). This may stem from the lower labor intensity of livestock farming compared to crop farming and the seasonal nature of pastoral activities, often reliant on family labor.

# 5.3 Estimates for Food Stress and Food Crisis Risks

The logit models show strong predictive performance, correctly classifying 69.14% of food stress cases and 87.89% of food crisis cases. These results highlight the importance of climatic and economic variables in shaping food security and demonstrate the models' ability to capture complex dynamics. The predictive power also points to their potential use in targeting and monitoring efforts.

To estimate the population at risk, we multiplied the population of each period by its predicted probability of food stress and food crisis. These estimates were rescaled by fitting a linear regression model, using the share of the population exceeding IPC levels 2 and 3 as the dependent variable and the population-weighted probabilities as the predictor. The rescaled values were then used to estimate the population at risk over time.

## **Geographical Variation in Modeled Risks**

Tables 4 and 5 provide a detailed breakdown of the probability of food stress and food crisis across Afghan provinces, along with provincial demographic data (NSIA, 2021) to capture population exposure to these risks. Overall, while food stress is widespread, food crisis risk is more concentrated but significant in certain areas.

Table 4 shows the probability of food stress, indicating moderate levels of food insecurity. Most provinces fall into higher risk categories, with 28 of 34 provinces showing an average probability of food stress above 50%, representing over 28 million people (89% of the country's population). Notably, 12 provinces have probabilities between 61%-70%, and two provinces exceed 71%, exposing large populations to chronic food stress. The distribution highlights widespread vulnerability, particularly in provinces with larger populations facing high food stress likelihood.

## **Table 4: Probability of Food Stress across Provinces in Afghanistan**

Table 4 shows the average probability of food stress over the full study period across Afghan provinces, broken down into ranges of 0.1. The columns provide a demographic breakdown of the provincial statistics to understand overall population exposure to relative risks. The majority of administrative areas are in the higher risk ranges, with probabilities over 50%.

|**Probability of food**<br>**stress**|**Number of**<br>**Province**|**Female Population**|**Male Population**|**Total**<br>**Population **|
|---|---|---|---|---|
|0.31-0.4|1|302,409|311,072|613,481|
|0.41-0.5|5|1,442,998|1,490,696|2,933,694|
|0.51-0.6|14|5,583,803|5,799,679|11,383,482|
|0.61-0.7|12|7,506,594|7,781,242|15,287,836|
|0.71 and above|2|906,737|943,930|1,850,667|

24
Annex 6: Liberia - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**25% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**50% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**25% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**50% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|---|---|---|---|---|---|---|---|
||<br>||**L**<br>|<br>**iberia National**<br>|<br>|<br>|<br>|
|Local Rice|9.6|60.1|**63.8**|64.3|64.8|63.9|64.3|
|Imported Rice|13.2|84.9||66.6|69.0|||
|All|22.8|99.0||67.1|69.8|66.6|69.4|
|||||**Liberia Urban**<br>||||
|Local Rice|1.1|17.1|**55.1**|55.2|55.3|55.1|55.2|
|Imported Rice|13.5|97.3||57.6|60.3|||
|All|14.6|98.6||57.8|60.5|57.6|60.4|
|||||**Liberia Rural**<br>||||
|Local Rice|16.2|80.0|**67.7**|68.4|69.0|67.8|68.4|
|Imported Rice|12.9|79.2||70.6|72.8|||
|All|29.2|99.2||71.2|74.0|70.6|73.4|

Source: Authors’ estimation using country household survey data 25
**Figure 2: Number of ninis aged 15-24 and their share of**

# **age group in LAC, 1992-2010**

<!-- Start of picture text --> 100.0 20.0<br>18.0 18.2 18.2<br>16.4 17.1<br>80.0<br>15.0<br>60.0<br>10.0<br>40.0<br>23.4 23.2 22.6 20.5 19.3 5.0<br>20.0<br>0.0 0.0<br>1990 1995 2000 2005 2010<br>Year<br>Number of ninis (right axis)<br>Percentage of ninis<br>in relevant age group (left axis)<br>Ninis (millions)<br>Ninis (% relevant age group)<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) A 14 percent increase in the number of ninis over a 20 year period may seem small, but disaggregating by gender reveals that the increase for young men was much more dramatic. The number of male ninis jumped 46 percent between 1992 and 2010, with the additional males accounting for the entire increase in ninis throughout the period (1.8 million individuals). This is an important distinction, since the number of male ninis is correlated with crime and violence in contexts where violence is already a serious problem (de Hoyos, Gutiérrez, and Vargas 2015). The combination of this surge in male ninis, the link with crime and violence, and the very emergence of the term “ _ninis_ ” may explain the increased media attention to the phenomenon in recent years.<sup>7</sup> Not all countries show the same trend, of course. The change in the national percentage of ninis between 1992 and 2010 ranges from a decrease of 12.3 percentage points in República Bolivariana de Venezuela to an increase of 1.6 percentage points in Argentina. For each country, Annex 4 provides graphs showing how the total number and percentage of ninis aged 15 to 24 have evolved over that period, while Figure 3 and Annex 3 summarize these national levels and trends using maps. Using data for 2010, Figure 3 shows that Peru and República Bolivariana de Venezuela have the lowest shares of ninis (at 10.9 and 13.0 percent, respectively), while the highest shares are concentrated around Central America. The highest numbers of ninis are found in Brazil, Colombia,

> 7 According to an analysis using Google Trends, searches from Mexico using the term “ _ninis_ ” surged in late 2010, after having been non-existent before that year. Moreover, this was not simply due to economic circumstances: searches using “desempleado” (unemployed) had already risen sharply some two years earlier, just after the global financial crisis hit.

7
checking the sensitivity of the analytical results may help to ensure that the present investigation remains useful.

The estimation results corresponding to the above four scenarios were reported in the second and third panels of Table S.29 (for progression) and Table S.30 to Table S.33 (for GPA scores) in this supplemental appendix. Considering all these estimation results, there is no strong evidence to suggest that the “no academic performance effects” of solar lamps should be attributed to a positive learning externality. Note that the absence of spillover effects is consistent with both views - that learning interaction between the treated and remaining students existed or did not exist. Given the absence of significant treatment effects, the former case may suggest that solar lamps did not provide treated students with any educational benefits (in terms of performance) that could have been transferred from them to the remaining students.

Finally, as the measured treatment intensity is unavoidably imprecise, for the reasons mentioned above, it may not be seen as strictly exogenous, even if the estimated eligible fraction is controlled for. To somewhat alleviate this concern, we also collected information on students’ academic performance in the final examination held in December 2012; we checked for a relationship between this pre-experiment test result and the subsequent treatment intensity in the first panel of Table S.29 (for progression) and Table S.30 to Table S.33 (for GPA scores). This falsification test revealed no relationships that were noticeable in a statistical sense, thereby arguably supporting the assumption that measured treatment intensity has exogenous variation (given the control of the eligible fraction).

# **S.3.3 Floods**

One remaining concern for the 2014 examination results is that continuous rainfall struck northern Bangladesh in August 2014, causing flash floods in our study area. Consequently, some families sought shelter during that time. This weather shock may explain no improvement of academic performance attributed to the provision of solar products. This is possible if this (only) temporary shock was sufficiently large to make the subsequent learning of all the sample students indiscriminate.

However, this study sees this concern as less likely for several reasons. First, apparently, the proportion of people that evacuated to shelters was not so remarkably large. Based on Martin (2014), for example, the population targeted due to this flood by aid agencies for humanitarian assistance was only less than 12% of the vulnerable population (i.e., extremely poor people) in the affected district.<sup>34</sup> In fact, during our school visit in August, when the area was already affected by the flood, the rate of children’s attendance (i.e., 61.7%) was comparable to that in the remaining visits (61.5% and 52.7% in February and April, respectively). Notably, while only less vulnerable students might have remained in school in August, such family characteristics are likely to be orthogonal to the treatment status, which

> 34See also http://reliefweb.int/disaster/fl-2014-000117-bgd for the flood issues.

38
4 percent of non-oil government revenue in 2006.<sup>13</sup> Trade diversion exceeds trade creation leading to a relatively small welfare loss of US$ 12 million.

# **Box 2.  Trade Diversion at the Sectoral Level**

In addition to the initial tariff structure and the product specific substitution elasticities, a main determinant for trade diversion in the TRIST modelling framework is the share of the preferential trading partner (in this case, the EU) in overall imports. If the overall import share is very low, trade diversion will be small because percentage changes are based on very small initial values. If the trade share of the EU is already very high, trade diversion is naturally limited by the size of the remaining market share that the EU could still take over. Thus, as illustrated by the graph (where each dot represents a specific product), the strongest trade diversion can be expected for products where the EU's market share is close to 50%.

<!-- Start of picture text --> EU share in initial imports vs trade diversion /<br>total imports<br>20%<br>10%<br>0%<br>0% 20% 40% 60% 80% 100% 120%<br>EU share by product<br>imports<br>trade diversion / total<br><!-- End of picture text --> Top 10 <u>sectors with strongest trade diversion</u>

|ISIC|description|EU<br>market<br>share|EU<br>applied<br>tariff|trade<br>creation /<br>pre-<br>reform<br>imports|trade<br>diversion /<br>pre-reform<br>imports|
|---|---|---|---|---|---|
|11|Crops and horticulture|23.9%|5.3%|0.6%|6.6%|
|242|Other Chemical Products|41.1%|8.2%|1.2%|4.9%|
|342|Bodies for Motor Vehicles|62.3%|9.3%|2.1%|4.5%|
|272|Basic Precious and Non-ferrous Metals|50.2%|8.9%|1.8%|4.5%|
|171|Spinning, Weaving and Finishing of Textiles|60.5%|7.2%|1.7%|3.8%|
|152|Dairy Products|72.3%|8.3%|1.4%|3.6%|
|241|Basic Chemicals|33.0%|3.8%|0.6%|3.4%|
|341|Motor Vehicles|38.8%|11.3%|0.8%|3.2%|
|271|BasicIronand Steel|37.0%|6.4%|0.6%|3.0%|
|252|Plastics Products|31.5%|16.0%|0.6%|2.9%|

Source: Compiled by authors

> 13 Based on government revenue estimations from IMF article IV consultations, see IMF country report No. 08/64. Estimate is 6,376 billion Naira (49.4 billion US$) for total and 931 billion Naira (7.2 billion US$) for non-oil government revenue in 2006.

16
A new strand of literature, surveyed in Volpe (2011), has turned to “clinical” (firm-level) evaluation of Export Promotion Agencies (EPAs). Using “difference-in-difference” (DID) estimation at the firm level, Alvarez and Crespi (2000) found that Chile’s EPA, PROCHILE, had an impact on the beneficiaries’ number of destinations, although not on their number of export products. Since then, a number of firm-level studies have shown that export promotion seems to be more successful at affecting the performance of established exporters than at encouraging non-exporting firms to start exporting (Bernard and Jensen, 2004; Görg, Henry and Strobl, 2008; Girma, Gong, Görg and Yu, 2009), as exporters differ from non-exporters in terms of productivity and other characteristics (see, e.g. Bernard, Jensen, Redding and Schott, 2007), which export promotion may not be able to offset. The impact seems stronger along the extensive margin than along the intensive one (Alvarez and Crespi, 2000; Volpe and Carballo, 2008).

Cadot et al. (2012) evaluated the effects of the FAMEX export promotion program in Tunisia on the performance of beneficiary firms. While much of the literature assesses only the short-term impact of such programs, their paper also considers the longer-term impact. Propensity-score matching, DID and weighted least squares estimates suggest that beneficiaries initially see faster export growth and greater diversification across destination markets and products. However, three years after the intervention, the growth rates and the export levels of beneficiaries are not significantly different from those of non-beneficiary firms. Exports of beneficiaries do remain more diversified, but the diversification does not translate into lower volatility of exports. The authors also did not find evidence that the program produced spillover benefits for non-beneficiary firms. However, the results on the longer-term impact of export promotion must be interpreted cautiously because the later years of the sample period saw a collapse in world trade, which may not have affected all firms equally.

19
<!-- Start of picture text --> Figure 5. Prevalence of Different Forms of Corruption in Colombia, Honduras, and Perua<br>Bribes for:<br>Bribes to obtain Public<br>Services (administrative<br>corruption)<br>Public Procurement<br>Shaping Laws and<br>Regulations (State<br>Capture)<br>Divenion/Improper use<br>of Budgetary Public<br>Resources<br>5 15 25<br>% of officials reporting frequent/very frequent form of corruption by firns<br>Peru * Honduras U Colombia<br>|<br>Source World Bank Institute (2001).<br>a. The graph shows the percent of public officials rating the indicated forms of bribery as being frequent or very<br>frequent, based on separate surveys of public officials carried out in 2001 (different months).<br><!-- End of picture text -->

45
Table 5: Trade Impacts by Tax Source (% of GDP) and Development Levels

||total taxes<br>(1)|indirect<br>(2)|CIT<br>(3)|property<br>(4)|PIT<br>(5)|payroll<br>(6)|
|---|---|---|---|---|---|---|
|Trade|0.218*|0.002|0.102***|0.025|0.010|0.055|
||(0.112)|(0.047)|(0.028)|(0.025)|(0.025)|(0.045)|
|Trade_∗_1(High-Inc)|-0.270|-0.146|-0.128**|-0.012|-0.061|0.090|
||(0.251)|(0.132)|(0.061)|(0.042)|(0.062)|(0.093)|
|Implied coef. for|-0.052|-0.144|-0.026|0.013|-0.0506|0.145**|
|<br>Trade in High-Inc|(0.188)|(0.105)|(0.036)|(0.026)|(0.042)|(0.056)|
|_N_|_4518_|_4518_|_4518_|_4518_|_4518_|_4518_|

_Notes:_ This table presents results for the impact of trade on different sources of taxation, estimated using the IV. Across columns, the outcome is the revenue collected by each tax as a percentage of NDP: total taxes, then indirect taxes, corporate income tax, property and wealth taxes, personal income tax, and social security taxes. The regression coefficients for _Trade_ as well as the interaction with a dummy for high-income countries, _Trade ∗_ 1(High-income) are presented. The bottom row reports the coefficient for the linear combination of _Trade_ and the interaction term. Trade is measured as the sum of export and imports divided by GDP. We instrument for trade using the oil-price and the gravity-equation instruments from Egger, Nigai, and Strecker (2019). All estimates include country and year fixed effects and observations are weighted by net domestic product in constant 2019 USD at PPP. We use dummy variable controls for significantly interpolated revenue data (rare) or imputed factor share data (frequent) in all columns. * p<0.10 ** p<0.05 *** p<0.01. Standard errors in parentheses are clustered at the country level.

53
combination of certain incomes. The following categories are distinctively considered for the purposes of calculating the taxable income of individuals in Turkey: 1) Business profits, 2) Agricultural profits, 3) Salaries and wages, 4) Income from independent personal services, 5) Income from immovable property and rights (rental income), 6) Income from capital investment, and 7) Other incomes and earnings.

Property taxes are paid according to the type of the property. The residence property tax is paid according to the value of the residence in the administrative records, rather than the actual market value of the property. The rate is 0.1 percent in rural areas and 0.2 percent in urban areas. The motor vehicle property tax is paid according to the characteristics of the vehicle (i.e., age, cylinder, power).

# _Social Security Contributions_

Social security contributions comprise a quarter of total revenues. In Turkey, employers and employees are obliged by law to pay social insurance contributions for health services, unemployment benefit, and retirement pensions. Contributions are paid directly from the salaries of formally employed workers. Overall, total contributions represent 32.5 percent of the gross salary. While formal employees are obliged to pay their contributions, self-employed or employees working in the informal sector can pay voluntary contributions. The informality rate in Turkey amounts to 32.7 percent of workers.<sup>7</sup>

# _Indirect Taxes_

Indirect taxes are the main tax collection channel in Turkey, raising 36 percent of total revenue. Among them, VAT on domestic and imported goods is the largest revenue source, amounting to almost half of indirect tax revenue. The VAT rate in Turkey is 18 percent, but some items have reduced tax rates. For instance, food and beverages are subject to 8 percent VAT, while bread purchases pay only 1 percent.

The second largest indirect tax is the special consumption tax (SCT). Certain products that are considered to be harmful to the health of the population, create pollution or are considered

> 7  Measured as the share of persons working without social security in their main job (TUIK Labor Force Statistics, December 2016).

7