rafmacalaba/datause-encoder-data huggingface dataset:
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The recent economic expansion seems to be registering across an increasing number of countries (figure 3). The number of countries with economic declines dropped from 15 to 18 in the early 1990s to about 2 to 5 countries in recent years. Only one country has significant economic contraction—Zimbabwe.<sup>4</sup> In contrast, about 40–45 countries have positive growth, and 14–19 countries are growing by more than 5 percent a year.

<!-- Start of picture text --> Figure 3: Growth Pattern of African Countries<br>Number of Countries by Different GDP Growth Range<br>50<br>45<br>40 14 10 10 14 11 10 8 11 8 13 13 16 12 15 12 19 14 19 17<br>35 19 4 20 18 >5%<br>9<br>30 8 8 6 7 8<br>9 10 13<br>25 14 16 13 8 11 6 16<br>20 12 15 13 7 10 11 12 11 13 15 15 13 17 3~5%<br>15 6 11 14 14 14<br>11 12 12<br>105 11 11 13 12 17 15 18 15 15 10 6 6 10 9 11 12 9 0~3%<br>7 6 4 6 7 7 7 7 5 2 3<br>0 <=0%<br>1985198619871988198919901991199219931994199519961997199819992000200120022003200420052006<br>No. of Countries<br><!-- End of picture text --> So urce: AFRCE vario us years ; Wo rld Bank vario us years g.

During 2000–06, about 26 countries had GDP growth exceeding 4 percent a year, while as many as 14 countries exceeded 5.5 percent. Countries with at least 4 percent GDP growth now constitute a sizable portion of Sub-Saharan Africa—about 70 percent of the region’s total population and 80 percent of the region’s GDP. As a group, these countries have been growing consistently at nearly 7 percent a year, whether considered in the more recent period or a longer period since the mid-1990s. The classification of countries in table 1 is fairly stable in the two time periods. There are some exceptions near the cut-off point: the performance of São Tomé and Príncipe, Sierra Leone, South Africa, and Zambia has improved in performance during the more recent period (2004– 06), whereas that of Malawi and, to a smaller extent, Cameroon and Mauritius has deteriorated.

> 4 This assessment does not include Somalia, where no good data are available, and the numbers for Sudan may not fully cover its southern regions. Both current and past data face the same limitations.

3
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the Ravallion and Chen results suggest that it would have taken half the time to achieve the reduction in poverty observed over 1981-2001.

This type of calculation assumes that the same overall rate of growth would have been possible without the sectoral imbalances. In principle, that is a strong assumption. However, it is not as strong as one might guess in the China context. The sectoral imbalance in China’s growth process is in part the result of deliberate policies on the part of the government. A number of specific policy instruments were used for this purpose, including:<sup>3</sup>

- Subsidized prices for key inputs (including energy, utilities and land), weak or weakly enforced regulations (including environmental protection);

- Favoured treatment for industry in access to finance, especially for large (private and state-owned) enterprises;

- Restrictions on labor movement through the Hukou system and discriminatory regulations against migrant workers in cities; and

- Local administrative allocation of land, with the effect that out migrants from rural areas face a high likelihood that they will lose their agricultural land rights.<sup>4</sup>

Given that the sectoral pattern of growth was far from being a wholly market-driven process, it would clearly be hazardous to assume that the specific pattern of growth was efficient and (hence) promoted the maximum overall rate of growth. Ravallion and Chen (2007) address this issue empirically, and argue that the national-level data do not provide compelling evidence for believing that lower growth rates in the primary sector were the “price” of higher growth in the secondary and tertiary sectors.

The main contribution of the present paper is to assess the contribution to poverty reduction of the sectoral and geographic pattern of China ́s growth, by extending the Ravallion-Chen analysis to the provincial level. By adding the extra variability in the geographic (inter-provincial) dimension we are able to enhance the power of the various tests of the PGH that we undertake—enhancing the scope for identification and precision of the estimates over past studies. By allowing us to introduce a latent provincial effect in the error term, our provincial panel-data analysis also addresses concerns about omitted variables. Additionally, the common origin and methodology of the primary data make this empirical

> 3 For further discussion on these points see the useful overview in Kuijs and Wang (2006).

> 4 The contrast with neighbouring Vietnam in land policies is notable; while China kept the nonmarket institutions of local administrative land allocation intact after embarking on its reform process, Vietnam introduced the essential features of a free market in land-use rights, Ravallion and van de Walle (2007) study these policies in depth and argue that Vietnam’s policy was more pro-poor than China ́s.

4
# **Appendix A: Data description**

<mark>The price data were taken from the World Bank’s world commodity price data system. The sample covers 50 years, January 1970 through December 2020 (612 observations). The prices, which are reported in nominal US dollar terms, were deflated with the U.S. CPI (taken from the St. Louis Fed). Although the World Bank covers more than 70 commodity price series, this paper uses only 27 series. The selection of the commodities was based on the following criteria:</mark>

- **<mark>Substitutability</mark>** <mark>. If two commodities are close substitutes only one was included. For example, because the edible oils are close substitutes, only soybean oil is used in the analysis.</mark>

- **<mark>Importance</mark>** <mark>. Several commodities that used to be very important in the past are not included in the current study because they have lost their importance during the past few decades, often due to the development of synthetic products. Notable exclusions are wool, hides and skins, sisal.</mark>

- **<mark>Price determination process</mark>** <mark>. Prices should be determined by a market-based mechanism such as exchange (or auction in the case of tea). Notable exclusions are iron ore (until recently, its price used to be the outcome of a negotiation process among key players of the steel industry) bananas (its price reflects quotations from a few large trading companies), and sugar (too many policy interventions reduce the significance of the world price indicator).</mark>

<mark>The prices were grouped into six broad categories, each of which contained at least three but no more than six series in order to maintain a balanced representation. The rest of this Appendix lists the commodities used in the analysis, the groupings, and their main uses. A technical description is also given for each series. More details on sources and description can be found in</mark> World Bank (2020, <mark>pp. 77-82).</mark>

# <mark>A.1. Energy (3 series)</mark>

<mark>The energy group includes three series: Coal, crude oil, and natural gas.</mark> **Crude oil** is primarily used for transport and, to a lesser degree, petrochemicals while most **coal** and **natural gas** are used for electricity generation and less so for industrial purposes. The prices belonging to the energy group are defined as follows:

- **Coal** (Australia): Thermal, f.o.b. Newcastle, 6,000 kcal/kg, spot price.

- **Crude oil** : Average price of **Brent** (38° API), Dubai Fateh (32° API), and West Texas Intermediate (WTI, 40° API). Equally weighed.

- **Natural gas** (Index): Weights based on five-year consumption volumes for Europe, U.S., and Japan (Liquefied Natural Gas).

_— 33 —_
In Handbook of Labor Economics, volume 4, chapter 16, pages 14871541. Elsevier.

- Bohrnstedt, G. and Goldberger, A. (1969). On the Exact Covariance of Products of Random Variables. Journal of the American Statistical Association, 64(328):14391442.

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- Chancel, L., Piketty, T., Saez, E., and Zucman, G. (2022). World inequality report 2022. Harvard University Press.

- Chetty, R., Hendren, N., Kline, P., and Saez, E. (2014). Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States. The Quarterly Journal of Economics, 129(4):15531623.

- Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., and York, R. L. (1966). Equality of Educational Opportunity. Government Printing Oce, Washington, DC.

- Dahan, M. and Gaviria, A. (2001). Sibling correlations and intergenerational mobility in latin america. Economic Development and Cultural Change, 49(3):53754.

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- Emran, M. S., Jiang, H., and Shilpi, F. J. (2020). Gender Bias and Intergenerational Educational Mobility : Theory and Evidence from China and India. Policy Research Working Paper Series 9250, The World Bank.

- Emran, M. S. and Shilpi, F. (2015). Gender, Geography, and Generations: Intergenerational Educational Mobility in Post-Reform India. World Development, 72:362380.

- Emran, M. S. and Shilpi, F. (2021). Economic approach to intergenerational mobility: Measures, methods, and challenges in developing countries. In Iversen, V., Krishna, A., and Sen, K., editors, Social mobility in developing countries: Concepts, methods, and determinants. Oxford University Press.

- Emran, M. S. and Sun, Y. (2015). Magical transition? intergenerational educational and occupational mobility in rural China : 1988-2002. Policy Research Working Paper Series

27
largest randomized evaluation in the financial education literature, covering 892 public high schools in six Brazilian states and approximately 25,000 students.

As part of the original study, schools in the study sample were assigned their treatment status through stratified and matched randomization, with schools within each state matched into pairs based on their pre-existing school- and community-level characteristics. Within each pair, one school was then randomly assigned as treatment while the other served as control.

Treated schools received financial education material and teacher training. Control schools did not receive any material or training but participated in surveys and testing in the same manner as the treated schools. One eleventh grade class in each school participated in our study. Treated classes received the first semester of financial education during the second semester of eleventh grade (Fall 2010) and the second and third semesters of financial education throughout twelfth grade (Spring 2011 and Fall 2011), the last year of high school. Most students in the sample were between 15 and 17 years of age at the start of the intervention. The rationale for choosing this age group was to engage with students who were already making some personal financial decisions, for example, when purchasing consumer products. Many students in this cohort also worked and earned income so there was an opportunity to apply newly learned financial concepts to their concurrent decisions.

The short-term analysis was based on three rounds of data collection: baseline (August 2010), follow-up 1 (December 2010), and follow-up 2 (December 2011). In addition, complementary administrative data on school graduation and dropout rates was compiled for the study period. Finally, teachers and principals were interviewed for feedback on the program.

# **_2.b. Short-Term Results_**

The empirical analysis of short-term impacts used the three surveys described above and applied the following intent-to-treat OLS specification: where yi,s,f is a measure of the financial knowledge, attitude, or behavior, of student _i_ in school pair _s_ at follow-up _f_ . The variable Treatmenti,s indicates whether an individual is in a school that was randomized into treatment or not and is thus equal to one for the treatment group and equal to zero for the control group. Equation 1 includes a set of dummy variables, ds, for the school pairs formed prior to randomization, the baseline outcome variable, yi,s,b, and a dummy for whether student is female, fi,s. The standard errors, εi,s, are clustered at the school level. ii This original analysis found unambiguous positive treatment effects on student financial proficiency and graduation rates, but the effects on student financial behavior, especially their use of credit, were mixed with some cautionary results.

6
The 2005/06 household budget survey (HBS) indicates that, in the survey year, 35 percent of the Yemeni population lived under the national moderate (expenditure) poverty line, with a much higher rate in rural areas (40 percent as opposed to an urban rate of 21 percent). Disparities between different governorates are also strong, with poverty incidence highest in the Amran governorate (64 percent), and lowest in Al Mahrah (9 percent). The 2005/06 rate was significantly lower than the 1997/98 rate of 40 percent, generated by a comparable household budget survey. The reduction was remarkable in urban areas, with the poverty rate falling by over one third, while the rural rate remained stubbornly high at above 40 percent. However, according to more recent estimates, the poverty rate is once more on the increase, reaching 42.4 percent in 2010, primarily as a result of increased food prices (MOPIC, 2011).

Analysis carried out based on data from the 2005/06 HBS also demonstrates that the substantial amounts spent on fuel subsidies only partially benefited the poor, with around 80 percent of the benefits accruing to the non-poor, while the high cost of health care discourages the poor from seeking care (Government of Yemen and others, 2007). The survey also confirms that public health care services and the increasing share of education expenditures allocated to the tertiary sub-sector do not target the poor. There are also major leakages in the public transfer schemes targeting the poor.

Trends over the last few years in primary education point to an increase in the gross enrolment rate (GER) in basic education from 58 percent in 1997/98 to 66.5 percent in 2003/04, with adult literacy reaching 50 percent by 2006. Despite substantial progress in girls’ education, the GER for girls only reached 51.5 percent in 2003. These average rates of school enrolment in the country hide serious geographic disparities. Boys and urban children enjoy greater education opportunities and higher enrolment rates.

While the gender gap in primary enrolment decreased from 37.2 in 1990/91 to 24.8 in 2002, the female enrolment rate in the first year of basic education was only 75 percent of the male enrolment rate. Both boys and girls intake rates in the past few years have increased in large measure due to the emphasis on improving primary school education.

13
**_Rfrences_**

_75_ Table C2. Expert assessments of mortality rates and deaths (continued)

|_Region and_|_Rat_|_es_|_Deat_|_hs_|
|---|---|---|---|---|
|_age group_|_Male_|_Female_|_Male_|_Female_|
|Industrial|||||
|Total|13|21|48|81|
|0|0|0|0|0|
|1-4|0|1|0|0|
|5-14|0|0|0|0|
|15-44|1|0|2|1|
|45-64|12|9|10|7|
|65+|101|132|37|73|
|Nonmarket|||||
|Total|5|9|11|20|
|o|0|0|0|0|
|1-4|0|0|0|0|
|5-14|0|0|0|0|
|15-44|1|1|1|1|
|45-64|9|10|4|5|
|65+|46|53|6|14|
|LAC|||||
|Total|14|17|28|34|
|o|1|1|0|0|
|1-4|0|0|0|0|
|5-14|0|0|0|0|
|15-44|2|2|2|2|
|45-64|41|41|10|10|
|65+|200|232|16|22|
|Sub-Sahara|||||
|Total|5|5|10|11|
|o|o|0|0|0|
|1-4|0|0|0|0|
|5-14|0|0|0|0|
|15-44|2|0|2|0|
|45-64|10|10|2|2|
|65+|100|120|6|9|
|MENA|||||
|Total|9|13|18|25|
|o|3|4|0|0|
|14|0|0|0|0|
|5-14|0|0|0|0|
|15-44|2|3|1|2|
|45-64|31|49|6|10|
|65+|166|194|10|12|
|Asia|||||
|Total|6|10|74|119|
|0|5|5|1|2|
|<br>1-4|<br>3|<br>1|<br>3|1|
|5-14|0|0|0|0|
|15-44|1|2|8|10|
|45-64|14|26|25|43|
|65+|71|109|36|63|
# **References**

1. Weichenthal, S. _et al._ How low can you go? Air pollution affects mortality at very low levels. _Science Advances_ **8,** eabo3381 (2022).

2. Shaddick, G., Thomas, M., Mudu, P., Ruggeri, G. & Gumy, S. Half the world’s population are exposed to increasing air pollution. _NPJ Climate and Atmospheric Science_ **3,** 1–5 (2020).

3. Fuller, R. _et al._ Pollution and health: a progress update. _The Lancet Planetary Health_ (2022).

4. Richardson, E. A., Pearce, J., Tunstall, H., Mitchell, R. & Shortt, N. K. Particulate air pollution and health inequalities: a Europe-wide ecological analysis. _International journal of health geographics_ **12,** 1–10 (2013).

5. Tessum, C. W. _et al._ PM2.5 polluters disproportionately and systemically affect people of color in the United States. _Science Advances_ **7,** eabf4491 (2021).

6. Mohai, P., Pellow, D. & Roberts, J. T. Environmental justice. _Annual review of environment and resources_ **34,** 405–430 (2009).

7. Clark, L. P., Millet, D. B. & Marshall, J. D. Changes in transportation-related air pollution exposures by race-ethnicity and socioeconomic status: outdoor nitrogen dioxide in the United States in 2000 and 2010. _Environmental health perspectives_ **125,** 097012 (2017).

8. Mikati, I., Benson, A. F., Luben, T. J., Sacks, J. D. & Richmond-Bryant, J. Disparities in distribution of particulate matter emission sources by race and poverty status. _American journal of public health_ **108,** 480–485 (2018).

9. Jbaily, A. _et al._ Air pollution exposure disparities across US population and income groups. _Nature_ **601,** 228–233 (2022).

10. Bell, M. L. & Ebisu, K. Environmental inequality in exposures to airborne particulate matter components in the United States. _Environmental health perspectives_ **120,** 1699– 1704 (2012).

11. Miranda, M. L., Edwards, S. E., Keating, M. H. & Paul, C. J. Making the environmental justice grade: the relative burden of air pollution exposure in the United States. _International journal of environmental research and public health_ **8,** 1755–1771 (2011).

12. Banzhaf, S., Ma, L. & Timmins, C. Environmental justice: The economics of race, place, and pollution. _Journal of Economic Perspectives_ **33,** 185–208 (2019).

13. Liu, J. _et al._ Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990–2010. _Environmental Health Perspectives_ **129,** 127005 (2021).

17
# **Appendix Table A (Continued): Plot-Level Characteristics by Region**

||**Total**|**Central**|**Eastern**|**Northern**|**Western**|
|---|---|---|---|---|---|
|Any Chemical Fertilizer Applied|1.2%|2.4%|1.2%|1.2%|0.5%|
|Kilograms of Fertilizer per Acre|0.74|1.62|0.33|0.76|0.51|
|Value of Fertilizer per Acre (USD)|0.64|0.62|0.26|1.42|0.12|
|Any Crop Protection Chemicals Applied|4.9%|11.0%|4.9%|2.7%|3.5%|
|(pesticides, fungicides, etc.)||||||
|Value of Crop Protection Chemicals per Acre<br>(USD)|0.79|1.34|0.50|0.16|1.41|
|**Cropping Patterns**||||||
|Plot is Intercropped|32.5%|44.7%|34.8%|28.5%|27.1%|
|Share of Plot Planted with Roots|27.9%|26.9%|29.2%|29.5%|25.5%|
|Share of Plot Planted with Pulses and Oils|26.1%|17.9%|22.8%|32.1%|27.6%|
|Share of Plot Planted with Cereals|24.0%|18.0%|31.2%|26.7%|18.2%|
|Share of Plot Planted with Banana|12.9%|24.3%|9.2%|3.9%|19.3%|
|Share of Plot Planted with Coffee|3.9%|8.7%|3.7%|0.7%|4.7%|
|Share of Plot Planted with Other Cash Crops|2.4%|0.6%|1.9%|4.1%|2.1%|
|Share of Plot Planted with Vegetables|1.7%|1.5%|1.5%|1.9%|1.8%|
|Share of Plot Planted with Fruits|1.0%|2.0%|0.5%|1.1%|0.8%|
|Share of Plot Planted with Other|0.0%|0.1%|0.0%|0.0%|0.1%|
|**Plot Manager Characteristics**||||||
|Age of Plot Manager|42.5|43.4|44.6|41.5|41.2|
|Education Years of Plot Manager|5.4|6.9|5.4|4.9|5.0|
|Plot Manager Received Government (NAADS)|19.9%|11.9%|16.3%|28.4%|18.6%|
|Extension Services||||||
|Plot Manager Received Other Extension|10.0%|12.6%|7.0%|14.9%|5.4%|
|Services||||||
|Number of Observations (Plots)|6999|1231|1711|2177|1880|

24
invoice expires, regardless of whether the buyer pays the bank the full amount.<sup>14</sup> Nafin and the buyers help banks reduce their losses in two ways: First, buyers must “invite” sellers to join their chain and participate in the program. Buyers generally require sellers to have a relationship of a minimum length and performance record before participating. Second, in the case of returns, the factor receives future receivable payments directly from the buyer and the buyer adjusts the amount of the negotiable documents on future receivable payments posted on the Nafin website by the amount of the receivables due to returns.

The Nafin factoring program is successful because it uses an electronic platform for cheaper and quicker transactions. All transactions are completed within 3 hours and money is credited to suppliers account by the close of business. This provides immediate liquidity to suppliers. The Nafin factoring program is also less expensive than commercial factoring because Nafin waives the service fee by paying the overhead and legal costs associated with maintaining the electronic platform and writing the contracts.

The Nafin factoring program has succeeded because of supporting electronic security laws.<sup>15</sup> For example, in May 2000, the “Law of Conservation of Electronic Documents” was passed giving data messages the same legal validity as written documents, which is necessary for electronic factoring. In April 2003, the “Electronic Signature Law” was enacted, which allows secure transactions substituting electronic signatures for written signatures, which permits the receiver of a digital document to verify with certainty the identity of the sender. In January 2004, modifications to the

> 14 As a result of this condition, Nafin has reported zero loses from factoring. 15 See AMECE (2004) for additional details on electronic security laws in Mexico and other countries.

22