rafmacalaba/datause-encoder-data huggingface dataset:
creative problem-solving on PISA 2012 found that East Asian students, including students from China, outperformed other countries. In addition, there is evidence that exam-driven systems develop persistence (“grit”), goal orientation, and self-control and willpower that can help students in their careers and lives (Baumeister, Vohs, and Tice 2007; Choi 2014; Duckworth et al. 2007). The success of East Asian emigrants in Silicon Valley, where China and Japan are among the top five source countries for immigrant entrepreneurs, also suggests that growing up in an exam-driven system may not inhibit creativity.

However, exam-driven systems require tremendous amount of student commitment both within and outside of normal school hours, as evidenced by significant household costs on tutoring. With the universally high demand for tertiary education, the fact that nearly all general secondary school graduates can be admitted to a tertiary institution, while a very limited number of graduates from vocational schools are able to do so, has basically raised the stake of the examination at the end of Grade 9 (Zhongkao) extremely high. The competition to gain seats at good quality general senior high school is among students as well as schools. Schools often extend guided study hours for Grade 9 students, and families often pay for extra tutoring to prepare their children for the test. Figure 3.17 compares the tutoring expenditure of households on Grade 7 and Grade 9 students by location of schools attended. Not surprisingly, families of students at city schools spend the highest amount. However, all families with Grade 9 students spend much more on tutoring than families with Grade 7 students. In city centers, there is a 67 percent increase in the household expenditure on tutoring from CNY 1,800 (about US$300) spent on Grade 7 students to about CNY 3,000 (about US$500) on Grade 9 students per term. On average, tutoring spending is around CNY 1,600 (about US$250) per term for Grade 9 students, compared with CNY 950 (US$150) for Grade 7 students.

Figure 3.17 Household tutoring expenditure per term <!-- Start of picture text --> 3,500<br>2,977<br> 3,000<br> 2,500<br>2,031<br> 2,000 1,781 1,642<br> 1,500<br>867 950<br> 1,000 570 700<br> 500 369 293 396 276<br> -<br>city center city outskirt city/rural Town outside rural Total<br>bordering city<br>Grade 7 Grade 9<br><!-- End of picture text --> _Source_ : Authors’ calculation based on the CEPS.

Household tutoring expenditure is highly associated with student school performance. While parental education and household location (urban versus rural) are naturally correlated with household tutoring spending, parents tend to spend less on tutoring when their children’s performance rank in class is higher (Figure 3.18). Gaining an edge in Zhongkao serves as a strong incentive for households to spend more on improving their children’s school performance. In contrast, household economic condition, which normally has a strong wealth or income effect on spending, does not appear to have significant effect on tutoring spending.

40
**<u>Table 3: Household characteristics by treatment category over time</u>**

|||**No cert**|**ificate**|||**With ce**|**rtificate**||
|---|---|---|---|---|---|---|---|---|
||**1999**|**2002 **|**2004 **|**2007 **|**1999**|**2002 **|**2004 **|**2007 **|
|Total owned land in hectares|1.34|1.35|1.61|1.47|1.57|1.59|1.89|2.06|
|Owned land per aeu in ha|0.36|0.34|0.36|0.32|0.37|0.34|0.36|0.38|
|Share of good quality land|0.36|0.32|0.31|0.44|0.36|0.33|0.28|0.39|
|Number of dependents|2.50|2.59|2.70|2.68|2.83|2.97|2.84|2.83|
|Number of adult male|1.24|1.41|1.65|1.79|1.41|1.54|1.71|1.95|
|Number of adult female|1.21|1.33|1.58|1.68|1.36|1.53|1.71|1.93|
|Number of oxen|1.20|1.20|1.17|1.29|2.22|1.98|1.99|2.06|
|Value of livestock (B)|1,628|1,670|1,771|1,962|2,883|2,839|2,888|3,081|
|Value of other animals (B)|857|924|1,026|1,124|1,432|1,309|1,591|1,737|
|Roof corrugated iron sheet|0.55|0.60|0.71|0.79|0.61|0.70|0.86|0.91|
|Age of household head (years)|44.17|45.66|48.08|49.81|44.87|46.67|48.72|50.46|
|Female headed household|0.13|0.12|0.15|0.17|0.10|0.07|0.17|0.19|
|Head can read and write|0.39|0.38|0.35|0.27|0.48|0.48|0.42|0.43|
|Value of cropoutputper ha(B)|1,926|634|2,596|3,283|2,564|880|2,187|2,804|
|Number of households|462|463|475|477|229|233|347|356|

**Source:** Own computation from AAU/Gothenburg/WB Survey

20
<!-- Start of picture text --> Manuf. Food/Bev. th Construction| HH<br>Manuf. Raw Goods ro Vehicle Services| DEA<br>IT, Communication Hal =6Manuf. Finished Goods| EH<br>Manuf. Finished Goods mm Manuf. Raw Goods|<br>Retail i Manuf. Food/Bev.| EH<br>Wholesale Hi Transport) =<br>Vehicle Services | Print/Publish| ==<br>Construction | Retail| as.3—<br>Transport Tis Wholesale) Ds<br>Manuf. Clothing Ss Food Services, Hotels| ITs<br>Food Services,Print/PublishHotels SSS,7 IT, Communication|Manuf. Clothing| E D s<br>70 -60 -50 -40 -30 -20 20 30 40 50<br>Pred. Average Reduction in Pred. Prob. of Pivoting (3)<br>Sales (96), with 95% Cls with 95% Cis<br>Manuf. Finished Goods| IT: Food Services, Hotels| h<br>Manuf. Raw Goods| Vehicle Services| fh<br>Manuf. Food/bev.| Construction| Mik<br>IT, Communication| Retail| Milk<br>Print/Publish| Eh Manuf. Food/Bev.| EH<br>Vehicle Senices| Dh Manuf. Clothing| HEH<br>Wholesale| Es Manuf. Finished Goods| EH<br>Transport| is, Manuf. Raw Goods| EH<br>Retail| Ds: Transport| De<br>Construction| Print/Publish| E24<br>Food Manuf.Services,Clothing|Hotels| is,HEEH IT, Communication|Wholesale] Ee<br>60 70 80 90 100 20 3 40 3 & 70<br>Pred. Prob. of Remaining Pred. Prob. of Starting/Increasing<br>Open (96), with 95% Cls Remote Work Arr. (96), with 95% Cis<br><!-- End of picture text -->
statistically significant. The magnitudes and signs of other coefficients are similar to those shown in the top panel of table 3.

Next, we investigate the heterogeneity of the effects of the electoral cycle on public expenditures by the degree of political competition. Several recent studies demonstrate that political budget cycles are positively correlated with electoral competitiveness. Using data on 27 countries in the European Union for 1997–2008, Efthyvoulou (2012) shows that tighter electoral competition is correlated with a higher probability of observing a political budget cycle. Analysis of the composition of public expenditures in 69 developing countries from 1975 to 1990 demonstrates that in competitive elections, incumbent politicians are willing to incur the economic costs of changing the composition of public spending (Block 2001). <u>Prichard (2018)</u> and Vergne (2009) show similar results in a sample of 42 developing countries over the period 1975–2001. <u>Eibl and</u> - <u>Lynge Mangueira (2017)</u> show the mediating effect of electoral competitiveness on the political budget cycle on a sample of 112 countries from 1960 to 2006.

To test the effect of political competition on public spending during the electoral cycle in the pandemic, we split countries in our sample into two groups based on the political competitiveness index. The 53 countries in the first group have an index of political competition with a value of 8 or less; the index for the 72 countries in the second group is greater than 8. A value of 8 in the political competition index represents a transitional situation between factional/restricted competition (values 7 and below) and institutionalized electoral competition (values of 9 and 10).<sup>7</sup> <u>Figure 2 shows the semiparametric graphs of the relationships between the share of the social</u> protection stimulus budget spent on cash and in-kind transfers and the number of months since the last election for these two groups of countries.

The share of social assistance and income protection in the social protection stimulus budget in countries with a low political competition index (the left-hand panel of <u>figure 2) seems to increase</u> almost linearly from the early post-election month, peaking at about 97 percent of the total budget by the fourth year after the election. The political budget cycle is much more pronounced for

> 7 Our robustness checks indicate that the results that follow are not affected by including in either of the two groups countries with a value of 8.

15
Policy Research Working Paper 7262

# **Abstract**

Women comprise 50 percent of the agricultural labor force in Sub-Saharan Africa, but manage plots that are reportedly on average 20 to 30 percent less productive. As a source of income inequality and aggregate productivity loss, the country-specific magnitude and drivers of this gender gap are of great interest. Using national data from the Uganda National Panel Survey for 2009/10 and 2010/11, the gap before controlling for endowments was estimated to be 17.5 percent. Panel data methods were combined with an Oaxaca decomposition to investigate the gender differences in resource endowment and return to endowment driving this gap. Although men have greater access to inputs, input use is so low and inverse returns to plot size so strong in Uganda that smaller female-managed plots have a net endowment advantage of 12 percent, revealing a larger unexplained gap of 29.5 percent. Two-fifths of this unexplained gap is attributed to differential returns to the child dependency ratio and one-fifth to differential returns to transport access, implying that greater child care responsibilities and difficulty accessing input and output markets from areas without transport are the largest drivers of the gap. Smaller and less robust drivers include differential uptake of cash crops, and differential uptake and return to improved seeds and pesticides.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at dali1@worldbank.org, dbowen@worldbank.org, kdeininger@worldbank.org, and mduponchel@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 1: Impact of the Brain Drain on Education**

||log increm<br>education at<br>(Change|ent of tertiary<br>home 95-2000<br>in Stocks)|log tertiary<br>enrollment|
|---|---|---|---|
||ALL|Low Income||
|Ln|-0.366|-0.331|-0.256|
|brain drain|(3.53)|(2.21)|(2.32)|
|Ln|0.567|1.400|0.691|
|income|(9.11)|(9.48)|(9.31)|
|Ln|0.887|0.797|-0.112|
|population|(14.95)|(8.74)|(2.18)|
|No. of obs.|91|39|55|
|R-square|.90|.91|.69|

Table reproduced from Lucas (2005).

OLS; SE Robust; t-stats in parentheses; intercepts included, not shown. Brain Drain: OECD (2000) Tertiary Enrollment: UNESCO (several years) 36
**<u>Table A.14. The placebo test of the program effect on individuals’ outcomes using the threshold of the poverty rate at 40%</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|Log of<br>monthly<br>wage (wage<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Log of<br>education<br>subsidy for<br>students<br>(children<br>aged 6-17)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Districts with the 2006 poverty above|0.0453|0.0070|-0.0186|0.0430|0.1368|0.1083|-0.0519*|0.2058|
|40% * Year 2008|(0.0389)|(0.0155)|(0.0300)|(0.2323)|(0.1777)|(0.1742)|(0.0268)|(0.3198)|
|(Poverty rate – 40) * Year 2008|-0.0034*|0.0001|0.0031**|-0.0147|-0.0067|-0.0057|0.0001|0.0339***|
||(0.0018)|(0.0010)|(0.0013)|(0.0109)|(0.0082)|(0.0079)|(0.0009)|(0.0111)|
|Districts with the 2006 poverty above|0.0098|-0.0056***|-0.0066|-0.0174|-0.0532**|-0.0508**|0.0075|-0.0805|
|40% * (Poverty rate – 40) * Year 2008|(0.0060)|(0.0021)|(0.0045)|(0.0294)|(0.0241)|(0.0232)|(0.0050)|(0.0525)|
|Age|0.0320***|0.0084***|0.0094***|0.1108***|-0.0090***|-0.0084***|0.1544***|0.1415***|
||(0.0008)|(0.0004)|(0.0004)|(0.0039)|(0.0024)|(0.0022)|(0.0055)|(0.0187)|
|Age squared|-0.0003***|-0.0001***|-0.0001***|-0.0015***|0.0004***|0.0003***|-0.0079***|-0.0071***|
||(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0003)|(0.0009)|
|Male|-0.0737***|-0.0208***|0.1152***|1.2509***|-0.1637***|-0.1555***|0.0130***|-0.0085|
||(0.0086)|(0.0039)|(0.0048)|(0.0421)|(0.0170)|(0.0156)|(0.0047)|(0.0129)|
|Ethnic minorities (yes=1, Kinh=0)|0.1133***|-0.0679***|0.0083|0.1569*|-0.1082**|-0.1096**|-0.1046***|0.7840***|
||(0.0122)|(0.0055)|(0.0089)|(0.0829)|(0.0523)|(0.0502)|(0.0101)|(0.1201)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|-0.0105|-0.0526***|-0.0442***|-0.4257***|0.6811***|0.6157***|0.2591***|-0.4258***|
||(0.0182)|(0.0072)|(0.0092)|(0.0872)|(0.0297)|(0.0283)|(0.0292)|(0.1131)|
|Observations|322,913|322,913|322,913|322,913|216,298|216,298|106,531|106,531|
|R-squared|0.174|0.058|0.137|0.133|0.114|0.113|0.196|0.301|

Note: This table reports district fixed-effect RDD-DD regressions of employment of individuals using individual-level observations. The placebo treatment includes districts with the 2006 MOLISA poverty above 40%. The sample consists of individuals living in districts with the 2006 MOLISA poverty rate between 30% and 50%. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

64
Once the initial marketing process ends, the recyclers are responsible for keeping families active in the program. Administrative data from PRISMA pertaining to the period before this study suggest that approximately 50 percent of households contacted by PRISMA’s marketers join the program. However, because of the inability to contact all families, the program enrolls only approximately 34 percent of households residing in the intervention areas.

Working with PRISMA, we designed a two-part study to help expand the recycling program. The first part of the experiment tested different messaging treatments designed to increase enrollment in the door-to-door recycling program (the participation study), and the second tested different methods of increasing recycling compliance for those who participated in the program (the participation intensity study).

For the participation study, the experiment was built into PRISMA’s expansion into new neighborhoods.<sup>7</sup> In areas that had not previously received the program, we randomly provided 6,718 households with messages encouraging individuals to recycle their waste. We tested four main information campaigns: i) a focus on social norms, encouraging recipients to conform to the proenvironmental behavior of their peers; ii) a focus on social sanctions, informing individuals of others within and outside their community with whom the participation rates for their street would be shared; iii) an injunctive to recycle that was endorsed by religious or government authorities; and iv) a purely informational campaign with messages that stressed the social or environmental benefits of recycling and urged individuals to participate.

For the participation intensity study, we worked with a different sample of individuals than for the participation study. The participation intensity study used a sample frame of those already enrolled in the recycling program, meaning they had 7
shows a positive and (marginally) significant correlation between the share of SOEs in total industry output and industry upstreamness, suggesting that SOEs have a dominant position in upstream industries. Fig. 6 shows a positive and significant relationship between SOEs’ share in the industry’s gross exports and industry upstreamness. Figs. 7-8 show no particular relationship between upstreamness, output, and exports for SMEs. In sum, these findings confirm that the high VAX ratio for SOEs is partly driven by their dominance in the upstream sectors, while SMEs’ high VAX is due to other reasons. One possibility is that exporting is associated with high fixed costs and only large (productive) firms can make sufficiently high export revenue to amortize them. Thus, SMEs tend to export indirectly and have a high VAX ratio.

We use the split IO table from 2010 and estimate the industry measures of upstreamness for different firm types again (see Table A3 in the appendix for the estimates). Fig. 9 shows that for 27 of the 40 industries, the upstreamness measure increased. This finding is exactly the opposite of what recent studies have documented for the U.S., where industries have shown to become more downstream over time (Fally, 2012). If more upstream activities are being offshored from the U.S. to China, our results can provide the “mirror-image” support to Fally (2012).

# **5. Concluding Remarks**

This paper proposes methods to incorporate firm heterogeneity in the standard IO-table based approach to portray the domestic segment of global supply chains in a country. Using conventional IO tables, firm census data for both manufacturing and service sectors, and constrained optimization techniques, we are able to estimate direct and indirect value added exports (VAX) for different types of firms in China, and decompose a firm type’s indirect VAX into different channels through which they are realized.

Based on our split IO table, we find that in China, both state-owned enterprises (SOEs) and small and medium domestic private enterprises (SMEs) have much higher shares of indirect exports and ratios of value-added exports (VAX) to gross exports, compared to foreign-invested and large domestic private firms. Using China’s IO tables for 2007 and 2010 respectively, we find evidence of increasing VAX ratios for all firm types, particularly for SOEs. By extending the method proposed by Antras et al. (2012), we find that SOEs are consistently more upstream while SMEs are consistently more downstream within industries. These findings suggest that SOEs still play an important role in shaping China’s downstream exports.

Our findings imply that years of privatization have led to the dominance of SOEs, not only large firms, in 29
however, the study uses a carbon tax instead of ETS to meet the NDC, and the study does not explicitly represent provincial economics. To the knowledge of the authors, this is the first study that uses a recursive dynamic, multi-regional CGE model with latest available data (2012 provincial SAM) to analyze an ETS to meet China’s NDC under the Paris Agreement. The study finds that meeting China’s NDC through a national emission trading system would cause 0.4% to 1.5% reduction of economic output at the national level (GDP) in 2030 depending upon allowances allocation as well as baseline setting rules.

The study is organized as follows. Section 2 presents the details of the multi-region, recursive dynamic CGE model developed for the study, followed by a discussion on data in Section 3. Section 4 presents discussions of results from the model simulations followed by key conclusions drawn in Section 5.

# 2. **Methodology**

In this section, we present a description of the regional CGE model developed for this study.

_The Naming rules for variables in the equations:_ (1) Endogenous variables are named as uppercase letters, and exogenous variables are named as uppercase letters with a cross line above; (2) Variables are generally named as their recognized prefix, as Q is quantity, P is commodity price, W is the factor price, Y is income, E is expenditure; (3) The number of provincial regions in this model is m, and each region has n production sectors; (4) The initial value of all variables in the business-as-usual scenario are used as parameters and are named as original names with 0 added behind, while other parameters are named as lowercase letters.

_Definition of sets:_ I,J = {production sectors or commodities}; R,S = {regions}; F = {factors, including capital and labor}; E(<sup></sup> I,J) = {energy sector or commodity}; 5
|Degrees of freedom<br>8|7|7|
|---|---|---|
|Pseudo R2<br>0.542|0.455|0.280|
|Note: ** p < .01, *** p < .001|||

**Controlling for year of assessment.** The above analyses were repeated with dummy variables introduced to control for the PISA cycle year. Controlling for PISA cycle did not change the size or statistical significance of the coefficients for any of the models (table 8).

**Table 8: Odds of participating in PISA, all countries and non‐OECD countries, controlling for year of**

**assessment**

|In PISA|All countries|All countries|Non‐OECD countries|
|---|---|---|---|
||Odds Ratio(std. error)|Odds Ratio(std. error)|Odds ratio(std. error)|
|Lower‐middle income|5.12 (2.55)**|4.99 (2.50) **|4.95 (2.47) **|
|Upper middle‐income|14.60 (7.24)***|21.08 (10.43) ***|15.15 (7.51) ***|
|High income|13.97 (7.05)***|40.65 (19.91) ***|12.68 (6.41) ***|
|ECA region|3.07 (0.71)***|6.24 (1.24) ****|3.14 (0.74) ***|
|In TIMSS|5.10 (1.08)***|5.55 (1.07) ***|5.47 (1.17) ***|
|In Regional|1.22 (0.39)|1.33 (0.44)|1.12 (0.39)|
|National|3.58 (0.77)***|5.99 (1.16) ***|3.40 (0.74) ***|
|OECD|145.84 (109.47)***|‐‐|‐‐|
|Constant|.003(.002)***|.004(.002)***|0.004(0.002)***|
|Observations|1269|1269|1081|
|LR chi2|832.28|689.57|278.29 ***|
|Degrees of freedom|13|12|12|
|Pseudo R2|0.558|0.463|0.305|

Note: ** p < .01, *** p < .001; dummy variables for PISA year as controls

**Single-year logistic regressions.** Separate analyses were conducted for each PISA cycle, using fewer indicator variables, since the number of observations ranged from 207 to 215 countries per year (table 9). As previously noted, OECD membership perfectly predicted participation in PISA in all years so the variable was not included in the regression.

**Table 9: Odds of participating in PISA, by** **<u>year, PISA 2000, PISA 2006 and PISA 2015</u>**

|In PISA||Odds Ratio(std|. error)|
|---|---|---|---|
||PISA 2000|PISA 2006|PISA 2015|
|Upper middle‐income|3.18 (2.17)*|5.09 (3.06)**|9.34 (5.78) ***|
|High income|8.84 (4.89)***|9.02 (4.85)***|13.63 (19.91) ***|
|ECA region|7.25 (3.75)***|12.37 (6.19)***|6.74 (3.40) ****|
|In TIMSS|9.04 (4.46)***|7.71 (3.92)***|8.01 (4.20) ***|
|National|3.90 (2.11)*|9.42 (4.76)***|9.77 (4.69) ***|
|Constant|.012(.007)***|.010(.006)***|.008(.005)***|
|Observations|207|211|215|
|LR chi2|92.63|117.29|137.03|
|Degrees of freedom|5|5|5|
|Pseudo R2|0.444|0.476|0.497|

14