rafmacalaba/datause-encoder-data huggingface dataset:
The SVS appear to have a decidedly greater latitude than either the STS or the SWS to determine the subjects and the course content of their curricula, though they have by no means complete freedom. For example, the SVS must follow the curricula establish-d at the State level (by the State Education Commission for about 80 percent of thG general education material that they teach; the remaining 20 percent is at local discretion). In addition, for some of their vocational-technical courses, they must follow curricula established at the relevant central technical ministry level (for example, in Accounting). But where they do have curricular discretion, the SVS have evolved a rather impressive range of mechanisms to seek labjor market information as the basis for determining curri:ula, as well a% enrollment patterns.

These include formal surveys and both formal and informal meetings. For example, in Wuhan, Hubei province, an SVS has run a conference to advertise the school's program to factoriss and other organizations that might hire its graduates. In some cases, this flow of information is part of a larger and formalized relationship with a given employer. The Shenyang Municipal Vocational Education center has such a relationship with a computer research institute, which serves as an umbrella for a range of exchanges, including placement of graduates and industry input into the curricula in the form of technical advice and equipment. In Shashi, Hubei province, the municipality's Vocational Education Center (an SVS) is run in close cooperation with a select number of enterprises. The enterprises assist with teaching staff, equipment, and money, and it was estimated that about 10 percent of the school's total resources come to it in this way. In return, 'graduates will be assigned to enterprises who have supported the school.' Input of this type from industry takes several forms, and is present in both SVS and SWS, the former through school and industry initiative, the latter by virtue of enterprise ownership. Equipmena and other resources are donated; in the SWS, these are in addition to regular budgets. The Anshan Iron and Steel Company requires each of 23 affiliated enterprises to donate one piece of equipment to the SWS each year. Enterprises provide practice sites routinely through the SWS curriculum, and for 12-15 week summer assignments for the SVS. The SVS hire part-time teachers from employers, and they are a core featura of the staffing of SWS.

In the STS visited, linkages of this type are rare, with the formal planning and job assignment process being more .mportant. The reasons for this are not totally clear, but may be the result of strong excess demand for STS graduates, which makes it unnecessary for STS to make any particular effort to "sell" their graduates, or to consider changes in their curricula to meet employers' wishes.

Many schools regularly provide training and technical services to employers under contract. These relationships provide important flexibility in meeting training demands, significant income to schools, and a firm basis for school-employer cooperation. Training

- 37 -
the 4-5 and 6-7 year-old groups are as large as the 0-1 and 2-3 year-old groups (in absolute terms and relative to men). This indicates that tremendous churn at the entry margin is unlikely to be occurring, providing evidence against pre-trends that are influencing the timing of elections. Based upon Figure 4’s deviations for never-adopting states, we will specifically test below that our results are robust to this group’s inclusion or exclusion.

As a second approach, we look for pre-trends in women’s involvement in manufacturing on dimensions other than entrepreneurship. We can do this with respect to women’s raw employment in manufacturing and their share of manufacturing employment in three ways: the unorganized sector through the 1989 and 1994 NSSO surveys, the organized sector through the 1989 and 1994 ASI surveys, and the household surveys from 1987 and 1993. Appendix Table 2 reports an analysis similar to that in Figures 2 and 3.

We examine the degree to which the log growth in women’s employment by state or state-industry in the years preceding our sample period correlates with future election timing. These estimations find no evidence that changes in women’s employments in manufacturing in the pre-period predict the ordering of implementations across states. These tests are also admittedly imperfect, as we do not later observe a strong response on this margin, and we unfortunately cannot measure the entry margin similarly. Nonetheless, the combination of these tests with those of the 1994 establishment age distributions do provide substantial support to our identification assumption that the election timings are exogenous. After we introduce our estimation strategy, we further examine dynamic estimations that also show the responses in women’s entrepreneurship follow the reforms in a timing that is consistent with the reservations causing the increased entry rates.

# **5.  Empirical Analysis**

This section provides our core empirical results. We begin with a broad analysis of the impact for women in manufacturing that includes wage employment and establishment ownership. After observing the concentrated impact on entrepreneurship, we conduct a focused analysis on the industry differences and household-basis of the entrepreneurship finding. We close with an analysis of district pairs along state borders. The next section will test several hypotheses about channels through which this effect operates.

17
# **_Importance of better planning and coordination on data collections_**

It is important to entertain the possibility that the poverty data gaps discussed in this note may not result from the lack of household survey data _per se_ , but result from the lack of planning or coordination in survey implementation. According to Demombynes and Sandefur (2014), who utilized the International Household Survey Network (IHSN) database, the SAR region on average has two household surveys every year. However, according to the WDI data used to construct the statistics in this note, each country in the SAR region on average only has slightly more than one poverty estimate for every five years. Similarly, according to Demombynes and Sandefur (2014), the SSA region has 1.5 surveys every year on average but the database of poverty estimates used in this note indicates that countries in Sub-Saharan Africa on average have roughly around one poverty estimate every five years.

Our own examination of the IHSN database and the World Bank’s Microdata Library in conjunction with the WDI database reinforces that there is a disconnect between available surveys and poverty data. Figure 6 shows that low income countries on average had 16.6 household surveys between 2002 to 2011, slightly lower than the average number of surveys for lower-middle income and upper middle income countries (with about 19 surveys between 2002-11). However, low income countries had far fewer poverty estimates available, as demonstrated by their very high survey to poverty data point ratio (10.8) compared with lower middle income and upper middle income countries (7.5 and 5.6 per year).

These differences stem from the fact that the IHSN includes all surveys conducted in countries, while we focus on surveys that include consumption data used for poverty estimation. Thus, while many countries may have an abundance of surveys, most of the surveys are not suited for or tailored to poverty monitoring. While this does not mean that every survey carried out should include a consumption module, carrying out surveys in a more coordinated fashion would be effective in reducing key data gaps.

**Figure 6: Availability of Household Surveys vis-à-vis Poverty Estimates**

**(a) By  Region (between 2002-2011)**

<!-- Start of picture text --> Poverty Total Number Average Ratio<br>Data Points of Surveys<br>Sub-Saharan Africa 1.7 16.8 10.5<br>South Asia 2.4 28.8 12.7<br>Middle East & North Africa 1.9 11.5 7.2<br>Latin America & Caribbean 5.2 19.5 3.9<br>Europe & Central Asia 7.7 21.7 3.0<br>East Asia & Pacific 2.1 16.0 6.4<br>0 5 10 15 20 25 30<br><!-- End of picture text --> 19
associated with these two types of reforms, although their impacts on trade costs themselves are largely the same.

Fourth, the GTAP model deployed in this analysis covers the entire world and is not limited to the APEC economies. The model we use allows for observation of the impacts of bilateral trade flows between APEC member and non-member economies, placing a focus on the issue of trade diversion or creation under differing scenarios.

The paper proceeds as follows: in Section 2 we survey the empirical literature on relationships between transparency and trade costs. Section 3 discusses our methodological approach and model, and Section 4 provides results and implications from the analysis.

# **2.  Trade Facilitation, Transparency and Trade Costs**

Trade facilitation reform to lower transactions costs is one of the major policy goals in the Asia Pacific region and among APEC member economies. Traditionally in APEC, trade liberalization involves the reduction of traditional trade barriers, such as tariffs and import quotas. Trade facilitation generally covers measures to reduce trade costs both at the border and internal to member economies. The Bogor Declaration of APEC leaders in 1994 include this dichotomy. With the reduction in traditional trade barriers over the past decade, however, attention has shifted to trade facilitation measures as a major step toward lower trade costs and more open trade in the region.

Trade facilitation measures most often refer to administrative or procedural steps in the trading environment that affect costs. This includes streamlining customs clearance procedures, harmonizing product standards and conformance certifications, or deregulating licensing requirements in the transport sector, for example. The improvement of transparency in trade policies, however, is also a factor to consider when addressing reform steps toward lowering trade transactions costs.

Among previous estimates of the potential gains to reform in trade facilitation, a research report by APEC Economic Committee (APEC (1997)), drawing on the survey 3
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1.0<br>0.5<br>5 1.; Madhya Pradesh Maharashtra Orissa Punjab<br>1.0<br><!-- End of picture text -->

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# **Table 1 Educational Attainment of the Rural Registered Population Evidence from the 2003 Rural Household Survey**

Registered Population by Agricultural/Non-Agricultural/Migrant Distinction

||Act|ivityand Locati<br>Local, Non-|on||
|---|---|---|---|---|
||Agricultural|Agricultural|Migrant|Total|
|Age|41.4|38.58|28.62|38.7|
|Male Share|0.46|0.63|0.63|0.52|
|Years of Education|7.35|8.98|8.87|7.87|
|Educational Attainment|||||
|Illiterate|0.09|0.03|0.02|0.07|
|Primary|0.37|0.19|0.16|0.30|
|Middle School|0.45|0.55|0.67|0.51|
|High School|0.09|0.21|0.14|0.12|
|College|0.00|0.02|0.01|0.00|
|Observations|130533|30241|30936|191710|

Source: National Bureau of Statistics (NBS) 2003 Rural Household Survey (Full National Sample), from Park et al (2006).

38
than do indirect effects. In Scenario 3, indirect effect shares are smaller for the upper quintile than for the poorest quintile.

**Table 20. Jordan: Direct and indirect impacts on well-being of removing electricity subsidies**

|Quintiles,consumptionper capita|S|cenario 2.I,fla|t||Scenario|3|
|---|---|---|---|---|---|---|
||total<br>direct<br>(JD)|total<br>indirect<br>(JD)|share of<br>indirect<br>intotal|total<br>direct<br>(JD)|total<br>indirect<br>(JD)|share of<br>indirect in<br>total|
|Quintile 1|49|21|30%|10|4|26%|
|Quintile 2|59|31|34%|13|5|29%|
|Quintile 3|66|41|38%|18|7|28%|
|Quintile 4|78|54|41%|24|9|28%|
|Quintile 5|109|102|48%|58|17|23%|
|Total|72|50|41%|24|8|26%|
|Source:Authors’calculations based on|extrapolatedH|EIS2010 dat|a and the Jor|dan 2010|Input/Outpu|t table.|

# **5. The Political Economy of Reforms**

The simulations indicate that the current subsidy system provides valuable assistance to the poor, but at the same time it is pro-rich and inefficient. Eliminating subsidies and compensating the poor and vulnerable with a direct cash transfer would be a more effective form of social protection. Subsidy reform, however, is a politically sensitive issue. A major reform of the bread subsidy in 1996 involving the complete elimination of the price support and its replacement by a cash transfer was rapidly overturned following widespread social unrest (and what came to be known as ‘food riots’). Thus, even as the government is burdened by high subsidy costs, reform efforts are hampered by political economy considerations.

**<u>Figure 5: Opposition to reform consumption subsidy on any product</u>**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Poor Lower Middle Upper middle and rich<br>Egypt Lebanon Jordan Tunisia<br>Source: Silva et al. (2013) calculations using the MENA SPEAKS survey<br><!-- End of picture text --> The opposition to subsidy reform appears particularly strong in Jordan, especially when compared with neighboring countries (figure 5). Data from the MENA SPEAKS (Social Protection Evaluations of Attitudes, Knowledge and Support) Survey, indicates that about 56 percent of Jordanians were opposed to 18
# **Appendix B: Variable Declarations**

|age:<br>age2:|age<br>squaredage|
|---|---|
|emplyd:|0-1dummyforemployed|
|escola:|0-1variable,1:childattendsschool|
|expir:<br>expir<sup>2</sup>:|experience(age-school-6)<br>experience squared|
|famsize:|familysize|
|faM2:|famsizesquared|
|female:|0-1genderdummyforwomen|
|femHH:|0-1dummy forfemalehouseholdhead|
|m5yNESE:|0-1dummyformigrantsfromtheSEinto NEoverthelast5years|
|m5ySENE:|0-1dummyformigrantsfromtheNEinto SEoverthe last5years|
|moverNS:<br>moverSN:|linearpredictedwage/incomeformigrantsfromNEtoSE<br>linearpredicted wage/incomeformigrantsfromSEtoNE|
|NE:|Northeast|
|PO:<br>1997|0-1dummyforhouseholdincomebelowpovertylineofR$65in|

# prices

- 0-1 dummy for primaryl primaryl education (4years of schooling)of schooling)schooling)

- priml: 0-1 dummy for primaryl primaryl education (4years of schooling)of schooling)schooling) primlH: 0-1 dummy for household head with primaryl education (4years of schooling)

- 0-1 dummy for primary2 education (8 years of schooling)

# prim2:

- 0-1 dummy for household head with primary2 education (8 years

- prim2H: of

# schooling)

scholage:

- 0-1 variable, scholage if 1 if:

- primaryl-aged pupile _(+/-1_ one year, i.e. 7 to 10 years old) attending primaryl

- primary2-aged pupile _(+/-1_ one year, i.e. 10 to 14 years old)

- attending primary2

- secondary-aged pupile _(+/-1_ one year, i.e. 14 to 18 years old) attending

- school:

- years of completed schooling

- SE: Southeast

- 0-1 dummy for household head with for household head with household head with with secondary education

secH: 0-1 dummy for household head with for household head with household head with with secondary education secu: 0-1 dummy for secondary education (11 years of schooling) stayerNN: linear predicted wage/income for non-migrants in NE stayerSS: linear predicted wage/income for non-migrants in SE uni: 0-1 dummy for higher education (more than 11 years of schooling) uniH: 0-1 dummy for household head with higher education (more than 14 years of schooling 38
Table 9: Impact of therapy on human capital accumulation at 12 and 24 months.

||(1)|(2)|(3)<br>**Primary **|(4)<br> **outcomes**|(5)|(6)|(7)<br>**Sec**|(8)<br>**ondary outco**|(9)<br>**mes**|
|---|---|---|---|---|---|---|---|---|---|
||Enrolled<br>in school|Compet-<br>encies|Self-<br>efcacy|Pregnant|Married|Risky sex|Time<br>prefs.|Paid work|Desired<br>fert.|
|_Panel A: Midline (12 month _|_follow-up)_|||||||||
|IPT-G|0.075|-|0.468|-0.018|-0.025|-0.009|0.056|-0.007|-0.275|
||(0.032)||(0.334)|(0.013)|(0.012)|(0.023)|(0.030)|(0.018)|(0.072)|
|Control mean|0.328|-|31.649|0.096|0.084|0.273|0.429|0.751|3.786|
|Control SD|0.470||5.049|0.296|0.277|0.446|0.495|0.257|1.289|
|Observations|993||987|973|982|993|998|996|988|
|H0: IPT-G=0 p-values|0.021|-|0.166|0.194|0.042|0.715|0.063|0.711|0.000|
|FDR adj. q-values|0.104||0.242|0.242|0.106|0.715|0.095|0.711|0.001|
|_Panel B: Endline (24 month _|_follow-up)_|||||||||
|IPT-G|0.060|-0.060|-0.630|0.012|0.011|-0.032|0.055|0.024|0.014|
||(0.030)|(0.091)|(0.815)|(0.024)|(0.021)|(0.036)|(0.033)|(0.012)|(0.080)|
|Control mean|0.257|1.444|29.743|0.213|0.170|0.381|0.395|0.793|3.813|
|Control SD|0.437|1.188|9.074|0.410|0.376|0.486|0.489|0.251|1.385|
|Observations|884|774|884|877|885|870|881|879|866|
|H0: IPT-G=0 p-values|0.048|0.515|0.442|0.606|0.584|0.375|0.095|0.055|0.859|
|FDR adj. q-values|0.289|0.606|0.606|0.606|0.606|0.606|0.143|0.143|0.859|

Notes: This table shows ITT estimates of the impact of IPT-G on the primary and secondary human capital outcomes at midline and endline, comparing the control group to the IPT-G only group; All regression models include randomization block fixed effects centered and fully interacted with treatment; Standard errors clustered at the club level in parentheses; Estimates at endline are weighted to account for the probability of selection for intensive tracking; Columns 1-6 show results for the primary outcomes: whether they are enrolled in school if school is in session or were enrolled in school when school was last in session (or if they have completed secondary schooling), the score on the Competencies test, the score on the Schwarzer and Jerusalem (1995) self-efficacy scale, whether they have been pregnant since baseline, whether they have married since baseline, and risky sex (whether they used a condom at their last intercourse); Columns 7-9 show results for the secondary outcomes: Time preferences (prefers 110,000 Uganda shillings in one month instead of 90,000 today), the self-reported probability of engaging in paid work at age 25, and desired fertility (desired number of children at time of survey); The rows marked as _H0:..._ show the p-value for a t-test of the indicated null hypothesis on the regression coefficients and the corresponding q-values adjusting for the false discovery rate within primary and secondary outcomes and round, as per Benjamini and Hochberg (1995); Sample size differences across outcomes are due to shorter tracking questionnaires being administered to difficult-to-reach subsets of CRs.
business sector, as new private firms rely less on informal and family sources of finance and more on retained earnings, to a similar extent observed in privatized firms in TEs. The other major trend over time has been the large decline in state financing of SOEs, which we take as a sign of harder budget constraints.

The analysis of restructuring and innovation activity shows that the scale of these processes is as high or higher in the TEs as in the DMEs, in line with convergence patterns. We also find, somewhat unexpectedly, an inverse U-shape pattern of restructuring in the transition countries between 1999 and 2005, with the peak of restructuring occurring in 2002 and not in 1999 or 2005. We relate this finding either to the standard pattern of firm development (most sampled firms are new private firms, established after the collapse of socialism) or to the largely common timing of transformational recession and the resumption of growth in TEs. With respect to ownership, our analysis suggests that state-owned firms are the least active in restructuring and innovation activities, followed by privatized enterprises and new private firms. We did not find any significant effect of market structure on restructuring. This may be related to the fact that the market structure, an exogenous factor at the start of transition, has already adjusted to the market environment in most of the TEs, suggesting convergence. Our results, however, suggest that highly elastic demand is associated with less restructuring of firms. Finally, the BEEPS data provide evidence that restructuring is positively associated with external financing. The impact of access to external financing is, interestingly, quantitatively as qualitatively very similar in both the developed market economies of the pre-2001 EU members and in the TEs.

44
diarrhea lead to poor absorption of nutrients particularly for children at early ages, which can in turn cause growth faltering and reduced socio-emotional development in the longer term (Victora et al., 2008).

# _Program Description_

The hand washing campaign programs in Senegal started with a nationally aired television and radio spots, education-entertainment events in market places, and smallgroup discussions conducted with women’s associations and in the waiting rooms of health centers. This first phase of the hand washing promotion by the government was nationally implemented and all households were exposed to this campaign. In the second phase, the promotion campaigns were delivered in a 10-month communications campaign rolled out in 7 of Senegal’s 11 regions<sup>4</sup> . The second phase had three large components: Local Communications Campaigns (LCC), Direct Communication Contact (DCC)—to stimulate hand washing behavior using an _entertainment-education_ approach—and an Inter-Personal Communication (IPC) campaign—that was conducted at the household level and focused on influencing other hand washing determinants, such as beliefs or skills for which mass media was less suited.

The target population for the second phase included mothers and other caregivers aged 14 to 49, and children up to 13 years of age living in urban and rural areas. In order to measure the health and development impacts of the second phase of the campaigns, an Impact Evaluation was designed with an experimental approach. For simplicity, the treatment consists of all information campaigns delivered (exposed) to communities: mainly local mass media campaign, DCC and IPC activities<sup>5</sup> . The control group received only the less intense and limited mass media campaigns.

> 4 Administratively, Senegal has 11 regions, (7 covered by WSP campaigns), with 34 departments and 430 localities. In rural areas such localities can be subdivided in villages or communities. The National Statistic Office divides the country for census purposes in census districts named _District de Recensement_ (DR) which were also considered in the sampling strategy of the IE surveys to gain external validity in rural areas. The DRs were used as the unit of clustering the sample of the study.

> 5 Other programs outside of WSP—run by the Ministry of Health and UNICEF in 2011—also contained elements of handwashing promotion through community events and household visits, and they implemented these activities in regions where data was collected for this impact evaluation. These other programs do not have documentation on the specific localities (communes and rural communities) where they implemented their activities, but it is possible that they covered control areas to the same extent that WSP‐sponsored activities

6
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