rafmacalaba/datause-encoder-data huggingface dataset:
79

# Table **6**

Percenatge Deviatione in National Sducetlenel. Invavotuota Adjusting ter the tffecta ot Iceae. Teacher Selaries. and Demographic Composition: 1960-1930

|**Region**|Pr<br>Numberof<br>CountryfYear<br>Observed|siary Se<br> <br>Zarel<br>Rate<br>Terele|bol**S**<br>Lent<br>s<br> **Hale**|**ystem**<br>srpenditures<br>**per**Scbool<br>Ag*d Child|**Second**<br> <br>**Numberf **<br>o<br>**Country/Tear**<br>Observed|**ary Sch**<br>**morel**<br>Rate<br>**emnal-**|**ool S**<br>**Lment**<br>s<br> **Hale**|**yatem**<br>**Izpenditures***<br>perSchool<br>**Aged**Child|
|---|---|---|---|---|---|---|---|---|
|lurop. Ocea3nic<br>and North America|<br>|+6<br>+|+<br>6|-|21<br>1+|+3|+7<br>7|<br>-t<br>--|
|LatinAmerica|43|-10|-15|-5|35|-30|-39|-16|
|last Aila|21|+22|+12|+9|16|+27|+19|+2|
|Sauthand|24|-49|-24|-21|16|-49|-7|-4|
|Atrics|62|+26|+16|+15|49|+43|+34|+17|

aDependent **variables are** t1) the logerithm of jhe number of children *nrolled in primary schools divided by tho number ot ehildren **age** 6 to 11, and (2) the logarithm of the secondary enrollSent divided by the number of ehildren **age** 12 to 17; and the local publie ezponditures on thb school level divided by the cbildren in the respective benetiting **age** clasa **erpreased** in real 1970 local currency and converted to 1970 U.S. dollare on the basis of 'M *achange rates prevailing on average for 1960 to 1971. See source for more detail.

Source: T. P. Sehults (1967: Table 13).
16-D Figure 6.c <!-- Start of picture text --> Devaluation and Parallel Premium<br>Turkey: 1970-89<br>200 60<br>150 50<br>e%40<br>100<br>30<br>50<br>20<br>0 ~~~~~~~~~~10<br>-0 1970 1972 1974 1976 1978 1980 1982 198i4 1,986 1988 0<br><!-- End of picture text -->
# **Table A18:  Cingranelli Richards Human Rights Database & Political Terror Scale (HUM)**

**Data Provider** University of Binghamton Cingranelli-Richards Human rights database (CIRI) and  University of North Carolina Political Terror Scale (PTS) **Description** United States based universities **Website** www.humanrightsdata.com **Data Source** Cingranelli-Richards Human Rights Dataset (CIRI) and the Political Terror Scale (PTS) **Type** Expert assessment **Respondents** Coding by Cingranelli-Richards and Gibney teams **Frequency** Annually since 1980 **Coverage** Global sample of countries **Public Access** Yes **Description** The Cingranelli-Richards dataset is a numerical coding on a 2 or 3 point scale of data on 13 human

The Cingranelli-Richards dataset is a numerical coding on a 2 or 3 point scale of data on 13 human rights, as reported in Amnesty International Human Rights Reports and the U.S. Department of State Country Reports on Human Rights Practices. It is produced by Professor David Cingranelli at the University of Binghamton, U.S.A. and Professor David Richards at the University of Memphis, U.S.A. and is available at www.humanrightsdata.com. The Political Terror Scale is a numerical coding on a 5-point scale of state-sponsored domestic political terror through imprisonments, torture, disappearances and violations of the rule of law. It is produced by Prof. Marc Gibney at the University of North Carolina and is available electronically at: http://www.unca.edu/politicalscience/images/Colloquium/faculty-staff/gibney.html. Note that the two sources are averaged and treated as a single source in Political Stability and Absence of Violence since they are based on the same underlying publications.

|**Voice and Accountability**|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|
|Restrictions on domestic and foreign travel (CIRI)|X|X|X|X|X|X|X|X|X|
|Freedom of political participation (CIRI)|X|X|X|X|X|X|X|X|X|
|Imprisonments because of ethnicity, race, or<br>political, religious beliefs? (CIRI)|X|X|X|X|X|X|X|X|X|
|Government censorship (CIRI)|X|X|X|X|X|X|X|X|X|
|**Political Stability and Absence of Violence**||||||||||
|Frequency of political killings (CIRI)|X|X|X|X|X|X|X|X|X|
|Frequency of disappearances (CIRI)|X|X|X|X|X|X|X|X|X|
|Frequency of tortures (CIRI)|X|X|X|X|X|X|X|X|X|
|Political terror scale (PTS)|X|X|X|X|X|X|X|X|X|
|**Government Effectiveness**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||||
|Independence of judiciary (CIRI)|X|X|X|X|X|X|X|X|X|
|**Control of Corruption**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Country coverage (CIRI)**|**192**|**192**|**192**|**192**|**159**|**159**|**159**|**159**|**159**|
|**Country coverage (PTS)**|**176**|**176**|**176**|**178**|**178**|**178**|**178**|**177**|**174**|
|**Year of Publication**|**2007**|**2007**|**2006**|**2005**|**2004**|**2003**|**2001**|**1999**|**1997**|

56
- 16 -

# V. Subjects in the Curricula. 1980s

We begin our survey of the results by examining the propensity of less-developed countries to offer instruction in certain subject areas. The first series of four tables examines the proportion of LDCs that provide instruction in specific subjects of the primary curriculum in the 1980s. Table 1 reports that in the main subject areas of primary education -- language, math, science and social science -- there is relatively little variation among regions. Almost all national educational systems offer classes in these core subjects of the curriculum. Tnese findings -- at least as they relate to math and science -- substantialy confirm the conclusions of a recent global study of math and science instruction by UNESCO (1986). There are a few interesting exceptions, however, mainly involving microstates in Oceania: for example, Vanuatu offers no instruction in science; Kiribati and Tonga offer no instruction in the social sciences. Table 1 also reports that almost all countries provide some instruction in aesthetic education and physical education at the primary level. Countries that do not offer instruction in these areas include Cameroon, Ecuador, Thailand, Grenada and Qatar (for aesthetic education) and Tunisia, Trinidad and Tobago, Suriname, Thailand and St. Kitts (for physical education).

Within core subject categories, there are several interesting regional differences as to whether specific subjects are offered. For example, we find that countries in Sub-Saharan Africa (SSA) are more likely (than countries in other regions) to teach an official language that is not a mother tongue of a majority of the population. Countries in this region also offer more instruction in local languages than countries in other regions. Moreover, all countries in the EMENA region ( Southern Europe, Middle East, North Africa) offer instruction in an official language; they are also more likely to offer instruction in foreign languages than other regions.

For subjects falling under the category of "social science," we note the following regional differences: (1) Countries in the LAC region (Latin America and the Carribean) offer more instruction in social studies and less in history and geography than other
Gender offers additional information that suggests more comparative advantage considerations than barriers to entry into formality. For women, there is an especially dynamic corridor between self-employment and OLF: The propensity of women to move from OLF to self-employment is 2.3 (Brazil), 3.3 (Mexico) and 8 (Argentina) times higher than for males, and the reverse flows are higher than to any sector of work. In fact, the rapid transitions between these two sectors largely explain the higher mobility indexes for women than for men (See tables 3a-3c). As with older workers, this pattern may reflect especially high barriers, perhaps arising from discrimination, to women entering formal salaried work. However, in an alternative view, with lineage to Becker’s (1991) work stressing structural determinants of employment patterns, Cunningham (1996) argues that Mexican women’s patterns of participation and particularly their gravitation toward self-employment are driven by their need to balance their other responsibilities in the household: child raising requires a greater job flexibility than the salaried sectors offer. Overall, women do show a lower propensity than men to transit to self-employment from unemployment suggesting that this is not a sector of last resort after search.

But a stronger test should be that women without family responsibilities should show patterns closer to those of men. Table 5 extracts a cohort of single women for the two surveys with a marital status variable, Argentina and Mexico. The intensity matrices of single women are now very similar to those of men and this similarity holds up when disaggregated by duration and propensity. Further, most of the difference in OLF duration,<sup>11</sup> the propensities to transition into OLF from every sector, and transitions from OLF into self-employment are explained by marital status.<sup>12</sup> In sum, comparative advantage considerations, in this case generated by the woman’s need to balance child

> 11 In fact Argentine single women now spend _less_ time OLF than men do. Similarly, Mexican single women spend 2.8 years in OLF instead of 3.9 for their gender overall, far closer to the mean spell of men, 2.21.

> 12 In other dimensions, single women appear to have largely standard male labor market patterns.  In both countries, they appear to enter from OLF into search and directly into formal salaried employment at the same rates as men and from unemployment they enter with propensities as high or higher than men.

18
107. Few public institutions in Tanzania offer training for HEs. IST training is part of the core mandate of VETA, but the latter‟s own corporate plan has little to say about HE training. VETA also does not distinguish between training for wage-earners and for entrepreneurs, but its funding base (payroll taxes) favors wage-earners. Its trainees are mostly wage earners from both the informal and formal sectors, including the government. Moreover, a cursory review of information from VETA shows that demand for their programs far exceed their existing training capacity.

108. Among the limited number of training providers in Tanzania, nongovernment training institutions from the private, FBO, and NGO sectors produced about three-fourths of the total vocational graduates in 2007, compared to only one-fourth trained by public institutions funded by the central government, local governments, and VETA (Figure 5.2). Other training providers are business services and credit providers, including SIDO and SIDO-VETA, and other business development agencies such as the Tanzania Gatsby Trust (TGT).

109. _There has been no rigorous evaluation of these programs to show their impact on the prospects for employment and income generation of trainees._ Apprentices and trainees of government programs are expected to contribute to production as soon as possible, often after one to six months. However, no records are kept on trainee progress. Most trainees do not receive certification upon completion of training, except those trained in VETA or other similarly bigger and more formal training institutions (see Box 5.3 for an example of a successful women‟s training supported by VETA). Box 5.4 describes a _g_ overnment program to build skills in the rural areas, but one that suffers from weaknesses in quality and results monitoring.<sup>27</sup> Another training program showing promise for HEs include the CSSC program, which has heightened awareness of the importance of training for jobs in the informal sector based on local market assessments.

**Figure 5.2:  Total Vocational Graduates, by Institutional Ownership**

<!-- Start of picture text --> Tanzania- Total Vocational Training Graduates by Ownership<br>35000<br>30000<br>25000<br>2005<br>2006<br>20000 2007<br>15000<br>10000<br>5000<br>0<br>Central Gov. FBO Local Gov. NGO PRIVATE VETA<br>Number of Graduates<br><!-- End of picture text --> _Source: Calculations based on Johanson and Wangu, 2009._ 27 The UDEC program, one of the training programs on entrepreneurship in the University of Dar Business School, has addressed the problem by customizing the training program to fit the business needs of specific entrepreneurs. This program is not targeted towards HEs, and needs to be rigorously evaluated.

47
2023/24) to illustrate the impact of partially and gradually introducing market mechanisms in the formulation of monetary and exchange rate policy (see Box 11).

|**Box**|**11. Effects of a liberalization scenario on the NBE financial program for 2019/20 – 2023/24**|
|---|---|
|A tent<br>liberal<br>the fis<br>and s<br>sector|ative financial program has been prepared to incorporate the effects of a partial and gradual<br>ization of Ethiopia’s financial system during 2019/20 – 2023/24. The reform scenario focuses on<br>cal, monetary and external sector measures. Along with public finance, the SOEs, public banks<br>ocial security agencies will be reformed. The scenario does not include other structural and<br>al reforms.|
|Main<br>_Real a_<br><br><br><br>_Fiscal_|assumptions in the baseline scenario:<br>_nd price developments_<br>Real GDP growth projected at 7 percent in line with recent performance and the projections of<br>the IMF and World Bank<br>Imports of goods and services are projected to grow at around 6.5 percent (on average in US$ terms) to reflect the ongoing decrease in the ratio of import to domestic credit (from 85 percent<br>in 2015/16 to 63 percent in 2017/18)<br>Inflation rate projected at 8 percent over the medium term in line with current NBE policy<br>_policy_<br>|
|<br><br><br><br><br><br>|Tax revenue of the central government projected to increase slightly over the medium term<br>from 7.8 percent of GDP in 2017/18 to 8.2 percent of GDP in 2023/24<br>Direct budget support and relief projected based on data from IMF and World Bank<br>Privatization proceeds assume to reduce the outstanding debt of SOEs<br>Expenditure of the central government projected constant as a share of GDP to 12 percent of<br>GDP as in the 2018/19 budget<br>The fiscal deficit is contained below 3.5 percent of GDP throughout the period<br>Net external borrowing of the government grows by 10 percent annually in line with IMF and<br>World Bank projections<br>Annual government net borrowing from NBE remains at 1.1 percent of GDP|
|_Mone_<br><br><br><br>|_tary and exchange policy_<br>The nominal exchange rate of birr per US$ depreciates at the annual rate of 6 percent,<br>consistent with current NBE policy<br>Net foreign assets are projected to increase marginally, consistent with the balance of<br>payments projections from the IMF and the World Bank. The NBE reserves accumulate by an<br>average of around U$300 million per year during the period<br>Domestic deposits and credits in commercial banks continue to grow on their current trends<br>(23 percent and 22 percent on average during the period, respectively)<br>Broad money grows at a slightly higher pace than nominal GDP based on an estimate of the<br>change in money velocity over time. Money velocity projected to decrease from 3 in 2017/18<br>to 2.1 in 2023/24|
||Commercial bank reserves at the NBE grow in line with deposits and current levels of reserves<br>and liquidityrequirements(5percent and 15percent,respectively)|

36
- 13 -

loans, and so on. Weekly meetings of the group are used to track the effects of the loans, provide suggestions and support and implicitly Induce peer pressure to comply with the terms of the loans. The second Institutional Ingredient Is that lending takes place at market or commerclal Interest rates. Finally, the only requirement imposed by the bank for the use of the loan Is that It be Income producing (housing loans being occasional exceptions). Similar programs featuring these Institutional Innovations are being developed elsewhere In Malaysia, Indonesia, Rwanda, Kenya, Sri Lanka, Pakistan, Egypt and even In the United States (a neighborhood South Shore Bank In Chicago and the new good faith fund In rural Arkansas). This rapid expansion of the Grameen Bank In Bangladesh and Grameen-type banks elsewhere will test If the success of these institutlonal Innovations Is strongly linked with smaliscale operations or If It can witi;,tand largescale implementation with Its usually problem ridden and sometimes fatal (in developing countries) bureaucratic apparatus.<sup>8</sup> Other credit programs with related Institutional Innovations which have enjoyed some success have been the voluntary joint liability and mandatory joint liability programs In Zimbabwe. Under the former, loans are made and accounted for on an individual basis, but the farmer has to show membership In an active cooperative or farming group. Emphasis Is placed on mutual ald and collective responsibility. In the event of a member's default, the loan conditions do not require that the other members cover the loan. Rather It stipulates that the whole group loses eligibility for future loans. Under the alternate mandatory joint liability program, responsibility for loan administration and repayment rests with the group as a whole. The group requests loans 8Hossain (1986] provides an extensive analysis of the Grameen Bank.
- 49 -

# Table 4s Growth, Equity and Poverty Alleviation

|Poverty|Poverty|Sector|Poverty|alleviation due|to;|Elasticity|
|---|---|---|---|---|---|---|
|measure|line||higher<br>mean*<br>_(Z)_|lower<br>inequality**<br>(2)|residual<br>(Z)|w.r.t. mean<br>consumption<br>1984<br>(la)|
|Headcount|11000|Urban|78.25|18.29|3.46|-3.27|
|Index||Rural|82.97|7.72|9.31|-2.00|
|(a=0)||Total|86.12|6.43|7.44|-2.05|
||15000|Urban|92.95|3.64|3.41|-2.19|
|||Rural|103.85|-10.65|6.80|-1.18|
|||Total|100.93|-5.73|4.80|-1.27|
|Poverty|11000|Urban|65.81|38.43|-4.24|-3.51|
|gap||Rural|69.82|30.23|-0.05|-2.82|
|(a= 1)||Total|72.82|26.93|0.25|-2.88|
||15000|Urban|78.83|20.38|0.79|-2.84|
|||Rural|80.65|15.40|3.95|-1.98|
|||Total|83.02|13.85|3.13|-2.06|
|Preferred|11000|Urban|56.07|53.63|-9.71|-3.83|
|measure||Rural|64.11|43.14|-7.25|-3.35|
|(a = 2)||Total|66.81|39.93|-6.74|-3.38|
||15000|Urban|69.65|33.17|-2.82|-3.26|
|||Rural|72.35|27.98|-0.33|-2.52|
|||Total|75.19|25.23|-0.42|-2.58|

* (Pa<sup>7</sup> * - Pj<sup>4</sup> )/(PJ 7 - PQ<sup>4</sup> ) expressed as a percentage. ** (PS<sup>7</sup> ** _- Pa_<sup>_4_</sup> _)/(Pa 7_ - P1<sup>4</sup> ) expressed as a percentage.
# **Robustness checks**

Next, we implement robustness checks that were outlined in the pre-analysis plan to assess the robustness of our findings. For each of the three outcomes, we first show the baseline model with covariate adjustments in the first column, followed by

- Balance: A model estimated only on the subset of countries where we cannot reject balance for any covariate at the .05 level

- Attrition: A model estimated only on the subset of countries where treatment status does not predict completing the survey

- Attrition (cond): A model estimated only on the subset of countries where, jointly with covariates, treatment status does not predict completing the survey

- Bottom manip: A model estimated only on the subset of countries where we can reject the hypothesis that the bottom-down treatment had no effect on the corresponding manipulation check

- Top manip: A model estimated only on the subset of countries where we can reject the hypothesis that the top-down treatment had no effect on the corresponding manipulation check

Table 6: Main Model and Robustness Checks: Tax Index (OLS)

||**Main**|**Balance**|**Attrition**|**Attrition**<br>**(cond)**|**Bottom**<br>**manip**|**Top**<br>**manip**|
|---|---|---|---|---|---|---|
|**Top**|0.231***|0.235***|0.273***|0.273***|0.265***|0.223***|
|**down/Control**|(0.017)|(0.018)|(0.044)|(0.044)|(0.021)|(0.025)|
|**Bottom**|0.164***|0.175***|0.199***|0.199***|0.185***|0.169***|
|**up/Control**|(0.018)|(0.019)|(0.044)|(0.044)|(0.022)|(0.026)|
|**N**|65426|57526|10827|10827|42074|31995|
|**p**|0.0|0.0|0.0|0.0|0.0|0.0|
|**Log-likelihood**|-132601.4|-116858.9|-22154.0|-22154.0|-85417.0|-65171.8|
|**AIC**|265230.7|233731.8|44322.1|44322.1|170848.0|130357.5|

Notes: Each column shows a different model predicting the tax index dependent variable on different subsamples of countries. In each case, countries are excluded if they do not meet certain quality criteria. In all cases the effect sizes increase.

Table 6 shows that, using the combined Tax Index outcome, estimated treatment effects are remarkably stable when restricting the universe of cases considered, and if anything are larger when discarding observations from countries where treatment assignment predicts attrition. Table 11 and Table 12 in the appendix show this is also true of the two constituent parts of the Tax Index measure: namely the Tax Morale outcome and the Tax Fine Attitude outcome.

# **Attrition and Lee trimming bounds**

Lee trimming bounds describe the worst case scenario for how attrition could affect the results and therefore provide bounds for the size of the effect.<sup>1</sup> The control group had statistically lower attrition than the treatment groups in most countries in this study (84% of countries). However,

> 1 Note that the differential attrition only appears after the treatment is administered.

14
# **Table 16: Access to deposit and loan services and financial inclusion agenda**

|(1)<br>FIeffectiveindex|(2)<br>FI index|
|---|---|
|GDP er caita (lo)<br>-0.268**|-0.251*|
|p p g<br>(0.119)|(0.143)|
|Poulation densit (lo)<br>-0.049|-0.067|
|p y g<br>(0.053)|(0.069)|
|Etd GDP l<br>0.124**|0.126**|
|xpece (og)<br>(0.054)|(0.058)|
|# dit t/1000 dlt (l ld)<br>-0.008|-0.085|
|epos accouns, aus og, agge<br>(0.097)|(0.127)|
|# loans/1000 adults (lo laed)<br>0.195**|0.133|
|, g, gg<br>(0.095)|(0.136)|
|Deosit insurance<br>-0.308*|-0.358*|
|p<br>(0.154)|(0.181)|
|Crdit rank<br>0.001|-0.001|
|e<br>(0.002)|(0.002)|
|Constant<br>1.370**|2.432***|
|<br>(0.537)|(0.652)|
|<br>0.439***|0.521***|
|σ<br>(0.064)|(0.077)|
|Number of observations<br>61|61|
|Log-likelihood<br>-44.33|-51.07|
|PseudoR<sup>2</sup><br>0.116|0.120|
|Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1||
|Results of Tobit estimation,dependent variable is between 0 and 1.||

42
|Interaction Rest*Rel.U Values|5.19e-07**||||
|---|---|---|---|---|
||(2.32e-07)||||
|Interaction Rest*R&D||1.494**|||
|||(0.591)|||
|Interaction Rest*Training|||1.311***||
||||(0.367)||
|Interaction Rest*RD&Training||||1.206***|
|||||(0.365)|
|PolicyRestrictiveness in ManufacturingFDI|-0.134|-0.531|-0.512|-0.208|
||(0.807)|(0.870)|(0.866)|(0.789)|
|Tariffs on Inputs(Effective)|-0.00852*|-0.00943*|-0.00948*|-0.00834*|
||(0.00490)|(0.00525)|(0.00530)|(0.00490)|
|% of Output byForeign Firms in the Sector|-0.000106|-9.80e-06|-2.29e-05|-0.000105|
||(0.000390)|(0.000398)|(0.000397)|(0.000389)|
|Output Tariffs(Effective)|-0.000222|0.000818|0.000777|-0.000253|
||(0.00366)|(0.00412)|(0.00412)|(0.00365)|
|Constant|0.494***|0.413***|0.410***|0.502***|
||(0.0669)|(0.0824)|(0.0811)|(0.0636)|
|Year Fixed Effects|Yes|Yes|Yes|Yes|
|Firm Fixed Effects|Yes|Yes|Yes|Yes|
|Standard Errors|Clustered<br>ISIC2/Year|Clustered<br>ISIC2/Year|Clustered<br>ISIC2/Year|Clustered<br>ISIC2/Year|
|Observations|40,083|33,385|33,385|40,208|
|R-squared|0.034|0.037|0.037|0.035|
|Number of id|25,317|19,647|19,647|25,425|
|Robust standard errors inparentheses,***p<|0.01,**p<0.0|5,*p<0.1|||
|***p<0.01,**p<0.05,*p<0.1|||||

26