rafmacalaba/datause-encoder-data huggingface dataset:
- 13 -

our measures combine information for the six years most commonly considered elementary education. When the primary cycle is longer than six years, we only consider the curriculum of the first six years; when it is shorter, we include information from the first year or two of the lower secondary cycle. Such decisions produce very little impact on our results. Third, some curricular timetables combine topics we code separately (e.g., mathematics and science). In such cases, we treat both subjects as taught, and treat the allocated periods as split equally between them. Other decisions would alter our results very little.

(3) Nature of the Data: First, our data consist of official declarations of subject matter to be taught in primary schools, generally produced by government education officials in the form of national timetables. We assume that official policies themselves reflect commitments widely understood to carry authoritative intent in a world in which education is predominandy a creature of the nation-state. Thus, official statements of curricular policy are of interest in their own right, but one cannot infer corresponding instructional practices. Curricular policies can be -- and many times are -- poorly related to actual educational practice in local schools. This "slippage" or "loose coupling" between the intended and implemented curriculum occurs, in various degrees, in developcd countries (Weic'-. 1976; Meyer and Rowan, 1983), and implementation is thought to be even weaker in developing ones with newer educational systems. Critics might argue that official policy statements represent more ideology than practice, and that official ideologies of educational content as reported to international agencies reflect worldwide standards much more than classroom reality does. This argument assumes, however, that local teachers and administrators are sampling from different (perhaps, local) content frames than national elites. Given the widespread growth of national networks of teacher training institutes, professional associations and the like, the validity of this assumption is questionable. In any case, it is interesting to see whether national educational leaders reflect world standards of public policy.

Second, the data describe emphasis on general curricular topics (e.g., science or history) and do not describe specific content of these topics. Country differences or temporal changes
which of these two extremes-serially uncorrelated prices or prices which follow a random walk-is nearer the truth.

2.2 Consumption smoothing with serial correlation Many of the major traded primary commodities exhibit significant annual serial correlation which<sup>dramatically affects the</sup> costs and the benefits, and the feasibility<sup>of price stabilization.</sup> Cuddington and Urzdia (1987) have attempted<sup>to identify the extent</sup> to which price changes of primary commodities<sup>persist, using annual</sup> data on 24 commodity prices for the period<sup>1900-1987, deflated by</sup> an index of manufacturing unit values. They regress the change in the log of the real commodity price on a constant<sup>plus error, e(t),</sup> which is in turn expressed at A(L)u(t), where<sup>u(t) is white noise.</sup> Their measure of persistence is then ai,<sup>where ai are the coefficients</sup> of A(L), and is a measure of the extent to which<sup>the price change will</sup> persist.

Table 4, reproduced from Cuddington and<sup>Urzua (1987), gives</sup> the persistence measures of three groups of<sup>commodities, each group</sup> ranked in increasing order of persistence. (It also gives the highest order significant lag for the more parsimonious<sup>lag specification).</sup> Thus if one looks at cocoa, 65 percent of a<sup>price change is expected to</sup> persist, and the remaining 35 percent can be<sup>accounted by short</sup> term fluctuations, with a maximum (statistically<sup>significant) lag of</sup> two years. In each group the average persistence is over<sup>_50_percent,</sup> and for many commodities prices seem to follow<sup>a random walk with</sup> persistence of 100 percent. It was impossible to reject the null hypothesis that all commodity price series followed<sup>a random walk</sup> 13
were unemployed and 5 percent left the labor force, relative to approximately 15.5 percent of other age groups becoming unemployed (Table 5-17).

**Nationality:** Conclusions based on reported nationality should be interpreted with caution due to the small sample size for national minorities and potential biases in data collection. Nonetheless, for Jordan, our method predicts the general pattern. As shown in Table 5-14, our model predicts higher rates of PFPS job loss for Palestinian and “other” workers, and lower rates of job loss for Syrian workers. This pattern is confirmed, indeed magnified, in the CMMHH data.  However, the survey-measured projections do not project this general pattern. Once again, the model-based prediction gives a closer approximation to the demographic pattern of job losses observed in the CMMHH, lending support to that approach of assessing jobs at risk.<sup>51</sup>

# **5.5.2 Georgia**

**Gender:** In Georgia, our model similarly projects impacts disfavoring men more than women (Table 5-18). The model shows a 17.4 percent rate of PFPS job loss for females compared to 20.0 percent for men in wave 1 of the CFUWBES and a 2.8 percent rate of PFPS job loss for females compared to 7.9 percent for men in the Wave 2. However, the projected gender gap narrows when we utilize the survey-measured job loss rates.

_Table 5-18 Georgia: Comparison of PFPS Job Losses, Modeled, and Survey-Measured (including jobs loss due to firm closures), by Demographic Group_

||CFUWBES1<br>|CFUW<br>|BES2 (Cumulative)<br>|
|---|---|---|---|
||Modeled|Model-predicted|Survey-measured|
|By gender||||
|Female|17.4|2.8|6.4|
|Male|20.0|7.9|8.7|
|By age group||||
|below 25|19.4|6.8|7.1|
|25-45|18.3|3.7|6.6|
|above 45|19.8|8.4|9.4|
|By nationality||||
|Georgian|18.8|5.6|7.6|
|Azeri|22.6|17.2|12.8|
|Armenian|26.9|12.4|12.5|
|Other|16.2|2.0|3.2|
|Total|19.0|5.9|7.8|

Sources: CFUWBES waves 1 and 2. Notes: Jobs lost in the table are reported as cumulative. The cumulative jobs lost by the time of CFUWBES2 is predicted by aggregating modeled jobs lost in CFUWBES 1 and the modeled incremental jobs lost from R1 to R2.

These patterns are not consistent with those seen for the larger group of all private sector workers according to the GHFPS. In that data, 22.9 percent of men had lost their job between March and December 2020 due to COVID (and were not working in December 2020) in contrast to 40.8 percent of women (Table 5-19). More

> 51 The CMMHH results must be considered tentative, because Palestinians and Syrians each represent a very small share of pre-COVID PFPS workers (approximately 3 percent each), and sample sizes for the CMMMHH were even smaller, making these statistics somewhat unreliable.

38
**Table** **<u>6: Models with ending as dependent variable</u>**

||(1)<br>any<br>ending|(2)<br>any<br>ending|(3)<br>any<br>ending|(4)<br>any<br>ending|(5)<br>major<br>ending|(6)<br>major<br>ending|(7)<br>major<br>ending|(8)<br>major<br>ending|
|---|---|---|---|---|---|---|---|---|
|P t|-0.047|-0.053|-0.037|-0.038|0.019|0.020|0.038|0.061|
|x ()|(0.036)|(0.034)|(0.033)|(0.030)|(0.059)|(0.077)|(0.041)|(0.057)|
|P 1|0.040|0.033|0.036|0.028|-0.004|-0.013|-0.045|-0.056|
|x (t-)|(0.054)|(0.055)|(0.052)|(0.052)|(0.065)|(0.059)|(0.061)|(0.055)|
|P 2|-0.002|-0.024|-0.011|-0.043|0.009|0.034|0.014|0.030|
|x (t-)|(0.036)|(0.036)|(0.037)|(0.036)|(0.053)|(0.058)|(0.050)|(0.057)|
|P|-0.044|-0.046|-0.033|-0.042|0.242*|0.237*|0.094|0.179*|
|m (t)|(0.070)|(0.080)|(0.059)|(0.063)|(0.127)|(0.140)|(0.088)|(0.105)|
||0.129|0.098|0.101|0.077|-0.142|-0.143|-0.131|-0.142|
|Pm (t-1)|(0.102)|(0.101)|(0.083)|(0.079)|(0.169)|(0.159)|(0.127)|(0.119)|
|P 2|0.020|0.007|0.021|0.002|0.075|0.082|0.101|0.044|
|m (t-)|(0.080)|(0.089)|(0.071)|(0.078)|(0.134)|(0.167)|(0.098)|(0.098)|
|MP|-0.093|-0.078|-0.050|-0.030|0.123|0.181|0.357|0.399|
|(t)|(0.180)|(0.191)|(0.148)|(0.172)|(0.347)|(0.343)|(0.252)|(0.305)|
|MP 1|0.060|-0.064|0.187|0.094|0.114|-0.084|-0.379|-0.571|
|(t-)|(0.332)|(0.312)|(0.268)|(0.258)|(0.479)|(0.432)|(0.409)|(0.394)|
|MP 2|-0.079|-0.018|-0.278|-0.249|-0.320|0.032|-0.038|0.229|
|(t-)|(0.293)|(0.334)|(0.234)|(0.260)|(0.426)|(0.604)|(0.331)|(0.412)|
|Trade with|0.249**|0.199*|0.221**|0.155|0.402|0.463|0.359|0.375|
|neigh. (t-1)|(0.095)|(0.116)|(0.094)|(0.108)|(0.276)|(0.436)|(0.268)|(0.415)|
|Observations|1,104|1,104|1,101|1,101|540|540|537|537|
|R-sq. (within)|0.058|0.196|0.056|0.196|0.168|0.297|0.168|0.303|
|Countries|80|80|80|80|41|41|41|41|
|Shocks|logP|logP|logP|logP|logP|logP|logP|logP|
|Weight|[t-12; t-2]|[t-12; t-2]|[t-4; t-2]|[t-4; t-2]|[t-12; t-2]|[t-12; t-2]|[t-4; t-2]|[t-4; t-2]|
|Time trends|NO|YES|NO|YES|NO|YES|NO|YES|
|Sum Px|-0.008|-0.045|-0.011|-0.053|0.024|0.041|0.007|0.035|
|Sum Pm|0.105***|0.058|0.089**|0.037|0.174**|0.177|0.064|0.081|
|Sum MP|-0.113|-0.16|-0.141|-0.185|-0.082|0.129|-0.059|0.056|

_Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; all regressions include country fixed effects, year effects and controls (any conflict since 1946 and war border (t-1))._ 31
Gawande, K. and B. Hoekman (2006). ―Lobbying and US Agricultural Policy,‖ _International Organization_ , 60: 527-61.

Gillson, I. and M. Datt (2011) ―Using Trade to Overcome Food Insecurity,‖ World Bank, mimeo.

- Goldberg, P. and Maggi, G. (1999)  ̳Protection for sale: an empirical investigation.‘ _American Economic Review_ 89(5)1135-55.

- Gouel, C. and S. Jean (2011),  ̳Optimal food price stabilisation in a small open developing country‘ Mimeo, Paris School of Economics.

- Grossman, G. and Helpman, E. (1994),  ̳Protection for sale‘ _American Economic Review_ 84(4):833-50.

- Hamilton, A. (1791),  ̳Manufactures‘, Communication to the House of Representatives, December 5.

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- Hoekman, B. and K. Saggi (2007), ―Tariff Bindings and Bilateral Cooperation on Export Cartels,‖ _Journal of Development Economics,_ 83, 141-56.

- Hoekman, B. and J. Trachtman (2008), ―Canada-Wheat: Discrimination, Non-Commercial Considerations, and the Right to Regulate through State Trading Enterprises,‖ _World Trade Review,_ 7(1): 45-66.

- Hoekman, B., F. Ng and M. Olarreaga. 2004. ―Reducing Agricultural Tariffs versus Domestic Support: What is More Important for Developing Countries?,‖ _World Bank Economic Review,_ 18(2): 175-204.

- Irwin, D. (1996), _Against the Tide: an Intellectual History of Free Trade_ , Princeton University Press, Princeton.

- Ivanic, M and Martin, W. (2010), ―Promoting Global Agricultural Growth and Poverty Reduction,‖ World Bank, mimeo.

- Japan (2000),  ̳Negotiating proposal by Japan on WTO agricultural negotiations‘ World Trade Organization, 21 Dec. G/AG/NG/W/91

- Japan and Switzerland (2008), Proposal on export prohibitions and restrictions: Communication from Japan and Switzerland, Committee on Agriculture, Special Session, JOB(08)/34, 30 April.

- Jordan (2001) WTO  ̳Agriculture negotiations: Proposal by Jordan‘ World Trade Organization 21 Mar  G/AG/NG/W/140.

- Korea (2001),  ̳Proposal for WTO negotiations on agriculture‘ World Trade Organization, 9 Jan. G/AG/NG/W/98

- Martin, W. and Alston, J. (1997),  ̳Producer surplus without apology? Evaluating investments in R&D‘ _Economic Record,_ 73(221):146-58, June.

- Martin, W. and Anderson, K. (2012),  ̳Export restrictions and price insulation during commodity price booms‘ _American Journal of Agricultural Economics_ 94, forthcoming. (World Bank Policy Research Working Paper 5645).

- McCorriston, S., R. Sheldon and I. Sexton, (2004). ―Vertical Market Structure, Commodity Exports and Trade Reform,‖ University of Exeter, mimeo.

31
Figure 5.4: Prots, Wages, and the Rate of Return to Capital <!-- Start of picture text --> Profits and Wages Rental Rate Capital Stock<br>Average Earnings top 1%<br>Wage Rate<br>Average Profits<br>1998 2002 2006 2010 1998 2002 2006 2010<br>Year Year<br>8<br>4<br>7<br>3 6<br>%<br>5<br>Relative to SS 1998 2<br>4<br>1<br>3<br><!-- End of picture text --> the removal of prot taxes, in explaining the aggregate and micro-level dynamics predicted by the model. To do so, we compute a partial reform where only the productivity-elasticity of distortions follows its calibrated path of reversal, while the prot tax remains at its initial steady state value.

Figure 5.5 shows that the abstracting from the elimination of prot taxes reduces the aggregate productivity growth to half as much as the one yielded by the full reform and is accompanied by micro-level dynamics that are counterfactual. The average rm size increases and stabilizes above the initial steady-state's level, a feature that the benchmark reforms anticipate should happen in response to reductions in the degree of productivity-dependent idiosyncratic distortions, but one that is at odds with China's experience. Moreover, the increase in top income inequality is also substantially lower than the growth generated by the full reform. When prot taxes are kept in place, the incentive to increase innovation expenses is subdued, thereby mitigating the divergence between entrepreneurial prots among the top entrepreneurs, the average entrepreneurial prot, and the wage rate.

34
<!-- Start of picture text --> 2<br>1.5<br>r=)o. 1<br>So<br>ml<br>}<br>s 05<br>0<br>-0.5<br>Mauritius Uganda Tanzania South Africa Kenya Ethiopia<br>Btelecom UOelectricity Wroads<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>5<br>4<br>o.<br>S<br>‘es 3<br>=<br>2<br>1<br>0<br>Niger Uganda Ethiopia Tanzania Kenya South Africa<br>Btelecom UOelectricity Wroads<br><!-- End of picture text -->
Table 7: Marriage penalty by attitudes toward domestic violence

|Dependent variable|||Working|||
|---|---|---|---|---|---|
|Sample|SAR|BGD|IND|MDV|NPL|
||(1)|(2)|(3)|(4)|(5)|
|_Panel _|_A: No child _|_sample_||||
|Post-marriage|-0.113***|-0.101***|-0.115***|-0.156***|-0.007|
||(0.012)|(0.023)|(0.013)|(0.046)|(0.030)|
|Post-marriage _×_ Attitude towards violence|-0.035|0.029|-0.049*|0.143|-0.146*|
||(0.022)|(0.053)|(0.025)|(0.101)|(0.079)|
|No. of obs.|8812|1712|4847|440|1805|
|R-squared|0.525|0.326|0.489|0.566|0.380|
|_Panel B:_|_Ignore chil_|_d sample_||||
|Post-marriage|-0.148***|-0.101***|-0.151***|-0.257***|-0.083***|
||(0.008)|(0.014)|(0.008)|(0.046)|(0.018)|
|Post-marriage _×_ Attitude towards violence|-0.059***|-0.038|-0.075***|0.095|-0.045|
||(0.015)|(0.030)|(0.017)|(0.068)|(0.050)|
|No. of obs.|10884|2163|5887|578|2249|
|R-squared|0.568|0.357|0.536|0.594|0.376|

Note: Standard errors in parentheses clustered at the cohort level. All estimates weighted by cohort size. All models include birth cohort and country-by-year fixed effects. SAR - SOuth Asia Region, BGD - Bangladesh, IND - India, MDV - Maldives, NPL - Nepal. No child sample refers matching post-marriage observations in the sample without children. Ignore child sample refers matching post-marriage observations in the full sample. Cohort FE refer to birth cohort. Attitude toward violence is measured as the sum of agreement indicator variables for 5 DHS questions on the acceptability of domestic violence, averaged by cohort, with larger values indicating greater agreement. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

29
# **_2.3. Poverty_**

Estimates of poverty are generally measured using household per capita income or consumption derived from nationally representative household surveys. In this paper, we rely on Mali’s 2001 poverty evaluation survey or EMEP which covered 7,500 households. Since the procedure for measuring the impact of shocks on poverty using the CGE results is discussed in Rumpalla, Semega and Vellutini (2006) and Nouve, Rumpalla and Vellutini (2007) we only present the most relevant relationships. Household _h_ from group _g_ has a level of per capita consumption spending _Ehg_ defined as the product of a price vector **p** and a consumption bundle **xhg** ( **p** , _yh_ g), where _yh_ g is the household’s disposable income, so that _Ehg_ = **p’*xhg** ( **p** , _yh_ g). Denoting the base scenario by the superscript _0_ and a scenario after the food price shock by the superscript _s_ , per capita consumption spending after the shock _Ehgs_ in terms of the base year price vector is: The term in the bracket is computed from the CGE model for each scenario and each year, whereas _Ehg_ 0 is derived from the household survey data in the base year. This formulation assumes that the food price shock has different impacts across household groups, but the withingroup impact is identical for all households belonging to the group. Said differently, we combine the CGE results and the household surveys to perform micro simulations2. We use standard _FGT_ measures (Foster, Greer and Thorbecke, 1984) to compute the effects of the food price shock on poverty. For a vector of household expenditures _E_ , a poverty line _z_ , a total

> 2 Alternatives to our approach to fully account for within-group heterogeneity of impacts are twofold. A first alternative is to specify a density function for the within-group distribution of consumption and to use this distribution to compute individual impacts within each household group. Examples include de Janvry, Sadoulet and Fargeix (1991) and Decaluwé et al. (1999) who used the lognormal and beta distribution functions, respectively. A second alternative is to include all households in the survey in the CGE model and compute the impact of the shock directly from the macro model. Cockburn (2002) is an example of the latter approach.

8
groups may encounter greater challenges in adapting to this process. For instance, younger workers may be more readily able to adjust to AI than older workers.

# 2.3. Data and methodology

We rely on two different types of data to examine AI exposure:

- (1) Worker-level information: We use the global labor database (GLD) that provides access to harmonized labor force surveys for 25 countries. The surveys are harmonized on all levels for a set of key variables and give access to the 4-digit International Standard Classification of Occupations (ISCO). The countries included in the database are predominantly low- and middle-income countries. To enable comparisons with high income countries, we also include household survey data for the United States and Chile.<sup>6</sup> We rely on the latest labor force survey data collected from 2014 to 2023 for these 25 countries. Overall, the analysis encompasses data from approximately 3 million workers.

- (2) Indicator on AI occupational exposure (AIOE): This study adopts the AIOE index, developed by Felten et al. (2021) to explore the potential for AI exposure on occupations. The construction of the index is twofold. In a first step, Felten et al. (2021) identified a set of applications of AI building on the information provided by the Electronic Frontier Foundation AI Progress Measurement Project. In a second step, Felten et al (2021) combine information on the AI applications with the tasks and abilities as listed in the Occupational Information Network (O*NET). This yields the AIOE, a measure of occupational exposure to AI applications. This measure ranges from the lowest AIOE of -2.67, which is for “Dancers”, to the highest calculated AIOE of 1.58 for “Genetic Counselors”. The occupations the index reports stem from the Standard Occupational Classification (SOC) system in the 2018 version. As our harmonized surveys are stored in the International Standard Classification of Occupations (ISCO), we map the SOC level information to ISCO 2008. We then normalize the original values spanning from -2.67 to 1.58 to the range from 0 to 100 and interpret them as the occupational exposure to AI.<sup>7</sup> As an example in our dataset the occupations with the lowest AIOE of 0 is the ISCO 2008 category for “Dancers and Choreographers”, the ISCO 2008 category “Weaving and knitting machine operators” has an AIOE value of around 25, examples for ISCO 2008 categories with a value at around 50 are “Bus and Tram drivers” and “Aircraft Engine mechanics and repairers”. Further up the scale, “Medical records and health information technicians” report a value of around 75 and the highest AIOE value in our dataset is 100 for “Financial Managers”.

> 6 See Appendix Table 1 for an overview of countries, sample size and observations.

> 7 See the Github Felten et al. (2021) set up to describe the construction of the AIOE as well as the AIOE values for each occupation for further information: https://github.com/AIOE-Data/AIOE.

8
# **8.  Poverty-Reduction Strategy for Bahia**

Over the medium to long run what is needed to alleviate the high levels of poverty is broad-based growth. However, this is not enough to alleviate poverty, particularly in the short run. Measures are needed to protect vulnerable groups and to ensure that the poor are able to take advantage of the greater opportunities in the economy. In order to address these latter needs, this paper has examined the profile of the poor in Bahia and recommended priority policies and programs targeted to them.

The government of Bahia has taken important steps to reduce poverty, for example the recent generation of SECOMP (the Secretariat of Poverty Reduction) and the conduction of poverty related research by SEI are encouraging signs. However, it needs a poverty alleviation strategy that sets clear and appropriate priorities and goals for poverty reduction efforts within a framework of a continuation of economic policies that would promote growth. The challenge and test of the government’s resolve will be to what extent current and future policies and programs are governed by that strategy. In order to ensure that the poor reap the benefits, poverty measurement and monitoring are called for, including tracking changes and making appropriate adjustments in existing programs to reflect these changes.

## **A SIX-PRONGED POVERTY-REDUCTION APPROACH FOR BAHIA**

The poverty profile, determinants of poverty, and social risk and incidence analysis provide guidance on a social agenda and poverty alleviation strategy for Bahia.<sup>17</sup> The strategic principles for reducing poverty involve seeking to strengthen the key assets of the poor, taking into account geographic differences in the poverty situation and priorities. The government of Bahia could apply a six-pronged poverty-reduction approach:<sup>18</sup> 17 It is worth to keep in mind that this paper is a fast assessment of poverty in Bahia and does by no means capture all areas of poverty in the state. Further research include: public expenditure analysis (incidence and management), the links between poverty, rural and urban living, health, and the environment, impact evaluations of social assistance programs, participatory research on the obstacles to increased school enrollment among black and mulatto children, and participatory research on poverty, crime and violence.

18 Some key steps for immediate action could include: (1) conducting a thorough review of public expenditure allocations and developing proposals for reallocating expenditures such that they better reach the poor; (2) developing a set of indicators to monitor implementation of poverty reduction interventions (including key budget categories) and agreeing on an inter-institutional process for reporting on such indicators and implementation; and (3) applying a poverty map as a tool for targeting and resource allocation.

47