rafmacalaba/datause-encoder-data huggingface dataset:
Indonesia (March 1980) and Egypt (1981), are highly comprehensive and show great depth of analysis. Both these reports also provide a good secondary analysis of survey data.

Most sector reports are strong in demographic and population analysis but weak in analyzing program effectiveness issues, especially those which are salient at the program-client interface level. Typically, sector reviews do not go beyond description of the service delivery and IEC system and preliminary assessment of the management structure at the central government level. Since management issues are likely to be sensitive and sector reports need government clearance, the reluctarce to deal with certain issues is understandable, but since chese issues lie at the heart of effective program and project development, it is important that a mechanism be found to address them. The Mauritius (1983) and Egypt (1985) reports are exceptions, as they focus on management aspects in some detail.

The coverage and depth of sector reports vary considerably. Basic demographic analysis is handled with reasonable consistency, but the discussion of program management, other government departments and NGOs does not conform to any se,pattern. It seems that the composition of the review team may account for.some of the variance in scope and auality.

# 4. Impact of Sector Work

It is very difficult to assess the impact of sector work on Bank activities and the development of population policies. The Health Sector Review concluded that sector work had had a considerable impact in terms of both these criteria. Our interviews also brought out that the operations staff find the sector work useful in project development and policy dialogue.

We have already discussed sector work in relation to policy dialogue in the previous section. We found that sector reports have been an important input in policy dialogue in Africa and Latin America. It is not clear that the same holds for some of the mature programs in India, Indonesia, Egypt, Pakistan and Bangladesh. In fact, it should be mentioned that sector work regarding mature programs hai been extremely limited.

At the aggregate level, there seems to be a clear correspondence between the volume of sector work and the number of projects: compared to 71 PHN related sector and economic reports, there are 60 projects. However, we find that sector work has not always been a precursor of project development. Our tentative analysis indicates that there are 22 projects in 17 countries where no sector work was done before the project was appraised (Table A-2). There are 23 sector reports for 22 countries where the sector work has been completed, but where no 18
_yc,t_ = _αc_ + _λt_ + _Postt ×_ ( _β_ 1 _T_ 1 _c_ + _β_ 2 _T_ 2 _c_ ) + _uc,t,_ (2) where the dependent variable will be one of the labor market outcomes. The right-hand side of the model includes canton and year dummies. The latter account for aggregate economic fluctuations, such as the downturn affecting Ecuador in 2014 and the years that followed, which clearly impacted labor market outcomes at the national level.

_Postt_ is an indicator taking a value of one for years 2016-2019 and zero prior to that (2013-2015). The key explanatory variables are _T_ 1 _c_ and _T_ 2 _c_ . These indicator variables identify _cantons_ with, respectively, a medium (2 _._ 5% _−_ 5 _._ 0%) and high ( _>_ 5%) density of Venezuelan migrants in the canton’s population in year 2019. The omitted category are _cantons_ with the lowest density of Venezuelans ( _<_ 2 _._ 5%).

Table 4 presents the estimates. The top panel estimates the models using the labor market indicators constructed using the whole working-age population. The estimates do not indicate any effects of Venezuelan migration on participation or employment rates, that is, we do not detect deviations in these labor market indicators of the highVenezuelan-density regions in the post-2016 period relative to the low-density regions. However, the estimates do suggest a worsening in employment conditions, measured by the adequate employment and informality rates, along with reductions in monthly earnings and employment, in the regions receiving Venezuelan migrants. The magnitudes are relatively small but with a clear pattern of larger effects (in absolute value) in the regions with the highest density of Venezuelan migrants (identified by indicator _T_ 2). Next, we turn to the labor-market indicators for the female population, which the literature has often shown to be adversely affected by inflows of refugees (e.g. Ceritoglu et al. (2017), Tumen (2016), DelCarpio and Wagner (2015), Ruiz and Vargas-Silva (2018)). The second panel in Table 4 presents estimates based on the indicators for the female working-age population. In this case the estimated coefficients are very small and none is statistically significant at conventional levels. Thus, it does not seem to be the case that the labor market conditions of Ecuadoran women are being affected by the arrival of Venezuelan migrants.

The third panel in Table 4 focuses on the labor market indicators referring to young workers (ages 15-40) with low educational attainment, defined as having completed at most primary education. Once again, we do not find evidence of effects on the participation or employment rates. However, a clear pattern emerges suggesting substantial worsening of the quality of employment and earnings for this group of workers in the 15
**Table 5. Education Districts with Average Scores in the** **_Ser Maestro_ and** **_Ser Bachiller_ Assessments** **<u>that are Below One Standard Deviation from the Mean</u>**

|District<br>ID|Number<br>of<br>Schools|%<br>Schools|Province|Region|_Ser Bachiller_<br>Average Score|_Ser Maestro_<br>Average Score|
|---|---|---|---|---|---|---|
|21D02|2|2.33|Sucumbíos|Amazonía|6.70|484.38|
|14D05|8|9.30|Morona Santiago|Amazonía|6.23|497.28|
|16D02|4|4.65|Pastaza|Amazonía|6.53|497.31|
|13D01|1|1.16|Manabí|Costa|6.75|499.50|
|21D04|5|5.81|Sucumbíos<br>|Amazonía|6.65|500.96|
|19D01|1|1.16|Zamora<br>Chinchipe|Amazonía|6.52|509.60|
|16D01|9|10.47|Pastaza|Amazonía|6.53|512.91|
|15D01|7|8.14|Napo|Amazonía|6.74|520.99|
|06D01|1|1.16|Chimborazo|Sierra|6.57|521.00|
|14D01|1|1.16|Morona Santiago|Amazonía|6.35|527.70|
|09D20|1|1.16|Guayas|Costa|6.94|528.00|
|08D02|4|4.65|Esmeraldas|Costa|6.60|528.67|
|08D04|3|3.49|Esmeraldas|Costa|6.75|530.48|
|14D02|4|4.65|Morona Santiago|Amazonía|6.51|532.68|
|08D03|2|2.33|Esmeraldas|Costa|6.60|536.65|
|14D03|4|4.65|Morona Santiago|Amazonía|6.60|537.36|
|08D01|7|8.14|Esmeraldas|Costa|6.76|541.35|
|22D01|2|2.33|Orellana|Amazonía|6.80|543.06|
|09D02|1|1.16|Guayas|Costa|6.83|545.67|
|22D02|7|8.14|Orellana|Amazonía|6.76|547.01|
|21D03|1|1.16|Sucumbíos|Amazonía|6.56|552.50|
|13D07|1|1.16|Manabí|Costa|6.60|552.67|
|08D06|2|2.33|Esmeraldas|Costa|6.76|555.42|
|05D04|1|1.16|Cotopaxi|Sierra|6.80|555.60|
|08D05|1|1.16|Esmeraldas|Costa|6.93|556.25|
|09D11|1|1.16|Guayas|Costa|6.61|561.60|
|12D06|1|1.16|Los Ríos|Costa|6.76|562.17|
|21D01|1|1.16|Sucumbíos|Amazonía|6.45|564.67|
|12D01|1|1.16|Los Ríos|Costa|6.95|568.00|
|19D02|1|1.16|Zamora<br>Chinchipe|Amazonía|6.84|572.80|
|09D06|1|1.16|Guayas|Costa|6.79|575.94|
|**Total**|86|99.97|||||

_Source:_ Author using _Ser Maestro_ 2016 and _Ser Bachiller_ 2017/2018 datasets.

18
Ravallion (2000). The reason is that TD better reflects differences in coverage—the proportion of the poor receiving the program—than other standard measures, which focus more on how well the scheme avoids leakage—the proportion of the non-poor receiving the program. This is likely to hold for other programs besides _Di Bao_ .

In the present context, this can be defined as the difference between the MGNREGS participation rate for the poor and that for the non-poor. In obvious notation: Here _P_<sup>_i_</sup> (i=poor, non-poor) is again the participation rate (as defined by equation (1)), but this time differentiated between the poor and non-poor. To interpret the targeting differential, note that when only poor people get help from the program and all of them are covered, _TD_ = 1, which is the measure‘s upper bound; when only the non-poor get the program and all of them do, _TD_ = -1, its lower bound. This measure is easy to interpret, and it automatically reflects both leakage to the non-poor and coverage of the poor.

Table 4 gives the _TD_ and participation rates for the poor and non-poor.<sup>16</sup> Participation rates among the poor vary enormously across states, from a low of 0.10 in Maharashtra to a high of 0.73 in Rajasthan. They also vary a lot among the non-poor. Although participation rates are always higher for the poor, the gap with that for the non-poor is not large. The targeting differential for India as a whole is 0.12. (The _TD_ for China‘s _Di Bao_ program mentioned above was 0.27.) Madhya Pradesh has the highest _TD_ , at 0.22, while Kerala has the lowest, at 0.01.

Table 4 gives the rationing rates for the poor and non-poor. Consistently with the allIndia results in Table 3, we see that the non-poor are rationed more than the poor in almost all states (the only exceptions are Kerala and Rajasthan).

The _TD_ is determined by how the demand rates and the rationing rates vary between the poor and non-poor. We can use a simple decomposition method to show how much of the _TD_ is due to each factor:

> 16 To divide the population into poor and non-poor, we use poverty lines that deliver the same poverty rates using the abridged consumption module in the Employment Schedule, as those that are obtained from the Consumption schedule, and reported in Table 1.

11
We first use these data to run similar regressions to those reported in Panel A. The results in Panel B show that the association between the CLASS and achievement in these data is similar to that which we estimate in the control group of the coaching pilot: A 1 SD increase in the CLASS is associated with a 0.082 SD increase in achievement. When we look at different domains of the CLASS simultaneously, only _Classroom Organization_ is consistently and significantly associated with test scores.

The results in Panels A and B of Table 2 are associations, not necessarily causal effects. Indeed, if better-off children attend higher-quality schools, as seems likely, the coefficients from these crosssectional regressions would overstate the importance of teacher behaviors for achievement. In the multigrade experiment, however, there was random assignment of children to classrooms within schools. As a result, estimates that use only the within-school, cross-classroom variation in the CLASS and achievement are more likely to have a causal interpretation (see Campos et al, 2020, for a detailed analysis of these data).

In Panel C of Table 2, we report the results from regressions of achievement on the CLASS, including school fixed effects. In these school fixed effects regressions, teacher CLASS scores should not be correlated with the observable or unobservable characteristics of the students in their classrooms, although the CLASS could still be correlated with other teacher attributes that affect test scores, as discussed in Araujo et al. (2016). The coefficient on the CLASS in these fixed effects regressions is somewhat smaller than in Panel B—0.062, rather than 0.082. Importantly, however, the last column of Panel C shows that—much as is the case in the results without school fixed effects—only _Classroom Organization_ , not _Emotional Support_ or _Instructional Support_ , consistently predicts achievement.

# **4. Identification strategy**

To estimate the impacts of the coaching program on teaching practices, we run regressions of the following form: The dependent variable is the CLASS (or one of its domains) of teacher _t_ in school _s_ and block _b_ , where the blocks refer to the groups of 20 schools that were the basis for the block randomization; δb is a set of block fixed effects; Ttsb takes on the value of 1 for teachers (in schools) randomly assigned to the coaching intervention, 0 otherwise; Ztsb and Xsb are the teacher and school controls in Table 1; and utsb is the regression error term. The coefficient of interest is ρ. To test for the possibility of differences in effects at the top and bottom of the distribution, we also run regressions in which the dependent variable is an indicator variable which takes on the value of 1 if the CLASS is below the 10<sup>th</sup> or 20<sup>th</sup> percentiles, or above the 80<sup>th</sup> or 90<sup>th</sup> percentiles, respectively (four separate regressions).

10
FISP/FSP support, farmers in the richest quintile were almost twice as likely to participate in government maize programs as were farmers in the poorest quintile.

Figure 8: Distribution of agricultural practices among farming households <!-- Start of picture text --> 100%<br>12%<br>90% 18%<br>25%<br>80% 16% 12% 43% Does not grow crops<br>70% 9%<br>74% Grow crops (not maize)<br>60%<br>6%<br>50% Grow maize w/o fertilizer or<br>seeds from govt. or coop.<br>40% 63% 60% 53%<br>Beneficiaries: Grow maize<br>30% 40% with fertilizer or seeds from<br>govt. or coop.<br>20%<br>20%<br>10%<br>9% 11% 13% 11%<br>5%<br>0%<br>Q1 Q2 Q3 Q4 Q5<br>(Poorest 20%) (Richest 20%)<br>Distribution of households by agriculture practices<br><!-- End of picture text --> The area cultivated by the farmer is relevant at the time of distributing benefits since, by regulation, to qualify for public subsidies cultivated plots must be at least one hectare in size. The average area cultivated per beneficiary increases with consumption levels from an average of 1.01 hectares among producers of the bottom quintile to 1.8 hectares for producers in the top quintile.<sup>16</sup> In addition, when looking at the aggregate distribution of land cultivated for maize, Figure 9 shows that producers belonging to the richest quintile cultivated 14 percent of the total maize hectares using inputs from the program. Producers in the poorest quintile cultivated a very similar share of that type of land, 16 percent. This again calls into question the targeting of the program, which does not seem to concentrate on the most vulnerable producers but rather focuses on producers in the middle quintiles, as they represent the lion‘s share of maize production in Zambia.

> 16 The average area cultivated for maize with inputs from government programs for the second to fourth quintiles were 1.23, 1.39 and 1.43 hectares, respectively. Average cultivated areas for maize (with or without inputs from public programs) are: 1.05, 1.02, 1.15, 1.39 and 2.17 hectares respectively. Source: Authors‘ estimates from LCMS VI.

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Table1. Role of Petroleum Subsidy in the Government Budget

|Sector<br>I|2007<br>n bill. YR<br>in %<br>exp|of total<br>enditures|20<br>in bill. YR|08<br>in % of total<br>expenditures|20<br>in bill. YR|09<br>in % of total<br>expenditures|
|---|---|---|---|---|---|---|
|Economic Affairs|473.5|27.3|827.2|37.1|483.0|27.5|
|of which:|||||||
|Industry/Trade|2.4|0.1|2.7|0.1|3.0|0.2|
|Trans./Comm.|3.2|0.2|2.6|0.1|4.4|0.2|
|Agriculture/Fishing|17.6|1.0|19.9|0.9|14.2|0.8|
|Petroleum Subsidy|401.7|23.2|759.3|34.1|391.0|22.2|
|Other Econ. Affairs|48.6|2.8|42.7|1.9|70.5|4.0|
|Health|59.3|3.4|70.2|3.2|61.5|3.5|
|Education|251.1|14.5|291.7|13.1|286.2|16.3|
|Social Protection|4.2|0.2|5.1|0.2|47.8|2.7|
|General Public Services|416.5|24.0|452.9|20.3|320.5|18.2|
|Defence|272.8|15.7|297.1|13.3|288.2|16.4|
|Other|256.3|14.8|283.2|12.7|270.3|15.4|
|**Total**|**1,733.8**|**100.0**|**2,227.5**|100.0|**1,757.6**|100.0|

Source: Ministry of Finance (2010).

The rising cost of the fuel subsidy has an adverse impact on the public investment program in infrastructure, including for transportation and telecommunication. Between 2007 and 2009, less than 1 percent of the economic affairs budget was allocated for infrastructure construction. Investment for development, was largely externally financed (about 2 to 4 percent of GDP). However, there is broad consensus that building and investing in economic infrastructure is a key ingredient for achieving longterm growth, economic diversification and sustainable poverty reduction. In Yemen, for example, road density, especially of asphalted roads, is generally low, and the average travel time by district to the nearest urban center can exceed three hours. Investments into the road network system, especially in rural areas, will therefore have large poverty-reducing effects (Breisinger et al. 2010b). Improvements of road infrastructure are also expected to facilitate people’s access to public service facilities such as hospitals, schools, and administration offices and have positive spillover effects on development and commercialization in rural areas. Lowering of transaction costs will directly lead to new economic opportunities and diversification.

The largest share of fuel subsidies goes to diesel, which made up more than two-thirds of all subsidized fuels in 2009 (Table 2): 69 percent of fuel subsidies goes to diesel; 14 percent goes to gasoline; and the remainder is split between LPG, kerosene, and jet fuel. In terms of total domestic fuel consumption, diesel accounted for the largest part, with 3.96 billion liters in 2009, followed by gasoline (2.04 billion), jet fuel (1.34 billion), and kerosene (0.12 billion).

5
where _Ki_ is the stock of productive capital in sector _i_ , and _Li_ the amount of damages to this sector productive capital.

The variable _αi_ is the over-production capacity; its modeling is the same than in H08 and is recalled in Section 3.1.3 below. This variable represents the ability of economic sectors to increase their production above its normal level thanks to, e.g., longer hours from workers, more efficient use of existing capital, and imports of qualified workers and equipment from other regions.

In absence of inventory constraint, the production in sector _i_ would be:

- _Limitation by supplies._ In the model, production is done using inventories only. Production can thus be limited by insufficient inventories. It is assumed that if inventories are lower than their required levels, then production is reduced. Each supplying-sector _j_ creates an additional constraint, if the corresponding inventory is lower than a share _ψ_ of its required level _S_<sup>_r_</sup> ( _i, j_ ), which is given by:

where _Pi_<sup>_a· A_(</sup><sup>_i, j_) is the required consumption flux of commodity</sup><sup>_j_to produce the level of</sup> production the previous time step<sup>3</sup> If the actual inventory is lower than the required one, it is

> 3 The required level of inventory _Sr_ ( _i, j_ ) and the target level of inventory _St_ ( _i, j_ ) are different. The former represents the amount of input necessary to produce (see a discussion of its impact on production below), while the latter represents orders to replenish inventories. The former takes into account constraints on other supplies; not the latter.

13
land intensive and more standardized industries could relocate from Singapore to the IDR (see Figure 3).<sup>23</sup>

**Figure 1: IDR Population Size (2005) – National and Regional Comparison**

<!-- Start of picture text --> 30,000.0<br>5,000.0<br>26748.1<br>4,500.0 4,234.0<br>25,000.0<br>4,000.0<br>20,000.0 3,500.0 3,170.0<br>3,000.0<br>15,000.0 2,500.0<br>2,000.0<br>10,000.0 1,353.2<br>1,500.0<br>1,000.0<br>5,000.0<br>500.0<br>0.0<br>0.0<br>Malaysia Johor State IDR Singapore<br>( '0 0 0 ) ( '0 0 0 )<br><!-- End of picture text --> Source: Projected from Census (2000) figures.

**Figure 2: Composition of Age Structure (2005) – National and Regional Comparison**

<!-- Start of picture text --> IDR<br>3.1%<br>30.5%<br>66.4%<br><<br> <15 years 15-64 years > 64 years<br><!-- End of picture text --> Source: State/District Data Bank 2005

> 23 Kenney and Patton (2007) find that industrial clustering and the proximity of venture capitalists is greater for electronics and telecommunications – the main manufacturing industries in S–IRD than for biotechnology.

10
The composite error term in parentheses contains the heterogeneity in weights. Now it is far from clear what standard subjective welfare regressions are telling us. Even if _Cov_ ( _X i_ ,  _i_ )  0 , a linear regression model for subjective welfare will not in general deliver the mean weights in the population in large samples. That requires that the interaction effects between the latent differences in weights and the values taken by the covariates are orthogonal to those covariates, i.e., that _Cov_ ( _X i_ ,  _i X i_ )  0. However, we can easily imagine that people with high weights on some welfare dimension tend to be better endowed with that dimension ( _Cov_ ( _X i_ ,  _i X i_ )  0); this may reflect parental decisions on schooling, say. Then (ignoring all other problems) the regression coefficient will tend to over-estimate the mean weight. Alternatively, suppose that people with low innate attributes (such as health) tend to put a higher value on those attributes; then one could find that _Cov_ ( _X i_ ,  _i X i_ )  0 . We will underestimate the mean weight.

Thus the likely heterogeneity in the weights attached to non-market goods clouds inferences from subjective-welfare regressions. Indeed, while one would presumably like to find weights appropriate to the trade-offs of poor people, it is far from clear that the weights implied by standard regressions are appropriate. One could try to model the heterogeneity in weights by adding interaction effects. This will presumably help, although latent heterogeneity in weights can never be ruled out.

A possible direction for research is to use surveys to try to focus directly on the subjective tradeoffs adopted by respondents. This would require more complicated and difficult survey questions, but a not dissimilar approach has been used with some success for inferring attributes of preferences, such as risk aversion or valuations of environmental factors.

# **6. Conclusions**

Subjective data offer to expand the information set traditionally used for assessing welfare and measuring poverty. Self-assessed welfare is not equated with welfare. Rather, the promise of subjective data is to help identify the weights on the dimensions of welfare for which prices are missing (or unreliable) and in determining the social subjective poverty line—below which people tend to think they are poor, but above which they do not.

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