rafmacalaba/datause-encoder-data huggingface dataset:
and building. GPS data were collected from all schools, the proposed program-school site, and surveyed households.

The follow-up survey consisted of three instruments: a school survey; a household survey; and a child survey, which included a test. The household and child surveys, and child tests, were administered at the child’s home. The household survey was administered to households with at least one child aged 5–9. In large villages, up to 42 randomly sampled households in the village were interviewed; in villages with fewer than 42 households, which comprised the majority, all households in the village were interviewed. The survey was multi-topic, and had extensive modules on past and current schooling and other activities for children aged 5–17, answered by the household head or another primary adult household member.

A child survey was administered to each child aged 5–9. It asked questions mainly on work activities performed inside and outside the home, past and current schooling, and aspirations. Each child was then administered tests on language (either Urdu or Sindhi, as preferred) and mathematics.

The school survey gathered information from interviews of head teachers and all other teachers, and visual checks by the survey interviewers of school infrastructural and environmental conditions. The survey also collected student attendance information through a headcount, with the attendance lists used during the household survey to verify the child’s enrollment status reported by the household. GPS data were gathered from all surveyed households and schools.

Table 1 reports sample sizes of the baseline and follow-up surveys, by treatment status. The baseline survey interviewed 2,089 households and 5,556 children aged 5–9, and the follow-up survey interviewed 5,966 households and 17,720 children aged 5–17.

# **5 Empirical strategy**

The primary outcomes of interest are child enrollment and test scores. We estimate the intention-to-treat (ITT) based on the following specification: where _Yi_ is the outcome of interest for child _i_ , _Ti_ is an indicator variable indicating whether child _i_ resides in a village assigned a program school, and _Xi_ is a vector of child and household controls. In other specifications, we examine the differential impacts of the program by gender, by the two subsidy treatments, and by the two subsidy treatments interacted with gender. Standard errors are clustered at the village level, _j_ .

_Internal validity:_ The validity of our results depends upon the comparability of populations across the 9
during 2006-2013 compared to 1995-2005. Elsewhere, growth slowed down or the subsector actually contracted (food, sugar, chemicals, metallurgy and machines). These outcomes are driven by a combination of declining productivity in agriculture and constraints to manufacturing productivity, including the poor state of infrastructure reflected in, for example, the poor quality of electricity supply (Senegal ranks 111<sup>th</sup> in the Global Competitiveness Index 2014-2015, worse than its WAEMU neighbors such as Mali, 101<sup>st</sup> and Côte d’Ivoire, 96<sup>th</sup> ).

<!-- Start of picture text --> Figure 2: Growth of manufacturing subsectors (%)<br>manufacture of furniture, manufacturing nec<br>building materials transport<br>manufacturing machines<br>metallurgy, foundry, manufacturing<br>manufacture of glass, pottery<br>manufacture of rubber products<br>chemical manufacturing<br>oil refining, coking,<br>manufacture of paper, cardboard,<br>woodworking and manufacture of articles<br>manufacture of leather; manufacturing<br>cotton ginning and textile manufacturing<br>manufacture of tobacco products<br>manufacture of beverages<br>Food manufacturing nec<br>manufacture of sugar processing<br>Manufacture of grain food<br>Working grain product manufacturing<br>Processing and preservation of meat, fish<br>‐10.0 ‐5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0<br>2006‐2013 1995‐2005<br><!-- End of picture text --> Source: Senegal country sources

# **2.4. VALUE ADDITION IN THE SERVICES SECTOR**

The prominent drivers of services growth are communications, financial and business services, and transportation, in decreasing order of importance (figure 3).

9
located in seven regions of Ethiopia, and 42 percent of the observations came from Addis Ababa. The survey included firms with at least five permanent employees in four sectors: furniture, wood, and metal; food and beverages; leather and leather products; and textiles and garments. In all, 360 manufacturing firms were surveyed. The data from the Sudanese firms were collected as part of the Investment Climate Survey, which was launched in November 2007 and conducted by H&H Consultancy. This company is a Sudanese management consulting firm with expertise in conducting complex surveys.<sup>9</sup> Thus, the data were collected before South Sudan seceded from Sudan in July 2011; therefore, our sample includes firms in what is now South Sudan (see note 1). The survey covered 432 manufacturing firms in eight states, and most of these firms were private. The capital city of Khartoum accounted for 52 percent of the sample observations. No sector represented more than 20 percent of the sample; hence, the survey was diverse in terms of sector. The largest sectors were food and beverages (18 percent) and fabricated metal products (16 percent). Microenterprises were not covered. After deleting the observations that had too many missing values, we obtained a sample of 304 firms for Ethiopia and 401 firms for Sudan.<sup>10</sup> This subset of the observations formed our baseline sample.<sup>11</sup> Summary statistics are shown in table 1, and the variables that constitute our control vector are presented first. More mature firms and firms with higher-quality management should be more adept at recognizing the value of new technologies and business practices. Female ownership is included because female-headed businesses have been shown to be less growth oriented (de Mel, McKenzie and Woodruff 2009; Fafchamps 2003). We also include firm size, which is represented by the (log of the) total firm employment. The average log employment is 3.37 in Ethiopia (which corresponds to 29 employees) and 2.91 in Sudan (which corresponds to 18 employees).

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19 Figure 4b: Productivity Growth and Vertical Specialization in China, Czech Republic, Indonesia and the United States by Industrial Sector, 1986-2014 <!-- Start of picture text --> CHN CZE<br>31<br>29 34 34 35<br>33 3429<br>26 30<br>3626 33 28 25 31 362626 3333 28 2525 2930 3429 31 35 3232<br>31<br>25<br>36 28 36 28<br>0 20 40 60 0 20 40 60<br>IDN USA<br>3626 33 2828 2525 29 343429 35 313135 32 36362626 3333 2828 2525 293034342930 313135 32<br>3626 33 30 32 35<br>32<br>30<br>0 20 40 60 0 20 40 60<br>1986-2000 2001-2014<br>Vertical specialization(world imports of parts and components<br>as a share of world total exports, average 1986-2014, percent)<br>300<br>200<br>150 200<br>100<br>100<br>50<br>0<br>0<br>-50 -100<br>300 200<br>200<br>100<br>100<br>0<br>0<br>-100<br>Productivity Growth (average by period, percent) -100<br>-200 -200<br><!-- End of picture text -->

# _Source:_ UNIDO (INDSTAT2), UN Comtrade, and authors’ calculations.

_Notes:_ ISIC3 industry codes are used; sample includes 85 countries; productivity is normalized using average industry productivity; weights are represented by value added.

ISIC3 codes: 25 Manufacture of rubber and plastics products; 26 Manufacture of other non‐metallic mineral products; 28 Manufacture of fabricated metal products, except machinery and equipment; 29 Manufacture of machinery and equipment n.e.c.; 30 Manufacture of office, accounting and computing machinery; 31 Manufacture of electrical machinery and aparatus n.e.c.; 32 Manufacture of radio, television and communication equipment and apparatus ; 33 Manufacture of medical, precision and optical instruments, watches and clocks ; 34 Manufacture of motor vehicles, trailers and semi‐trailers; 35 Manufacture of other transport equipment; 36 Manufacture of furniture; manufacturing n.e.c.

Motivated by this preliminary evidence, we try to quantify the impact of the diminished responsiveness of world trade to world GDP on productivity growth. We do this in two steps.

First, we use estimates from Constantinescu, Mattoo and Ruta (2016b) to assess the impact of vertical specialization on productivity growth. Second, we do a back-of-the-envelope calculation
reverting error, attenuation is not guaranteed. If the ‘shrinkage’ of the variance in the first term in the denominator due to multiplying by λ<sup>2</sup> (for 0<λ<1) exceeds the effect of adding the variance of the random noise term ( σ _v_ 2 ), the response coefficient is overstated rather than understated. Also, the variance of the error-ridden variable is less than that of the true variable, contrary to what is possible with the reliability ratio interpretation.

Knowing if the coefficient on the proxy for permanent income is either attenuated or exaggerated seems important for many of the policy conclusions that economists may draw from regressions where surveyed household consumption is a key explanatory variable. Moreover, when economists attempt to deal with possible attenuation by using instrumental variables for household consumption (e.g., Alderman et al, 2006) they are using an estimator that is inconsistent when measurement errors are correlated with true values (Black _et al._ 2000). Once again, knowing more about the nature of measurement error in household consumption data may help improve modeling practice and interpretation of empirical results.

# **III. The Survey Experiment and Evidence from Village Averages**

The designs of the eight alternative consumption modules are described in Table 1. They differ by method of data capture (diary versus recall), by respondent (individual versus household reporting), by recall period (7 day, 14 day and usual month), and by number of items in the recall list. The designs were strategically selected to reflect the most common consumption modules used in multi-topic living standards surveys in developing countries (which typically seek less commodity detail than do household budget surveys). Design variation was restricted to foods and frequently purchased non-foods, but assignment to different modules also affects reports on infrequently purchased items, which were covered by a set of annual recall questions common to all households. Beegle et al (2012) ascribe this impact on the infrequent items as due to either respondent conditioning or fatigue, since questions on infrequent items came after the lengthy food and frequent non-food recall sections in modules 1-5 and after the two-week diary for modules 6-8.

6
**Table 4. Changes in education, work experience, and political affiliation over time**

||1991|1993|1995|1997|2000|2002|2004|2006|2008|2010|2012|Total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Panel A. Educati**|**on level (%):**||||||||||||
|Illiterate|2.36|0.83|0.38|0.31|0.16|0|0|0|0|0|0|0.29|
|Primary school|16.19|9.86|8.42|6.3|2.74|2.18|1.7|1.52|0.86|1.22|1.12|3.87|
|Junior HS|47.9|36.18|35.56|31.46|19.57|17.56|12.88|12.58|8.05|9.11|8.23|18.78|
|HS/technical|28.36|35.97|37.55|41.68|39.11|41.93|33.62|36.59|29.19|28.37|25.36|33.26|
|College|5.19|16.6|17.36|19.58|35.03|33.47|46.06|44.85|49.01|54.21|57.15|38.51|
|Graduate deg.|0|0.56|0.73|0.67|3.39|4.86|5.74|4.46|12.89|7.09|8.13|5.29|
|Total|100|100|100|100|100|100|100|100|100|100|100|100|
|**Panel B. Distribu**|**tion of years o**|**f schoolin**|**g over fi**|**rm size (**|**by asset q**|**uantiles)**|||||||
|0-25%||10.26|10.33|10.88|11.98|12.24|12.99|13.06|13.86|13.62|13.97||
|25%-50%||10.55|10.92|11.25|12.48|12.54|13.48|13.17|14.1|13.98|14.24||
|50%-75%||11.18|11.13|11.53|12.87|13|13.7|13.71|14.7|14.27|14.56||
|75%-100%||11.71|11.93|12.07|14.02|13.81|14.58|14.63|15.5|15.25|15.56||
|**Panel C. Work ex**|**perience befo**|**re startin**|**g private**|**firms**|||||||||
|||||1997|2000|2002|2004|2006|2008|2010|2012|Total|
|gov't cadre||||0.272|0.274|0.313|0.251|0.179|0.18|0.14|0.163|0.167|
|state firms||||0.56|0.642|0.651|0.804|0.993|.|0.287|0.257|0.599|
|village cadre||||0.075|0.071|0.067|0.077|0.099|0.261|0.228|0.282|0.1|
|farmer||||0.385|0.278|0.26|.|0.259|0.0778|0.136|0.156|0.196|
|household<br>enterprise||||0.182|0.0771|0.26|.|0.521|0.371|0.351|0.493|0.31|
|soldier||||0.418|0.241|0.058|0.074|0.088|0.1|0.134|0.126|0.154|
|other||||0.562|0.373|0.183|0.289|0.309|0.198|0.309|0.272|0.313|
|**Panel D. Political**|**connections o**|**f private**|**entrepre**|**neurs**|||||||||
||1991|1993|1995|1997|2000|2002|2004|2006|2008|2010|2012|Total|
|party mem|.|0.123|0.176|0.202|0.207|0.17|0.346|0.405|0.335|0.415|0.34|0.297|
|congress|.|.|.|0.101|0.166|0.174|0.18|0.19|0.216|0.218|0.164|0.146|
|consultative<br>conference|.|.|.|0.238|0.415|0.351|0.306|0.262|0.296|0.305|0.258|0.242|

_Data sources: National Surveys of Privately-Owned Enterprises in China_ 40
Results are largely unchanged, with a few small differences. First, the effect of the information treatment on attendance increases (from 4.5%-points to 6.1%-points), which, as discussed above, is consistent with the idea that the quality of monitoring may be worse in larger schools at baseline. Despite all treatment arms providing the same attendance information to parents, there is no similar increase in the effect of the transfers treatments when weighting schools proportionally to their potential (pre-treatment) enrollment. This suggests either that the effect of incentives is smaller in larger schools (e.g., because the population served by these schools is wealthier, which is the case), or that the information component of the conditional transfers treatments may be less salient than when provided on its own.<sup>24</sup> A second difference in results compared to the unweighted case is that, despite the larger increase in attendance in response to the information treatment compared to the unweighted estimates, the effect on the math score is slightly smaller in magnitude (0.15 instead of 0.18) and just statistically insignificant at the 10% significance level. A final difference between the weighted and unweighted estimates is that the population-weighted estimates of the negative effect of each treatment on the indicator variable for high girl empowerment increase in magnitude, although the smallest p-value remains slightly above 0.10 (0.12).

# **VConclusion**

Regular school attendance is widely believed to be important to support sustained learning (Aucejo and Foy Romano, 2016; Robinson et al., 2017). Child absenteeism is therefore understood to be detrimental both from the pupil’s point of view and from the point of view of the efficient functioning of education systems, which motivates governments around the world to spend vast amounts of tax-payer money incentivizing parents to ensure regular school population is defined as all girls who, based on the 173 schools’ records for 2013-2015, are eligible to enroll in Grade 6 or 7 in 2016.

38
29 Some countries expanded electricity generating capacity with little progress in increasing the access of the population to electricity, while others that increased capacity moderately but had low losses were able to expand coverage. Within the African continent there are striking contrasts. For example, Tanzania expanded generating capacity at an annual average rate of 8% during 2000-2004, but access to electricity remained constant (Table 6). Tanzania’s loss rate is 32%. Ghana increased its installed capacity at an annual average growth rate of 22 %, while it coverage rose modestly by 4 percentage points. On the other extreme, South Africa’s generating capacity rose by 1 percent, but coverage increased the same 4 percentage points, thanks to the low loss rates. India and Pakistan present an interesting contrast. While both have similar low loss rates and capacity expanded at the same rate, India increased coverage from 43% of the population to 56%, while Pakistan’s electrification rates remained practically constant.

Table 6 Where did the Electricity Go? Electrification Rates and Percentage Annual Increase in Installed Capacity in Selected Countries, 2000-2005

||Coverage 2000<br>(%)|Coverage 2005<br>(%)|Average annual<br>percentage change<br>in installed capacity|
|---|---|---|---|
|Ghana|45|49|22|
|Kenya|8|14|13|
|South Africa|66|70|1|
|Tanzania|11|11|8|
|India|43|56|4|
|Pakistan|53|54|4|
|Indonesia|53|54|2|
|Thailand|82|99|8|
|Vietnam|76|84|11|
|Guatemala|67|79|17|
|Source: IEA,WEO 200|2,2006.|||

It is also imperative to reduce losses in the water sector, where utilities in developing countries register physical and commercial losses equivalent to 35% of the total input; in developed nations the loss ratio is only 15% (Table 6A). The monetary cost of these losses will depend on the marginal cost of water and the average tariff charged, but conservative estimates of the loss suggest that it is around US$14 billion. The magnitude of the estimate is appreciated when compared to the estimated US$20 billion required to invest every year to reach the MDG for basic access to potable water in developing countries (Kingdom, et.al. 2006).
- Bigsten, A. Gebreeyesus, M. and Sőderbom, M. (2008) "Agglomeration effects in Ethiopian manufacturing," _Mimeo._ University of Gothenburg.

- Brown, S., Haggblade, S. and Hazell, P. (1989) “Farm-nonfarm linkages in rural Sub-Saharan Africa” _World  Development_ 7(8):1173-1201

- Carlin, W., Schaffer, M. and Seabright, P. (2006) “Where are the Real Bottlenecks? A Lagrangian Approach to Identifying Constraints on Growth from Subjective Survey Data” _Center for Economic Reform and Transformation Discussion Paper_ 2006/04.

- Carlin, W., Schaffer, M., and Seabright, P. (2006) “Where are the Real Bottlenecks? A Lagrangian Approach to Identifying Constraints on Growth from Subjective Survey Data” _Center for Economic Reform and Transformation Discussion Paper_ 2006/04.

- Collier, P. and Gunning, J. (1999) "Explaining African Economic Performance" _Journal of Economic Literature_ 37(1): 64-111.

- Collier , P. and Venables, A. (2008) “ Trade and economic performance: does Africa’s fragmentation matter?” _Paper prepared for the ABCDE conference._

- Cook, P. and Nixson, F. (2000) “Finance and Small and Medium-Sized Enterprise Development” _Finance and Development Research Programme Working Paper Series_ No. 14

- Dasgupta, P. and Ray, D. (1986) "Inequality as a Determinant of Malnutrition and Unemployment, 1: Theory" _Economic Journal_ 96(4): 1011-1034.

- ibid. (1987) "Inequality as a Determinant of Malnutrition and Unemployment, 2: Policy." _Economic Journal_ 97(1): 177-188.

- Deininger, K., Jin, S. and Sur, M. (2007) “Sri Lanka’s Rural Non-Farm Economy: Removing Constraints to Pro-Poor Growth” _World Development_ 35(12): 2056-2078.

- Deininger, K, and Jin, S. (2008) “Key Constraints for Rural Non-Farm Activity in Tanzania: Combining Investment Climate and Household Surveys” _Journal of African Economies,_ 1-43.

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trends -- of labor incomes in the urban informal sector and agriculture. As said above, analysis of total labor costs is extremely relevant vith regard to non-traditional ezports, which is an industry included **almost** entirely in the formal sector of the economy in LDCs.

The information on labor costs used in this study corresponds to production workers **(i.e.,** those directly involved in production). In so e countries, available statistics report wages data for this specific group of **the** labor force. In others *and due to lack of information concerning only production workers, **we** had to resort to data corresponding to all blue collar workers in **the** manufacturing **sector.** It **is** important to note that **the** wage data used in this paper correspond to an average for the entire asnufacturing sector, not only to **the** group of export industries, a fact that **may** prove crucial in interpreting some comparative statistics. Data on non-wage costs of labor vere obtained froa different national and international sources, thus reflecting unaudited and actual payments and contributions to s*voral rrograms associated to employment of a worker in formal activities. In **general** terms, however, our empirical labor cost data **are** comparable amoag countries, and they can be interpreted **as** the cost of unskilled labor in LDCs' manufacturing. Availability of comparable total labor costs among countries **has** preoccupied international agencies for a long time **[see,** for instance, ILO (1983)]. However, a standard compilation of similar labor cost data for LDCs to perform up$irical comparisons is non-existent. Most sources report **measures** of LABOR KAININGS, which include all the gross payments made to **the** workers before any deductions.1 In this paper, we **use** this concept **as** synonymous with WAGE COST OF LABOR (WC). **Given** that aeployers also face other outlays in employing labor, it is useful to distinguish between two other concepts. First, that of TOTAL
# **3. Econometric Methods**

The econometric analysis of contingent valuation data depends on the chosen formulation of the question. The choice between closed-ended questions or "payment cards" depends on several factors. Because of its specific nature closed-ended questionnaires "eat up" a lot of data. Since the identification of an effect rests on the random amounts offered to different individuals, one needs a large sample to achieve statistically significant effects. For similar reasons, the results are more likely to be misleading when a single closed-ended question approach is applied to a small sample.<sup>21</sup> When an open ended question is used, the result is usually analyzed by regressing the contingent valuation on a set of explanatory variables using OLS. We will use this approach as a benchmark. To analyze the answers from closed-ended questions, researchers initially used to run a simple logit or probit model where the explanatory variable was the offer, or the log of the offer. However Cameron and James (1987) show that there is an important difference between the traditional logit/probit model and the dichotomous choice generated by a closed-ended questionnaire. In particular, while in the traditional logit (probit), the β′ _s_ and the σ cannot be identified separately, this is possible in the statistical model generated by the closed-ended contingent valuation model. The reason is the following. Imagine that we ask individuals to report their willingness to pay an amount of taxes _T_ in order to enjoy a new national park. If they accept to pay that amount then _I_ = 1. Otherwise _I_ = 0 . Then where *<sup>_T_</sup> _i_<sup>is the unobserved upper limit of the willingness-to-pay of individual i. The</sup> willingness to pay is function of some variables which we can group under _xi_ .

where _ui_ follows a normal distribution. By the usual reasoning in probit models then

> 21 Cameron and Huppert  use a simulation to generate single closed-ended responses from a "payment card" questionnaire. They conclude that the results from single threshold open-ended questions are substantially different from to the ones obtained from using all the information from the "payment card". Their evidence suggests that if using a single closed ended question, a very large sample is required to achieve the level of accuracy obtained by the "payment card" method.

8