rafmacalaba/datause-encoder-data huggingface dataset:
estimate parameters of demand and simulate coverage rate. Unfortunately, this approach will often not work for service such as improved supply of safe, reliable and sufficient water because there are no historical data in the context where the plans are being made. By definition, new government policies and improved products are beyond the range of historical experience. In this setting, the analyst typically has to rely on stated preference (SP) approaches such as contingent valuation surveys (also called WTP surveys) that directly elicit willingness (and ability) to pay statements from respondents (Mitchell and Carson, 1989). In other words, respondents are directly asked about their WTP (Whitehead et al., 2005). The method involves the development of a hypothetical market in the context of in‐person, telephone, mail, or other types of surveys. In the hypothetical market respondents are informed about the current problem and the policy designed to mitigate the problem. The state of the environment before and after the policy is described. Other contextual details about the policy are provided such as the policy implementation rules (e.g., majority rule) and the payment vehicles (e.g., increased taxes or utility bills). Finally, a hypothetical question is presented that confronts respondents with a choice about improved environmental quality and increased costs versus the status quo. Respondents can be presented with multiple scenarios and make multiple choices.

In addition to solving the problem of missing ‘behavioral data’, consider two advantages of conducting WTP surveys in this context. First, seeking households’ opinions and preferences during the planning and design stage is widely viewed as an important ingredient of the economic development process. Paraphrasing Sen (1998), participation is development. Second, the typical WTP survey presents an important form of experimentation which lies somewhere along the spectrum of laboratory experiments and observational studies. Such survey based ‘field experiments’ represent a practical mix of control and realism. Control comes from the design of the survey sample and the structure of the survey instrument. Contrasted to the laboratory settings of experimental economics, realism comes from interviewing people in their homes about goods and services that are important to their daily life, and considering the infrastructure programs that are familiar to respondents.

What can we get from WTP surveys for improved piped water services? It is important to review some of the qualifications regarding the typical product – mean WTP – because the economic concept measured by WTP can be difficult to grasp, leading to misinterpretations and misuses of results. Analysis and interpretation of survey data usually generates a measure of 3
As far as Puzhehei water quality project is concerned, only 7% respondents have ever heard the project being proposed before. Thirty-nine percent of the households are currently living within the project domain, making them direct project beneficiaries. When responding why they perceive the project as important, income increase, lake view improvement, swimming condition improvement, environment protection, health benefit, tourism benefit, and next generation benefit are all frequently mentioned by the respondents (19%, 36%, 16%, 36%, 39%, 36%, and 46%, respectively). A majority of respondents (87%) have confidence that the project would prevent lake water from further deterioration. 56% of the interviews are conducted individually and the remaining 44% of interviews are conducted in a group setting.

# **5. WTP Estimation**

# 5.1 Analytical Framework

We followed the estimation strategy proposed in Wang and He (2010) to conduct the empirical analysis, which is summarized below. Suppose an individual i’s WTP is Vi, which is a random variable with a cumulative distribution function F(t). The mean value of Vi is  i and the standard variance is  i. The WTP model can be written as, Vi=μi+  i (1) where  i is a random term with a mean of zero. Individual i knows his valuation distribution. When given a price tij, the probability for the person to say “yes” to the offer tij will be, Pij = Prob(Vi>tij)=1-F(tij) (2) Once Pij, the probabilities for individual i to agree to the price tij, is known to a researcher, either by assigning numerical values to the verbal MBDC data or by directly asking individuals of their numerical likelihood information as did with the SPC approach, equation (2) can be estimated for each individual.

The estimation model can be constructed as follows: Pij = 1-F(tij) + λi (3) where λi is an error term with a mean of 0 and a standard variance of <sup>2</sup> .  can be constant for a respondent i, but different for different respondents. Pij is a dependent variable, which is the uncertainty answer given by a respondent i at price j. Pij takes values between 0 and 1, and can be viewed as a continuous variable. tij is an independent variable, which corresponds to the bid price proposed in the questionnaire, and tij is also a continuous variable.

10
# **2.2 Economic and Social Outcomes**

Undeniably, the demand‐led growth model summarized earlier has had positive effects on both economic and human development in Morocco.

## **2.2.1 Growth, Inflation, and Fiscal Balance**

After increasing at about 3.2 percent per annum during the period 1990‐99, Morocco’s GDP growth expanded by 4.7 percent per annum during the period 2000‐09 and by 3.6 percent over the period 2010‐2017 (Figure 2.3). Concomitantly, after increasing by about 1.5 percent per annum between 1989 and 2000, real GDP per capita increased at an annual rate of 3.1 percent between 2001 and 2017, thereby raising its level from about 13,702 Moroccan dirhams (MAD) in 1990 to nearly 26,217 dirhams in 2017 (Figure 2.4).

At the same time, the volatility of the GDP growth rate (as measured by its standard deviation) dropped from 6 during 1990‐99 to about 1.8 during 2000‐2017 (Ministry of the Economy and Finance, 2019). For agriculture alone, that volatility fell by two‐thirds during the same period. Thus, not only did Morocco maintain a sustained growth rate, its fluctuations fell markedly in the past two decades. This increased stability was also accompanied by major changes in the sectoral structure of the Moroccan economy, including a reduction in agriculture’s and manufacturing’s share in GDP, a sharp increase in the tertiary sector (mainly in connection with the sustained development of telecommunications and financial services), and rapid development of off‐shoring activities.

**Figure 2.3 Morocco: Growth rate of real GDP and non‐agricultural GDP, 1990‐2017 (Annual percentage change)**

<!-- Start of picture text --> 15 Average<br>Average 4.7 % Average 3.6 %<br>3.2 %<br>Average 4.6 % Average 3.6 %<br>10 Average<br>3.5 %<br>5<br>0<br>-5<br>-10<br>Non agricultural GDP Real GDP<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source_ : World Bank.

6
the probability of working, either exclusively or in combination with school attendance, increases (top panel in Figure 6). The average number of hours that a child works steadily increases with the number of animals (bottom panel in Figure 6). Consistent with previous results for _Progresa/Oportunidades_ (Gertler et al., 2012), using evidence follow-up survey we find that among households with at least one child aged 8-13, PAL increased the number of animals owned, with an effect that is statistically larger for cash than in-kind ones (column 1 in Appendix Table A8). Households from both treatment groups are more likely to report being involved in agricultural activities after the program inception (column 2 in Table A8), with the effect being statistically larger among those from cash localities than in-kind ones. We also find that households in the cash group are more likely to report a higher number of family members involved in agricultural activities, and a higher number of hours farming, but in both cases differences are not statistically significant at conventional levels (columns 3 and 4 in in Table A8). Results presented in Skoufias et al. (2013) find that PAL did not lead to any change in the overall labor supply of adult males and females, but adult males substituted agricultural activities with non-agricultural ones. Also, Tagliati (2019) finds that among older children (age 15-16 at the baseline) both transfers increased child labor, with a stronger impact for cash transfers. Taken all together, the available evidence is consistent with the hypothesis that at least in households with primary school age children, the transfer contributed to a partial reallocation of agricultural tasks from adults and older children to younger ones.

In summary, the results in this section are highly suggestive that the PAL transfer, especially when distributed in cash, led to an increase in the return to labor of young children and a higher probability of combining work and school attendance. The fact that children in inkind localities, despite the improved nutritional intakes, do not reduce their labor supply can potentially help explain why they did not experience any improvement in their test scores.

# **5.3 School quality**

As discussed above, children in our sample have access to three types of school service, that are very different in terms of quality and costs. Using the information from the Formato 911 for the school associated to the individual score, we test whether the two transfer modalities had an impact on the probability of attending a general school (vis a vis an indigenous or a community one), and characteristics that make up for most of the direct costs of school attendance: school distance from the community of residence, the sum of school contributions, material and uniform costs, and the student teacher ratio. Finally, we look at student-teacher ratio as a proxy for resource congestion.

20
Table 2.5a. Shapley Decomposition of Regional Differences in Poverty for 2001

|||Headcoun|t|P|overty Ga|p|Squar|ed Povert|y Gap||Watts||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Difference|Scale|Inequality|Difference|Scale|Inequality|Difference|Scale|Inequality|Difference|Scale|Inequality|
|Douala|-29.29|-29.85|0.56|-10.71|-10.52|-0.19|-4.84|-4.64|-0.19|-14.76|-14.32|-0.45|
|Yaoundé|-26.84|-30.08|3.24|-10.13|-10.56|0.43|-4.70|-4.73|0.04|-14.10|-14.45|0.35|
|Adamaoua|8.20|14.54|-6.34|2.60|7.02|-4.42|0.83|3.70|-2.88|2.94|10.27|-7.33|
|Center|8.00|15.07|-7.07|2.19|6.42|-4.24|1.08|3.28|-2.21|3.67|9.40|-5.73|
|East|3.80|9.34|-5.54|2.58|5.70|-3.12|1.20|3.07|-1.88|3.48|8.43|-4.95|
|Far-North|16.11|22.73|-6.62|6.05|11.11|-5.05|2.62|5.96|-3.33|7.97|16.45|-8.48|
|CoastT|-4.70|2.47|-7.17|-2.70|0.71|-3.40|-1.38|0.36|-1.74|-3.95|1.02|-4.97|
|North|9.90|13.30|-3.40|2.71|6.36|-3.65|0.81|3.30|-2.49|3.05|9.24|-6.19|
|North-West|12.30|12.69|-0.39|8.11|7.08|1.03|5.15|4.10|1.05|13.45|11.00|2.45|
|West|0.15|10.17|-10.02|-1.69|4.33|-6.02|-1.36|2.21|-3.57|-3.19|6.22|-9.41|
|South|-8.63|3.94|-12.57|-5.43|1.61|-7.04|-3.13|0.76|-3.89|-8.34|2.24|-10.58|
|South-West|-6.36|-2.49|-3.86|-2.28|-1.05|-1.23|-1.04|-0.54|-0.50|-3.25|-1.53|-1.72|

Source: Authors’ Calculations Table 2.5b. Shapley Decomposition of Regional Differences in Poverty for 2007

|||Headcount||Po|verty G|ap|Square|d Povert|y Gap||Watts||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Difference|Scale|Inequality|Difference|Scale|Inequality|Difference|Scale|Inequality|Difference|Scale|Inequality|
|Douala|-34.40|-26.66|-7.73|-11.44|-8.57|-2.87|-4.81|-3.51|-1.30|-15.10|-11.23|-3.88|
|Yaoundé|-33.96|-26.34|-7.62|-11.35|-8.54|-2.80|-4.79|-3.55|-1.23|-14.99|-11.25|-3.73|
|Adamaoua|13.05|17.73|-4.68|2.17|7.41|-5.23|0.39|3.69|-3.31|2.35|10.51|-8.16|
|Center|1.29|14.76|-13.46|-2.83|5.76|-8.59|-1.93|2.69|-4.62|-4.43|7.99|-12.43|
|East|10.51|16.43|-5.92|3.37|7.98|-4.61|1.20|4.25|-3.05|4.14|11.57|-7.44|
|Far-North|25.97|24.77|1.20|12.26|14.59|-2.33|6.18|8.43|-2.25|17.23|22.02|-4.79|
|Coast|-8.82|3.09|-11.91|-4.66|1.34|-6.00|-2.32|0.65|-2.97|-6.51|1.87|-8.38|
|North|23.76|24.15|-0.39|8.67|12.63|-3.96|3.55|6.68|-3.13|11.32|18.35|-7.03|
|North-West|11.10|9.66|1.44|4.30|5.61|-1.31|1.81|3.00|-1.19|5.67|8.15|-2.48|
|West|-10.95|2.85|-13.80|-5.68|0.98|-6.66|-2.76|0.47|-3.22|-7.87|1.36|-9.23|
|South|-10.64|-2.96|-7.68|-4.94|-1.27|-3.67|-2.38|-0.62|-1.76|-6.80|-1.77|-5.03|
|South-West|-12.39|-4.46|-7.93|-5.45|-1.78|-3.67|-2.55|-0.85|-1.70|-7.46|-2.47|-4.99|

Source: Authors’ Calculations 16
# Unpacking the MPI: A Decomposition Approach of Changes in Multidimensional Poverty Headcounts<sup>_∗_</sup>

Jorge Eduardo P ́erez P ́erez<sup>_†_</sup> Carlos Rodr ́ıguez Castel ́an<sup>_‡_</sup> Jos ́e Daniel Trujillo<sup>_§_</sup> Daniel Valderrama<sup>_¶_</sup>

**Keywords** Multidimensional Poverty, Decomposition and Non Parametric Methods, Shapley Value, Poverty Transitions

## **JEL Codes** I32, I38, C14

> _∗_ The authors would like to thank Paola Ball ́on, Wendy Cunningham, James Foster, and participants of the 2014 Summer Initiative for Research on Poverty, Inequality and Gender of the Poverty Global Practice at the World Bank, and the 2015 LACEA meeting for their useful comments and suggestions. The views, findings, and conclusions expressed in this paper are entirely those of the authors and do not necessarily reflect those of the World Bank, its Executive Board or member country governments. P ́erez P ́erez gratefully acknowledges financial support from Fulbright-Colciencias.The do-file code that implement this decomposition could be provided upon request

> _†_ Corresponding author. Department of Economics, Brown University.

Email: jorge ~~p~~ erez@brown.edu

> _‡_ World Bank - Poverty Global Practice. Email: crodriguezc@worldbank.org

> _§_ DANE. Email: jdtrujillos@dane.gov.co

> _¶_ World Bank - Poverty Global Practice. Email: dvalderramagonza@worldbank.org
**Table 2: School Construction and Child Health: Difference-in-differences estimates of intentto-treat.**

||1966-1970|Maternal Cohort|Differen|ce in Dif|ferences|
|---|---|---|---|---|---|
||High INPRES|High-Low INPRES||||
||Mean|Gap|[1]|[2]|[3]|
|_Panel A: Health at birth_||||||
|Child’s birthweight (grams)|3,076.8|-44.6|44.07|37.01|37.21|
||||(45.93)|(44.68)|(44.44)|
||||[0.456]|[0.480]|[0.464]|
|spaceLow birthweight incidence (%)|20.5|8.7|0.20|0.47|0.42|
||||(2.84)|(2.79)|(2.77)|
||||[0.916]|[0.736]|[0.980]|
|_Panel B: Health in infancy_||||||
|Child was sick in past fortnight|57.4|1.1|-2.32|-2.07|-2.02|
|(cough/fu/fever/stomach ache) (%)|||(4.36)|(4.29)|(4.33)|
||||[0.664]|[0.656]|[0.668]|
|Child’s weight-for-age (WHO z-score)|1.64|-0.30|0.09|0.10|0.10|
||||(0.09)|(0.09)|(0.09)|
||||[0.456]|[0.420]|[0.460]|
|spaceChild is wasted (%)|33.5|13.4|-3.95|-4.26|-4.45|
||||(3.85)|(3.82)|(3.83)|
||||[0.408]|[0.364]|[0.404]|
|spaceChild is extremely-wasted (%)|8.7|4.7|-3.61|-3.90|-3.94|
||||(2.04)|(1.97)|(1.97)|
||||[0.148]|[0.108]|[0.112]|
|Child’s height-for-age (WHO z-score)|1.41|-0.30|0.14|0.15|0.15|
||||(0.07)|(0.07)|(0.07)|
||||[0.072]|[0.056]|[0.080]|
|spaceChild is stunted (%)|20.7|8.1|-2.58|-2.76|-2.96|
||||(2.32)|(2.32)|(2.31)|
||||[0.188]|[0.244]|[0.224]|
|spaceChild is extremely stunted (%)|4.6|1.5|-2.55|-2.68|-2.72|
||||(1.25)|(1.23)|(1.22)|
||||[0.064]|[0.048]|[0.048]|
|_Controls (D-in-D’s)_||||||
|Caregiver district and year of birth FE’s|||YES|YES|YES|
|Treated cohort interaction with # school-|aged children in|1973 (ln scale)|NO|YES|YES|
|Child gender|||YES|YES|YES|
|Child-age FE’s|||YES|YES|NO|
|Child’s age(linearly,in months)|||NO|NO|YES|

Notes: Sample is 2,118 children. Standard-errors in parentheses are clustered at the district of maternal birth. Wildbootstrap significance p-values in brackets.
<!-- Start of picture text --> a) 600,000 60,000<br>< _<br>=<<br>ax=<br>=<2= 500,000 50,000 =§_vn<br>xo<br>=}<br>400,000 40,000 oO<br>oe<br>a<br>c<br>c<br><x<br>300,000 30,000<br>200,000 20,000<br>100,000 10,000<br>0 0<br>Angola DRC Ethiopia Kenya Madagascar Mozambique Sudan Tanzania Uganda<br>Ma Number of HAlsNumberof HAls ==<<=Deaths: HAls (no AMR) <———<=Deaths: HAls withAMR —=Deaths: Deaths<br><!-- End of picture text -->
The GIDD’s framework is based on micro-simulation methodologies developed in the recent literature, including Bourguignon and Pereira da Silva (2003); Ferreira and Leite (2003, 2004); Chen and Ravallion (2003); and Bussolo, Lay, and van der Mensbrugghe (2005). The starting point is the global income distribution in 2000, assembled using data from household surveys (see above).<sup>12</sup> The hypothetical distribution is then obtained by applying three main exogenous changes to the initial distribution: (a) shifts in the sectoral composition of employment; (b) economic growth, including changes in relative wages across skills and sectors; and (c) changes in real income derived from the shifts in food prices.

The empirical framework is depicted in Figure 5 **_._** The starting point is the price and quantity effects following the removal of agricultural distortions which are computed using the global CGE model (top part of Figure 5). The CGE will compute the values of the three variables linking the macro and micro levels of the model (middle part of Figure 5): overall economic growth, real wage premiums among agricultural/non-agricultural and skilled/unskilled groups, and the consumption (or real income) effects brought about by the change in relative price of food. These CGE results are passed-on to the household survey data, creating a new, hypothetical household income distribution (bottom link in Figure 5). Distribution and poverty comparisons between the initial and the counterfactual income distributions will capture the welfare effects of the removal of global agricultural distortions. By taking into account labor market (returns to skills in and out the agricultural sector) and consumption effects whilst evaluating macro policies, GIDD’s framework closely maps the theoretical linkages outlined in the previous section.<sup>13</sup>

**Figure 5 GIDD methodological framework**

<!-- Start of picture text --> Removal of Global Agricultural Distortions<br>CGE – LINKAGE<br>Growth Real Wages Food Prices<br>Effects (by skill and<br>sector)<br>Hypothetical Income<br>Distribution<br><!-- End of picture text --> 12 Throughout the paper, when we talk about the global distribution, we are indeed referring to the GIDD’s sample covering 92 percent of the world population.

13 The GIDD does not take into account the welfare impacts via changes in transfers resulting from the trade reform.

10
Using a combination of a manufacturing survey and data on expansion of preschools in Indonesia, we provide causal evidence of the impact of institutional childcare availability on manufacturing plants’ productivity. We do so by employing a triple diff-in-diff estimation comparing the outcomes of plants in sectors that rely on female labor at baseline to different degrees. This allows us to address several key threats to the identification by employing a large array of time varying fixed effects. Our preferred results suggest that an additional preschool per 1,000 children (approximately 1/3 standard deviation increase in preschool density) boosts TFPR by 11 percent for plants with average fraction of women among their employees. The increase is driven by changes in technical efficiency of the firms: we find zero impact on markup and impacts on TFPR and TFPQ are of similar magnitude.

Our results are robust to various changes in specification. In addition to our preferred OLS specification, we measure impacts of preschool expansion on plant productivity in an instrumental variables framework. We also confirm the robustness of the estimates to using different levels of sectoral aggregation, different methods of accounting for missing years in preschool data, and to including a rich set of time-variant control variables. We find suggestive evidence in support of two channels underlying the observed increase in productivity: better allocation of talent leading to improved matching between firms (à la Pissarides, 2000), and increase in job stability, which we measure through a reduction in turnover.

Our study offers the first evidence that provision of childcare may enable governments to reap short-term economic benefits through higher firm productivity. To what extent this effect could hold beyond the specific Indonesian context remains an open question. At the beginning of the study period only 49 percent of Indonesian women worked, and a large fraction of these women were highly educated. Moreover, Indonesian firms, more than their counterparts in the neighboring countries, suffered from low access to qualified personnel. Thus, removing childcare constraints to women’s labor force participation resulted in an influx of educated workers in the labor market. In a context where women who are out of the labor force have lower education levels, investment in childcare may generate lower immediate benefits. As such, more research is needed to test for the external validity of the impacts of access to childcare on firm productivity.

21
extended to children’s _general_ needs. We find no correlation or causal link between maternal time on children’s general needs and maternal education. The OLS estimate is precisely 0 and the IV estimate is of the wrong sign (Table 6). Take together these results strongly suggest that improvements in education at low starting levels do not alter bargaining power within the household. However, education still has an impact. The channel that figures prominently and causally is the way in which both mothers and children allocate their time in a household learning environment created by “educated” mothers.

# **Section 4: Robustness and Limitations**

There are some limitations to the estimates presented here. First, like with all IV estimates, we estimate the Local Average Treatment Effect, or LATE. It is likely that the compliers—mothers who shifted their education levels as a result of school presence—behave differently from the sample of all mothers (Card, 1999). Second, our sample sizes are much smaller than previous studies in the literature (Currie and Moretti (2003) for instance, use more than 600,000 observations in their IV sample.). This is primarily because there are no standard datasets from low-income countries that allow mothers to be matched to their birth-villages. The data therefore have to be collected from scratch and villages have to be matched manually after data collection. We also required detailed data on child learning and time-use that precluded using larger pre-existing household surveys. Thus achieving large sample sizes was beyond the scope our data collection exercise. Consequently, given the well known problems of poor efficiency in IV estimates for smaller samples (in the case of discrete variables, Chiburish and others, 2006, show that effect sizes of 0.15 can be detected in 95 percent of cases with IV estimates only once the sample size crosses 22,000), several of our estimates are plagued by low precision.

Given the small sample size, we were also unable to include birth-village fixed effects in the primary specifications. This introduces a concern that variation across villages in the availability of schools could be directly correlated with current child outcomes, perhaps because of the long-term presence of the school itself. Table A2 in the Appendix presents the main results in the paper after introducing a dummy variable for whether the mother’s birth-village ever received a school; the excluded instrument then captures only the variation in age-specific enrollment possibilities for the mother. Our results on the time spent by children in 23