rafmacalaba/datause-encoder-data huggingface dataset:
this assumption by looking at the association between coffee mills and two key health outcomes that have a significant bearing on the cognitive development of children: high-for-age (HAZ) scores and stunting (Grantham-McGregor et al., 1996; Mendez and Adair, 1999).

Our results in Table 8 provide evidence that coffee mills are associated with an improvement in child health. Children living within the catchment area of coffee mills are 5 pp less likely to be stunted (column 3) and experience 0.1 std increase in their HAZ scores (column 1).

# **6.3 School Attendance**

We conclude our section on mechanisms by evaluating the effects on school attendance. Again, our working hypothesis is that the positive income shocks associated with coffee mills enable parents to increase educational investments in their children thereby allowing (incentivizing) the children to attend school and learn.

Ideally, testing this assumption will require granular data on student attendance at the school level before and after the opening of coffee mills. However, this data is unavailable in the study context. The available data on attendance is aggregated at the district level, thereby limiting its use, at least for this study. To this end, we rely on proximate measures of attendance from the DHS dataset, which asks if a "person attended school during the current school year", and restrict the sample to children within the primary school-going age (7 and 16) in Rwanda. Admittedly, this measure is imperfect as it may reflect enrolment rather than (regular) attendance, but we use it as a proxy for attendance.

We, therefore, combine the DHS data and information on the location and opening of the coffee mills to estimate the effects of the coffee mills on our proxy measure of "school attendance". The results as shown in Table 9 suggest a positive effect of the mills opening on school attendance rates. Starting in column 2, we find that children living within the catchment area of an operational coffee mill are about 2 pp more likely to attend school than their counterparts in control communities. Splitting the sample into rural and urban sub-samples, we find that the effects of the coffee mills on schooling are largely concentrated in rural areas (column 4). There is no statistically significant effect on school attendance on children in urban areas. This result concurs with our main results in Table 5<sup>22</sup> which also shows that exposure to coffee mills increases the test scores of children in rural areas with no (statistically) discernible effects on urban students. Overall, the results herein provide suggestive evidence that schooling and learning are potential channels through which the income shocks associated with coffee mills affect students’ performance. This result is in line with prior literature which establishes a

> 22As well as Table A6 in the Appendix.

18
17 Table 3: Household Expenditures Per Person and Social Capital

|Column:|1|2|3|4|
|---|---|---|---|---|
|Source of Data:||Social Capit|al and Poverty Sur|vey|
|Level of Data:|Cluste|r Averages|Hou|sehold|
|Estimation method':|OLS|IV (a)|IV' (A)|IV (B)|
|Social Capital|.119<br>1.80|.496<br>2.75|.559<br>5.374|.345<br>1.29|
|Household size|-.011<br>.21|-.033<br>.424|-.073<br>8.66|-.075<br>6.21|
|Average adult schooling<br>2|-.030<br>.789|-.105<br>1.66|0087<br>.512|.013<br>.761|
|Female head of household<br>(1 =yes)|-.439<br>.714|-.458<br>.566|-.090<br>.810|.070<br>.676|
|Asset ownership (in)<sup>2</sup>|.102<br>1.24|-.038<br>.297|.176<br>2.78|.207<br>3.58|
|Self-employed in agriculture|-.99|-.975|-.207|-.203|
|(1 =yes)|2.76|1.92|2.12|2.32|
|Distance to nearest market<br>|-.023|-.015|.0062|0018|
|(cluster)<sup>2</sup>|1.57|.801|286|.090|
|Agroclimatic zones<sup>4</sup>|||||
|Regre-gsinn RtatigtiCs|||||
|Number of Obs.|53|53|846|846|
|Adjusted R-Squared|.272||||
|First Stage Incremental R2|-|.119|.099|072|
|Instrument test (p-level)|--|.274<br>||<br>.004|.345|

Notes: 1) The t-statistics are Huber corrected standard errors that are heteroskedasticity consistent and account for stratified random sampling.

2) If observations for any of these variables for any households were missing, a value a missing value dummy variable is set equal to one (not reported).

3) The instrument sets are a: trust in strangers, tribesman, cell leader, village chairman (government), district officials, central government while instrument set B excludes strangers.

4) Included in the regressions but not reported are dummy variables for each of six agro-climatic zones.

Instrumental variables estimation uses the correlation between social capital and another variable--the instrument--which is not determined by, and does not directly determine, income
increased participation intensity in a sample of participants in the recycling program. One treatment provided a free recycling bin,<sup>6</sup> and a second treatment provided a bin with a sticker displaying instructions on how to sort recyclables from nonrecyclables (that is, information on the logistics of recycling). These treatment groups were compared with a control group that received nothing. A separate treatment, randomized within the same group, sent weekly short message service (SMS) reminders the day before recyclables would be picked up from the curb.

None of the enrollment messages had significant effects on recycling behavior. This null result is precisely estimated; we can rule out effects larger than 5 percentage points (or approximately 10 percent of the mean level of participation). Similarly, SMS reminders had no impact on recycling behavior. In contrast, providing a bin significantly increased the frequency and amount of recycling. Overall, the results suggest that reducing the time and effort of ongoing usage is more effective than messaging.

# **<<A>>** SETTING AND EXPERIMENTAL DESIGN

Global polling data show that a majority of Latin Americans express concern about the environment, on par with other parts of the world. In the most recent round of the World Values Survey, 65 percent of Latin Americans agreed that they would “give part of [their] income if [they] were certain that the money would be used to prevent environmental pollution.” In contrast, 52 percent of respondents in the United States, 37 percent of respondents in Germany, and 74 percent of respondents in Canada agreed with that statement (World Values Survey Association 2009). In another poll by the Pew Research Center, 73 percent of Latin Americans stated they would prioritize protecting 5
skills sets. In many Asian countries, skills development may not be the primary constraint since a significant pool of educated are unable to find productive employment. The ESI and ASI in this sense provide a more comprehensive picture of the process by which human capital is translated into economic output. The variables used in constructing the ESI and ASI are reported in Tables 1A and 1B.

The ASI tries to preserve the underlying logic of the ESI as is evident in comparing Tables 1A and 1B. Some variables are essentially similar. Preprimary pupil-teacher ratios; percentage enrolled in vocational education; youth not in education, employment, and training; labor force participation rate; etc. are in both indexes. There are, however, some significant differences. The inclusion of LAYS in the ASI in place of PISA scores in the ESI is one major difference. Another has to do with substituting ‘Low Wage Workers’ in the ESI with ‘Vulnerable Employment’ in the ASI. In addition, to Vulnerable Employment, the ASI also includes ‘Unemployed with Advanced Education’. Labor market characteristics such as ‘Vulnerable Employment’ and ‘Unemployed with Advanced Education’ are distinctive features of many Asian labor markets and should be part of any index that purports to capture labor market conditions in such countries. In addition to the inclusion of some variables that are distinctively Asian, the exclusion of other variables in the ESI (Recent Training, High Computer Skills, for instance) were driven by the lack of consistent, uniform data for Asian countries.<sup>11</sup>

> 11 We looked at almost all the series in WDI and Ed Stats to home in on the indicators that were most relevant. This was not a straightforward task. One of the cardinal principles we followed in the paper was that each variable used in the index needed to be from _the same source with precisely the same series code_ . There are, in fact, numerous series that have similar names reported by different organizations using different collection methodologies and, in some cases, quite dissimilar values. In addition, not surprisingly, many series had missing values. Balancing out all these considerations to create a reasonably cohesive, comparable data set was the most challenging aspect of creating the index.

15
and Kenya are included in tabular form. The results are broadly comparable, suggesting some degree of generalizability to other parts of Africa. After describing the estimation of household demand, we discuss estimation of the cost of a centralized grid system that provides complete service coverage to all urban and rural areas. Following that, we describe the cost estimates for stand-alone household-level and minigrid options that exploit locally available non-renewable or renewable energy. Then we compare the levelized costs of each technology to determine the lowest cost options in each geographic area.<sup>5</sup> The result is a spatially-explicit set of expansion paths that delineate frontiers between centralized and decentralized service areas. We then introduce technological change and carbon mitigation economics. We incorporate learning rates to assess future costs and estimate the carbon tax rates necessary to make non-renewables competitive with grid supplied electricity in each part of the country. In these scenarios, we do not consider population growth, which would force a scaling up of supply, but would be unlikely to change relative supply prices; especially since most population growth will likely occur in high density areas where grid connected options dominate.

# **3.1. Household Demand for Electricity**

In accord with a recent engineering feasibility study for Kenya (KMOE 2008), we assume that each connected rural and urban household consumes a fixed quantity of electricity, 120 kWh/month or 4 kWh/day. This is somewhat higher than the combined household and productive demand assumed in Parshall et al. (2008) for all but the most densely populated non-poor areas. Obviously the assumption of fixed average demand across households is a simplification. For our purposes, we are interested more in the question of whether households are connected or not than marginal changes in demand.

> 5  Levelized cost is the cost of supplying a unit of energy over a system’s lifetime that incorporates the initial investment in generation, transmission and distribution infrastructure; capital costs; and operations and maintenance costs including fuel costs.  Levelized costs allow us to compare different technologies on the basis of the minimum unit price a user must pay for each system to break even.

7
17 The decompositions proposed in this project consist of estimating the effects of changing one or more of the arguments of _D[._ on Dt. The simplest decomposition applies to those arguments which are exogenous to the household: that is, the _fis,_ ~S, and the variance of the various residual terms. Changing the _fis_ amounts to assuming a change in the rate of return on human capital variables in equation (2) and (3). We refer to this as a "price effect". In algebraic terms, it can be expressed as: This expression measures the contribution to the overall change in the distribution _Dt'_ - _Dt_ of a change in ,l between t and t', holding all else constant. Likewise, the "labor supply effect" may be defined by: The price effect _Btt'_ is obtained by comparing the distribution at date t with the hypothetical distribution obtained by simulating on the population observed at date t the remuneration structure of period t'. A price effect can be computed individually - that is, for one element of the vector f3, or collectively - that is, for all elements of the vector P. Both types of simulations are reported below.

Likewise, the labor supply effect, _Ltt',_ is obtained by comparing the initial distribution with the hypothetical distribution obtained by simulating on the population observed at date t the occupational preferences observed at date t'. Again, a labor-supply effect can be computed individually - that is, for one element of the vector y, or collectively - that is, for all elements of the vector y. We only report collective labor-supply decompositions in this paper.

_'4_ This section draws heavily on Bourguignon et. al. (1998), adapting it to our specifications.
Sapling biomass (trees less than 5cm at a height of 1.3 m) was estimated using a 100 m<sup>2</sup> concentric circle within the 250 m<sup>2</sup> circle. Biomass was estimated for stem, branch and foliage. On average, sapling biomass is only 3% of total plot biomass, however, and virtually all biomass is contained in trees. Seedling biomass was not estimated, but numbers of tree seedlings were counted using a 3.14 m<sup>2</sup> concentric circle (1 m radius). On average, 1 meter radius plots contained 3.03 seedlings, implying 30,356 seedlings per hectare.

During the plot survey a variety of environmental data were collected that are believed to affect biomass and carbon. Community level data were also collected and four variables are used in the analysis. Community data are directly collected for NCFs and for CFs taken from MoFSC (2013). Both sources use interviews with executive committee members. For CFs, pairing communities with forests was straightforward, because forest metrics and user lists are approved at CF establishment and any changes must be recorded. For NCFs the <u>one</u> forest analyzed is the forest identified by users and/or their leaders as the most important forest used by communities to collect subsistence products, such as fuelwood and fodder and for grazing. NCFs present other challenges. For example, NCFs generally have not been officially mapped. Forest mapping was therefore done based on identification of the periphery by user group leaders, GPS points were taken, area calculated, etc. User households may also be less well defined than in CFs. Member lists sometimes do not exist and there may even be disagreements about the composition of NCFs. Numbers of households in NCFs were therefore calculated onsite after developing user group lists in consultation with user group leaders.

# **4.2 Variables**

Forest data are collected at the plot level and it is at this level that all dependent variables and most independent variables are measured. Relying on our random sampling methodology,

**14 |** P a g e
Interest rate guarantees were introduced on new policies from the early 1980s.<sup>1</sup> The guaranteed interest rate was to some extent linked to the maximum technical discount rate used in the actuarial calculations of liabilities, and was set at 4.5 percent in 1982. New EEC insurance directives were implemented in Denmark in 1994, causing the guaranteed interest rate on new policies to be reduced to 2.5 percent and in 1999 it was reduced to 1.5 percent.

_Table 3: Pension Liabilities Distributed on Interest Rate Guarantee Levels_

||None|0 to 2percent|2 to 4percent|Over 4percent|
|---|---|---|---|---|
|Life insurance companies|3 %|25 %|20 %|52 %|
|General Pension funds|2 %|12 %|31 %|55 %|

Source: Danish FSA As table 3 shows more than 50 percent of the accumulated liabilities of Danish pension institutions had a guaranteed interest rate of more than 4 percent<sup>2</sup> . Furthermore, these American type guarantees could be exercised at any point before or at maturity, which added significantly to the provisions required to cover the obligations. In 1994, when the maximum guaranteed interest rate was lowered to 2.5 percent, most pension institutions also took the opportunity to switch to European type guarantees, which can only be exercised at maturity. Therefore the liabilities have a long duration and the composition is very difficult to adjust to changing investment return expectations, because policies already written cannot be changed and new contributions paid into old policies, under the terms of those policies, continue to be covered by the original guarantees throughout the life of the contract.

The state of the liabilities is of course mirrored in the provisions required to cover future obligations. With falling interest rates and decreased mortality<sup>3</sup> the provisions have increased considerably over the past few years, putting pressure on investment returns and squeezing solvency.

_Table 4: Pension Obligations Return on investments and solvency ratio, 2000 - 2005_

||Provisions for future<br>pension obligations<br>(annual change)|Return on investments<br>(after tax)|Solvency ratio<br>(Buffer Capital,<br>weighted Average)|
|---|---|---|---|
|||Percent||
|2000|7.9|4.95|4.2|
|2001|3.5|-1.2|2.4|
|2002|6.0|1.71|2.2|
|2003|7.9|6.05|2.6|
|2004|11.4|9.11|2.7|
|2005|15.7|12.46|2.6|

Source: Danish FSA 1 This practice was also followed in several other countries in continental Europe. The Danish pension institutions were not unique in offering these guarantees, although the size of the Danish market may have been much greater, relative to GDP, than in any other country.

2 The majority of these carry a guaranteed interest rate of 4.5 percent.

3 Like most European pension institutions the Danish pension sector has also had to adjust provisions to allow for the fact that policy holders are living longer than originally projected.

8
|P125018|2014|2017|6/3/2017|9 Closed|Western Africa|
|---|---|---|---|---|---|
|P125019|2012|2012||0 Closed|Indonesia|
|P125019|2012|2013||0 Closed|Indonesia|
|P125020|2011|2012||0 Closed|Indonesia|
|P125020|2011|2013||0 Closed|Indonesia|
|P125021|2013|2013|5/21/2013|1 Closed|China|
|P125021|2013|2014|12/17/2013|2 Closed|China|
|P125021|2013|2014|6/24/2014|3 Closed|China|
|P125021|2013|2015|12/28/2014|4 Closed|China|
|P125021|2013|2015|6/17/2015|5 Closed|China|
|P125021|2013|2016|12/11/2015|6 Closed|China|
|P125021|2013|2016|6/20/2016|7 Closed|China|
|P125021|2013|2017|12/21/2016|8 Closed|China|
|P125021|2013|2017|6/22/2017|9 Closed|China|
|P125021|2013|2018|12/24/2017|10 Closed|China|
|P125021|2013|2018|6/28/2018|11 Closed|China|
|P125021|2013|2019|12/27/2018|12 Closed|China|
|P125022|2013|2013|6/20/2013|1 Closed|China|
|P125022|2013|2014|12/22/2013|2 Closed|China|
|P125022|2013|2014|6/24/2014|3 Closed|China|
|P125022|2013|2015|12/10/2014|4 Closed|China|
|P125022|2013|2015|6/15/2015|5 Closed|China|
|P125022|2013|2016|12/9/2015|6 Closed|China|
|P125022|2013|2016|6/24/2016|7 Closed|China|
|P125022|2013|2017|12/21/2016|8 Closed|China|
|P125022|2013|2017|6/12/2017|9 Closed|China|
|P125022|2013|2018|7/19/2017|10 Closed|China|
|P125022|2013|2018|12/28/2017|11 Closed|China|
|P125022|2013|2018|6/22/2018|12 Closed|China|
|P125022|2013|2019|12/27/2018|13 Closed|China|
|P125022|2013|2019|4/17/2019|14 Closed|China|
|P125022|2013|2019|6/26/2019|15 Closed|China|
|P125022|2013|2020|10/15/2019|16 Closed|China|
|P125022|2013|2020|11/27/2019|17 Closed|China|
|P125022|2013|2020|12/28/2019|18 Closed|China|
|P125024|2014|2015|10/29/2014|1 Closed|Gambia,The|
|P125024|2014|2015|3/1/2015|2 Closed|Gambia,The|
|P125024|2014|2016|8/6/2015|3 Closed|Gambia,The|
|P125024|2014|2016|10/30/2015|4 Closed|Gambia,The|
|P125024|2014|2016|4/27/2016|5 Closed|Gambia,The|
|P125024|2014|2017|10/31/2016|6 Closed|Gambia,The|
|P125024|2014|2017|5/24/2017|7 Closed|Gambia,The|
|P125024|2014|2018|12/29/2017|8 Closed|Gambia,The|
|P125024|2014|2018|6/29/2018|9 Closed|Gambia,The|
|P125024|2014|2019|12/24/2018|10 Closed|Gambia,The|
|P125024|2014|2019|6/25/2019|11 Closed|Gambia,The|
|P125024|2014|2020|12/20/2019|12 Closed|Gambia,The|
potentially many charters, and vice versa. Finally, we model charter heterogeneity in curricular focus, grade coverage and costs.<sup>3</sup> Second, we contribute to the empirical literature on school choice. While others have estimated school choice models with endogenous peer characteristics (Ferreyra 2007, Altonji et al 2010), we rely on the full choice set of private and charter schools, and model unobserved school quality. In addition to market shares, we match school peer characteristics, neighborhood fraction of children enrolled in charter schools and neighborhood average travel distance to public and charter schools.

Using the full school choice set in addition to modeling school unobserved quality poses severe computational challenges. Hence, we recast our demand-side estimation as a mathematical programming with equilibrium constraints (MPEC) problem following Dube et al (2012) and Skrainka (2012). While these authors supply analytical gradients and Hessians to optimization software, we combine two software solvers in such a way that requires only first-order derivatives, and for these we use a symbolic differentiation tool. We can therefore experiment with different model specifications without recoding derivatives. Thus, our research lies at the frontier of computational methods and estimation.

Third, we contribute to the literature on firm entry in industrial organization, recently reviewed by Draganska et al (2008). We develop a supply-side model of charters featuring the regulator’s key role. The model is realistic as well as tractable, and could be applied to other regulated industries such as child care provision and for-profit tertiary education. The entry literature typically uses reduced-form demand specifications, yet we specify a structural model of school choice and allow for unobserved school quality, as in Carranza et al (2011). A major focus of the entry literature is the strategic interaction between entrants and/or incumbents. We do not, however, model public or private school decisions because there is limited school entry and exit activity during our sample period, and this precludes the identification of a strategic decision-making model for them. Moreover, the six superintendents DCPS has had between 1998 and 2007, coupled with its financial instability, suggests that it may not have been able to react strategically to charters during our sample period.

The rest of the paper proceeds as follows. Section 2 describes the institutional framework and data sources. Section 3 describes basic data patterns. Section 4 presents the model. Section 5 describes the estimation strategy, and Section 6 presents estimation results. In Section 7 we discuss counterfactual results, and Section 8 concludes.

3Other related work includes Walters (2012) and Neilson (2013). Using data on charter school lotteries and individual-level school choice and achievement, Walters estimates preference and achievement parameters. Neilson (2013) uses Chilean student-level data to estimate achievement and BLP-style preference parameters. Neither Walters nor Neilson model school entry or endogenous peer characteristics.

5