rafmacalaba/datause-encoder-data huggingface dataset:
as an indicator of quality of roads - measured as the percentage of the road paved from the capital of region ‘i’ to the capital of region ‘j’.<sup>20</sup> A road that is paved in most of its length links the four markets under consideration. In addition to this, there are no significant differences in terms of infrastructure. The deeper penetration of cellular phones in Santa Cruz relative to La Paz and Cochabamba is noticeable. In terms of GDP per capita, differences are not significant, and in terms of road quality and density, the departments with poorer indicators are those with larger areas, and lower population densities.

Transport cost and not distance between a pair of cities is what isolates one from the other. This is suggestive of markets working with relative efficiency, and gives some scope for public policy to improve transport infrastructure. There are not other visible patterns that explain market integration. In general, distance between markets does not seem to be an insulating factor. Rice is produced mainly in Santa Cruz and it travels 473 kilometers to reach the market of Cochabamba, and 848 kilometers to reach that of La Paz. That distance between production and the consumption shows in the price difference between producing and consuming regions. This applies for most of the products considered: transport costs are, generally, not binding constraints.

However, the effect of distance on integration seems be product-specific. There are products for which a given distance implies higher transport costs than for other products. In the case of beef, distance is more important due to the fact that it has to be refrigerated during transportation. Unlike most of the other products, beef is mostly transported by air from Santa Cruz to La Paz. Higher transportation costs for beef may be inhibiting trade of low-quality cuts 20 In Table (14), road density is measured as the kilometers of roads divided by the area in sq kilometers of the department. Road quality is the percentage of total roads that are paved. Telephone and cell phone lines are the number every 100 inhabitants. GDP PC is GDP expressed in dollars. Surface is measured in squared kilometers and population density is population/surface. The LHS portion of table (15) shows the distance in kilometers from the capital of one department to the capital of another. C is Cochabamba, SC is Santa Cruz, EA is El Alto and LP is La Paz. The RHS portion of that table shows the portion of the road that links each of the capitals that is paved.

17
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# **Table 8. WTP Regressions**

||Model 1|Model 2|Model3|Model 4|
|---|---|---|---|---|
|Dependent|variable||||
||WTP|log(WTP)|log(WTP)|Log(WTP)|
|independen|t variable<br>||||
|Income|0.0001026***|0.057892***|0.0582288***|0.0561636***|
||0.0000219|0.0095805|0.0096227|0.0100136|
|Cityws|0.0801782|0.0294668|0.0239479|0.0099459|
||0.0558026|0.0279399|0.028052|0.0291706|
|Satisfy|0.0014649|-0.001029|0.0073955|0.0060255|
||0.0381318|0.0191974|0.0191982|0.0199786|
|Cwp|0.4042867***|0.3550591***|0.3562884***|0.3800915***|
||0.0777847|0.0839215|0.0842922|0.0876725|
|Avemwu|0.001246|0.0007092|0.0009485|0.0010851|
||0.0017857|0.0008964|0.0008989|0.0009357|
|Age|0.0019545|0.0010087|0.0009579|0.0010257|
||0.0016269|0.0008166|0.0008202|0.0008538|
|Edu|0.0249529|0.0087343|0.0096548|0.0104211|
||0.01592|0.0079538|0.0079892|0.008316|
|Male|0.0972388**|0.0600857***|0.060987***|0.0742992***|
||0.0433189|0.021718|0.0218148|0.0227041|
|Urban|0.601073***|0.3045403***|0.3099979***|0.3114547***|
||0.0404013|0.0200129|0.0200768|0.0208773|
|Wwqtype|-0.0221602|-0.0096263|-0.0150638|-0.0175809|
||0.0296438|0.0148717|0.0149121|0.0155186|
|order1|-0.2476063***|-0.1259277***|-0.0866356***||
||0.0418999|0.021011|0.0185558||
|order2|0.1264611***|0.0658993***|0.103608***||
||0.0398537|0.019992|0.0174932||
|order3|-0.1726949***|-0.0856627***|||
||0.0431202|0.0216331|||
|ordernorm||||-0.0195295|
|||||0.0154392|
|Con_|.5940617***|-.2081305*|-.2498855**|-.2360578**|
||.2205951|.1149269|.114904|.1189831|
|Overall Mo|del Fit||||
|p-value|0.0000|0.0000|0.0000|0.0000|
|R-squared|0.2250|0.3207|0.3164|0.2894|

***= significantly level at 10%; **=significantly level at 5%; ***=significantly level at 1%.**
3. Impute or not impute monetary outcomes into DHS and MICS, and how? Imputing monetary outcomes into a non-monetary survey is one way of avoiding “wasting” some of the data present in separate surveys, like LSMS and DHS. However, imputation methods have limitations, mainly that their underlying assumptions may not hold in practice. One open empirical question is whether they still improve (inter-households and/or cross-regions) well-being comparisons when their underlying assumptions do not hold. Hence, is it the case that using data from two separate surveys more than compensates for the errors made on their joint distribution? Another empirical question is which imputation method performs best to impute monetary outcomes into non-monetary surveys. The paper should study these questions and quantify how different are the well-being comparisons based on these imputations from currently used comparisons and from “ideal” comparisons made when the joint distribution is known (see sections 3.4.1 and 3.4.2).

4. Accounting for local public services through the total consumption approach

- LSMS consumption surveys often provide very limited information on households’ outcomes in dimensions such as health, education or security. One pragmatic solution is to construct a household’s “total consumption” from monetary valuations of the public services provided in their area. The paper should investigate the best approach to make these monetary valuations at scale given prevailing data constraints. It should then quantify the impact of such valuations on well-being comparisons. Conceptually, it remains an open question how to aggregate consumption aggregates with monetary valuations of public services. Indeed, households cannot freely allocate the monetary valuations from these services. As a result, assuming perfect substitution (i.e., summing these valuations) may not be conceptually ideal. The paper should provide a conceptual discussion of alternative aggregation methods.

5. Multidimensional poverty weights selected from information on preferences The “equal-weights” procedure is regularly used when designing national multidimensional poverty measures and always used for global measures. The use of this obviously ad-hoc procedure runs two risks. First, measures based on ad-hoc weights may yield implausible well-being comparisons in the sense that they would deviate too much from people’s views. Second, ad-hoc weights may reduce the perceived legitimacy of these measures. The paper should propose a conceptually sound method to select values for these weights that is consistent with information on preferences (see section 3.6). It should also study empirically the extent to which the obtained weights yield different well-being comparisons than “equal-weights”. The information can come from either subjective well-being data or from a one-off dedicated preference elicitation survey.

40
figures refer to a definition of rural as disperse areas. Including grouped or semi-urban areas, the share of workers declaring nonfarm activities as their primary occupation would rise significantly. In Mexico, for example the number increases from 44 to 55 percent.

# **4. Correlates of Participation in Rural Nonfarm Employment**

What determines what type of workers are most likely to seek employment outside the agricultural sector? This section examines factors, which are associated with employment in nonagricultural activities in dispersed rural areas in Argentina. We present an exercise carried out on the basis of the RNS 2003, using a probit model to determine the probability of individual involvement in non-farm activities as primary occupation, conditional on a range of personal, household and geographical characteristics. The specification of the model draws on findings from the poverty analysis, which suggests that the choice of primary occupation is affected by for example education and gender. Rather than reporting the parameter estimates, which are difficult to interpret on their own, Table 4.1 presents the marginal effects associated with each explanatory variable. These can be interpreted as indicating the effect of a percentage change in the explanatory variable on the probability of involvement in nonfarm business activities, taking all other variables in the specification at their means.<sup>12</sup> Because of limitations in the RNS survey, some important variables cannot be considered, including ethnicity and social networks.

> 12 For dummy variables, the marginal effect is calculated as the change in the dependent variable associated with a move from a value of zero for the dummy, to one, holding all other variables constant at mean values.

13
_Policy credibility_ was measured by creating an indicator covering three dimensions: whether policies were consistent; whether they are coordinated between the units of government e.g. ministries; and whether political interference/micro- management was felt likely. Responses from six questions were used to calculate the policy credibility indicator as described in Appendix 2.

_Resource adequacy and predictability_ was measured by creating an indicator compounded from six questions about: unpredictable seasonal absences of personnel due to severe weather conditions e.g. hurricane; anticipated supply of necessary skills; and about more general anticipated capacity. Appendix 2 provides details.

Using a similar method, an overall institutional environment indicator was also constructed on a scale of 0 to 10 (O = worst, 10 = best) by taking the simple arithmetic mean of the rule credibility, policy credibility and resource adequacy and predictability indicators. In this way, as Appendix 2 illustrates, an overall institutional environment indicator was calculated for the entire public sector of each of the East Caribbean states.

Using this approach, it is possible to measure rule credibility, policy credibility or resource adequacy and predictability _of a particular organization_ (e.g. Finance Ministry) or by _types of organizations_ (e.g. regional administrations) when sufficient data is available for each type of organization. This was possible in the Argentina, Bolivia, Guyana and Indonesia data sets, and such calculations were made.

19
# For Online Publication: Appendices

## **A Data**

### **A.1 School-Level Data**

#### **A.1.1 Public Schools**

The starting point for this dataset is audited enrollments from the District of Columbia Office of State Superintendent of Education (OSSE), available at http://osse.dc.gov. This gives us the list of public schools and their grade-level enrollment. From the original list we exclude alternative schools, special education schools, early childhood centers (as long as they never include any of grades 1 through 12 during the sample period) and schools with residential programs. Our data pertain to grades kindergarten through 12th. For each school we collect the information listed below:

- Address: from the Common Core of Data (CCD). We geocode all addresses.

- School enrollment: total school enrolment excluding ungraded and adult students. Source: own calculations based on OSSE.

- Grade-level enrollment: for each grade between kindergarten and 12th. Source: OSSE.

- Focus: Source: Filardo et al (2008).

- Percent of white students: calculated based on CCD. For cases in which CCD data are not available, we use the demographics reported to OSSE in order to fulfill No Child Left Behind (NCLB) requirements (see http://www.nclb.osse.dc.gov/). Note, however, that the NCLB requirements pertain to students enrolled in the grades tested by law, not to the entire student body. We include “other ethnicities” (such as Asian students) among white students.

- Percent of black students, percent of Hispanic students: constructed similarly to percent of white students.

- Percent of low income students: calculated as the percent of students who receive free or reduced lunch. Source: own calculations based on CCD. For the few cases in which the CCD data are not available we use the demographics reported in fulfillment of NCLB requirements.

- Reading proficiency: percent of students who are proficient in reading. Source: OSSE. In 03 and 04, proficiency levels were determined according to the Stanford9 assessment. To be considered proficient, a student was supposed to score at the national 40th percentile or higher. Since 05, proficiency has been determined according to DC CAS (Comprehensive Assessment System).

Prior to 05, grades tested were 3, 5, 8 and 10 (according to the School Performance Reports for PCBS-authorized charter schools, and according to our own calculations comparing grade-level enrollment with number of students tested). Since 05, grades tested have been 3, 4, 5, 6, 7, 8 and 10 (according to OSEE, School Performance Reports and our own calculations comparing grade-level en41
**Table 3: World shares of Intra‐regional trade by GVC categories, final and intermediate goods, 2014**

|**Final or**<br>**Intermediate**|**Individual GVCs**|**Intra-regional**<br>**Exports**|**Intra-regional**<br>**Imports**|
|---|---|---|---|
|Intermediate|Electronics - Intermediate Good|64%|70%|
|Intermediate<br>Intermediate|Footwear - Intermediate Good<br>Motor Vehicles - Intermediate<br>Good|59%<br>56%|60%<br>57%|
|Intermediate|Apparel - Intermediate Good|55%|52%|
|Final|Motor Vehicles - Final Good|52%|53%|
|Final|Textiles - Final Good|41%|43%|
|Final|Electronics - Final Good|39%|47%|
|Final|Apparel - Final Good|37%|45%|
|Final|Footwear - Final Good|36%|45%|

<u>Notes: Values in percentage shares from original variables expressed in thousand current US$ and calculated on the total number</u> of 256 countries/geographies with available data on GVC Exports in at least some years of the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits, mirror data). All trade data is drawn from Comtrade on WITS, and data on countries' region and income group in each year comes from WB classifications.

<u>Source: Trade: World Bank MC‐GVC database</u> (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).

**Table 4: World shares of Intra‐regional trade by GVC categories, 2014**

|**Individual**<br>**GVCs**|**Intra-**<br>**regional**<br>**Exports**|**Intra-**<br>**regional**<br>**Imports**|
|---|---|---|
|Motor Vehicles|54%|55%|
|Electronics|50%|57%|
|Textiles|41%|43%|
|Apparel|41%|47%|
|Footwear|38%|46%|

<u>Notes: Values in percentage shares from original variables expressed in thousand current US$ and calculated on the total number</u> of 256 countries/geographies with available data on GVC Exports in at least some years of the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits, mirror data). All trade data is drawn from Comtrade on WITS, and data on countries' region and income group in each year comes from WB classifications.

<u>Source: Trade: World Bank MC‐GVC database</u> (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).

25
Chart 3. Current Account<sup>Deficits as a Percent of GDP</sup> Annual Average from First<sup>Year of Capital Inflows to 1994</sup> <!-- Start of picture text --> 8<br>1990<br>7<br>~~~1988<br>6<br>'1989<br>5 _ Y 1991 19-92<br>4<br>1991<br>3 1990<br>1990<br>2<br>1992<br>Mexico Thailand Malaysia Colombia Philippines Argentina Indonesia Chile Brazil<br><!-- End of picture text --> Source: World Economic Outlook, International<sup>Monetary Fund.</sup> Note: The date for each country denotes the year<sup>in which the surge in capital inflows began.</sup>
21

**Table 3:** Poliev Change and Growth in Devoloping Countries

|_____________.________|(1)<br>_______________________|(2)<br>________________________|(3)<br>__|(4)|(5)|
|---|---|---|---|---|---|
|Dep.Var.|GR6589|GR6589|GR6589|GR6589|GR6589|
|GDP65<br>HXS65<br>M3Y65<br>IY6589<br>SEGRY6S89<br>DHKS|-1.25<br>(0.427)*<br>0.40<br>(0.168)*<br>0.041<br>(0.021)*<br>0.068<br>(0.029)*<br>-0.189<br>(0.137)<br>-0.087<br>(0.216)|-1.259<br>(0.42)*<br>0.441<br>(0.154)*<br>0.044<br>(0.018)*<br>0.061<br>(0.021)*<br>-0.235<br>(0.096)*<br>-----|-0.768<br>(0.432)*<br>0.478<br>(0.165)*<br>0.041<br>(0.014)*<br>0.059<br>(0.019)*<br>-0.165<br>(0.087)*<br>-----|-0.836<br>(0.458)*<br>0.445<br>(0.159)*<br>0.039<br>(0.014)*<br>0.063<br>(0.019)*<br>-0.142<br>(0.083)*<br>-----|-0.825<br>(0.457)*<br>0.856<br>(0.428)*<br>0.083<br>(0.040)*<br>0.038<br>(0.029)<br>-0.19<br>(0.101)*<br>----|
|DXY|0.016<br>(0.032)|-----|-----|-----|----|
|DINF|-0.002<br>(0.004)|----|-|-----|----|
|DBMP<br>DBSRCB|-0.0005<br>(0.0005)<br>-0.035<br>(0.021)|-0.0007<br>(0.0003)*<br>-0.034<br>(0.020)|-0.0009<br> <br>(0.0002)0<br>-0.037<br>(0.020)*|-0.0009<br> <br>(0.0003)*<br>-----|-0.0008<br> <br>(0.0003)*<br>-0.027<br>(0.019)|
|DCPY|------|-----|-----|0.026<br>(0.015)|----|
|DCGY|------||-----|-0.012|----|
|||||(0.019)||
|M3Y6589*IY65|89 ------|-----|-----|-----|0.0004<br>(0.0008)|
|M3Y65*HKS65|------|-----|-----|-----|-0.018<br>|
||||||(0.015)|
|DA|-0.70<br>(0.458)|-0.687<br>(0.0465)|-----|-----|-----|
|DLA|0.933|0.852|-----|-----|-----|
||(0.778)|(0^0724)||||
|c|<br>-1.290<br>(0.774)|-1.355<br>(0.715)*|-1.545<br>(0.647)*|-1.858<br>(0.745)*|-2.073<br>(0.92)*|
|Adj. R2|0.441|0.468|0.434|0.42|0.433|
|S.E.R.<br>Obser.|1.306<br>52|1.274<br>52|1.314<br>52|1.331<br>52|1.316<br>52|

Notes: Coefficients in parentheses are the standard errors of the coefficients; ***** shows the significance of the coefficient at the 5% level; SER is the standard error of regression.

Definition of variable: OR6589 is the annual average real GDP per capita growth rate; ODP65 is the log of real GDP per capita in 1965; ER865 is the estimated years of schooling in 1965; N3Y65 is the ratio of total assets of the financial system to GDP in 1965; IY658T is the average ratio of real investment to GDP; 5S0RY6589 is the average ratio of weignorage revenue to GDP (defined as the change in claims of the central bank on government to GDP in the same year); DHKS is the change in human capital stock, DXY is the change in export to GDP ratio, DIN? is the change in inflation rate, DISP is the change in black market exchange rate premium; DBSRCB is the change in commercial banks' reserves with the central bank to total financial assets, DCPY **is the** change in. private sector credit to GDP ratio, DCOY is the change in the government sector credit to GDP ratio, U3Y6589*XY6589 is the interaction term between average financial development and investment rate; K3Y65*HKS6S is the interaction term between initial financial development and human capital stock; DA and DLA are dummy variables for Africa & Latin America.
# XXIV INTRODUCT1ON

**Figure** 8 Percentage Distribution of Wc,ld Population by Continent, 1985

<!-- Start of picture text --> ELrVe on: U.S.S.R CIS.SM<br><!-- End of picture text --> <!-- Start of picture text --> eRanIn CD5)<br><!-- End of picture text --> Figure 9 Percentage Distribution of World Population by Continent, 2000 <!-- Start of picture text --> Europe Or4 U.S S R C`3 sW Africa (1C 0%)<br>M~eICO C13 3%)<br>Oceamnl CO 3M<br>Agla CS9t<br><!-- End of picture text --> Figure 10 Percentage Distribution of World Population by Continent, 2025 <!-- Start of picture text --> Eurae am U.S S . (to 1P)<br>Asia csa A c1<br>/ / / / / / / / r~~~~~~eanc CO 4%)<br>Asia ////<br><!-- End of picture text -->