rafmacalaba/datause-encoder-data huggingface dataset:
by savings, investment and depreciation across sectors. The second scenario introduces the soil productivity and nutrient flow parameters in the agricultural production functions in order to examine how nutrient losses might affect broader outcomes. The third scenario builds on the second by introducing targeted ODA for agricultural inputs. We dub this the “green revolution” scenario, recognizing that this is an analytical simplification of the actual mix of activities that might enable an African green revolution in practice. The external support program starts in Year 3, and then is gradually phased out over the following five years, such that it decreases to zero for Years 8, 9 and 10. All scenarios include the same annual improvements in the road network.

The model is applied using General Algebraic Modeling System (GAMS) software and the “CONOPT” nonlinear solver. Indicative parameters are set to match the general nature of the Ugandan economy as of the mid-2000s (see Appendix S4). More precise and detailed countryspecific refinements could be applied as desired. For the purposes of the illustrative scenarios presented here, the model is structured to match the country’s four main regions. Kampala is located within the Central region, so Central has the lower initial transport loss when sending food to the urban area.

For each scenario, Table 4 reports values for a selection of key variables, including: soil productivity, modern agricultural input use, labor movements, prices, real wages, savings, capital stocks, government aggregates, external balances, aggregate aid flows, and total private output (i.e., excluding the government wage bill) indexed to first period prices. Specifying unit measures can commonly present a challenge in an analytically distilled CGE model like this one. For purposes of intuition, each labor unit can be roughly interpreted as representing a million workers, while prices and economic aggregates can be interpreted, as previously mentioned, in domestic currency terms relative to a fixed nominal exchange rate and constant numeraire 18
- 11 -

21. As discussed, the population distribution is highly skewed. The fokontany population density differs from 31 to 331,000 per km<sup>2</sup> . As locational attributes, travel time to the nearest health facility is calculated ( _CSB_ ). In Antananarivo, in general, people have good accessibility to health care services, but in suburban areas, the accessibility may be poorer ( **Figure 12** ). In addition, the climatic vulnerability, _VUL_ , is measured by the projected changes in travel time to the CBD when a 10-year flood happens, which is essentially assumed to reduce the average speed along affected roads. How much the speed would be reduced depends on flood depth.

22. Available transport space is also measured by the share of road space in total built-up areas, _RD_ . It is expected that greater road space would be desirable because traffic congestion might be eased. Moreover, the information is included on whether each fokontany is connected to the commune capital by all-weather roads during the rainy season. In Madagascar, the passability during the rainy season has been a matter of particular concern.

23. The impact of rice fields on land prices is also of particular interest. It is measured by the share of rice fields in the total fokontany land area, _RICE._ In some fokontany, a significant amount of land is used for rice production.

24. Proximity to public transportation is expected to be captured by two dummy variables: _TAXIBE_ and _SUBU_ are set to unity if there is a taxi-be and suburban bus route within 200m of fokontany (centroid). This threshold of the distance will be relaxed later in the analysis.

**<u>Table 1. Summary statistics</u>**

|<br>Variable|Abb.|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Land price (Ar/m2)|PRIX|548|64,308|172,984|110|1,298,500|
|Travel time to CBD (minutes)|MIN|548|42.20|21.42|2.41|98.07|
|Job Access Index (0 to 100)|JAI|548|19.83|11.52|0.01|100.00|
|Population density (per km2)|POPDEN|548|16,244|30,794|31|331,850|
|Travel time to the nearest basic health center|CSB|548|5.07|4.33|0.002|28.58|
|(minutes)|||||||
|Vulnerability (change in travel time to CBD under the|<br>VUL|548|1.20|3.68|0|43.40|
|flood scenario)|||||||
|Share of road areas in total built up areas|RD|548|0.06|0.05|0|0.36|
**Table 9**

Regressions of **the Fraction** of **Wage Zamnera oan 1s_c_:** (1) Combination of InitLl and Final Year Crosm **Section** of **Coontries,** and (2) An---- **Rates** of ChOage Within Cimtries, by Sector of E **ploYut and Sex** of _Norker_

|Sector|Sam|ple||Men|||Women||
|---|---|---|---|---|---|---|---|---|
||Siz|e|Intercept|LogCNP/A|R|Intercept|LogGNPIA|_R-_|
|Agriculture|1|122|.088|.0286|.060|.740|-.0810|.146|
||2|61|(1.45)<br>-|(2.77)<br>.136<br>|.213|(7.09)<br>-|(4.53)<br>.278<br>|.150|
|||||(4.10)|||(3.26)||
|Mining|1<br>2|122<br>61|.649<br>(21.7)<br>-|.0395<br>(9.94)<br>.025<br>|.464<br>.021|.394<br>(9.92)<br>-|.0678<br>(12.4)<br>.062<br>|.584<br>.073|
|||||(1.10)|||(2.07)||
|Manufacturing|1|122|-.153<br>|<br>.131<br>|.868|-.619<br>|.185<br>|.787|
||||(4.40)|(28.1)||(9.16)|(20.8)||
||2|61|<br>-|<br>.105<br>(5.50)|.335|<br>-|.035<br>(2.54)|.100|
|Utilities|1|122|.821|.0199|.406|.121|.102|.751|
||2|61|(47.1)<br>-|(8.91)<br>.072<br>|.110|(2.81)<br>-|(18.7)<br>.192<br>|.075|
|||||(2.67)|||(2.16)||
|Construction|1|122|.457<br>|<br>.0457<br>|.206|.387<br>|.0542<br>|.280|
||||(7.27)|(5.38)||(6.18)|(6.82)||
||2|61|<br>-|<br>-. 033<br>143|.033|<br>-|-. 045<br>196|.060|
|||||(.)|||(.)||
|CAxozorce|1|122|-. 653<br>(157)|.165<br>(296)|.878|-1.067.<br>(170)|.219<br>(276)|.862|
||2|61|.<br>-|.<br>.120<br>|.422|.<br>-|.<br>.127<br>|.310|
|||||(6.62)|||(5.19)||
|Transportation|1|122|.335<br>|<br>.0681<br>|.497|.535<br>|.0508<br>|.425|
||2|61|(7.22)<br>-|(10.9)<br>.016<br>|.014|(12.0)<br>-|(9.42)<br>.003<br>|.001|
|||||(91)|||(.23)||
|Services|1|122|.503<br>(133)|<br>.0404<br>(845)|.373|.284<br>(531)|.0757<br>(113)|.515|
||2|61|.<br>-|.<br>.122<br>|.194|.<br>-|.<br>.117<br>|.143|
|||||(3.80)|||(3.16)||
|Bonagriculture|1|122|.0599<br>|<br>.0968<br>|.859|-. 348<br>|.147<br>|.817|
||||(2.25)|(27.1)||(6.96)|(23.1)||
||2|61|<br>-|<br>.076|.282|<br>-|.105|.174|
|||||(4.85)|||(3.55)||
|Total|1<br>2|122<br>61|(-.439)<br>(12.6)<br>-|<br>.147<br>(28.7)<br>.188|.873<br>.511|-. 487<br>(6.93)<br>-|.154<br>(15.6)<br>.2'2|.570<br>.276|
17 patients to them." Underlying the changes in utilization evidenced in Table 6, are the income growth we have discussed but also the concomitant large expansion in the overall number of health facilities and personnel during this period. The results are clearly showing that the primary health care movement has brought significant changes in rural Indonesia in terms of the treatment of illnesses.

# 5. Access to Education and Health Caret Availability and Costs

Utilization incidence patterns are the outcome of several interlinking factors. At the household level, the key determinant is probably the full cost of using the facility, comprising the price charged, transport costs, forgone income, and any disutility incurred. Most of these factors can be linked to broad structural issues, including the aggregate resource levels spent on the sector, allocation within the sector, the degree of private financing, pricing policies, and the organization of sector inputs. This paper approaches the issue instead from the perspective of household level decisions. These will be determined in great part by the supply and demand for services, whizh are themselves directly linked to the country's broad structural policies. So, before turning to household level factors, it is instructive to review the general conclusions which two survey studies of education and health systems for different Asian countries irn the mid 1980s, have drawn with respect to Indonesia (Tan and Mingat, 1991; Griffin, 1990).

Indonesia's public spending on education, at 3.7 percent of GNP and 15 percent of total public spending, is slightly above the Asian mean. However, Indonesia allocates a larger share of expenditures to basic education and is generally more favorable to the lower levels of education (primary and " In July 1991, World Bank staff were told by hospital and administrative doctors in both Bali and East Java that there exists cooperation between bone setters and modern medicine with referrals back and forth. One district health officer related the story of a patient whose leg, which would have been amputated by them, was saved by the bone setter.
# **II.4 Cross-Border Trade between Affiliated Firms**

A significant proportion of cross-border trade takes place between affiliated firms. Figure 5 provides an alternative breakdown of “other private services” into payment flows associated with services imports from affiliates and non-affiliates respectively.<sup>9</sup> Over the longer term, the growth of intra-corporate payment flows has generally been more volatile. This volatility may reflect in part the greater discretion firms normally have in reporting the value of flows between affiliates (in terms of both recording the flow and pricing it). However, during the crisis, intra-corporate flows have continued to grow at rates of about 10 percent per annum whereas the growth of arms-length payments has declined in each of the past three quarters and is now close to zero. The relative resilience of intra-corporate flows may reflect a greater sensitivity on the part of firms to maintaining employment in affiliated firms rather than in arms-length suppliers.

Figure 5: Payments from US Companies for Imports of Other Private Services to Affiliated and Non-Affiliated Entities, 2006 – 2008, Annual Growth Rates <!-- Start of picture text --> 50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>-10.0<br>US parent to FA US to foreign unaffiliated<br>2006:I2006:II2006:III2006:IV2007:I2007:II2007:III2007:IV2008:I2008:II2008:III2008:IV<br>%<br><!-- End of picture text --> Source: BEA, US International Transactions Accounts Data, Table 3a: Private Services Transactions, breakdown by affiliation. Millions of dollars, months seasonally adjusted. 4th quarter 2008 preliminary data. “FA” denotes “foreign affiliate”.

# **II.5 Trade of Other OECD Countries**

The US accounts for 17 percent of all OECD imports of services and 20 percent of all OECD exports of services. How far does its experience reflect that of the OECD countries more generally? Trade data for the fourth quarter of 2008 have been released by a number of other OECD countries as well as two non-OECD countries (Brazil and Indonesia). These data are only available at an aggregate level, i.e. for services trade as a whole and not for its subcomponents. Figure 6 shows for each country the quarterly growth rate of goods imports

> 9 Figure 5 does not present the time services for payments and receipts involving non-US parent companies.

11
# **5 Theoretical model**

In light of the existing evidence for the US, our finding that an expansion in the supply of municipal preschool does not crowd-out local private enrollment in Brazil might be somewhat surprising. In this section we present a simple theoretical framework in order to rationalize these mixed empirical results.

Consider a market for institutional preschool education with potentially two providers: one private and one public. The private provider offers preschool services at quality _q_ 1 and price _p_ . For simplicity, we take a short-term perspective by assuming that the quality level is fixed, making price the only choice variable of the private provider. The public provider offers preschool education at quality _q_ 0 for free (zero price).<sup>14</sup> We assume that the public provider has lower quality than the private provider: _q_ 1 _> q_ 0. This assumption is fully consistent with the descriptive statistics on the Brazilian child care market presented in Section 3, which show that the quality indicators are systematically higher for private providers.

We assume that consumers have unit demand, where each consumer demands either one unit of child care from the most preferred provider, or zero units if that is the utilitymaximizing choice. The net utility of buying one unit of preschool education is given by where _v_ + _θqi_ is the willingness to pay for one unit of child care of quality _qi_ , with _θ_ being the marginal willingness to pay for quality. A key assumption of our model is that consumers are heterogeneous with respect to the parameters _v_ and _θ_ . More specifically, we assume that demand for preschool services comes from two different demand segments, henceforth referred to as Segment _H_ and Segment _L_ , where each consumer in Segment _H_ is characterized by _v_ = _vH_ and _θ_ = 1, whereas each consumer in Segment _L_ is characterized

> 14The assumption of fixed quality is consistent with the evidence presented in Tables 8 and 9 in Section 4, which shows that an expansion of public supply of preschool has no systematic effect on the quality offered either by private or public providers.

17
<!-- Start of picture text --> Table 4B: Regression results from the full sample of 153 countries and all years 1988-2015<br>) Q) 8 @) 6) 6 0 8 ©) 1) dy) (2<br>Number of projects Number of HD projects Share of HD projects<br>IBRD country -0.571** -1.634*%** -0.680** = -0.852**« -0.189%** = -0.423*** = -0.342*** = -0.360*** -0.040*<< -0.043** -0.071** = -0.068**<br>(1=yes) (0.274) (0.286) (0.347) (0.398) (0.059) (0.070) (0.089) (0.097) (0.015) (0.021) (0.032) (0.034)<br>Log population 0.619*** 1.431** 0.142*** 0.474**« -0.004 -0.063<br>(0.085) (0.618) (0.019) (0.198) (0.004) (0.078)<br>Log GNI per capita 0.298** 0.387* 0.097*** 0.109** 0.014 0.040<br>(0.129) (0.207) (0.032) (0.055) (0.010) (0.025)<br>Net aid flow 0.178*** 0.072*** 0.015* 0.002 -0.001 -0.001<br>(millions of 2010 dollars) (0.024) (0.021) (0.008) (0.009) (0.001) (0.002)<br>Number of observations 3,894 3,402 3,894 3,402 3,894 3,402 3,894 3,402 2,602 2,365 2,602 2,365<br><!-- End of picture text -->

|||Table 4B:|Regression|results from|the full samp|le of153 cou|ntries and al|l years 1988-|2015||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||)|Q)<br>Numbero|8<br>fprojects|@)|6)|6<br>Numberof|0<br>HDprojects|8|©)|1)<br>ShareofH|dy)<br>Dprojects|(2|
|IBRD country|-0.571**|<br>-1.634*%**|<br>-0.680**|= -0.852**«|-0.189%**|<br> = -0.423***|<br>=-0.342*** =|-0.360***|-0.040*<<|<br>-0.043**|<br>-0.071**<br>|= -0.068**|
|(1=yes)|(0.274)|(0.286)|(0.347)|(0.398)|(0.059)|(0.070)|(0.089)|(0.097)|(0.015)|(0.021)|(0.032)|(0.034)|
|Logpopulation||0.619***||1.431**||0.142***||0.474**«||-0.004||-0.063|
|||(0.085)||(0.618)||(0.019)||(0.198)||(0.004)||(0.078)|
|LogGNI per capita||0.298**||0.387*||0.097***||0.109**||0.014||0.040|
|||(0.129)||(0.207)||(0.032)||(0.055)||(0.010)||(0.025)|
|Net aid flow||0.178***||0.072***||0.015*||0.002||-0.001||-0.001|
|(millionsof2010dollars)||(0.024)||(0.021)||(0.008)||(0.009)||(0.001)||(0.002)|
|<br>Numberofobservations|3,894|3,402|3,894|3,402|3,894|3,402|3,894|3,402|2,602|2,365|2,602|2,365|
|R2|0.023|0.433|0.616|0.646|0.024|0.159|0.262|0.268|0.024|0.027|0.131|0.130|
|Country fixed effects|no|no|yes|yes|no|no|yes|yes|no|no|yes|yes|
|Fiscal year fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|MeanofIDA/Blend|2.282|2.443|2.282|2.443|0.526|0.558|0.526|0.558|0.238|0.235|0.238|0.235|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, *** Significant at 95%.
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39
“With financial resources, anybody can become a businessman” (1 = No), “There is no relationship between product marketing or advertising and the amount of sales” (1=No), “Product variety is important for sales” (1=Yes), “Customers only look at the price when deciding whom to purchase from” (1=No), “A successful business person cares about customer loyalty” (1=Yes), “What is an important aspect of the marketing and sale of products and services?” (Packing and Packaging = 1), “Profit is Revenue minus?” (Cost = 1), “How do you determine the full cost of a product or service?” (Check cost of inputs, materials and time spent = 1).

_Business Practices_ , simple average for standardized z-scores of the following dummy variables: allowing purchases on credit (1=Yes), record of sales (1=Yes), record of money taken from business (1=Yes) and record anything at all (1=Yes).

_Business Operations_ , simple average for standardized z-scores of the following dummy variables: having a fixed location (1=Yes), year-round operation (1=Yes), investing in marketing (1=Yes), having a business open to the public (1=Yes), or having secured buyers (1=Yes); and z-scores of two indexes built around a number of questions on business appearance (“Is business visible from the street?” (1=Yes). Does business have a sign board?” (1=Yes), Does business have adequate lighting? (1=Yes), “Does business have good ventilation?” (1=Yes), “What is the condition of the wall/roof/floor?”), and ownership of several business assets including “Land and buildings”, “Plants, machinery etc”, “Hand tools”, “Transport equipment”, “Furniture and fixtures”, “Office equipment”, “Other durable goods owned by the business”, “Goods in inventory”, and “Raw material in stock”.

_Sales and Profits,_ simple average for standardized z-scores of the following variables: log of sales and profits in November 2008, and the log of sales at baseline for a good, average, and bad month.

_Expenditures/Assets,_ simple average for standardized z-scores of the following variables: log of monthly expenditures, log of savings, log of livestock value, and a PCA index of housing quality measured by ownership of house (owns house=1) , number of rooms, type of walls and roof.

_CO Cohesion,_ simple average for standardized z-scores of four dummies taking the value of 1 if the member considers that can rely more on other CO members, if reports more collective action among the group, or if the member lends or borrows to/from other members.

_General Outlook of Life_ simple average for standardized z-scores of three PCA indexes for questions related to trust in people’s intentions, stress and satisfaction with life. The _Trust index_ includes the 3 GSS questions “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?”, “Do you think most people would try to take advantage of you if they got a chance, or would they try to be fair?” and “Would you say that most of the time people try to be helpful, or that they are 38
the effects on youth education, longer-term impacts of rural water treatments, and we pay particular attention to gender-specific and exposure-time-specific effects.<sup>7</sup>

# **3 Sample, Variables, and Estimation Strategy**

We primarily rely on the China Health and Nutrition Survey (CHNS), which includes nine waves of survey so far in 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009 and 2011, respectively. CHNS covers nine provinces including Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Liaoning, and Shandong. The subsample in each of the provinces was selected based on a multistage, random cluster sampling process. Since the rural drinking water program has only been implemented in rural areas, we only use the rural sample of CHNS.

Our sample consists of young people between the age 18 and 25, a period in which the school-to-work transition for rural youth is largely complete. The starting age of 18 was chosen because China’s Compulsory Education Law (CEL), which took effect in 1986, mandates that children must enroll in school by age six (in some areas it can go up to age seven), and thus the vast majority of rural youth presumably would had finished their high school had they chosen so at age 18. Figure 1 plots the proportions of individuals who are currently working and in schools for each age group from 12 to 40 in our sample between 2004 and 2011.<sup>8</sup> Over 80 percent of children at age 15 stay in school, but the majority of people over 25 (87 percent) work.<sup>9</sup> In our sample around 65.8 percent of the individuals in this age group graduate from a middle school, but only 16.4 percent of them actually graduate from a high school, and less than 5 percent continue their study after that. The vast majority of the rural youth clearly do not pursue more

> 7 Our paper is also related to Maimaitwe and Siebert (2009), who use the CHNS data to test the hypothesis that poor _access_ to water hurts girls’ education due to unmet female hygiene needs after menarche. The major distinction between their study and this paper lies in the discussion on the roles of water _access_ versus water _quality_ . While their focus is on water access (tap water in the house or courtyard), ours is instead on water quality. In the robustness check, we control for households’ water access status in the regressions, and our baseline treatment effects remain intact. Not dealing with the improvement in water quality, the central element of the rural drinking water program, their paper does not evaluate this specific program’s impacts. Moreover, we pay particular attention to genderspecific and exposure-time-specific effects which do not concern their paper.

> 8 CHNS only recorded work status of individuals aged above 16 before 2004.

> 9 According to the CHNS survey, people not currently working includes housewives, the disabled, students, retirees and those on job hunt.

> In the waves before 2000, the work status is recorded for children whose are 16 or older. This question is also asked for children not in schools in the waves after 2004. To be consistent across waves, we only plot the work status of children over 16

8