rafmacalaba/datause-encoder-data huggingface dataset:
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biased when shifts in the share of unreported or smuggled exports changed.<sup>This could have</sup> resulted in an positive bias in the results above.

2. The producer price data have been carefully compiled to distinguish between official prices and actual producer prices where divergences were apparent. Nevertheless,<sup>'he prices</sup> used are for most countries officially reported prices and in some cases will<sup>not accurately</sup> reflect what farmers are being paid.

3. The index of producer prices computed is based on from 1 to 4 export crops per country. For countries where the composition of exports includes significant other crops, the price incentives are omitted from the analysis, while their export volumes are included.

4. Changes in input prices have been ignored. To the extent that producers are responding to changes in farm profits, changes in fertilizer and other prices should<sup>be taken</sup> into account. Typically, higher output prices and devaluation have been associated with<sup>raising</sup> the prices for imported inputs such as fertilizer. To the extent that this phenomenon<sup>has been</sup> omitted from the above analysis, the elasticities with respect to price and exchange<sup>rate have</sup> been biased downward.

_5._ There are simultaneity problems for countries where their exports account for a sizable hare of inteational nmarkets anid face less than perfectly elastic demand (Ghana, Cote d'Ivoire, Nigeria, Kenya, Cameroon). This problem could bias the results downward. For countries where the farm price reflects the highly variable international price (coffee in Kenya and Ethiopia, tobacco in Malawi), occasional sharp increases in price will not affect current production decisions, and will be viewed and temporary in nature. This may partly explain<sup>the</sup> low, or negative, results for these three countries.
Table B4: Mechanism at play, exchange rate volatility as proxy for uncertainty

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|(5)<br>CR5|(6)<br>CR5|
|---|---|---|---|---|---|---|
|ExchangeRateVolatility (standardized)|0.614|-0.857|3.827***|3.669***|3.067***|4.255***|
||(1.461)|(1.910)|(0.856)|(0.818)|(0.824)|(0.936)|
|ExchangeRateVolatility _×_ Developing||4.075**<br>(1.979)|||||
|Observations|2,349|2,349|1,489|1,472|1,777|1,170|
|R-squared|0.830|0.830|0.819|0.824|0.843|0.801|
|Controls|No|No|Yes|Yes|Yes|Yes|
|Data Coverage|All co|untries||Devel|oping||
|Minimum # of|||||||
|Foreign Investors|||3||1|10|

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” stands for the concentration ratio of the top 5 investing firms; the minimum number of foreign investors to be included in the sample is indicated in the last row of the table. The _ExchangeRateV olatility_ is calculated from the monthly exchange rate of the local currency against the US dollar, normalized by the whole-period mean and standard deviation. For a given destination-year, _it_ , _ExchangeRateV olatility_ is proxied as the average score in the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

29
# Village sanitation and children’s human capital: Evidence from a randomized experiment by the Maharashtra government

Jeffrey Hammer and Dean Spears<sup>_∗_</sup> August 20, 2013

**Keywords:** sanitaton, anthropometry, human capital, height, health, nutrition, open defecation, field experiment, India **JEL Codes:** O12, I15, H43

> _∗_ Hammer: Woodrow Wilson School, Princeton University. Spears: Centre for Development Economics, Delhi School of Economics and r.i.c.e (www.riceinstitute.org). We acknowledge with thanks support for this project from the World Bank Water and Sanitation Program. We are grateful for and applaud the vision of the leaders in the Maharashtra government who championed this project. The work in this paper could not have come about without the leadership of Secretary Khatua of the Government of Maharashtra whose vision of the needs of the people led him to try novel and untested methods to improve their lives and health. He also pioneered the use of rigorous methods to help prove to others the value of behavior change over construction as the goal. The team at the Water and Sanitation Program in India: Soma Ghosh-Moulik, Ajith Kumar and J.V.R. Murty under the guidance of Junaid Ahmad were, likewise, early voices in the debate supporting Secretary Khatua and putting real evidence into “evidence-based policy.” We also benefitted from comments by Luis Andres, Bertha Brice ̃no, Nazmul Chaudhury (an early collaborator), Diane Coffey, Juan Costain, Oliver Cumming, Angus Deaton, Paul Gertler, Avinash Kishore, C. Ajith Kumar, Soma Ghosh Moulik, Rinku Murgai, and seminar participants at the World Bank in Washington, DC and in Delhi and at World Water Week in Stockholm. The views and conclusions presented here (and any errors) are our own, and are not the responsibility of any government or organization, or of those who were kind enough to comment on this draft.

1
Most of Yunnan’s access gains at the upper secondary level has been disproportionately driven by academic track expansion. Yunnan’s upper secondary enrollment growth in recent years has largely been a phenomenon of expansion of academic secondary education while the total enrollment in vocational education has been flat (Figure 4.5). This represents a deviation from the national trend where declining total enrollment is observed in both tracks. Population trends play an important role where due to Yunnan’s relatively high birth rates in the past two decades the negative effects of a declining population on high school enrollment observed in many other parts of the country have not been prominent in Yunnan. The disproportional increase of academic track enrollments has widened the gap between shares of academic and vocational students. In 2016, only about 37 percent of high school entrants are absorbed in vocational secondary education, while the rest 63 percent entered academic secondary education, despite the 50-50 allocation requirement at the national level.

Figure 4.5 Upper secondary enrollment (2013–2016): Yunnan versus nationwide <!-- Start of picture text --> Yunnan Nationwide<br>850000 30000000<br>800000<br>25000000<br>750000<br>700000 20000000<br>650000<br>15000000<br>600000<br>550000 10000000<br>500000 5000000<br>450000<br>0<br>400000<br>2013 2014 2015 2016<br>2013 2014 2015 2016<br>academic vocational<br>academic vocational<br><!-- End of picture text --> _Source_ : National Bureau of Statistics of China 2017.

Several factors could influence the widening gap between academic and vocational education enrollment. These factors include (a) remaining financial constraints mentioned earlier, (b) a concurrent expansion for tertiary education created for demand for academic track, and (c) different education quality and perceptions of career opportunities and economic returns associated with each track. To help better understand these factors we conducted a stakeholder analysis to gain in-depth understanding of the key stakeholders’ attitudes and opinions toward upper secondary education at the local level in addition to the analysis on the national CEPS data in Section 3.

For the stakeholder analysis, we surveyed a convenient, albeit large, sample from Tengchong County, Yunnan. The sample included a large variety of key stakeholders including middle school students (n = 381), high school students (n = 169), students from vocational high schools (n = 277), and parents of middle school students (n = 473), with a total of 1,300 valid responses. We also interviewed education administers at the county level (n = 7), school principals (n = 9), teachers (n = 25), students (n = 57), and parents (n = 15) using semi-structured questions. Tenchong County presents typical challenges faced by upper secondary education in Yunnan and many parts of China. It is a relatively poorer county in Yunnan Province. Due to a relatively lack of industries (as evidenced by its CNY 24,034 GDP per capita<sup>10</sup> versus CNY 28,806 of the provincial average 49
Figure 4: Share of Students in the Different Categories of the IHA <!-- Start of picture text --> 45,68<br>23,38 24,26<br>5,33<br>1,35<br>0 1 2 3 4<br>Poverty index<br>Share of sample (%)<br><!-- End of picture text --> To test for heterogenous impacts by socio-economic status the evaluation sample was divided in two groups according the value of the IHA: those with a value of the IHA of 2 or less and the rest. Table 4 shows the effects of PROBEMS on graduation rates, by socioeconomic status in a specification with and without controls. The effects of PROBEMS are zero, even within the group of more marginalized individuals (IHA of 2 or below). As a complementary exercise we estimate the effects of PROBEMS among individuals with a value of the IHA of 0 or 1 (the most marginalized) and the results are shown in Table A6 in the Appendix. For students with a value of the IHA of zero or one, in the specification without controls, being awarded a scholarship increases the probability of graduation by 8 percentage points (p-value of 0.06), on a mean value of 60% among the control group. However, when the controls are included in the specification, the positive effect remains but is no longer statistically significant (p-value of 0.104). The lack of statistically significant effects among the group of poorest students could be explained by the relatively small number of students with a value of the IHA of 0 or 1 (483). We also test for heterogenous effects on test scores by different levels of IHA, splitting the sample as indicated in Table 4 and found very small and statistically insignificant effects.<sup>11</sup> To identify how well targeted PROBEMS was, we use the Mexican national household

> 11The results are available from the authors upon request.

17
regulatory frameworks, in contrast with vulnerability to political pressure and lower levels of autonomy—ERC’s tariff regime has been stable, if not exemplary.

ERC performs poorly in quality regulation, computed as the average of quality-of-service standards and quality-of-service enforcement. The main reason for the low score is the lack of enforcement of quality-of-service standards. Moreover, ERC does not specify how technical performance data are to be collected and does not independently review or validate technical performance data. Measurements of reliability of the power supply are not publicly available, and there are no incentives for utilities to increase customer satisfaction.

**Table 11. Formal regulatory substance in Kenya and comparators, 2015**

|**Indicators**|**Kenya**|**Senegal **|**Tanzania **|**Uganda**|**International**<br>**benchmark**|
|---|---|---|---|---|---|
|**Regulatory Substance**|81%|73%|83%|78%|76%|
|**_Tariff Regulation_**|88%|76%|88%|92%|77%|
|_Regulatory Framework for Tariffs_|_100%_|_86%_|_100%_|_100%_|90%|
|_Determination of Tariffs_|_75%_|_67%_|_75%_|_83%_|64%|
|**_Quality Regulation_**|54%|42%|63%|58%|75%|
|_Quality of Service Standards_|_75%_|_50%_|_75%_|_50%_|82%|
|_Quality of Service Enforcement_|_33%_|_33%_|_50%_|_67%_|68%|
|**_Market Entry Regulation_**|100%|100%|100%|83%|77%|
|_Permitting New Entrants_|_100%_|_100%_|_100%_|_100%_|90%|
|_PPA Approvals_|_100%_|_NAP_|_100%_|_67%_|57%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform

# _3.4.3 Summary_

Kenya has been able to maintain tariffs at a level sufficient to cover operating and limited capital costs, supported by a comprehensive tariff-setting framework that has included an automatic tariff adjustment mechanism since the late 1990s. However, regulatory autonomy remains a challenge, and tariff reviews are regularly delayed, even as the rules and regulations surrounding the regulator are very clearly articulated in the concerned laws. Consequently, Kenya scores much lower when it comes to comparing how the regulation on paper translates in real life (table 12).<sup>21</sup> Kenya is a (positive) outlier in the region when judged on the financial viability and sustainability of its primary power company (in large part due to its tariff regime), but political involvement in regulatory decisions remains a concern.

21 The “perception” index is calculated based on expert assessment of whether the provisions that exist on paper are applied in practice. The expert assessment was provided by the local consultant in each country, whose professional opinion was also informed by some 20 stakeholder interviews he or she conducted with key stakeholders in the power sector reform process. The resulting “perception” index was also reviewed by the World Bank country energy team, which is also knowledgeable about the local context. Despite best efforts, it is important to acknowledge that this second index is inevitably more subjective in nature than the de jure index.

38
|_Sector_|_0c _|at|**_0q _**|_au_|
|---|---|---|---|---|
|Primary|2.00|3.00|0.75|1.25|
|Manufacturing|1.25|1.50|0.75|0.85|
|Services|0.60|1.00|0.75|1.25|

Table 1: Trade and Production Elasticities The model is calibrated to generate a reference steady state run with values that match the benchmark data of the Philippine economy. The derivation of these data and parameters are based on a 1979 social account matrix (SAM) and CGE model for the Philippines in Go (1986 and 1988). For this study, there are three production sectors - the primary sector, manufacturing, and services; two labor types - urban and labor; and, one representative household but four distinct institutions - urban labor, rural labor, firms, and government. The trade-related elasticities, oc for the Armington functions and ot for the CET functions, are generally higher in the commodity sectors than in the less-tradable service sector (see Table 1). In the nested production function, the elasticity of substitution a, between intermediate input and value-added is set at less than unity to allow for flexible and increasing shares of material costs in the event of an input price shock. The elasticity of substitution between labor and capital _a,,_ in value added is less in manufacturing than in other sectors, given the assumption that skill and training are required more in manufacturing. However, the 0V's in manufacturing are still close to unity.<sup>12</sup> The manufacturing sector is the most capital-intensive while the primary sector is the most labor-intensive. The balanced-growth rate, g, is specified at the long-term growth of the Philippine economy at 5.5% a year.

12This is consistent with the findings in other studies, Behrman (1982) which uses cross-country data and Estanislao (198O) which uses Philippine time series.

13
35 While aggregate indicators pointed to a boom, there were also more detailed banklevel data that pointed to a movement beyond the frontier, i.e. towards an unsustainable level, including a shift towards foreign currency lending. In 2008, the share of foreign currency debt in total debt ranged from less than 20% in the Czech Republic to over 47% in Lithuania (Allen et al., 2011). Obvious (at least ex-ante) arbitrage possibilities were exploited by banks and households, taking out Swiss Franc or Euro mortgages as lower interest rates than local currency mortgages, betting on the seemingly unavoidable long-term appreciation of local currencies, following the Balassa-Samuelson hypothesis. This trend towards both foreigncurrency loans in many countries thus took the character of carry trades for consumers and producers of non-tradables. While the offer of mortgages in Euros and Swiss Francs was for a long time seen as innovation, allowing households to directly benefit from these seemingly riskless arbitrage possibilities, this also exposed them to currency shocks.

In addition, there were also macroeconomic warning signs, pointing to aggregate imbalances that had been observed earlier in the lead-up to banking and currency crises (Berglöf et al., 2009, Sirtaine and Skamnelos, 2007). This included a private sector deficit and lack of savings, which could be interpreted as an overshooting in dis-savings after the transition, ultimately resulting in negative net asset positions (Figure 9).

**Figure 9: Net foreign currency asset as share of GDP**

<!-- Start of picture text --> 30<br>20 Bulgaria<br>10<br>Czech<br>0 Republic<br>-10 2000 2005 2008 Estonia<br>-20<br>Hungary<br>-30<br>Latvia<br>-40<br>-50<br>-60<br><!-- End of picture text --> Source: Mihaljek (2009) 35
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their competitors. For the 42 tariff line products exported exclusively by air from Haiti the nominal freight rate of 8.5 per cent is about one-half that of its competitors, while Jamaica and St. Kitts experience a favorable freight rate differential of about the same magnitude. In only two cases (Dominica and Guyana) is the normal pattern reversed with Caribbean exporters paying higher ad valorem air transport costs. 19/ While Table 6 shows that the Caribbean countries typically encounter lower ad valorem freight rates for products shipped by air, the same findings do not hold for goods transported exclusively by vessel. For six of the 16 Caribbearn countries (Anguilla, Barbados, Guyana, St. Kitts-Nevis, St. Lucia and Suriname) the overall ad valorem sea transport rate exceeds that of other suppliers with Anguilla and Suriname facing adverse differentials of 5 to 10 points. In Haiti and Jamaica, where some of the most favorabie air transport differentials occurred the vessel freight rates are less than one percentage point below those for non-Caribbean exporters.

Columns 5 through 8 provide similar comparisons for products exported partly by air and partly by vessel. These tabulations also indicate the cost margin of air over water transport for a common group of products. For example, the six tariff line products that Antigua and Barbuda exports both by 19/ Detailed statistics on Dominica's transport costs presented in the appendix shows that the reversal of the normal pattern is due to shipments of one pharmaceutical good (TSUSA 4400000) where Dominica's air freight rate is about 125 points higher than its competitors. Similarly, the adverse differential for Guyana is due to shipments of "reptilian leather" (TSUSA 1144562) where an adverse freight differential of over 100 points occurred. If these two products were excluded the average ad valorem air freight rate for Dominica and Guyana would be lower than those of their competitors.
# **_Section 2. There is ample evidence that important choices about the carbon intensity of future long-lived capital stock are being made today or will be made in the near future, particularly in developing countries; and that avoiding similar demand-side or supply-side lock-ins in highly carbon-intensive pathways is important for global climate mitigation_**

43. **Economic growth—spurred on by urbanization and globalization—is inducing rapid expansion of long-lived capital stock in developing countries** , similar to the development of long-lived capital stock in Western Europe or in Japan post-World War II.

44. **First** , **urbanization** (i.e., an increase in the share of urban population in total population) **will be accompanied by major decisions about urban forms** (category 4 of long-lived capital), which will influence the energy system. In fact, as noted by Jaccard and Rivers (2007), density configurations and land-use patterns affect the energy intensity of various end uses (e.g., ratios of external wall to floor space for space heating), the energy requirements for urban transportation (e.g., travel distances for shopping and commuting), and the character and prospects for alternative energy supply and delivery systems (solar access, combined heat and power, public transit, hydrogen refueling networks, etc.). This component of long-lived capital stock has been estimated to have a 120 year turnover rate based on Canadian data. We are not aware of any comparable estimates for developing countries (or even other industrial countries).

45. More than 80% of the people on Earth live in developing countries. The bulk live in countries that are predominantly rural. The sectoral shift from a primarily agricultural-based economy to one based on manufacturing and services will be accompanied by a shift in the spatial location of the population, i.e., urbanization. Approximately one third of developing countries population lived in cities in 1990, whereas two thirds of that population is expected to live in cities by 2050. In China alone, it is estimated that the share of urban residents in the total population will increase by 50% over the next 25 years, from 40% in 2005 to about 60% in 2030.<sup>36</sup> This will generate a lot of construction in a relatively short period as discussed in box 5, with potentially significant consequences for emissions.

> 36 Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2006 Revision and World Urbanization Prospects: The 2007 Revision, http://esa.un.org/unup .

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