rafmacalaba/datause-encoder-data huggingface dataset:
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE allow for full cost recovery if a premium to amortize capital is taken into account. A first approach to full recovery (operating and capital costs) is presented in table 8. Based on a first order estimation of full operating _and_ capital costs, it would appear that WUC faces significant underpricing.<sup>5</sup>

**Table 8. Evolution of operational indicators associated with the Botswana Water Utility Corporation**

||Water<br>delivered<br>(million<br>m<sup>3</sup>/year)|System<br>losses<br>(%)|Collection ratio<br>(%)|Averagetotal<br>cost<br>($/m<sup>3</sup>)|Average<br>effective tariff<br>($/m<sup>3</sup>)|Total hidden<br>costs<br>($ million/year)|Total hidden<br>costs<br>(% revenues )|
|---|---|---|---|---|---|---|---|
|2005|41|13|95|1.21|0.61|23|29|
|2006|55|12|95|1.10|0.61|19|26|
|2007|45|11|95|0.80|0.61|9|11|
|2008|48|11|95|0.91|0.61|14|19|

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

In a regional context, Botswana’s utility ranks among the most efficient utilities, with hidden costs that are one-fourth of the next-most-efficient utilities (figure 11b). In 2009 mispricing of water services and—to a lesser extent—collection inefficiencies accounted on average for 19 percent of revenues, down from 29 percent in 2005. As a result of these major performance improvements, WUC ranks as one of the most efficient utilities in the continent.

**Figure 11. Hidden costs**

<!-- Start of picture text --> a. Evolution of hidden costs in Botswana’s water sector<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2005 2006 2007 2008<br>Collection inneficiencies Losses Under-pricing<br>Percentage of the revenues<br><!-- End of picture text -->

> 5 The AICD exercise used full cost recovery as the norm to estimate underpricing or mispricing. Here we are following that definition to allow for consistent cross-country comparison. Admittedly, this norm is out of reach for most African utilities that by design aim at achieving ONLY operating cost recovery, leaving capital cost and a transfer or subsidy from the central government.

22
with randomization assignment and successful delivery of the program components. This is notable given the challenging implementation context.

## Attrition

The lottery assigned 491 households to treatment and 728 households to control. The followup survey was successfully completed among 458 treatment households (93%) and 689 control households (95%). The difference in attrition rates across treatment and control groups is not statistically significant.<sup>15</sup>

# **Data analysis**

## Pre-analysis Plan

To present a comprehensive overview of impacts on a large set of variables while balancing this with the threat of false positives, we registered the trial and published a PAP before analysis began.<sup>16</sup> The results presented in this report follow the variable construction and econometric specifications defined in the PAP, with a few exceptions. First, we include an index generated through principal component analysis of number and type of assets, rather than value of assets, since we did not collect asset values at follow-up due to a questionnaire design oversight. Second, we present both the pre-specified measure of women’s empowerment (aligned with other studies) along with a measure that incorporates a broader set of empowerment dimensions to highlight the differences in results. Third, conditional average treatment effects compare bottom, middle, and top thirds of the distribution for each respective outcome of interest, rather than median splits, to better understand the distribution. Beyond these deviations, all other analysis follows the PAP.

## Econometric Specification

Since treatment compliance with randomization was not perfect, we use the intention-totreat (ITT) estimator – simply, the difference between average outcomes across treatment and control groups at follow-up. The basic specification is: Here, Yi is the outcome of interest for household i at follow-up, Ti is a dummy variable equal to 1 if household i is assigned to receive treatment and 0 otherwise. β is the estimate of the average effect of the TUP intervention at follow-up. Since randomization is stratified by community, we follow Bruhn & McKenzie (2008) and include Vj , which is a dummy variable equal to one if household i comes from community/PRA group j (of a total of 133 PRA groups). For household-level variables, standard errors are not clustered. Where data on multiple

> 15 We define response rates based on households with a complete LHH survey. We experienced an 88% response rate for the MHHH survey, however all main outcomes can still be estimated without this survey. 16 https://www.socialscienceregistry.org/trials/2665

18
effect on paid employment (Grimm and Paffhausen, 2015). Management consulting services to larger firms have also often not led to jobs, with the one study that has found impacts only finding these over the medium-term with administrative data on formal employment (Bruhn et al, 2013). Moreover, the YouWiN! program has other important impacts other than just direct job creation. I turn next to examining these other impacts.

# **5.4 Impacts on Innovation and Business Practices**

The other stated objective of the program was to encourage innovation. We examine whether YouWiN! winners are employing better business practices in their firms, and whether they are innovating more. Business practices are measured as the proportion of 22 business practices employed, following McKenzie and Woodruff (2015). Innovation is an index of 12 different types of innovation, including product, process, marketing, pricing, quality control, and use of the internet. The first four columns of Table 6 report the unconditional estimates, where we see positive increases in both business practices and innovation for both new and existing firms. The point estimates show a 14 to 22 percentage point increase in innovative activities for experimental winners. However, some of this reflects that firms need to be in business in order to innovate, and that the program had large impacts on start-up and survival. Therefore in the last four columns we look descriptively at the treatment-control difference conditional on the business operating. We see that the winning firms among new applicants are doing more innovation and using better business practices on average than the control firms, and also some more innovation in the first two rounds for the existing firm winners. Appendix 9 examines the types of innovation occurring, and shows the new applicants are innovating more in multiple dimensions, introducing new products, processes, pricing methods, quality control systems, using the internet, and using new channels for selling goods.

# **5.5 Impacts on Profits and Sales**

Table 7 reports the estimated impacts of winning the business plan competition on several measures of sales and profitability.<sup>17</sup> Figures 2 and 3 show the CDFs of profits and sales respectively. Appendix Table 10 reports impacts conditional on the firm being in operation, and Appendix Table 11 reports impacts on other pre-specified measures of sales and profitability.

> 17 Nominal Naira were converted into real (November 2012) Naira using the Consumer CPI of the Central Bank of Nigeria.

25
# **Appendix Table 2a.  Decision-making: Person who usually decides on large household purchases***

||||Respond|Share<br>ent alone|of responden<br>Res|ts (R) and<br>pondent an|husband/partners (H)<br>d husband/partner|reporting<br>Hu|:<br>sband/ p|artner alone|
|---|---|---|---|---|---|---|---|---|---|---|
|||R|H|Diff|R|H|Diff|R|H|Diff|
|Benin|2011|0.10|0.07|0.03***|0.44|0.20|0.24***|0.46|0.73|-0.27***|
|Burkina Faso|2010|0.01|0.03|-0.02***|0.16|0.15|0.01|0.81|0.80|0.01*|
|Burundi|2010|0.06|0.02|0.04***|0.54|0.50|0.04**|0.40|0.48|-0.08***|
|Cameroon|2011|0.12|0.12|0.00|0.38|0.27|0.11***|0.48|0.61|0.13***|
|Comoros|2012|0.23|0.21|0.02|0.30|0.36|-0.06***|0.46|0.42|0.04|
|Côte d’Ivoire|2011|0.08|0.04|0.04***|0.28|0.28|0.00|0.63|0.66|-0.03*|
|Congo, Dem. Rep.|2013|0.13|0.10|0.03***|0.46|0.48|0.02*|0.41|0.41|0.00|
|Ethiopia|2011|0.05|0.04|0.01***|0.60|0.67|-0.07***|0.34|0.28|0.06***|
|Gambia, The|2013|0.06|0.03|0.03***|0.43|0.36|0.07***|0.50|0.60|-0.10***|
|Liberia|2013|0.22|0.17|0.05***|0.60|0.58|0.02|0.17|0.25|-0.08***|
|Mali|2012|0.06|0.09|-0.03***|0.11|0.07|0.04***|0.82|0.82|0.00|
|Mozambique|2011|0.13|0.11|0.02**|0.48|0.50|-0.02|0.37|0.37|0.00|
|Namibia|2013|0.24|0.18|0.06***|0.59|0.65|-0.06***|0.17|0.17|0.00|
|Nigeria|2013|0.05|0.23|-0.18***|0.32|0.26|0.06***|0.62|0.50|0.12***|
|Rwanda|2010|0.04|0.03|0.01|0.67|0.67|0.00|0.29|0.29|0.00|
|Senegal|2014|0.02|0.01|0.01**|0.18|0.25|-0.07***|0.69|0.56|0.12***|
|Sierra Leone|2013|0.05|0.17|-0.12***|0.50|0.38|0.12***|0.44|0.44|0.00|
|Uganda|2011|0.14|0.10|0.04***|0.46|0.48|-0.02|0.40|0.42|-0.02|
|Zambia|2013|0.10|0.08|0.02***|0.55|0.58|-0.03***|0.34|0.33|0.01|
|Zimbabwe|2011|0.17|0.14|0.03***|0.71|0.76|-0.05***|0.12|0.10|0.02***|

Notes: (1) Source: Demographic and Health Surveys (Waves 5 and 6) for Sub-Saharan Africa.

(2) Diff = T-tests for whether differences in respondent’s (R) and husband’s (H) responses were statistically significant. *** p<0.01, ** p<0.05, * p<0.1

*In the men’s questionnaire, the question was asked “who should have a greater say in large household purchases?”

57
Izquierdo, and Mejia, 2008; Calvo, Izquierdo, and Talvi, 2006; Calvo, Izquierdo, and LooKung, 2006; Guidotti et al., 2004; Ortiz et al., 2007). This literature studies the role of various types of macroeconomic imbalances in the incidence of sudden stops, and focuses on determining the role of ex-ante and ex-post policies in dampening their real consequences. The nature of the microeconomic frictions that transform sudden stops in output collapses is not only of academic interest, but also crucial for the correct design of policy responses to prevent and address these episodes and the lack of evidence on this regard is an important shortcoming. In reaction to this situation, Chari et al. (2005) conclude: _“The key frictions that generate output drops in the existing literature on sudden stops are subtle ones for which so far there is little evidence. Finding that evidence is a challenge for future research.”_ This paper provides evidence of the importance of financial frictions for the propagation of sudden stops to the real economy using detailed industry level data for a large set of developed and emerging countries. The paper exploits the heterogeneous response of different industries to the occurrence of a sudden stop, and the relationship between these responses and industry characteristics to identify the mechanisms at work in the propagation of the sudden stops to the real economy. This approach has several advantages over existing studies based on aggregate (country-level) behavior. Most importantly, the reverse causality between macroeconomic performance and the occurrence of sudden stop is much less of a concern when looking at the relation between detailed industry-level activity and aggregate shocks, such as sudden stops. Furthermore, the heterogeneous responses of industries to sudden stops allow us to use a difference-indifference approach to identify the empirical importance of various possible mechanisms of transmission of sudden stops to the real economy. In particular, the paper tests whether, according to most existing theories, sudden stops result in a larger output decline in industries that are more sensitive to financial frictions and less likely to 4
Table A.7: Eligible S1 coefficients for base specifications

|**Index**|**Ivs**|**Moving average **|**Significant, f>10**|**Significant, f>10, positive**|
|---|---|---|---|---|
|Rise|UNGA aff.|5|784|540|
|Rise|UNGA aff.|3|698|516|
|Composite|UNGA aff.|5|713|479|
|Composite|UNGA aff.|3|656|471|
|Summation|UNGA aff.|5|703|471|
|Summation|UNGA aff.|3|640|449|
|Summation|EU agreements|5|262|194|
|Rise|EU agreements|5|248|180|
|Rise|EU agreements|3|245|177|
|Composite|EU agreements|5|243|175|
|Summation|EU agreements|3|242|174|
|Composite|EU agreements|3|240|172|
|Rise|Trade w. donors|3|953|31|
|Rise|Trade w. donors|5|934|28|
|Composite|Trade w. donors|3|914|25|
|Summation|Trade w. donors|5|907|24|
|Composite|Trade w. donors|5|885|23|
|Summation|Trade w. donors|3|897|21|

_Notes: Table is ordered by based specifications that, in addition to being significant at a p- value of 0.05 with an F-statistic of at least 10, had the sign we theorized for the IVs. Column four indicates the number of coefficients with a p-value below 5% and an f-statistic above 10. Column five contains the same information filtered for coefficients with a positive sign._ 59
- Marshall, N. L., C. G. Coll, F. Marx, K. McCartney, N. Keefe, and J. Ruh (1997). After-school time and children’s behavioral adjustment. _Merrill-Palmer Quarterly (1982-) 20_ (3), 497–514.

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To understand the Rwandan genocide’s impact on children’s schooling, we discuss the mechanisms by which the conflict could have affected education. The genocide was extremely violent and disrupted the school year throughout the entire country. Schools were closed, school buildings and supplies destroyed, teachers killed, students and teachers alike became refugees abroad, families lost their savings and became poor, and many students became orphans losing one or both parents to the genocide. However, the genocide lasted a short time, concentrated on 100 days during the spring of 1994, and at the micro-level households could have rebounded from this massive shock as is evidenced in the macroeconomic indicators discussed previously.

# _2.2 Data Overview_

Our analysis, focusing exclusively on primary schooling as few Rwandans complete more than six years of school, attempts to measure the medium-term effects of the genocide on educational achievements several years after the conflict ended. We use the 1992 and 2000 Demographic and Health Surveys (DHS) for Rwanda to measure educational attainment and grade completion rates.<sup>4</sup> Table 1 reports the overall educational attainment for all individuals aged 6 to 35, and then for males and females separately, in 1992 and in 2000. For both genders and for all individuals overall, on average schooling outcomes improved between 1992 and 2000. The fraction of people with no education decreased from 30.0 to 23.8 percent, and the proportion with some primary education increased from 63.99 to 69.16 percent. There was also a small increase in secondary schooling from 6.01 to 7.04 percent. Similar results are seen for both men and women, although the improvements seem to be slightly larger for women. These results

> the current province of residence and shows that approximately 88.5 percent of individuals currently live in the same province in which they were born.

> 4 Rwanda’s education system has a 6-3-3-4 structure: six years of primary schooling, three years of lower secondary schooling, three years of upper secondary schooling, and four years of higher education (World Bank, 2003).

5
_Low-Income Countries’ Access to Private Debt Markets_

**Figure 22 Short-term debt as a share of total domestic debt, 2004**

<!-- Start of picture text --> Short-term 1 / total domestic debt (%)<br>100<br>75<br>50<br>25<br>0<br>4 5 6 7 8 9<br>Log(GNI per capita)<br><!-- End of picture text --> _Sources:_ World Bank staff calculations based on estimates reported by Christensen (2005), Jeanne and Guscina (2006) and Cowan and others (2006).

_Note:_ 1. Domestic debt with original maturity of less than one year.

Expanding borrowing from commercial creditors would signify a major shift in most low-income countries where the outstanding debt is largely comprised of highly-concessional long-term loans from the official sector. In 31 of 42 low-income counties more than 80 percent of external debt is owed to official creditors, compared to just 2 of 35 upper middle-income countries (figure 23).<sup>23</sup> The average grant element of (official and private) new debt commitments exceeds 50 percent in 36 of 43 low-income countries (figure 24) with an average interest rate of less than two percent (figure 25) with an average maturity structure of over 20 years (figure 26).

> 23 The two exceptions are Botswana and Montenegro.  Montenegro declared independence in June 2006 and has cumulated little private debt, while Botswana has a very low external debt burden.

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TbC studies using firm-lovel data provide support to the oft-made claim of Japanese officials that policy-based finance made a positive contribution to industrialization and economic devolopment. Given the small relative size of the policy-based industrial funds, which oven in the 1950s amounted tc less than 5% of the total funds mobilized by the financial system, but declined over timc to less than 1% in the 1970s and 1980s, no claim can be made that policy-based finance was the main factor behind Japan's economic success. The studies do not show that the roquired finance could not have boen obtaincd from private sources, but they suggest that policy-based finance acted as a catalyst for financing particular scctors or firms, supporting the argument of Japancse ofricials with regard to the "pump-priming" function and "crowding in" effect of policy-based finance.

23
not evident to most people whether a decline in under-five mortality from 200 deaths per 1,000 live births to 180 per 1,000 is a more significant achievement than a decline from 30 to 10. A more numerically salient target would be life expectancy. People generally have a sense of how long the average life is in their country, and also how long it might be in the richer countries. The target could be a “normal lifespan” (which sounds less technical than life expectancy). Because life expectancy is typically calculated as life expectancy at birth, and is very sensitive to reductions in mortality in the early years of life, life span might be calculated as life expectancy of all citizens currently living in a country, or in some other way weight lifespan information from older individuals as much as (or almost as much as) young children.

In education, several have proposed a target that focuses on outcomes, not just school attendance. Perhaps the most salient psychological target would be “universal literacy.” The ability to read and write is easily understood. And universality, 100%, is obviously a salient number.

A second characteristic of psychological salience involves causal narrative. When confronted with confusing or even meaningless information, human beings nevertheless search for a storyline; and in the absence of an available causal explanation, they invent one (Kahneman 2011). The MDGs entailed no explicit or implicit storyline for either why many people and countries are poor, or how the obstacles to shared prosperity might be overcome. To some extent this was by design, on the view that countries should be free to use a local approach, based on a contextual model of the development process and the local “binding constraints,” to achieve the targets. But if the primary effect of the MDGs is about nudging, the importance of narrative needs to be taken seriously.

There are many possibilities for a narrative about development, but one part of the storyline may be crucial. Most people, when asked to consider why people are poor and why efforts to promote development have had mixed success, find themselves thinking about the widespread use of patronage to distribute resources and opportunities, the creation of “dependency” on the part of recipients of ODA and 12
# Appendix D: Structural Vector Autoregression

To understand the relative importance of external factors in driving each country’s business cycle or output fluctuations around trend, we estimate a VAR per country with a set of external and internal factors and calculate the impact of each factor in determining short term movements of real output growth. This effort translates into computing the variance decomposition of each VAR system. We run a VAR for 54 countries with quarterly data typically ranging from the early 1990s to 2017.

The variance decomposition proceeds as follows. Using an orthogonalized VAR system, we can compute an accounting of the forecast error variance; that is, what percent of the k-step ahead forecast error variance is due to which variable. Formally, we start with the moving average representation of our orthogonalized VAR system:<sup>27</sup> , E( ′)=I, )=I, =I, where is a vector including our variable of interest, real domestic output growth, a set of external xxtt = CC(LL)ηηtt ηηttηηtt , E( ′)=I, )=I, =I, where factors represented by the log change in CTOT, US real output growth, and change in real US policy tt rates and a set of internal factors represented by the change in real public expenditure, change in real xx exchange rate, change in domestic real interest and change in real trade balance. C(L) = C0 + C1L + C2L2 + ... where C is the matrix of orthogonalized coefficients and L the lag operator.

In our specification, we use one lag in all VAR systems and we report the variance of the 8th step ahead forecast errors. The choice of lag length was based on several information criterion tests. The choice of the 8th step ahead was made to ensure that the role of different factors had already begin to stabilize (some factors may take more than one period to affect growth).

> 27 See Cochrane (2005) for a complete textbook reference.

33