rafmacalaba/datause-encoder-data huggingface dataset:
expatriate outside director with management expertise for its Board, and provided training to its staff on audit, portfolio valuation, and loan restructuring.

- In _Viet Nam_ , the two IFIs provided technical assistance and on-site staff training in a broad range of topics, including statistical data collection and analysis, macroeconomics, econometrics, and banking supervision, supported by the assignment of long-term external experts to the central bank; as well as assistance in strengthening the payments system, including the introduction of new payments instruments.

**It should be underscored that this list underestimates the true extent of capacity-building effort and resources expended in these countries during their exit from fragility** . The source documents focus on economic policies and conditionality, and provide only a high-level summary of the two IFIs’ technical assistance activities. In addition, they do not cover the extensive training activities of the IMF Institute for officials from member countries. Last but not least, with very few exceptions, the source documents do not identify capacity-building activities by bilateral donors.

# **Lessons learned**

## **Before generalizing from the findings of the analysis of the country cases, it is worth recalling**

**the caveats** . Although the sample of cases was deliberately selected to be as broad and as representative of different experiences of fragility as possible, it is still small. The analysis is based on publicly available documents of the World Bank and the IMF. Although these documents contain the most comprehensive summary of financial sector reforms during the process of exit of these countries from fragility during the 1980s and 1990s, they were produced at a time when the two institutions—especially the IMF—did not see financial sector issues as a core part of their mandate. This is likely to result in less detailed coverage of these issues than could perhaps be warranted.

## **With these caveats in mind, the findings suggest certain lessons for policy makers in countries trying to exit fragility, as well as their international partners.**

- **_Do not forget the financial sector_** . Regardless of its original causes, exiting state fragility requires a strategy of far-reaching economic reforms across a broad front. The financial sector plays a central role in a well-functioning economy, and financial sector interventions should be part of this strategy. If there are pressing humanitarian, security, or other needs, these should, of course, be given priority. But it is unlikely that a successful exit can be achieved and sustained without some early action in the financial sector.

- **_Ensure that the fundamentals are in place before launching other reforms_** . Taking steps to create an efficient financial sector that can mobilize savings to finance growth is futile if

18
Thus, the rate of new ideas produced depends on both the allocation of resources to the R&D sector ( _HA,t_ ), the productivity of those resources ( _λ_ ), the stock of ideas already in existence ( _At_ ) and the ability of that stock to support new invention ( _φ_ ). Note that if _φ_ > 0, prior research increases current R&D productivity (the "standing on shoulders" effect), but if _φ_ < 0, prior research has discovered the easier ideas and new invention becomes more difficult.

Our measures of new ideas production are total patent applications, total patents awarded, and patents granted to U.S.-based universities and other institutions and firms. All of these data refer to activities within the United States. Patents are not an ideal measurement of innovative output, primarily because patents vary widely in their economic and technical significance (Griliches, 1984). However, patenting activity is the most commonly used proxy in innovation studies and does capture three important aspects of innovation (Kortum, 1997; Stern, et al, 2000). First, patents do reflect an important portion of innovative output and are likely correlated with others, such as trade secrets and copyrights. Second, to be awarded a patent, inventions must be novel and non-obvious, suggesting that patent grants capture something new. Third, it is costly to apply for a patent, so the patenting entity believes there is something economically valuable about its technological innovation.

The primary novelty of our approach is in the definition of _HA,t_ . In prior studies these resources have been measured by R&D expenditures (perhaps broken into university and non-university sources) and scientists and engineers. We retain the use of these basic variables but incorporate international students and skilled immigrants as 11
**Table 3: Importance of the "Growth" Component in the Total (“Growth” + “Convergence”) Explained Change in the Quantity of Assets, %**

|Wealth class||Type of a|sset|a|verage|
|---|---|---|---|---|---|
||Freezers|AWMs|CTVs|Cars||
||1995-2001<br>|||||
|poorest|28.0|45.1|52.7|27.8|38.4|
|middle (lower)|76.5|87.3|90.3|76.4|82.6|
|middle|65.1|79.8|84.2|64.9|73.5|
|middle (upper)|47.1|65.3|71.8|46.9|57.8|
|richest|36.3|54.7|62.0|36.1|47.3|
||2001-2003|||||
|poorest|98.0|99.0|99.3|98.0|98.6|
|middle (lower)|33.1|51.1|58.5|32.9|43.9|
|middle|32.1|49.9|57.4|31.9|42.8|
|middle (upper)|34.2|52.4|59.8|34.0|45.1|
|richest|47.8|65.9|72.3|47.5|58.4|

Source: Author’s estimates.

- 30 -
Table A5: Future employment expectations and outcomes do not differ by treatment

||(1)<br>Day labor|(2)<br>Salaried|(3)<br>Any work|(4)<br>Daily wage|(5)<br>Expects work|(6)<br>Total expected|
|---|---|---|---|---|---|---|
|Work|0.001|-0.004|-0.111|-0.009|-0.064|-0.619|
||(0.015)|(0.009)|(0.043)|(0.341)|(0.143)|(2.713)|
|Cash|0.023|-0.011|-0.070|0.119|0.120|-1.787|
||(0.022)|(0.008)|(0.049)|(0.344)|(0.200)|(3.588)|
|Shrp. q-val Work|1.000|1.000|0.060|1.000|1.000|1.000|
|Test Work = Cash|0.321|0.113|0.356|0.635|0.327|0.681|
|Shrp. q-val Work = Cash|1.000|1.000|1.000|1.000|1.000|1.000|
|Observations|743|743|698|193|698|698|

**Notes:** This table reports the treatment effect of receiving employment or cash relative to the control group on future employment. The columns show OLS estimates of a regression of the outcome on a dummy for assignment to the work treatment, as well as the baseline measure of the dependent variable, camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure Belloni, Chernozhukov, and Hansen (2014).

Outcomes collected during the six-week followup survey. ‘Day labor’ is a binary variable that equals 1 if the respondent reports engaging in agriculture or construction work in the prior month, and 0 otherwise. ‘Salaried’ work equals 1 if the respondent reports engaging in service or teaching work, and 0 otherwise. ‘Any work’ equals 1 if the respondent reports engaging in any work in the prior month, and 0 otherwise. ‘Daily wage’ is the wage in USD received if a respondent worked in the previous month. ‘Total expected’ is the total compensation expected in the coming month if one were to find work (USD). Standard errors are clustered at the block level.

Table A6: Mental health is correlated with family but not community value-ranking

||(1)<br>PHQ|
|---|---|
|Rank within family|0.052<br>(0.020)|
|Rank within|0.003|
|community|(0.019)|
|Observations|726|

**Notes:** This table reports the baseline relationship between ranking of oneself within one’s family and one’s community on PHQ score. The PHQ score is reversed such that lower PHQ reflects a poorer outcome, or higher likelihood of depression. Regression includes camp and enumerator fixed effects. Standard errors are clustered at the block level.

50
most as soon as possible. However, if pregnancy in the McCrary and Royer (2011) population is timed in relation to age rather than schooling, such variation in educational attainment would have no effect. In my case, however, young teens are given or denied the opportunity to continue schooling (thus varying age at exit) at the KCPE discontinuity. The KCPE discontinuity only has an effect on those who choose to continue beyond primary education (delaying school exit), and who must be considering tradeoffs between continuing their education and raising a family. These may be higher ability students, relative to the Kenyan distribution, than are the McCrary and Royer (2011) respondents in relation to the US distribution. Thus, while they find essentially no impact of education on early fertility using variation in age at school entry, it may still be sensible that in contrast to their work, I find large effects. Filmer and Schady find no effect of additional schooling in Cambodia on pregnancy rates, but the non-effect in their setting may be due to the fact that the median respondent in their survey was still only 14 years old.

Other studies in Sub-Saharan Africa have found similar (though smaller) effects of schooling on teen pregnancy in relation to those I show here. Ferr ́e (2009) finds that a policy shift reclassifying 8th grade from secondary to primary school increased the fraction of students reaching 8th grade, thereby reducing teen pregnancy by 10 percentage points in Kenya in the 1980s. Duflo, Dupas, and Kremer (forthcoming) observe a 1.5 percentage point reduction in teen childbearing in Kenya in response to a school uniform distribution program that helped girls stay in school; and Baird, Chirwa, McIntosh, and ̈Ozler (2010) find that a conditional cash transfer to bring dropouts back into 26
**Table 5: Marginal effects of the logit model of awareness and interest**

|**Indicator**|Interested in how|Informed about|Knows that|
|---|---|---|---|
||local budget<br>expenditures are<br>done|the local<br>budget issues|information about<br>local budget should<br>be available|
|Personal characteristics||||
|Age|0.002 ***|0.002 ***|0.004 ***|
|Female|-0.050 ***|-0.057 ***|-0.040 ***|
|Uzbek<br>|-0.116 ***|-0.148 ***|-0.106 ***|
|Russian|-0.008|-0.010|-0.042|
|Other ethnicity|-0.010|-0.144 ***|-0.127 ***|
|Years of schooling|0.020 ***|0.020 ***|0.024 ***|
|Risk attitude|0.011 ***|0.010 ***|0.012 ***|
|Labor market status||||
|Inactive|-0.041 **|-0.047 ***|-0.063 ***|
|Unemployed|-0.090 *|-0.095 **|-0.141 ***|
|Social capital||||
|Internal migrant|-0.070 *|-0.042|0.090 **|
|Member of a social group|0.032|0.035|0.035 *|
|Can get financial help from others|0.190 ***|0.171 ***|0.084 ***|
|Trust in local government|-0.013|0.012|0.015 *|
|Political attitudes||||
|Supports current political system|-0.078 ***|-0.092 ***|0.030|
|No preference for political system|-0.152 ***|-0.134 ***|-0.087 ***|
|Is going to vote in local elections|0.050 ***|0.048 **|0.040 **|
|Household characteristics||||
|Female HH head|0.009|0.022|0.059 ***|
|Children|0.018 ***|0.019 ***|0.002|
|Income per person|0.002|0.003|-0.004|
|Land size|-0.001|0.012 **|0.000|
|Livestock|0.000 *|0.000 *|0.000|
|Asset index|0.000 ***|0.000 ***|0.000 ***|
|Community characteristics<br>1||||
|Population|0.003 *|0.003 *|-0.002|
|Kindergarden is available|0.026|0.018|0.030|
|Medical service is available|-0.021|0.050 **|0.025|
|Frequent energy interruptions|-0.163 ***|-0.122 ***|-0.125 ***|
|Oblasts||||
|Issyk-Kul|0.147 ***|0.027|0.026|
|Naryn|-0.092 **|-0.026|-0.128 ***|
|Batken|-0.049|0.011|0.076 **|
|Osh|-0.094 ***|-0.049 *|-0.153 ***|
|Talas|0.294 ***|0.019|0.030|
|Chui|0.082 **|-0.131 ***|-0.091 ***|
|Pseudo R<sup>2</sup>|0.152|0.149|0.172|
|No of observations|5,042|5,042|5,042|
|Source: Life in Kyrgyzstan (LiK) Survey, 2|012<br>Note:|1/ Community data|are from 2011 wave|
|Standard errors are corrected for cluster|effects at the househo|ld level.||
|Significant estimates are indicated by * p|<0.1, ** p<0.05, *** p<0|.01.||
|‘Kyrgyz’, ‘Employed’, and ‘Jalalabad oblast|’ are the reference gro|ups.||

Household characteristics indicate that awareness and interest may be driven very much by household economic status. Proactive individuals seem to originate from households that have fewer members, 14
# Table S3. Features of total debt booms

||World|Advanced<br>economies|EMDEs|World|Advanced<br>economies|EMDEs|
|---|---|---|---|---|---|---|
|**All debt booms**|||||||
|Amplitude|43|63|40***||||
|(Percentage points of GDP)|[25, 78]|[33, 103]|[24, 71]||||
|Duration|8|9|7***||||
|(Years)|[4, 12]|[5, 15]|[4, 11]||||
|Speed|6.0|6.1|5.8||||
|(Percentage points of GDP per year)|[3.8, 10.1]|[4.6, 8.9]|[3.6, 10.1]||||

## **All debt booms: Completed versus Ongoing**

||**Completed**|||**Ongoing**|||
|---|---|---|---|---|---|---|
|Amplitude|42|62|39|54*|76|49**|
|(Percentage points of GDP)|[24, 76]|[34, 103]|[23, 68]|[32, 81]|[32, 102]|[32, 76]|
|Duration|7|9|6|10***|9|10***|
|(Years)|[4, 11]|[5, 15]|[4, 10]|[6, 13]|[5, 14]|[7, 13]|
|Speed|6.0|5.8|6.1|5.9|6.5|5.5|
|(Percentage points of GDP per year)|[3.8, 10.5]|[4.2, 10.3]|[3.8, 10.5]|[3.8, 9.0]|[5.4, 8.1]|[3.4, 9.8]|

## **Completed debt booms: With crises versus Without crises**

||**With crises**|||**Without cri**|**ses**||
|---|---|---|---|---|---|---|
|Amplitude|61|100|52|36***|53***|33***|
|(Percentage points of GDP)|[33, 101]|[62, 183]|[32, 92]|[20, 61]|[24, 90]|[19, 49]|
|Duration|8|12|7|6**|8*|6*|
|(Years)|[4, 14]|[7, 17]|[4, 13]|[4, 10]|[4, 15]|[4, 9]|
|Speed|7.8|7.4|8.2|5.3***|5.5**|5.1***|
|(Percentage points of GDP per year)|[4.9, 12.1]|[5.4, 11.7]|[4.6, 12.1]|[3.5, 8.3]|[3.7, 8.7]|[3.3, 8.3]|
|**Completed debt booms: Before 2000**|**versus After**<br>**Before 200**|**2001**<br>**0**||**After 2001**|||
|Amplitude|43|55|41|40|74**|37|
|(Percentage points of GDP)|[23, 72]|[26, 92]|[23, 70]|[24, 82]|[41, 172]|[23, 64]|
|Duration|7|8|7|7|12|6|
|(Years)|[4, 11]|[5, 14]|[4, 11]|[4, 11]|[6, 21]|[4, 10]|
|Speed|5.8|5.5|6.2|6.2|7.1**|5.7|
|(Percentage points of GDP per year)|[3.8, 10.2]|[4.0, 7.8]|[3.8, 10.5]|[3.9, 11.0]|[4.7, 11.5]|[3.8, 10.4]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), based on 448 total debt booms, of which 92 debt booms are in advanced economies and 356 are in EMDEs. Amplitude is the trough-to-peak change in the total debt-to-GDP ratio during a total debt boom. Speed is the ratio of the amplitude to the duration. *, **, and *** denote that medians between advanced economies and EMDEs in all debt booms, and between completed and ongoing booms or between completed debt booms with and without crises in respective country groups, are statistically significantly different at the 10 percent, 5 percent, and 1 percent levels, respectively.

35
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE percent in 2000 to 40 percent in 2007 in rural parts of the country (figure 10). Similarly, urban access to safe water within 30 minutes from home decreased from 81 percent in 2000 to 75 percent in 2007.

**Figure 10. Access to safe water is declining, 2000-2007**

<!-- Start of picture text --> 100<br>2000<br>2007 2000<br>80 2007<br>60<br>2000<br>2007<br>40<br>2007<br>20<br>2000<br>0<br>urban rural urban rural<br>access to clean and safe water<br>access to clean and safe water<br>within 30 min (rural 2000 data NA)<br>percentage of households<br><!-- End of picture text --> Source: Monitoring Progress in Water and Sanitation, WaterAid, Tanzania, March 2009 (HBS 2007).

In addition, Tanzania’s utilities are highly inefficient (table 6a). Distribution losses are typically around 45 percent, compared with 33 percent among peers. At around $0.40 per cubic meter, Tanzania’s water tariffs are substantially lower than those found in other African countries, covering only two thirds of the cost of service provision. The hidden costs of these inefficiencies are very substantial amounting to 145 percent of sector revenues.

There is significant performance variation across Tanzania’s utilities (table 6b). System losses range from 33 to 52 percent. Collection ratios range from 85 to over 100 percent. Cost recovery ratios range from 32 to 60 percent. Only the better performers (Dodoma, Mwanza) have hidden costs around the level of the low income country benchmark for Africa, while the worst performers (Dawasco, Mbeya, Moshi) have hidden costs in excess of 200 percent of revenues. In absolute terms, the hidden costs associated with Dawasco dwarf those of the other utilities amounting to US$47 million annually.

# Irrigation

## **Achievements**

The 2002 National Water Policy (NAWAPO - 2002) stipulates an integrated approach to water resource management and development. Within this framework, Tanzania’s government has successfully implemented institutional reform in the irrigation sector. As a result, the country now has four out of five good practice features for sector institutional development: a specialized agency for basin level management, infrastructure development separated from agronomic management, empowerment of the WUAs, and irrigation strategy. The government published a National Irrigation Policy in 2009 to support implementation of its Agriculture Sector Development Program.

Like many African countries, Tanzania has realized very little of its irrigation potential. According to analysis by IFPRI, currently, 184,330 hectares are irrigated—only 3.6 percent of the total cultivated area 21
# Figure 2.2: TURKEY, evaluation of performance of the ICA method

I. Kernel<sup>1</sup> estimates of output and input densities in the complete case and in the sample after imputing missing values by the ICA method <!-- Start of picture text --> A. Sales (log) B. Materials (log)<br>5 10 15 20 5 10 15 20<br>log-Sales log-Materials<br>Complete case ICA method Complete case ICA method<br>C. Capital stock (log) D. Employment (log)<br>0 5 10 15 20 8 10 12 14 16<br>log-Capital log-Employment<br>Complete case ICA method Complete case ICA method<br>.2<br>.2<br>.15<br>.15<br>.1 .1<br>Density Density<br>.05 .05<br>0 0<br>.25<br>.25<br>.2 .2<br>.15 .15<br>Density Density<br>.1 .1<br>.05 .05<br>0 0<br><!-- End of picture text --> II. Table of descriptive statistics and tests of equality of distributions of output and inputs in the complete case and in the sample with imputation by the ICA method

|||**# Obs.**<br>**(# imputed)**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**One-sample K-S**<br>**Test (p-value)**|
|---|---|---|---|---|---|---|---|
|Sales (log)|Complete case|1497|14.24|2.10|7.78|19.40|0.004|
||ICA meth.|1821 (324)|14.30|1.99|7.78|19.40|0.000|
|Materials (log)|Complete case|1293|13.19|2.31|4.33|18.65|0.020|
||ICA meth.|1822 (529)|13.37|2.13|4.34|18.65|0.000|
|Capital (log)|Complete case|1289|11.39|2.26|0.63|19.65|0.015|
||ICA meth.|1816 (527)|11.32|2.05|1.05|19.65|0.004|
|Empl (log)|Complete case|2529|11.63|1.45|7.64|15.42|0.001|
||ICA meth.|2548 (19)|11.63|1.45|7.64|15.42|0.001|

Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values. The null hypothesis of the one-sample Kolmogorov-Smirnov Test is that the cumulative distribution differs from the hypothesized theoretical normal distribution.

Source: Authors’ estimations with ICSs data.

22
-13-

autoregulatory homeostatic nature of the body allows for substantial variability in energy intakel5, and that such variability creates a band of up to 20X around the reference nutrient requirement where intakes can vary. It is only when intake is below the lower bound<sup>of the band for a sustained</sup> period of time (e.g. below 1,800 calories per day per adult) that undernutrition appears as a permanent condition, with its associated risks.

These arguments imply that longitudinal data is required to identify the extremely-poor and, in addition, that corrections for individual variances in nutritional requirement **s** hould be made. Unfortunately, the data to carry out these computations in Mexico is at present unavailable. As a result, if 'access to adequate sources of nutrition' is used to measure the line of extreme-poverty, the use of averages is unavoidable and, as with any other procedure, some errors of classification will be made.

Estimates of undernutrition in Mexico vary widely, from 20 to 50X<sup>of the</sup> population (cf. Lustig, 1984, pp. 443-47). Most of<sup>these estimates</sup> are made comparing the monetary costs of a 'desired' reference intake of nutrients (given, in most cases, by an exogenously determine **d** iet as in the Coplamar study mentioned before) with actual food expenditures by households1 6. These estimates are: one, subject to the Srinivasan-Sukhatme type of criticisms 1<sup>5</sup> "There is considerable evidence to show that<sup>a healthy, active</sup> individual engaged in fixed tasks and maintaining near constant body weight enjoys wide flexibility in intake" (Sukhatme, 1988, pp. 374-75). On the other hand, Behrman and Deolalikar (1988, pp. 654-55) argue that this evidence is based on small sample sizes that are<sup>not representative.</sup> (But Edmundson and Sukhatme (1990) present more evidence to reinforce Sukhatme's thesis.) 16The Coplamar estimates imply that undernutrition in Mexico is almost non-existent for the lowest decile of the income distribution: urban (rural) households consume 92.65 and 95.71X (117.68 and 98.89X) of the recommended daily intake of 2082 calories and 63 grams of protein, respectively (see Coplamar, 1985a, tables A-3.14 and A-3.15). On<sup>the other hand, the World Bank</sup> (1989b, p. 32, table V.1) presents data to show average intake of calories in rural households in 1979 in the following states as: Chiapas, 1609; Oaxaca 1483; Guerrero 1638; Coahuila and Nuevo Leon 1684; Hidalgo 1703; Veracruz 1746; Yucatan 1755. Unfortunately, it is not clear what the reference point is: if it is average intake at a point in time,<sup>or average intake for a</sup> sustained period of time. In the latter case<sup>the situation</sup> is very serious, but need not be in the former.
2001; EMC, 2011; Bouahom and Douangsavanh, 2013). While many rice farmers in Lao PDR would potentially benefit from higher prices, the fact is that only a quarter of the households sell rice and over a third buys it (Hill and Christiansen, 2006; Lao Census, 2012; World Bank, 2015).<sup>6</sup> It is thus understandable that many less-developed countries, including Lao PDR, resort to extensive trade restrictions with the aim of ensuring stable staple food prices (Galtier, 2013). And, in fact, appropriate trade restrictions may also agree with optimal trade and storage models, see e.g. Gouel and Jean (2014) and Gouel et al. (2014).

Nonetheless, it is commonly believed that government policy interventions encountered in practice are more likely to generate price spikes than to curb them. Interventions are frequently of a discretionary nature, creating an environment of uncertainty that hampers private market development and ultimately increases prices; see for example the recent studies of maize markets in Sub-Saharan Africa by Chapoto and Jayne (2009), Jayne and Tschirley (2010) and Tschirley and Jayne (2010). But even transparent and rule-based trade restrictions may lead to price spikes as rational agents anticipate the lifting of these restrictions (Porteous, 2012). The same trade restrictions, if collectively enforced by a sufficiently large share of the market, have also been linked to price spikes on the world market (see e.g. Anderson and Nelgen, 2012; Barrett and Bellemare, 2011; Martin and Anderson, 2012; Ivanic and Martin, 2014a).<sup>7</sup> Yet, not all government interventions are bad. There are examples where government interventions

> 6 It should be noted that in 2012 about a third of the households that are net-buyers of rice spend less than 10% of their food expenditures on purchasing rice (World Bank, 2015).

> 7 The 2008-2009 global food price crisis is arguably an example of this.

3