rafmacalaba/datause-encoder-data huggingface dataset:
farmers.<sup>16</sup> Of those who over- (under-)estimated initially, 76 (48) percent still over- (under-) estimated post-treatment.

**Table 5: Information-induced changes in reporting behavior (treatment group plots only)**

||Post-ha|rvest reportingbeh|avior|Total<br>|
|---|---|---|---|---|
|Post-planting reporting behavior|SR:<br>Overestimate|SR:<br>Underestimate|SR:<br>Accurate|number of<br>observations|
|SR: Overestimated|76.0|15.5|8.5|647|
|SR: Underestimate|36.5|48.4|15.1|304|
|SR: Accurate|22.6|0.00|77.4|31|
|Total numberofobservations|610|247|125||

**Notes:** Values are shares or percentage of plots over(under)-estimated across the post-planting and post-harvest rounds.

Second, the response of farmers to accurate information appears highly asymmetric. Those who overestimated their plot size pre-treatment remain highly likely to continue to overestimate it, while less than half of those who underestimated their plot size initially continue to underestimate post-treatment. Moreover, as shown in Table 6, the average magnitude of the overestimation hardly changes, with the difference statistically significant only at the ten percent level. Even though pre-treatment underestimates were only about one-fifth as large in percentage terms as overestimates, the average magnitude of change in underestimation was slightly larger, and the information-induced change had a highly significant effect in correcting underestimates. For instance, Table 6 shows that the average underestimation rate for the treatment group plots declined by 64 percent (from 32 percent to 12 percent) while the corresponding overestimation rate only declined by about 12 percent (from 149 percent to 131 percent). These results clearly reveal asymmetric responses both at the extensive (Table 5) and intensive margins (Table 6).

**Table 6: Magnitude of information-induced changes in reporting behavior (treatment group plots only)**

||**Post-planting reporting behavior**<br>ME magnitude in<br>post-planting (%)|**Post-harvest r**<br>%Underestimation<br>in post-harvest|**eporting behavior**<br>%Overestimation in<br>post-harvest|Difference<br>between PP<br>and PH<br>|
|---|---|---|---|---|
|Overestimated in post-planting|149.1(214.9)|3.5(11.9)|131.3(222.3)|17.8<sup>*</sup><br>|
|Underestimatedinpost-planting|31.8(22.5)|11.67(18.2)|34.1(128.0)|20.2<sup>***</sup>|

Note: Values in bottom panel are mean percentage over/under-reporting in each round (standard deviations in parentheses).<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

> 16 In the pre-treatment survey, only 31 farmers accurately reported their plot size; this increases to 125 in the posttreatment survey .

19
All three countries were initially affected by the collapse in export demand. China was hit hardest, with exports falling 25 percent in the first half of 2009 compared with the same period the previous year, leading to an estimated 20–36 million people losing their jobs. India’s exports, which grew 28 percent in 2007, fell 19 percent between October 2008 and March 2009. Brazil, whose exports grew at an average annual rate of 14 percent during the pre-crisis boom, posted double-digit declines in the months following September 2008.

Foreign direct investment fell in China from $143 billion in 2007 to $70 billion in 2009, before recovering to $186 billion in 2010. In India, portfolio investment flows fell from an annual average of $45 billion during 2003–08 to $16 billion in 2009. In Brazil, such flows shifted direction, with an outflow of $23 billion between September 2008 and March 2009.

Brazil was most affected through the credit channel. The disruption in the international credit markets led to a drop in investment and GDP contracted in 2009. In India, the global crisis occurred after a downturn in the domestic investment cycle had already begun; as a result, the negative impact on growth was magnified.

The immediate impact on growth was most severe in Brazil; the impact over three to four years has been equally severe in India (figure 2).

**Figure 2 Real Annual Changes in Gross Domestic Product in Brazil, China, and India, 2003–12**

<!-- Start of picture text --> Percent<br>12<br>10<br>China<br>8<br>6<br>India<br>4<br>2<br>Brazil<br>0<br>2003-07 2008 2009 2010 2011 2012<br>-2<br><!-- End of picture text --> Source: Table 1 above.

18
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# **Labor Market Transitions in Egypt Post-Arab Spring**

**Jingyuan Deng** , **Nelly Elmallakh** , **Luca Flabbi and Roberta Gatti**<sup>**1**</sup>

**Keywords:** employment, informality, transitions, labor market, Egypt. **JEL codes:** J10, J21, J22, J45, J46.

> 1 Deng: World Bank’s Office of the Chief Economist for the Middle East and North Africa (jdeng1@worldbank.org); Elmallakh: World Bank’s Office of the Chief Economist for the Middle East and North Africa (nelmallakh@worldbank.org); Flabbi: University of North Carolina at Chapel Hill (lflabbi@email.unc.edu); Gatti: World Bank’s Office of the Chief Economist for the Middle East and North Africa (rgatti@worldbank.org).
to adapt to outages by purchasing a back-up generator is not an ideal scenario for firms, and thus our results are unlikely to be capturing the nuanced ways in which electricity outages affect firms.<sup>7</sup> Table 3 also shows that foreign ownership and exporting status are positively related to sales, with coefficients statistically significant at the 5% level. Formal training is positively related to sales, but the coefficient is not significant. As expected, security costs are negatively related to sales, with the coefficient statistically significant at the 1% level. Finally, the coefficient for the variable capturing whether or not firms compete with informal firms is negative but not statistically significant.

For this study, the translog production function is the base specification and all findings discussed above are based on this specification. However, we also present the results from estimating a CobbDouglas production function (column 2 of table 3) to show the robustness of our findings to an alternative functional form commonly used in the literature. Survey weights are used in all the estimations, taking advantage of the levels of stratification by sector, size, and location.

Just as our results presented earlier have shown that the impact of drought on water supply is asymmetric across income level, it is also plausible that the relationship between water shortages and productivity also differs according to countries’ levels of economic development. Again, we explore the findings for two income groups – upper-middle and high-income economies on one hand and lower-middle and low-income economies on the other hand. The findings for both groups are presented in columns 1 and 2 of table 4. The results show that the number of water shortages per day has a negative and statistically significant coefficient for low and lower-middle income countries, but is not statistically significant for upper-middle and high-income economies. This result serves to underscore our earlier finding that there is an increased vulnerability to water shortages in less-developed economies. And that, unlike in low and lower-middle income economies, firms in high income economies appear to have the necessary resources to adapt to water shortages.

> 7 Allcott et al. (2016) found that, for Indian manufacturing firms, average productivity losses due to electricity outages are small because, for the firms in their analysis, most inputs can be stored during such outages. Furthermore, their analysis shows that electrical outages more severely affect firms without generators relative to firms with generators. This has recently been shown to be the case for firms in Sub-Saharan Africa also, by Cole et al. (2018).

13
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_Roe of NGOs in Health V*llvezy Afica Halth Policy Study_ _-8_

# _Reaching Peripheral Arew_

20. While not consistently the case, NGOs including missions, involved in healthrelated activities tend to be particularly represented in poorer, more remote arcs% e'ther out of commitment to serve the under privileged (e.g., religious missions often state this explicitly) or because they can fll a gap in such aieas not already met by gove- nent services. In Ghana, for Instance, while missions provide 25 percent of total hospital beds in the country, they provide about 46 percent of beds in thesix under-privileged northern regions (Bradley 1989). NGO activities may also be concentrated in areas that tie govermment is not serving for political reasons, thus potentially bringing themselves into conflict with the government (as in Ethiopia, for example, during the famine of 1984) NGOs may choose to be in a position to defend those discrimina ed against by government or to provide servicec to groups in conflict with the government (e.g., r edecins sans Frontieres) (Cumpe, 1986).

21. There Is debate, however, on the extent to w-hich NGOs real!y do reach the poor or the underserved. A Finnish evaluation found tdat aid provided te or throurA NGOs - did reach the poor more successfully (Van derJeijden cited in Hellinger -8, p. 200), yet an analysis of U.S. private voluntary organization (PVO) projects concluded that many of these do not distribute benefits to the bottom third of the populadon (Tendler 1982).

# _in ter-Sectoral Scope_

22. Perhaps one of the greatest strengths of NGOs is the inter-sectoral scope of their health actividtes-that they tend to see their mandate in terms of raising the social welfire of target groups or communities rather than along sectoral or professional lines. The perceived value of this approach of NGOs Increased with the promotion of the primary health care movement and its emphasis on broader definitions of health and equity. Even where NGOs are not active in other sectors such as education, they often have more awareness of the wider, cross-sectoral determinants of health status, given their focus on small-scale activities.4 4. NGOs often express frustration about their dealings with governmert pcisely because they are forced to deal administratively according to strict sectoral lines, not reflecting the true nature of their activities. See articles in _WorldDevelopmentsupplement-_ 1987 (Drabek 1987) for this and other complaints of NGOs regarding their dealings with governments.
To gauge the effects of the rural drinking water program on youth education, we estimate the following equation: the following equation: i i i i (1) ii ii ccii ii Here _i_ YYindicates an individual, = XX ββ+ TT γγ+ ww _v_ + εεa village, _c_ a county, and _t_ a year. i is a person’s educational status such as grades of education completed and the dummy variables of being a ii YY graduate of a middle school and of being a graduate of a high school.<sup>13</sup> While strict enforcement of the Compulsory Education Law should lead to a 100 percent graduation rate from middle schools, its enforcement had been weak until recently. The dummy variable of being a graduate of a middle school is therefore still a meaningful measure of the youth’s educational achievement.

i refers to the characteristics of individuals, households, and villages, including the individual’s age, gender, and the relationship to the household head.ii<sup>14</sup> We also control for XX household income in the first wave rather than the income in the current wave. Since water treatments also benefit adult health which further increases household income and youth education, the inclusion of the current household income as an explanatory variable may lead to an underestimation of the program’s impacts on the youth--current household income absorbs part of the benefits of a water plant on the households.

part of the benefits of a water plant on the households. i is the treatment variable, Water Plant. Note that Water Plant is one as long as the village in which he/she resides had access to treated plant water in some part of his/her sample TT years.<sup>15</sup> Thus in the base regressions, we do not allow the treatment effect to depend on the earliest age at which the youth had exposure to plant water; later we shall distinguish exposureyear effect. represents the county-year fixed effects. i is the error term. A consistent estimate requires the following condition to be satisfied: ccii ii ww εε

> 13 These two dummy variables are constructed based on the grades of education completed. If it is greater or equal to 9, the dummy variable of being a graduate of a middle school is set to one, and if it is over 12, then we consider the individuals as a graduate from a high school. We should mention that the CHNS questionnaire uses different terms for the middle and high schools: i.e. the lower middle school and the upper middle school.

> 14 The distances to schools were not surveyed in the first wave. Therefore, we use the information in 1991 as the proxies for the information in 1989.

> 15 Here the subscript _t_ merely indicates that the observation year for each individual could differ.  Our treatment variable here is essentially a cross-sectional measure.

10
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# **<mark>Table A3A – Comparison of Means Between States with Low and High Public Debt Before FD Law</mark>**

This table displays the summary statistics of states given their public indebtedness in March of 2016, one month prior to the implementation of the FD Law. A state is defined as with low (high) debt if its public indebtedness in March 2016 is below (above) the median. _Lending to states_ is the share of liabilities to net income. _Population_ is the total population of the state in millions. _Employment_ is the quarterly growth rate of employment. _GDP_ is the quarterly GDP growth. _Investment_ is the ratio of spending on public investment projects relative to the state’s GDP. _Services_ is the ratio of spending on public services relative to the state’s GDP. _Social Services_ is the ratio of spending on social services relative to the state’s GDP. _Debt Servicing_ is the ratio of spending on debt outlays relative to the state’s GDP. _Transfers_ is the ratio of federal transfers relative to the state’s GDP. _Tax Rate_ is the ratio of tax revenues of a state relative to the state’s GDP _. Poverty_ is the share of households in a state living in poverty _. Gini_ is the Gini coefficient in a state _. Northern State_ is an indicator of whether the state is classified as a northern state by the national statistics agency. The first two columns in each panel show the mean and standard deviation of each variable. The last column— _Standardized Difference—_ shows the normalized differences of variables between states with high and low debt (based <u>on Imbens and Wooldridge, 2009). All differences are insignificant, except for</u> _<u>Liabilities/Net Income</u>_ <u>, which is higher in states with high debt (by definition).</u>

|on Imbens and Wooldridge, 2009). All differen|ces are insignificant, except<br>**States**|for_Liabilities/Net Income_<br>**with Low Public Debt**|, which is higher<br>**States with Hi**|in states with high<br> **gh Public Debt**<br>Stdd|debt (by definition).<br>Stddid|
|---|---|---|---|---|---|
||Mean|Standard Deviation|Mean|anar<br>Deviation|anarze<br>Difference|
|Lending to states (share)|0.391|0.12|1.128|0.423|-1.68|
|Population (millions)|3.64|3.848|3.227|1.77|0.1|
|GDP|-0.031|0.022|-0.035|0.043|0.09|
|Employment|-0.013|0.022|-0.011|0.021|-0.05|
|- Primary|-0.044|0.114|-0.061|0.116|0.11|
|- Secondary|0.009|0.064|0.025|0.067|-0.17|
|- Tertiary|-0.007|0.023|-0.005|0.03|-0.05|
|Investment|0.006|0.004|0.006|0.005|-0.04|
|Services|0.005|0.003|0.007|0.004|-0.46|
|Social Services|0|0|0|0|-0.02|
|Debt Servicing|0.002|0.002|0.008|0.007|-0.84|
|Transfers|0.129|0.053|0.146|0.075|-0.18|
|Tax Rate|0.485|0.197|0.483|0.169|0.01|
|Poverty Total|0.416|0.12|0.415|0.179|0|
|- Moderate|0.351|0.076|0.337|0.106|0.1|
|- Extreme|0.065|0.055|0.078|0.089|-0.13|
|Gini|0.451|0.039|0.469|0.043|-0.3|
|Northern State|0.4|0.507|0.533|0.516|-0.18|

66
Table 1: Tariff water (Latest Available Year)

|**Country**|**MFN**|**Bound**|**Water**|**Country**|**MFN**|**Bound**|**Water**|
|---|---|---|---|---|---|---|---|
|All Countries|0.05|0.15|0.11|Lesotho|0.15|0.66|0.50|
|High Income|0.04|0.11|0.07|Macedonia,FYR|0.06|0.06|0.00|
|Middle Income|0.08|0.24|0.16|Madagascar|0.11|0.60|0.49|
|Low Income|0.09|0.45|0.36|Malawi|0.33|1.41|1.08|
|||||Malaysia|0.04|0.21|0.17|
|Antigua and Barbuda|0.14|0.69|0.55|Mali|0.11|0.47|0.36|
|Argentina|0.08|0.36|0.28|Mauritania|0.10|0.39|0.29|
|Australia|0.03|0.10|0.07|Mauritius|0.03|0.85|0.81|
|Bahrain|0.05|0.68|0.63|Mexico|0.13|0.39|0.25|
|Bangladesh|0.12|0.94|0.81|Mongolia|0.05|0.19|0.14|
|Barbados|0.19|0.96|0.77|Morocco|0.19|0.43|0.24|
|Belize|0.08|0.63|0.54|Mozambique|0.08|0.73|0.65|
|Benin|0.16|0.56|0.40|Myanmar|0.06|0.64|0.58|
|Bolivia|0.08|0.45|0.36|Namibia|0.09|0.33|0.24|
|Botswana|0.11|0.23|0.12|Nepal|0.14|0.30|0.16|
|Brazil|0.11|0.31|0.20|New Zealand|0.02|0.11|0.09|
|Brunei|0.10|0.43|0.33|Nicaragua|0.07|0.44|0.37|
|Burkina Faso|0.09|0.61|0.52|Niger|0.07|0.49|0.41|
|Burundi|0.13|1.04|0.91|Nigeria|0.08|0.78|0.70|
|Cambodia|0.09|0.20|0.10|Norway|0.01|0.11|0.10|
|Cameroon|0.14|1.07|0.93|Oman|0.05|0.13|0.08|
|Canada|0.03|0.09|0.06|Pakistan|0.11|0.61|0.50|
|Central African Rep.|0.14|0.45|0.31|Panama|0.07|0.20|0.14|
|Chad|0.14|1.09|0.95|Papua New Guinea|0.01|0.31|0.30|
|Chile|0.06|0.30|0.24|Paraguay|0.06|0.38|0.32|
|China|0.05|0.06|0.00|Peru|0.04|0.36|0.32|
|Colombia|0.14|0.50|0.36|Philippines|0.05|0.49|0.44|
|Costa Rica|0.05|0.40|0.34|Qatar|0.05|0.17|0.12|
|Cote d'Ivoire|0.11|0.51|0.40|Rwanda|0.16|0.91|0.75|
|Croatia|0.05|0.06|0.01|Saudi Arabia|0.04|0.10|0.06|
|Dominica|0.12|0.73|0.62|Senegal|0.10|0.29|0.19|
|Ecuador|0.10|0.25|0.15|Singapore|0.00|0.65|0.65|
|El Salvador|0.07|0.36|0.29|South Africa|0.07|0.30|0.24|
|European Union|0.05|0.08|0.03|Sri Lanka|0.09|0.67|0.58|
|Gabon|0.15|0.24|0.09|St. Kitts and Nevis|0.14|0.80|0.66|
|Georgia|0.01|0.08|0.07|St. Lucia|0.10|0.87|0.77|
|Ghana|0.10|1.00|0.90|St. Vincent|0.12|0.77|0.65|
|Grenada|0.12|0.58|0.46|Swaziland|0.09|0.32|0.23|
|Guatemala|0.07|0.44|0.37|Taiwan|0.06|0.07|0.01|
|Guinea|0.13|0.51|0.38|Tanzania|0.11|0.99|0.88|
|Guyana|0.12|0.60|0.47|Thailand|0.07|0.54|0.47|
|Honduras|0.08|0.23|0.15|Togo|0.14|1.06|0.92|
|Iceland|0.02|0.26|0.24|Trinidad and Tobago|0.23|0.61|0.38|
|India|0.10|0.37|0.27|Tunisia|0.20|0.68|0.48|
|Indonesia|0.06|0.35|0.28|Turkey|0.06|0.48|0.43|
|Israel|0.04|0.36|0.32|Uganda|0.10|1.02|0.92|
|Jamaica|0.08|0.55|0.46|United States|0.03|0.03|0.00|
|Japan|0.02|0.20|0.18|Uruguay|0.07|0.36|0.29|
|Jordan|0.09|0.14|0.05|Venezuela|0.14|0.36|0.22|
|Korea,Rep.|0.08|0.28|0.20|Zimbabwe|0.10|0.63|0.52|
|Kyrgyz Republic|0.04|0.08|0.04|||||

Our estimates<sup>6</sup> suggest that world’s tariff water is 11 percent, but is close to zero in the US and China, and higher than 70 percent in many least developed countries and 6 Estimates are computed using observations for the latest available year, which is at best 2008.

4
# **1.1. IBRD Default History**

Figure 1 details the history of IBRD nonaccruals up to the end of April 1999. The first default occurred in 1985, and we see that during the late 1980's there was a rash of subsequent defaults, peaking with 9 countries, representing 4.13% of the IBRD loan portfolio, in nonaccrual status in 1989. The 1990's have been characterized by a declining share of nonaccrual loans in the overall IBRD loan portfolio, and a fairly steady number of countries in nonaccrual. This illustrates the fact that default to the IBRD is a rare phenomenon, and thus we will not have all that many cases of defaults from which to draw our conclusions.

**Figurel: IBRD history of Nonaccrual 1984-99**

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Source: Finance Credit Risk (FINCR) records, The World Bank.

Over the past 10-15 years the heavily indebted poor countries have experienced a withdrawal of private lenders and have become increasingly dependent on official financing, with multilateral institutions becoming the main source of loan finance for most low-income countries. The World Bank is the largest multilateral lender, with its share in total multilateral debt of developing countries exceeding 50 percent in 1996. (IMF, 1998). Much of this lending has occurred on concessional terms, in the form of IDA credits. Figure 2 shows the shares of IBRD loans and all Multilateral loans in Total debt stocks over the period 1980-97, averaged over the 63 IBRD borrowers in our sample for which we had data for each year in this period. We see the rise in the importance of multilateral loans over this period, but with the share of IBRD loans falling over the 1990's.

3