rafmacalaba/datause-encoder-data huggingface dataset:
one are automatically filled.<sup>This feature of flow-of-funds can</sup> readily be exploited to fill<sup>gaps in the data. Given that very</sup> limited independent information<sup>is available on the private sector,</sup> we choose this sector as<sup>the residual of our system and focus on</sup> the collection of data for<sup>the other five sectors.</sup> This also provides a check on the collection<sup>of data since the variables</sup> obtained as a residual in<sup>the private sector should behave</sup> normally.

The problem of stock-flow<sup>consistency arises because of the</sup> use of discrete time periods during<sup>which flow data are collected.</sup> In an accounting sense, flow-of-funds<sup>do not require the stock</sup> data. However, implicitly, we require<sup>stock data to determine some</sup> flows, for example, interest payments. In addition, from the economic perspective stocks play a crucial role in the determination of asset prices,<sup>interest rates and inflation.</sup> We start the construction of<sup>the flow of funds by ensuring</sup> consistency between stocks<sup>and flows of external debt and the</sup> balance of payment's capital account.<sup>Then we take the data from</sup> the current account of the balance<sup>of payments and relate them to</sup> the national accounts and interest<sup>payments on external debt. Next</sup> we consider the monetary sector<sup>and ensure stock-flow consistency</sup> of domestic assets as well as consistency<sup>between Tl flows and the</sup> Tl value of foreign currency denominated<sup>assets. Given explicit</sup> and implicit interest rates we<sup>derive the current account for the</sup> Central Bank and the banking<sup>system. Then we move to the fiscal</sup> accounts where we assure<sup>consistency between financing and the</sup> current accounts of the budget<sup>and the SEEs. We make sure that</sup> the key macroeconomic identitie of the national accounts<sup>are</sup>
and rural, etc.), and a p-score of 0 indicated that the school had a background very different from the background of a typical CONAFE school.

It is interesting to know not only how CONAFE affects student achievement, but to know how CONAFE differently affects the most and least disadvantaged students in Mexico. To make such comparison, we divided students into groups of relative disadvantage with p-scores between 0.1 and 0.3, p-scores between 0.4 and 0.6, and for primary schools, p-scores between 0.7 and 0.9, and we compared performance over time of matched CONAFE and non-CONAFE students by relative disadvantage. For example, we compared the improvement of matched CONAFE and non-CONAFE students with levels of disadvantage between 0.4 and 0.6 between 1998 and 2002. According to equation (2.2), the amount by which CONAFE student improvement exceeded non-CONAFE student improvement represented the effect of CONAFE on student learning.

# **Data**

We used three types of databases for this evaluation: student-level test databases (Test) containing test scores and student background information, school-level databases (Drop) containing data on student repetition and failure, and school-level background databases (Bgrd) with data from school principal and school teacher questionnaires. Entries in each of the three databases included a school code that let us identify the same school in all three databases. A summary of databases used appears in Tables 3 and 4 below.

**<u>Table 3. Primary School Databases</u>**

|**Respondent**|**Year(s)**|**Type**|**Grade**|**Obs**|**Schools**|
|---|---|---|---|---|---|
|Principal|1996-2000|Bgrd||3,681|3,681|
|Student|1998|Test|Second|43,398|2,581|
|Student|1998|Bgrd|Fourth|47,497|3,140|
|Student|1998|Bgrd|Fifth|44,912|3,037|
|Student|1999|Test|Third|49,136|3,098|
|Student|2000|Test|Fourth|46,252|3,040|

15
The signs of the estimated coefficients in the parsimonious regressions are as anticipated and are mostly in line with the literature. First, we see that countries that experienced a higher number of past crises had a lower probability of experiencing another crisis after 2007, likely as a result of efforts to address macroeconomic, financial, and institutional vulnerabilities after the past crises. Second, countries with greater deviations of real GDP from its potential (greater output gap) showed a higher propensity to experience banking crises after 2007. This result agrees with results of other studies of banking crises, such as Kaminsky and Reinhart (1999) and Gourinchas and Obstfeld (2012) that estimated predictors of banking crises during 1973–2011, and with results of Frankel and Saravelos (2012), which investigated the determinants of the 2008 global crisis. Our results also show a negative impact of the real interest rate on the probability of a banking crisis: low real interest rates support excessive borrowing that can ultimately generate banking crises. Frankel and Saravelos (2012) also associate higher saving rates with lower incidence of crises. In contrast, Demirgüç-Kunt and Detragiache (1998, 2005) find that exposure to high real interest rates, which could intensify credit risk as well as negatively affect bank profits, was a source of bank fragility during 1980–2002.

Third, we confirm the results from earlier studies that countries with greater financial deepening (ratio of private credit to GDP) and countries taking greater aggregate liquidity risk (ratio of private credit to deposits) are significantly more prone to banking crises. Demirgüç-Kunt and Detragiache (2005), Gourinchas and Obstfeld (2012), Schularick and Taylor (2012), and Babecky et al. (2011) find, as we do, that the credit-to-GDP ratio is a consistent predictor of banking crises. In addition, Lane and MilesiFerretti (2011) find that the credit-to-GDP ratio and its change over 2004–07 could also help explain the intensity of the 2008–09 global recession.<sup>24</sup> Our finding that high aggregate liquidity risk increases the tendency toward banking crises confirms earlier results from Caprio et al. (2014).<sup>25</sup> The alternative

> 24 This literature brings supporting evidence for Rajan’s (2005) claim that larger, more complex financial systems may be inherently more risky.

> 25 Berkmen et al. (2009) find that higher aggregate liquidity risk can explain a larger variation in the growth forecast revisions during the global crisis period, also a possible indicator of macrofinancial stability.

17
the need for an infrastructure-specific survey instrument to examine the extent of corruption in private provision across sectors and countries.

The nature of corruption surveys and the nature of the sector should dampen expectations of highly robust data being produced by such a survey, particularly at the sector/country level, where confidentiality issues may not allow the publication of any data at all for many countries. Nevertheless, the pilot study referred to suggests that it will be possible to obtain important indicative evidence and insights. Many respondents are willing to inform about their experiences and frustrations. Even if precise quantification of grand corruption may be difficult, a broad survey may reveal important mechanisms and offer insights about the efficiency of current anti-corruption measures. Such understanding is needed to develop better policy advice on how to prevent challenges associated with grand corruption and curb its influence on the performance of utility provision.

# **References**

Acemoglu, D. and T. Verdier, 2000. The choice between market failures and corruption. _The American Economic Review,_ 90 (1): 194-211.

Ades, A. and R. Di Tella, 1999. Rents, competition and corruption. _The American Economic Review_ , 89 (4): 982-993.

Ahmed, L. T. and R. Wells. 2007. _Making foreign investment safe: property rights and national sovereignty_ . Oxford University Press.

Aidt, T. S. 2003. Economic analysis of corruption: a survey. _The Economic Journal,_ 113:63252.

Andvig, J.C Christian, 1995. “Corruption in the North Sea Oil Industry: Issues and Assessments,” Crime, Law, and Social Change 28: 289-313.

Andvig, J.C. and K.O. Moene, 1990. How corruption may corrupt. _Journal of Economic Behavior and Organization_ , 13: 63-76.

Auriol, E. and A. Blanc. 2007. Public private partnerships in water and electricity in Africa. AED Working Paper 38. Agence Francaise de Developpement, France.

Ayres, I. and R. Gertner. 1992. Strategical contractual inefficiency and the optimal choice of legal rules. _Yale Law Journal_ . 101:729.

Bardhan, P. 1997, Corruption and development: a review of issues. _Journal of Economic Literature_ . 35: 1320-46.

Batra, G., D. Kaufmann, A.H.W. Stone. 2003. _Investment climate around the world: Voices of the firms from the World Business Environment Survey_ . The International Bank for Reconstruction and Development/ The World Bank, Washington, D.C.

Bayliss, K., D. Hall and V. P. Corral. 2001. FDI Linkages and Infrastructure: Some Problem Cases in Water and Energy. Public Services International Research Unit (PSIRU).

Beato, P. and J.J. Laffont. 2002. Competition in public utilities in developing countries. _Sustainable Developing Department Technical Papers Series_ . Inter-American Development Bank, Washington D.C.

18
-s

# ANLYTICAL YM OF TNIRIY EM S1DIES

INDEPEMT VMAKES OWIN RATE ----------------------------------.-.------aWTERCNA.ATION RNUTAL U OCR. WuMes PRMLCT IRICS OF PEIW **_m_** MI ElOMCEPUUINI SOCIOE TYPE AM SA **_UE_** AULISIS 0MIIITAL POLICY PTlclC POLITICAL Of V SMlCE MIXIMnIES MPD VAIBESt VMUAU Ou e FACTfS WUIAES IEIS rIIXel Tet the chief or GOP 46 devloped **Nexhem** Initial per Rate of Invst rate Civil 3-eq **a** 0.64 and '(prtiaulty (acmb to wd dwevcping 28 v_s capita inee s hvath of librty IIIE the nt _ea*rv)_ otris (in Cyrvirs bt - public pm-, indicator R-e a 0.59 (195) coersal) ,eee1 s (INF) p _Afo_ ice no contry) Avarage rat PopAstion WUIUh **rat*** ditlO rate - n.e. (slentwridlt The effect of rative to coatrie lass 1950-77 Rat of growth taken fro erg rnic policy wo fhct Souh Africa) Stdrad of t rate GASIIL _1979)we_ deaud to Ecltulen of dwiation of + act tuWuh BiLm rate*of Ruofgrath factor atier veriation In of Inflatien proctivity L pict **rate** Stufrd dwistion of rate of _dwue_ in w aeof g9itof _-Wo afA_ + n.

UAL nd COnfi ad (Wbrld 18 lwoirfcome 12 as RAPATIRM criticia crtain lwi) **untr"i** Avera raft (11W) tests Of 1973-1 uqmorts/'owh raationip .................................................................................................................................................................

Growth of UP Investam rate **R-eq** - 0.30 ,1.% of + orts frm Growth of ldbor t~19 to 1973 force (irdicator of + qm prior to 173)
growth of town in-migration has been staggering over the last five years, hovering around 1.5 percent in average each year during the 1996-2000 period. During whole period of 1995-2000, in-migration only accounted for 7.3 percent of town population. In Sichuan, which was economically in the middle range among three provinces, in-migration into towns has shown an upward trend during this period although growth was not been as pronounced as that in Zhejiang province.

Table 5 Migration in towns as percentage of total population:2000

||live<br>in tow<br>since<br>born|before<br>nOct31<br>1995|11/1- 1996<br>12/31<br>1995|1997|1998|1999|2000|total of<br>last five years|
|---|---|---|---|---|---|---|---|---|
||||(|%)|||||
|Zhejiang|61.70|19.71|0.38<br>1.64|1.96|3.21|4.91|6.49|18.21|
|Henan|70.51|21.88|0.33<br>1.15|1.28|1.73|1.79|1.34|7.29|
|Sichuan|57.65|26.99|0.38<br>1.69|2.16|3.22|4.01|3.84|14.92|

________________________________________________________________________ Sources: calculated from Tabulation on 2000 Population Census of the P. R. China, Table L7-2.

A main expectation underlying the advocacy for town development is to have the towns to absorb excessive laborers who would otherwise head for large cities. In the following two tables, we present the geographic composition of in-migrants to see if towns absorb excessive labor force from the rural areas. In Table 6, we categorize town in-migrants by original place of residence. Data suggest that towns in three provinces have indeed absorbed rural population. In Zhejiang, 38 percent of in-migrants were originally rural residents, living in villages. In Henan and Sichuan provinces, 53 percent and 37 percent came from rural areas respectively. If we include those who were originally rural town residence, then the majority of town in-migrants came from rural areas. Whereas the percentage of in-migrants from city has been small, accounting for 4.6%, 15.6% and 12.4% respectively for Zhejiang, Henan and Sichuan.

Table 6 Town in-migration by original residence place: 2000

|Province<br>Zhejiang|village<br>residence<br>38.49|town rural<br>residence<br>(%)<br>45.19|town urban<br>residence<br>11.69|urban<br>residence<br>4.64|
|---|---|---|---|---|
|Henan|52.99|20.38|11.00|15.63|
|Sichuan|37.34|27.89|22.38|12.40|

Sources: calculated from Tabulation on 2000 Population Census of the P. R. China, Table L7-6.

7
For t=1,..., T. Note that, under the above assumptions, the growth differential is accounted for by the civil war effect and the mechanical convergence term only. This simulation allows us to compute the marginal contribution of civil war to the EA-SSA income differential. Taking 1970-74 to be the initial period (t=0), we note that average GDP per capita for EA in this period was about three times the average income in SSA, which is already very high compared to the latter. However, as a result of vastly superior and sustained growth, the East Asian per capita income reached almost 4 times that of the SSA in 1999. Therefore, we ask the question as to how much has civil war contributed to this wide and expanding income gap between the two regions. Our estimates suggest that up to the second half of the 1980s, EA was assessed as the riskier region. Therefore, the war effect contributed, albeit marginally, to the narrowing of the gap between the two regions. However, by the turn of the 1990s, especially toward the end of the decade into the new century, SSA emerged as the substantially riskier region and the growth impact of civil war started to make an increasingly large contribution to the expanding income gap (Figure 4). For example, the contribution of civil war to the EA-SSA income differential precipitously rose from 3.5% in 1990 to 22.5% in 1999. With the further deterioration of political security in SSA in the new Millennium, while most other regions started to enjoy a significant decline in political violence<sup>32</sup> , civil war is likely to become even more important as a cause of Africa’s economic decline, especially relative to the increasingly peaceful EA.

# <u>Estimating the costs of war</u>

Finally, we analyze the evolution of income during the conflict cycle in Sudan, expressed relative to its level in an initial period (1975-79) prior to the onset of the civil war.<sup>33</sup> Assuming that the risk of riots and other growth fundamentals remain the same as in the initial period, we have:

> 32 See, for example, the Human Security Centre’s report on Human Security in 2005.

> 33 This is an appropriately chosen period, so that it is close but not too close to the war start year of 1983.

24
of transporting rice significantly decrease the use of fertilizer in rice production.<sup>3</sup> Controlling for soil fertility (and thus for non random placement of roads), they demonstrate that crop yields for the three major staple items in Madagascar (rice, maize, and cassava) are lower in isolated areas relative to non-isolated areas. Sahn and Stifel (2003) also demonstrate that living standards in rural areas lag far behind those in urban areas.

There is also evidence that road improvement can exert a direct impact on poverty. In Lao People’s Democratic Republic, rural poverty decreased by 9.5 percent between 1997-98 and 2002-03, of which improved road access played a significant part. The road improvement came in the form of providing all-season roads to areas that previously had only dry-season access (Warr, 2005). Similar evidence was identified in Ethiopia, where 15 villages were surveyed about their links to market towns (Dercon and Hoddinott, 2005). From the finding the authors conclude that increases in the road quality, i.e. from a dry to an all-season road, have strong positive effects on the consumption growth for rural households. Consistent with intuition, these results imply that is not just the presence of a road, but the presence of a road that is passable yearround that aids in poverty reduction. Low service to rural areas can be worsened during the rainy season in high-rainfall Africa, when roads are wiped out (Ellis and Hine, 1998).

The World Development Report (WDR) 2008 reports evidence of increasing economic inequality between leading and lagging areas within countries, as well as a threshold effect of investments in low economic density areas. Countries are not homogenous entities, but are comprised of areas that are combinations of economic development and population. The term “low economic density area” refers to areas lacking economic development, i.e. industries and services, usually coupled with high poverty rates and occasionally high population density. Additionally, developing countries also face the paradox of declining poverty rates occurring simultaneously with increasing inequality between leading and lagging regions, examples provided in the WDR 2008 include China, Ghana and India. In response to the situation in lagging areas, governments have attempted to attract industry and disperse economic development more

> 3 In fact, the simple regression model they use to illustrate the correlates of transportation costs demonstrates that distance is a significant determinant of transporting 50 kg of rice to nearest major city. A multicolinearity problem then arises and may bias results.

5
# **Appendix(Figure(C1:(Sample(Details**

<!-- Start of picture text --> Sample Control( HighDsaturaEon LowDsaturaEon<br>776villages,666 756villages,666666 1146villages,666666<br>2496schools6 2286schools6 3786schools6<br>Treatment+Offers<br>06schools6 2286schools6 1146schools6<br>Take0up 06schools6 2136schools6 1096schools6<br><!-- End of picture text -->

# **Appendix(Figure(C2:(Project(Timeline**

<!-- Start of picture text --> 2012 2013 2014<br>Round 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12<br>Baseline6Survey<br>Baseline6Child6Testing6<br>Randomization6Ballot<br>Disbursements<br>Round61<br>Round62<br>Round636<br>Round64<br>Round656<br><!-- End of picture text --> Notes:6Rounds61T36correspond6to6the6first6school6year6and6rounds646and656refer6to6the6second6school6year6after6treatment.6A6school6 year6in6this6region6is6typically6from6AprilTMarch,6with6a6three6month6break6for6summer6between6JuneTAugust.6

# **Appendix(Figure(C3:(Data(Availability(by(Survey(Rounds**

|Outcome|Baseline|Round1|Round2|Round3|Round4|Round5|
|---|---|---|---|---|---|---|
|Enrollment|✓|✓|✓|✓|✓|✓|
|Fees|✓|✓|✓|✓|✓|✓|
|PostedRevenues|✓|✓|✓||✓||
|CollectedRevenues|||✓|✓|✓|✓|
|Expenditures|✓|✓||||✓|
|TestScores*|✓|||✓|||
|Teachervariables*|✓|✓||||✓|

Notes:6This6table6shows6data6availablity6in6each6round6for6key6outcomes.6Different6modules are6administered6in6different6rounds6based6on6cost6and6attrition6concerns.6Variables6with6a star6marking6are6only6collected6for6half6of6the6sample6at6baseline.6See6Appendix6C6for6details.
production at the plot-level, as in the case of Malawi IHS, but for the past 6 months. The R1 households received their first visit in February 2016 to collect plot-level cassava production information for the period of August 2015–January 2016. The R1 households were visited for a second time in July/August 2016 to collect the comparable harvest information for the period of February 2016-July 2016.

The fourth group of households (R2) were visited once, at the end of the 12-month fieldwork in each EA (i.e. in July/August 2016), and were asked to report fresh cassava production at the plotlevel for the past 12 months, in line with the prevailing approach to household and farm surveys collecting information on production of tree and root crops, as in the case of the Malawi IHS.

Similar to the cash/airtime incentives provided to the diary households, each R1 and R2 household was provided a cash incentive of approximately US$7. Half of the cash incentive was paid at the end of the first visit, while the remaining half was paid in the final visit to the recall households.

# **3.2. Crop Cutting**

Although a well-implemented diary is the gold standard for _household-level production_ measurement, crop cutting has been recognized as the gold standard for _plot-level yield_ measurement for seasonal crops since the 1950s by the Food and Agriculture Organization of the United Nations (FAO). Besides the cost- and supervision-intensive nature of the exercise, several concerns have been raised regarding the accuracy of the method. Even if one places only one random crop cutting sub-plot within the sampled plot, the resulting yield estimate may carry a sampling error if the yields exhibit within-plot heterogeneity. Further, Fermont and Benson (2011) mention the following additional possible sources of error: (1) More thorough-harvesting of crop cut sub-plots vis-à-vis the typical farmer harvesting practices, (2) possible rounding of crop cut production estimates obtained through scales; (3) using faulty or inappropriate scales; (4) omitting to net out the weight of the measurement container from the measured production; (5) including plants that fall outside of the sub-plot; and (6) non-random placement of crop cut sub-plots. The CVIP crop cutting exercise attempted to overcome these potential concerns through random subplot placement; the use of well-monitored weighing scales of acceptable precision; and intensive training of enumerators on the sub-plot placement, harvesting and weighing. In each CVIP household, regardless of the survey treatment, 1 cassava plot was selected at random for crop cutting, and a 5x5m subplot was set up, also at random, in line with the international best practices.P21F<sup>22</sup> Further, the crop cut sub-plot was cordoned off until the respondent was ready to harvest this area of the plot. Each EA had a designated crop cutting assistant that paid periodic visits to the households to check on the integrity of the sub-plots, and to inform the enumerator regarding the

> 22 Refer to Appendix I for CVIP Crop Cutting Protocol.

11