rafmacalaba/datause-encoder-data huggingface dataset:
from each moshav and “other village” for the sample. In each kibbutz, we analyzed five questionnaires, one from each agricultural branch. These branches were randomly selected and the manager for that activity was interviewed for the survey.

A total of 381 farmers were interviewed out of which 230 grow crops and the rest have animal husbandry farms. In this paper, we concentrated on crop farms only and thus most of the analysis is conducted on the 230 crop farms observations. 95% of the farmers in our sample irrigate at least part of their land. There were a few mixed farms but we decided to concentrate on crops growers only. The mixed farms will be analyzed in the future with the livestock farms.

Climate data on temperature and precipitation were taken from Bitan and Rubin (2000). Average annual temperature calculations are based on data collected in 38 meteorological stations over the period 1965-1979 while average annual precipitation were calculated on data collected from 32 meteorological stations over the years 19611990. The periods and the stations for the temperature and precipitation calculations are slightly different because precipitation were not measured in all of the 38 stations and in some stations data for temperature were not available for all the years.

Following MNS (1994) we used an extrapolation of physical data of each village location to predict climate data for 230 farms based on data from 32 meteorological stations. Annual average temperature and precipitation were described by a polynomial function of the altitude, longitude, and latitude of each village. The models’ OLS coefficients appear in Table 2. The R<sup>2</sup> values are high in both models: 95% for temperature and 89% for precipitation. This means that the model can predict quite accurately the variation in climate data and thus predictions of these models can be used for climate data at the village level.

Unlike similar studies we use annual climate data only and not monthly or seasonal data. The main reason for this is the small size and geographical location of the country in our study and thus the lack of significant variation in climate conditions over a year period. The use of monthly or seasonal climate data led to high multicolinearity in the regression analysis. As a result almost all the monthly or seasonal climate variables were not found to be significantly different than zero.

Data for water quotas were obtained from the annual water consumption report of the Water Commissioner 2001 (Israel Water Commissioner, 2001).

7
Financial Development and Fragility: A Clustering Analysis<sup>∗</sup> Igor Custodio Jo ̃ao<sup>_a_</sup> Pietro Calice<sup>_b_</sup> Andre Lucas<sup>_a_</sup> Julia Schaumburg<sup>_a_</sup>

> _a_ Vrije Universiteit Amsterdam and Tinbergen Institute

> _b_ The World Bank

> ~~∗~~ <u>This paper was funded by the</u> State and Peacebuilding 2.0 Umbrella Trust Fund (SPF2.0). SPF2.0 is a global multi-donor fund administered by the World Bank that works with partners to address the drivers and impacts of fragility, conflict, and violence (FCV) and strengthen the resilience of countries and affected p opulations, communities, and institutions. The SPF2.0 is kindly supported by : Denmark, Germany, Netherlands, Norway, Sweden, and Switzerland. The views expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# 1. Introduction **1. Introduction**

a major resource to electrical energy systems boQi for hydropowergenerationandforcoolingthermalgenerators. It is essential to many transportation systems. Inland and are also extensively used for the disposal of treated or tansport chemical or other wastes. Fresh water is used to ties. In many places large investments are required to fundable, by Bank resources.

agriculture, urban and industrial supply, recreation, wildmental quality. Manifestations of this competition and our to the rapidly growing city ofPune. In China industries are Bank urban water supply projects for the same water, and many proposed irrigation projects and most hydro project proposals are on hold because of environmental concerns.

The water sector is also a substantial portion of the Bank's unnual lending. For example, between 1984 and 1988, out of a total lending of $83 billion, water project and drainage accounted for $4.6 billion, hydropower for $2A billion, and water supply and wastewater _$3.5_ billion. No data are available for flood control,water transport, and Until recently the approaches taken to water plantries and by the analysts at the funding agencies were, by and large, appropriate and adequate to the task at hand. The increased competition for water has, however, made most are no longer sufficient, the Bank has initiated a Comprehensive Water Resources Management Policy Study to investigate how best to resolve them. This paper is an attempttooutline the issueswhichshouldbe included inthe Bank's Policy Paper.

Water is dealt with independently in each of the Bank's regional bureaus and in its central departnents. This situationhas sometimes ledto inconsistencies inBank policy between counMies or between water uses. Under conditions of water suipluses such inconsistencies are not necessarily bad, given the wide diversity of the countries and range of uses for water. Indeed, this is what the Bank's tion projects, for example, perfonned quite well in the and

## 1.1 The Water Sector at the World Bank

have broad impacts throughou **t** purposes. It is used extensively for irrigating crops, and is

**I**
cross-section model estimated with the OLS estimator with those from the dynamic panel estimated with the GMM estimator below (see Table 5).

# **4. Growth forecasts**

We forecast real per capita GDP growth rates for all developing countries for the period 200514 assuming no changes in any of the explanatory variables in matrix _G_ of forecasting model (5). Under this assumption transitional convergence is the only factor determining the change in real per capita growth from one period to the next (α=-0.0176). Table 3 displays the country forecasts, which are point estimates representing the most likely growth outcomes, jointly with their 60% confidence bands.<sup>26</sup> From a policy perspective these confidence intervals are more useful than the 90% confidence bands reported in Table 5 since the later are large as is the case for other forecasting models, and most policy analysts are not interested in low probability outcomes.

The confidence intervals are computed by rewriting equation (4) as shown below: and assuming that matrix _Z_ , which incorporates all right-hand side variables other than initial income, is a function of non-random variables, and φ , which includes the respective coefficients, is also non-random.

From equation (5) it follows that the variance of the forecast for the next period is:

> 26 In the case of the 60% confidence bands, we can infer that growth rates higher (lower) than the upper (lower) end of the confidence interval occur with 20% probability.

20
- 19 -

(2.9) log(A'), = **30 +** **_PNI ),_** - **Ntkgc,+^** - **_loget)_** Solving (2.7) and (2.9) together yields the following reduced-form dyn,-rnic equation<sup>for the real</sup> exchange rate: (2.10) **_loget_** - )llog **_et-I_ -0** - **811ogP",** - 259ogP;, - 6310g( -tX)t where A = **_c43 2 /(1_ 0** 4 1)<1, and where the 3's are the corresponding coefficients on the right hand side.

The equilibrium real exchange rate, L, is that value of the RER that satisfies equation<sup>(2.10)</sup> for sustainable values of the right-hand side variables.' Equation (2.10) can be solved forward for log (g L) by recursive substitution. Imposing the no-bubbles solution, and<sup>defining the parameter</sup> vector 6 by 8 = (80, -81, **-82...,** -6)' and the vector of fundamentals F by we get the following forward-looking expression **L'** for given sustainable values of<sup>the</sup> fundamentals vector (we denote sustainable values of F by **_F):_** (2.11) **logJ,** = **_ZA} 'F_** It can be shown that if _F_ are stationary in first differences (ie I(1)) then the following cointegration 'Notice that the ERER is not unique, since it is a function of policy fundamentals as well as external fundamentals.
percent. We include exit rates for surveys in the Republic of Yemen and the Arab Republic of Egypt in Table 3 below for comparison. These show that Syria’s restricted exit rate is relatively similar to the Republic of Yemen’s and Egypt’s which is surprising given both the conflict in Syria and the longer time between surveys. The unrestricted rate is, however, higher in Syria than the comparators which suggests it is likely that a large percentage of the unreachable firms in Syria really have exited the market and therefore the unrestricted upper end of the range is more likely to be accurate for Syria. Table 3 provides exit rates for Syria broken down by sector, region, and size for both the restricted and unrestricted models. As per the above discussion, the highest exit rate is for firms in Aleppo. Small firms were also particularly likely to exit.

**Table 3 Exit Rates in Syria and Comparators**

|**Restricted(a) **<br>|**Un-restricted(b)**|
|---|---|
|Syria (2009-2017)<br>27.2%|56.3%|
|Yemen (2010-2013)<br>33.1%|44.2%|
|Egypt (2013-2016)<br>23.8%|27.9%|
|**Syria (2009-2017)**||
|Manufacturing<br>28.0%|56.4%|
|Other Services<br>25.7%|53.6%|
|Retail<br>25.0%|61.7%|
|Damascus<br>17.6%|48.1%|
|Aleppo<br>66.0%|82.1%|
|Hama/Homs/Lattakia<br>23.5%|54.5%|
|Small (<20 emp.)<br>30.2%|64.0%|
|Medium (20-99 emp.)<br>29.8%|57.3%|
|Large (100+emp.)<br>19.8%<br>Un-weighted.<br>(a) Restricted omits those firms that were unre<br>(b) Un-restricted considers unreachable firms a|43.0%<br>achable<br>s exiting the market|

Beyond these readily observable characteristics we also considered the relation between firm productivity in 2009 and exit in 2017. These regressions largely found results in line with expectations: larger and more productive firms are less likely to exit.<sup>5</sup> This result seems to hold in Syria despite the conflict, however, there are some exceptions. In Aleppo, more productive firms were instead more likely to exit, possibly a sign that these firms were more able to be footloose and leave business altogether or move their production elsewhere. For example, in Aleppo, using an unrestricted model, a firm in the 75<sup>th</sup> percentile of productivity in 2009 is 12 percentage points _more likely_ to exit than a firm in the 25<sup>th</sup> percentile of productivity in 2009. Productive firms facing the particularly severe destruction in Aleppo may have made a different calculation to productive firms elsewhere that likely faced slightly less severe circumstances: to use their capabilities to leave rather than to use their capabilities to weather the storm.

> 5 Regression tables are included in Annex A.

5
Policy Research Working Paper 10336

# **Abstract**

Forest fires are among the main drivers of deforestation and forest degradation in the drylands of Sub-Saharan Africa. This paper uses remote sensing data on forest fires and remaining tree cover to estimate the effectiveness of a project targeted at reducing fire incidences in twelve protected forests in arid Burkina Faso. The project consisted of two components that were implemented in the villages surrounding the target forests: a campaign aimed at raising community awareness about the detrimental effects of forest fires, and a program to support establishing and maintaining forest fire prevention infrastructures. Using the Synthetic Control Method the paper finds that the project resulted in a 35% reduction in forest fire occurrences in the period of the year when they tend to be most prevalent —in November, at the very end of the agricultural season. However, this impact is short-lived (as the reduction only occurred in the first four years of the program). The reduction in forest fires also did not result in a detectable increase in vegetation cover—because the reduction in November was not sufficiently large to be captured via remote sensing, or because the duration of the reduction was too short for the vegetation to recover. The paper then tries to uncover the underlying mechanisms to shed light on which of the project’s components were effective and to also learn how the program can be improved.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at d.p.vansoest@tilburguniversity.edu. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
and if there were more interested children than subsidies, then the subsidies would be allocated to children based on a lottery in each locality.<sup>7</sup> During January and the beginning of February, the program was advertised in the two localities through posters, newspapers ads, radio spots, loudspeakers in cars, churches, and community leaders, including principals of schools and priests. Potential candidates for the subsidy were registered during 15 days between the end of February and the beginning of March 2005. The registration was conducted in various schools of the two localities. In order to be included in the program, at least one parent / guardian was required to be present at the registration.

In order to be eligible for the program, children had to meet several criteria. First, the potential candidate had to have finished grade 5 and not yet graduated from grade 11. To focus on lower income families, all children’s families had to have been classified into the bottom two categories on Colombia’s poverty index, the SISBEN.<sup>8</sup> To verify the classification, the student had to present an identification card (which the vast majority of students have). The SISBEN categorization of the household was confirmed online by the SED at the time of registration. In order to eliminate the possibility that families would move to take advantage of the program, only those households that had been classified by the SISBEN system as living in San Cristobal or Suba prior to 2004 were eligible to participate in the program. In total, 17,309 eligible students were registered for the two experiments: 10, 947 in San Cristobal and 6,362 in Suba.

The randomization was publicly conducted on April 4 in each locality. The research team conducted the actual lottery, but in order to ensure transparency of the process, the code was inspected prior the exercise by researchers from the National University. The randomizations were done publicly (projecting the code onto a screen), with representatives of the community, school and local authorities present. The lists of beneficiaries were immediately printed, signed by local officials, and made available to the communities so that parents were able to determine if their children were included.

The randomization was stratified on locality, type of school (public / private), gender, and grade level. Panel A of Table 1 shows the distribution of registrants. In all, 6,875 students from

> 7 The over subscription and recruitment process are similar to the techniques used in the assignment of school vouchers in the PACES program implemented nationally in Colombia.  This process is described in Angrist et al. (2002).

> 8 See Vélez et al (1999) for details for the description of SISBEN.   The SISBEN classified households according to 6 levels, 1 being assigned to the poorest.  Most of the families in these areas were surveyed in 2003 and 2004.

- 11 -
|**Appendix table 2: Coefficients from Cobb-Do**|**uglas Crop Produc**|**tion Function (OLS and panel)**|
|---|---|---|
||OLS|Fixed-effect|
|Total Expenditure on crop production (log)|0.376***<br>(35.43)|0.151***<br>(22.65)|
|Total area cultivated (log)|0.243***<br>(14.43)|0.400***<br>(28.75)|
|Total labor days on crop production (log)|0.179***|0.048***|
||(18.79)|(6.86)|
|Total value of agricultural assets (log)|0.025***<br>(10.44)|0.006**<br>(2.51)|
|Head’s age (log)|1.635***|0.449|
||(4.29)|(1.11)|
|Head’s age (log) squared|-0.229***|-0.065|
||(4.49)|(1.18)|
|Share of village irrigated|0.125***|-0.013|
||(5.49)|(0.76)|
|Head with secondary education or higher|0.019**<br>(2.19)|0.028***<br>(3.06)|
|Household-year observations|50234|50234|
|Households||13125|
|R-squared|0.60|0.32|

Year dummies included but nore reported. Robust t statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

23
# **References**

- Aggarwal, R., Demirg ̈uc ̧-Kunt, A., Per ́ıa Mart ́ınez, M.S., 2011. Do remittances promote financial development? J Dev Econ 96, 255–264.

- Allen, T., Dobbin, C.d.C., Morten, M., 2018. Border walls. Technical Report. National Bureau of Economic Research.

- Ambrosius, C., Cuecuecha, A., 2016. Remittances and the use of formal and informal financial services. World Development 77, 80–98.

- Amuedo-Dorantes, C., Pozo, S., 2005. On the use of differing money transmission methods by Mexican immigrants. Int Migr Rev 39, 554–576.

- Beylier, P.A., Fortun ́e, C., 2022. Cross-border mobility in Nogales since Trump’s election. Journal of Borderlands Studies 37, 187–208.

- Canales, A.I., 2008. Las cifras sobre remesas en M ́exico.¿ Son cre ́ıbles? Migraciones Internacionales 4, 5–35.

- Capps, R., Batalova, J., Gelatt, J., 2020. COVID-19 and unemployment: Assessing the early fallout for immigrants and other US workers. Report. Washington, DC: Migration Policy Institute. https://www. migrationpolicy. org/research/covid-19-unemployment-immigrants-other-us-workers.

- Caruso, G.D., Cucagna, M.E., Ladronis, J., 2021. The distributional impacts of the reduction in remittances in Central America in Covid-19 times. Research in Social Stratification and Mobility 71, 100567.

- Chen, H., Chand, S.S., et al., 2020. Impact of Covid-19 on remittance inflows to Samoa. Asian Economics Letters 1, 17894.

- Clemens, M.A., McKenzie, D., 2018. Why don’t remittances appear to affect growth? Econ J 128, F179–F209.

- Demirg ̈uc ̧-Kunt, A., C ́ordova, E.L., Martinez Per ́ıa, M.S., Woodruff, C., 2011. Remittances and banking sector breadth and depth: Evidence from Mexico. J Dev Econ 95, 229–241.

- Docquier, F., Rapoport, H., Salomone, S., 2012. Remittances, migrants’ education and immigration policy: Theory and evidence from bilateral data. Reg Sci Urban Econ 42, 817–828.

- El Economista, 2020. UBS espera contracci ́on de 7.6% para M ́exico este a ̃no — El Economista. URL: https://www.eleconomista.com.mx/economia/UBS-espera-contraccion-de-7. 6-para-Mexico-este-ano-20200413-0146.html.

- El Financiero, 2020. Remesas ’destrozan’ pron ́osticos y tienen en marzo su mejor mes desde septiembre de 2003. URL: https://www.elfinanciero.com.mx/economia/ remesas-tienen-en-marzo-su-mayor-incremento-desde-2001.

14
fulfillment of obligations and the avoidance of social sanctions, such as guilt, shame or ridicule, or fear of witchcraft. It can also include altruism, which we do not attempt to distinguish from social norms.

A recent empirical literature relying on experimental design highlights the importance of these forces. Chandrasekhar, Kinnan and Larreguy (2011, 2012) find that in the presence of hidden income and limited commitment, social proximity between the risk-sharing partners increases the amounts transferred. The field experiments of Leider et al. (2009) and Ligon and Schechter (2012) show that altruism is more important than repeated interaction in determining the size of the transfer. Furthermore, social norms could weaken the constraints to risk sharing to the extent that they never bind and allow for the existence of sustained, unreciprocated transfers.<sup>9</sup> Below we will find evidence of such unilateral relations and argue that this is consistent with risk sharing motivated by social norms.

# **3. Data and descriptive analysis**

Kagera is a region in the north-western part of Tanzania. A large part of Lake Victoria is contained within this region and it shares a border with Burundi, Rwanda, and Uganda. The region is overwhelmingly rural and agricultural production is the most important source of income, with more than 80 percent of the region’s economically active population engaged in it (URT 2012). Bananas, beans, maize, and cassava comprise the main food crops while coffee, tea, and cotton are important cash crops. Recent years have seen a rise in improved banana varieties and sugar for use as cash crops. At the time of the last national census in 2002, Kagera had a population of roughly two million people.

The Kagera Health and Development Survey (KHDS) was originally designed and implemented by the World Bank and the Muhimbili University College of Health Sciences. It consisted of 915 households from 51 villages that were interviewed up to four times from autumn 1991 to January 1994.<sup>10</sup> The KHDS-2004 survey aimed to reinterview all individuals that were ever interviewed in the baseline survey and were

> 9 Schechter and Yuskavage (2011) empirically document unreciprocated relations in Paraguay. 10 See World Bank (2004).

9
<!-- Start of picture text --> Child Poverty rates at the $6.85 poverty line<br>Gree3 RS eh© <i -es -_. ee&, .<br>> st of f ~<br>“yf ea? iF<br>Poverty Rate Tie, . os hata’ f<br>ee - ‘ Rn,<br>* M310 P ; 7 ain a)<br>1020 ¢ ><br>P pete F v v4<br>40-50 Sig i<br>i so-6o /<br>60-70 a Peay<br><!-- End of picture text --> <!-- Start of picture text --> Number of poor children at $6.85 poverty line<br>PRS: raé af - TM.&,<br><é i<br>- : S--<br>< cave " a AS a<br>Number of Poor (millions) : S ‘4¥.)<br>OMissing ‘ i -<br>= g- 3<br>_ B30 5 2 ;<br>Bs a : . ;<br>4<br>2 Bi<br><!-- End of picture text -->
**Table 7.** Models of Reported Forest Collective Action Behaviors by Analytical Method (no Household <u>Controls).</u>

|||Probit||Negative B<br>Regres|inomial<br>sion|Interval<br>Regression|
|---|---|---|---|---|---|---|
||MEETING<br>_DUM|BIOGAS_DUM|ANYTREE<br>_CF|NUMTREES<br>_FARM|MONITOR<br>ING|NUMTREES<br>_CF|
|CONTRIBU<br>TION1|-0.000|0.002|0.007|0.005|0.002|0.006|
||(0.02)|(0.54)|(1.09)|(0.70)|(0.40)|(0.80)|
|CF_<br>MEM|1.063|-0.621|-0.321|-0.794|-1.624|-0.731|
||(2.16)**|(1.84)*|(0.90)|(1.35)|(2.52)**|(1.25)|
|CF__<br>OFFICER|0.648|0.460|0.431|0.442|1.130|0.625|
||(2.15)**|(2.12)**|(1.21)|(1.03)|(2.54)**|(0.89)|
|CONSTANT|-1.200|-0.748|-1.447|2.886|1.769|0.818|
||(2.38)**|(2.18)**|(3.20)***|(4.72)***|(2.80)***|(1.22)|
|LN_ALPHA||||1.580|1.977||
|||||(6.86)***|(7.17)***||
|_LN_SIGMA||||||0.807|
|||||||(3.54)***|
|Wald Χ<sup>2</sup>|10.40|6.71|10.79|8.32|11.23|7.89|
|Prob> Χ<sup>2</sup>|0.015|0.082|0.013|0.04|0.011|0.048|
|PseudoR<sup>2</sup>|0.074|0.032|0.032|-|-|-|
|Log<br>pseudo<br>likelihood|-208.60|-128.57|-113.76|-963.66|-496.17|-707.93|

N=327 in all models; * _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; robust t-statistics and errors clustered at the forest user group level 34