rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 4980

# **Abstract**

There has been a steady increase in the occurrence of natural disasters. Yet their effect on economic growth remains unclear, with some studies reporting negative, and others indicating no, or even positive effects. These seemingly contradictory findings can be reconciled by exploring the effects of natural disasters on growth separately by disaster and economic sector. This is consistent with the insights from traditional models of economic growth, where production depends on total factor productivity, the provision of intermediate outputs, and the capital-labor ratio, as well as the existence of important intersector linkages. Applying a dynamic Generalized Method of Moments panel estimator to a 1961–2005 cross-country panel, three major insights emerge. First, disasters affect economic growth—but not always negatively, and differently across disasters and economic sectors. Second, although moderate disasters can have a positive growth effect in some sectors, severe disasters do not. Third, growth in developing countries is more sensitive to natural disasters—more sectors are affected and the magnitudes are non-trivial.

This paper—a product of the East Asia and Pacific Social Protection Unit and the Development Research Group—is part of a larger effort to study main sources of vulnerability. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at nloayza@worldbank.org, and jrigolini@worldbank.org.

_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
who do not take up the training, we can also estimate the treatment-on-the-treated (TOT) effect – the effect of training for those who take it up. The required assumption seems plausible if we define take-up as going to at least one training session, but could be violated if we try to measure the effect of finishing the training course, since training may have an effect on businesses of owners who start training and drop-out. We therefore look at the TOT for having attended at least one training session. Since almost all individuals who attended at least one session completed the training, in practice the distinction will not matter much. We estimate the TOT by instrumenting receipt of some training with assignment to training only, and receipt of training plus cash with assignment to this treatment.

# **5.1 Impact on the business practices of women who were already in business**

In Table 3 we examine whether the training led to any improvements in business practices taught in the course. These practices were measured in the baseline, and again in the round 2, 4 and 5 surveys. The first three columns show that the use of these practices increased in both the short-term (3-4 months post training), and medium-term (15-16 months after training and 24-25 months after training), with the magnitude of the increase similar at all follow-up intervals. In the last survey round, and for all rounds combined, we can reject equality of the training and training plus cash treatments at the 10 percent level, with the improvement in practices slightly higher for those receiving the training plus grant treatment.

The magnitude of the increase is large relative to the low baseline levels, with the TOT showing an increase of two to three total practices relative to a baseline mean of five practices. However, given the maximum possible practices score is 29, the treated firms are still clearly a long way from implementing all the practices taught in the SIYB training. Columns three through seven pool together the different rounds and show that the training programs significantly improved each subcomponent of the overall business practice score, with improvements in marketing, stock control, financial planning, and record-keeping.

# **5.2 Impact on the business outcomes of existing business owners**

Eighty-three percent of the current business owners remain in business by the round 5 survey, and we cannot reject that survival is unrelated to treatment status (p=0.37). Moreover, the treatments do not appear to have affected which firm survived: treatment status is unrelated to 17
|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
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|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
|Yemen Water for Urban centers- Output-based serviceprovision byEdward PhilipEnglish|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal<br><br>|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|Syrdarya Water SupplyProject<br>KhairyAl-Jamal|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|EnhancingSME Access to Finance<br>Johanna Jaeger|
|NI Public Financial Management Modernization Project<br>Josef S. Trommer|
|NI Public Financial Management Modernization Project<br>Josef S. Trommer|
|NI Public Financial Management Modernization Project<br>Josef S. Trommer|
|NI Public Financial Management Modernization Project<br>Josef S. Trommer|
|NI Public Financial Management Modernization Project<br>Josef S. Trommer|
|NI Public Financial Management Modernization Project<br>Josef S. Trommer|
|NI Public Financial Management Modernization Project<br>Josef S. Trommer|
|NI Public Financial Management Modernization Project<br>Josef S. Trommer|
# **Web Data Appendix References**

Bairoch, Paul. 1988. _Cities and Economic Development: From the Dawn of History to the Present_ . Chicago: The University of Chicago Press.

Bairoch, Paul, Batou, Jean, and Pierre Chevre. 1988 _Population des villes européennes de 800 à 1850 : banque de données et analyse sommaire des résultats._ Geneva: Librairie Doz.

Barro, Robert J., and Rachel M. McCleary. 2003. Religion and Economic Growth Across Countries. _American Sociological Review_ 68(5): 760-781.

CRED. 2013. _EM-DAT: International Disaster Database_ . Louvain: Centre for Research on the Epidemiology of Disasters (CRED), Université Catholique de Louvain.

DHS. 2013. _Demographic and Health Surveys_ . Washington, DC: USAID.

FAO. 2013. _FAOSTAT_ . Roma: Food and Agriculture Organization.

Gollin, Jedwab and Vollrath (2013). Urbanization with and without Industrialization. Unpublished manuscript, Department of International Development, University of Oxford.

ILO. 2013. _ILOSTAT Database_ . Roma: International Labor Organization.

IPUMS. 2013. _Integrated Public-Use Microdata Series, International_ . Minneapolis, MN: Minnesota Population Center.

Malanima, Paolo, and Oliver Volckart. 2007. _Urbanisation 1700-1780._ Chapter prepared for the Third RTN/CEPR Summer Symposium in London: An Economic History of Modern Europe.

Polity IV. 2013a. _Polity IV Project: Political Regime Characteristics and Transitions, 1800-2011: Polity IV Annual Time-Series 1800-2011_ . Vienna: Center for Systemic Peace.

Polity IV. 2013b. _Polity IV Project: Political Regime Characteristics and Transitions, 1800-2011: Major Episodes of Political Violence, 1946-2011_ . Vienna: Center for Systemic Peace.

Ross, John A., and W. Parker Mauldin. 1996. Family Planning Programs: Efforts and Results, 197294. _Studies in Family Planning_ 27(3): 137-147.

Sluglett, Peter. 2008. _The Urban Social History of the Middle East: 1750-1950._ Syracuse: Syracuse University Press.

UN. 2013. _Millenium Development Goals Indicators_ . New York: United Nations.

WB. 2013. _World Development Indicators_ . Washington, DC: World Bank.

Wikipedia. 2013. _City of X, in the United States_ , _Economy of Country X_ , _Landlocked country_ , _Largest Cities of Country X_ and _List of Island Countries_ . San Francisco, CA: Wikipedia.

WUP. 2011. _World Urbanization Prospects, the 2011 Revision_ . New York: United Nations.

A. 8
a result, the greater number of returned migrants will just clear the higher labor demand in those locations.

To address the endogeneity of immigrants’ location choices, we implement the instrumental variables approach described in the empirical methodology section. The results are presented in panels C-D in Table 3. For each outcome, we present two models: a model that includes the homicide rate at the municipal level as control, and a second one that does not include it. As discussed above, the rationale for the inclusion of this variable is to control for the potential effects of crime on labor market outcomes at the municipal level. As shown in Banegas and Winkler (2020), unlike other countries where crime tends to be concentrated in more disadvantaged areas (Sacket, 2016), homicide rates in El Salvador are higher in municipalities with greater levels of economic activity.

Our estimations indicate that an inflow of repatriations equivalent to one standard deviation (an increase of 0.04 percentage point in the inflow of returnees that accounts for an 80 percent increase in the exposure of the average firm) has negative impacts on the average monthly salary paid by the firms of approximately 9.4 percent (exp( _−_ 0 _._ 099) _−_ 1 = 0 _._ 094), after controlling for homicide rates. This estimated impact corresponds to a reduction of about US$31.4 from the average monthly salary paid by the average firm.

In terms of the impacts of returned migrants on employment, the estimated coefficient is not statistically significant. However, the magnitude of the coefficient is economically relevant and similar to the effects on wages: A one standard deviation increase in repatriation inflows can reduce employment in the formal sector by about 9 percent (exp( _−_ 0 _._ 095) _−_ 1 = 0 _._ 090) after controlling for crime.

We next explore the differential effects of returned migrants on wages by gender. Considering that most Salvadoran migrants are men (around 80 percent in our sample), it is possible that they will mostly affect male salaries upon their return. However, it is also likely that the reduction in the cost of labor would induce firms to hire women too. Estimated effects on wage data separated 15
<!-- Start of picture text --> 63 6.3 63<br>so<br>o ”<br>g g<br>as 45 47<br>ce<br>ss<br>=2 i<br>os<br>28otee<br>Ls<br>EPa<br>&8<br>ce 1.2<br>o<br>Ghana Kenya Fambia<br>HE— v iethodente of arbitration_ (I—— votrePost Harvest Senices_<br>Source: Authors' calculations based on survey responses<br><!-- End of picture text --> <!-- Start of picture text --> 10.7 494<br>°<br>g 8.4 8.5<br>BB o 79 79 77<br>ce<br>$3<br>oe 6.5<br>peo<br>=3ontPz<br>Sey<br>Ee<br>£3<br>Bn 1.7<br>1.1 99<br>0.2<br>2 a<br>Ghana Kenya Zambia<br>BER ticof tivation BER 0s: Havent Sences<br>Source: Authors' calculations based on survey responses<br><!-- End of picture text --> <!-- Start of picture text --> Ghana Kenya Zambia<br>-<br>Proportion of farmers (%): Non- Contract Non- Contract Non- Contract<br>contract contract contract<br>Using technology 543 | 55.0 | 633 | 75.0 | 82 | 93 |<br>Receiving more than half of maize seeds from out<br>grower<br>externalReceivingssourcemore than half of maize seeds from other 2.7 67.5 66.7<br>Using fertilizer<br>Receiving togrow maize 30.0 | 600 | 49.2 | S500 | 441 | 66.7 |<br>from external moresource than (incl.half ofoutfertilizer grower)(to grow maize) 80.0. 29.7.<br><!-- End of picture text -->

||Gh|ana|Ken|ya|Zam|bia|
|---|---|---|---|---|---|---|
|-<br>Proportion offarmers (%):<br> <br>|Non-<br>contract<br>|Contract<br>|Non-<br>contract<br>|Contract<br>|Non-<br>contract<br>|Contract<br>|
|Usingtechnology<br>|543|55.0|633||75.0|82|93||
|<br><br>Receiving more than half of maize seeds from out<br>grower|||||||
|Receiving<br>more than half of maize seeds from other<br>s<br>external source<br> <br><br>|2.7<br>||67.5<br>|66.7<br>|||
|Usingfertilizer<br>togrowmaize<br><br>Receiving|0.0|600|49.2|S500|441|66.7||
|<br><br>morethan halfof fertilizer (togrowmaize)<br>fromexternal source (incl. outgrower)<br> <br>|<br>|<br>|<br>|<br>80.0<br>.<br>|<br>29.7<br>.<br>|<br>|
|Usingherbicide/pesticidetogrowmaize<br>|379|33.3||970|75.0|00||00||
Table 4: Incidence of malaria <u>(since</u> campaign launch)

||1 if|any HH m|ember had|malaria|Sh|are of HH|members wh|o had malaria|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||Full|Full|Full|Solid house|Full|Full|Full|Solid house|
|T|0.003|0.001|0.017|-0.024<sup>_∗∗_</sup>|-0.002|-0.002|0.003|-0.008<sup>_∗∗∗_</sup>|
||(0.011)|(0.009)|(0.013)|(0.011)|(0.003)|(0.003)|(0.004)|(0.003)|
|T _×_ Solid house|||-0.038<sup>_∗∗_</sup><br>(0.018)||||-0.011<sup>_∗∗_</sup><br>(0.005)||
|Solid house||-0.019<sup>_∗_</sup>|-0.001|||-0.007<sup>_∗∗_</sup>|-0.001||
|||(0.010)|(0.014)|||(0.003)|(0.005)||
|Individual controls||✓|✓|✓||✓|✓|✓|
|Cluster controls||✓|✓|✓||✓|✓|✓|
|Observations|3052|3052|3052|1316|3052|3052|3052|1316|
|Adj. R-squared|-0.000|0.028|0.029|0.020|0.001|0.026|0.027|0.033|
|Mean in control|0.058|0.058|0.058|0.055|0.016|0.016|0.016|0.015|
|SD in control|0.235|0.235|0.235|0.229|0.081|0.081|0.081|0.076|

Each observation is an individual. Responses collected from January 7, 2021 to March 31, 2021 are included. In columns (1-4), the dependent variable takes value 1 if the respondent/family member has had malaria since August 2020. In columns (5-8), the dependent variable is the share of household members who have had malaria since August 2020. T is the treatment indicator. Solid house is a binary indicator equal to 1 if the individual lives in a dwelling made of stones, bricks, cement or concrete. State fixed-effects are included in all specifications. Individual controls include gender, age, household size, caste, education, distance to the medical center, employment and student status, religion, presence of pregnant woman in the house, ownership of bednet and of an air conditioner. Cluster controls are reported in Table 1. Standard errors are clustered at the district level.<sup>_∗_</sup> _p <_ 0 _._ 1, _∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01 Figure 4: Heterogeneous treatment effects by bed net ownership <!-- Start of picture text --> All households Households with 1 or more bednets<br>At least 1 bednet<br>Has bednet<br>every 2 members<br>Less than 1 bednet<br>Has no bednet<br>every 2 members<br>-.02 -.01 0 .01 .02 -.03 -.02 -.01 0 .01 .02<br>Treatment effect on share of HH members who have had malaria Treatment effect on share of HH members who have had malaria<br>Solid house Non-solid house Solid house Non-solid house<br><!-- End of picture text --> Notes: The figure reports coefficient estimates from the regression described in columns (5-6) of Table 4, estimated on different sub-samples, based on bed net ownership and across dwelling type. Non-solid houses include semi-solid dwellings. Bars represent the 95% confidence intervals. Standard errors are clustered at the district level.

at home, the campaign increased by 6.7 p.p. or (31%) the share of individuals reporting to be extremely worried about getting malaria. However, as discussed above, we do not find evidence that this increased level of concern translated into higher bed net ownership and lower malaria incidence. Consistently with the above evidence, we find no significant effects among respondents living in non-solid dwellings.

24
Figure 3. Cross-country correlations: asset prices and output (correlation coefficient) <!-- Start of picture text --> 0.8<br>0.6<br>***<br>***<br>0.4<br>0.2 **<br>0.0<br>House Prices Equity Prices Output<br>1971:1-2016:4 1971:1-1984:4 1985:1-2016:4<br><!-- End of picture text --> Note: The average cross-country correlation for each variable in the respective periods is presented. The sample consists of 18 advanced economies. *** and ** indicate that average correlations for the period Q1 1971 to Q4 1984 are statistically different from those for the period Q1 1985 to Q4 2016 at the 1% and 5% levels, respectively.

Figure 4. Variance due to the global factor: asset prices and output (in percent) <!-- Start of picture text --> 70<br>60<br>50 ***<br>40 ***<br>30<br>**<br>20<br>10<br>0<br>House Prices Equity Prices Output<br>1971:1–2011:3 1971:1–1984:4 1985:1–2011:3<br><!-- End of picture text --> Note: The average fraction of the variance explained by the global factor is presented. The sample consists of 18 advanced economies. *** and ** indicate that variance explained by the global factors for the period Q1 1971 to Q4 1984 is significantly different from that for the period Q1 1985 to Q3 2011 at the 1% and 5% levels, respectively.

55
|URBAN WATER SUPPLY AND WASTEWATER|AbedalrazqF. Khalil|
|---|---|
|URBAN WATER SUPPLY AND WASTEWATER|AbedalrazqF. Khalil|
|URBAN WATER SUPPLY AND WASTEWATER|AbedalrazqF. Khalil|
|BuildingCapacityand StrengtheningMonitoringand Eval|uation CapGuillemette Sidonie Jaffrin|
|BuildingCapacityand StrengtheningMonitoringand Eval|uation CapGuillemette Sidonie Jaffrin|
|BuildingCapacityand StrengtheningMonitoringand Eval|uation CapGuillemette Sidonie Jaffrin|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|IN National Ganga River Basin Project|Xavier Chauvot De Beauchene|
|CADF Preparation Grant for China CPF Projects|Garo J. Batmanian|
|CADF Preparation Grant for China CPF Projects|Garo J. Batmanian|
|CADF Preparation Grant for China CPF Projects|Garo J. Batmanian|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|
|Hospital Waste Management Support Project|Anh ThuyNguyen|