rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 10: Children aged 7-12 attending school (%)**

<!-- Start of picture text --> 88 99 91 90 87 94 88 88 98 91 95 92 97 92 92 96 89 97 96 90 86 10086 85 87 90 98<br>79 76 76 74 75 73 78 72<br>55<br>Bamako Gao Timbuktu Kidal Niger Mauritania<br>IDPs Returnees Refugees<br>14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014 and 2015.

# **5. Challenges Faced by Returnees**

The results suggest that returnees were less affected by the crisis than IDPs and refugees. This is reflected in data on asset and livestock ownership, but also in the information pertaining to exposure to violence. Returnees reported fewer victims; fewer returnees reported to have lost income as a consequence of the crisis; more of their children were able to continue schooling; and relative to IDPs and refugees, fewer perceived being poorer in June 2014 than before the crisis. Returnees are also the group that feels most secure, that has high levels of trust in the Malian army and police and that has a positive attitude towards most government policies.

17
7. Other expenditures

# 7.  Debt service payments

Source: Adapted from the Budget Law (1994).

Given the absence of specific assignments, in a broad sense the four levels of subnational governments share joint responsibility for all functions not assigned to the central government. In practice, concurrent assignments are common, with all four levels of subnational governments engaged in virtually all budgeted activities (World Bank 2006a). This is illustrated in Table 4, showing a range of major services provided by several levels of government, including rural governments at the county and township levels.

**Table 4.  Expenditure Assignments by Government Level**

||**Central**<br>**(T1)**|**Provinces**<br>**(T2)**|**Municipalities**<br>**(T3)**|**Counties**<br>**(T4)**|**Townships**<br>**(T5)**|
|---|---|---|---|---|---|
|Higher education|x|x|x|||
|Intermediate education||x|x|x|x|
|Basic education||||x|x|
|Culture|x|x|x|x|x|
|Basic R&D|x|x|x|||
|Health|x|x|x|x|x|
|Family planning|||x|x|x|
|Administration|x|x|x|x|x|
|Law andjustice system|x|x|x|x|x|
|Social security||x|x|x|x|

Source: Adapted from Zhao et al. (2005).

These assignments are largely inherited from the planned economy, when they were designed for purposes of administration, rather than finance. For ease of administration, responsibilities were mostly divided by catchment areas, rather than by function, and the system explicitly permitted concurrent assignments.<sup>13</sup> For example, in education, primary and junior middle schools are the responsibility of subprovincial government at the lowest level – districts and “street communities” in urban areas, and townships in rural areas.<sup>14</sup>

> 13 This principle also permitted state-owned enterprises – using pre-tax profits (i.e. state funds) – to run schools and clinics and hospitals for their own staff and their dependents, phenomena that were common with Soviet-type economies.

> 14 "In July 2001 the State Council issued an edict that counties should pay all rural teachers salaries for primary through junior middle schools.  Implementation began in 2002 in most provinces but negotiations are continuing in many localities over whether the State Council intended a change in expenditure assignment, or simply a cashiering responsibility --  where the county would disburse against township funds."

11
# **1. Introduction**

Natural disasters can cause large economic damage both by directly impacting the population and their resources, as well as by indirectly affecting production capacity and economic decisions. Economic losses due to natural disasters are estimated to have been close to US$ 3,000 billion between 1998 and 2017 (UNISDR 2018). Over three-quarters of the damage have been caused by climate-related disasters, with the United States suffering the most, followed by China, Japan, and India. In Japan and the United States, most of the damage has been caused by earthquakes and storms, while for China and India, the major cause has been floods. Globally, floods account for nearly half of all natural disasters.<sup>1</sup> In India, more than 300 floods have been recorded since 1950, killing at least 75,000 people and rendering more than 900 million individuals injured, homeless, or otherwise affected.

The number of extreme rainfall events resulting in landslides, flash floods and crop damage has been increasing in India over time (Goswami et al. 2006; Roxy et al. 2017). The incidence of natural disasters has been growing in other parts of the world too. In the Asia-Pacific region, for example, the number of natural disasters grew from an average of 11 per country during the 1970s to more than 28 in the 2000s (Cavallo and Noy 2011). With accelerating climate change, the increase in natural disasters and flooding is likely to speed up.

In this paper, we study the short-run impact of a major flood in India on the economy and economic agents using several monthly and quarterly outcome variables. Kerala, a state in Southern India, experienced severe rainfall, landslides, and floods between June and August 2018. These were the worst floods in the state since 1924 and the third worst in India since 1900. A total of 504 people died and 23 million people were directly affected.<sup>2</sup> Since

> 1The publicly available Emergency Events Database (EM-DAT), maintained by the Center for Research on the Epidemiology of Disasters, is the most widely used database in economic research related to natural disasters. EM-DAT defines a disaster as a situation or event that overwhelms local capacity, necessitating national or international assistance and is often unforeseen and sudden. See https:www.emdat.be and Cavallo and Noy (2011) for more information.

> 2https://www.emdat.be/cred-crunch-53-flash-floods-sharing-field-experience-kerala

2
|Stormwater Mgt. and Climate Change Adaptation Project<br>Isabelle Celine Kane|
|---|
|Stormwater Mgt. and Climate Change Adaptation Project<br>Isabelle Celine Kane|
|Stormwater Mgt. and Climate Change Adaptation Project<br>Isabelle Celine Kane|
|Stormwater Mgt. and Climate Change Adaptation Project<br>Isabelle Celine Kane|
|Lao Seventh PovertyReduction Support<br>Genevieve F. Boyreau|
|Lao Seventh PovertyReduction Support<br>Genevieve F. Boyreau|
|Lao Seventh PovertyReduction Support<br>Genevieve F. Boyreau|
|GFDRR: South-South Cooperation Program Grant for FacilitatingWoSaurabh Suresh Dani|
|GFDRR: South-South Cooperation Program Grant for FacilitatingWoSaurabh Suresh Dani|
|GFDRR: South-South Cooperation Program Grant for FacilitatingWoSaurabh Suresh Dani|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Mineral Governance Support Project<br>Boubacar Bocoum|
|Comoros Development PolicyGrant 2<br>Shireen Mahdi|
|Comoros Development PolicyGrant 2<br>Shireen Mahdi|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|IDP LivingStandards and Livelihoods Project<br>Robert Wrobel|
|Second Rural Investment Project<br>Tara Sharafudheen|
|Second Rural Investment Project<br>Tara Sharafudheen|
|Second Rural Investment Project<br>Tara Sharafudheen|
|Second Rural Investment Project<br>Tara Sharafudheen|
|Second Rural Investment Project<br>Tara Sharafudheen|
|Second Rural Investment Project<br>Tara Sharafudheen|
|Second Rural Investment Project<br>Tara Sharafudheen|
|Second Rural Investment Project<br>Tara Sharafudheen|
The analysis of DHS data was complemented and cross‐checked with the 2010/2011 wave of the Household and Budget Survey (HBS), administered quarterly by Lesotho’s Bureau of Statistics. Data on financial inclusion was obtained from the 2011 Global Findex database.

However, the range of analyses that were feasible using existing nationally representative data was limited, especially because of restrictions in how data on migrants are collected in these surveys.<sup>6</sup> To complement the available data sources, we conducted fieldwork to collect primary data in March‐April 2015 in Lesotho. Utilizing a mixed methods approach, this fieldwork focused on female migrants engaged in formal (textile) and informal (domestic) sectors, and their sending households and communities. With the assistance from nine local research assistants, the mission undertook (i) a survey with 210 female migrants, (ii) seven in‐depth, semi‐structured, interviews with selected migrant women, as well as five of these women’s family households in rural sending communities, (iii) four urban focus group discussions with migrants, and (iv) six focus group discussions with women and men from sending communities.

First, the survey included 210 female migrants in urban Maseru and Maputsoe: 103 textile workers, most of whom worked at three selected factories of varying size and ownership, and 107 domestic workers from four neighborhoods of diverse income levels located throughout urban Maseru. Two cases were ultimately dropped from analyses based on their migration and current work status, resulting in a working sample of 208 female migrants. Concurrent with the structured interview, we conducted four separate focus group discussions with textile and domestic workers: one group consisted of two domestic workers and three groups consisted of 8 to 11 textile workers. Details on factory and neighborhood selection and sampling of workers are provided in Annex 2.

_Table 1: Descriptive statistics of survey respondents_

||All|Textile|Domestic|
|---|---|---|---|
|**N**|208|102|106|
|**Age (years)**|31.2|32.2|30.3|
|**Marital Status**||||
|Single/never married|36.41|25.49|47.12|
|Married (monogamously)|33.98|37.25|30.77|
|Married (polygamously)|0.97|1.96|0.00|
|Living together/cohabitating|0.97|1.96|0.00|
|Widowed|14.08|18.63|9.62|

> 6 Discussed further in Annex 1.

11
**Figure 12: Average expenditure in medical consultation on the month before the survey, by type of centre**

<!-- Start of picture text --> 3,472.60<br>SESPAS (Public) Private<br>2,273.30 2,270.60<br>2,075.40<br>1,809.40 1,764.30<br>1,425.60<br>896.2 952.4 988.5<br>Q1 Q2 Q3 Q4 Q5<br>Source: ENDESA survey, 2007<br>DR pesos<br><!-- End of picture text --> As it is shown in the graph, people from the first quintile<sup>11</sup> is spending an average of RD$ 896 (US$ 23) when they go to a SESPAS public center for examination. This confirms that there is a share of the poorest population that is not totally covered by the fully-subsidized regime. The main implication is that those that have to attend to the doctor may be spending most of its monthly income in health services, even when they use the public system (the upper income boundary for this quintile is RD$ 1,904 a month).

People from the middle and higher strata (Q4 and Q5) are on average paying 68% more than people in the lower quintiles (Q1-3) when they opt for a public centre for a check up<sup>12</sup> . One of the reasons for this divergence may be that the richer strata of the population are not subsidized, which affects averages, or that they “consume” more medicines and are more able and willing to pay for additional analysis and medical check-ups in order to obtain a better diagnosis. In any case, the narrower difference in cost the upper intermediate class (Q4) is facing when they are choosing between public or private health is likely to be one of the reasons why they are more propense to opt for private services, especially when some public centres often lack of good reputation.

> 11 ENDESA income quintiles are defined as follows: Q1 includes individuals earning between 0 and RD$ 1,904per capita a month; Q2: RD$ 1,904 to 3,138; Q3: RD$ 3,138 to 4,866; Q4: RD$ 4,866-8,323; Q5: 8,323 and above. 12 Again, check up fees plus medicine and laboratory analysis expenditure.

[18]
decline of fertility in relation with structural transformations such as the spread of mass education, urbanization, or the shift from agriculture to services, than on the role played by culture and values in demographic change. As a consequence, empirical evidence is scanty: most, if not all, large fertility surveys designed on a highly standardized and comparative scheme<sup>13</sup> have disregarded ideas and values, with the exception of a handful of questions directly related with family building, such as the ideal number children, sex preference, or views about marriage. For lack of individual data on more fundamental ideas and values expressing the way people see their lives, this paper will content itself with a macro approach, and will not reach the micro level which would allow a true validation of the hypothesis that migration is a vehicle for alternative ideas<sup>14</sup> .

**Figure 1:** A framework of the impact of international migration on birth rates

<!-- Start of picture text --> Initial patterns of Diffusion of new patterns<br>family building and of family building<br>their determinants and their determinants<br>in countries of origin in countries of origin<br>(I) Flows of (II) Flows of<br> migrants ideas and money<br>Demographic adjustment to<br>patterns of family building<br>and their determinants in<br>countries of destination<br><!-- End of picture text -->

> 13 The core questionnaire of the World Fertility Survey (WFS) devised in the 1970s has been reproduced—with some amendments—in all major subsequent surveys, notably the Demographic and Health Survey (DHS) of the 1980s-1990s and the Papchild (1990s) and Papfam (2000s) initiated by the Arab League.

> 14 At the other side of the spectrum, for lack of accurate time series on migration in many countries, the paper will not offer any estimation of the global demographic impact of international migration, that is the reduction of the world population rate of growth which can be attributed to migration.

10
question of how much of the surge in recorded remittances reflects changes in measurement, and how much reflects genuine changes in remittances.

To address this question, we can carry out a decomposition of remittances. Let L<sup>∗</sup> = L<sup>�</sup> −L be the stock of workers overseas, out of the total stock L<sup>�</sup> born in the country of migrant origin. Let _w_<sup>_*_</sup> be the overseas wage and _θ_ the fraction of migrants’ earnings abroad remitted home. The flow of remittances is described by: This is an accounting identity to the extent that only migrants send remittances. This seems a reasonable assumption to a first approximation, although it ignores remittances sent by secondand third-generation migrants. Existing research suggests second generation migrants are much less likely to remit, and send less remittances when they do than first generation migrants (Lee 2003; Kasinitz et al. 2008).

If we now assume that migrants have not changed the share of income which they remit over the period 1990 to 2010, then (4) implies That is, the growth of remittances should depend on the growth in migration, and on the growth in the incomes that these migrants earn.

# _3.1.2. Migration growth over the 1990 to 2010 period_

We can measure the first term in equation (5) using data from the United Nations Population Division, which has constructed a bilateral migration stock matrix for 1990, 2000, and 2010 using national census records. According to these estimates, there were 123.4 million migrants from developing countries in 1990, growing to 185.0 million in 2010, a 49.9 percent increase.

12
Table 6: Effect of the interaction between treatment and education and between treatment and income

||<br>A)Aggreg|<br>ate/allproposal|<br>s|<br>B)Ind|<br>ividual proposals|<br>|
|---|---|---|---|---|---|---|
||Mean<br>giving|Frequent<br>giver|Uncond.<br>cash<br>transfer|Uncond.<br>food<br>transfer|Uncond. cash<br>transfer and<br>school|Cash transfer<br>conditional on<br>training|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Panel A: Interaction with|education||||||
|Treatment|0.02|0.01|0.04|0.00|0.04|0.01|
||[0.048]|[0.051]|[0.061]|[0.017]|[0.059]|[0.055]|
|Treatment*Skilled|0.09|0.14|0.12|0.06|0.11|0.06|
||[0.093]|[0.101]|[0.129]|[0.083]|[0.151]|[0.147]|
|Skilled|0.00|-0.04|-0.07|0.03|-0.02|0.06|
||[0.003]|[0.091]|[0.094]|[0.054]|[0.190]|[0.065]|
|Number of observations|420|420|420|420|420|420|
|Panel B: Interaction with i|ncome||||||
|Treatment|0.06|0.04|0.08|0.07|0.05|0.02|
||[0.041]|[0.055]|[0.058]|[0.050]|[0.050]|[0.052]|
|Treatment*High income|0.02|0.09|0.03|-0.10|0.08|0.05|
||[0.045]|[0.164]|[0.070]|[0.063]|[0.228]|[0.124]|
|High income|0.04|0.04|0.03|0.10|0.01|0.03|
||[0.033]|[0.046]|[0.023]|[0.062]|[0.019]|[0.052]|
|Number of observations|406|406|406|406|406|406|
|Panel C: Interaction with|_"_subjective_"_|income|||||
|Treatment|0.04|0.03|0.05|0.00|0.08|0.01|
||[0.050]|[0.058]|[0.051]|[0.027]|[0.057]|[0.036]|
|Treatment*High|0.11|0.13|0.17|0.11|0.04|0.13|
|"subjective"income|[0.144]|[0.159]|[0.101]*|[0.116]|[0.080]|[0.165]|
||-0.04|-0.07|-0.06|-0.05|-0.02|-0.04|
|High"subjective"income|[0.219]|[0.120]|[0.194]|[0.103]|[0.048]|[0.146]|
|Numberofobservations|404|404|404|404|404|404|

Notes: The estimation method is a linear probability model. Each column reports regression on participants ’ decisions on all (columns 1 and 2) or specific proposal (columns 3-6) controlling for gender, location in the capital city, and number of cars in the household (Panel A), and for gender, three education levels, location in the capital city and number of cars in the household (Panels B and C). Results are maintained if excluding education levels and number of cars in the household as control variables. Standard deviation reported below (in square brackets). Standard errors clustered at the PSU level using wild bootstrap-t are reported in brackets. Skilled is a dummy variable equal to one if the participant has completed high school or more. High income is a dummy variable equal to one if the participant lives in a household with an income per capita level above the sample mean. High "subjective" income is a dummy variable equal to one if the participant reports that his relative position on an income scale from one (lowest) to four (highest) is three or four. ***, **, * significant at 1%, 5%, and 10% level.

28
(2016). Impact of cash transfer programs on food security and nutrition in sub-Saharan Africa: A cross-country analysis. _Global Food Security_ , _11_ , 72–83. https://doi.org/10.1016/j.gfs.2016.07.009

- UNCTAD. (2023). _A World of Debt: a growing burden to global prosperity_ . Washington DC. Retrieved from file:///C:/Users/user/Downloads/osgmisc_2023d4_en.pdf

- UNICEF. (2022). _Social Protection Budget Brief 2022/23_ (Vol. 2022/23). Lilongwe, Malawi.

- Vaitla, B., Coates, J., Glaeser, L., Hillbruner, C., Biswal, P., & Maxwell, D. (2017). The measurement of household food security: Correlation and latent variable analysis of alternative indicators in a large multi-country dataset. _Food Policy_ , _68_ , 193–205. https://doi.org/10.1016/j.foodpol.2017.02.006

- Verduzco-Gallo, Í., Ecker, O., & Pauw, K. (2014). _Changes in food and nutrition security in Malawi: Analysis of recent survey evidence_ (Working Paper No. 06). Washington, DC. Retrieved from

http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/128205/filename/128416.pdf

- Waldman, K. B., Ortega, D. L., Richardson, R. B., & Snapp, S. S. (2017). Estimating demand for perennial pigeon pea in Malawi using choice experiments. _Ecological Economics_ , _131_ , 222–230. https://doi.org/10.1016/j.ecolecon.2016.09.006

- Walls, H. L., Johnston, D., Tak, M., Dixon, J., Hanefeld, J., Hull, E., & Smith, R. D. (2018). The impact of agricultural input subsidies on food and nutrition security: a systematic review. _Food Security_ , _10_ , 1425–1436. https://doi.org/10.1007/s12571-018-0857-5

- WFP. (2008). _Food Consumption Analysis: Calculating and use of the Food Consumption Score in Food Security Analysis_ . Rome, Italy.

- Zingwe, D. E., Manja, L. P., & Chirwa, E. W. (2021). The effects of engendered intra-household power dynamics on household food security and nutrition in Malawi. _Journal of Gender Studies_ . https://doi.org/10.1080/09589236.2021.1940110

- Chirwa, E. W., Mvula, P., & Matia, M (2016) Rapid Assessment of the Eligibility of SCTP Beneficiaries for Emergency Interventions in the Scope of the Malawi Vulnerability Assessment Committee (MVAC) Response, Report submitted to UNICEF, Lilongwe, Malawi.

30
# Table 6. Incumbent Victories and Conflict: Rich vs. Poor Municipalities

||Poisson RD Estimates<br>Post-Election Period|Pre-El|ection Period|
|---|---|---|---|
|Dependent variable:|Casualties<br>Violent Incidents<br>(1)<br>(2)|Casualties<br>(3)|Violent Incidents<br>(4)|
|Panel A: All Municipalities (|153 municipalities)|||
|Efect of Incumbent Victory|0.89**<br>0.31***<br>(0.29)<br>(0.12)|0.059<br>(0.44)|-0.057<br>(0.19)|
|Mean of dep. var.|0.50<br>0.20|0.72|0.36|
|Panel B: Rich Municipalities|(78 municipalities)|||
|Efect of Incumbent Victory|0.40<br>0.12<br>(0.36)<br>(0.15)|-0.77<br>(0.73)|-0.26<br>(0.37)|
|Mean of dep. var.|0.17<br>0.09|0.46|0.28|
|Panel C: Poor Municipalities|(75 municipalities)|||
|Efect of Incumbent Victory|1.65**<br>0.56**<br>(0.79)<br>(0.27)|-0.38<br>(0.25)|-0.15<br>(0.18)|
|Mean of dep. var.|0.84<br>0.31|0.99|0.44|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC). _Note:_ Regressions are based on the 2007 Philippine mayoral elections. All models flexibly control for incumbent margin of victory on both sides of the threshold. Specifications are identical to those reported in columns 2, 4, 6 and 8 of Table 3. All regressions are weighted by a triangular kernel with a bandwidth of 0.05. Marginal effects calculated at the sample means are reported. Standard errors are in parentheses. _∗_ , _∗∗_ and _∗∗∗_ denote statistical significance at the 10%, 5% and 1% levels. Standard errors in column (3) are clustered at the municipality level. All specifications include the same control variables that were used in Tables 3 and 4.

29