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# Natural Disasters and Fiscal Drought

## Lazar Milivojevic<sup>∗</sup>

The World Bank _Keywords:_ natural disasters, fiscal sustainability, climate change _JEL-Codes:_ C51, E62, Q54.

> ∗The author thanks Robert Beyer, Alexander Meyer-Gohde, Gauthier Vermandel, Volker Wieland, and participants at the Asian Economic Development Conference (ADB), 85th Anniversary Conference at the Faculty of Economics and Business (University of Belgrade), and South Asia Chief Economist Office internal seminar (World Bank) for helpful comments.
Figure 3. Wealth Index Distribution <!-- Start of picture text --> (a) Wealth Index - Total (b) Adequate Housing Materials (% of Total)<br>-5 0 5 10 15 0 .2 .4 .6 .8 1<br>Wealth Index Average - Dwelling Material<br>Colombian Venezuelan Colombian Venezuelan<br>Venezuelan: Before migrating Venezuelan: Before migrating<br>(c) Asset Ownership (# Total) (d) Access to Services (% of Total)<br>0 10 20 30 40 0 .2 .4 .6 .8 1<br>Total Assets Average - Access to Services<br>Colombian Venezuelan Colombian Venezuelan<br>Venezuelan: Before migrating Venezuelan: Before migrating<br>4<br>.25<br>.2 3<br>.15<br>2<br>k-Density k-Density<br>.1<br>1<br>.05<br>0 0<br>.15 3<br>.1 2<br>k-Density k-Density<br>.05 1<br>0 0<br><!-- End of picture text --> _Notes:_ Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household’s cumulative living standard constructed following The Demographic and Health Surveys (DHS) methodology. It includes the following variables: i) dwelling materials is the average of dichotomous variables that take the value of one if the materials of the floors and walls are of adequate quality (including block, brick, stone, polished wood, and cement); ii) asset ownership is a set of count variables for 15 consumer items (telephones, cellphones, TVs, computers, tablets, microwave, washing machine, fridge, blenders, water heater, radio/recorder, cars/taxis, motorcycles, bikes and trailers); and iii) access to services characterizing access to energy, gas, garbage, aqueduct, sewage, sewerage, sanitary, kitchen, internet, TV, telephone. The index is constructed using a principal component analysis. Panel (b), (c), and (d) illustrate the proportion of adequate materials, total number of assets, and proportion of services that the household has as a percentage of the maximum possible ownership, materials, and access, respectively. For each case it is constructed as the average of the dichotomous variable equal to one if the materials are adequate, the assets are owned, or the household has access to the service.

average 0.3 higher standardized value on the years of education, this corresponds to a lag of approximately 1 year of education (considering 1 SD= 3.06 years).

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households in the top wealth quintile. Nearly a quarter of the mothers had received no formal education (up to half in the lowest wealth quintile) and about one-fifth of the children were perceived to be born with low birth weight by mothers (up to one-quarter in the lowest wealth quintile).

Table 1. <u>Sample characteristics: overall and by wealth quintiles</u>

|||_W_|_ealth quintil_|_es_||_Whole_|
|---|---|---|---|---|---|---|
||_Q1 _|_Q2 _|_Q3 _|_Q4 _|_Q5 _|<br>_sample_|
|_Village characteristics_|||||||
|Village classification (“urban”=1)|0.212|0.105|0.155|0.110|0.328|0.179|
|Household characteristics|||||||
|Rooms (number)|1.319|1.458|1.528|1.827|1.980|1.615|
||(0.775)|(0.881)|(0.701)|(0.975)|(1.107)|(0.926)|
|House has cement floors (yes=1)|0|0.018|0.085|0.230|0.543|0.168|
|Access electricity (yes=1)|0|0|0|0|0.277|0.051|
|Thatched roof (yes=1)|0|0.014|0.057|0.049|0.113|0.045|
|Iron-concrete roof (yes =1)|0.962|0.921|0.833|0.739|0.539|0.804|
|Cooking fuel: charcoal (yes =1)|0.003|0.018|0.092|0.159|0.512|0.150|
|Cooking fuel: wood (yes=1)|0.997|0.975|0.904|0.834|0.465|0.842|
|Toilet open pit (yes=1)|0.014|0.152|0.327|0.551|0.766|0.354|
|Household has no toilet (yes=1)|0.986|0.819|0.659|0.410|0.148|0.614|
|House walls: bricks (yes=1)|0.017|0.058|0.131|0.201|0.270|0.133|
|Drinking water: public well (yes=1)|0.410|0.332|0.313|0.219|0.176|0.292|
|Drinking water: river (yes=1)|0.372|0.289|0.195|0.173|0.082|0.225|
|Own radio (yes=1)|0.188|0.473|0.567|0.731|0.844|0.554|
|Own tv (yes=1)|0.003|0.000|0.000|0.007|0.262|0.051|
|Own cart or bicycle (yes=1)|0.174|0.224|0.279|0.410|0.496|0.313|
|Own cattle or sheep (yes=1)|0.431|0.430|0.407|0.438|0.266|0.397|
|Own poultry=1|0.684|0.668|0.673|0.742|0.656|0.685|
|Home language local dialect (yes=1)|0.951|0.845|0.741|0.664|0.578|0.760|
|Maternal characteristics|||||||
|Unschooled =1|0.462|0.260|0.245|0.155|0.047|0.238|
|Primary education =1|0.469|0.628|0.620|0.625|0.430|0.556|
|Secondary/higher education =1|0.069|0.112|0.135|0.219|0.523|0.206|
|Height (cm)|153.14|152.48|152.63|152.03|153.27|152.70|
||(5.83)|(6.17)|(6.33)|(5.92)|(6.44)|(6.15)|
|Receptive vocabulary* (log odds ratio)|-0.143|-0.054|0.015|0.179|0.587|0.112|
||(0.484)|(0.537)|(0.567)|(0.602)|(0.713)|(0.636)|
|Child characteristics|||||||
|Male =1|0.451|0.455|0.480|0.516|0.559|0.491|
|Birth order =1|0.212|0.209|0.192|0.180|0.270|0.211|
|Birth order=2|0.160|0.199|0.224|0.230|0.234|0.209|
|Birth order=3 or higher|0.125|0.166|0.176|0.166|0.164|0.159|
# **Table V: Children's Health Measures Regressions of Health Outcomes on Program Dummy - Children Aged 0 - 5**

|**Dependent Variable**|**Control Group**<br>**Mean(Std.Dev.)**|**Model 1**|**Model 2**|**Model 3**|
|---|---|---|---|---|
|_Parasite count_|0.333(0.673)|-0.061<br>[0.032]**<br>-18.217|-0.060<br>[0.031]**<br>-18.182|-0.061<br>[0.031]**<br>-18.363|
|||-0.018|-0.019|-0.018|
|_Diarrhea_|0.142(0.349)|[0.009]*<br>-12.445|[0.009]**<br>-13.079|[0.009]**<br>-13.018|
|||-0.083|-0.077|-0.078|
|_Anemia_||[0.028]***|[0.027]***|[0.027]***|
||0.426(0.495)|-19.446|-18.070|-18.204|
|||4.037|5.341|5.384|
|_McArthur Communication Development Test score_||[1.650]**|[1.642]***|[1.620]***|
||13.354(18.952)|30.230|39.995|40.314|
|||2.476|3.033|3.083|
|_Picture Peabody Vocabulary Test percentile score_||[1.689]|[1.430]**|[1.472]**|
||30.656(24.864)|8.077|9.893|10.055|
|||0.005|-0.003|-0.003|
|_Height-for-age z-score_||[0.043]|[0.038]|[0.038]|
||-0.605(1.104)|-0.784|0.491|0.469|
|||0.012|0.005|0.013|
|_Weight-for-height z-score_||[0.034]|[0.036]|[0.037]|
||0.125(1.133)|9.900|3.777|10.016|

Notes: (i) Model 1: no controls; Model 2: age, demographic, health-habits and economic controls; Model 3: age, demographic, health-habits, economic and federal-programs controls.

(ii) Reported results: estimated coefficient, clustered standard error at census-block level in brackets and (coefficient/control mean) * 100. * significant at 10%; ** significant at 5%; *** significant at 1%. (iii) Missing values in covariates were imputed with zero, and a corresponding dummy variable was then added to the regressions.

32
2014). A third and more pessimistic view considers informal businesses as parasites engaged in unfair competition with formal businesses that pay tax and abide by regulations (Farrell, 2004). Finally, a more nuanced view argues that informality is a rational decision where businesses choose to stay informal when the perceived benefits of formally registering are outweighed by costs such as paying taxes, registration fees, and meeting regulatory requirements (Maloney, 2004; Perry et al., 2007; Ulyssea, 2020; Kanbur, 2017).

Informal Sector Enterprise Surveys ask respondents questions about some of the reasons why businesses chose to remain informal and the perceptions about any potential benefits from formally registering. This may provide useful information for policy makers to understand the potential barriers faced by these businesses to formalize. Figure 15 shows the reasons given (not mutually exclusive), for each country. In Somalia, Mozambique, and Lao PDR, the lack of any perceived benefit from formally registering is the most common reason cited, for Iraq it is taxes, and in Zambia and Zimbabwe, it is lack of information and the time and fees required respectively.

In three of the surveys (Lao PDR, Somalia, and Zambia), an additional question asks owners to choose the main reason, out of seven possible options, for not registering. Figure 16 displays the relative importance of each reason per sampled city. The results largely support the view that businesses decide to remain informal due to the perception that formalizing imposes additional costs and confers few, if any, benefits. By far, the two most common main reasons cited for not registering were: first, no perceived benefit, and second, the fees and time associated with formalizing. Each option was chosen by around 30% of businesses. Costs associated with registering, along with taxes and meetings or inspections with government officials were reported as the main reason for 12% and 3% of businesses, respectively. Overall, the primary reasons for not registering appear to be related to either a lack of benefit or the presence of additional costs for over 75% of businesses. On the other hand, a lack of information does not appear to be the driving force behind a reluctance to formalize, cited by less than 13% of businesses as their main reason for not registering. This is consistent with findings from studies based on field experiments that show limited formalization following the presentation of information pertaining to the registration process (De Mel et al., 2013; Benhassine et al., 2018; Campos et al., 2018). It is therefore unsurprising that under 15% of businesses report having considered formally registering their businesses in the three years prior to the Informal Sector Enterprise Survey interview.

28
the Ivorian side is a very important cocoa and coffee production area. The year 1993 marked the climax of an enormous macroeconomic crisis in Cˆote d’Ivoire, triggered by the collapse of international prices for cocoa and coffee in 1987 and the halving of administered producer prices in 1990. In contrast, Ghana began a progressive recovery from two decades of economic and political turmoil. In the case of Cˆote d’Ivoire, survey figures exhibit a greater fall than national accounts (table 2, lower top panel). This may be due to under-reporting biases in the less detailed 1992–3 survey and/or to the inaccuracy of national accounts, which compute private consumption as a residual. Cˆote d’Ivoire remains the wealthiest country, even in border districts, where a significant gap of +76 USD per capita is observed. Cash crops’ income differences remain statistically significant but are ten-fold lower (+17 USD). Unfortunately, the absence of geographical coordinates in the 1992 Ghanaian income survey prevents us from using these consumption and income figures for the computation of BDs.

The BD estimates are reported in table 3, again for the two periods of 1986–8 (top panel) and 1993 (bottom panel), and for the set of four welfare indicators. Column (1) reports the difference between survey means at the national level, and columns (2a) to (4b), respectively, report the BD obtained when implementing our three estimation methods with two bandwidths, 50 or 75 km, as in table 1. In 1986–8, we are able to restrict the estimates to the sub-sample of heads born in border administrative districts and in the country of residence. This allows us to test whether discontinuities hold among people sharing the same region of origin or “ethnicity” because part of the above-mentioned literature is concerned with this dimension, especially the fact that shared preferences or intense trade flows may equalize welfare among ethnic groups. We favor the district of birth variable 21
# Salvador the number is 50 percent. Clearly there is a high degree of substitutability between

the two types of providers.

Table 6: Results from simulation studies on own- and cross-price effects on the probability of provider chosen: Percent deterred from public sector who go to private sector

|Sample|Price (or implicit price) changed<br>Increases in prices|Percent|
|---|---|---|
|Benin (rural)|Community Health Center (CHC) fees|61|
|Bolivia (urban)|Fees in Ministry of Public Health (MoPH) facilities, effect on adults 16 and over|33|
|Bolivia (urban)|Fees in Ministry of Public Health (MoPH) facilities, effect on children 15 and under|0|
|Bolivia (urban)|Fees in Ministry of Public Health (MoPH) facilities, effect on children 4 and under|0|
|El Salvador (urban)|Fees in Ministry of Health (MoH) facilities, effect on males|56|
|El Salvador (urban)|Fees in Ministry of Health (MoH) facilities, effect on females|50|
|Ghana|Public fees|60|
|Kenya (rural)|Fees in gvt. facilities from 0 to 10 Ksh (US$0.20), effect on adults 15 and over|37|
|Nigeria|Public prices, effect on adults 16 and over|100|
|Pakistan (urban)|Government clinic price, effect on children 5 and under|71|
||Increases in distance or time||
|Bolivia (urban)|Waiting time in Public Health (MoPH) facilities, effect on adults 16 and over|100|
|Bolivia (urban)|Travel time to Public Health (MoPH) facilities, effect on children 15 and under|20|
|Bolivia (urban)|Waiting time in Public Health (MoPH) facilities, effect on children 4 and under|20|
|Ghana|Distance to nearest public facility|50|
|Kenya (rural)|Distance to government facilities|44|

Sources: Benin: Bolduc, Lacroix and Muller (1996); Bolivia: Ii (1996); El Salvador: Bitran and McInnes (1993); Ghana: Lavy and Gerrnain (1994); Kenya: Mwabu, Ainsworth, and Nyamete (1993); Nigeria: Akin, Guilkey, and Denton (1995); Pakistan: Alderman and Gertler (1989); Note: See Appendix Tables B- 1 and B-2 for further details on the results from these studies _The more the private sector expands with increased earning opportunities, the smaller_ _the impact ofpublic spending._ Government intervention may induce a private supply response which can mitigate any actual impact on health outcomes. For example, a case-control experiment in changing user fees in public facilities in Indonesia showed that, although there was an effect on the use of public facilities, there was a strong private sector supply response, and the effect on health outcomes was very small (Gertler and Molyneaux, _1995)._ After a liberalization of health care provision in Tanzania in 1991 the number of private sector providers skyrocketed, as non-profit units operated by "approved voluntary organizations" increased from 697 to 780 while the number of private for profit facilities increased from 41 to 36
**Table 11: Instrumental variable regression results of project impact on child development: difference between Batch 1 and Batch 3. Dependent variable is standardized Early Development Instrument score reported by child’s caregiver.**

||Physical<br>Well-<br>Unadjust<br>|Health and<br>being<br>|Social Co<br>|mpetence<br>|Emotiona<br>Unadjuste<br>|l Maturity<br>|Langua<br>Cognitive D<br>Unadjuste<br>|ge and<br>evelopment<br>|Communi<br>General K<br>|cation and<br>nowledge<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||ed|Adjusted|Unadjusted|Adjusted|d|Adjusted|d|Adjusted|Unadjusted|Adjusted|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Child’s village is Batch|||||||||||
|1 (1=Yes)|-0.0732|-0.0669|0.0188|0.0285|0.00938|0.00193|0.0397|0.00847|-0.0258|-0.00267|
||(0.0584)|(0.0601)|(0.0618)|(0.0573)|(0.0606)|(0.0582)|(0.0683)|(0.0595)|(0.0595)|(0.0580)|
|Constant|-<br>0.682***|-<br>1.960***|-0.102|-1.271***|-0.490***|-1.550***|-0.535***|-1.820***|-0.472***|-1.440***|
||(0.0979)|(0.357)|(0.0882)|(0.293)|(0.0932)|(0.315)|(0.0775)|(0.280)|(0.0718)|(0.298)|
|Observations|1909|1709|1911|1713|1913|1714|1917|1716|1918|1717|
|R-squared|0.108|0.126|0.121|0.169|0.138|0.173|0.252|0.357|0.245|0.284|
|**Controls Included**|||||||||||
|Child Characteristics<br>Household|N|Y|N|Y|N|Y|N|Y|N|Y|
|Characteristics|N|Y|N|Y|N|Y|N|Y|N|Y|
|Height and Weight|N|Y|N|Y|N|Y|N|Y|N|Y|
|Parenting practices at<br>baseline|N|Y|N|Y|N|Y|N|Y|N|Y|
|District dummies|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|First stage F-Statistic||102.530||97.570||98.060||97.530||97.630|
|First stageR-squared||0.777||0.776||0.776||0.775||0.775|

_Note:_ Robust standard errors clustered at the village level in parentheses. Child characteristics include: child’s age and gender. Household characteristics include household size, household wealth and mother’s education. Height and weight include stunting and wasting status at baseline. Parenting practices are self-reported by caregiver at baseline. District dummies are controlled for in both adjusted and unadjusted models.

*** p<0.01, ** p<0.05, * p<0.1

45
Policy Research Working Paper 10975

# Maternal Mental Health and Its Influence on Children’s Early Development

Evidence from Khyber Pakhtunkhwa, Pakistan _Mahreen Tahir-Chowdhry Elizabeth Hentschel Heather Tomlinson Amna Ansari Amer Hasan Aisha Yousafzai Naveed Hussain_ Education Global Practice November 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
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<!-- Start of picture text --> Upward mobile Downward mobile Vulnerable in both periods Secure in both periods<br>©©©©<br>Qbe] ar] be]<br>xx~+<br>°o4 7... oo °<br>se ae oo ib Ss ae eS 5 Oi Ss a ae rt cv $e Be ge a Og<br>se<br>& F&F FS KF SS KKK SC SF FS KKK FS SK SE KKK SK S<br>Population shares are based on synthetic panel estimates for household heads aged between 25 and 60 in the first round of each interval.<br>The poverty line used is RM 527, the vulnerability line RM 1120.<br><!-- End of picture text -->