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45
Pkt 44'= -.

Khan and Hoshino (1992) are correct on this point in their definitions, they use the term "aid" interchangeably with the sum of the two - a practice which is incorrect terminology.

The rationale for the separate budget constraints is that governments will not finance recurrent expenditure out of domestic borrowing. This rationale is asserted by Mosley _et at._ (1987: 619), Gang and Khan (1991: 358) and Khan and Hoshino (1992: 1483). Examination of the data for Greece by McGillivray and Papadopoulos (1991), Papua New Guinea by Forster (1993), and Tanzania by Doriye _et al._ (1993) show that this assertion is not true in general" - and defies the notion that developing country governments are rather prone to printing money to finance their spending.12 But this criticism withstanding, it is odd to write the budget constraint in a way which predetermines the allocation of the income terms in given proportions (that is, the p parameters). Such an allocation should be the outcome of the utility maximization problem. By predetermining the allocation of aid, government behaviour is over-restricted - a problem which is apparent from simulations. White (1994a) calculates the model's solution with the values of the ps obtained by Gang and Khan and a hypothetical set of as which give equal weight to each target? The hypothetical policy targets and aid inflows are set exogenous to the model, being chosen to ensure that the government's targets can all be met. Using equation (3.16) as the utility function, utility will be maximized to a0 by each endogenous variable being equal to its target level - hence, since the targets are attainable, this outcome is the solution that should be found by the model. But the solution is not that - utility is not maximized since government fiscal behaviour is detexmined by the two separate budget constraints.

,On account of this problem we may wish to use instead the single budget constraint: _Is + G + Ge =_ **_T ._** _B + Ag_ **_,_** _A, (3,_ It is shown in White (1993) that the fiscal response model will produce the result of fungibility when the utility function is combined with the budget constraint given in equation (3.19). That is aid inflows do not fully fund expenditure increases, but are pardy used **to** decrease taxes and borrowing - which is what the model is intended to demonstate - even when a budget constraint is used.

However, the budget constraint in equation (3.19) is problematic since different types of 42
confuse the research findings on the effects of a given increase in actual capital ratios with the effects of the same magnitude increase in capital requirements.

In theory, capital requirements reduce leverage risks because if equity capital is higher as a result of these requirements, it serves as a cushion to absorb losses, reducing leverage risks. To the extent that the requirements result in higher actual capital ratios, these requirements may also reduce moral hazard incentives to take on excessive portfolio risks are engendered by limited liability, deposit insurance, and other government protections, such as “too big to fail.” Moral hazard incentives to take on risks are blunted by forcing shareholders to bear more of the losses from these risks (e.g., Admati, DeMarzo, Hellwig, and Pfleiderer, 2013; Anginer and DemirgüçKunt, 2019; Thakor, 2019). Risk-based capital requirements may additionally reduce portfolio risks by encouraging safer choices that reduce the capital ratio denominators. To the extent that capital requirements successfully operate through this prudential mechanism of encouraging banks to reduce their risks, there may also be a certification mechanism in which the nonbank public is better assured of bank safety, reducing the incidence of destructive runs by risk-sensitive counterparties.

It is alternatively argued by some that banks may react to higher capital standards by taking on other portfolio risks to counteract reduced leverage risk and increase rates of return for shareholders (e.g., Koehn and Santomero, 1980; Kim and Santomero, 1988; Calem and Rob, 1999). This could also increase the incidence of destructive runs. See Freixas and Rochet (2008) for a review of the theories that suggests that the reduced-risk view generally dominates.

The empirical literature overwhelming suggests that higher capital ratios are associated with lower bank risk. Almost all U.S. bank failure studies find that higher capital reduces the likelihood of failure (e.g., Cole and Gunther, 1995; Wheelock and Wilson, 2000; Cole and White, 43
Table 2 Potential Structural Breaks according to Zivot-Andrews Recursive Unit Root Test

|Commodity|7Dp<br>Full Sample|iTDr<br>Lcvel Break|Datc|min-rDp<br>Trend Break|Date|
|---|---|---|---|---|---|
|Aluminum|-2.63|-4.75"|1940|-4.76*|1940|
|Bananas|-2.28|-4.44|1924|-3.44|1932|
|Beef|-2.72|-4.52|1958|-3.12|1984|
|Cocoa|-1.98|-4.37|1947|-3.23|1923|
|Copper|-2.84|-4.71*|1953|-3.50|1922|
|Cotton|-1.30|-4.22|1985|4.48*|1962|
|Jute|-2.67|-4.49|1973|-5.05*|1967|
|Rubber|-3.12|4.88*|1917|-4.38*|1919|
|Silver|-1.83|-3.40|1962|-2.58|1931|
|Tea|-1.05|-4.26|1985|-5.04*|1963|
|Tobacco|-2.43|-4.88*|1917|-4.03|1935|
|Wool|-1.65|-4.07|1974|-5.69*|1952|

ote: * rejects te null hypothesis of non-stationarity at 10% The critical values are -3.45 for e -samp e test, and -4.58 for the level break and -4.11 for the trend break (Zivot and Andrews, 1992).
involves comparing two groups over time. Due to their distinct characteristics, we expect differences in outcomes between the groups. However, the evolution of these differing outcomes over time, while holding group characteristics constant, should follow a similar trend (i.e., the common trend assumption) until an exogenous shock occurs. The presence of this parallel trend is crucial for establishing causal evidence of impact. The difference-in-differences research design is particularly suitable for ‘event’ studies and the quantification of the impact of unexpected shocks on economic outcomes. This method has been extensively employed in the reviewed literature (Card & Krueger, 2000; Galiani et al., 2005). In our case studies, we are using the canonical difference in difference, which means two groups and two time periods (before and after).

The difference-in-difference regression is specified as follows: = T T T 1 + P P 2 + Treatment 3 4 (1) YYwhere ii,tt is the average log of nightlights data for each flooded pixelTTiiββ PPTT PPT TTttββ ii,ttββ + ccPPccT PPT TTPPii,tt _i_ ββat time + εεiitt _t._ The use of remote sensing data allows us to explore immediate to short term impact of the flood over our two groups. ii,tt T T T YY is a dummy variable equal to 1 for flooded pixel _i_ located within the conflict buffer zone _i_ before the flood event, and to 0 for flooded pixel located in a non-conflict affected area before the ii flood eventTT _._ P P is a dummy variable that represents the period after the exogeneous shock.<sup>10</sup> T T T T is the treatment variable, i.e., the variable of interest in a difference-intt PPTT PPT TT difference specification which accounts for the interaction of the treated and Post period variable; iitt TT is the set of additional explanatory variable suspected to impact the level of nightlights radiance; and is an independent and identically distributed error term, clustered at the iitt administrative level 2, to avoid spatial autocorrelationccPPccT PPT TTPP _._ iitt εε One of the challenges in our analysis is the definition of conflict-affected areas. Conflict events in Mozambique are clustered geographically as such that it is readily ascertainable what regions are most impacted by these events, and thus defined as conflict-affected areas for the purpose of this study.

Nigeria, conflict-affected areas in Nigeria are not defined based on number of events alone but are based on the WBG FCS conflict classification. This classification uses publicly available data to annually assess countries, pinpointing those most affected by fragility and conflict. This method differentiates between territories experiencing _Fragility_ and/or _Conflict_ situations. Aligned with this definition, the study employs the following conflict indicators identified by the FCS index to delineate conflict-affected areas in Nigeria at the Local Government Area (LGA) scale:

> 10 This variable takes a value of 1 if _t_ = August 2020 to estimate the effect 1 month after the disaster, _t_ = November 2020 for the effect 3 months after the disaster, etc. and 0 otherwise.

8
42 and boys under 14 years working in Sri Lanka in 2012 was approximately the same—0.05 and 0.06 percent, respectively.

**Table 13. Hours Worked in Sri Lanka**

|||Pe<br>|rcent of<br>|workers<br>|working<br>|more than<br>|40 hour<br>|s a week<br>|<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2008|2011|2012|
|Male|52.34|52.64|54.26|54.05|49.23|46.31|49.48|49.79|48.16|55.43|51.39|52.23|65.80|66.59|
|Female|38.59|40.31|40.57|40.84|42.89|35.15|36.07|36.64|36.99|41.16|37.60|35.75|43.33|43.61|
|Agriculture|35.86|36.25|37.17|35.08|37.49|29.68|32.56|33.58|30.36|33.81|30.21|32.35|43.00|43.27|
|T&G|57.02|65.56|63.69|68.76|64.36|59.46|63.60|63.08|62.03|68.57|66.60|62.28|72.99|74.78|
|All sectors|48.60|49.06|50.24|50.13|47.24|42.82|44.98|45.46|44.44|50.86|46.91|46.61|58.51|59.30|
|||Percent o<br>|f childre<br>|n less tha<br>|n 14 yea<br>|rs old in t<br>|he emplo<br>|yed pop<br>|ulation<br>||||||
||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2008|2011|2012|
|Male|0.26|0.17|0.23|0.11|0.26|0.15|0.47|0.49|0.23|0.18|0.15|0.15|0.10|0.05|
|Female|0.50|0.47|0.31|0.21|0.24|0.10|0.63|0.61|0.38|0.33|0.29|0.10|0.05|0.06|
|Agriculture|0.21|0.15|0.2|0.11|0.38|0.22|1.02|1.07|0.55|0.49|0.5|0.43|0.22|0.05|
|T&G|0|0|0|0|0|0|1.1|0|0.75|0|0.24|0|0|0|
|All sectors|0.32|0.26|0.26|0.14|0.25|0.14|0.53|0.53|0.28|0.23|0.2|0.14|0.08|0.06|

_Source:_ Calculations based on the Sri Lanka 1992–2002 2008, 2011, and 2012 Labor Force Surveys. _Note:_ T&G = textiles and garments.

# Employment fluctuates with changes in labor demand, suggesting imperfect mobility

between industries. Working conditions fluctuate less over time, suggesting that our model which focuses on wage changes in the context of imperfect labor mobility is not at odds with the labor market characteristics of Cambodia and Sri Lanka.

Table 14 shows the regression results using the hourly wages for both Cambodia and Sri Lanka. The qualitative results are similar. The higher apparel prices in the MFA period are characterized by higher industry-specific wage premiums and smaller male-female wage differences. After the MFA, when apparel prices were lower, the apparel wage premiums are lower and the male-female wage gap is larger. These results are consistent with the hypothesis
# **Are Cash Transfers Made to Women Spent Like Other Sources of Income?**<sup>♣</sup>

Norbert Schady World Bank Washington, DC, USA <u>nschady@worldbank.org</u> José Rosero Secretaría Técnica del Frente Social Quito, Ecuador JEL Codes: I32, I38

> ♣ We thank Harold Alderman, Caridad Araujo, Orazio Attanasio, Kathleen Beegle, Angus Deaton, Eric Edmonds, Francisco Ferreira, Deon Filmer, Sebastian Galiani, John Hoddinott, Elizabeth King, Peter Lanjouw, David McKenzie, Christina Paxson, and Martin Ravallion for their comments.  Any remaining errors are our own.  The findings in this paper do not necessarily represent the views of the World Bank or the Government of Ecuador.
-43/44-

# TABLE A-3 GOVERNMENT WAGES

(in local currencr, nd percent)

|MIDDLE EAST<br>AND<br>NORTH AFRICA|GOP per<br>Capita<br> <br>**(local currOncy)**|GNP per<br>Capita<br>(US Dollars)|Average<br>Govemment<br>wages|Wages in<br>Manufacturing|Government wages to<br>Manufacturing<br> <br>wages|Govemment<br>wages<br>as percent<br>of GDP|<br>Average<br>Govemment<br>wages to<br>per capita GDP|
|---|---|---|---|---|---|---|---|
|Algeria|43,474|1,690|<br>124,236||0.9|9.7|2.9|
|Bahrain|3,208|8,030|4,212|||7.1|1.3|
|Egypt|2,278|710|10,746|<br>3,274|<br>3.3|8.4|4.7|
|Jordan|877|1,300|<br>2,401|<br>1,296|<br>1.9|<br>14.4|<br>2.7|
|Lebanon|6,461,155||8,775,259|||1.5|1.4|
|Morocco|9,546|1,020|42,676|<br>33,000|<br>_1.3_|12.1|<br>4.5<br>|
|Syria|21,998|1,150|65,214|||8.7|3.0|
|Tunisia<br>1|<br>1,697|1,800|5,091||_|7.1|3.0|
|Yemen|27,273|280|90,000||1|<br>8.0|3.3|
|AVERAGE|||||1.0|9.8|3.|

|OECD|GDP per<br>Capita<br>**(local curcncy)**|GNP per<br>Capita<br>**(US Donars)**|Average<br>Govemment<br>wages|<br>Wages In<br>Manufacturing|Govemment wages<br>to manufacturing<br> <br>wages|Government<br>wages<br>as percent of<br>GDP|Average<br>Govemment<br>wages to<br>per capita GDP|
|---|---|---|---|---|---|---|---|
|Australia|24,183|17,490|31,745|33,475|0.9||1.3|
|Austria|265,189|23,500|256,421|296,916|0.9|3.7|1.0|
|Belgium|725,040|21,650|1,758,526|<br>836,141|2.1|7.3|2.4|
|Canada|24,489|19,960|40,561|33,370|0.2|2.2|1.7|
|Denmark|170,726|26,390|227,705|228,645|2.0|12.9|<br>1.3|
|Finland|94,634|19,400|119,767|112,971|1.3|3.3|1.3|
|France|123,340|22,490|127,220|92,189|3.9|3.3|1.0|
|Germany|39,150|23,560|48,994|32,757|1.5|2.4|1.3|
|<br>Greece|1,618,620|7,480|6,725,656|2,318,765|2.9|9.5|4.2|
|Ireland|9,139|13,010|11,300|<br>13,179|<br>0.9|5.3<br>|<br>1.2<br>|
|Italy|27,312,197|19,840|38,866,464|||5.9|<br>1.4|
|Japan|3,764,873|31,360||4,456,272||||
|<br>Netherlands|30,467|20,950|61,500||||2.0|
|New Zealand|21,703|12,600|69,093|||3.6|3.2|
|Norway|152,305|25,960|160,141|||3.6|1.1|
|<br>Portugal|1,452,906|8,950|2,286,753|2,578,752|0.9|11.5|1.6|
|Spain|1,542,623|13,580|2,773,000|||5.3|1.8|
|<br>Sweden|169,750|24,740|129,729|47,689|2.7|3.0|0.8|
|Switzerland||35,750||13,073||0.8|1.5|
|United Kingdom|10,838|18,050|16,789|47,689|0.4|4.5|1.5|
|<br>US|24,263|24,263|36,941|13,073|1.3|2.3|1.5|
|AVERAGE|__<br>24_263|__<br>4|||_1.6_|<br>4.S|1.6|

Column averages encompass only those countiles for **which** the informabon is complete.
Table 8. Regressions on Africa Interaction or Africa Subsample

|**Panel A. Regressions**|Africa Interacti|on|Afr|ica Subsample||
|---|---|---|---|---|---|
|Dep. Variables|TFP<br>log LP|LP Growth|TFP|log LP|LP Growth|
||(1)<br>(2)|(3)|(4)|(5)|(6)|
|FemHead|-0.078**<br>-0.163***|-0.019**|-0.142|-0.095|0.002|
||(0.036)<br>(0.040)|(0.008)|(0.094)|(0.068)|(0.018)|
|FemHead*Africa|-0.167**<br>0.041|0.026||||
||(0.082)<br>(0.090)|(0.021)||||
|Foreign|0.387***<br>0.493***|0.051***|0.647**|0.697***|0.109***|
||(0.049)<br>(0.049)|(0.012)|(0.225)|(0.102)|(0.021)|
|OwnLargest|-0.113<br>-0.352***|-0.036***|-0.342*|-0.643***|-0.116***|
||(0.070)<br>(0.088)|(0.010)|(0.170)|(0.181)|(0.037)|
|Middle|-0.049*<br>0.176***|0.032***|-0.040|0.213**|0.025|
||(0.027)<br>(0.028)|(0.005)|(0.086)|(0.096)|(0.022)|
|Large|-0.158***<br>0.284***|0.045***|0.127|0.628***|0.089***|
||(0.050)<br>(0.051)|(0.007)|(0.145)|(0.152)|(0.023)|
|Age6_10|0.053<br>0.089***|-0.009|0.363**|0.142**|0.011|
||(0.047)<br>(0.022)|(0.007)|(0.164)|(0.059)|(0.012)|
|Age10plus|0.014<br>0.089***|-0.015**|0.370**|0.232**|0.021|
||(0.041)<br>(0.028)|(0.008)|(0.143)|(0.084)|(0.014)|
|Exporter|0.198***<br>0.227***|0.029***|0.172*|0.028|0.022|
||(0.038)<br>(0.045)|(0.006)|(0.085)|(0.208)|(0.023)|
|Initial LP||-0.104***|||-0.144***|
|||(0.017)|||(0.010)|
|Year FE|Yes<br>Yes|Yes|Yes|Yes|Yes|
|Industry FE|Yes<br>Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes<br>Yes|Yes|Yes|Yes|Yes|
|Observations|20,157<br>51,079|45,711|1,329|5,279|4,500|
|Adjusted R<sup>2</sup>|0.283<br>0.388|0.372|0.142|0.278|0.622|
|**Panel B. PSM for the**|**Africa sample**|||||
|||Ttd<br>C|l|||
|Dep. Variable|**Sample**<br>|reae<br>(Female)<br>on<br>(Ma|ros<br>le)<br>Differenc|e<br>S.E.|T (Z)|
|TFP|<br>Unmatched|<br> <br>-0.734<br>-0.5|<br>23<br>-0.212|0.181|-1.17|
||ATT (Bootstrap)|-0.734<br>-0.5|80<br>-0.154|0.258|-0.60|
|Log LP|Unmatched|8.272<br>8.3|41<br>-0.070|0.080|-0.87|
||ATT (Bootstrap)|8.270<br>8.3|95<br>-0.126|0.087|-1.46|
|LP Growth|Unmatched|-0.030<br>-0.0|61<br>0.031|0.030|1.05|
||ATT (Bootstrap)|-0.031<br>-0.0|04<br>-0.027|0.051|-0.53|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country level in columns. For Panel B, standard errors are obtained with bootstrapping replicated 100 times.

38
# **Table 4. Poverty among Low Elevation Coastal Zone Populations, 2000**

||**Popula**<br>**Areas**<br>**High I**<br>**Mort**<br>**(mill**<br>**Total**|**tion in**<br>**with**<br>**nfant**<br>**ality**<br>**ions)**<br>**Rural**|**Rural**<br>**Share**<br>**(%)**|**Popula**<br>**Areas**<br>**Malnou**<br>**Child**<br>**(milli**<br>**Total**|**tion in**<br>**with**<br>**rished**<br>**ren**<br>**ons)**<br>**Rural**|**Rural**<br>**Share**<br>**(%)**|**Share (%) of**<br>**Rural LECZ**<br>**Population in**<br>**Areas with**<br>**High Infant**<br>**Mortality**|<br>**Share (%) of**<br>**Rural LECZ**<br>**Population in**<br>**Areas with**<br>**Malnourished**<br>**Children**|
|---|---|---|---|---|---|---|---|---|
|**Developing Country**|**119.3**|**64.3**|**53.9%**|**106.8**|**62.4**|**58.4%**|**27.2%**|**26.4%**|
|_By Income:_|||||||||
|Low Income|50.8|34.3|67.5%|33.5|25.0|74.6%|59.2%|43.1%|
|Lower Middle Income|61.0|26.8|43.9%|63.7|34.2|53.6%|23.8%|30.3%|
|Upper Middle Income|7.6|3.3|43.2%|9.6|3.2|33.6%|5.0%|4.9%|
|_By Region:_|||||||||
|East Asia & Pacific|18.4|12.9|70.3%|41.0|24.0|58.5%|10.5%|19.5%|
|Europe & Central Asia|0.0|0.0|--|0.2|0.1|36.2%|0.0%|6.0%|
|Latin America & Caribbean|2.9|1.2|41.3%|1.9|0.6|31.0%|16.1%|7.9%|
|Middle East & North Africa|4.0|1.9|47.4%|1.7|0.8|49.8%|11.4%|4.9%|
|South Asia|71.1|40.3|56.6%|56.8|34.9|61.4%|50.8%|44.1%|
|Sub-Saharan Africa|22.9|8.0|35.1%|5.2|2.0|38.1%|98.4%|24.1%|
|**Developed Country**|**0.02**|**0.02**|**86.1%**|**0.7**|**0.1**|**14.5%**|**0.2%**|**0.6%**|
|**World**|**119.4**|**64.4**|**53.9%**|**107.5**|**62.5**|**58.1%**|**25.5%**|**24.7%**|

The low elevation coastal zone (LECZ) is the contiguous area along the coast that is less than 10 m above sea level.

High infant mortality is 50 or more deaths within the first year of life per 1,000 births (or 500 deaths/10,000 live births).

Child malnutrition counts are the product of malnutrition rates and population counts. Malnutrition rates are based on the percentage of children with a weight‐for‐age z‐score more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population.

See the appendix for details on the spatial methods in calculating these coastal population distributions.

Low‐income economies are those in which 2013 per capita income was $1,045 or less, lower‐middle‐income economies are those in which 2013 per capita income was between $1,046 and $4,125, upper‐middle‐income economies are those in which 2013 per capita income was between $4,126 and $12,745, and developed countries are those in which 2013 per capita income was more than $12,745 , as defined by the World Development Indicators, available at <u>http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=world‐development‐ indicators.</u> 14
# Figure 7. International trade costs in EMDEs, by country characteristics

A. Trade costs by free trade agreements B. Trade costs by tercile of logistics performance index <!-- Start of picture text --> Percent Percent<br>200 200<br>150<br>150<br>100<br>100<br>50 50<br>0 0<br>Non-FTA FTA Bottom Top<br>C. Trade costs by tercile of liner container D. Trade facilitation<br>shipping index<br>Percent Index<br>200 3.0<br>150 2.5<br>2.0<br>100<br>1.5<br>1.0<br>50<br>0.5<br>0 0.0<br>Bottom Top Advanced economies EMDEs<br><!-- End of picture text --> Sources: Comtrade (database); Gurevich and Herman (2018); OECD; UNESCAP-World Bank Trade Costs Database; World Bank; World Trade Organization.

Note: EMDEs = emerging market and developing economies; FTA = free trade agreements; LSCI = Liner Shipping Connectivity Index; LPI = Logistics Performance Index. Orange whiskers indicate minimum and maximum range. Sample includes 52 EMDEs.

A. Average trade costs (unweighted) of countries based on their membership in free trade agreements as defined in Gurevich and Herman (2018).

B. Average trade costs (unweighted) for countries ranked in the bottom and top quartiles of the Logistics Performance Index.

C. Bars show average trade costs (unweighted) for countries in the bottom and top quartiles of the liner shipping index.

D. Unweighted average for 36 advanced economies and 122 EMDEs. Trade facilitation index is an unweighted average of 11 subindexes, all scored on a scale of 0-2. A higher index indicates greater trade facilitation. The indexes score countries on information availability, trade consultations, advance rulings, appeals procedures for administrative decisions by border agencies, fees and charges on imports and exports, simplicity of trade document requirements, automation of border procedures and documentation, simplicity of border procedures, cooperation between domestic agencies, cooperation with neighboring agencies, and governance and impartiality. The data is collected from publicly available sources, country submissions, and private sector feedback. Orange whiskers indicate minimum and maximum range