rafmacalaba/datause-encoder-data huggingface dataset:
# REiFERENCES

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|ASHBY, J. A.<br>1981<br>New Models of Agricultural Research and Extension: The Need to<br>Integrate Women, in Lewis (1981).|
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|<br>1981<br>Females, Farming, and Food: Rural Development and Women's<br>Participation<br>in Agricultural<br>Production Systems, in Lewis (1981).|
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|1970<br>Women's Role in Economic Development.<br>New York: St. Martin's<br>Press.|
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|1979<br>Women and Economic Development in Cameroon. Yaounde, Cameroon: US<br>Agency for International<br>Development.|
|1980<br>The DeveloRment Imolicazions<br>of Female Involvement in Agriculture:<br>The Case of Cameroon. Manchester,<br>UK: University of Manchester,<br>Faculty of Economic and Social Studies.|
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|<br>1981<br>Sociological<br>Dimensionu of Extension Organization:<br>The<br>Introduction<br>of the T & V System in India, in Crouch and Chamala<br>(1981).|
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|1986<br>Compact for African<br>Development:<br>Report<br>of the Committee on|
- Baum, C. F., M. E. Schaffer, and S. Stillman (2007): “Enhanced Routines for Instrumental Variables/GMM Estimation and Testing,” _Stata Journal_ , 7(4), 465–506.

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- Caliendo, M., and S. Kopeinig (2008): “Some Practical Guidance for the Implementation of Propensity Score Matching,” _Journal of Economic Surveys_ , 22(1), 31–72.

- Cambodian Center for Human Rights, . (2014): “Workers’ Rights are Human Rights,” Policy Brief: The Garment Industry in Cambodia.

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- Cardona Sosa, L., and C. Medina (2006): “Migration as a Safety Net and Effects of Remittances on Household Consumption: The Case of Colombia,” Discussion paper, Banco de la Republica de Colombia.

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- Cooperation Committee for Cambodia, . (2005): “Impact of the Garment Industry on Rural Livelihoods: Lessons from Prey Vey Garment Workers and Rural Households,” .

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34
18

- Malaysia Korean import duties actually exceed 100 percent in several processing chains." The importance of these observations iF accentuated by the fact that average applied tariffs on processed comnmodities in the EC (our standardfor comparison) are about 6 percent (Laird and Yeats, 1987), w}ich is less than one third the average duty on Indonesian, Korean or Malaysian commodity imports.

The second important point that emerges from Table 5 is that tariff escalation occurs in most commodity processing chains. In Japan, 89 percent of the processed commodities have higher average applied tariffs than do the primary stage components, and in several cases the spread in duties over a chain exceeds 30 percentage points (e.g., the cocoa and tobacco chains). Overall, the average difference in tariffs on primay and processed commodities is 16 percentage points or more for Thailand and Indonesia and about 12 percentage points for New Zealand. Japan's tariffs increase by 7 percentage points, which is approximately double that for the EC taiffs over the same processing chains.'<sup>2</sup> The overall tariff difference for the Republic of Korea (about 9 percentage points) understates the true importance of escalation in most chains due to the perverse results in two chains -vegetables and tea, where relatively high tariffs are applied on the primary stage items. In both cases, nontariff protection may have been substituted for tariffs on the final stage good (see Table 6).

While Table _5_ indicates most countries' tariffs escalate sharply, other trade barriers may also contribute to Asia's import bias against processed commodities. Several Asian countries employ nontariff barriers, like quotas, licensing requirements, ot variable import levies to control imports. Thus, the presence (or absence) of NTBs over commodity processing chains should be examined to determine if these restrictions generally reinforce Asian tariffs.

**"** The Japanese rates are 'applied' tariffs in that they are the average of the MFN or GSP duty actually paid on imports. The GSP rates may provide developing countries with important margins of preferences for many manufactured goods, but our analysis indicates they are not of major importance for primary or processed commodities. In some cases Australia, New Zealand and other countries that face MFN tarrifs may pay higher tariffs on a few products than indicated by the average rates shown in Table 5.

12 Yeats (198X p. 115) provides a cross-country comparison of the degree of escalation in 10 OECD countries' tariffs. Japan's tariffs escalate far more sharply than any of the other countries used as comparators. The increase in Japanese tariffs over commodity processing chains was about three times greater than that for the United States and at least six times greater than that for Norway, Sweden and Switzerland.

;'
**Table T3.IU:  Father's Education and  Educational Expenditure on Children**

|||**URBAN**<br>**IHDS**<br>|**INDIA**<br>**2005**<br>||
|---|---|---|---|---|
||**(6-22) Yea**|**rs Children**|**(8-23) Yea**|**rs Children**|
|Father's Sch.|384.4***|338.7***|403.9***|348.0***|
||(14.9)|(14.40)|(17.4)|(16.9)|
|**Father's Sch. * Daughter**|**-34.00***|**-37.50***|**-36.5**|**-40.2***|
|**Dummy**|**(20.5)**|**(20.3)**|**(23.9)**|**(23.5)**|
|**Daughter Dummy**|**-290.2*****|**-189.9**|**-325.1****|**-174.3**|
||(136.9)|(138.7)|(164.4)|(162.9)|
|Intercept|784.8***|2763.50***|917.3***|3220.7***|
||(105.1)|(148)|(124.9)|(175.6)|
|No. of Children||-673.1***||-777.7***|
|||(36.0)||(41.9)|
|No. Observations|13086|13086|11051|11051|
|||**NSS**<br>|**1995**<br>||
||**(6-18) Yea**|**rs Children**|**(8-18) Yea**|**rs Children**|
|Father's Sch.|108.4***|108.4***|110.4***|110.9***|
||(2.817)|(2.817)|(3.070)|(3.039)|
|**Father's Sch. * Daughter**|**-11.3*****|**-11.3*****|**-11.2*****|**-11.2*****|
|**Dummy**|(3.906)|(3.906)|(4.295)|(4.238)|
|**Daughter Dummy**|**-39.4**|**-39.4**|**-41.6**|**-11.1**|
||(26.12)|(26.12)|(29.00)|(29.03)|
|Intercept|505.9***|505.9***|543.6***|1004.0***|
||(17.95)|(17.95)|(19.75)|(26.13)|
|No. of Children||||-178.0***<br>(7.963)|
|No. Observations|27810|27810|24443|24443|

Notes: (1) The data used are IHDS 2005 and NSS 1995 respectively. (2) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
Antimicrobial resistance increases the costs of medical care, through the need for more procedures, drugs and longer stay. In Ghana, Otieku et al (2023) found that an AMR infection increased drug cost by 30%, increased laboratory cost by 42%, and ALOS by 4.9 days. From 20 studies (19 OECD countries and 1 China), multi-drug resistant HAIs cost an additional 33%, with 27% increase in ALOS (Serra-Burriel et al, 2020).

**At the macroeconomic level** , the World Bank concludes that the economic costs of AMR may be within the same order of magnitude as those of the major 2008 global financial crisis, or a loss of global GDP of 0.14% every year, with more pronounced negative impacts in lowincome countries (World Bank, 2017). Their estimates include AMR in both animals and humans. Dadgostar (2019) reports that about 10 million people will die across the world annually by 2050 if strong and effective action against AMR is not taken and could cost health systems from US$300 billion to more than US$1 trillion annually by 2050 worldwide. OECD (2018) estimates that between 2015 and 2050, up to US$3.5 billion (at Purchasing Power Parity) is expected to be spent yearly on AMR-related complications across 33 OECD and EU countries. This corresponds to 10% of health care costs caused by communicable diseases.

**The costs of interventions** to improve basic WASH and HCWM services in healthcare facilities was estimated for 46 low-income countries (Chaitkin et al, 2022), finding that the cost per capita needed is US$0.30 for capital cost and starting at US$0.10 for recurrent cost going up to US$0.51 per year at full coverage (Chaitkin et al, 2022). A review on the costs of WASH in healthcare facilities in low- and middle-income countries found 36 studies, with piecemeal information for different service types and levels but limited evidence for giving a picture of the resource needs on the continent (Anderson et al, 2021).

# Study focus on Eastern and Southern Africa

In SSA, there is a lack of routine monitoring on WASH, HCWM and environmental cleaning in healthcare facilities, although it has been improving since the WHO/UNICEF Joint Monitoring Programme (JMP) started compiling statistics in the SDG era. Figure 3 shows the latest data available in the nine countries from Eastern and Southern Africa included in this study. Data on environmental cleaning are available for only 2 of the 9 countries, and there are major data gaps for basic sanitation and basic hygiene. Lack of data would suggest that these basic conditions for patient and health worker safety are largely absent. To provide a better picture of coverage in healthcare facilities, these data compiled by the JMP can be supplemented with other data available from the study countries, although attention is needed on quality and national representation of additional data. For example, in Tanzania between 2005 and 2010, it was found that only 24% of health facility delivery rooms were “WATSAN-safe” (Benova et al, 2014).

6
# Table A.4.2 Panel unit root tests

## A. All countries

||Int<br>Im-Pesaran<br>Shin|ercept and tr<br>-<br>ADF<br>Fisher|end<br>PP Fisher|Intercept<br>Im-Pesaran-<br>Shin<br>ADF<br>Fisher|PP Fisher|
|---|---|---|---|---|---|
|Total|-23.1|1167.2|560.8<br>|-15.2<br>853.4|444.3|
|sensitivity|(0.00)***|(0.00)***|(0.00)***|(0.00)***<br>(0.00)***|(0.00)***|
|Gross public|<br>0.0|102.0|42.9|-4.9<br>405.2|78.2|
|debt|(0.52)|(0.27)|(1.00)|(0.00)***<br>(0.00)***|(0.88)|
|Ptti|<br>-2.2|145.3|112.2<br>|-0.9<br>96.1|89.2|
|eneraon|<br>(0.01)***|(0.00)***|(0.10)*<br>|(0.19)<br>(0.42)|(0.62)|

## B. Advanced economies

||Int<br>Im-Pesaran<br>Shin|ercept and tr<br>-<br>ADF<br>Fisher|end<br>PP Fisher|<br>Im-Pesaran-<br>Shin|Intercept<br>ADF<br>Fisher|PP Fisher|
|---|---|---|---|---|---|---|
|Total|-3.7|96.7|95.2<br>|-0.4|71.8|98.4|
|sensitivity|(0.00)***|(0.00)***|(0.00)***|(0.34)|(0.01)***|(0.00)***|
|Gross public|<br>1.1|38.2|14.2|-0.6|55.8|27.9|
|debt|(0.86)|(0.84)|(1.00)|(0.26)|(0.21)|(0.99)|
|Ptti|<br>-3.4|86.3|53.3<br>|-0.9|49.3|47.2|
|eneraon|<br>(0.00)***|(0.00)***|(0.28)<br>|(0.18)|(0.42)|(0.50)|

## C. EMDEs

||Inte<br>Im-Pesaran-<br>Shin|rcept and tre<br><br>ADF<br>Fisher|nd<br>PP Fisher|Im-Pesaran<br>Shin|Intercept<br>-<br>ADF<br>Fisher|PP Fisher|
|---|---|---|---|---|---|---|
|Total|-29.1|1070.5|465.6<br>|-21.3|781.6|345.9|
|sensitivity|(0.00)***|(0.00)***|(0.00)***|(0.00)***|(0.00)***|(0.00)***|
|Gross public|<br>-1.0|63.8|28.6|-6.4|349.5|50.3|
|debt|(0.16)|(0.04)**|(0.98)|(0.00)***|(0.00)***|(0.31)|
|Ptti|<br>1.0|48.2|60.5<br>|0.1|43.9|43.8|
|eneraon|<br>(0.85)|(0.38)|(0.07)*<br>|(0.53)|(0.56)|(0.56)|

Note: P-values are in parentheses. ADF = augmented Dickey-Fuller unit-root test; EMDEs = emerging market and developing economies; PP = Phillips-Perron unit-root test.

A. Results for the full sample of 47 countries, using data for 1995-2016.

B. Results for 24 advanced economies, using data for 1995-2016. C. Results for 23 EMDEs, using data for 1995-2016. The null hypothesis of a unit root is rejected at significance levels of *** p < 0.01, ** p < 0.05, * p < 0.1.

39
|Total Yearly Cash & In-kind Income from Casual Labor in Rs. -<br>winsorized-1 percent|25,335.92|24,897.71|438.212|1.8|889.509|
|---|---|---|---|---|---|
|Total Yearly Cash IncomefromSalaryin Rs. -winsorized-1percent|13,635.42|12,429.95|1,205.47|9.7|1,835.09|
|Total Yearly Cash & In-kind Income from Agriculture in Rs. -<br>winsorized-1percent|17,214.42|16,755.76|458.662|2.7|976.439|
|Total Yearly Cash & In-kind Income from Land-winsorized-1 percent|228.881|247.379|-18.499|-7.5|252.873|
|Total Yearly Cash & In-kind Income from Fisheries in Rs. -<br>winsorized-1 percent|382.772|124.212|258.56|208.2|284.29|
|Total Yearly Cash & In-kind Income from Enterprises in Rs. -<br>winsorized-1 percent|3,306.83|3,249.69|57.141|1.8|2,186.95|
|Total Yearly Income from Public Transfers MGNREGS and other<br>Government Programs|7,752.07|8,317.96|-565.885|-6.8|350.217|
|Total Yearly Income from Private Transfer in Rs. -winsorized-1<br>percent|820.44|370.61|449.830*|121.400*|251.578|
|Total Yearly Cash Income from Other Sources in Rs. -winsorized-1<br>percent|1,535.52|1,475.68|59.847|4.1|162.189|
|<br>Total Paid days for all Household Members under MGNREGS work<br>(in a Year)|122.855|167.339|-44.484|-26.6|43.295|
|Number of Members Migrated to Urban Areas for Employment or<br>Search of Employment|0.059|0.034|0.025***|74.100***|0.008|
|<br>Number of Members Migrated to Rural Areas for Employment or<br>Search of Employment|0.023|0.006|0.017***|296.400***|0.005|
|Total Numberof Enterprisesin Household|0.04|0.033|0.007|20.2|0.007|
|Total number of employees (Permanent and Temporary)|0.055|0.043|0.013|29.5|0.017|
|Total number of external employees (Permanent and Temporary)|0.013|0.015|-0.001|-9.9|0.013|
|<br>Does anybodyintheHouseholdhaveNon-Farmbusiness?|0.098|0.08|0.018*|23.100*|0.01|
|Does the Household Own Any Enterprise? (Y/N)|0.039|0.033|0.006|18.2|0.007|
|Any external Funding for enterprises|0.021|0.007|0.014***|213.400***|0.004|
|Log Total funds invested in enterprise|0.367|0.218|0.149**|68.200**|0.065|
|<br>Log Total funds borrowed for Enterprise|0.207|0.061|0.146***|237.600***|0.039|
|LogTotalCash&KindRevenuefrom Enterprisesin Rs.|0.394|0.352|0.042|11.9|0.074|
|Households cultivating HVA Crop (Non-Paddy-Wheat-and-Millets)-<br>(Y/N)|0.375|0.291|0.084***|28.800***|0.016|
|Total Number of Village Level Group Participation|0.084|0.051|0.033***|63.800***|0.01|
|Total Number of Different Programs|1.102|1.153|-0.051*|-4.400*|0.027|
|Does anybodyintheHouseholdhave aNREGS card?(Y/N)|0.662|0.607|0.055***|9.100***|0.017|

34
**18**

**fr.naa % acs.tt** . doLt; is', theF favoured **ErUri oef** in\eswtmcnt f:nan,re. prl;>.tF au-- Ferhap _.f_ ta :nsiderat ios). Ar internal sciin fc:r the optimal dett choice of the firm still! e<1-ts. though. prnxbided that v.e assume that h!gher debt, relativel',y tD the<sup>firm's</sup> capita! stor k, is as,lt '.52th increasing agenc, costs. Emal'l, because **of** -oQnstraints in the credit market, at each point of time the adebt<sup>tocapital r.ticfk</sup> rati,- of the firm is b.ound by an upper ronstraint. Formally,:

s.t.

'x.here. in standard notations, Y, N, K and I represent the level _of_ pr oduction, employment, capital stock and investment respectively, whereas B denotes the outstanding stock of debt. The function et ! with c >0. cjj') and c2<0, is the agency cost function, which is. assumed to be quadratic. The output price is indicated as p, the wage as w, the price oF investment goods as q, while r and i denote the interest rate and the discount rate respectively. We assume that I+i= (l+r) (I+k) N.here k is a multiplicative risk premium. Finally d is the depreciation rate. Eq. 6 is the leverage constraint whi-ch defines the madmum amount of debt as a time varying proportion (E*) cf the capital stock, while eq. 7 and 8 describe the production relationships and the capital accumulation identity respectively. Suppose first that the debt constra;nt is not binding. It can be easily shown<sup>that, at an optimum,</sup> debt Vill be a fixed proportion , say y, of nominal capital stock. In turn v is a function of the risk premium k and oF the parameters aFfecting the position of the agency<sup>cost function</sup> **2** Notice that y does not depend on the interest rate. As matter of fact, variations in r (or in the discount rate i) affect the level of<sup>investment,</sup> not the con.position of its financing. The choice oF y in turn afFects the demand<sup>for capital</sup> which is otherw.ise determined in a standard way. IF _we_ consider the case where the debt constraint is binding (i.e. B*<y), financial conditions have a direct impact on the demand for capital. Under linear
_Figure 4 Heterogeneity in wealth accumulation_ <!-- Start of picture text --> a) b) c)<br>Proportion of households Proportion of households Percentage of population<br>with net worth greater than with net worth greater than with financial wealth greater<br>6 months of consumption 60 months of consumption than 6 months of<br>consumption<br>1 0.6<br>0.8 0.5 0.4<br>0.4 0.3<br>0.6<br>0.3<br>0.4 0.2<br>0.2<br>0.2 0.1<br>0.1<br>0 0 0<br>25 29 33 37 41 45 49 53 57 61 65 25 29 33 37 41 45 49 53 57 61 65 25 29 33 37 41 45 49 53 57 61 65<br>2001 2010 2018 2001 2010 2018 2001 2010 2018<br><!-- End of picture text -->

# 6.2 Heterogeneity in household wealth accumulation

The decomposition of age profiles can be applied to subgroups as long as they are defined by permanent household characteristics such as level of permanent income, schooling of the head, rural or urban status to examine whether the patterns above hold for different types of households. We approximate quintiles of permanent income by computing per capita equivalent consumption quintiles over 5-year age bins, as described in the data section. Because schooling strongly correlates with permanent income and results in coarser and uneven groups, we only report results disaggregated by urban/rural status and permanent income quintiles.

Saving rates follow similar life cycle progressions across all consumption quintiles (Figure 5, left panel): saving rates are constant in the 20s and 30s before increasing by 15-30 percentage points in the second half of the life cycle. While households in the bottom quintile exhibit average saving rates that are 17pp lower than those in the top (Table 2) their progression over the life cycle progressions are not distinguishable.

The saving rates of rural and urban households exhibit very different life cycle patterns (Figure 5, right panel). In urban areas, saving rates decline early in the life cycle before stabilizing and then growing again from age 45 to age 60. In rural areas, the pattern found in the overall population holds. There again, this describes changes in the saving rate relative to the age 25 baseline. Table 2 suggests that this baseline is much lower for rural than for urban households, with the measurement caveats noted at the beginning of this section.

16
Policy Research Working Paper 9016

# **Abstract**

Interventions in highly insecure and fragile contexts are always confronted with the latent risk of not being able to implement the program as intended. Despite its high policy relevance, little is known about the impacts of program disruption or cancellation on beneficiaries. This study uses the unplanned cancellation of the South Sudan Youth Business Start-Up Grant Program to assess the socioeconomic, behavioral, and psychological consequences of a program that fails to be implemented as intended. Originally planned as a randomized trial, the Youth Startup Business Grant Program consisted of an unconditional cash grant combined with a business and life skills training targeting the youth in South Sudan. Due to the intensification of violence in the country, the disbursement of the grant was terminated in late 2016 before most of the intended beneficiaries had accessed the grant. The study uses survey data from face-to-face interviews and experimental data from lotteries, trust games, and a list experiment to assess the consequences of the cancellation in a comprehensive form. The empirical analysis employs instrumental variable regressions to control for individual characteristics that might have made it more likely to access the grant before disbursement was frozen. The results show that participants who received the originally planned treatment displayed significant improvements in their consumption, savings, and psychological well-being. However, participants who vainly expected to receive the cash grant showed reduced levels of consumption and women among this subgroup also experienced strong reductions in their trust level. In addition, the study finds some evidence that these women were less likely to migrate.

This paper is a product of the Poverty and Equity Global Practice and the Social Protection and Jobs Global Practice. 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 upape@worldbank.org and lralston@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