rafmacalaba/data-use-mentions-extended huggingface dataset:
the CPI present during the survey period in 2010/11. The crop loss figures from LEAP show the proportion of crops lost in each community, weighted by crop type (where zero means no crop losses suffered). In 2010, few if any households experienced crop losses of more than 30%, whereas in 2005 there were some zones that experienced very high crop losses. In sum, 2011 was a better year for crops than 2005 (and better than a 15 year average) but a worse year for food price inflation (the worst in the last 15 years).

Table 2 shows the mean and standard deviations of the main variables used in the analysis.

# **3 Estimating vulnerability to poverty**

## **3.1 Measuring vulnerability**

Vulnerability to poverty is conceptually distinct from poverty, due to the uncertainty that is incorporated into the analysis (Moser, 1998; Alwang et al., 2001). The focus

> 6Note that this does correlate strongly with the self-reported price shock.

> 7See `http://www.wfp.org/disaster-risk-reduction/leap` , last accessed 28th October 2016. The LEAP model also provides corresponding estimates of the number of households in need of assistance based on a household economy model which can be seen as an alternative to our consumption based approach.

> 8Merging the two datasets together was a complex exercise, and approximately 8,000 observations were lost along the way mainly in pastoral areas.

7
entially impacts perceptions of empowerment. In particular, we expect urban based displacement to be especially conducive to empowerment, while we expect camp-based displacement is especially harmful to IDP empowerment.

# **4 Research Design**

To evaluate our hypotheses, we analyze original cross-section surveys we conducted that sample IDPs and similarly at-risk but not displaced populations in Iraq (2019) and the Philippines (2010). To explore temporal change within a single case, we additionally analyze a repeated cross-sectional survey in northern Uganda (in 2007 (n=1404) and 2010 (n=2498)) that also captures IDPs and similarly at risk populations. We restrict the analysis of the Uganda repeated cross-sectional waves just to Acholi, the main site of displacement within northern Uganda and the region for which we have consistent and comparable coverage across the two survey waves. Figure 2 plots the spatial distribution of our samples in the Philippines (Figure 2a), Iraq (Figure 2b), and Uganda (the 2010 wave, Figure 2c). Combined, these surveys provide 11,170 unique responses that capture the IDP experience across three different continents, regimes, and conflict zones. An additional case contributing to our reflection, Colombia, is presented in appendix because of significant methodological differences.

Each survey was collected independently as part of our broader research agenda to understand the consequences of forced displacement. As a result, the survey samples and instruments are not identical across cases.<sup>12</sup> Each survey was designed to capture and respect the particularities of the local context. We bring these surveys together precisely because they enable us to examine the experience of IDPs across contexts while prioritizing the internal validity of each case. The three surveys presented in this paper provide the best balance of consistency in questions, similarity of populations sampled, and analytically fruitful contextual differences between cases in our broader research agenda and data gathering efforts on the topic.<sup>13</sup> Although specific design decisions vary by country, consistency across the
and of industrial countries by 1.8 percent. The declines are nearly twice as large in an amplified pandemic scenario in which containment is assumed to take longer.

It is still too early to make an assessment of the impact of the virus based on full statistical evidence. High frequency data are providing some indicators, but it is hard to assess the depth and the breadth of the pandemic as it spreads, and to precisely estimate how long it will take countries to return to normal activity levels. This paper seeks to illustrate the transmission channels and heterogenous impact of COVID19 on output and trade in different scenarios. The results presented here should be regarded as scenario analyses, not as projections. The implemented shocks are illustrative and based on previous episodes of global epidemics or on preliminary data.

The assumptions on the spread of the disease are not grounded in epidemiological projections, they do not take into consideration the quality of the health systems in the affected countries, transport connections to affected countries, and health policy responses to the outbreak. The model incorporates the decline in demand due to reduced production and incomes but does not fully capture the independent contraction in demand, except for the reductions in tourism and other services that require close human contact. It also does not include the decline in investor confidence and any financial repercussions. We capture some aspects of global value chains trade, but a fuller analysis will require a richer data set. This analysis will evolve as we fine tune assumptions in line with early impacts and evaluate potential scenarios of the spread of the virus.

- 2 -
<!-- Start of picture text --> A Daily Number of Tweets that Report a Crash B Daily Number of Geolocated Crashes<br>150 150<br>100 100<br>50 50<br>2013 2014 2015 2016 2017 2018 2019 2020 2013 2014 2015 2016 2017 2018 2019 2020<br>Cc Crashes, August 2012 - July 2020<br>) * 2 Pa LETS<br>ee \s ‘3 Oty iW ee ae a ere :<br>Moy ave . ey y oe are ° By, fs ot oc ae ae<br>* oR ° g Pee ce 5 Aen eho > HORE! Bo +z oe °° ° AS on i veer<br>a e Cog < Sp ° y0§ iN A<br><!-- End of picture text -->
_Table 6 – The effect of shocks on social cohesion measures. The sample includes households in the HEIS sample. Shock is measured as the year-onyear difference in log(USD to IRR rate) in each month._

|||||**Regre**|**ssion**|||||**Mat**|**ching**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**A**|**id**|**Tra**|**nsfer**|**Free**|**items**|**don**|**ation**|**Aid**|**Transfer**|**Free items**|**donation**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|**Afghan dummy**|1.485***|1.471***|1.380***|1.386***|1.449***|1.425***|-0.94***|-0.90***|1.194***|0.443*|1.255***|-0.102|
||(0.162)|(0.160)|(0.212)|(0.203)|(0.155)|(0.151)|(0.
such as educational attainment and employment status of household members, household composition and the like. This S2S approach is useful if price adjustments or poverty lines are not comparable over time, but non-consumption data are comparable. Given some key assumptions are satisfied, Christiansen et al. (2012) and Ahmed et al. (2013) corroborate the reliability of this approach using data from various countries.

NISR (2016) estimates a poverty trend between 2010/11 and 2013/14 using the S2S approach and compares it with the official poverty trend between 2010/11 and 2013/14 in NISR (2015). Following Tarozzi (2002), NISR (2016) estimates a logistic regression model to predict the probability of being poor from nonconsumption data using EICV4, predicts a poverty rate from a model using EICV4, and applies it to nonconsumption data in EICV3. Using this approach, they find that the poverty rate of EICV3 is 44.6 percent, which is almost the same as the official poverty estimate for EICV3, i.e., 44.9 percent. NISR (2016) also estimates the poverty rate of EICV4 using this approach (39.0 percent), which is almost identical to the official estimate of EICV4 (39.1 percent). The S2S analysis, therefore, also confirms that the official trend is reliable.

In Rwanda, the reduction in food budget shares over time also corroborates the poverty decline. Typically, increases in income are strongly associated with a declining share of the budget spent on food (Engel’s law). We examine if this is true in the case of Rwanda. We estimate real food budget shares, expressed in January 2014 prices, for 2010/11 and 2013/14 and find that the real food share declines from 64 percent in EICV3 to 61 percent in EICV4. We also find that these differences are statistically significant.<sup>11</sup>

> 11 The real food budget share is estimated by deflating food expenditures (numerator
financial audit gathered information on O&M cost, water tariff, connection charges, and revenue collection from the water tariff _Household Survey:_ Household surveys were conducted to find out the benefit and satisfaction from the service provided. The survey asked questions related to availability of water at the household level, adequacy of water, the quality of water provided, reliability of service etc. It also included questions related to satisfaction with service quality, and affordability of water tariff and capital contribution/connection charges.

_Institutional Assessment:_ The BG survey was conducted to assess the institutional strength of the community management schemes. The process included interviews of the key stakeholders from the Beneficiary groups as well as focus group discussions of members of the BGs. Institutional assessments collected information on composition of executive committee, frequency of meetings, regularity of election to the Executive Committee, maintenance of records, preparation of annual report, and financial and investment decisions of the BGs.

Field surveys were undertaken from March to June 2014. The survey team was unable to locate some of the schemes. A total of 2,583 households from 157 schemes were surveyed. Technical and financial audits were carried out in 172 schemes. Moreover, 135 BGs were surveyed for the institutional assessment. Table 2 provides a summary of the data collected by scheme type through various survey instruments.

**<u>Table 2: Data Collection by Survey Instrument and Scheme Type</u>**

|**Dataset**|**Household**<br>**Survey**|**Technical**<br>**Audit**|**Financial**<br>**Audit**|**Institutional**<br>**Assessment**|
|---|---|---|---|---|
|Jalanidhi|87|87|87|90|
|KWA BM|22|35|35||
|Other Community
Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jul-2018<br><!-- End of picture text --> Notes: Authors’ calculations using the WFP mobile Vulnerability Analysis and Mapping Survey. The survey publicly reported the national share of respondents receiving food assistance since August 2015; and the survey publicly shared the governorate-level share of respondents receiving food assistance since January 2017. IPC groupings are as reported in the 2017 IPC announcement, and IPC 3 + governorates refers to regions that were classified as IPC 3 but would have been IPC 4 in absence of humanitarian assistance.

27
mining costs, are on par with Chile. As a result, the projects that Zambia has managed to develop have copper cutoff grades equivalent to those in Chile. Yet Zambia has attracted only a small fraction of the exploration spending and general project activity compared with Chile, despite the sizes of the two countries being comparable. A confounding factor is that Zambia has not conducted a geological survey, which means that companies will only explore in regions that have yielded success in the past. One cannot disentangle the impact of lack of geological survey from political risk.

The second and most important message is that maximizing government revenues from mining is best achieved by encouraging exploration. It is the seed from which mining projects emerge: more exploration results in more projects being found and developed, and more projects being found and developed results in more taxation being received by the government. Notions of increasing fiscal receipts from mining – by, say, implementing special resource rent taxes on existing projects, or special mining taxes on top of corporate taxes, or anticipating increased fiscal revenues as metal prices rise – is of second-order importance. Taking an intertemporal and project lifetime cash-flow approach, there is very little resource rent that has been left untaxed in the first place, and any attempt to increase mining taxation on all projects will likely lead to less exploration, not more. Increased taxation rates should likely be limited to projects that are extraordinary in their resource size and quality. So far, despite the green energy revolution, we are not seeing an increase in metal prices; we are, however, seeing a rapid inflation of mining costs that reduces the tax base. What will lead to more exploration is a national geological survey that maps the countries’ areas of mineral potential, an effective and efficient exploration and mining permitting system, security of land tenure, and stability of fiscal regime. These are the steps that successful mining countries like Canada and Chile have pursued, and that Botswana is using to attract companies to its newly developed Kalahari copper belt.

44
0.03)|(0.04)|(0.06)|(0.11)|(0.14)|
|Private sector credit (% of GDP)|-1.33|-0.00|-0.03|-0.15**|-0.27|-0.73***|
||(0.92)|(0.03)|(0.06)|(0.06)|(0.18)|(0.23)|
|Constant|1.72|-0.00|-0.01|0.11|0.42|2.08***|
||(1.21)|(0.09)|(0.10)|(0.20)|(0.31)|(0.56)|
|R-squared|0.1791|0.0012|0.0160|0.1031|0.1674|0.1706|
|Number of observations|1,093|1,093|1,093|1,093|1,093|1,093|
|Panel B) Fund investments, vintage|pre-2010||||||
|De-jure fnancial openness index|0.17|0.08|-0.04|-0.09|-0.17|-0.03|
||(0.72)|(0.12)|(0.11)|(0.16)|(0.26)|(0.11)|
|Private sector credit (% of GDP)|-3.10|-0.16|-0.22**|-0.16|-0.52**|-0.72***|
||(2.49)|(0.
The paper posits three main hypotheses to reconcile the mixed results discussed so far. First, the relationship between FLFP and inequality follows an inverted U-shaped pattern if developing countries are also included; this can also allow us to identify a “turning point” at which the disequalizing factors, such as the assortative mating, attenuate depending on the FLFP level. Second, along this curve, it is possible to identify groups of countries (Gornick et al., 2019) with common features and a similar FLFP to inequality gradient. Third, by means of an unconditional quantile regression (Firpo et al., 2009) we can replicate the macro-level results using micro data, and show the asymmetric impact of FLFP on different quintiles, as well as confirm whether a pro-inequality or pro-equality impact exists in a given country.

To sum up, increasing FLFP is widely considered an important societal target with numerous positive developmental outcomes; it also increases the level of resilience, boosting productivity and accelerating economic diversification (IMF, 2018). Increasing FLFP is also part of a broader women’s empowerment agenda being actively promoted by many international institutions and governments (UNHLP, 2016). Moreover, it clearly represents a priority in those countries where FLFP is low and women are burdened with a disproportionate proportion of unpaid care work, arising from dominant social norms regarding gendered division of roles (Chopra and Meenakshi, 2019). However, while the consensus on this issue is almost unanimous, the picture becomes a bit fuzzier when it comes to translating the principles into active policies. As pointed out in this section, neither the direction nor the shape of the nexus between FLFP and inequality is clearly established. However, as we will discuss in the concluding section, policy makers should take this important piece of information into account when formulating policies aimed at promoting FLFP.

# 3. Data and methods

Different data sources are used for this paper. The macro analysis (first part of Section 4) is conducted using the World Bank’s PovcalNet<sup
needed to ensure that all parts of the educational system are aligned with the goal of improving learning, overcoming political challenges and technical constraints that are often particularly severe in developing countries (Pritchett, 2013; World Bank, 2018).

**Within countries, access to formal education varies substantially across gender, location and socio-economic classes, contributing to inequalities across these groups.** Figure IVa highlights the gender dimension, displaying the difference between men’s and women’s average years of schooling against GDP per worker. In most of the poorer countries, women are less likely to be educated than men, while this gap disappears or even reverses with development.<sup>3</sup> Figure IVb visualizes the extent of educational disparities across administrative units for countries at different deciles of the income distribution. Poorer countries feature very large gaps in average education between regions, while richer countries tend to be more homogeneous. In addition, the importance of the family’s socio-economic status for children’s educational achievements has been widely documented in several rich countries (Bj ̈orklund and Salvanes, 2011), and its role might be even larger in poorer countries (though intergenerational data are less readily available for developing economies). These different sources of within-country heterogeneity highlight the importance of understanding the barriers faced by different groups in human capital accumulation and utilization, an issue discussed in greater detail in Section 3.

Figure IV: Schooling Gaps within Countries <!-- Start of picture text --> (a) Gender Gap (b) Across Regions<br>4 15<br>EGY<br>CAFCOD LBR HTITGOGMBYEM SENBEN PAK IND ARM USA<br>20 BDI MWINERMOZ SLEUGAMLIRWA TZANPLZWEZMBTJKCMRLSOKHMKENBGDSDNMRT CIV COGMMRKGZHNDNICVENGHASYRVNM BLZ LAOPHLBOLMDASLVMARGTMJAMSWZIDNUKRJORIRQMNGTUNNAMGUYARMECUALBPERPRYLKADZABRBCHNZAFFJIIRNCOLBRADOMBWAGAB SRB THACRIBGRMDVMEXMUSURYARGR HRV OUCMYSKAZHLLVAPANHUNRUSTURGRCLTUPOLSVKCYPESTPRTTTOSVN CZE MLT ESPITANZLISRKORJPNFRAFIN GBR BELTWNCANISLDNKDEUBHRAUSAUTSWEHKGNLDSAUKWTUSA NORCHEARESGPIRLLUX MAC QAT 105 ETH NPL SEN IRQ PERPAN URY POL FRA<br>-2 0<br>7 8 9 10 11 12 6 7 8 9 10 11<br>Log GDP per worker in PPP (2015) Log
that help companies to grow by providing a combination of capital, mentorship, technical support, infrastructure, and other critical resources. Their ultimate goal is to prepare companies for growth and eventual investment from VC firms. Unlike VC firms, however, incubators and accelerators are not funds per se and generally provide only small amounts of financing. Often the capital invested by such intermediaries is start-up capital and is less than US$25,000–US$50,000.

> 73 This was previously an Aureos fund investment.  Aureos merged with Abraaj in 2012.

> 74 Injaro also classifies itself as an impact investor.

> 75The investments are in Sproxil, Pagatech, Medeem, and GADCO.  Source: Ghana Entrepreneurship Ecosystem Study led by DFID.

> 76 Manager of SIFEM.

> 77 Draws from Ghana Entrepreneurship Ecosystem Study (DFID), Ghana Climate Innovation Center feasibility study (infoDev) and Ghana Science, Technology and Innovation Policy (STIP) Review (World Bank and UNCTAD).

17
Policy Research Working Paper 8827

# **Abstract**

This paper examines the labor market impacts of a largescale marine environmental crisis caused by toxic chemical contamination in Vietnam’s central coast in 2016. Combining labor force surveys with satellite data on fishing-boat detection, the analysis finds negative and heterogeneous impacts on fishery incomes and employment and uncovers interesting coping patterns. Satellite data suggest that upstream fishers traveled to safe fishing grounds, and thus bore lower income damage. Downstream fishers, instead, endured severe impact and were more likely to substitute fishery hours for working secondary jobs. The paper also finds evidence on an impact recovery to fishing intensity and fishery income, and a positive labor market spillover to freshwater fishery.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 hanguyen@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
than half the average loan size received by men. In rural areas, loans have been mainly used for agriculture production (including livestock), while in urban areas they have been mainly used for enterprise development. In both rural and urban areas, some loans have been used for consumption purposes, particularly for households with low income potential. One study that gathered detailed consumption expenditure data finds that borrowers reported improvements in their diet.

There are very few studies of the rates of return to investments, but the ones available tend to find returns to enterprise development to be between 8 and 30 percent. One difficulty in accurately assessing rates of return is that families often have multiple sources of income, with family members working elsewhere providing substantial support, some of which is funneled into family businesses. Also, only a few studies examine the impact of microfinance programs on employment. A study of the Kashf microcredit program interviewed 129 female respondents and finds that the program led to more family members participating in family enterprise activities. Also, women borrowers reported higher levels of confidence, self-esteem, and measures of empowerment and demonstrated increased awareness of social issues.

In summary, microfinance programs in Pakistan mainly have had male borrowers unless they have been explicitly targeted at women, and they tend to have benefited the near-poor and middle-income groups rather than the poorest, unless they explicitly targeted the poorest. The rate of return studies suggest that there were relatively high rates of returns to the loans, although the returns are somewhat difficult to measure given multiple household income sources. There is suggestive evidence that the programs have had some nonmonetary benefits, such as an increase in women‟s confidence and empowerment and improvements in the household‟s diet.

# **4.1.4  Microcredit in India**

Banerjee, Duflo, Glennerster and Kinnan (2009) evaluate the effects of placing a microfinance institution in a new market using a large-scale randomized experimental design. In 2005, 52 of 104 neighborhoods of Hyderabad (a large city in India) were randomly