rafmacalaba/datause-encoder-data huggingface dataset:
the inverse of the distance increased from 0.96 to 1.7. The respective weighted number of fatalities rose from 0.93 to 5.2.

For 2018, the descriptive statistics show that the women in our sample are, on average, 31.8 years old, 58 percent are Muslim, and 41 percent are Christian. About 44 percent have no formal education. The education level of the husbands is distributed in a similar way, with the exception of the percentage of men who report having no formal education, which is 25 percent. We observe similar descriptive statistics for men. The main differences are that men are about eight years older, a larger share of men are Muslim (52 percent), and a smaller share of men report having no formal education (25 percent). For men, we do not report the education level of their partners. Although it is possible to identify the partner in some cases, the information is missing for a large proportion of the sample, which means including this variable as a control would reduce the size of the sample considerably. Therefore, we refrain from including this variable. The average household size is six. Finally, 60 percent of women in the sample reside in rural clusters with a population density of 1,640 inhabitants within a 10 km radius.

# **4 Identification Strategy**

To examine spatial spillovers of violent conflict, we regress the outcome variables on the number of distant violent attacks in the five-year period before the DHS wave in 2013 and the wave in 2018. Our empirical strategy exploits that both the location and severity of distant attacks are exogenous to individuals and household clusters. We set up the following model: Here, _Yiclt_ indicates the outcome variable of individual _i_ from DHS household cluster (that is, village) _c_ located in local government area (that is, district) _l_ at time _t_ .

For the outcome variables, we focus on different financial indicators (employment, asset ownership, and decision-making autonomy) and nonfinancial indicators (fertility 20
# **_2. Literature review_**

## **2.1 What is investment promotion?**

Wells and Wint (2000) define investment promotion as activities through which governments aim to attract FDI inflows. These activities encompass: advertising, investment seminars and missions, participation in trade shows and exhibitions, distribution of literature, one-to-one direct marketing efforts, facilitating visits of prospective investors, matching prospective investors with local partners, help with obtaining permits and approvals, preparing project proposals, conducting feasibility studies and servicing investors whose projects have already become operational. Their definition of promotion excludes granting incentives to foreign investors, screening potential investment projects and negotiations with foreign investors, even though some IPAs may also be engaged in such activities.

Investment promotion activities can be grouped into four areas: (i) national image building, (ii) investment generation, (iii) investor servicing, and (iv) policy advocacy. Image building activities aim to build a perception of the country as an attractive location for foreign direct investment. Investment generation involves identifying potential investors who may be interested in establishing a presence in the country, developing a strategy to contact them and starting a dialogue with the purpose of having them commit to an investment project. Investor servicing involves assisting committed investors in analyzing business opportunities, establishing a business and maintaining it. Policy advocacy encompasses initiatives aiming to improve the quality of the investment climate and identifying the views of private sector in this area (Wells and Wint, 2000).

## **2.2 How can investment promotion affect the decision process of a potential investor?**<sup>**5**</sup>

A company that has decided to engage in FDI usually starts the process of selecting the investment location by drawing a long list of potential host countries. The list is put together by the company executives or by a consulting firm hired for the purpose of site selection. The long

> 5 This subsection draws on MIGA (2006) and the authors’ interviews with former professional consultants assisting companies in establishing facilities abroad.

7
TABLE 9: Impact of leaders’ exposure on intermediary outcome,s by leaders’ prior beliefs

||(1)<br>Nutrition|(2)<br>Stimulation|(3)<br>Health|(4)<br>Micronutrients|(5)<br>Proteins|(6)<br>Attitudes|(7)<br>Hygiene|
|---|---|---|---|---|---|---|---|
|ITT Leader|-0.12|0.06|-0.01|-0.25<sup>_∗∗∗_</sup>|-0.25<sup>_∗∗∗_</sup>|0.01|-0.10|
||(0.09)|(0.06)|(0.04)|(0.07)|(0.09)|(0.07)|(0.08)|
|Agreeing leader|0.09|0.11|0.07|-0.11|0.06|0.01|0.06|
||(0.09)|(0.09)|(0.06)|(0.10)|(0.10)|(0.08)|(0.10)|
|ITT leader X Agreeing leader|-0.04|-0.12|0.01|0.22<sup>_∗_</sup>|0.05|-0.03|-0.02|
||(0.11)|(0.11)|(0.07)|(0.12)|(0.12)|(0.10)|(0.12)|
|Observations|2062|2062|2073|2059|2062|1970|2068|
|ITT Leader + ITT leader X Agreeingleader = 0|0.12|0.58|0.96|0.77|0.11|0.82|0.19|

_Note:_ Agreeing leader is a binary variable indicating that the closest leader agreed with at least 4 out of 5 program messages on parenting practices at baseline. All outcome variables are standardized using the mean and standard deviation of the control household. The first 6 columns show ITT estimates on indices aggregating information regarding early childhood investments, as reported by the caregiver. Column 7 aggregates information regarding hygiene of the child, as observed by the enumerator. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 43
GDP GROWTH by about 0.005 (i.e., = 0.10 x 0.05) or about<sup>**1/2**</sup> of one percentage<sup>point, whichis economically</sup> significant relative to the mean GDP GROWTH of 0.030 or 3.0%.

The marginal effect of an increase in the SMALL EFF RANK is oGDP GROWTHISSMALL EFF<sup>RANK</sup> = P2 + P12 x SMALL SHARE. Using the coefficient estimates from Regressions 5 and 6 in Panel A of<sup>Table 3</sup> and a value for SMALL SHARE of 0.25 gives 0.033 + 0.026 x 0.25 = 0.0395 and 0.005 + 0.013<sup>x 0.25 = 0.0083.</sup> Thus, an increase in SMALL EFF RANK of 0.10 is estimated to increase GDP GROWTH<sup>by about 0.004 (cost)</sup> and 0.001 (profit) or about I to 4 tenths of one percentage point.

In Panel B of Table 3, we add SME EMP and BKLENDGDP as additional regressors<sup>tofull</sup> specifications of the GDP GROWTH equations using cost and profit efficiency<sup>ranksin Regressions7and8,</sup> respectively. The estimated coefficients of both SME EMP and BKLENDGDP are positive in all 4 cases,<sup>but are</sup> statistically significant at the 10% level in just 2 cases. In terms of economic significance, an increase<sup>in these</sup> variables of 0.10 is estimated to increase GDP GROWTH by about 2 tenths of one percentage point (SME EMP) and less than one tenth (BKLENDGDP) of one percentage point. The measured effects of community bank<sup>health</sup> on GDP GROWTH do not appear to be substantially diminished by the inclusion of<sup>theSME and bank lending</sup> variables, which casts some doubt on the strength of the hypothesized transmission mechanisms.<sup>As noted, these</sup> regressions may be subject to additional endogeneity problems to the extent that GDP GROWTH<sup>directly affects</sup> SME EMP or BKLENDGDP.

In Regressions 9-12 of Panel B in Table 3, the estimated coefficients on SMALL SHARE, SMALL EFF RANK, and the interaction term SMALL SHARE x EFF RANK are positive and statistically significant<sup>at the</sup> 10% level or better in all cases for both the SME EMP and BKLENDGDP equations. This<sup>is consistent with the</sup> two hypothesized transmission mechanisms, although we are mindful of the potential<sup>endogeneity problemin</sup> which SME EMP or BKLENDGDP may affect the relative health of community<sup>banks.In terms of economic</sup> significance, an increase in SMALL SHARE of 0.10 is estimated to increase SME<sup>EMPby 0.03545using cost</sup> efficiency ranks in Regression 9 and by 0.0785 using profit efficiency ranks in Regression<sup>10,or about 3 to8</sup> percentage points, which are economically significant relative to the mean SME EMP of<sup>0.600 or 60 percent.An</sup> increase in SMALL EFF RANK of 0.10 is estimated to increase SME EMP by<sup>0.01408 (cost) andby 0.05540</sup> (profit) or about I to 6 percentage points. In terms of effects on overall bank lending, an increase in SMALL SHARE of 0.10 is estimated to increase BKLENDGDP by 0.0168 (cost) and by 0.02175 (profit)<sup>or about 2</sup> percentage points, and an increase in SMALL EFF RANK of 0.10 yields estimated effects on BKLENDGDP of 19
28

- Honohan, Patrick and Daniela Klingebiel. 2000. "Controlling the Fiscal Costs of Banking Crises." World Bank Policy Research Working<sup>Paper, No: 2441.Washington,D.C.</sup>

- Intemational Monetary Fund. 1998. _IMF World Economic Outlook (WEO): Financial Turbulence and the World Economy._ October.<sup>Washington, D.C.</sup>

- **1** 999a. "Financial Sector Crisis and Restructuring:<sup>Lessons fromAsia,"September.</sup>

- Washington, D.C.

- 1 999b. _IMF International Capital Markets._ September.<sup>Washington,D.C.</sup>

- 1999c. "Republic of Poland: Selected Issues."<sup>InternationalMonetaryFundStaff</sup>

- Country Reports, No. 99/32. Washington, D.C.

- . 2000. _IMF International Capital Markets._ September.<sup>Washington,D.C.</sup>

2002. _Global Markets Monitor._ International Capital<sup>MarketsDepartment.April18.</sup> Washington, D.C.

- Kanaya, Akihiro and David Woo. 2000. "The<sup>JapaneseBanking Crisisof the1990s:Sources</sup> and Lessons." International Monetary Fund Working<sup>paper,WP/00/7.Washington, D.C.</sup> January.

- Kane, Edward J. 1989. _The S & L Insurance_<sup>_Mess: How Did It Happen?_UrbanInstitutePress.</sup> Washington, D.C.

- Karlsen, Harald. 1994. 'The Norwegian Banking Crisis-Rescue<sup>Operationsin the Period1988-</sup> 1993." _Norges Bank Economic Bulletin, Q1._

- and Robin R. Nilsen. 1996. "Finance Institutions<sup>in1995."</sup><sup>_Norges Bank Economic_</sup>

- _Bulletin._ Norway; 67 No.1:56-71. March.

- . 2000. E-mails to the author. February 11 and 14.

- Klingebiel, Daniela. 2000. "The Use of Asset<sup>ManagementCompaniesintheResolutionof</sup> Banking Crises: Cross-Country Experience." World Bank Policy Research Working Paper, No. 2284. Washington, D.C.

- Kopanyi, Mihaly. 2000a. "Workout of Classified<sup>ClaimsandEnterpriseRestructuring</sup> Experiences in Hungary 1992-1999." World Bank. Mimeo.

- . 2000b. E-mail to the author. April 26.

- Larrain, Felipe. 1989. "Debt Reduction Schemes<sup>and theManagementofChileanDebt."</sup> International Discussion Paper. World Bank. March. Washington,<sup>D.C.</sup>
country manages to reduce poverty (Goal 1) but also increases inequality (Goal 10) at the same time? Or what is the trade-off between strong economic growth (Goal 8) and deteriorating environmental quality (Goal 11)? The next two examples illustrate such situations.

Table 2 presents recent estimates on the dynamic changes with poverty and vulnerability for 21 African countries, drawing with some modifications on Dang and Dabalen (2019), who analyzed household consumption surveys mostly implemented in the late 2000s. These countries together account for two-thirds of the population in Sub-Saharan Africa. We rank the countries in a decreasing order of reduction in the headcount poverty rate (column 3), and show the changes to the population shares of the vulnerable group and the middle class (columns 4 and 5). We also show the growth in the mean consumption levels for the bottom 40 percent of the consumption distribution (column 6) versus the growth for the whole distribution (column 7), which represents the World Bank’s definition of shared prosperity (see, e.g., Basu, 2013; Jolliffe et al., 2015). This measure of shared prosperity is the first SDG indicator for tracking inequality reduction (Indicator 10.1.1). However, since concerns have been raised that this measure tracks only the (anonymous) growth of the bottom 40 percent, we also show for comparison another measure of shared prosperity that tracks dynamic changes of the population shares of the poor and the vulnerable groups. This latter measure is based on a simple typology of pro-poor growth scenarios recently proposed by Dang and Lanjouw (2016).<sup>7</sup>

> 7 In particular, we consider three welfare categories that correspond respectively to the poor, the vulnerable, and the middle class groups. There are in total six possible growth scenarios depending on whether (the population share for) each of the three categories is expanding or shrinking. The most positive pro-poor growth scenario is one where both the poor and vulnerable categories decrease while the middle class expands (denoted by three stars in Table 2). The opposite happens with the worst pro-poor growth scenario (denoted by three minuses in Table 2) where both the poor and vulnerable categories expand while the middle class shrinks. The first three scenarios relate to the reduction of the poor group and thus indicate positive pro-poor growth, and the remaining scenarios suggest negative pro-poor growth. The growth of the vulnerable category helps further determine the rate of pro-poor growth, for example, whether pro-poor growth is more positive or simply positive. See Table 2.1 in Appendix 2 for further details.

8
**Figure 1: Treasury bill rates by country (2009-2013)**

<!-- Start of picture text --> 25%<br>20% Kenya (91 days)<br>Nigeria (T-bill<br>15%<br>rate)<br>Rwanda (91 days)<br>10%<br>South Africa (91<br>5% days)<br>Tanzania (91 days)<br>0%<br>2008 2009 2010 2011 2012<br><!-- End of picture text --> Source: Central Bank of Nigeria, National Bank of Rwanda, South African Reserve Bank, Bank of Tanzania, and Central Bank of Kenya.

# **_b. The role of competition and innovation_**

A strong determinant of banks’ involvement with SMEs is the degree of competition in the market and the innovation introduced either by domestic institutions or foreign entrants. Competition in the SME market segment is strongest in Kenya where a large number of commercial banks are targeting different market segments. The difference between Kenya and most other Sub-Saharan African countries in that regard is that innovation started through a combination of microfinance-rooted institutions scaling up to becoming commercial banks and innovation with lending models and technology in the retail banking segment by other institutions, most notably Equity Bank. In addition, in Kenya there are lively markets for hirepurchase and invoice-discounting – provided by banks to SMEs which deliver to Government and larger enterprises with reputable payment histories. Adoption of these instruments has facilitated entry by a tier of mid-sized Kenyan banks into the SME financing space. These developments have resulted in the SME market being served both from the lower end through institutions growing their group- and micro-clients, from the upper end through banks that downscale to grow smaller enterprises as well as by mid-size banks. Rather uniquely in the SubSaharan African context, Kenya has fostered a tradition of prudent innovation to the advantage of financial deepening both by larger and smaller market participants. This goes back to the 10
# **A PPENDIX C: LIQUIDITY INFORMATION REQUIREMENTS**

(Note: The forms referred to in this list are included in the OCC MIS liquidity monitoring package. They are only samples or ideas. Tailor reports to fit your specific needs.)

# **Daily Bank Information Needs**

- Management should contact examiners immediately if significant activity occurs in the

- market or deterioration of any kind occurs throughout the day.

- Press articles, potentially damaging media, applicable Dow reports, and other

- pertinent information received over the wire—throughout the day.

- Wire room activity report—daily and periodically as appropriate.

- Daylight OD numbers.

- Stock quote.

- CD rate survey.

- Funds Flow Analysis (see form).

- Fed funds summary (see form).

- Net intercompany funding positions report (see form).

- Total deposit trends report (see form).

- Damage assessment report (see form).

- Summary balance sheets on all banks and affiliates.

- Sources and uses analysis (see form).

- Meet with examiners to discuss reports.

- Close of business estimates (see form).

# **Daily Examiner Requirements**

- Management should contact examiners immediately, if significant activity occurs in the market or deterioration of any kind occurs throughout the day.

- Press articles, potentially damaging media, applicable Dow reports, and other pertinent information received over the wire—throughout the day.  +

61
refugees. The proportion of school-age Syrian refugees hosted in LICs and MICs is equivalent to 25 percent of the total school-age refugee population; yet the countries’ hosting these refugees received approximately 75 percent of ODA for refugee education in 2021 (REFD, 2024).

host countries, with the average cost of inclusive education in countries hosting Venezuelan refugees (e.g. Colombia, Peru, Ecuador and Brazil) exceeding the average cost of inclusive education for the major host countries of Syrian refugees (Türkiye, Jordan, and Lebanon). Furthermore, education ODA for refugee situations does not correspond to the costs of education in the majority of countries, as shown in Figure L. It is thus clear from this analysis that there is a distinct preference among donors to finance education for Syrian refugees at the expense of other nationalities/crises, with Venezuelans displaced abroad receiving noticeably less ODA for refugee education in comparison to other nationalities/crises.

<!-- Start of picture text --> 2,100,000 1,400<br>1,800,000 1,200<br> School-age refugees (5-17)<br>Financing for refugee education<br>1,500,000 1,000<br>1,200,000 800<br>900,000 600<br>600,000 400<br>300,000 200<br>0 0<br>1,258<br>1,836,421<br>1,312,406<br>955,797<br>787,110<br>Refugee at school-age (5-17)<br>333,945 300,875 288,005<br>245,606 227,526<br>153,029 Financing for refugee education (US$ mns)<br>65<br>18 24 38 25 28 15 26 11<br><!-- End of picture text --> _Source: REFD (2024)_

# Conclusion and Recommendations

education in LICs and MICs is not sufficient to meet the education needs of these countries’ refugees. As Figure N shows, almost US$ 3 billion of additional financing was required to finance inclusive refugee education in LICs and MICs in 2021. This represents the financing gap if all refugees were educated through national systems. However, with many refugees currently educated through costlier parallel systems, the finances required for all refugees in LICs and MICs to access education are likely to be far higher.

21
we construct using principal components analysis. Nevertheless, the quality of the data corresponding to the variable on the costs of starting a new business across countries is notoriously weak. Consequently we test the robustness of our results reported below to the exclusion of that factor in the composite index.<sup>7</sup> In addition, we also use the number of days that it takes for exports to clear customs. This indicator is also available from the World Bank’s _Doing Business_ database. This variable allows us to test whether export-specific barriers affect the magnitude of the effect of export growth along the intensive margin on export-discovery counts. It has been used in recent research on the effect of trade costs and trade flows (Djankov, Freund and Pham 2010).

> Our measure of exogenous potential returns to export discoveries ( ~~~~ ) is the annual growth of exports along the intensive margin within Leamer-commodity export baskets relative to the realized growth rate of non-export economic activities. That is, we use the growth of exports excluding export goods identified as discoveries, minus the annual growth rate of non-export GDP. The growth of non-export GDP is netted out of our measure so that it directly captures the returns to exporting compared to producing for domestic consumption or producing services, which are not captured in the merchandise trade data used to identify export discoveries. This seems to be an important adjustment, especially when firms can choose goods and services to sell to foreign or domestic markets, depending on the relative profitability of the different activities.

More formally, the exogenous returns to export in country _c_ and commodity group _i_ ,  _i_ , _c_ was calculated as:

> 7 We thank Caroline Freund for pointing this out.

11
where _βSf_<sup>_p_isthepartduetoparentaleducationand</sup><sup>_λR_</sup> _f_<sup>istheresidualsiblingeect.</sup> Taking variance of equation (5), we have: Dividing through by _σs_<sup>2weget:</sup> If one assumes stationary distributions across generations, then _σp_<sup>2=</sup><sup>_σ_</sup> _s_<sup>2andwehave</sup> In fact, Solon (1999) derived the decomposition under the assumption of stationary distributions as in equation (8), while Bjorklund et al. (2010) used equation (7).

Residual sibling correlation represents all other factors shared by siblings but uncorrelated with parental education. Many studies on intergenerational mobility in developed countries used equations (7) and (8), and the conclusion from this literature is that only a small part of sibling correlation could be explained by the parental education. According to the estimates for years of schooling reported by Bjorklund and Jantti (2020), the IGC estimate for Sweden is 0.30 and sibling correlation is 0.43. The squared IGC is thus 0.09, only about 20 percent of sibling correlation is explained by IGC.

However, equation (5) is motivated by the workhorse linear mobility equation for estimating IGC which imposes a number of assumptions that are likely to be rejected on both theoretical and empirical grounds. Recent theoretical advances suggest that the assumption of linearity is likely to be violated in many cases. Becker et al. (2015) develop a model of intergenerational educational persistence between parents and children where the mobility equation can be concave (due to diminishing returns) or convex (due to complementarities).<sup>26</sup> A concave or convex intergenerational persistence equation has two important implications:

> 26Recent evidence suggests that the mobility equation is not linear in most of the cases. Emran et al. (2020) nds that the mobility equation in India is concave irrespective of gender. Ahsan et al. (2022) provides evidence suggesting concave or convex mobility equations for years of schooling in China, India, and Indonesia.

13