rafmacalaba/datause-encoder-data huggingface dataset:
methodology. Section V summarizes results. Section VI discusses the implication of World Bank internal knowledge sharing. The final section concludes.

# **II. Generating Knowledge in the World Bank: External Policy Reports**

**The World Bank has three key types of core knowledge products: external client knowledge, public goods, and knowledge for internal use.** Over three-quarters of expenditures on knowledge products are for external clients (Figure 1a). There are four distinct external knowledge products: Economic and Sector Work (ESW), Technical Assistance (TA), Impact Evaluation and external training. Along with TA, ESW has increasingly become a core part of the Bank’s engagement with clients—in fiscal years 2000– 2006 the Bank spent 26 percent of its spending on country services on ESW and TA (IEG, 2008). Between April 2012 and March 2013, expenditures for these two products constituted 39 percent of its spending on country services, a substantial increase from previous years (World Bank, 2013b, Figure 1b).

**Figure 1: Distribution of Expenditures**

<!-- Start of picture text --> a) Distribution of Expenditures of b) Distribution of Expenditures on Country<br>Knowledge Products 1% Services<br>0% 6% Project Supervision<br>15%<br>8% Lending Preparation<br>External Clients<br>31%<br>Economic and Sector Work<br>8%<br>Public Good Technical Assistance<br>27% Aid Coordination<br>Internal Use<br>Country Program Support<br>77% 14%<br>Client Training<br>13%<br>Impact Evaluation<br><!-- End of picture text --> Source: World Bank Quarterly Business and Risk Review FY13 Quarter 3. Note: Data is for the period Q4 FY12 to Q3 FY13.

**Economic and Sector Work is the World Bank’s primary knowledge product line and often complements technical assistance (TA)** .  It is the World Bank’s primary country-based analytical and advisory business line, intended to provide a basis for i) conducting policy dialogue; ii) developing and implementing country strategies; iii) formulating effective lending programs; iv) building institutional capacity and informing the international community about a country’s development challenges. It must involve original analytical effort, be undertaken with the intent of influencing an external client’s policies and program, and be owned by a specific Bank unit (World Bank, 2012b).<sup>7</sup> The product represents the view of the Bank, and as such is not attributable to individual authors.  ESW is distinct from public good

> 7 There are three ESW report types: First, core diagnostic reports, which include Country Economic Memoranda, Development Policy Reviews, Poverty Assessments and Public Expenditure Reviews; second, other diagnostic reports, which cover a range of sector-specific topics; and third, advisory reports, which address high priority sector-specific/thematic issues.  Each type of diagnostic report has its own guidelines (World Bank, 2002).

5
To explore the firm’s choice of equity raising mechanism, we first examine if the preissue stock price performance differs systematically across PIPE and SEO issuers. Any study of follow-on equity issuance needs to account for widely documented “market –timing” effect. Firms that undertake SEOs experience a significant run up in their share price immediately prior to the SEO announcement (see, for example, Asquith and Mullins, 1986, Jung et al. 1996, Loughran and Ritter, 1997, and Hovakimian et al. 2001). Baker and Wurgler (2002) argue that this pattern can be explained by managers attempting to time the market when issuing equity to benefit existing shareholders by selling overvalued equity. This argument has been supported by an anonymous survey of CFOs reported by Graham and Harvey (2001). Almost two-thirds of the CFOs admitted to being influenced in their equity issue decision by the prevailing share price of their firm. We extend the tests of such market timing to our sample of PIPE as well as SEO issuers.

Thus, we test if a “market-timing” effect exists for our sample of international issuers. We find that firms with poorer pre-issue stock market performance are more likely to issue PIPEs. In the 12 months before the issue date, the stock market return of SEO issuing firms is almost twice as large as that of PIPE issuing firms and firms with a stronger pre-issue stock market performance are more likely to issue equity via a SEO. Even though we find that SEO issuers have a significantly higher run-up preceding the issue, both SEO and PIPE issuers have a significant increase in stock price before the equity issue.

Next we test whether, compared to SEO issuing firms, PIPE issuing firms suffer from higher levels of asymmetric information and/or are more financially distressed. The literature on the US market suggests that both issues are present and that PIPEs in that sense present a “financing of last resort” for firms that may otherwise not have access to more traditional capital 4
were used as collateral.

Related, when asked about the primary use of their WEDP-X loans, almost half of the treatment group say they used the loan for marketing materials or services; while 25% say they purchased inputs, and 21% equipment. While marketing may have been an important use of funds, for loans of this size we suspect inputs and equipment may have been a larger use, and that there may have been some reticence to report this use linked to the mistaken belief that business assets might count as collateral for the loan.

# **6.2 Impacts on Firm Survival**

Table 3 shows that treatment increased the firm survival rate from baseline to endline from 67% in the control group to 83% in the treatment group. Put differently, access to psychometric-appraised loans reduced firm death over three years to 17%, from the controlgroup rate of 33% through the COVID-19 pandemic, conflict in Ethiopia (including in the Oromia region where the study took place), and the related challenges in the economic environment. For illustrative comparison, McKenzie and Paffhausen (2019) use data from small firms in 12 developing countries to estimate firm death rates of 50% over six years. As a rough benchmark, we might interpolate this linearly to a business-as-usual closure rate of 25% over three years — our treatment group faring better than this, our control group faring worse. It appears the loan helped slightly smaller firms to survive: firms that stayed open (closed) had 2.33 (1.41) employees in the control group versus 1.75 (1.73) employees in the treatment group. The additional firms kept alive in the treatment group are roughly evenly distributed across sectors.

There is no significant impact on women exiting entrepreneurship altogether (Column 2), since many of those control-group respondents who closed their original firm reopened a new firm, typically in a different sector (Column 4). These sector switches did not follow a — — clear pattern for example, as many women move into retail as out of it and thus it is not clear whether these adjustments were in response to COVID-19 or idiosyncratic factors.

15
16 in 1992) and cohorts who should have completed their mandatory school education before the conflict started. The education of the “young” cohort may have been affected by various disruptions associated with the conflict, such as lack of school facilities and teachers, decrease in household income, uncertainty and insecurity associated with the conflict.

Figures 1 and 2 show the average years of schooling completed by women and men by year of birth using the 2003 and 2007 data respectively. The solid lines represent the average educational attainment by individuals who lived in the regions not significantly affected by the war (mostly in Sugd and GBAO) while the dashed line represents the average educational attainment by cohorts of individuals who lived in the more conflict affected regions (mostly in Dushanbe, Khatlon and RRS). Figure 1 indicates that the younger cohort of women in the conflict affected regions (age 2-16 in 1992) obtained about 0.54 fewer years of schooling than women who were of the same age but lived in the lesser affected regions.

Figure 2 presents longer-term evidence of the effect of the conflict on education. The gap in education between the more and less conflict affected areas is greater for the younger cohorts (aged 2-16 in 1992) (0.52 years less) than the gap for women who were aged 18-34 in 1992 (0.21 years less) and who should have completed their schooling before the conflict started. Interesting observation: The gap is the largest for those aged 9-16 in 1992, averaging 0.62 years of schooling, and those aged 3-5 in 1992, averaging 0.71 years of schooling. The gap is the smallest for those aged 6-8 in 1992, at 0.28 years.

# **Regression results**

In the regression analysis that follows, I first use the 2007 TLSS data to estimate the determinants of completion of “basic level of schooling or higher” (that constitutes eight or nine grades depending on when an individual entered schooling) and “secondary school or higher” levels of education. The choice of these dependent variables is based on the system of education in Tajikistan where the basic level of education (nine grades) is compulsory. Students who completed secondary education level qualify for a secondary school diploma. Only students who completed secondary school level can apply for admission 16
approximately 72,000 children were unable to write their examinations in the North (UNICEF, 2005).

A recent study by UNESCO (2010) used 2006 Multiple Indicator Cluster Survey (MICS) to conduct a quantitative study on the relationship between education and war in Côte d‟Ivoire. This study finds an increase in the uneducated proportion of male cohorts in war-affected areas. Looking separately at the educational attainments for males and females, it concludes that for both genders the average educational attainment has dropped since the conflict broke out. To our knowledge this is the only quantitative study so far that examined the impact of war on education in Côte d‟Ivoire. However, this study does not draw any causal inference on the potential impact of war on education. In addition, the MICS survey was undertaken in 2006 just after conflict had reached its peak, and as a result it might not have demonstrated the full impact of war.

This study aims to bridge this knowledge gap. We calculate the average causal effect of civil war on education in Côte d‟Ivoire using the Household Living Standards Survey (HLSS) data collected in 2008 and the data on local incidences of conflict taken from the Armed Conflict Location and Event Database (ACLED). We use a number of empirical strategies to identify the causal effect of war on education for the school-going age-cohort. First, we use year and department of birth to determine an individual‟s exposure to war. The difference-in-difference outcomes indicate that the average years of education for individuals aged 10 to 22 is .94 years fewer compared to the individuals aged 23 to 32 in war-affected regions. However, the underlying assumptions for difference-in-difference estimation can possibly be violated in the present study by several factors - internal migration, heterogeneous selection into victimization within regions and varying intensity of conflict across regions. These could be potential sources of bias in the estimated causal effect.

The causal inferences on the impact of war can also be affected if education itself worked as a catalyzer of war. Moreover, there were reasons other than the conflict that could produce a detrimental effect on the educational performances (for example deaths of teachers from HIV/AIDS). The NGO-run primary and secondary schools, which stepped in to fill the education gap in the North during and after the war, might create a downward bias in the estimated impact of war on education. As a second identification strategy, we use a set of victimization indicators to measure the potential effect of war and estimate a counterfactual comparison group based on 3
Instability.” Prepared for the Annual Meeting of the APSA, Washington DC, September 1-4 2005. Accessed November 7, 2006 at <u>http://globalpolicy.gmu.edu/pitf/pitfdata.htm</u> Green, William H. 2003. Econometric Analysis. 5<sup>th</sup> Edition.

- Goldin, Claudia and Lawrence Katz. 1997. “Why the United States led in education:  lessons from secondary school expansion, 1910 to 1940, “ NBER Working paper 6144, August.

- Gyimah-Brempong, K. and M. Corley. 2005. “Civil Wars and Economic Growth in SubSaharan Africa,” Journal of African Economies, 14(2):270-311.

- Hegre, Havard, Tanja Ellingsen, Scott Gates, and Nils Petter Gleditsch. 2001. “Toward a Democratic Civil Peace? Democracy, Political Change and Civil War 1916-1992.” <u>American Political Science Review</u> 95(1): 33-48.

- Human Security Centre, University of British Columbia. (2005). Human Security Report <u>2005: War and Peace in the 21st Century.  Oxford University Press: New York, Oxford.</u>

- International Institute for Strategic Studies. 2000.  Strategic Survey 2000/2001.  Oxford: OUP for IISS.

- Landes, D. 1998. The Wealth and Poverty of Nations: Why Some Are So Rich and Others <u>So Poor. W. W. Norton.</u>

- La Porta, Raphael. 1999. “The quality of government,” <u>Journal of Law, Economics, and organization, 15: 222-279</u>

- Li, D. 2002. “Is the _AK_ model still alive? The long-run relation between growth and investment re-examined,” Canadian Journal of Economics 35,  92-114.

- Lijphart, Arend. 1977.  Democracy in Plural Societies: A Comparative Exploration.  New Haven.  Yale University Press.

Loayza, N. and R. Soto. 2002. ” The Sources of Economic Growth: An Overview,” in N. Loayza and R. Soto (editors). Economic Growth: Sources, Trends and Cycles. Central Bank of Chile, Santiago.

Loayza, N., P. Fajnzylber and C. Calderon. 2005. Economic Growth in Latin America and <u>the Caribbean: Stylized Facts, Explanations, and Forecasts. The World Bank.</u>

- Lorentzen, P., J. McMillan, R. Wacziarg. 2006. ”Death and development,” unpublished mimeo, Graduate School of Business, Stanford University, Stanford CA 94305-5015, USA.

Mauro, Paolo. 1995. “Corruption and Growth,” Quarterly Journal of Economics _<u>,</u>_ CX 681712.

- McGrattan, Ellen.  1998.  “A Defense of AK Models,” Federal Reserve Bank of Minneapolis <u>Quarterly Review</u> 22(4), 13-27.

39
observations in the regressions and cluster standard errors at the MFI level. All but one of the most recent observations are from 2003 or 2004.<sup>2</sup> Participation by microfinance institutions in the MIX is voluntary, and thus the sample is skewed toward institutions that have stressed financial objectives and profitability. These institutions also tend to be large by the standard of MFIs, covering 16.1 million active microfinance borrowers with a combined total of $2.8 billion in assets. Most of the clients (10 million) are found in the top 20 largest institutions. Honohan (2004) finds that the largest 30 MFIs account for more than three-quarters of the customers of 2,572 MFIs that report to the Microcredit Summit. While we cannot be certain, it seems highly likely that our sample covers a significant proportion of the customers in that database. The relatively large, profitable MFIs in our dataset are likely to be in the best position to absorb the costs of prudential supervision. If we find evidence that they decrease outreach to help absorb those costs, we would presume that the smaller MFIs not covered in our dataset would face even more severe tensions.

The data are collected for publication in the _Microbanking Bulletin_ (MBB) and have been adjusted to help ensure comparability across institutions when measuring profitability.3 In addition to standard entries from the balance sheet and income statements, the dataset contains qualitative information on the lending style employed by the MFI (group versus individual-based lending), the range of services it offers, its profit status, ownership structure, charter status, and sources of funds. Many of these serve as important controls in the regressions that follow.

The key variables in the analysis summarize whether an MFI faces prudential supervision. As already pointed out, most MFIs face some form of non-prudential regulation, and thus it is not surprising that the average for the regulatory dummy 4 variable that is included in the _MBB_ and our dataset is 0.85 (Table 1). It also comes as

> 2 We have one observation from 2002, 50 from 2003, and 194 from 2004.

> 3 These include adjustments for inflation, the cost of subsidized funding, current-year cash donations to cover operating expenses, donated goods and services, write-offs, loan loss reserves and provisioning, a reclassification of some long-term liabilities as equity, and the reversal of any interest income accrued on non-performing loans.

> 4 The institutions included in MicroBanking Bulletin data tends to be larger and more profitable than the broader set of MFIs included in Mixmarket data. This difference could account for why 85 percent of MFIs

7
# Figure A.13: The Cost of VAT Rebates and POS Subsidies

<!-- Start of picture text --> A. VAT Rebates B. POS Subsidies<br>I. Nominal Cost<br>2014m8 2015m1 2015m7 2015m12 2012m9 2013m1 2013m7 2014m1 2014m7 2015m1 2015m7 2015m12<br>Month-Year Month-Year<br>II. Cost as Share of Total VAT Revenue<br>2014m8 2015m1 2015m7 2015m12 2012m9 2013m1 2013m7 2014m1 2014m7 2015m1 2015m7 2015m12<br>Month-Year Month-Year<br>III. Cost as Share of VAT Liability<br>2014m8 2015m1 2015m7 2015m12 2013m1 2013m7 2014m1 2014m7 2015m1 2015m7 2015m12<br>Month-Year Month-Year<br>Rebate Granting Firms Subsidy Receiving Firms (Left Axis)<br>Firms with a POS Firms with a POS (Right Axis)<br>All Firms All Firms (Right Axis)<br>180 1500<br>160<br>1000<br>140<br>Cost of VAT rebates 500<br>Cost of POS Subsidies<br>Millions-Uruguayan Pesos 120<br>Thousands-Uruguayan Pesos<br>100 0<br>.015<br>1.6<br>1.4<br>.01<br>1.2<br>.005<br>Cost of VAT rebates as 1 Cost of POS Subsidies as<br>Share of National VAT Revenue Share of National VAT Revenue<br>.8 0<br>8 4 .04<br>6 3 .03<br>4 2 .02<br>2 1 .01<br>Cost of VAT Rebates as Share of Net VAT Liability Cost of POS Subsidies as Share of Net VAT Liability<br>0 0 0<br><!-- End of picture text --> Notes: This figures examines the cost of the VAT rebates and POS subsidies. Panel A1 plots the nominal cost (in millions or Uruguayan pesos) of the VAT rebates. Panel A2 plots the cost of the rebates as a share of total VAT revenue (extracted from dgi.gob). Total VAT revenue includes domestic VAT revenue and VAT collected at customs. Panel A3 plots the cost of the VAT rebates of VAT-filing-firms relative to the net VAT liability of three different groups of firms, as per the labels. Panel B displays similar measures for the POS subsidies. For panel B, the values for November and December 2013 are an average over the two months, as we observe no subsidy payments in December 2013, and a disproportionately high number in November, suggesting that December payments were erroneously recorded in November. This figure is mentioned in the conclusion, Section 7.

47
integration (i.e., the material and productive assets lost during displacement), but also by granting them the same civil and political rights as natives. Furthermore, new schools were opened to teach refugee children the same education and civic values taught to Greek natives in order to promote a cohesive national identity.<sup>6</sup> Consistent with this historical account, I find evidence that the supply of new schools played an important role in the refugee children’s progress in literacy and in the catching up with natives in the interwar period.

These results may yet be of limited external validity for current inflows of refugees with totally different ethnic and cultural backgrounds compared to the local population. The successful integration of the 1923 refugees stands in stark contrast with the social marginalization of more recent Albanian immigrants who, unlike past refugees, neither spoke Greek nor had the same religion as locals upon arrival (coming from a Muslim-majority country). Albanian immigrants have not experienced the same social assimilation as refugees, neither in terms of education, intermarriage, nor civic participation.<sup>7</sup> The discrepancy in the socio-economic trajectories of the two groups of newcomers highlights the specific challenges of providing integration policies for migrants who have profoundly different linguistic, religious and cultural profiles relative to those of the locals.

The remainder of the paper is organized as follows. Section 2 provides some elements of historical context. Sections 3 reviews the related literature and presents the paper’s contribution. Section 4 describes the data and empirical strategy. Section 5 presents the main results, while section 6 concludes.

# **2 Context**

## **2.1 The Exodus**

Since it gained its independence from the Ottoman Empire in 1832, Greece pursued the goal to unite the ethnic Greek population scattered across the Ottoman territories. After the First World War, backed by the Allies, the Greek Army occupied territories in western Turkey that were heavily populated by ethnic Greeks. However, while the Greek advance stalled as the military support of the Allies faded, the Turkish Army’s counter-offensive at the end of August 1922 was successful. The persecution of Greek Orthodox communities by the advancing Turkish Army, in particular the fire in the Christian neighborhoods of Smyrna in September 13, 1922, triggered a mass exodus of population to Greek islands and mainland. Almost one million of Greek Orthodox, whose ancestors have been living in Asia Minor for millennia, left Turkey within a few months’ time.

> 6Kontogiorgi (2006), p. 241.

> 7As I cannot provide evidence on the second generation of Albanian immigrants, I examine the integration outcomes of first generation migrants who arrived in Greece at different age in order to measure intergenerational trends. Evidence from both the 2001 census and the ESS suggest that Albanian immigrants have not yet experienced any progress in integration outcomes between younger and older immigrants of the first generation.

3