rafmacalaba/datause-encoder-data huggingface dataset:
despite the adverse economic environment in which refugees are hosted, is eight times more important than aid.

Relative to Chadian hosts, and despite receiving assistance, Sudanese refugees are worse off. This too is reflected in Figure 8, which shows that nearby hosts generate more of their own consumption (worth FCFA 498). Like refugees, Chadian hosts face significant consumption shortfalls: to the tune of almost 27 percent of the poverty line, resulting in some 40 percent of hosts who are unable to meet their minimum caloric intake (World Bank 2021)! Chadians living in rural areas elsewhere are better off than local hosts and refugees, as they generate some FCFA 581 per day of their own consumption. Yet, they too face considerable hardship. Their consumption falls almost 15 percent short of the poverty line and 19 percent is unable to meet the minimum caloric intake (World Bank 2021).

Four implications can be drawn from this: first, not only do refugees need assistance to attain an acceptable minimum standard of living (the poverty line), so do their Chadian hosts. Second, the amount of participation savings is significant: FCFA 367 per refugee per day or 54 percent of the cost of basic needs. This even though refugees live in remote areas with high levels of poverty. In fact, and this is the third implication, Chad’s contribution to ensuring that refugees attain an acceptable standard of living is more than three times larger than the aid contribution by the international community (97 FCFA, or 14 percent of the cost of basic needs). And finally, in terms of the ability to increase participation savings and raise the incomes of refugees, there is potential for growth by allowing refugees to catch up with their Chadian hosts. The potential is even greater if refugees were to attain incomes attained by rural Chadians living elsewhere.

**Figure 8: Composition of consumption relative to the poverty line**

<!-- Start of picture text --> 700<br>Poverty line<br>600<br>Sudanese<br>500 Rural Chadian Nearby host 498 refugee<br>367<br>581<br>400<br>300<br>97<br>200<br>Own generated<br>Assistance<br>100 186 220<br>103 Shortfall<br>0<br>FCFA<br><!-- End of picture text -->

# 5. Determinants of refugee earnings and participation savings

The previous section evidenced how minimum basic consumption needs of refugees can be covered through aid and through own earnings. Once a poverty line is selected and the pre-assistance poverty gap estimated, it is possible to calculate the aid needed to bring refugees to the poverty line. The difference between the poverty line and the aid needed to bring refugees to the poverty line are participation savings, which reflect the reduction in aid host countries generate by enabling refugees to earn an income.

11
all family incomes and dividing by the number of members in the household provided us with household per capita income which we used to construct income quintiles for the population of Cali.<sup>12</sup> Based on the income per capita estimation, households were grouped into population quintiles. Further, for the purposes of this study, we defined _poverty_ to be the bottom two income quintiles comprising 40 percent of the city population (following the result of Urrea and Ortiz, 1999). Similarly, we defined _extreme poverty_ to constitute the bottom 20 percent of the population.

# 4. Quantitative and Qualitative Results

## _4.1. Some Characteristics of Poverty_

Given that the EPSOC included an income module which allowed the ranking of households according to their welfare level, simple cross-tabulations between income quintile and key characteristics of living conditions allowed development of a detailed profile of poverty and welfare in the city. Such a profile can help in understanding common as well as distinguishing factors between population groups relating to service access, educational attainment, labor market characteristics and so on. Many of the poverty correlates derived from the survey confirmed earlier work, especially relating to the correlation between household welfare and (i) service access (universal access to electricity but high dependence of poorer households on shared use of public water and sanitation services), (ii) years of formal schooling of the household head (strong relations between formal schooling and welfare), (iii) school enrolment rates (57,000 girls and 44,000 boys not enrolled in primary school) (iv) gender of the household head (little differences across welfare quintiles), (iv) shelter (close to 300,000 people lived in houses without a proper title – 60 percent of whom were poor), or (v) crime (relatively little difference in incidence of experiencing crime across income groups but geographical concentrations in certain _comunas_ ). But the analysis of welfare characteristics caught the attention of policy makers and academics/civil society alike with respect to four findings: first, the nexus between the labor market, education and poverty;

> as a function of individual (education, experience, gender), occupational, and area characteristics to impute the missing data.

11
- N. Kabeer. Women, wages and intra-household power relations in urban bangladesh. _Development and Change_ , 28(2):261–302, 1997.

- N. Kibria, United Nations Research Institute for Social Development, and United Nations Development Programme. _Becoming a garments worker: The mobilization of women into the garments factories of Bangladesh_ . United Nations Research Institute for Social Development New York, 1998.

- J. Kim, G. Ferrari, T. Abramsky, C. Watts, J. Hargreaves, L. Morison, G. Phetla, J. Porter, and P. Pronyk. Assessing the incremental effects of combining economic and health interventions: the image study in south africa. _Bulletin of the World Health Organization_ , 87(11):824–832, 2009.

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- M.A. Koenig, S. Ahmed, M.B. Hossain, and A.B.M.K.A. Mozumder. Womens status and domestic violence in rural bangladesh: individual-and community-level effects. _Demography_ , 40(2):269–288, 2003.

- C.B. Lloyd, J. Young, and Population Council. _New Lessons: The Power of Educating Adolescent Girls: a Girls Count Report on Adolescent Girls_ . Population Council, 2009.

- N. Luke and K. Munshi. Women as Agents of Change: Female Income, Social Affiliation and Household Decisions in South India. _Journal of Development Economics_ , 94(1):1–17, 2011.

- R. Macmillan and R. Gartner. When she brings home the bacon: Labor-force participation and the risk of spousal violence against women. _Journal of Marriage and the Family_ , pages 947–958, 1999.

- Kaveh. Majlesi. Labor market opportunities and sex-specific investment in children’s human capital: Evidence from mexico. 2011.

- A. Malhotra, S.R. Schuler, et al. Womens empowerment as a variable in international development. _Measuring empowerment: Cross-disciplinary perspectives_ , pages 71–88, 2005.

- S. Mathur, M. Greene, A. Malhotra, and International Center for Research on Women. _Too young to wed: the lives, rights and health of young married girls_ . International Center for Research on Women, 2003.

- R.T. Naved and L. A. Persson.<sup> ̊</sup> Factors associated with spousal physical violence against women in bangladesh. _Studies in Family Planning_ , 36(4):289–300, 2005.

- E. Oster and M.B. Millett. Do Call Centers Promote School Enrollment? Evidence from India, 2010.

- P. Panda and B. Agarwal. Marital violence, human development and women’s property status in india. _World Development_ , 33(5):823–850, 2005.

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8 Since Section 5.1.6 details the procedure that we have adopted, we shall only briefly outline it here. Its major steps are as follows: First, we calculate RCA indices in parts in reference not to world exports but we do it in respect to EU exports of manufactures.

Second, we supplement RCA calculations on exports with those on imports. Ng and Yeats (1999) propose using the revealed comparative advantage concept calculated on imports (rather than exports) to identify specialization pattern in terms of upstream operations. The reasoning behind this modification is simple: Except for an imported part used as replacement for a failed component in an assembled good, parts and components are exchanged for further assembly into a product. It follows that countries with above average import shares for components have a comparative advantage in the assembly operation. In other words, the value of RCA index above unity suggests that firms in a country i are better endowed to carry out assembling than those in the EU. It follows from our detailed analysis that values of 'import' RCA indices are a relatively reliable predictor of specialization up the production chain, i.e., the assembly of a final product or component.

Third, since it does not necessarily imply that they do, we identify items with comparative advantage in both exports and imports. Since the likelihood increases when a product has RCA exceeding unity for both producing (as measured on exports) and assembly operation, we use the values of RCA in the manufacture and assembly jointly to identify the phenomenon of production sharing. The intuition behind it is that a double RCA (above unity) means more specialization in exports of part i and relatively larger imports of part i than in the EU. In other words, while various combinations of values of RCA indices for exports and imports may occur indicating different layers of involvement in production sharing activity, a double 'revealed comparative advantage' indicates a stronger likelihood of participation in production fragmentation.

Fourth, we also calculate the values of RCA indices for components and final products in the production and marketing networks. A country may have a comparative advantage in exports of a component while not having comparative advantage in assembling simply because a significant portion of parts used in the assembly operation may be provided by local suppliers. Furthermore, imports used by local suppliers may appear in trade statistics as parts of other products or may be simply not identified as such.

The approach outlined above implies the following steps of analysis. First, we use a narrow definition of parts to assess major developments in comparative perspective and identify variation, if any, in CEEC performance. Second, we examine areas of specialization of CEEC in
##

# **Self-reported barriers to use of formal accounts**

Non-account-holders reporting barrier as a reason for not having an account (%)

||**Not enough**<br>**money**|**Religious**<br>**reasons**|**Family member**<br>**already has**<br>**account**|**Too**<br>**expensive**|**Too**<br>**far away**|**Lack of**<br>**necessary**<br>**documentation**|**Lack of**<br>**trust**|**None of**<br>**the reasons**<br>**given**|
|---|---|---|---|---|---|---|---|---|
|**DEVELOPING ECONOMIES**|||||||||
|All|66|5|23|25|20|18|13|11|
|GENDER|||||||||
|Male|67|5|20|25|21|18|13|12|
|Female|66|4|26|24|20|17|12|11|
|AGE GROUP|||||||||
|15–24|65|4|26|25|19|23|11|10|
|25–64|68|5|21|25|21|16|13|11|
|65+|60|4|24|21|19|13|13|17|
|WITHIN-ECONOMY INCOME QU|INTILE||||||||
|Poorest|75|4|13|28|23|17|10|10|
|Q2|71|4|21|26|22|17|13|11|
|Q3|64|5|23|24|21|19|15|12|
|Q4|58|6|31|23|18|17|14|12|
|Richest|52|4|39|17|14|17|13|12|
|EDUCATION LEVEL|||||||||
|Primary or less|67|5|23|24|22|17|10|11|
|Secondary|66|4|22|26|17|20|17|11|
|Tertiary or more|53|5|30|24|13|12|21|16|
|RESIDENCE|||||||||
|Rural|68|4|22|25|25|18|12|11|
|Urban|61|5|26|23|8|18|15|13|
|**WORLD**<br>INCOME GROUP|||||||||
|Low income|76|6|10|32|28|23|16|8|
|Lower middle income|69|6|24|26|23|20|11|9|
|Upper middle income|58|2|26|20|14|12|14|15|
|High income|45|6|31|21|10|14|24|24|
|REGION|||||||||
|East Asia & Pacifc|64|1|24|18|20|14|6|13|
|Europe & Central Asia|65|4|18|17|15|15|31|16|
|Latin America & Caribbean|55|2|17|40|15|21|26|17|
|Middle East & North Africa|77|12|9|21|8|10|10|7|
|South Asia|65|8|34|23|22|16|9|9|
|Sub-Saharan Africa|81|4|7|36|31|30|16|7|

_Note:_ Respondents could choose more than one reason. Regions exclude high-income economies. See the annex to the methodology section for regional and income group classifications. Data by education level exclude Zimbabwe; data by income quintile exclude Morocco; and data by rural or urban residence exclude Guatemala and Morocco.

_Source:_ Demirguc-Kunt and Klapper 2012.

**56**

MEASURING FINANCIAL INCLUSION
Importing firms are significantly larger in terms of their employment and output, significantly more capital intensive and more productive than firms that do not import in the agribusiness sector as well as in the manufacturing sector overall. In the apparel sector importing firms are significantly larger in terms of their employment and output but they are neither more capital-intensive nor more productive than firms that do not import.<sup>11</sup>

**Table 4. Performance Premia for Importers based on EEA Data**

||**Log**<br>**(Employment)**|**Log (Real**<br>**Output)**|**Log (Capital/**<br>**Employment)**|**Log (Labor**<br>**Productivity)**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|||**All Manufa**|**cturing Firms**||
|**Dummy for Importers**|0.829***|1.074***|0.422***|0.164***|
||(0.037)|(0.039)|(0.039)|(0.018)|
|**Log (Capital/Employment)**||||0.243***<br>(0.004)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|
|**Industry (ISIC 3‐digit)**<br>**Fixed Effects**|Yes|Yes|Yes|Yes|
|**Observations**|12,076|12,533|12,018|11,645|
|**R‐squared**|0.122|0.196|0.194|0.452|
|||**Agrib**|**usiness**||
|**Dummy for Importers**|0.983***|1.280***|0.612***|0.221***|
||(0.159)|(0.145)|(0.136)|(0.074)|
|**Log (Capital/Employment)**||||0.308***<br>(0.018)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|
|**Industry (ISIC 3‐digit)**<br>**Fixed Effects**|Yes|Yes|Yes|Yes|
|**Observations**|897|933|891|864|
|**R‐squared**|0.065|0.102|0.029|0.27|
|||**Ap**|**parel**||
|**Dummy for Importers**|1.357***|1.267***|‐0.05|‐0.027|
||(0.159)|(0.126)|(0.135)|(0.061)|
|**Log (Capital/Employment)**||||0.162***<br>(0.015)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|
|**Observations**|1,028|1,071|1,015|990|
|**R‐squared**|0.129|0.131|0.022|0.114|

Notes: Robust standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

Next, we focus on the subset of firms in the EEA that are exporters so as to characterize the differences between those that are exporter-importers and those that are exporters-only, particularly in terms of productivity. The top part of Table 5 shows very clear evidence that exporter-importers overall are significantly larger in terms of employment and output, they are older and more likely to be foreign-owned, and they are also more capital-intensive and

> 11 We also considered the subset of the EEA data that is a panel covering only the largest Peruvian firms, as described in Iacovone et al. (2015). That sample is limited but allows us to estimate the same regressions but controlling for firm fixed effects. Unreported results show that even after controlling for firm fixed effects, firms that import exhibit significantly higher capital-labor ratios and labor productivity (controlling for the capital-labor ratio).

16
# _2.2 Trends in Education Spending_

Average government education spending around the world doubled in real terms between 1999 and 2015 but spending in terms of share of GDP increased only slightly (Figure 2). Much of the 4 percent annual growth in public education spending was fueled by economic growth rather than significant increases in the share of GDP devoted to education. The share of GDP devoted to public education spending increased from an average of 4.6 percent in 1999 to an average of 4.7 percent in 2015.

**Figure 2: Real government spending on education has grown significantly since 1999** _Public education spending, constant 2015 PPP dollars and percentage of GDP, 1999-2015_

<!-- Start of picture text --> 40,000 5.0<br>30,000 4.5<br>20,000 4.0<br>10,000 3.5<br>0 3.0<br>Public education spending Public education spending as % of GDP<br>Linear trend (spend) Linear trend (spend as % of GDP)<br>Total public education spending (Milllions, constant 2015 PPP $)<br>Public education spending (% GDP)<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> _Source:_ World Bank calculations based on UIS and IMF online databases.

_Note:_ The bars show the country average of total public spending on education for all countries with data available in each year.

# **_Low-Income Countries Have Seen Faster Growth of Public Education Spending as Share of GDP_**

Low-income and upper-middle-income countries have registered relatively large increases in the share of GDP devoted to government education spending since 1999 (Figure 3). For example, countries that were classified as low-income in 2017 increased their public education spending from 3.2 percent of GDP in 1998 **–** 2001 to 4.0 percent in 2014 **–** 17. In contrast, government education spending as a share of GDP has remained relatively constant for upper-middle-income countries (4.6 percent) and high-income countries (4.8 percent) (Figure 3). These trends have resulted in a narrowing of the gap between country income groups in terms of share of GDP allocated to public education spending.

7
show a clear break after 2000. Exit rate increases from around 23% in 1998-1999 to around 27% in the 2000-2002 period. Entry rate shows an even bigger gap between the two periods, from around 15% in 1998-1999 to 9-10% in the later years. Aggregate employment also contracts from a net job creation of about 10000 workers in 1999<sup>27</sup> to a net loss of about 18000 jobs in 2000 and decreases further in 2002 and 2003.

As shown in figure 5, foreign and Ivorian-owned firms have similar entry and exit rates in 1999 but diverging trends of increasing exits among domestic firms and decreasing entry among foreign firms starting in 2000. One possible explanation is that foreign firms weather shocks better than domestic firms and thus are more likely to stay in business. However, because foreigners have higher opportunity costs of establishing businesses in the country given the conflict, they are less likely to enter. Figures 6 and 7 show that the spike in exit rates since 2000 is also accompanied by an increase in the average size and age of exiting firms. Given the common finding in the literature that larger and older firms are more productive, this trend suggests that more productive firms might be exiting during the conflict.

These figures indicate a highly volatile economy comparing to entry-exit rates of firms in other countries. It is difficult to find census data for other economies with comparable income level but estimates from surveys in other sub-Saharan countries indicate lower annual firm exit rates. For example, Soderbom et al. (2006) find that the attrition rate for a sample of firms from Ghana, Kenya and Tanzania averages 6% between 1993 and 1999. Shiferaw (2009) reports annual exit rates from the manufacturing census in Ethiopia, whose income per capita is about one third the level of Cˆote d’Ivoire, to be 16% in the 1996-2002 period. In general, coinciding with the surge of violence, the formal sector starts to show signs of contraction in 2000 as evidenced by decreased overall employment, increased exit and decreased entry.

# **5.3 Output**

Figure 8, panel A plots the simple trend in firms’ output - measure as log of value added - over time (the base level in 1998 has been normalized to zero). It shows a sharp decline in average output, which is 38-56% lower in 2000-2003 compared to 1998-1999, the pre-conflict period. In panels B and C, we plot the coefficient estimates _γ_ 2 _t_ from the following regression: where _yit_ denotes log value added, _Foreignit_ denotes foreign ownership/employment and _Dt_ are year dummies. The coefficients _γ_ 2 _t_ tell us the percentage differentials between firms with/without foreign ownership and employment over time, conditional on firm fixed effects _μi_ and industryspecific year effects _μjt_ . The results show that output has been reduced by more than 20% for firms

> 27The size of the total workforce in Cˆote d’Ivoire in the industrial sector is half a million (Bureau of International Affairs 1999).

18
(2000), Filmer et. al. (2001) and Pradhan and Sparrow (2000) all report some impact of the crisis on enrollment, although the effects tend to be small.

This paper analyzes the effect of the prolonged economic crisis in Peru between 1988 and 1992 on school attendance, employment, and the number of years of completed schooling. The Peruvian case is appealing from an analytical point of view for a number of reasons. First, the crisis in Peru was particularly deep and prolonged: National accounts data suggest that GDP per capita fell by almost 30 percentage points between 1988 and 1992. The depth and duration of the crisis could severely stretch the ability of households to protect investments in schooling. Second, reliable household data are available for Peru. Three Living Standards Measurement Surveys (LSMS) cover the period before the crisis (1985/86), during the crisis (1991), and after the crisis (1997). These LSMSs include detailed information on household characteristics, including demographic structure, income, consumption, access to credit, and parental education, and child outcomes, including educational attainment and employment. The length of the questionnaires and therefore the potential list of controls is much richer than that found in the labor force surveys which have typically been used for analyses of crisis impact (for example, in Brazil and Mexico), although the sample sizes are also smaller. Moreover, it is rare to have highquality data for a middle-income country which covers the periods before, during, and after a crisis.

The rest of the paper proceeds as follows. Section 2 briefly discusses the Peruvian setting during this period. Section 3 describes the data used for the analysis; more details on the data can be found in a Data Appendix. In section 4, I describe the econometric specification, and present results. Section 5 concludes.

# **2. The Peruvian setting**

Economic developments. 1985-1997: Peru followed an erratic economic course in the 1980's and 90's. Between 1985 and 1990, the government of Alan Garcia attempted to stimulate the economy with a "heterodox" stabilization program, which relied, inter alia, on reduced foreign debt payments, a price freeze, and economic reactivation via wage increases, job creation programs, and increased investments in education and health. These policies encouraged high growth rates in 1986 and 87, but the inflationary pressures and budget deficits quickly proved

> **2** A summary of the evidence for Latin America can also be found in de Ferranti et. al. (2000, pp. 73-87). 3Note that it is important to distinguish between the effect of _systemic_ crises, such as macroeconomic contractions, and _idiosyncratic_ crises, such as loss of employment or illness of a household breadwinner on schooling decisions. Systemic crises are generally associated with worse employment prospects for children, whereas idiosyncratic crises need not be. As a result, household responses may differ.

_5_