rafmacalaba/data-use-mentions-extended huggingface dataset:
19 results (not shown) are qualitatively unchanged, the sign reversion disappears for debt and moves further to the upper extreme of the distribution for reserves.

# _6.1 Robustness: omitted variables_

Could the negative indirect effect of workers’ remittances be spuriously due to other unobserved country-specific features that are correlated with both workers’ remittances and current account reversal?

For example, the degree of development of financial markets could sustain the flows of workers’ remittances – especially those channeled through the banking system and, as such, more likely recorded in the official balance of payments statistics – and, at the same time, reduce the probability of financial crises. Similar effects could descend from a higher flexibility of exchange rates and greater freedom in capital movements. Another potential channel for a spurious relationship is political instability that could reduce migrants’ willingness to send money back home and, at the same time, be correlated to financial instability. To the extent that there is some relationship between the literacy rate of migrants and their propensity to remit<sup>25</sup> , a spurious correlation might arise if education<sup>26</sup> , through its positive effects on potential growth, reduce the probability of financial crises.

We address some of these issues by adding to our empirical specification the interactions between international reserves and external debt on one side, and indicators of financial development, exchange rate arrangements, political instability, literacy rate of working-aged residents and country size on the other side. As a further robustness exercise, we also add interactions with dummy variables for African and Asian developing scountries to prove that our results are not specific to any particular geographical area, and for small countries. Given the results in the previous section, we present, for both measures of reversals, only the most clear-cut results based on the 75<sup>th</sup> percentile workers’ remittances-dummy variable.

> 25 As far as we know, only Faini (2002) empirically tests how migrants’ skills affect their propensity
3 regional and socioeconomic patterns of poverty, the results are disaggregated by socio-economic groups.

This paper is made up of six sections, including the present Introduction section. Section II looks at the sources of data used for the study. Section III looks into household income and expenditure distribution in Nigeria and provides an interpretation of poverty indices and the calculation of a relative poverty line for Nigeria. Section IV makes up the core of this paper, presenting the spatial characteristics of poverty in Nigeria and how they have evolved over a seven year period from 1985 to 1992, indicating in which regions and states the poor are located in and the extent and severity of their poverty; the basic demographic characteristics of the poor including time use and employment patterns, describing in detail how these characteristics have evolved over the period; and how the poor feature in various sectors of the economy and the poverty decompositions, vulnerability and targeting; discusses how the changes in poverty could be explained by factors that are growth-related and those that are due to redistribution. Section V discusses the evolution of expenditures in Nigeria and explains how the patterns of expenditure correspond to poverty, by decomposing the sources of expenditure. Section VI concludes with some final remarks.

# **2. NATIONAL CONSUMER SURVEY**

The main data sets used in this study were collected by the Federal Office of Statistics (FOS) of Nigeria under the National Integrated Survey of Households (NISH). Both the 1985/86 and 1992/93 surveys were used to construct the poverty profile and consequent analysis. Both surveys start in April of the first year and end in March of the following year. The main objective of the data set is to provide periodic information to revise the consumer price index. The data on income is used to prepare a system of national accounts. The expenditure data and socioeconomic information are used to assess the impact of policy on general consumption patterns. The coverage of the data set is not as wide as one would wish, for detailed welfare and poverty analysis.
Even though the literature on international banking is quite vast, a limitation of existing studies is that the determinants of financial FDI (or local claims) and those driving cross-border claims have been considered largely in isolation.<sup>4</sup> Furthermore, though some studies have documented differences in the behavior of international banks’ cross-border and local claims across countries (see Haas and Lelyveld, 2002, for Eastern Europe, Peek and Rosengren, 2000, for Latin America, and Palmer, 2000, for U.S. bank claims around the world), the implications of the compositions of foreign bank claims have been largely overlooked in the international banking literature and still require a systematic empirical analysis.

The majority of existing papers on international or foreign banking have examined why international banks enter certain markets. An early strand of the literature on FDI by international banks focused on the experience of developed countries (especially the U.S.) with foreign bank entry and bank internationalization during the 1970s and 1980s (e.g., Goldberg and Saunders, 1980, 1981a, b; Ball and Tschoegl, 1982, Nigh et al., 1986; Cho et al., 1987; Hultman and McGee, 1989; Goldberg and Johnson, 1990; Goldberg and Grosse, 1994; Brealey and Kaplanis, 1996; Fisher and Molyneaux, 1996). More recently, several authors have examined the decision of international banks to establish operations overseas during the 1990s, especially in developing countries (e.g., Buch, 2000; Claessens et al., 2000; Focarelli and Pozzolo, 2001, 2006; Guillen and Tschoegl, 2000; Buch and DeLong, 2001; Moshirian, 2001; Galindo, Micco

> 4 Buch and Lipponer (2004) is an exception. This study recognizes that banks have a choice to lend cross-border or to do so locally in host countries via their foreign affiliates. Using data for German banks, the authors test whether these two forms of financing are complements or substitutes by running regressions
# **5. Findings**

- 5.1. What is the current status of textbook distribution in Zambia and what are the factors related to textbook availability?

## **Textbook availability in DEBS**

Before the intervention, textbooks were distributed across the districts unsystematically and unpredictably. According to the DEBS survey, 85 percent of DEBS reported receiving Grade 2 textbooks. However, after the curriculum revision in 2014, the old curriculum books were still circulated and only 63 percent of DEBS received new curriculum books that were procured in 2016 (Appendix Table A.1). The average time for books to reach DEBS offices from MoGE’s central warehouse is 13 months, but times vary significantly across DEBS. A quarter of DEBS reported receiving books within two months and another quarter of DEBS reported receiving the books after 19 months. For the case of Grade 4 textbooks that were targeted for the intervention of last-mile delivery of textbooks, distribution from the central warehouse to DEBS was organized in a way that all the textbooks were delivered to 27 selected DEBS in July/August in 2019. Based on the number of books distributed and the number of Grade 4 students in the sample districts, about 2–3 students are supposed to share the new curriculum textbooks (Ministry of General Education in Zambia 2019). In terms of the distribution practice from DEBS to schools, DEBS reported that on average 44 percent of schools had collected the Grade 2 books from DEBS on their own, compared to 55 percent for the Grade 4 textbooks.

## **Textbook availability in schools**

According to the school survey, 49 percent of schools reported receiving Grade 2 textbooks, including both new and old curriculum books delivered after the curriculum revision in 2015. Yet, only 28 percent schools received the Grade 2 books for the new curriculum. This low number is partly due to the fact that many DEBS did not receive the new curriculum books. However, even after taking into account textbook availability at DEBS, a large
deeper understanding of the issue, we use firm level census data to compare the manufacturing sectors in Ethiopia and Vietnam.

# _Ethiopia versus Vietnam: A Case of the Missing Huge Firms_

Ethiopia is the second most-populous country in Africa. Its government has pursued an aggressive industrialization strategy, which largely revolves around attracting investment in laborintensive manufacturing for export. As of 2021, Ethiopia’s strategy mirrors to a large degree the strategy pursued by Vietnam; as noted earlier, both countries have been heavily influenced by China’s use of special economic zones.

In some ways, the manufacturing sectors of Ethiopia and Vietnam look similar. In both countries, annual average employment growth between 2000 and 2017 was between 7 and 8 percent. In both countries, employment growth was driven almost entirely by the entry of new firms, not the expansion of existing firms. Employment growth in both countries is more rapid in firms with between 10 and 49 employees at 7.5 percent in Ethiopia and 12.1 percent in Vietnam. Average annual labor productivity growth in Vietnam was 7.5 percent, higher but not that much higher than the 5.2 percent registered in Ethiopia.

There are high rates of entry and exit in both countries indicating a significant degree of dynamism. Capital-labor ratios are comparable in both, yet Ethiopia is much poorer than Vietnam – a point to which we will return. In both countries, public sector firms played a significant role in the early stages of industrialization. Public-sector firms are still involved in manufacturing in both countries today but by 2017, the employment share of public-sector firms was considerably lower at around 8 percent in Ethiopia and 5 percent in Vietnam (in 2017).

There are of course some striking differences. The most notable is the sheer size of the formal manufacturing sector in Vietnam relative to Ethiopia. An 8 percent increase in formal sector manufacturing in Vietnam adds an additional 520,000 jobs while an 8 percent increase in formal sector manufacturing in Ethiopia adds around 28,000 jobs. If employment in Ethiopia
NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes.

There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014).

Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation. Some data sources (such as registration systems and population censuses) are more appropriate for estimating stocks of asylum-seekers, refugees and IDPs at a particular point in time, while other data sources (such as population tracking systems and border crossings) are more appropriate for estimating flows over a specific period. In general, there is a lack of comprehensive and up-to-date data available on all stocks and flows for a particular displacement situation (see Table 4). Consequently, data on flows might be used to estimate stocks, for example in the absence of government data, the stock of refugees in many industrialized countries is estimated by UNHCR based on 10 years of individual asylum-seeker recognition. And, especially in the case of IDPs, changes in the total population combined with some contextual analysis, may be used to deduce estimates of new internal displacement or returns. However, these approximations are flawed unless data on all other flows (births, deaths, repatriation etc.) are
studies the poverty trend from 2015 to 2017/18 and its decomposition. Section 4 quantifies the impacts of the non-standard features of the official methodology. Section 5 provides concluding comments.

# **Section 2: Description of Egypt’s official poverty lines**

In this section, we briefly present the data and methodology used to produce poverty figures for Egypt. More complete information on Egypt’s official poverty measurement methodology can be found in World Bank (2011) or CAPMAS (2019).

# **Data**

Living standards in Egypt are monitored through the Household Income, Expenditure, and Consumption Survey (HIECS), which captures income, expenditures, and consumption at the household level. These surveys are conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS).

Our analysis is based on two rounds of these surveys. First the 2015 round, which covered a representative sample of 23,976 households for the period January to December 2015.<sup>4</sup> Second, the 2017/18 round, which covered a representative sample of 24,971 households from October 2017 until September 2018.

# **Total household expenditures**

The consumption indicator that we use is annual household total expenditures. Total expenditures include all spending on consumption of goods and services, including food and beverages; clothes and footwear; housing; housing services (water, electricity, gas, and fuel); furniture and household equipment and maintenance; health care; transportation; wired and wireless communication; culture and recreation; education; restaurants and hotels; and miscellaneous goods and services. The implicit value of housing is also added.<sup>5</sup> In the remainder, we sometimes use “consumption” or “expenditure” interchangeably.

# **Official poverty lines**

The definition of the official poverty lines is relatively complex, which makes the interpretation of the poverty trend challenging. The main complexity comes from the way in which the poverty lines are computed and
Urban|77,318|0.51|0.50|
|**Panel C: Individual means**<br>||||
|Number of surveys|10,340|3.54|1.13|
|Number of years|10,340|22.01|5.00|
|Number of PODES years|10,340|7.48|1.46|
|Age of first marriage<br>|10,329|20.23|4.59|
|Age of first birth|10,337|22.13|4.52|
|Number of children|10,340|2.74|1.59|
|Years of education|10,140|7.75|4.39|
|**Panel D: District-year means (PODES years only)**||||
|Number of districts|290|||
|Public preschool density|2,559|0.24|0.35|
|Private preschool density|2,559|4.18|3.24|
|Public preschool count|2,592|10.27|14.11|
|Private preschool count|2,592|227.48|243.76|
|Child age 3-6 population|2,566|61,206|56,815|

Notes: We constructed individual-year panel of mothers aged 19-45 in at least two IFLS rounds. Panel A describes the pooled observations of mothers across all the observed years. Panel B describes the pooled observations of mothers in PODES years only. Panel C describes unique observations of individual mothers. Panel A, B, and C are constructed from IFLS 1-5. Panel D is constructed from multiple rounds of PODES and Susenas from 1990-2014, as described in Section 3. Panel D describes pooled observations of districts across PODES and Susenas years. Preschool counts are obtained from PODES and child age 3-6 population from Susenas; densities
outpatient as well as inpatient care in their RCMS schemes (19), generating incentives to providers to admit patients for ailments that could be treated more cheaply at home.

Out-of-pocket payments as a share of medical bills remain significantly higher for outpatient care compared to inpatient care in both urban and rural areas. The most recent National Health Services Survey (NHSS) (11) shows that in 2008, 33% of patients received partial reimbursement for outpatient care compared to 85% who received partial reimbursement (in the average amount of 35% of the medical bills) for inpatient care. This is confirmed by field research. Citizens’ scorecard survey shows that out-of-pocket payments as a share of medical bills remain significantly higher for outpatient care compared to inpatient care, and reach 84% of _monthly_ per capita income among patients in the poorest quintile (compared to about 11% in the richest quintile) for an average outpatient visit, and a staggering 140% of _annual_ per capita income in the poorest quintile (compared to about 10% in the richest quintile) for a single average hospitalization episode. (20)

# _Earmarking of vertical programs to promote equity may also be failing in some areas_

To implement national priorities, the central government has been circumventing the problem of resource allocation at local level by increasingly relying on vertical programs and earmarked specific purpose transfers (that are conditional, to incentivize sub-national governments, as opposed to general-purpose transfers that are unconditional to provide general budget support). As part of China’s HSR, six new public health initiatives (for hepatitis B vaccination of older children, folic acid supplementation, fluorosis prevention, cervical and breast cancer screening for rural women, cataract treatment, and rural water and sanitation initiatives) have been recently added to the hundreds of earmarked transfers already in existence in the health sector. Currently, the entire central government budget for the health sector consists of earmarked transfers (accounting for RMB 112 billion in 2009). Central government spending on health does not include any general-purpose transfers to benefit poorer
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the case of flooding or in the Tsunami, populations living close to the river bank/sea were likely very different from those further away.<sup>8</sup>

# **Data**

Our data are based on a survey conducted in 2009 of 126 randomly selected villages in four earthquake-affected districts. Two of the districts—Bagh and Muzaffarabad are in the Azad Jammu Kashmir area of Pakistan and two—Abbottabad and Mansehra are in the North Western Frontier Province, or NWFP. The data-collection was done in two parts. The first part, conducted in spring 2009, was a complete census listing of more than 28,000 households living in these villages including their GPS coordinates. As part of this data-collection exercise, we also collected information on socioeconomic characteristics of all individuals within the household and mortality from the earthquake. In addition, the census also asked households about organizations that came to help them in the 3 months after the earthquake. Following the detailed census, we randomly sampled 10 percent of the households for detailed follow-up on the impact of the earthquake and the post-quake recovery. In addition to standard modules on living conditions and socioeconomic status, we also implemented a complete module on attitudes towards foreigners. This module included questions on trust, the willingness and ability of different communities to work together and the kindness of strangers (the questions are detailed further below). We emphasize that the data-collection was conducted by surveyors local to each district surveyed, led by supervisors who have been trained by the authors over a 4-year period in the context of other survey-based work in Pakistan on education (www.leapsproject.org).

> 8 A similar argument could be made in the context of earthquakes a well—people can _choose_ to live far from a faultline. The key observation for our results is that the households we compare are never those living far from _any_ faultline to those living close to the activated fault-line. By including district fixed-effects, we compare households who vary in distance from the fault-line by 20-40Km and
||-0.056|-0.050|
||||||(0.145)|(0.144)|
|# of Observations|3642|3642|3642|3642|3642|3642|
|# of Cities|281|281|281|281|281|281|
|Adj R<sup>2</sup>|0.60|0.60|0.60|0.60|0.60|0.60|

_Notes_ : This table presents the political cycle of GDP manipulation with more measures of political connections included. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. All the specifications include city socioeconomic controls, leader controls, city FEs, and province-year FEs. Leader controls and city controls are the same as those in Table 2. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

42
2000). Bias in proxies' reports of disability: data from the National Health Interview Survey on disability. _American Journal of Public Health_ , 90(8), 1248.

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United Nations Statistics Division (UNSD). (2019). _Guidelines for Producing Statistics on Asset Ownership from a Gender Perspective._ Retrieved from: https://unstats.un.org/edge/publications/docs/Guidelines_final.pdf <mark>Zhang, L., Cheng, W., Cheng, E., & Wu, B. (2020). Does land titling improve credit access? Quasi-experimental evidence from rural China.</mark> _<mark>Applied Economics</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>(2), 227-241.</mark> 21