rafmacalaba/datause-encoder-data huggingface dataset:
sources not previously covered. The combination of all these sources makes this review among the largest and most thematically comprehensive undertaken. In contrast to earlier work, the review will focus mainly on specific types of infrastructure – namely energy, digital, and transport – and specific dimensions of development impact – such as output and productivity, poverty and inequality, labor market outcomes, human capital formation, and trade. In this way, the paper seeks to develop a more nuanced understanding of the pathways through which infrastructure contributes to the development process. To avoid publication bias, the corpus of papers contains a significant number of unpublished working papers and reports. Nevertheless, about 70 percent of the papers are published in peer reviewed journals, and 80 percent include at least one academic co-author.

<mark>Following the selection of the papers, their most salient characteristics were recorded in a database, from which it is possible to obtain a descriptive overview of the literature under review. What emerges very clearly is the large and sustained increase in the volume of research addressing infrastructure and development linkages since 2005, with over 60 percent of the articles considered being published in the last decade, 2011-2021 (Figure 1). However, in terms of the historical period analyzed within these papers, two-thirds relate to data representing the period 1980-2000 (e.g., Bogart, 2009; Burgess et al, 2010; Haines and Margo, 2006), while only around one-third is based on post-2000 data (e.g., Aggarwal et al, 2018; Rodríguez-Castelán et al, 2022). About 70 percent of the studies are based on longitudinal data exceeding one decade in length.</mark> Over time, there has also been a noticeable shift from articles focusing on cross-sectoral infrastructure broadly defined, to research that examines specific types of energy, digital and transportation literature, again primarily during the last decade (Figure 1). From 2010 the literature became more oriented towards micro-economic approaches, exploiting increasingly granular data that is often spatially explicit. In particular, the availability of satellite images, maps, and georeferenced surveys allows for a much higher degree of resolution in examining infrastructure and development linkages. This has allowed the literature to examine a broader range of development impact variables, with household welfare captured through expenditure, employment status, wages, human capital formation, or poverty and inequality, and firm performance captured through output, exports, or productivity. Moreover, spatially disaggregated data allows local output to be captured at the municipality, district, or city level. Nevertheless, just under half of the studies identified are still concerned with investigating the impact of broad infrastructure aggregates on macroeconomic measures of output.

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# **3 Determinants of destination choice**

## **3.1 The extended Todaro model**

The previous analysis has highlighted at least two stylized facts, which are not in line with the basic Todaro (1969) model. First, urban welfare levels clearly dominate rural welfare levels, across the whole distribution. This is true when we look at data on those who migrate to these locations in the KHDS, or on all those residing there in the census data. In the census, for example, Table 3 shows the constructed wealth index in the rural areas going from -0.99 at the 10th percentile to 0.29 at the 90th percentile, while in cities these same percentiles span 0.33 to 2.73: even the lowest city welfare level, measured this way, outperforms that of the highest rural ones. Similarly, KHDS shows that moves to towns or cities clearly outperform not moving or moves to rural areas.

These facts are problematic for the original Todaro model, as the model requires rural wages to equate the expected city wages in equilibrium. Kanbur et al. (2019) extend the model by introducing a cost to moving for each migrant (financial, social, psychological,...). The equilibrium condition now requires that rural wages plus migration costs need to equal the expected urban wage, so that there is a range of costs that can make the Todaro model fit the stylized facts.

Second, both KHDS and the census show that the economy is not dualistic, but displays a clear gradient from villages, over towns to cities. Thus, the second extension the authors make to the Todaro model is the introduction of multiple destinations.

The model highlights the role of a number of key variables when it comes to destination choice: the cost of moving and the destination wages. The next section introduces these variables into a regression framework to assess their relative importance and their interaction with poverty, education and age.

We use distance to destination as a proxy for the cost of moving. While the use of distance has been somewhat ignored in the more recent development literature, it has a long tradition 9
The results of the IMPACT model underscore the shifting in growth in cereal and meat consumption from developed to developing countries. Annual demand for meat will increase by between 6 and 23 kilograms per person worldwide by 2050, and the absolute increase will be largest in Latin America and the Caribbean (LAC) and East and South Asia and the Pacific (ESAP), with demand doubling in Sub-Saharan Africa (SSA) (Figure 3). Demand for maize and other coarse grains for animal feed will increase global cereal demand by 553 million metric tons by 2050, which is nearly half of the total increase in demand from 2000-2050. Consequently, the IMPACT model projects large and rapid increases in livestock populations. For example, between 2000 and 2050, the global cattle population will increase from 1.5 billion to 2.6 billion, and the global goat and sheep population will increase from 1.7 billion to 2.7 billion (Table 3).

Figure 3. Per capita availability of meats, 2000 and 2050, reference run. Source: IFPRI IMPACT model simulations.

<!-- Start of picture text --> CWANA 20 13<br>SSA 11 11<br>ESAP 28 23 2000<br>2000-2050<br>NAE 83 6<br>LAC 58 19<br>0 20 40 60 80 100<br><!-- End of picture text -->

# **kg per person per year**

CWANA Central and West Asia and North Africa ESAP East and South Asia and the Pacific LAC Latin America and the Caribbean NAE North America and Europe SSA Sub-Saharan Africa 17
to society beyond wages. Ideally, the social benefits should include non-monetary benefits of education, such as improved health and nutrition practices and inter-generational well-being. Given the scant empirical evidence on the social benefits (Oreopoulos and Salvanes 2011; see also McMahon 2004), estimates of the social rate of return are typically based on observable monetary costs and labor market earnings (Jimenez and Patrinos 2008).

The returns to higher education have changed over time. The returns to an additional year of schooling tend to decline as the level of schooling rises. But the returns have declined modestly in past decades despite rising average years of schooling. In 2010, the world population aged 15 and above is estimated to have had an average of 8 years of schooling, having increased steadily from just over 5 years in 1980 (Barro and Lee 2013). While the enrollment rate in higher education grew significantly over time, the returns to higher education remain high. This suggests that global demand for skills has kept the returns to schooling high. It is thus safe to say that education is a good investment globally.

Vietnam is following the general case of increasing returns to tertiary education. Analysis of the social returns to schooling in Vietnam is limited. Using published data on costs and wage differentials by level in Nguyen (2004), we estimate the social rate of return using the “short cut” method (Psacharopoulos 1995). The social rate of return to schooling was very low in Vietnam in 1993, and just about 0 percent at the tertiary level. By 1998, social returns had risen considerably, and the highest returns were for tertiary education, at 12 percent, compared to primary and secondary, both at 5 percent.

# **Financial Implications**

For the individual and her family, the good news is that the returns to higher education remain high. Higher education is a likely good investment for people to consider. But decisions on investing should be based on full information about the costs and benefits for an individual student, as well as full information on the course of study. Enrolling in higher education will not lead to higher earnings if the student is in the wrong school or faculty or discipline based on their own interests and capabilities.

For society, better educated citizens and workers are more productive and impart social benefits. However, before committing to increases in funding, governments would be wise to plant incentives for the efficient and equitable use of funds. This might mean selective fees near the social cost of higher education. Expanding opportunities could also mean student finance options, but given the experience with traditional student loans, options to tap future earnings to finance current education (e.g., income contingent loans and human capital contracts) might be considered.

Given the high private returns to tertiary education, it is right to expect a greater private contribution. In low-income countries, returns on investment reach 22.8 percent per additional year (Education Commission 2016).

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Table 9. The NTB Coverage of Major Product Categories of African Exports to OECD Markets.

|Exporter|Al<br>Goods|1992 Valu<br>Foods<br>and<br>Feeds|e of OECD Imp<br>Agricultural<br>Materials|orts(USS millio<br>Ores and<br>Non-Ferrous<br>Metals|ns)<br>Manufactures|All<br>Goods|Share of<br>Foods<br>and<br>Feeds|OECD lmports<br>Agricultural<br>Materials|Covered by Non<br>Ores, and<br>Non-Ferrous<br>Metals|tariff Measures<br>Manufactures|
|---|---|---|---|---|---|---|---|---|---|---|
|Angola|3,684|17|1|2|192|4.7|4.9|0.0|0.0|0.0|
|<br>Benin|<br>76|22|32|-|12|1.3|3.5|0.0|0.0|_1.5_|
|Burkina Faso|53|13|35|n.a.|4|12.3|51.1|0.2|6.5|0.5|
|Burundi|74|65|3|4|2|0.1|0.1|0.0|7.6|0.1|
|Carneroon|1,577|335|346|84|44|20.8|_5.0_|0.0|0.3|17.2|
|Cape Verde|7|6|0.1|-|1|39.5|67.9|0.0|0.0|7.9|
|Central African Rep.|102|5|19|-|78|0.1|0.0|0.0|4.4|0.1|
|<br>Chad|66|1|64|-|1|0.1|1.5|0.0|0.0|0.0|
|Comoros|28|20|-|-|8|0.6|0.0|28.8|0.0|0.6|
|Congo|1,440|11|122|6|365|38.5|58.5|1.2|0.0|0.1|
|Cote D'Ivoire|2,258|1,587|364|3|266|14.1|17.9|0.2|0.0|16.7|
|Djibouti|4|-|I|-|2|5.2|87.0|0.0|5.7|3.1|
|Equatorial Guinea|39|6|29|2|2|0.1|0.2|0.0|0.0|0.1|
|<br>Ethiopia|160|92|27|-|40|1.8|1.1|0.0|0.0|7.4|
|Gabon|2,119|16|189|180|70|15.8|0.6|0.0|0.0|2.8|
|Gambia|183|49|1|-|133|0.5|0.8|0.0|0.0|0.0|
|Ghana|886|386|130|266|88|0.4|0.5|0.0|0.0|0.1|
|Guinea|526|18|5|370|132|0.3|6.2|0.0|0.0|0.0|
|Guinea Bissau|8|4|2|-|1|1.6|2.3|0.0|2.7|1.8|
|Kenya|837|591|107|11|111|3.5|4.2|1.7|0.0|0.2|
|<br>Liberia|804|2|90|41|546|0.0|0.2|0.0|0.0|0.0|
|Madagascar|340|251|13|25|50|7.6|6.3|0.0|0.0|24.2|
|<br>Malawi|393|363|10|-|18|29.6|28.0|0.0|28.3|59.3|
|Mali|117|4|52|-|61|0.4|3.4|0.1|3.8|0.1|
|Mauritania|413|206|1|201|3|1.3|0.6|0.0|0.0|2.4|
|Mauritius|1,279|408|6|1|860|61.6|86.5|5.4|0.0|47.2|
|Mozambique|147|108|21|4|13|10.3|13.2|0.0|0.0|4.4|
|<br>Niger|184|1|2|1|179|0.2|10.4|1.4|<br>10.3|0.0|
|Nigeria|11,379|200|111|17|155|14.4|0.3|0.0|13.7|2.1|
|<br>Reunion|154|147|1|1|6|83.0|89.5|0.0|0.0|2.0|
|Rwanda|64|50|8|3|2|1.1|1.1|0.0|0.0|2.1|
|Sao Tome & Principe|5|4|-|-|1|17.2|18.1|0.0|0.0|0.0|
|Senegal|362|291|17|25|21|19.1|24.6|0.0|0.0|2.8|
|Seychelles|37|34|_|_|2|91.4|94.3|0.0|8.0|0.6|

=~~~~~~~~~~~~~~~~~~~~~~~~~~.
conducted in 2019 served as the frame for the Malawi HFPS. The sixth round of the Uganda National Panel Survey (UNPS) fielded in 2019/20 served as the frame for the Uganda HFPS. Lastly, the fourth round of the General Household Survey – Panel (GHS-Panel) in 2018/19 served as the frame for the Nigeria HFPS.

The four LSMS-ISA surveys that served as the frames for the HFPS are panel surveys that follow the same households over time. To facilitate recontact and tracking in the subsequent wave of the survey, interviewers collected phone numbers of up to 4 household members and 2 non-household member reference persons. It is this contact information that enabled seamless implementation of the HFPS surveys in each country. However, the availability of contact details varies between the countries, largely due to much higher mobile phone penetration in Nigeria than in Ethiopia, Malawi, and Uganda. Figure 3.1 summarizes the LSMS-ISA samples, contact information availability, and selection process for the HFPS in each country. The share of LSMS-ISA households with phone numbers (i.e. the coverage rate in Figure 3.1) ranges from 73 percent in Malawi and Uganda up to 99 percent in Nigeria. This at least partially reflects the much higher mobile phone penetration in Nigeria compared with the other three countries.<sup>7</sup> Figure 3.2 presents estimates of phone ownership in the four countries. Nigeria has the highest share of households who own a mobile phone at 76 percent, followed by Uganda (69 percent), Malawi (59 percent), and Ethiopia (48 percent). In all four countries, the LSMS-ISA sample households with contact information (row B in Figure 3.1) served as the frame for the HFPS.

**Figure 3.1 Survey sample and selection process for HFPS**

|Survey Sample and<br>Selection Processs|Ethiopia|Malawi|Nigeria|Uganda|
|---|---|---|---|---|
|F2F Survey Sample|ESS 2018/19|IHPS 2019|GHS 2018/19|UNPS 2019/20|
|Size (A)|6,770 Households|3,181 Households|4,976 households|3,308 households|
|With phone numbers<br>(B)|5,372|2,337|4,934|2,421|
|**Coverage Rate**<br>**(B/A)**|**79%**|**73%**|**99%**|**73%**|
|Initial HFPS Sample<br>(C)|5,372|2,337|3,000|2,421|
|Reached<br>(D)|3,350|1,743|2,057|2,274|
|Interviewed<br>(E)|3,249|1,729|1,950|2,259|
|**Response Rate**<sup>6</sup><br>**(E/C)**|**60%**|**74%**|**65%**|**93%**|

> 7 Included in the share of the LSMS-ISA sample with phone numbers are households that did not provide a phone number of a household member but did provide the phone number of at least one reference person. As a result, the share with phone numbers does not necessarily reflect whether the household has a phone, but whether they provided any phone number of a member or reference person.

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- 13 -

planning started in the late sixties following analysis of the 1961 population census. In its efforts to support and promote family planning the government gave the Family Planning Association an annual grant, allowed contraceptives to be available through Ministry of Health Facilities and allowed non-medical personnel to initiate and resupply family planning clients with oral pills and condoms. However, before Zimbabwe's independence in 1980, family planning was viewed with great suspicion by the Black majority and thus the progrv ime's effectiveness was rather limited to the urban few.

After independence in 1980, a new era was begun. The new government took over the Family Planning Association and changed its outlook completely. Through government and international donor support, the family planning programme was restructured and expanded. Personnel to provide family planning services were more than doubled in the CBD and Youth Advisory Service Units, more service delivery points were set up particularly in the rural areas and new units, the I.E.C. and Evaluation and Research Units were established. Through a World Bank assisted project (with grant funding from Norway and Denmark), the Ministry of Health has also started to strengthen its family planning provision capabilities.

All these efforts have led to an increase in the contraceptive prevalence rate from an estimate 14 percent in 1982 to 43 percent in 1988. However, it is now apparent that the programme's growth is beginning to stall. Much more effort and resources are going to be required if the programme is to grow or even maintain its present status. Future challenges and opportunities will have to include: designing new innovative strategies to reach the hard to reach population, more emphasis on EEC particularly for males and youths using multimedia approach, involvement of other sectors in family planning information and service delivery, broadening the contraceptive method mix particularly the promotion of long term and permanent methods of contraception, alternative family planning delivery systems such as the use of depot holders, volunteers and government extension workers, and the establishment of a National Population Policy. Cost recovery and other self sustainability measures will also be considered for programnme growth. Zimbabwe stili remains committed to making family planning services readily accessible to all the people throughout the country.

# 6. BIBLIOGRAPHY

- a. Government of Zimbabwe (GOZ)/UNICEF, July 1985, Children and Women in Zimbabwe- A Situation Analysis.

- b. GOZ, 1975, Drugs and Allied Substances Act

- c. GOZ, 1984, Manpower Inmormation Services: Women and the Economy.

- d. GOZ, 1985. The Zimbabwe National Family Planning Council Act No.1.

- e. ZNFPC/Westinghouse Public Allied Systems, June 1985, Zimbabwe Reproductive Health Survey 1984

- f. GOZ, 1985, Labour Relations Act No. 16.

- g. Central Statistical Office,December 1989, Zimbabwe Demographic and Health Survey 1988.
(Agwu and Okhimamhe, 2009). Women are also often excluded from decision-making processes inside and outside their homes, including farming decisions and participation in local community organizations and politics (Ogunlela and Mukhtar, 2009; Nagarajan, 2017; UN Women 2020).

The Women, Peace, and Security Index by the Georgetown Institute for Women, Peace and Security ranks countries according to 11 indicators in three different categories, namely inclusion, justice and security. Nigeria as a whole is ranked as number 145 of 167 countries (GIWPS, 2019). A pilot study to produce the index at the sub-national level shows that the extent of these issues also varies significantly within Nigeria, with the three states Adamawa, Borno and Yobe scoring worst.

Demographic and Health Survey data suggest that 30% of women aged 15-49 in North-east Nigeria have experienced physical violence and 35% state that a husband is justified in beating his wife (NPC and ICF, 2014). In addition, a recent World Bank study on Gender Based Violence (GBV) in Nigeria (World Bank, 2019) emphasizes that gender norms in North-east Nigeria are a key risk factor for Intimate Partner Violence (IPV). Such norms include, for example, the presumed social superiority of men, the belief that paying a bride price is equivalent to purchasing a wife, and men’s rights to ‘correct’ their wives’ behavior. In addition, only 23% of female survivors of GBV in Northeast Nigeria seek help.

# **3 Methodology**

# **3.1 Intersectionality approach**

To grasp the complex role of gender, forced displacement and violence for coping behaviors in conflict crises, we take an ‘intersectional’ perspective (Bastia, 2014). Such an approach aims to explore different forms and sources of disadvantage and emphasizes their interconnectedness and interdependencies, rather than approaching them as separate domains (Crenshaw, 1990; Valentine, 2007). Specifically, intersectionality seeks to understand outcomes by exploring the relationships and interactions between different factors and across the individual and group levels (Kapilashrami and Hankivsky, 2018).

Intersectional approaches are increasingly applied in development research and have been shown to improve analyses of labor market outcomes in Sub-Saharan Africa (Elu and Loubert, 2013; Grünenfelder and Schurr, 2015). From a gender perspective, it can help to uncover the interlinkages between the multiple sources of gender inequalities and “focus on the experiences of those who have been excluded thus far from feminist analysis” (Bastia, 2014).

We thus aim to analyze how different forms of disadvantage stemming from gender, forced displacement and violence outcomes (the ‘factors’) in conflict settings shape coping behaviors at the household, adult, and child levels (across ‘levels’).

# **3.2 Key indicators**

We study three main outcomes: harmful food coping at the household level, economic activity at the adult level (age 15 or over), and school attendance at the child level (ages 6-14).

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“formal” and “informal wage” sectors respectively.<sup>24</sup> The remaining 32.6 million workers (69.0 percent) were self-employed. In 2004, Vietnam’s workers on average had attained 7.7 years of education, and worked 34.8 hours per week,<sup>25</sup> and its wage workers earned .94 million dong per month. Formal sector workers worked longer hours (41.8 hours on average) than those who worked in the informal sectors (35.1 hours and 33.0 hours for those in the informal wage and self-employment sectors respectively). On average, workers in the formal sector were more educated with average total education of 11.1 years, whereas the corresponding figures were lower for the informal wage sector (6.6 years) and self-employed workers (7.1 years). In 2002, applying the sampling weight in the VHLSS 2002, only 29.6 percent of total workers had wage jobs: the increase in the proportion of wage workers in total workers is attributable to the rise in the proportion of workers employed in the formal sector from 14.2 percent in 2002 to 16.6 percent in 2004.

One limitation of using the VHLSS is that self-employed individuals cannot be included as they have no wage information. Whereas it is possible to calculate income at the household level, it turns out to be difficult to attribute household income to individual household members. However, as the VHLSS includes wage information for informal wage workers, this enables the researcher to extend the sample coverage beyond the formal sector. The data are also limited in that they are likely to exclude a substantial number of migrants who hold a temporary or no

> 24 The classification of employment is based on the response to a question “for whom has [name] worked?” in the VHLSS 2004: those who responded to work in “foreign-invested sector”, “state”, “collective economic sector” and “private economic sector” are classified as “formal”; “informal wage” workers are assumed to be represented by those who responded to “work for other households”; “self-employed” includes those who answered “selfemployment” and “working in his family owned company”. For 2002, based on the VHLSS 2002, those who responded to work for “government agencies, police, military”, “communist party, social organization”, “state owned enterprises”, “other state economic sector”, “collective economic sector”, “private capital economic sector”, “state capitalist economic sector” and “foreign shared enterprise” are referred to “formal”; “informal wage” and “self-employed” are represented those who responded to be ”self-employed” and “work for other households” respectively.

> 25 The hours worked per week are computed by combining the hours worked for the first and second jobs.

15