rafmacalaba/data-use-mentions-extended huggingface dataset:
in Jordan<sup>50</sup> . Furthermore, the economic transformation that occurred in the past decade in Jordan seems to have accentuated the role of rent in increasing household welfare as opposed to labor income. Indeed Tables 8 and 9 suggest that receipt of rent and income from capital assets has contributed positively to the increase in household consumption in both 2002 and 2010, with the

> 47 Access to services controls for the supply side effects and their implications on household welfare. It should be noted that services such as electricity and water are very much accessible by all Jordanians and were therefore omitted from the empirical model.

> 48 Sectors were grouped as to match the ones presented in the official Jordanian National Accounts.

> 49 A list of the above covariates along with summary statistics on the differences in means between 2002 and 2010 can be found in Table 7 (refer to Annex).

> 50 Reflects the results found earlier on the dominance of the between-group component for reducing inequality.

24
initiation of commercial oil production in 2011, together with a loose (and possibly election-driven) fiscal policy stance. The bust thereafter saw growth declining four consecutive years, down to 1.6 percent in 2015, and reflected a combination of declining commodity prices, energy rationing (partly due to reduced hydropower output in face of drought conditions), and a large fiscal crisis in 2013. Avoiding such boom-and-bust cycles is an important goal of stabilization policies, since such volatilities can themselves have negative impacts on long-term output.<sup>13</sup>

**Figure 6: Cyclical and trend GDP p.c.**

<!-- Start of picture text --> A. Real GDP p.c. against its trend potential B. Cyclical component of real p.a. GDP<br>1960 1970 1980 1990 2000 2010<br>year<br> HP 'trend' ln GDP p.c. ln GDP p.c. 1960 1970 1980 1990 2000 2010<br>year<br>.2<br>8.2<br>8 .1<br>7.8 0<br>7.6<br>-.1<br>7.4 lngdppc cyclical component from hp filter<br>-.2<br><!-- End of picture text --> Source: own calculation based on annual data from PWT9.0. The smooth blue line in the left panel is an estimate of potential output (measured as log GDP p.c.) with cyclical fluctuations (red line) around it. The latter component is separately displayed in the right panel. A positive value here indicates that output is ‘above potential’.

13 There is a vast literature addressing this relationship, staring with the seminal paper by Ramey and Ramey (1995).

14
should be interpreted as a push factor or as a pull factor. On the other hand, the bilateral migration data included in Poprawe (2015) lack the time dimension, with information on 230 countries only for the year 2000.<sup>4</sup> Although this paper improves on the existing literature by having access to a panel dataset, it may potentially still suffer from reverse causality. Because of this, the analysis remains about correlation, and all the conclusions of this paper should not be interpreted as causal statements.

Lastly, by controlling for many drivers besides corruption levels, this paper speaks to the large body of research interested in quantifying the role of the different determinants of migration flows. A key driver of migration found in the literature is the presence of networks of previous migrants of the same nationality in the destination country, with cultural distance affecting migration decisions (Clark et al. 2007, Bauernschuster et al. 2014, Falck et al. 2012, Falck et al. 2018, Krieger et al. 2018). Especially relevant for low-skill migration, these diasporas explain the majority of variation in migration flows (Beine et al. 2011). By sharing a common language and culture, the economic and psychological costs associated with migration are greatly reduced. Migrant networks provide an essential service to newer waves of migrants during their process of job search, improving labor market outcomes in the destination country (Edin et al. 2003, Munshi 2003). From the perspective of the destination country, good education and health systems tend to attract migrants (Geis et al. 2013). This is in contrast with the significantly less clear role of social safety nets, which only seem to be salient for migrants from the least developed countries (Pedersen et al. 2008). From the perspective of sending regions, local amenities (e.g., security and public services) play an important role in shaping the intentions to emigrate (Dustmann and Okatenko 2014).

The remainder of the paper is
processing approach. This approach relies on the use of water and machinery such as cherry hoppers, which sort the coffee cherries based on quality, after which the sorted cherries are pulped using coffee pulping machines. After pulping, the cherries are further sorted by immersion in water for several hours (Macchiavello and Morjaria, 2021).

Coffee processed at these stations attracts a higher market premium than that processed through traditional methods by farmers. Estimates suggest that the prices of wet-processed coffee via the mills are 40% higher than that of dry-processed coffee (Macchiavello and Morjaria, 2015). This suggests that the introduction of coffee mills induced significant income shocks to coffee farmers in the country. In addition, the introduction of the mills also led to an increase in employment opportunities through wage employment, as labor<sup>1</sup> was hired to work in these mills (Sanin, 2021).

Thus, the extent to which the local economic shocks associated with the introduction of these coffee mills affect the cognitive development of children exposed to these shocks in early life is the focus of this paper. To this end, we combine unique administrative data on test scores of the universe of primary school students in Rwanda with data on the staggered rollout of the coffee mills to estimate the effects of early life exposure to the coffee mills on students’ performance in a high-stakes national exam.

To causally identify the impact of these shocks on students’ performance in the medium term, we leverage the staggered variations in the rollout of the coffee mills across various locations relative to the timing of birth. Using a 2.5km radius from a coffee mill as the catchment area for coffee mills, we measure early life exposure to coffee mills as children born in a catchment area after the opening of the coffee mill. Essentially, our identification strategy is a difference-in-difference design whereby we compare the educational outcomes of children in a catchment area born after the opening of a mill with those in the catchment area but born before the opening of a mill
2

# **1. Introduction**

Between 2005 and 2008, driven by rapidly rising demand during a global economic expansion, the world prices of many staple food commodities rose substantially.<sup>2</sup> Rice prices rose by 25 percent, wheat prices by 70 percent, maize prices by 80 percent, and dairy prices by 90 percent (Ivanic and Martin, 2008). Price increases of this magnitude for basic foodstuffs, over a relatively short period, led to widespread concern about possible impacts on hunger and deprivation. A number of governments, including those of India and Vietnam, resorted to export restrictions in order to guarantee domestic supply, while international organizations fretted about possible reversals and delays in meeting the first Millennium Development Goal, related to the eradication of hunger and extreme poverty.<sup>3</sup> After falling during the “Great Recession” of 2008-2009, international food prices resumed their upward trend in mid-2009. The increase accelerated in late 2010, with the World Bank food price index rising by 15% between October 2010 and January 2011, to a level just 3% below its previous (2008) peak. Some international agencies began to describe the prevalence of higher average levels _and_ volatility for world food prices as the “new normal” (World Bank, 2011).

But it has also been argued that the present concern with the effect of higher food prices on household well-being may be fundamentally misguided, because it tends to ignore or underplay income gains to farmers or farm workers, who are numerous among the poor in many developing countries. As Swinnen (2010) notes, this represents a striking reversal in the mainstream opinion among development economists: “Only a few years ago the widely shared view was that low food prices were a curse to developing countries and the poor. However, since 2008 the vast majority of analyses and reports state that high food prices have a devastating effect on developing countries and the world‟s poor” (p.30)
. Most of these are developed countries, where efficiency may be pushed beyond the level assumed in the Chilean model.<sup>24</sup> Where this is the case, subsidies are assumed to be zero.

# **3.10 Estimating the subsidies for countries not in IBNET**

To extrapolate subsidy estimates to countries not included in the database, we first separate out China and India, while the remaining countries are grouped into four clusters—high income, upper middle income, lower middle income, and low income—based upon the World Bank’s country classifications by income for fiscal year 2019. Next, for water and sanitation separately, we calculate an average subsidy per person served for countries with representation in IBNET, disaggregated into the four clusters (see Appendix B). Then, for countries not in the IBNET database, we multiply the per person subsidy for its cluster by the total population served by the respective service (estimated by multiplying the country’s coverage rate<sup>25</sup> and its total population). Since the main drivers for these estimates are the unit cost in the asset base calculations, the results presented in the report assume a +/-10 percent variation in the unit asset base estimates.

> 22 China and India were not extrapolated due to low proportional representation in IBNET and a general lack of data availability.

> 23 Water and/or sanitation coverage data for 6 countries (Bahrain, Fiji, Indonesia, Kosovo, Kuwait, and Solomon Islands) were incomplete, and were thus supplemented by additional data and estimates. Refer to Appendix B for details.

> 24 See Appendix C for a list of all countries with “negative” subsidies.

> 25 Water and/or sanitation coverage data from the World Health Organization/United Nations Children’s Fund for 3 countries (Austria, Isle of Man, and Micronesia), in addition to the 6 previously cited with partial IBNET data, were incomplete, and were thus
corresponding import record. Conversely, the reported imports of both the intermediate countries (excluding the first one) and the destination will also lack a corresponding mirror. Carrère and Grigoriou (2015) estimated that as much as 23% of recorded trade flows on the aggregate level do not have a corresponding mirror. However, the relative effect of re-export reporting on partner misattribution in mirror data remains difficult to assess. Although the rising role of re-exporting in merchandise trade and its effect on reported discrepancies has been documented for the largest re-exporting hubs - Hong Kong SAR, China; Singapore; and the Netherlands (Ferrantino & Zhi Wang, 2008; Fortanier, 2016; Jones et al., 2020), there is no comprehensive methodology that considers re-exporting on a global scale. The challenge of tracking re-exports and goods undergoing processing is further complicated by the non-uniform application of re-export reporting guidelines across countries, with 118 reporters having never reported re-exports to the UN Comtrade system (Jones et al., 2020).

Therefore, structural constraints, such as differences in valuation methods, result in many inconsistencies in reported trade. However, it simultaneously suggests that the magnitudes of many observed discrepancies cannot be attributed to logistic constraints alone. Therefore, a growing number of studies have turned to country and bilateral characteristics to explain the recorded gap in reporting.

Besides logistic reasons, discrepancies in international trade reporting can also be non-logistic, i.e., they are often a deliberate misevaluation or misclassification for evading import tariffs (Cantens, 2015 and Demir and Javorcik, 2020) and export taxes (Chalendard et al., 2020), minimizing corporate profit and/or turnover tax, and other non-tariff measures (Kee and Nicita, 2022). The misclassification and misinvoicing of products have been shown to be substantial (Carrère & Grigoriou, 2015), with 77% of all bilateral trade flows reported by both partners at the aggregate level and only 47% of bilateral trade flows reported by both partners at the six-digit level. Larger discrepancies in disaggregated products
4.3|5.1|0.16|6%|68%|79%|16%|
|Singapore|4.2|3.9|-0.06|-2%|60%|57%|-5%|
|Taiwan(China)|5.5|6.0|0.11|3%|82%|91%|12%|

# **_How is health spending financed?_**

The five health systems vary in how they raise the revenues used to finance health care (Figure 3). At one extreme is Hong Kong (China), which finances just over half of its health spending through general tax and non-tax revenues. At the other extreme is Singapore, which finances as much as two thirds of its health spending through out-of-pocket payments. In the middle are Japan, Korea and Taiwan (China), all of which rely on social insurance for half (or nearly half in the case of Korea) of their health spending. Noteworthy is the fact that out-ofpocket payments account for over 30% of health expenditures in all systems, with the exception of Japan, and that private insurance accounts for a fairly small share in Hong Kong (China) and Taiwan (China), and a negligible share in the other three systems.

* The predicted figures are from the same regression as used in Figure 2. Data for 1998 are from the same sources as in Figure 2.
# 1 Introduction

## _1.1 The importance of transport for export competitiveness_

Exports are critical to a country’s development. Exports allow firms to access a larger market to exploit economies of scale; contribute to growth and employment; and generate foreign exchange needed to finance imports. The importance of exports to a country’s growth trajectory means that export competitiveness is often at the center of policy discussions.

In recent years the focus to support trade growth has moved beyond trade policy and market access to embrace “behind‐the‐border” issues as many countries have been unable to compete in global markets despite greater (often preferential) market access. This shift recognizes that a firm’s ability to compete in international markets is the combination of a complex set of demand and supply‐side issues, including macroeconomic policies, factor conditions, infrastructure and related services, transport and logistics, and coordination failures (World Bank, 2012). The transport and logistics sector ‐ which underpins a firm’s ability to access cheap inputs, and to land products in foreign countries at a competitive price – is particularly important given the rise of regional and global value chains. These value chains offer significant growth potential for developing countries, particularly in the context of rising Chinese wages. Sub‐Saharan Africa is expected to be the main beneficiary of up to 85 million manufacturing jobs which may migrate from China in the next generation (Lin, 2011). Exploiting this opportunity depends on the ability to integrate into global value chains. Surveys of developing country suppliers indicate that transportation costs are the single biggest obstacle to entering, establishing or moving up global value chains (OECD‐WTO, 2013).

Developing countries face large trade costs.<sup>3</sup> In 2009, trade costs for low income countries were on average 2.5 times higher than those in high income countries. Although trade costs have been falling globally, the rate of change has been slower in developing countries, which are also starting
), have important side effects that make other economic institutions more efficient. Norton (1992), using pooled time-series, cross-national data from 47 countries from post-World War II to 1977, finds that telecommunications networks not only increase investment, but also increase the rate of economic growth. Aschauer (1989, 1990) finds that road building increased economic growth in the United States.

I This paper is the product of the research carried out by Susan Randolph, Zeljko Bogetic, and Dennis Heffley under the World Bank research project RPO-677-66 'Enhancing Urban Productivity: Determinants of Optimal Expenditure on Infrastructure, Human Resources and Public Consumption Goods". It is the product of the Country Operations Division, Department One, Europe and Central Asia Region of the World **Bank.** In the course of this research many World Bank economists and researchers provided invaluable advice, input and data for this project. Particularly, we want to thank Shantayannan Devarajan, William Easterly, Gregory Ingram, Marianne Fay, and Kyu Sik Lee for valuable discussions and for sharing several important draft papers on the subject. In addition, we would like to thank Matthew Baker, Fareed Hassan, Stephen Onyiwu, Sergey Rumyantsev, Fadel-Rahman Sesay and Hemsanta Kum Shrestha for their contributions to data input and table preparation. The authors are solely responsible for the final product.
succeeded in developing undernutrition models for SAEs of undernutrition with R-squares as high as 0.6 and 0.7 [16]. However, some express concern for overfitting with R-squares above 0.35 [21]. The full regional regression models are enclosed in appendix Table A2.

As a check on model accuracy, Table 1 shows the model-imputed undernutrition rates and those measured in EMDHS at regional level. The table shows that measured undernutrition rates in EMDHS and the estimated rates from census are similar, and none of the estimates are outside the 95 percent confidence interval of the measured levels.

**Table 1. Measured and imputed undernutrition rates, 2014**

||Region|Imputed<br>undernutrition rate|Survey<br>undernutrition rate|95 % confidence<br>surveyesti|interval for<br>mates|Observations<br>in survey|
|---|---|---|---|---|---|---|
||Tigray|0.440|0.459|0.380|0.537|435|
||Afar|0.404|0.465|0.396|0.533|565|
||Amhara|0.413|0.432|0.376|0.488|518|
|Stun|Oromia|0.410|0.385|0.334|0.435|635|
|ting|Somali|0.420|0.360|0.292|0.427|565|
||B. Gumuz|0.412|0.406|0.353|0.459|426|
||SNNP|0.421|0.443|0.391|0.496|673|
||Gambela|0.275|0.219|0.149|0.288|403|
||Tigray|0.351|0
that are dampening firms' multinational production as trade costs increase, such as imperfect transmission of technology between parents and affiliates either because of imperfect codifiability as discussed in Keller and Yeaple (2013) or because of higher frictions in the match between firms and workers.

Addressing the long-standing issue that FDI data are not systematically available across countries and over time, Ramondo, Rodriguez-Clare and Tintelnot (2015) present a comprehensive data set on the bilateral activity of multinational firms using UNCTAD data and an extrapolation procedure, with focus on two variables: affiliate revenues and the number of affiliates across country pairs. Among the various stylized facts, the analysis shows that the effects of distance on multinational production shares are similar to the ones found for trade shares (close to -1) and the extensive margin of multinational production is much more elastic to distance than the intensive margin.

While most empirical studies have examined FDI as bilateral relationships, an emerging literature accounts for the multi-country spatial interdependence of FDI flows predicted in Yeaple (2003b) and Ekholm, Forslid and Markusen (2007) as most multinational firms today employ complex integration strategies and operate multilateral production networks. Head and Mayer (2004) examine hypotheses of export-platform FDI and show that a country's market potential, measured by the distance-weighted sum of domestic and export market size, plays a significant role in countries' ability to attract multinational firms. The results show that Japanese multinationals are more likely to locate in regions proximate to large markets, suggesting that geographic proximity between host and third countries could also influence the investment decisions of multinational firms, especially those seeking to engage in export-platform FDI.

Spatial interdependence across FDI flows is also shown in Baltagi, Egger and Pfaffermayr (2007), Blonigen et al. (2007, 2008), and Chen (2011). Baltagi, Egger and Pfaffermayr (2007) estimate a 5
>|-0.06<br>|
|ama<br>Zimbabwe|-0.91<br>1.87|-0.57<br>15.55|-0.03<br>0.06|-0.02<br>0.47|

I/Based on price changes in Scenario II by Goldin, et **al.** (1995). 2/Based on **pnice** changes in scenario IV by Goldin, et al. _(1995)._ Source of basic data: FAO Comxmdity Balance Database.

27
the percentage of male household heads with wage employment increased by approximately 8 percentage points. The initial decline in the percentage of men working as self-employed or in family business can be understood as men being better able to switch to wage labor as mobility restrictions affected the possibility of running businesses. However, men’s self-employment did not recover in the last months of 2020 and beginning of 2021, when restrictions were eased. Instead, men continued going back to work in wage employment rather than resuming their self-employed work.

**Women saw a disruption in employment patterns in the first year of the pandemic, with a return to early pandemic work types after a year into the crisis** . For women who were selfemployed or worked in a family business prior to March, the estimated probability of working in a self-employed or family business initially increased by 5 percentage points (Figure 2, Panel B). This may reflect a faster loss in wage employment or it may be a response to an increased demand for home production, forcing women’s market work into the home via household enterprises. This is consistent with the estimated decline in the share of wage employees from 30 percent down to approximately 22 percent (Figure 2, Panel C). The trend reversed in November 2020 and by April 2021, the female share in each type of employment was at a similar level as the beginning of the pandemic (April 2020).

> 36 According to estimates using the first wave of the HFPS for the SSA region, the rate of work stoppage with respect to the prepandemic period was 26 percent for women and 23 percent for men (Kugler et al., 2020).

24
# **Share of sectoral exports in total merchandise exports: several generations of flying geese**

**Figures 2.6a Shares of Textile exports in total merchandise exports:  Five generations: Japan, Korea,** **<u>China, Indonesia and Vietnam.</u>**

<!-- Start of picture text --> 0.05<br>Korea China<br>0.04 China<br>Hkong<br>0.03<br>Japan Indonesia Indonesia<br>0.02<br>Vietnam Japan<br>0.01 Thailand<br>0 Korea<br>Singapore<br>Figure2.6b Share of Clothing exports in total merchandise exports, two generations<br>0.4<br>cloth_export_chn<br>0.3 Turkey cloth_export_hkg<br>0.2 cloth_export_ind<br>cloth_export_idn<br>0.1<br>cloth_export_tha<br>0<br>cloth_export_tur<br>cloth_export_kor<br>Figure 2.6c Shares of Footwear exports in total merchandise exports, three generations<br>0.08<br>footwear_export_chn<br>0.06 Korea<br>footwear_export_hkg<br>China<br>0.04<br>footwear_export_idn<br>IDN<br>0.02<br>HongKong footwear_export_jpn<br>0 footwear_export_tha<br>footwear_export_kor<br>Figure 2.6d Shares of toys exports in total merchandise exports, two generations<br>0.08<br>HongKong toy_export_hkg<br>0.06<br>toy_export_idn<br>0.04<br>toy_export_jpn<br>China<br>0.02 toy_export_tha<br>Japan<br>0 toy_export_kor<br>toy_export_sgp<br>Source: calculated based on COMTRADE data, SITC Rev 1, 3-4 digits, extracted from WITS .<br>1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995