rafmacalaba/data-use-mentions-extended huggingface dataset:
only mixed evidence. In the case of China for instance, Knight, Song and Huaibin (1999) find that migrants of rural origin hold jobs that non-migrants shun. On the other hand, Roberts (2001) mentions the issue of local workers in Shanghai being laid-off by state enterprises and competing with migrant laborers in the formal sector.

# **The effect of migration on poverty at the place of destination**

Finally, the effect of internal migration on urban areas is not limited to an impact on wages and employment. In a country such as South Africa, migration from poor rural areas is often viewed as contributing to a strengthening of residential segregation, an increase in the number of poor, and a costly burden on local public services. These issues have seldom been studied empirically, probably due to the lack of adequate micro-data.<sup>33</sup> One exception is Mohtadi (1986) who uses cross sectional data of cities and rural regions from Iranian censuses in order to assess how migration from rural areas affects urban inequality. Not surprisingly, the finding is that inequality in urban areas tends to increase with migration when migrants are from a poorer, landless group, and to decrease when they are from a better off, landed group.<sup>34</sup>

**Policy implications** -- The considerations above suggest that the inflow of migrants to cities can be problematic if not accommodated properly. Accommodationist policies cover programs designed to assist migrants upon their arrival in urban areas in order to reduce the economic and social costs of migration and to maximize its gains. The idea is to provide rural migrants with an environment that ‘allows them to make use of their energy and skills’ (Skeldon, 1997). Measures can be as diverse as the creation of reception centers for migrants, helping them access essential public goods, or helping them acquire affordable housing (such as the programs launched by the South African

> 33 There is a real need to measure the characteristics
activity in 69 countries. _Journal of Public Economics_ , 76 (3): 459–493.

Hanousek, J. and F. Palda (2003). Why People Evade Taxes in the Czech and Slovak Republics: A Tale of Twins. In B. Belev (ed.) _The Informal Economy in the EU Accession Countries:_ _Size, Scope, Trends and Challenges to the Process of EU Enlargement_ . Sofia: CSD: 139-174.

Hazans, M. (2007). Looking for the Workforce: The Elderly, Discouraged Workers, Minorities and Students in the Baltic Labour Markets. _Empirica_ , 2007, 34(4): 319-349.

Hazans, M. (2011a). Informal workers across Europe: Evidence from 30 European countries. Background paper for _In from the Shadow: Integrating Europe’s Informal Labor, a World Bank regional report on the informal sector in Central, Southern Europe and the Baltic countries_ (Task number P112988).

Hazans, M. (2011b). Determinants of Employment Status and (In)Formality: A Nested Logit Approach (forthcoming in _IZA Discussion Papers_ ).

Heckman, J., and G. Sedlacek (1985). Heterogeneity, aggregation, and market wage functions: and empirical model of self-selection in the labor market. _Journal of Political Economy_ 93 (6): 1077–1125.

Heckman, J. and C. Pagés (2004). _Law and Employment: Lessons from Latin America and the Caribbean._ The University of Chicago Press: Chicago.

Heritage Foundation (2011). The 2011 Index of Economic Freedom, http://www.heritage.org/index/.

35
8 change in this index can provide a useful "yardstick" for assessing the importance of the expansion of intra-trade.<sup>6</sup> Table 3 reports intensity ratios which were computed using United Nations Series D COMTRADE statistics for Mercosur countries' trade with each other, and with NAFTA members for selected periods from the late 1970s to 1994. The index clearly documents the increased "intensity" of trade between Mercosur members. For example, Argentina's intensity index for trade with Brazil took a value of 39 in 1994 which was more than five times its corresponding level in 1979-81 - Brazil's index for trade with Argentina more than doubles over the same period. With the exception of those involving Paraguay, where the quality of trade data is a problem, the bilateral intensity index ratios for Mercosur intra-trade were markedly higher in 1994 than in any previous period. These results strongly reinforce the impression provided by the previous comparisons (Tables 1 and 2) that show a major re-orientation of exports toward regional markets has occurred.<sup>7</sup> A key question is whether these changes are along lines that are consistent with efficiency and the true comparative advantage of member countries.

> 6The intensity of trade index **(Iij)** is defined for country i's exports to country j as the share of i's exports going to j (Xij/X,) relative to the share of j's imports (M1) in world imports (Mv). That is,

# (1)

**Ii.** = (XjjXj)/(MjNlM

Brown (1947) appears to have been the first among many to use this index five decades ago. Some analyses have netted out country i's imports from global imports in the denominator of equation (1).

7In contrast to the pattem for Mercosur, no similar results occur for trade with Canada and the United States. With one exception (Brazil's exports to the United States
Policy Research Working Paper 10774

# **Abstract**

This paper explores the evolution and resilience of global value chains (GVCs) in the agrifood sector, which intensified since the 1994 Uruguay Round Agreement. Using unique data from the FactSet database, along with Fortune 500 lists, the comprehensive analysis of approximately 17,500 agribusiness companies worldwide examines more than 150,000 supplier and customer connections from 2014 to 2022. The findings reveal that large corporations, acting as central nodes, have increased their network centrality in global value chains, particularly through geographic diversification and a concentrated supply strategy. The study also indicates that there is a correlation between the complexity and depth of firm-to-firm linkages and increased resilience, suggesting that firms with greater connectivity are less likely to exit the industry. The analysis not only contributes new insights into the structure and dynamics of agribusiness networks, but also highlights the role of firm linkages in navigating recent disruptive global events, such as the United States-China Trade War, the COVID-19 pandemic, extreme weather episodes, and geopolitical tensions.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at abeck@worldbank.org; slim@agcenter.lsu.edu; and dtaglioni@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development
Table 9. India’s urban area and urban population by global urban layer products

||Urban area<br>(percent of land area)||Urban p<br>(percent o|opulation<br>fpopulation)||
|---|---|---|---|---|---|
|||Census|GHSL|LandScan|WorldPop|
|Prediction|3.2%|29.9%|39.6%|28.8%|22.7%|
|GHSL|2.9%|-|54.7%|29.5%|23.1%|
|BEAM|1.9%|-|29.0%|26.9%|20.1%|

Combining the GHSL’s classification of urban cores with population data from LandScan yields an urbanization rate that is less than half a percentage point apart from our preferred estimate (29.9 percent). Similarly, applying the same approach to BEAM urban areas leads to an urbanization rate that is 3 percentage points lower than our preferred estimate. Therefore, when relying on the same population data the urbanization rates predicted by other recent studies are quite similar to the one we obtain with our methodology.

Discrepancies between our proposed methodology and global urban layer products become much wider when using other population data. All predicted urbanization rates decline when relying on WorldPop data and increase when using GHSL data instead. But regardless of the population data used our predicted urbanization rate falls in between the BEAM-based and GHSL-based estimates. We interpret this as further evidence that our predicted urbanization rate is not an outlier.

# **6. Conclusion**

When assessing the urban extent there is value in relying on what one “sees,” especially in countries where urbanization is messy in nature. Subjective assessments can capture the multifaceted nature of cities—relatively large spaces with a higher density of construction, better
the Brazilian economy proves to be able to sell a large number of products in many markets. This suggests a considerable potential to be explored in terms of export growth, since the initial fixed costs to start new markets have been largely overcome.<sup>3</sup> Figure 3. Product and Markets (2008–10) <!-- Start of picture text --> CHN<br>IND<br>KOR<br>THA<br>MEX BRA<br>IDN<br>ARG<br>COL VNM<br>CHL<br>PER PHL<br>URY<br>0 50 100 150<br>Number of countries<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br><!-- End of picture text --> _Source:_ World Integrated Trade Solution.

Figure 4. Markets and Products—the Herfindahl Index (2008–10 and 1998–2000) <!-- Start of picture text --> Herfindahl Index - Markets Herfindahl Index - Products (HS6)<br>ARG 0.063 0.108 ARG 0.023 0.036<br>BRA 0.060 0.082 BRA 0.013 0.035<br>CHL 0.0800.087 CHL 0.067 0.122<br>CHN 0.076 0.158 CHN 0.0050.008<br>COL 0.151 0.249 COL 0.0930.101<br>IND 0.047 0.079 IND 0.0250.029<br>PER 0.088 0.148 PER 0.043 0.061<br>RUS 0.0370.039 RUS 0.072 0.188<br>ZAF 0.0480.054 ZAF 0.0230.023<br>0 .05 .1 .15 .2 .25 0 .05 .
days of employment for each RMG member. This equates to just over four days of work per month. The RMG members were paid wages of around 60,000 kip/day (US$7.3/day), resulting in a total average income of around $550 per RMG member over the implementation period. Since this was a part-time activity, the expectation was that it provided supplementary income for participants. Indeed, the RMG payment corresponds to 40% of household income of waitlisted women. This is a meaningful increase in income: for comparison, conditional cash transfers received by the poorest decile in eight Latin American countries are in the range of 7.8% to 33.6% of household income (Amarante and Brun 2018).

# 4. Data

Baseline data was collected between September and October 2018, before any salary payments were made. Women knew whether they were selected to be a part of a road maintenance group at the time of baseline data collection and had started work. We were able to interview 333 treatment households and women, and 813 control households and women. Not all women who were selected participated in the survey (6 treated households, 34 control households). We discuss attrition from selection to baseline data collection below. The baseline data allowed us to verify that treated and control samples are balanced across many characteristics. However, there are relatively fewer Hmong-Iumien women in the treated sample at baseline, and control group households are smaller and have somewhat higher monthly income<sup>7</sup> (Table A1 8). We control for these baseline characteristics in our estimates to account for potential bias from imbalance.

Endline data collection took place in December 2019 and January 2020, 18 months after the program started and the end of the contract period. At endline, we were able to interview 323 treated households and 776 control households. There was attrition of 47 households (4 percent): 10 treated, 37 control.

At both baseline and endline, we administered
through linear regressions; all estimates weighted using population weights._

# **Source: Authors’ own calculations based on HFS 2016‐2017 data.**

The incidence of poverty is much more widespread in rural areas compared to urban areas. Rural poverty was equal to 86 percent in 2016 compared to 65 percent in urban areas (p<0.001, Figure 5). The rural poor also experience deeper poverty than urban residents, with a higher poverty gap and poverty severity. In 2016, the urban poverty gap was equal to 31 percent compared to 50 percent for the rural poverty gap (p<0.001, Figure 5). A similar pattern can be observed for poverty severity, the urban severity index was equal to 19 percent and the rural index equal to 33 percent (p<0.001). A stochastic dominance analysis based on a comparison of the cumulative consumption expenditure distribution across rural and urban areas reveals that this is not due to the chosen poverty line but that at any point along the distribution the urban consumption expenditure curve lies consistently below the rural curve (Figure 5). The isolated nature of many rural areas contributes to these observed poverty rates, given that they are often cut off from public services as well as humanitarian assistance.

Measuring inequality, the Gini index in South Sudan declined from 2009 to 2016, from about 0.47 in 2009 to 0.41 in 2016 (Figure 4).<sup>35</sup> The average Gini index for countries in Sub‐Saharan Africa is approximately 0.44, with South Sudan at 0.41 indicating slightly lower inequality but higher inequality compared to the global average Gini index of 0.38. While all households suffered consumption losses because of the conflict and macroeconomic crises, the consumption losses experienced by better off households were larger than those of the poorer households (World Bank, 2018). Thus, the driver of the reduction in inequality was not pro‐poor growth but rather a
and admin data. This requires the availability of administrative tax data, at least in tabulation form for different income groups. This type of data is not easily accessible and may best be pursued on a country-by-country basis.

The use of other forms of data to check surveys in the absence of tax data is still uncommon. Van der Weide et al. (2018) concluded that the Egyptian Income and Expenditure household survey severely under-estimated inequality using external house price data but given the number of strong assumptions required for their methods to be valid, it is possible that some of differences may be due to the inadequacies of the method and/or the administrative house price data used rather than the surveys.

Researchers finding solutions to the challenges of measuring the top of the income or wealth distributions that use admin data generally do not investigate _why_ the surveys and admin data find substantially different incomes at the top of the distribution. To improve the methods for solving the measurement challenges described above, to establish new methods and to improve the quality of 25
breaking. Furthermore, the negative net entry is a result of both more products exiting and fewer products entering, relative to before the crisis.

In Figures 11 and 12 we perform the same decomposition as before, but on a quarterly basis, using data from Q1 2007 to Q3 2009. Entry, exit, price changes, and quantity changes are now defined relative to the previous quarter, rather than the same quarter of the previous year. Hence the magnitudes of these changes will not match the magnitudes given in the other tables, but they do show the specific timing of the collapse in trade. Figures 11 and 12 plot the value of each margin across time, with the vertical axis showing values in billions of US dollars. It is clear from the upper left quadrants that, for both US and Indonesian imports, the fall in value begins in Q3 2008, as the economic crisis was beginning. Furthermore, this is true for both price and quantity effects. Figure 11 shows the dollar values for each margin for US imports and Figure 12 for Indonesian imports. For all three types of goods, value drops off beginning in Q3 2008. For differentiated goods, the fall is almost entirely composed of the quantity effect. For reference priced goods, the value change is a combination of quantity and price effects. For homogenous goods, the fall in US imports is almost entirely a price effect, whereas for Indonesian imports there are negative price and quantity effects.

# **4. Conclusion**

In this paper, we identify a new set of stylized facts on the 2008-2009 trade collapse that we hope can be used to shed light on the importance of demand and supply-side factors in explaining the fall in trade. In particular, we decompose the fall in international trade into 22
pop|38.3|55.22|45.28|54.10|49.54|6.0|
|Access to surface water|% pop|37.4|13.00|7.69|7.72|9.20|13.0|

Source: Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from <u>http://www.infrastructureafrica.org/aicd/tools/data</u> **.** Côte d’Ivoire was a pioneer for private sector participation in the water sector. SODECI’s concession contract for potable water and lease contract for sanitation dates back to 1959 and was renewed and redesigned in 1987 for a 20-year period. The 1987 contract has withstood a range of financial, economic, and political shocks. The concessionaire implemented and almost entirely self-financed the impressive gains in utility water access described above. Moreover, operational performance has been very strong. Revenue collection rates are close to 100 percent, and distribution losses are close to best practice levels, although they have risen lately from 17 percent in 2001 to closer to 23 percent in 2008 (table 9).

17
Figure 1. Conceptual Framework of the Extended Cost-Benefit Analysis

|**=**<br>**Net**<br>**Income**|**Change in**<br>**tobacco**|+<br>**Change in**<br>**medical**|**Change in years**<br>**of productive**<br>+|
|---|---|---|---|
|**Effect**|**expenditure**|**expenses**|**life lost**|
||(A)|(B)|(C)|

In addition to this more comprehensive understanding of the costs and benefits of reducing the medical burden of smoking, the ECBA methodology allows the various behavioral responses to the tax shock to be taken into account. The stylized finding in the literature suggests that households in lower-income countries and lower-income groups may be more responsive to tobacco price changes (see above). Decile-specific elasticities of demand for tobacco allow the heterogeneity in the sensitivity to price changes to be taken into account and the distributional impact of various price shock scenarios to be estimated. For further methodological details, see annex A or refer to Fuchs and Meneses (2017a).

# **4. Data Sources and Descriptive Statistics**

## **a. Household expenditures and tobacco consumption**

Data on household expenditures and tobacco consumption are taken from national household budget surveys. If available, surveys with nationally representative data for 2016 have been used. In case of data limitations, the most recent data sets have been used; a 2014 survey in South Africa is the oldest survey that has been used. See annex A on the specific sources, variable definitions, and data limitations in each country.

Table 1 summarizes the most relevant indicators for each country in the sample. Indonesia, followed by Ukraine and
Differences in educational attainment predict large gaps in WFH measures: the jobs of college graduates are 0.70 standard deviation more amenable to WFH than those of their less educated counterparts in the PIAAC sample. That figure for the MNA region is 0.61. In all three samples, workers aged 25 and older have jobs less amenable to WFH than those 24 years or younger. In PIAAC countries, the relationship between amenability to WFH and age has an inverted U-shaped pattern, where those aged 25 to 34 years have the jobs most amenable to WFH, while those younger than 25 and older than 55 are at the opposite end. Among countries in the STEP and LMPS data sets, workers in the youngest age bracket have the jobs most amenable to WFH, but there are little differences by age among those aged 25 to 65.

**Table 3. OLS regression of the WFH index (adjusted for home internet access)**

||PIAAC|STEP|LMPS|
|---|---|---|---|
|Women|0.0611|0.267|0.38|
||[0.00613]***|[0.0108]***|[0.0167]***|
|College education|0.702|0.487|0.615|
||[0.00642]***|[0.0130]***|[0.0173]***|
|25-34|0.305|-0.138|-0.0788|
||[0.0141]***|[0.0181]***|[0.0213]***|
|35-44|0.302|-0.177|-0.144|
||[0.0121]***|[0.0186]***|[0.0215]***|
), 725–53.

- Morten, M. (2019). Temporary migration and endogenous risk sharing in village India. _Journal of Political Economy 127_ (1), 1–46.

- Naqvi, A. (2024). Stata package “bumpline” (version 1.21).

- National Bureau of Statistics (NBS) (2012). Nigeria General Household Survey (GHS), Panel 2010-2011, Wave 1. Public Use Dataset. Ref: NGA ~~2~~ 010 ~~G~~ HSP-W1 ~~v~~ 04 ~~M~~ . Downloaded from `https://microdata.worldbank.org/index.php/catalog/1002` on 6 September 2019.

- National Bureau of Statistics (NBS) (2014). Nigeria General Household Survey, Panel 2012-2013, Wave 2. Public Use Dataset. Ref: NGA ~~2~~ 012 ~~G~~ HSP-W2 ~~v~~ 02 ~~M~~ . Downloaded from `https:// microdata.worldbank.org/index.php/catalog/1952` on 6 September 2019.

- National Bureau of Statistics (NBS) (2016). Nigeria General Household Survey, Panel 2015-2016, Wave 3. Public Use Dataset. Ref: NGA ~~2~~ 015 ~~G~~ HSP-W3 ~~v~~ 02 ~~M~~ . Downloaded from `https:// microdata.worldbank.org/index.php/catalog/2734` on 6 September 2019.

- National Bureau of Statistics (NBS) (2019). Nigeria General Household Survey, Panel 2018-2019, Wave 4. Public Use Dataset. Ref: NGA ~~2~~ 018 ~~G~~ HSP-W4 ~~v~~ 03 ~~M~~ . Downloaded from `https:// microdata.worldbank.org/index.php/catalog/3557` on 9 February 2024.

- National
The franchise and grant treatment coefficients reported in panel C, columns (3) and (6) of tables 3 and 4 are estimated jointly from a regression specification where the omitted category is the group randomized to receive none of the treatments. The IV coefficients reported in column (7) of both tables are estimated from two separate where we each treatment to the control regressions compare sample independently group. All regressions control for baseline household size, education level, and indicators for having given birth, having received any vocational training, or having any paid work experience prior to the baseline survey.

# **C.4 Sri Lanka Microenterprise Survey (SLMS)**

The experiment was designed to study the effect of incremental cash investment on firms’ outcomes. Small cash or in-kind grants were provided to a sample of microenterprises with less than 100,000 Sri Lankan rupees (LKR, about $1,000) in capital other _∼_ than land and buildings. The four types of grants awarded are: (i) 10,000 LKR ( $100) of equipment or inventories, (ii) 20,000 LKR in equipment/inventories, (iii) 10,000 LKR in cash, or (iv) 20,000 LKR in cash. Control firms were granted 2,500 LKR after the fifth wave, since following waves represented an extension of the survey relative to what initially agreed before baseline.

We focus on the sample of male-owned businesses as DeMel et al. (2008) and DeMel et al. (2012) find no short- or long-term effects of the intervention on female-owned enterprises.

The survey took place in three districts of Sri Lanka (Kalutara, Galle, and Matara), and 25 Grama Niladhari (GN) divisions within these districts. Of the 618 firms initially surveyed, 210 are directly affected by the December 26, 2004, Indian Ocean tsunami. These are excluded from the since the affect firms analysis recovery process might returns to The effects 10 after the of