rafmacalaba/datause-encoder-data huggingface dataset:
Baldwin, Richard (2011), “Trade And Industrialisation After Globalisation's 2nd Unbundling: How Building And Joining A Supply Chain Are Different And Why It Matters,” NBER Working Paper No. 17716.

Borchert, Ingo, Batshur Gootiiz and Aaditya Mattoo (2012), “Guide to the Services Trade Restrictions Database”, World Bank Policy Research Working Paper No. 6108, available at http://documents.worldbank.org/curated/en/2012/06/16441094.

Borchert, Ingo, Batshur Gootiiz and Aaditya Mattoo (2013), “Policy Barriers To International Trade in Services: New Empirical Evidence”, _World Bank Economic Review_ , vol. 28, 162-188.

Deardorff, Alan V. and Robert M. Stern (2008), “Empirical Analysis of Barriers to International Services Transactions and the Consequences of Liberalization”, Chapter 5 in: Aaditya Mattoo, Robert M. Stern and Gianni Zanini (eds.), _A Handbook of International Trade in Services_ , Oxford University Press, pp. 169-220.

Findlay, Christopher and Tony Warren (ed.), (2000), Impediments to Trade in Services: Measurement and Policy Implications, London and New York: Routledge.

Fink, Carsten, and Martin Molinuevo (2008), East Asian Free Trade Agreements in Services: Key Architectural Elements, Journal of International Economic Law. 11(2), 263–311.

Hirawan, Fajar, and Wahyu Triwidodo (2012), “Examining the ASEAN MRA Implementation Process on Engineering and Architectural Services and its impact to the Professionals: Indonesian Perspective. 2012, ERIA.

Hoekman, Bernard (1996), Assessing the General Agreement on Trade in Services, in Will Martin and L. Alan Winters (eds.), The Uruguay Round and the Developing Countries, Cambridge: Cambridge University Press, pp. 88-124.

Marchetti, Juan A., and Martin Roy (eds.) (2009), Opening Markets for Trade in Services: Countries and Sectors in Bilateral and WTO Negotiations, Cambridge: Cambridge University Press.

Mattoo, Aaditya, and Carsten Fink (2004), Regional Agreements and Trade in Services: Policy Issues, _Journal of Economic Integration_ , vol. 19, 742-779.

Mattoo, Aaditya, and Pierre Sauve (2011), Services, Chapter 12 in _Preferential Trade Policies for Development_ , edited by Jean-Pierre Chauffour and Jean-Christophe Maur, World bank.

OECD (2009), “Methodology for Deriving the STRI”, paper presented at the OECD Experts Meeting on the Services Trade Restrictiveness Index (STRI), 2-3 July 2009, Paris.

OECD (2011), Services Trade Restrictiveness Index: Opening the Way for Services Trade Liberalization, available at http://www.oecd.org/trade/stri.

Tan, Alan Khee –Jin (2013), “Air Transport Integration in the ASEAN Region”. Unpublished paper, World Bank, Washington DC.

30
adjusted by household characteristics and number of children in school. Finances for the program are largely sourced from development partners, and the government contributes 5 percent of the cost, just enough to cover one district only (UNICEF, 2022). The SCTP has also been a key instrument to respond to shocks such as floods/cyclone occurrence, lean season food inadequacy and the COVID-19 pandemic. Recent reforms being implemented include the use of unified beneficiary register and associated information management system. Further, the government is piloting an electronic payment system that is posed to replace cash payments at designated outlets. Innovations to the program framework are also being sought to make it more shock and nutrition sensitive (UNICEF, 2022).

# **2.3 Food aid**

Food aid is another direct welfare transfer from government used to abate shock-induced hunger and food insecurity. The Malawi Vulnerability Assessment Committee (MVAC) estimates show that increasingly more people are vulnerable to food insecurity due to the occurrence of climatic shocks. For instance, the numbers have risen from 63,234 people in 2007/08 to 1.3 million in 2014/15 (GoM, 2015). A further increase to 6 million people in dire need of food assistance was reported in 2016/17 (GoM, 2017) which fell to 1.06 million in 2019/20 (GoM, 2019). Recent floods and dry spells have caused food insecurity for nearly a third of the population of almost 20 million people in 2022 (IPC, 2022). Apart from the cyclone Freddy that resulted in loss of crop in field, livestock, and livelihoods in 2022, the Malawi economy has been unstable. The Malawi kwacha lost half of its value against the US dollar and the associated increase in prices of basic commodities will spur further challenges to food access (FEWS NET, 2023). Therefore, the need for food aid distributed through MVAC response, public works programs, school meals, supplementary feeding or other programs is unprecedented. The government humanitarian response is coordinated by the Department of Disaster Management Affairs and typically provides cereals (maize), pulses (beans), cooking oil and nutritious food (corn soya blend). The supplementary feeding component often targets pregnant women and malnourished children at health centers.

6
Policy Research Working Paper 10297

# **Abstract**

This paper extends recent research on satellite-based carbon dioxide measurement to an easily updated template for tracking changes in carbon dioxide concentrations at local and regional scales. Using data from the National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 satellite platform and a large sample of urban areas, a comparison of trend estimation models suggests that the template can use a simple model that estimates trends directly from satellite data pre-filtered to isolate local concentration anomalies. Illustrative applications are developed for a long-period trend model and a short-period model focused on change in the most recent year. In addition, the paper estimates carbon dioxide emissions for thousands of urban areas and identifies cities whose emissions performance is above or below expectation. Although the tracking model is “simple,” it requires software and hardware that are beyond the means of many interested stakeholders. For this reason, the World Bank’s Development Economics Vice Presidency has established an open web facility that pre-filters data from the National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2satellite and publishes monthly mean concentration anomalies for all terrestrial cells of a 25-kilometer global grid. The website will also publish annual carbon dioxide tracking reports for urban areas and provide information that links the 25-kilometer global grid cell IDs to IDs for urban areas and national administrative units (levels 0, 1, and 2).

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 sdasgupta@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/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Our contributions to the debates in the literature are two-fold. Our primary contribution to this literature is to analyze the impacts of migration on left behind women but extending the usual examination of aggregate effects of migration on labor force participation to differentiate those effects by age cohort, and also by whether the migration is of husbands, children, or other household members and finally by whether the effects of return migration are symmetric with those of out-migration. In the process we also extend the documentation of recent trends in allocation to labor force activities, including work-related migration, by gender and age-cohort over time up through 2006. Most recently published analyses only take the trends up through 2000 (de Brauw et al., 2008). Some of the more recent trends are suggestive of a developing gender gap in farm work for middle aged women. We identify signs of changing patterns including potential increases in the participation of women in agricultural work.

# **3. Data**

The main data used for the analysis are from the China Health and Nutrition Survey (CHNS), conducted by the University of North Carolina at Chapel Hill.<sup>6</sup> This is a longitudinal survey that interviewed the same households in 1989, 1991, 1993, 1997, 2000, 2004, and 2006 in nine provinces: Guangxi, Guizhou, Henan, Hubei, Hunan, Jiangsu, Shandong, Liaoning and Heilongjiang. These provinces substantially differ geographically and with respect to their level of economic development. Attrition at the household level is less than 5% between waves, and some rotation of households began after 1993. The data for rural households are partly from the rural villages on the outskirts of county towns and partly from much more rural villages. We use the complete rural CHNS sample of about 3,800 households covering approximately 16,000 individuals.

The sample for each province followed a multistage, random cluster process whereby counties were stratified by income and a weighted sampling scheme was used to randomly select four in each province. Rural villages and rural suburban neighborhoods were selected randomly. The sample is made up of 36 suburban neighborhoods and 108 villages.

> 6  See <u>http://www.cpc.unc.edu/projects/china</u> for details.

7
**Table 3. Estimated global demand and supply of health workers by WHO/World Bank regions and income group, 2013; disaggregated by countries with shortage and surplus (Difference: Supply – Demand).**

|||**Shor**|**tage countrie**|<br>**s**|||**Surp **|**lus countries**|||
|---|---|---|---|---|---|---|---|---|---|---|
||**Demand(D)**|**Supply (S)**|**Need**|**Difference**<br>**(S‐D)**|**#**<br>**Countries**|**Demand(D)**|**Supply (S)**|**Need**|**Difference**<br>**(S‐D)**|**#**<br>**Countries**|
|WHO Region|||||||||||
|Africa|32,357|20,570|168,664|‐11,787|3|1,073,826|1,854,259|5,722,406|780,434|40|
|Americas|6,612,863|5,111,374|3,872,984|‐1,501,490|18|2,214,070|3,274,107|1,566,639|1,060,037|10|
|Eastern Mediterran|2,325,668|1,511,359|2,576,703|‐814,309|9|731,856|1,179,084|1,221,066|447,229|6|
|Europe|9,432,898|6,916,358|3,504,234|‐2,516,540|29|4,745,111|5,776,043|2,124,299|1,030,933|21|
|South‐East Asia|5,542,630|4,847,018|10,129,877|‐695,613|3|421,687|925,233|2,303,207|503,546|5|
|Western Pacific|13,132,605|7,142,858|10,183,203|‐5,989,747|6|2,000,685|3,151,769|1,355,351|1,151,084|15|
|WB Region|||||||||||
|East Asia and Pacific|13,132,605|7,142,858|10,183,203|‐5,989,747|6|2,349,380|3,998,780|3,509,697|1,649,400|17|
|Europe and Central Asia|9,262,072|6,818,133|3,453,923|‐2,443,939|27|4,745,111|5,776,043|2,124,299|1,030,933|21|
|Latin America|2,662,078|1,414,236|1,935,612|‐1,247,842|17|1,864,157|2,725,997|1,351,392|861,841|9|
|Middle East & North<br>Africa|1,617,766|1,177,244|1,160,165|‐440,522|9|899,235|1,393,641|1,194,530|494,406|6|
|North America|3,950,785|3,697,138|1,937,372|‐253,647|1|349,914|548,110|215,247|198,196|1|
|South Asia|6,421,358|5,279,358|11,596,725|‐1,142,000|5|72,992|78,221|148,861|5,230|3|
|Sub Saharan Africa|32,357|20,570|168,664|‐11,787|3|906,447|1,639,703|5,748,942|733,256|40|
|WB Income group|||||||||||
|Low|395,927|223,855|1,699,364|‐172,072|5|241,657|468,902|3,162,540|227,245|24|
|Lower‐middle|8,834,452|6,850,983|12,800,744|‐1,983,469|17|2,063,083|3,016,936|4,804,549|953,853|27|
|Upper‐middle|15,462,673|8,497,379|10,939,972|‐6,965,294|20|3,577,879|5,266,760|3,677,217|1,688,881|26|
|High|12,385,969|9,977,320|4,995,585|‐2,408,649|26|5,304,614|7,407,897|2,648,661|2,103,283|20|
|**World**|**37,079,021**|**25,549,537**|**30,435,665 **|**‐11,529,485**|**68**|**11,187,234**|**16,160,495**|**14,292,968**|**4,973,261**|**97**|

20
# **1 Introduction**

The impact of unemployment benefits on the quality of post-unemployment jobs is theoretically ambiguous and empirically not sufficiently researched. According to job search theory, an increase in the level of benefits raises the reservation wage at the beginning of the covered unemployment spell, thus leading to post-unemployment wage gains (and gains in stability and other aspects of newly found jobs). Another line of reasoning that also suggests that unemployment benefits increase the probability of finding a job and/or the quality of the job points to the fact that such payments increase the resources available for job search, thereby facilitating a more efficient job-matching process.<sup>1</sup> On the other hand, by introducing moral hazard, a higher level of benefits may depress job search intensity and may just prolong unemployment without improving the quality of post-unemployment jobs.<sup>2</sup> Skill obsolescence and loss of human capital resulting from longer unemployment spells may also contribute to worse post-unemployment outcomes.

There is no compelling evidence that unemployment benefits, by subsidizing job-search costs, improve job matches (Cox and Oaxaca 1990). Using U.S. data, Ehrenberg and Oaxaca (1976), Burgess and Kingston (1976), Hoelen (1977), and Barron and Mellow (1979) find a statistically significant and positive relationship between benefit levels and post-unemployment wages. Ehrenberg and Oaxaca (1976) estimate that a 10 percentage point increase in the benefit replacement rate increases post-unemployment wages by 7 percent for older men and 1.5 percent for older women. Using New Zealand data, Maani (1993) finds that a 10 percentage point increase in the benefit replacement rate is associated with a 4.5 percent increase in post-unemployment wages. Other studies show a weak or negligible effect on post-

> 1According to Klassen and Woolard (2001), the absence of unemployment benefits in South Africa affects household formation and residential choices in ways that are detrimental to jobfinding. The system forces the unemployed to base their location decisions on the availability of economic support generally available in rural areas, often in parental households rather than on the availability of job openings. Klassen and Woolard thus conclude that the absence of unemployment benefits may not only lower welfare of the unemployed and their dependents, but it may actually increase unemployment durations.

> 2Particularly credible evidence on the effects of unemployment insurance on duration of recipients spell is provided by micro-studies (for a recent summary, see Vodopivec, 2004).

3
Taking again the example of a disaster that makes capital losses equal to $500 million, in a country with a 10% interest rate, with a reconstruction period that is likely to span over 3 years, the discounted loss of consumption is $606 million, which is 21% larger than asset losses.

# **Box 2 – Indirect flood losses in Mumbai**

The ARIO model is then used to assess the output impact that would be caused by different floods, accounting for the characteristics of the Mumbai region economy. In particular, the model accounts for the financial constraints that can make it very difficult for poor households to finance the reconstruction, and can thus slow down the recovery significantly. In the model, the 2005 floods – that caused direct losses amounting to about $2 billion – led to indirect losses amounting to $425 million, i.e. 18% of direct losses (see figure). In the model, reconstruction is carried out over a 12 months period.

<!-- Start of picture text --> 4000<br>3000<br>2000 July 2005 event<br>1000<br>0<br>0 2000 4000 6000 8000<br>Direct losses (million USD)<br>Indirect losses (million USD)<br><!-- End of picture text --> _Figure. Relationship between direct losses due to an event and indirect losses (productive sectors plus housing sector), with the July 2005 event highlighted._ This number evaluated through a significant modeling exercise is broadly consistent with the rule of thumb proposed below and the fact that most of the reconstruction occurred in one year. With a marginal productivity of capital of 12.5% in Mumbai, the rule of thumb for the 2005 event would give: Of course, aggregated numbers hide the very large heterogeneity in indirect losses. As already mentioned, informal dwellers suffered from losses that were on average equal to their total savings, suggesting that their ability to invest has been strongly affected. Many of them also report forced reduced consumption, including of food. However, dwellers with stable jobs in the service sector reported only a few days of lost work.

19
variables that they have in common. Plausibly, the agricultural census is in comparison more informative of rural livelihoods.

Other poverty maps of Vietnam that have been constructed in the recent past include: Minot (2000) who combined the Vietnam Living Standard Survey (VLSS) from 1993 and the Agricultural Census from 1994 to estimate rural poverty at the province and district level; Minot _et al._ (2002) and Gian and van der Weide (2007) combined the 1998 VLSS and a 33 percent sample of the population census from 1999. Fujii and RolandHolst (2008) study the effects of Vietnam’s access to WTO on poverty. They too combine the 1998 VLSS and a 33 percent sample of the 1999 population census to estimate provincial poverty rates. Nguyen et al. (2007) attempt to bridge the three-year gap between the Vietnam Household Living Standard Survey (VHLSS) from 2002 and the 1999 population census to estimate poverty levels for 2002. Nguyen et al. (2005) and Nguyen et al. (2007) produce a district map of poverty and inequality of Ho Chi Minh City for the year 2004. Recently, most of these poverty maps, however, are out-of-date.

The paper is structured into seven sections. The second section describes data sources. The third section presents the method of small area estimation of Elbers _et al._ (2003). The poverty and inequality estimates and the models used for respectively the expenditure and income based measures are reported in sections four and five. Section six compares the estimates of expenditure based poverty to those based on income, and the poverty rate reported by the Ministry of Labour, War Invalids and Social Affairs. Finally, concluding remarks are presented in section seven.

# **II. Data**

# _II.1 Household survey and agricultural census_

The two data sources used are: The Vietnam Household Living Standard Survey (VHLSS) for 2006 and the 50 percent sample of the Rural Agriculture and Fishery Census (ARFC) for 2006. Both data sets have been collected by the General Statistic Office of Vietnam (GSO).

The VHLSS 2006 includes 9189 households (with 39071 individuals), of which 2250 are urban and 6939 rural households. The collected information on household characteristics includes: income, expenditure, employment status, education level, housing condition, fixed assets owned by household. The survey is designed to be representative at the regional level. This means that the survey is not able to guarantee consistent poverty estimates at lower levels of aggregation (such as at the province level).

3
deter imports of intermediate inputs, we would need to follow more closely the changes observed in the implementation of these policies - which we are able to do only on the implementation of the advance clearance system - or to have further information, for example on domestic intermediate inputs used so as to also exploit the choice to start importing intermediate inputs that were previously bought domestically.

First, we focus on the correlations between tariffs and Peruvian firms’ import choices. The data on nominal applied tariff rates at the transaction level was provided by SUNAT, and it is calculated for each firm as the ratio of total ad-valorem tariff duty paid over the total value imported across all of the firm’s transactions at the product-country of origin level in a given year.<sup>12</sup> We consider the effects of changes in the applied tariff rate on contemporaneous changes in firm total imports using the following specification in growth rates: where _i_ stands for an exporter-importer firm, _t_ stands for a year, ��� are total imports of intermediate inputs, �� are year fixed effects, ���� are fixed effects indicating the main HS 2-digit product imported by the firm, other variables are defined as above and ��� is an i.i.d. error. The firm fixed effects �� included allow for heterogeneity across firms in their rates of import growth. We consider also the specification in levels below: where all variables are defined as above. The results from estimating these specifications are shown in Table 6.<sup>13</sup> Appendix Table 5 shows yearly averages of applied tariffs on intermediate inputs for all sectors and for agribusiness and apparel separately.

The key pattern that emerges from Table 6 is a significant negative short-term effect of higher growth in tariffs and higher tariffs on imports of intermediate inputs by Peruvian exporterimporters when considering exporter-importers across all sectors. While the magnitude of the effects of changes in tariffs on import growth is stronger in the agribusiness sector than for all sectors combined, the negative effects in the apparel sector are not significant.

While the reduced-form specifications whose estimates are shown in Table 6 are simple, it is interesting to note that the magnitude of the responses of import flows to tariffs for Peruvian

> 12 Though tariffs are applied to imports of products at the HS 10-digit level, for these calculations products are aggregated up to the HS 6-digit level.

> 13 The main HS 2-digit fixed effects are not collinear with the firm fixed effects because firms may switch over time the main HS 2-digit sector that accounts for the largest share of their exports.

18
# TABLE 2. Monthly Cash Benefits of _Oportunidades_<sup>_a_</sup>

|Grants|Nutrition grant||15|0|
|---|---|---|---|---|
||Education grants:|Grade|Boys|Girls|
||Primary|3|100|100|
|||4|115|115|
|||5|150|150|
|||6|200|200|
||Middle school|7|290|310|
|||8|310|340|
|||9|325|375|
||High school|10|490|565|
|||11|525|600|
|||12|555|635|
|Maximum transfer|with high school children||1,5|50|
|to Household|Other households||91|5|
|Average transfer<sup>b</sup>|||35|0|

a In Mexican pesos (2002); 11 pesos is approx. US $1.<sup>b</sup> Urban households (2002). _Source:_ Administrative sources, authors’ elaboration.

28