rafmacalaba/data-use-mentions-extended huggingface dataset:
census has defined additional urban units, such as “census towns”, administrative units formally classified as villages but which satisfy various criteria common to urban settlements; and “urban agglomerations” (UA), typically an urban area that consists of a statutory town and its adjoining outgrowths. While the concepts of census town and UAs try to capture the urbanization process taking place outside the administrative boundaries of statutory towns, it is widely agreed that these understate the extent of urban growth. Nighttime lights lend strong support to this. Panel (a) of Figure 1 plots three versions of Chennai: the statutory town (dashed red boundary), the UA (dashed black boundary), and the natural city (the shaded area). Further, towns in the vicinity are also plotted. The figure serves to show that urban spread can well exceed the administrative boundaries of officially defined cities and encompass many settlements—towns and villages— which are otherwise treated as wholly sperate entities in official statistics.

China presents a different case. In the literature and policy documents, prefectures (nearly 300 in total) and four municipalities directly under the central government are considered administrative cities in China (e.g., Chauvin, Glaeser, and Tobio, 2017). However, there are vast suburban and rural areas in each prefecture. In the official City Statistical Yearbooks, data are reported for both the entire prefecture as well as a core area, called the “city proper”. The latter is often used in studies of Chinese cities (e.g., Anderson and Ge, 2005; Au and Henderson, 2006; Fang, Li and Song, 2017). Panel (b) of Figure 1 shows that Dalian prefecture, known for its seaport and role as an economic center in northern China, contains four natural cities with one located in the city proper and three outside. As the case of Dailan prefecture shows, a focus on the city proper can simultaneously lead to the inclusion of large amounts of rural areas
# _Calibrating the Model to Thai Data_

According to the Bank of Thailand, total government domestic debt not held by the central bank at the end of December 1996, was roughly 41.7 billion baht or 0.9 percent of GDP. We set bo = 0.009.17 The Bank of Thailand estimated that the foreign debt of the public sector in December 1996 was roughly equal to 430.2 billion baht. According to the IFS, net foreign assets of the Thai central bank equaled 988.6 billion baht in December 1996. This suggests that the level of net foreign assets of the consolidated public sector was equal to 558.4 billion baht or _-_ 12.1 percent of 1996 GDP. Thus, we set bo fo = -0.112. Using data from the Bank of Thailand we estimate that net foreign debt of the private sector at the end of 1996 was roughly 38.4 percent of GDP, so we set do = -0.384.'1 In Section 6 we argue that a rough estimate of the fiscal cost of the banking bailout to be 30 percent of GDP, so we set 0 = 0.3. The parameter I was chosen to match the decline in consolidated government assets that occurred in the period leading up to the Thai crisis in July 1997. We estimate that during this period (until the end of June 1997), net assets of the consolidated public sector fell to 7.5 percent of GDP. We set 1 = -0.075.

According to the IMF World Economic Outlook, the ratio of government revenue to GDP in Thailand was 18.9 percent in 1996. As in the Korean case, we subtracted an additional 26.5 percent from output to ensure that the consumption to output ratio in the model equaled the 1996 value of that ratio in the data, roughly 0.5.

"<sup>7</sup> Bank of Thailand data was obtained from their World Wide Web site at http:
can enable conditions for voluntary, safe and sustainable return of displaced people: their study of UN peacekeeping operations in South Sudan found that displaced people are more likely to return home if peacekeepers are deployed in their province of destination, and that the presence of peacekeepers contributes to better relations between local populations and returnees.<sup>9</sup> Our paper aligns with these findings by demonstrating that local peace agreements in Iraq, most of which were brokered or supported by international actors in cooperation with local peacebuilding actors, also contributed to safe and sustainable IDP return and better community relations post-return.

> 6 Scholars have started to identify the type of peace agreements most likely to contribute to durable peace, and the type of engagement with IDPs during peace processes most likely to support durable solutions. This issue is discussed throughout the present study, but for an overview, see: Andersen-Rodgers, D.R. (2015). _Back home again: Assessing the impact of provisions for internally displaced persons in comprehensive peace accords._ Refugee Survey Quarterly, 34(3), pp.24-45. 7 Juan F. Tellez and Laia Balcells (2021), “The social, economic, and political effects of displacement. Evidence from rural Colombia”, _Unpublished Working Paper._ Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group.

> 8 Isabel Ruiz and Carlos Vargas-Silva (2021), “Refugee Return and Social Cohesion”, _Unpublished Working Paper._ Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group.

> 9 Vincenzo Bove, Jessica De Salvatore and Leandor Elia (2021), “How does UN peacekeeping shape attitudes toward displacement and return for IDPs and host communities?”, _Unpublished Working Paper._ Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington,
that the yields are much higher in these ecologies but levels of cultivation are well below the upper bounds, suggesting sub-optimal use.

# _Model validation_

To replicate the base year, equivalent to the 2016 survey data, it was necessary to establish upper bounds on acreage for the two ecologies with the highest yields i.e. ‘riverain’ and ‘mangrove’ and lower bounds for the other three ecologies at the base-year level of cultivation. This was necessary to avoid any misspecification of the model constraint set (See Nugent, 1970). The rationale for doing this is that in the unbounded simulations, more use acreages were allocated to these ecologies as the model ‘optimized’ the objective function. This is already an important finding that suggests that the sector is operating at a sub-optimal level with respect to the maximization of consumer and producer welfare in not expanding the acreage in these two ecologies to take advantage of the higher yields, other things being equal. Lower bounds were established for each of the other ecologies to reflect the respective current base year land usage (area under cultivation).

As table 10 below shows, the model is well validated by the available base year survey data on domestic price, consumption, domestic production and imports. There is currently no export of rice from Sierra Leone, as is reflected in the survey data as well as the model results.

**Table 10: Baseline results of the model compared with survey data**

|Scenario|Consumer &<br>Producer<br>Surplus (US$ Mn)|Domestic<br>Consumption<br>(MT)|Domestic<br>Production<br>(MT)|Imports<br>(MT)|Price<br>(US$/MT)|Trade<br>balance<br>(Rice)<br>(US$Mn)|Employment<br>(Numbers)|
|---|---|---|---|-
# **5. Conclusions**

This paper develops a new method of measuring malnutrition across the world. The current key measures of malnutrition, such as stunting and wasting, are based on headcount measures, i.e. the proportion of children who are suffering from malnutrition. However, a potential drawback of these headcount measures is that they do not inform us about the depth and severity of malnutrition. It is possible that a country with a low headcount rate for a particular malnutrition measure also has a high severity of malnutrition compared to countries with a similar headcount rate, and vice versa. Therefore, it is important to develop a measure of the severity of malnutrition.

To develop a measure of the severity of malnutrition, this study adopts a particular technique used in the development literature, specifically the Foster, Greer and Thorbecke (1984) class of poverty indicators, in the context of child malnutrition. Employing this new technique, we develop eight new measures of malnutrition in this study: (i) stunting gap, (ii) stunting gap squared, (iii) wasting gap, (iv) wasting gap squared, (v) overweight gap, (vi) overweight gap squared, (vii) underweight gap, and (viii) underweight gap squared. We employ over 20 years of malnutrition data from 95 developing countries to calculate these measures of severity.

Due to space limitations, this paper presents the results on stunting only, although all results have been calculated. It is of our interest to explore all our results in our future studies to understand if the additional information provided by the gap and gap squared measures is more useful for one malnutrition indicator than for another.

The malnutrition gap as a new measure enables us to monitor the development progress of those furthest away from the reference line, serving the principle of SDGs. Employing the new measures, we are also able to identify countries that have low levels of headcount for a malnutrition 23
distributions from the most recent surveys or updates old distributions using GDP per capita or private consumption growth rates.<sup>5</sup> In addition, the World Bank uses consumer prices indexes (CPI) and PPP exchange rates to account for variations in prices across countries and to convert distributions from local currency units to a comparable international currency.

Lack of a recent HBS for a country leads to omission from global and regional poverty counts because outdated surveys provide outdated distributions, which, if included, would lead to inaccurate poverty estimates (this also depends on the quality of macroeconomic numbers and their underlying relationship with household well-being --see detailed explanation and empirical results illustrating the issues in the Annex). For these reasons, if surveys are two years older than the reference year selected for reporting of global poverty numbers, and outdated surveys account for more than 60 percent of regional population, regional poverty estimates are not reported.

Until recently, MENA did not report regional poverty due to the lack of recent HBSs but also because of the presence of unreliable PPP exchange rates for several MENA countries (Atamanov et al., 2018). Once

> 3 Several countries measure income poverty using labor force surveys, for example the Dominican Republic and Ecuador.  However, these cases are exceptions and this practice is rare. We discuss briefly the issue of using labor force surveys to measure poverty and its applicability to the MENA region in the Annex.

> 4 Leaving aside the debate about different approaches to measure household welfare, we use “consumption”, “expenditure” and “income” terms interchangeably as measures of household living standards.

> 5 Find a detailed explanation of issues related to monitoring of global/regional poverty in the Annex.

3
10 model correlates district-level yields from government statistics with a satellite-derived vegetation index (green chlorophyll index) known to be sensitive to rice yields for pixels identified as ricegrowing areas. A random forest-based machine learning model is added to incorporate additional environmental variables known to affect rice yields, resulting in pixel-level rice yield estimates across two decades (2000-2022).<sup>23</sup> For tea, district-level yield data is regressed on a district-level vegetation index derived from satellite data for pixels deemed to be tea plantations.<sup>24</sup>

**Fertilizer subsidies** . Crop-level fertilizer subsidy rates are defined as the “nominal rate of protection at farm gate”, which is computed by comparing the actual (farm-gate) fertilizer price to the price that would be expected given international fertilizer prices and the cost of bringing fertilizer from port to farm. The fertilizer subsidy rate is 81% for rice and 45%-54% for four other crops of interest (potato, maize, tea, and onions).<sup>25</sup>

**Potential agricultural yields** . Our use of FAO-GAEZ data for crop-district-level potential attainable yields follows the literature on agriculture and trade (Bustos et al., 2016; Costinot et al., 2016; Farrokhi and Pellegrina, 2023; Sotelo, 2020).

**Wages and household characteristics** . Three types of information in our survey data are particularly valuable: (i) on estate workers who live and work in Sri Lanka’s tea plantations, for whom changing jobs is nearly impossible in the short run as their housing is tied to the tea estate where they work (Chandrabose, 2015); (ii) on the fraction of income households spend on food; and (iii) on income earned “by chance”, including lottery gains and disaster/relief payments.

**Summary Statistics.** Fertilizer and non-fertilizer products whose imports were banned accounted for 15%-19% of total imports in 2017-2019
and services (Yotov et al. 2016).

New trade gravity models also hold additional insights for FDI analysis. In their seminal paper, Anderson and van Wincoop (2003) demonstrate that traditional gravity equations can overstate the effect of trade frictions on trade flows because they ignore the fact that countries operate in a multilateral world. That is – a change in the trade cost (say, a tariff) between countries A and B also affects the trade flows of countries A and B with all their other trading partners (because it may create or divert trade flows). New or ‘structural’ gravity models distinguish themselves by directly accounting for this multilateral resistance (MR) effect, and are therefore considered more robust and accurate in evaluating policy reforms. The issue of multilateral resistance is also clearly present within foreign investment dynamics. For example, investment climate reforms or IPA promotion efforts in host country A would affect its relative competitiveness compared to other host countries and can shift FDI across such countries. As such, new gravity models also hold theoretical and empirical benefits to study FDI dynamics.

The economic literature has recently started to apply gravity models to FDI. In a literature review of FDI determinants, Blonigen (2005) noted that “As with trade flows, a gravity specification actually fits crosscountry data on FDI reasonably well”. Kox and Rojas-Romagosa (2020) note that “given the success of the gravity trade model, one wonders why it has not yet become part of the standard empirical toolkit for the analysis of international patterns in bilateral FDI”. Part of the challenge here lies with the absence of tractable models, but a more important constraint has been the availability of bilateral direct investment data. Fortunately, more countries are now reporting detailed bilateral FDI statistics,<sup>2</sup> which is leading to the increased popularity of studying FDI in a gravity context. A recent example of this is Kox and RojasRomagosa (2020) which uses a structural gravity model of FDI to estimate the potential impact of
–546.

Edin, Per-Anders, Peter Fredriksson and Olof Åslund. 2003. “Ethnic Enclaves and the Economic Success of Immigrants—Evidence from a Natural Experiment,” _The Quarterly Journal of Economics_ , 118 (1): 329–357.

European Commission. 2015. “Labor Market Shortages in the European Union,” IP/A/EMP/ST/2013-06.

European Commission. 2020. “Communication from the Commission Guidelines concerning the exercise of the free movement of workers during COVID-19 outbreak,” 2020/C 102 I/03. - - <u>https://eur lex.europa.eu/legal</u> <u>content/EN/TXT/PDF/?uri=CELEX:52020XC0330(03)&from=EN</u> Eurostat. 2021. “Labor Market Database”, Eurostat, European Commission, reference period 2019q2/2020q2, Employment by sex, age and country of birth (1 000) [lfsq_egacob], <u>https://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do</u> (accessed on 02 November 2021).

Fasani, Francesco, and Jacopo Mazza. 2020a. “A Vulnerable Workforce: Migrant Workers in the <u>COVID-19 Pandemic,” European Commission, JRC - EUR 30225</u> 29
four primary components: support for production and employment, infrastructure investment, policies concerning personnel in poor districts, and educational assistance for individuals and public personnel. One significant form of support for households is the program provision of microcredit and cash subsidies, which aim to enhance poor households’ agricultural productivity and facilitate their transition to non-farm employment.

Overall, our analysis reveals no significant impact of the 30A Program on household income or poverty. We also do not find significant program effects on local economic development, as measured by commune-level nighttime light data, establishment of new firms or migration inflows and outflows. However, we find positive program impact on household transitions from farm self-employment to nonfarm self-employment, leading to increased nonfarm income and decreased farm income. The results are robust to various falsification tests, RDD bandwidths, model specifications, different ways of estimating the standard errors, and program spillover effects.

Notably, we find substantial and positive program effects on formal borrowing and microcredit utilization, which could be a plausible reason for these positive program effects on nonfarm employment. We also find heterogeneous program effects on nonfarm employment across gender, education level, and ethnicity and larger effects in the longer term. Moreover, the program has improved household access to public services, including electricity, public transfers, health care utilization, and educational subsidies for students.

We make several new contributions to the literature on evaluating poverty alleviation programs. Despite the existence of numerous poverty alleviation initiatives in low- and middle-income countries, there is still a dearth of empirical research investigating the impact of large-scale poverty reduction programs targeting impoverished areas. Recent studies have focused on other developing countries such as China, India, and countries in Latin America 3
With the data, we compute firm-level export and import values, volumes, prices, number of transactions, export destinations, import origin countries, and number of products. The average price calculation at the firm-level accounts for the fact that different firms produce different products. So, for every disaggregated product (8-digit HS code), we compute the average price per unit across all firms and all destinations in that month. For every firm, we compute a ratio most disaggregated produce-wise: the numerator is the price that the firm charges for that HS-8 product and the denominator is the average price of that product. Then, we compute a weighted firm-month average price per unit of these products in the firm’s export basket.

We define GVC firms as firms that imported and exported at least once in the Covid-19 preperiod (February 2019-November 2019). Non-GVC firms are either pure importers or pure exporters. There are 8,290 GVC firms (23.11%), 22,065 pure importers (61.52%), and 5,511 pure exporters (15.37%) in our data.

The data shows that 80.4 percent of export value for Indonesia was generated by GVC firms before Covid (i.e., before March 2020) and 82.1 percent after Covid. Overall, about 81.5 percent of Indonesia’s exports were generated by GVC firms in the sample period. These figures are slightly higher than what was reported in other studies where about two-thirds of export values were generated by importing-exporters (Cal`ı et al., 2022).

The data on non-tariff measures (NTMs) is sourced from the World Bank NTM database for Indonesia.<sup>1</sup> This dataset improves on existing data from UNCTAD-ERIA through more frequent updates, a wider variety of sources, and intensive forward and backward tracing that enables the creation of panel data. The data is hand collected through extensive regulatory checks, careful coding processes using the UNCTAD 3-digit MAST classification
# **Table 1. Country and Industry Coverage**

This table presents the distribution of observations and unique firms among the countries. Panel A presents the country coverage. Panel B presents the industry coverage. Industry definitions are from Institutional Shareholder Services (ISS) database.

||**Pane**<br>|**l A: Country Covera**<br>|**ge**<br>||
|---|---|---|---|---|
|**Countries**|**# of Observations**|**% of Observations**|**# of Unique Firms**|**% of Unique Firms**|
|Australia|479|1.42|125|1.48|
|Austria|95|0.28|23|0.27|
|Belgium|121|0.36|30|0.35|
|Canada|858|2.54|221|2.61|
|Denmark|116|0.34|26|0.31|
|Finland|153|0.45|33|0.39|
|France|413|1.23|97|1.15|
|Germany|427|1.27|100|1.18|
|Greece|193|0.57|43|0.51|
|Hong Kong SAR, China|252|0.75|65|0.77|
|Ireland|143|0.42|34|0.40|
|Italy|297|0.88|77|0.91|
|Japan|2,668|7.91|596|7.05|
|Netherlands|251|0.74|58|0.69|
|New Zealand|74|0.22|20|0.24|
|Norway|103
|-|

_Source:_ WHO 2015b, 2017.

_Note:_ The table shows the prices for the most widely sold brands in international purchasing power parity U.S. dollars. Cigarette prices are standardized to packs of 20. Bidi prices are standardized to 20 pieces. Chewing tobacco prices are standardized to 20 grams. Taxes are calculated for the most widely sold brands. For Bangladesh, the brand of cigarette reported in the 2017 WHO Report (for year 2016) was not the most widely sold brand. The price of the most widely sold brand was less than half of the price reported in the table.

Similarly, the taxes on bidis differentiate between filtered and unfiltered products. The taxes are estimated based on a predetermined tariff value per pack, not the retail price. The tax rate rose from 20 percent in 2011 to 30 percent in 2017 for nonfiltered bidis; for filtered bidis, it increased from 25 percent to 35 percent. This tariff-value-driven tax base actually reduces the effective rate of the supplementary duty and the value-added tax. For instance, in 2011, the average retail price for a pack of 25 bidis was Tk 6.0, while the tariff value was Tk 3.2 and Tk 3.4 per pack for nonfiltered and filtered bidis, respectively (Ahmed 2012). Thus, the effective tax rate of the supplementary duty was reduced to 10.5 percent in 2011, not the established 20.0 percent. Bidi manufacturers in South Asia have avoided many of the local and international tobacco regulations and taxes enforced on factory-made cigarettes. Hence, bidis are sold cheaply at various prices and in packaging with poorly visible health warnings (Jha 2015; Kostova et al. 2014; Sankaran, Hillamo, and Glantz 2015). Duong et al. (2017) find that, even though bidis have significantly less tobacco content than cigarettes, they are at least as harmful as cigarettes in terms of cardiorespiratory health and mortality. Moreover, bidi smokers in South Asia are typically among the poorest and