rafmacalaba/data-use-mentions-extended huggingface dataset:
>10% 6.1% 0 Russia<br>4.9%<br>Japan<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: Author‘s calculations based on WITS database.

One can see another dimension to the trend by focusing on particular geographic trade corridors. For example, if one focuses on the Pacific coast of Latin America, one can see that the ―Pacific Rim‖ has changed its pattern of trade as well over the last two decades. It is too early to tell on the basis of the last decade, but there may well be a new ―emerging Pacific Rim‖ characterized by rapidly increasing trade relations between developing countries on both sides of the Pacific Ocean. While it is true that regional trade agreements (NAFTA, CAFTA) have strengthened North-South trade with the United States in Mexico and Central America, in South America, China has become a major destination for Pacific Coast economies. Two illustrative examples are Chile and Peru. In the case of Chile, note how China has rapidly overtaken Japan‘s role as the major Asian export destination over the last decade (Figure 11(a)). Nearly a quarter of Chile‘s merchandise exports go to China. This is all the more remarkable since, at the start of the 1990s, this figure was nearly zero, and it remained below 5 percent until the start of the 18
Tea Board’s annual<br>reports|
|**Agricultural**<br>**production,**|**[6]**Production by crop at national level|2022|Central Bank of Sri Lanka|
|**cultivated**<br>**area, yields**|**[7]**Remote sensing estimates of rice yields and cultivated areas at<br>pixel level|2000-2022|Landstat and Sentinel-2 satellite, com-<br>puted by authors using Ozdogan et al.<br>(2024) methodology|
||**[8]**Remote sensing estimates of tea yields at pixel level|2014-2022|Planet Satellite Data, computed by au-<br>thors|
||**[9]**Remote sensing estimates of tea map at pixel level|2021|Planet Satellite Data, computed by au-<br>thors|
||**[10]**Producer prices at crop-district level for rice, potatoes, cinna-<br>mon, cloves, onions, groundnuts, maize|2019|DCS|
|**Agricultural**<br>**prices**|<br>**[11]**Producer prices at national level for tea|2019|Food and Agriculture Organization of<br>the United Nations (FAO)|
||**[12]**National Consumer Price Index<br>**[13]** Crop prices at national level for cinnamon, cloves, maize,<br>onions, potatoes,rice,tea|2021-2022<br>2022|DCS<br>Central Bank of Sri Lanka|
||**[14]**Fertilizer requirements by crop|2022|Sri Lanka’s National Fertilizer Secre-<br>tariat (NFS)|
||**[15]**Fertilizer prices|2022|NFS|
|*
_20_ served and unobserved factors. In this section, we perform heterogeneity analysis by firm size, industry subsamples, income groupings, and world regions. For the heterogeneity analysis by firm size, we split the sample into three groups in line with the predominant literature: small enterprises (5-19 workers), medium-sized enterprises (20-99 workers), and large enterprises (100+ workers). For the heterogeneity analysis by industry classification, we use the broad industry classification of manufacturing and services. To examine the heterogeneity by income, we use the 2023 World Bank country classification of income into low, middle, and high.

# Firm Size

Table 3 presents the results of the estimation of the effect of the deviation in WBT on log sales per worker for each sub-sample by firm size. The results are mixed. Although we do not detect a statistically significant negative effect on mediumsized firms located in the two hottest climate zones, there are strong and negative impacts on both small and large firms in the hottest climate zone. For small firms, a unit increase in WBT from the long-term average in the _Zone_<sup>_Q_</sup> (25<sup>4</sup> _._ 7<sup>0</sup> _C,max_ )<sup>group</sup> reduces productivity by 33.7 percent, compared to the reference group (coldest region). The productivity loss for large firms in the same WBT zone is almost three times that for small firms. Large firms experience significantly large productivity losses in all the temperature categories, with the largest losses for firms in the hottest climate zone. The large and negative effects on the productivity of large firms are evident in the various specifications and suggest that the general impact that we observed earlier was driven largely by these negative effects on large firms.

There are several plausible explanations, for why large firms are more prone to such effects compared to small or medium sized firms. First, large firms tend to be particularly concentrated
During the first, post-planting survey visit, each treatment group household completed a short parcel-plot-crop-level module on farm organization, which listed all parcels and plots within, in accordance with the IHS5 parcel and plot definitions.<sup>4</sup> Once the roster of parcels and plots was completed, one maize plot was selected at random. The manager of this plot was the target respondent for questions on the rest of the activities occurring on this plot. The target respondent then self-reported the plot area. Subsequent to the respondent self-reporting the selected plot size, the enumerator and respondent visited that plot together. The enumerator measured the plot area with a handheld Garmin eTrex 30 GPS unit,<sup>5</sup> then recorded and shared with the farmer both the GPS-based plot area and the measurement error in farmer-reported area vis-à-vis the GPS-based counterpart (both in levels and as a share of GPS-based plot area).<sup>6</sup> This was the information treatment, a demonstrably accurate measure of the plot area.<sup>7</sup> After finishing the plot visit, the enumerator and the farmer returned to the dwelling to administer the rest of the post-planting questionnaire, which asked the farmer to self-report labor, fertilizer and other inputs used on the plot before and during planting.

During a second, post-harvest visit to each treatment household,<sup>8</sup> the respondent was asked again to report the selected plot area, whose GPS-based measure had been shared with the respondent during the post-planting visit. If the self-reported plot area was different than the GPSbased plot area, the respondent was asked again his/her recollection of the GPS-based plot area. The respondent then re-reported all labor and non-labor inputs on the plot, in view of the possibility of non-labor input applications not having been finalized at the time of the post-planting interview.

Conversely, control group households received a single, post-harvest visit during which they completed a unified agricultural questionnaire, including the same self-reporting of plot
5 characteristics and on the hypothetical condition where they do not receive remittances (counterfactual incomes). We then use these predicted and counterfactual household incomes to compare the level of poverty and inequality in Indonesia with and without remittances. Second, we use the model to rigorously compare the marginal spending behavior of two groups of households: those with no remittances and those receiving international remittances. Since all survey households are separated into one of these two groups, it becomes possible to compare how remittance- and non-remittance receiving households spend at the margin on a broad range of consumption and investment goods, including food, education and housing.

The paper proceeds in eight further parts. Section 1 presents the data. Since the problems of selection and identification are so important, Section 2 presents the three-stage nested logit model and discusses the various identification issues involved in estimating this model. Section 3 estimates the selection model using an instrumental variables approach, employing variations in historical distance to the nearest railroad station and changes in rainfall patterns. Section 4 estimates the selection-corrected predicted and counterfactual expenditure functions, and Section 5 uses these expenditure functions to analyze the impact of international remittances on poverty and inequality in Indonesia. Section 6 presents the model for comparing the marginal spending behavior of remittance and non-remittance receiving households, and Section 7 presents the results of this model. Section 8 concludes.

# 1.  Data Set

The data come from the Indonesia Family Life Survey (IFLS), an on-going panel household survey in Indonesia. The IFLS Survey includes four waves of surveying, IFLS 1 (1993), IFLS 2 (1997), IFLS 3 (2000) and IFLS 4 (2007). However, since this paper is on
# **2.1 Aggregate evidence on the effects of the invasion**

Given the historically strong integration of Ukraine as well as Russia into global commodity markets,<sup>5</sup> the Russian invasion of Ukraine, in February 2022, together with the economic blockade of its export routes through the Black Sea, resulted in a spike of grain prices and led to major concerns about effects on global food security. A key reason was the economic blockade of Ukraine’s main seaborne export routes via the Black Sea that limited Ukraine’s ability to generate export revenue and forced a costly shift to overland routes with much lower capacity but high physical and transaction costs. The ‘grain deal’ agreed under the auspices of the UN in July 2022 and extended twice since then, allows a limited quantity of grain to be exported from Ukraine through the port of Odessa, albeit at higher cost and risk than before the invasion.<sup>6</sup> Yet, while it may have avoided food insecurity in countries heavily dependent on Ukrainian imports (FAO 2022a), higher transaction cost of this export route may translate into lower prices for farmers in Ukraine.

Trade models have been used to highlight likely impacts of different conflict scenarios on global wheat markets, especially for African countries (Balma _et al._ 2022), the resilience of the global food system (Ihle _et al._ 2022), or countries that have historically been characterized by heavy reliance on wheat imports from Ukraine (e.g., the Arab Republic of Egypt, Türkiye, Mongolia, Georgia, and Azerbaijan). A global model drawing on satellite imagery for different scenarios concludes that, even in the most optimistic case, reductions in welfare and shortages of food and energy are likely unless production gaps are compensated by increased production elsewhere (Lin _et al._ 2023). One concern has been that countries could use the crisis as a pretext for adopting protectionist policies (Ben Hassen & El Bilali 2022)
Becker Chris (2016), “Vulnerabilities of Isolated Small Island States in the Pacific” in _Resilience and Growth in the Small States of the Pacific_ (eds) Hoe Ee Khor, Roger P. Kronenberg, and Patrizia Tumbarello, International Monetary Fund.

Bharadwaj, Ritu, Tom Mitchell and N Karthikeyan (2023), “Protecting against sovereign debt defaults under growing climate impacts: Role for parametric insurance”, IIED, London. http://pubs.iied.org/21426IIED.

Burns Andrew, Charl Jooste and Gregor Schwerhoff (2021), “Macroeconomic Modelling of Managing Hurricane Damage in the Caribbean: The Case of Jamaica”, Policy Research Working Paper 9505, the World Bank.

Cabezon, Ezequiel, Leni Hunter, Patrizia Tumbarello, Kazuaki Washimi, and Yiqun Wu (2016), “Strengthening Macro-fiscal Resilience to Natural Disasters and Climate Change in Small states in the Pacific” in _Resilience and Growth in the Small States of the Pacific_ (eds) Hoe Ee Khor, Roger P. Kronenberg, and Patrizia Tumbarello, International Monetary Fund Cevik, Serhan (2022), “Waiting for Godot? The Case for Climate Change Adaptation and Mitigation in Small Island States”, Working Paper WP/22/179, International Monetary Fund.

Doan, Miki Khanh, Ruth Hill, Stephane Hallegatte, Paul Corral, Ben Brunckhorst, Minh Nguyen, Samuel Freije-Rodriguez and Esther Naikal (2023), “Counting People Exposed to, Vulnerable to, or at High Risk from Climate Shocks: A Methodology”, Policy Research Working Paper, 10619, World Bank.

Fresnillo, Iolanda and Illaria Crotti (2022), _Riders on the storm: How debt and climate change are threatening the future of small island developing states_ , EURODAD (European Network on Debt and Development).

Gall, Melanie (2015). “The suitability of disaster loss databases to measure loss and damage from climate change”, _International Journal of Global Warming_ , vol 8, no 2, pp170-190.

Guerson, Alejandro (2020), “
|30.129**|‐4.938|
||[7.765]|[10.896]|[9.295]|[11.931]|[8.244]|
|Observations|2345|1268|1456|353|940|
|R‐squared|0.859|0.862|0.876|0.709|0.809|
|Mean dependent variable|67.03|95.70|83.54|112.60|72.24|
|Overall effect of female illness|‐16.490|‐16.870|‐15.470|‐10.040|‐17.430|
|P‐value|0.000|0.000|0.004|0.178|0.001|

_Notes: Worker ill = 1 if a household member reported missing work for an entire week in which s/he was planning to work that week and the reason for missing work was injury/illness. Expected earnings loss from baseline is calculated by taking the taking the average usual reported income from the baseline survey of individuals who are unexpectedly sick over the course of the survey. All specifications include household fixed effects, week of interview dummies interacted with enumeration area, and controls for the number of household members surveyed in that round. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
# **1. Introduction**

The world has made remarkable progress during the past two decades in raising the living standards of the poorest. In 1990, approximately two billion people, or 37 percent of the global population, lived on less than the current international poverty line of $1.90 a day. By 2013, the year for which the latest global poverty estimates are available, the number of extremely poor persons had fallen by over 60 percent. During the same period, the proportion of the global population living in extreme poverty fell even faster, from 37 to 11 percent. The Millennium Development Goal of halving extreme poverty in developing countries between 1990 and 2015 was met in 2010, five years ahead of time.

Despite these impressive achievements, the latest estimate is that 770 million persons remained in extreme poverty as of 2013, a figure in the ballpark of the combined population of the European Union and the United States. Eradicating extreme poverty is a critical priority of the international development community. Ending poverty in all its forms is the first of the 17 Sustainable Development Goals adopted by the United Nations, and the World Bank has set an ambitious goal of reducing the rate of extreme poverty to 3 percent by 2030.

Achieving this goal poses a formidable challenge. Economic growth is a key driver for poverty reduction, but several studies conclude that maintaining the pace of economic growth of the recent past will not be sufficient to meet the target.<sup>2</sup> For example, based on current projections of GDP growth from 2005 to 2015, global poverty is projected to be 4.2 percent by 2030, falling shy of the World Bank’s goal by over a percentage point (Ferreira et al. 2015). Global growth has slowed in recent years, and it is far from clear that the high rates of economic growth observed in the developing world during the past decade can be sustained for the next 15 years. Therefore, the pace of poverty reduction depends critically on
821,489|0,15%|88,751,039|1,12%|

Source: Informe de Pasivos Contingentes, Ministerio de Hacienda, years 2007 onwards. For previous years, from the yearly Informe de Finanzas Públicas, Ministerio de Hacienda. Amounts are in UF, an inflation indexed unit of account that usually fluctuates in the range USD38-44.

21
In order to better present the project and the outcome, a package of visual aids were developed; examples of the visual aids, which include tables, pictures and maps, are presented in **Appendix 2** . The maps show where and how those projects would be implemented. Pictures and words are used to present the water qualities and the changes. A detailed description of different grades of water quality (Grade I to Grade V or worse, also see **Appendix 3** ) in a plain language and their impacts on daily lives and activities was also developed to describe the water quality changes. A good feature of these WTP surveys is that the 6
**|Armenia|-64|-23|-48|
||Georgia|-64|-84|-121|
||<br>Moldova|-93|-62|-76|
||Ukraine|-29|-23|-39|

_Source_ : Updated EWN dataset based on Lane and Milesi-Ferretti (2007). Note: This table reports foreign asset minus foreign liabilities.

**Table 3.1a. Net Foreign Assets, Percent of GDP**

|Country|2002|2007|2009|
|---|---|---|---|
|Euro Periphery|-48|-77|-102|
|Emerging Latin America|-47|-24|-24|
|Emerging Asia|-15|0|-10|
|New Member States|-35|-65|-77|
|Candidates and Potential Candidates|-30|-52|-61|
|Eastern PartnershipCountries|-63|-48|-71|

Source: Updated EWN dataset based on Lane and Milesi-Ferretti (2007). Note: These tables report foreign asset minus foreign liabilities.

It might be argued that the debt component of the external balance sheet poses more problems than the equity component in relation to overhang issues. Accordingly, we show the net debt and net equity positions in Table 3.2. For the new member states, Table 3.2 shows sizeable growth in net debt liabilities between 2002 and 2007, from 2 percent of GDP to 18 percent of GDP. There has been further expansion during the crisis, rising to 27 percent of GDP in 2009. Table 3.2 also shows that net equity liabilities are far greater than net debt liabilities for the new member states. Moreover, the relative stability of net equity positions between 2007 and 2009 also highlights the stabilizing role played by equity-type liabilities - the value of these liabilities naturally falls with a decline in performance in these economies
|_Share who are 35 and older_|59.3|65.8|48.8|
|---|---|---|---|
||(1.4)|(0.7)||
|_Share who have less than secondary_|4.1|54.4|55.1|
|_education_|(0.6)|(1.0)||
|_Share who report working in past week_|59.7|37.0|31.9|
||(1.5)|(0.7)||
|_Share who are refugees_|46.5|42.1|42.2|
||(1.4)|(2.0)||

Notes: All survey sample shares are of individuals aged 18 and older. Clustered standard errors are reported in parentheses for survey estimates. Population shares by region, gender, and age are derived from population projections provided by PCBS in early 2022 and refer to individuals aged 18 and older. Population shares by educational attainment are based on population projections published by PCBS for 2021 and refer to individuals aged 15 and older. Population shares by labor force status originate in the 2021 Palestinian Labor Force Survey and refer to individuals aged 15 and older; the share reported here is of employed individuals. Population shares by refugee status come from 2017 estimates published by PCBS and refer to all individuals.

# Precision-comparability tradeoffs in calibrating the Facebook weights to population proportions

The initial weighting strategy for the Facebook survey imposed a substantial set of calibration constraints to try to maximize comparability with the PPCS and correct for a skewed achieved sample. PPCS weights were calibrated to population proportions using two sets of constraints based on population projections based on the most recent census: (1) the number of households by governorate and urban/rural/camps, and (2) the number of adults by region, five-year age groups, and gender.
</mark>_ <mark>, 130(630): 1842– 1873. https://doi.org/10.1093/ej/ueaa035</mark> Mestre, V., Samper, P., Frías-navarro, D., and Tur-Porcar, A. (2009). Are Women More Empathetic than Men? A Longitudinal Study in Adolescence. The Spanish journal of psychology, 12(1): 76-83. 12.

<mark>Miller, A., Worthington, Jr., E., and McDaniel, M. (2008). Gender and Forgiveness: A Meta–Analytic Review and Research Agenda.</mark> _<mark>Journal of Social and Clinical Psychology,</mark>_ <mark>27(8): 843-876</mark> _<mark>.</mark>_ <mark>Mohanty, M. S. (2009). Effects of positive attitude on happiness and wage: Evidence from the US data.</mark> _<mark>Journal of Economic Psychology,</mark>_ <mark>30(6), 884-897.</mark> <mark>Montalvao, J., Frese, M., Goldstein, M., and Kilic, T. (2017</mark> ). Soft Skills for Hard Constraints: Evidence from High-Achieving Female Farmers. _World Bank Policy Research Working Paper_ , No. WPS 8095.

Mueller, G. and Plug, E. (2006). Estimating the Effect of Personality on Male-Female Earnings. _Industrial and Labor Relations (ILR) Review_ , 60(1): 3-22.

Mulac, A., Bradac, J. J., & Gibbons, P. (2001). Empirical support for the gender-as-culture hypothesis: An intercultural analysis of male/female language differences. _Human Communication Research_ , _27_ (1), 121152.

Nakhaie, M. R., Silverman, R. A
Survey data. Specifically, we utilize the GHS-FUA spatial package developed by Moreno-Monroy et al. (2020), which offers a global delineation of metropolitan or functional urban areas (FUAs) combining a morphological and functional approach. Metropolitan areas consist of urban centers with at least 50,000 inhabitants and their surrounding commuting zones. Since detailed GPS or place name data are not readily available for every Enterprise Survey, particularly for the earlier years, and not all relevant establishment characteristics – most notably sales and employee education – are recorded for every establishment, our initial _full sample_ consists of 51,702 establishments in 649 metropolitan areas of 98 economies.

Most surveys have been conducted in the main urban centers although in recent years it has become more common to conduct surveys in smaller urban centers and more rural areas. In our data set, we link the location of the establishments to FUAs (also called metropolitan areas) with at least 50,000 inhabitants. Hence, our data set excludes rural areas and towns with fewer than 50,000 inhabitants. Of the establishments located in metropolitan areas, 68% are located in metropolitan areas with more than 1 million inhabitants, 29% are located in medium sized FUAs (between 100,000 and 1 million) and the remaining 3% are found in small FUAs between (50,000 and 100,000). Thus, our data set covers predominantly the largest metropolitan areas in developing economies. Second, our _common_ sample, which we use to test out hypotheses on urban mobility, consists of 38,526 establishments in 356 metropolitan areas in 80 developing economies because only for these metropolitan areas we have information on urban mobility and congestion from the work of Akbar et al. (2021). Appendix A1 includes the list of economies included in the _common_ sample.

# _3.2. Description of variables_

# _Dependent variable: Labor productivity_

Labor productivity is measured as the natural logarithm of sales per worker, computed as the revenue deflated and represented in 2009 USD during the last fiscal