rafmacalaba/data-use-mentions-extended huggingface dataset:
Meyfroidt, P., Schierhorn, F., Prishchepov, A.V., Müller, D., Kuemmerle, T., 2016. Drivers, constraints and trade-offs associated with recultivating abandoned cropland in Russia, Ukraine and Kazakhstan. Global Environmental Change 37, 1-15 Mueller, H., Groeger, A., Hersh, J., Matranga, A., Serrat, J., 2021. Monitoring war destruction from space using machine learning. Proceedings of the National Academy of Sciences 118, e2025400118 Munteanu, C., Kuemmerle, T., Boltiziar, M., Lieskovský, J., Mojses, M., Kaim, D., Konkoly-Gyuró, É., Mackovčin, P., Műller, D., Ostapowicz, K., Radeloff, V.C., 2017. Nineteenth-century land-use legacies affect contemporary land abandonment in the Carpathians. Regional Environmental Change 17, 2209-2222 Nagy, A., Szabó, A., Adeniyi, O.D., Tamás, J., 2021. Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics. Agronomy 11, 652 Neyter, R., Dushko, D., Nivievskyi, O., Stolnykovych, H., 2022a. Agricultural war losses review Ukraine - rapid loss assessment. Kyiv School of Economics, Center for Food and Land Use Research, Kyiv Neyter, R., Stolnykovych, H., Nivievskyi, O., 2022b. Agricultural war damages review Ukraine - Rapid damage assessment. Kyiv School of Economics, Center for Food and Land Use Research, Kyiv Nivyievskyi, O., Halytsa, O., Deininger, K., 2021. The impact of land sales market restrictions on agricultural productivity in Ukraine In: Policy Research Working Paper, p. 29. The World Bank, Washington, DC Panek, E., Gozdowski, D., 2020. Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based
Policy Research Working Paper 7838

# **Abstract**

Household expenditures for children-related goods may change when one of the parent migrates and do so differently depending on whether it is the mother or the father that leaves. A sequential model that explains migration and budget allocation choices is proposed and its predictions are tested on data from Indonesia. Selection of households into female migration is accounted for using a set of instrumental variables derived from the model. Results show that when children are left with fathers, the household budget is significantly diverted toward the purchase of adult private goods, but the share of budget devoted to children remains unaffected because mothers compensate by giving up their own private consumption and sending home more remittances.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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://econ.worldbank.org. The author may be contacted at lucia.rizzica@bancaditalia.it.

_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
# **The impact on poverty**

The combination of taxes and social spending also led to an important decline in poverty in 2014. The overall decline amounts to 16 percentage points from market to consumable income for the legal poverty line and 18 percentage points for the extreme poverty line.<sup>11</sup> Most of this decline is on account of contributory pensions, which could be thought of as deferred incomes. Starting from market income, the extreme poverty headcount rate measured using the National extreme poverty line<sup>12</sup> declined from 27.2 to 6 percent mostly on account of contributory old-age pensions (Table 4). Similarly, the legal poverty rate declined from 29.2 to 7.5 percent.

Table 4. Main Scenario: Changes in Poverty on account of taxes and transfers

|Market<br>Income<br>|Market income<br>+ contributory<br>pensions|<br>Disposable<br>Income|Consumable<br>Income|
|---|---|---|---|
|(1)|(2) = (1) +<br>contributory<br>pensions|(3) = (2) - direct<br>taxes -<br>contributions +<br>direct transfers|<br>(4) = (3) -<br>indirect<br>taxes|
|**Poverty headcount**||||
|National legal<br>29.2%|7.5%|8.6%|12.9%|
|National extreme<br>27.2%|6.0%|5.5%|8.9%|
|US $5PPP a day<br>27.3%|6.1%|6.0%|9.2%|
|US $2.5PPP a day<br>
8 where N is the number of articles in the corpus and N� is the number of articles in which word _i_ is present. Hence, this weighting gives more weight to words that appear more rarely across documents.

To illustrate our dictionary-based approach to measuring sentiment, here is an example of an article in our corpus in which the tonal words are underlined.<sup>6</sup> It suggests that although our sentiment measure s� does not capture all the nuances in the text, it provides a good indication of its overall tone.

<u>Title: Argentina’s Peronists defend Menem’s labor reforms.</u> <u>Timestamp: 1996-09-02</u> Text: BUENOS AIRES, Sept 2 (Reuters) - The Argentine government Monday tried to counter **criticisms** of President Carlos Menem’s proposals for more **flexible** labor laws, **arguing** that not just workers would contribute to new **unemployment** insurance. Menem **angered** trade unions, already in **disagreement** over his fiscal **austerity** programs, by announcing a labor reform package Friday including **suspending** collective wage deals and replacing **redundancy** payouts with unemployment insurance.

<u>Topics: Labor/Personnel Issues, Corporate/Industrial News, Economic/Monetary Policy,</u> Economic News, Political/General News, Labor Issues, Domestic Politics <u>Locations: Argentina, Latin America, South America</u> After computing the sentiment of each article s�, we obtain a daily sentiment index for each country _c_ by taking the average of s� across articles published in any given day that contain country _c_ in its location tags.

# **B.** **<u>Asset prices and related variables</u>**

We explore the impact of news-sentiment measures on stock market returns. Equity returns are
>ITA<br>SVK<br>GEO<br>GBR<br>RUS<br>ALB MDA AZEMNECZE LVA SRB POL<br>BGR UKR SWEDEU<br>KSV BIH KGZ FRA<br>KAZ BLR ROU TJK<br>ARM<br>LTUMNGUZB<br>EST SVN<br>TUR MKD<br>0 .2 .4 .6 .8 1<br>Dep Var: 10-step scale<br>Source: LiTS II, 2010.<br>.4<br>.3<br>.2<br>Dep Var: 5-step scale<br>.1<br>0<br><!-- End of picture text -->

# **_4.2. Systematic biases: Consistency of subjective relative income assessments within households_**

For the economic ladder, we test the presence of systematic biases in reporting by examining within-household differences in ladder assessments. In 40 percent of the households in the sample, the economic ladder question was asked to two respondents – the head of the household who answered questions about household composition, assets and expenditures, and the randomly chosen household member who provided answers to the rest of the LiTS modules (including all attitudinal questions). There is clear variation in the economic welfare assessments provided for the same household by two different household members. However, the assessments are “roughly” consistent – for most of the ladder steps, more than 80 percent of the evaluations by the second household member are within -1 /+1 interval from the evaluation given by the head of the household.

The two variables that are available in the LiTS data for all respondents are their age and sex. It is thus possible to examine whether answers to the economic ladder question are influenced by these demographic characteristics. Table 4 presents household fixed effects regressions for the 17
with invested capital totaling</mark> Rub 4.5 billion (about US$ 150 million).

<mark>Among the main areas for investments are biomedical technologies, power engineering and energy</mark> saving, ICT systems, software manufacturing technologies, and “critical technologies.” Venture capital investors are forced to invest in mature and/or foreign companies. They are particularly affected by the lack of viable exit strategies due to the underdevelopment of the market for initial public offerings and the lack of depth of financial markets. Statistics from the Russian Venture Capital Association show that 80 percent of investment capital is dedicated to financing restructuring or business expansion and only 20 percent is earmarked for early-stage financing of new companies. Several of the leading Russian funds have searched for an answer by investing in international projects or by copying Western projects, but without any real technological innovation. The best example of this is the fund Digital Sky Technologies, investing in Facebook, ICQ, Vkontakte, Mail.ru, and others.

The problem with investing in Russian start-ups is the lack of investment projects that fit the investment criteria, rather than an absolute lack of sufficient volume of investment capital. Also, Russian technology start-ups lack experience in developing business plans that reflect the fundamentals required for equity investment, and lack access to affordable legal, accounting, and consulting services that are required to prepare an “investable” business plan and to properly protect their IP.

> 8 OJSC: Open Joint Stock Company LLC: Limited Liability Company CJSC: Closed Joint Stock Company CEIF: Closed Equity Investment Fund

18
all traded goods are storable to some extent, the certainty and knowledge of future tariff reductions provided firms with strong incentives to delay their purchases in advance of their implementation and then to stock up in their aftermath.

Indeed, we find strong evidence of _anticipatory slumps_ and _liberalization bumps_ in the response of trade flows to tariff cuts for US imports from Mexico of phased-out goods. We estimate these anticipatory dynamics by using a standard odds ratio or “tetrads” method (Head and Ries (2001), Head et al. (2010)) to construct trade flows, we then consider within-year growth rates of these. First, we find that in anticipation of an upcoming tariff reduction of 1%, imports dropped by a sizeable 6% in November and December relative to the middle of the year. Second, in the first few months after the tariff reduction, imports spiked by 12% with respect to the end of the previous year. The size of these high frequency reversals are large even in comparison to previously documented shortrun (annual) estimates of the trade elasticity. This paper is the first to our knowledge to document how (1) firms anticipate tariff reductions during gradual phaseouts and (2) that the trade elasticity can be quite large even at high frequencies.

Importantly, we find that the anticipatory dynamics are increasing in the degree of product storability. This suggests that trade flows and their consumption might significantly diverge during these episodes. Unfortunately, the consumption or usage of imports is not observed, especially at the level of aggregation used in the estimation of trade elasticities. To make progress, we propose a novel measure of the consumption of imports that builds on monthly trade data and the assumption that a constant fraction of inventory holdings are used for consumption. A product-source country specific inventory-usage ratio is obtained by exploiting the fact that more storable goods are traded less frequently.<sup>3</sup> We validate the measure of consumption by demonstrating that (1) it eliminates the anticipatory slumps documented for NAFTA’
ISIC), Revision 4, and cover 15 1-digit sectors.<sup>17</sup> A stratified twostage sample design was used to select the businesses for the UBI, which focused on getting

> 16The informal status of a business is recorded using a question in the survey on whether a business pays any taxes as its determinant; this is in line with other surveys conducted in Uganda such as the UNHS which were more specifically directed to studying informality in the economy.

> 17These are agriculture & fishing, mining & quarrying, manufacturing, construction, utilities, trade, transport & storage, accommodation & food services, information & communication, finance & insurance, real estate & business services, education, health & social work, recreation and personal services. Note that the survey excludes information on the following ISIC sections: Section O, i.e., public administration and defense; compulsory social security, and Section U, i.e., activities of extraterritorial organizations and bodies.

27
Our results have important implications for development policy and fill key gaps in the literature concerning survey methods and the quality of agricultural labor data. Ours is one of the few studies to test the accuracy of agricultural labor data in developing-country settings. While labor data have been an essential ingredient in a broad range of important studies on smallholder agriculture in developing countries, scant attention has been paid thus far to the quality and robustness of the underlying data on family farm labor.<sup>3</sup> By showing evidence that agricultural labor inputs may be substantially overestimated because of recall bias, we challenge the reliability of the traditional end-of-season labor estimates commonly used in development economics.

These findings also contribute to academic and policy debates concerning the agricultural productivity gap and the degree to which rural labor may be misallocated in developing economies. Several studies have been engaged in this debate. Gollin, Lagakos, and Waugh (2014) and McCullough (2015), question the accuracy of current labor measures and reconsider the agricultural productivity gap after adjusting for labor data quality. By conducting comparisons at the per hour level (McCullough 2015) and by adjusting for sectoral differences in hours worked as well as for levels of human capital (Gollin, Lagakos, and Waugh 2014), both studies find that the difference in the productivity between the agricultural and nonfarming sectors is narrower than usually thought. Our study suggests that when surveying irregular labor through recall an upward of hours worked on the farm may result, which would further explain part of the productivity gap.

Although our results call into question the accuracy of current farm labor data, they also propose specific ways to improve the accuracy of labor measurement. The consistency of the labor reported across face-to-face and phone surveys suggests that season-long phone surveys are an option for reducing error in the measurement of rural agricultural labor.

The rest of the paper proceeds as follows. In section 2, we offer background on labor measurement. In section 3, we provide an overview of the empirical approach, including details on
<br>639|2,803|3,079|5,760|7,580|6,690|
|Private sector|100|<br>165|137|<br>917|2,002|3,509|5,756|12,267|15,734|15,607|
|Loans|100|<br>165|137|<br>767|1,380|<br>126|1,536|6,271|6,007|11,086|
|Bonds|0|<br>0|0|<br>150|<br>500|<br>740|1,155|3,867|5,810|3,262|
|Equity|0|<br>0|0|<br>0|121|2,643|3,065|2,130|3,918|1,259|

_Source: World Bank, Global Development Finance_

# **II. Reasons for increases in capital flows to infrastructure**

At first glance, infrastructure finance does not seem to be a viable vehicle for attracting foreign capital to developing countries. First, exposure to currency risk, which for foreign investment in export-oriented manufacturing industries is of a relatively minor concern, is a critical feature of infrastructure project investment. Project revenues are often generated in local currencies, while servicing of foreign capital, whether debt or equity, involves payment in foreign currency. Fluctuations in the exchange rate of the

> 6 According to World Bank estimnates (Global Development Finance, 1997).

4
|0|3|988|

Table 1: Summary statistics.

Our central independent variable is CBI. We use a recently updated version of the CBI measure as proposed in Bodea and Hicks (2015 _b_ ). Following the coding protocol of Cukierman, Miller, and Neyapti (2002), the index is built around four dimensions of CBI (e.g., the mandate of a central bank, monetary financing prohibitions) that are normalized on a scale between 0 and 1. Higher scores of the index indicate a greater degree of CBI. The data available from Bodea and Hicks (2015 _b_ ) covers up to 144 countries between 1972 and 2014 and are one of the most comprehensive 10
(DiNIECE

> 6 These figures do not include special and adult education.

> 7 According to De Hoyos, <u><mark>Holland, and Troiano</mark> (2015), there is a gradual increase in Argentina’s Science scores between 2006 and</u> 2012, but it is not statistically significant at conventional levels (95 percent).

> 8 Argentina participated in PISA 2015, but its results were excluded from the main report due to problems with sample design. However, CABA participated as an adjudicated region and was included in the results.

- 9 In a similar vein, results of the UNESCO Second Regional Comparative and Explanatory Study (SERCE), applied to students of third and sixth graders, show that only 11.4 percent of sixth-grade students were able to explain everyday situations based on scientific evidence, use models to explain natural phenomena, or draw conclusions based on data (UNESCO 2009).

10
**Table 4: Summary of RUSLANA firm panel data**

|Year|Total<br>number of<br>firms|Number of<br>“entering” firms|Firms in<br>slumping<br>sectors|Firms in<br>surging<br>sectors|Firms in sectors<br>that are<br>neither surging<br>nor slumping|
|---|---|---|---|---|---|
|2004|65942|7618|5464|19688|40790|
|2005|65136|12683|10357|699|54080|
|2006|64536|11068|1090|31595|31851|
|2007|65498|10616|829|40423|24246|
|2008|64026|8908|60563|13|3450|
|2009|61035|7075|41876|1416|17743|
|2010|59179|7874|7630|4589|46960|

Note: A firm is classified as an “entering” firm in year _t_ if year _t_ is the earliest year in which that firm exists in the RUSLANA panel, regardless of the year of incorporation. The total number of firms in year _t_ is inclusive of entering firms.

21
Table 1: Descriptive statistics

|Variable|Obs|Mean|Std. Dev.|
|---|---|---|---|
|ln simple tarifs|41,043|2.034|1.221|
|ln weighted tarifs|40,606|1.582|1.680|
|ln rain|41,043|2.469|0.757|
|agric|41,043|0.129|0.335|
|agric2|41,043|0.209|0.406|
|irrig|18,812|13.626|17.476|
|North America|41,043|0.046|0.211|
|Latin America and Caribbean|41,043|0.369|0.482|
|Europe|41,043|0.053|0.225|
|Africa|41,043|0.200|0.400|
|Asia-Pacifc|41,043|0.329|0.469|
|Number of countries||70||
|Number of products||65||

Country-level data on the proportion of agricultural land that is irrigated are available for 32 countries.

Table 2: Baseline estimates

|Dep. variable|ln simp<br>(1)|le tarifs<br>(2)|ln weig<br>(3)|hted tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.064<br>[0.076]|0.035<br>[0.076]|0.125<br>[0.094]|0.082<br>[0.093]|
|ln rain_jt ∗_agric_i_||0.230***<br>[0.047]|
energy policy in the country. However, from 2015 onwards momentum towards sustainable cost recovery reforms has been created.

In fact, tariffs have been very low in relation to costs over most of the period covered by this paper, with the exception of the years from 2015. As a result, the economy and society have enjoyed a massive subsidy flow over many years, supporting disposable incomes and employment, and postponing the need to painfully achieve international competitiveness. And in consequence, the power sector has been deprived of self-generated resources, so has needed very extensive propping up.

This policy combination is now starting to change in favor of actual cost recovery, and the relevant institutions may perhaps be adapting to make that changes at last. Time will tell, and much will depend on the political will and ability to ride out the adjustment process, and society’s will and ability to endure it.

It is very important to note in the data given below, that the most recent years are not reflected, although that is where the recent policy changes have been mostly happening, and the large effects on electricity of underpricing from and underpayment to the natural gas sector are not really captured either. But we do still get a sense of what the performance has been, albeit a partial one. In short, what we see is tariffs well below costs (even on a financial basis without accounting for the economic cost of underpricing natural gas), and a high degree of cross-subsidies between customer classes.

The analysis of cost recovery focuses on the data for the holding company- EEHC. The Quasi fiscal deficit (QFD) measures the revenue gap for a utility by measuring the difference between what an ideal utility with no underpricing, no under collection and technical losses within the 5 percent level would earn and what the real utility actually collects as revenues. The QFD can be divided into three main components: revenue gap attributable to under collections, underpricing, and excessive losses. For Egypt given the structure of the sector, it makes
<br>|e y y g , <br>located just in the south of Dakha<br>|
|Brazil<br>China|Presquisa<br>sobre Padr6es de Vida<br>China Health and Nutrition Survey|1995-96<br>1991<br>|Regional<br>Regional<br>|<br>4940<br> <br>3616<br>|1697<br>865<br>|1791<br>883<br>|1678 Month<br>850 Month<br>|0-4.99<br>0-4.99<br>|The areas are south-east and north-east regions<sup>only.</sup><br>8 provinces are covered by the survey covering<sup>both urban</sup><br>and rural areas within them. This sample is diverse in termns<br>of socioeconomic<br>factors (income, employment, education<br>and modernization) and other related health,<sup>nutritional and</sup><br>demographic measures.|
|Cote<br>d'Ivolre|LSMS<br>|1988<br>|National<br>|1600<br>|2121<br>|2121<br>|2120 Month<br>|0-4.99<br>||
|Egypt|Egypt Integrated Household<br>Survey|1997<br>|National<br>|2500<br>|1427<br>|1434<br>|1430 Month<br>|**0-4.99**<br>||
|Ghana<br>|LSMS<br> <br>|1987-88<br>|National<br>|3200<br> <br>|2349<br>|2350<br>|2341 Month<br>|0-4.99<br>|f 22<sup>i ttl