rafmacalaba/data-use-mentions-extended huggingface dataset:
indicated in the individual studies. But it should be noted that they differ considerably as between commodities e.g. most of the wheat crop in the north-west is harvested and sold during a short six week period, whereas rubber and coconut trees in the south produce year round. There are also differences for the same commodities between producing regions. In addition the data available for some commodities(e.g. rubber) in earlier years is for "crop year " averages, which differ from Indian fiscal years. Some of the harvest periods also overlap fiscal years but have been assigned to particular fiscal years in presenting the estimates. The correspondences between these periods and fiscal years are indicated in the individual studies, but they should borne in mind in interpreting the time series, and especially the NPC estimates for agriculture as a whole and the various other aggregations.

There can be substantial differences between the prices of different varieties and grades of a commodity, and we have been careful to specify which ones have been used in the price comparisons. As far as possible we have used price data for the same or similar varieties in the estimates for each year, including the long term estimates. But doing so has inevitably involved making some _ad hoc_ judgments which are noted in the text or the relevant tables, and also in the original commodity studies. The varieties for which price comparisons have been made are generally those with the largest production that the authors of the original commodity studies 2 If the levy were of a fixed quantity rather than a fixed proportion, it could be argued that the free market price should be used, as this is the relevant incentive that producers will take into account in deciding whether to increase or decrease their production. On the other hand in the long run the size of the levy and the acquisition price will affect the total return from the crop and long run investment and production decisions. In practice the Indian levies have been applied to processors (mainly sugar mills and rice mills)and have been fixed
Distribution: Linking Macroeconomic Models with Household Survey Data at the Global Level”. Washington, DC: World Bank.

- Bussolo, M., R. de Hoyos, D. Medvedev,  and D. van der Mensbrugghe. 2008. “Global Climate Change and its Distributional Impacts”. Washington, DC: World Bank.

- Chen, S. and M. Ravallion. 2008. "The developing world is poorer than we thought, but no less <u>successful in the fight against poverty", World Bank Policy Working Paper  4703.</u>

- Cline, W. 2007. _Global Warming and Agriculture: Impact Estimates by Country._ Washington, DC: Center for Global Development and Peterson Institute for International Economics.

- de Gorter, H. and D. Just. 2010. “The Social Costs and Benefits of Biofuels: The Intersection of Environmental. Energy and Agricultural Policy”. _Applied Economic Perspectives and Policy_ . 32 (1):  4-32.

- de Hoyos, R. 2008. “Global Income Distribution Dynamics (GIDD)”. Washington, DC: World Bank.

- de Hoyos, R, and D. Medvedev. 2011. “Poverty Effects of Higher Food Prices: A Global Perspective,” _Review of Development Economics_ , forthcoming.

- Food and Agricultural Organization. 2008. “Soaring Food Prices: Facts, Perspective, Impacts and Action Required”. High-Level Conference On World Food Security. FAO, Rome.

- Habib-Mintz, N. 2010.  Biofuel investment in Tanzania: Omissions in implementation. Energy Policy 38, pp. 3985–3997.

- Hertel, T. 2009. “Analyzing the Poverty Impacts of Biofuel Mandates”. Transition to a Bioeconomy: Global Trade and Policy Issues Conference, Washington DC.

- Hertel, T., Tyner, W., and Birur, D. 2008. “Biofuels for All? Understanding the Global Impacts of Multinational Mandates”. GTAP Working Paper No. 51.

- Hahn, R.,
correlated with project outcomes. Curiously, they also find that measures of World Bank project task manager quality matter significantly for the ultimate outcome of projects.

Knowledge products of a variety of forms have long been part of the Bank‘s ―arsenal‖ for promoting economic development; country-based economic analysis dates back to the 1950s. Some of these analyses have been conducted on broad economic issues (e.g., Country Economic Memorandum), as well as sectoral issues (e.g., Water Resource Management in Country X). Bank-wide sector strategies are also produced, as are regional ―flagship‖ reports on selected topics.

Research reports are also produced as a means of consolidating existing global knowledge on a selected topic. Policy Research Reports and the World Development Report are two examples—the former produced from time-to-time, and the latter on an annual cycle. Last, but certainly not least, the Bank is a central figure in the collection/collation/processing/documentation and presentation of data on developing countries. The publicly available World Development Indicators are the primary vehicle in this realm, but there are other databases – including those compiled by researchers--that are available to the public as well.

The Research function<sup>67</sup> at the Bank itself has been led by the research department under the direction of the Chief Economist and leadership of the Research Department Director. Both in the past and in the present, these leaders have been leading thinkers in development economics. In addition, the Bank‘s researchers themselves have strong publication records.<sup>68</sup> That said, it would probably be an overstatement to say that the Bank has been the guiding force in development thinking. Instead, there has been a symbiotic relationship between the Bank‘s research department and the research community outside the Bank.

Measuring the impact of these activities on development is notoriously difficult. The Independent Evaluation Group (IEG) of the World Bank attempted an evaluation of country level studies
concentration of imports in each of the Asian countries. Said this differently, it measures from how many countries an HS6 product is imported by each of our possible destination countries. Using notations, this concentration measure indicates the number of exporting countries, _k_ , from which a product _g_ is imported in each of our possible destination countries, _j_ , thus: where time subscripts are omitted for simplicity. In this expression, _s_<sup>_jk_</sup> _g_ = _m_<sup>_jk_</sup> _g_<sup>_/mg_,and</sup><sup>_mjk_</sup> _g_<sup>isthe</sup> import value of product _g_ imported by country _j_ from country _k_ and _ng_ is the total number of countries exporting the product _g_ .<sup>16</sup> To obtain a competition measure at the firm-country level, the next step is to match the above productspecific index with information on French exports at the firm-product-country of destination level over the period 1999-2005. This allows us to compute the average of the Herfindahl index at the firm level. To avoid compositional effects and potential endogeneity concerns, we take the average of this firm level index keeping constant the HS6 product range of French exporters at 1999, while allowing the concentration index to vary over time.<sup>17</sup> In this first measure, the number of foreign competitors is proxied by the number of countries exporting the same product line towards the Asian markets. To account for the competitive pressures that could emerge from other firms selling the same product line to the same destination, a second measure of competition is needed. To proxy foreign competition suffered from other firms, we introduce a second measure that captures the degree of competition due to the mass of French firms selling the same HS6 product in the liberalized market.<sup>18</sup> Using French Customs export data at the firm-product-country of destination level, we construct an Herfindahl index that this time
in private pre-school institutions as well.

# III. **Data and methodology**

## **Data sources**

This analysis combines household survey data with data from the national accounts, Ministry of Finance public finance data, and reports of the Statistical Office of the Republic of Serbia. The household surveys used as the basis for the analysis are the Survey on Income and Living Conditions (SILC) for 2017 (which refers to 2016 income) and Household Budget Survey (HBS) for 2016. The SILC is the most comprehensive survey used in the European Union countries to collect microdata <mark>on income, poverty, social exclusion and living conditions.</mark> The SILC has been the basis for officially monitoring household welfare, poverty, and social inclusion in Serbia since 2013. The HBS contains both income and expenditure data, but the level of details on income components and benefits received is less than the SILC, and the sample size is smaller. Therefore, this analysis is primarily based on the SILC while household consumption information in the HBS is used to estimate the impacts of indirect taxes. Appendix 4 explains how value added tax and excises are estimated from the HBS and imputed for each household in the SILC data.

## **Approach**

This paper follows the Commitment to Equity (CEQ) approach developed by Lustig (2018) to analyze the distributional impacts of fiscal interventions. For each household, we define the following income concepts and calculate them for each household in the survey data: 10
clusters.<br>We have identified 32,339 vascular plant species that are in both databases. As before, we rasterize our<br>occurrence maps and compute cell counts at 5 km resolution. Borgelt provides species maps in a raster<br>stack with much coarser resolution (50 km). We extract raster layers for the 32,339 common species and<br>add across layers to obtain relative incidence scores for the 50 km grid cells. Then we use mean smoothing<br>to approximate the effect of higher resolution.<br>Figure 5 provides the comparative results, displayed as ranks in 10 groups. Inspection of the two maps<br>reveals essentially the same density pattern, with the exception of somewhat more extensive high-ranking<br>In this section, we have compared our estimated GBIF occurrence maps with species-matched sets of<br>expert maps from recent publications for mammals, ants and vascular plants. In all three cases, the<br>comparative global patterns of species density are quite similar. Where the patterns diverge, the<br>maps and expert “native range” maps are attributable to “undershooting”, where the expert map<br>boundaries exclude many GBIF occurrences, or “overshooting”, where GBIF occurrences are persistently<br>absent in parts of the native range maps. For ants, where the research also employs aphahull estimation,<br>the differences are attributable to differences in the source databases and the relative conservatism of our<br>approach to map estimation. For plants, where the research employs GBIF occurrences and the global<br>pattern similarity is most striking, the few discrepancies are attributable to temporal and spatial<br>restrictions imposed by the expert research team.<br><!-- End of picture text -->
Eaton, J., and S. Kortum. 1999. “International Technology Diffusion: Theory and Measurement.” _International Economic Review_ 40 (3): 537–70.

Eaton, J., and S. Kortum. 2002. “Technology, Geography, and Trade.” _Econometrica_ 70 (5): 1741–79.

Feenstra, R., R. Lipsey, H. Deng, A. Ma, and H. Mo. 2005. “World Trade Flows: 1962– 2000.” NBER Working Paper 11040. Cambridge, Mass.: National Bureau of Economic Research.

Feenstra, R., and A. Rose. 2000. “Putting Things in Order: Trade Dynamics and Product Cycles.” _Review of Economics and Statistics_ 82 (3): 369–82.

Fieler, A.C. 2011. “Nonhomotheticity and Bilateral Export Trade: Evidence and a Quantitative Explanation.” _Econometrica_ 79 (4): 1069–101.

Foley, C.F., and W. Kerr. 2013. “Ethnic Innovation and U.S. Multinational Firm Activity.” _Management Science_ 59 (7): 1529–44.

Golub, S., and C. Hsieh. 2000. “Classical Ricardian Theory of Comparative Advantage Revisited.” _Review of International Economics_ 8 (2): 221–34.

Gould, D. 1994. “Immigrant Links to the Home Country: Empirical Implications for US Bilateral Trade Flows.” _Review of Economics and Statistics_ 76 (3): 500–18.

Hakobyan, S., and J. McLaren. 2016. “Looking for Local Labor Market Effects of NAFTA.” _Review of Economics and Statistics_ 98 (4): 728–41.

Hall, B., A. Jaffe, and M. Trajtenberg. 2001. “The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools.” NBER Working Paper 8498. Cambridge, Mass.: National Bureau of Economic Research.

Harrigan, J
characteristics, such as their household size and demographic composition, which can influence welfare in ways that may not be evident in consumer demand behavior. More precisely, if demands _q_ ( _pi_ , _yi_ , _xi_ ) can be integrated back to a utility function _u_ ( _qi_ ,<sup>_x_</sup> _i_ ) then the same demands also maximize the broader welfare function _w_ [ _u_ ( _qi_ , _xi_ ), _xi_ ]. Thus there is no unique utility function that can be inferred from market demands alone.<sup>13</sup> We need stronger identifying assumptions, or more data. The rest of this paper will consider the scope for using data on selfassessed welfare for addressing these problems.

# **3. Anchoring poverty measures to subjective welfare**

There are essentially two approaches to collecting subjective data on welfare. The first uses qualitative categories in the welfare space. An example is the ―economic ladder question‖ (ELQ): _“Imagine six steps, where on the bottom, the first step, stand the poorest people, and on the highest step, the sixth, stand the rich (show a picture of the steps). On which step are you today?”_ A popular indicator of overall welfare has been the ―satisfaction with life‖ (SWL) question: _“Overall, how satisfied (content, happy) are you with your life? Are you (1) very unsatisfied; (2) unsatisfied; (3) neither unsatisfied nor satisfied; (4) satisfied; (5) very satisfied?”_ The ELQ is arguably better suited to poverty measurement as it is tied to a concept of economic welfare, while ideas such as SWL or ―happiness‖ are broader and more nebulous in scope.

The second approach asks for a money-metric of subjective welfare. An example is the ―minimum income question‖ (MIQ): _“What income level do you personally consider to be absolutely minimal? That is to
of classrooms in poor condition that cannot be

> 20 Gunnerson et al. (2009) use retrospective data to study the impact of parental participation and school outcomes on education outcomes in eight Latin American countries. They find that correcting for endogeneity substantially increase the estimated impact of parental participation on students‟ test scores.

> 21 Note that this may be also due to the family moving to a different location, or the student being transferred to a different school, where more than one are available.
26

# **4.3 Measuring the Level of Infrastructure in Information and Communications Technologies**

Empirical assessments of the effects of ICTs on aggregate output and economic growth typically entails distinguishing the economy-wide capital stock into ICT and nonICT capital stock. However, due to the current non-availability of data to construct figures for the ICT capital stock, it will be seen in the empirical methodology section that we take an alternative approach. The approach entails including as a separate regressor a proxy for the stock of ICTs currently available in the economy, in addition to the aggregate capital stock variable as dictated by the assumption of the aggregate CobbDouglas production function. By this estimation methodology, we are implicitly testing for additional effects of ICTs on economic growth over and beyond that of its contribution as a “normal” component of the economy-wide capital stock. We will now consider the various indicators that we will use as proxies for the stock of ICTs available in the economy.

## **Number of Computers (per 1,000 persons)**

Personal computers per 1,000 population refers to the number of self-contained computers designed to be used by a single individual and is an indicator of personal computer penetration and use of relatively new technology for information processing. The data series was obtained from the World Bank World Development Indicators.

## **Number of Internet Users (per 1,000 persons)**

Internet users are people with access to the worldwide network. The data series was obtained from the World Bank World Development Indicators.

## **Number of Internet Hosts (per 10,000 persons)**

Internet hosts per 10,000 population refers to the number of computers with active Internet Protocol (IP) addresses connected to the Internet. All hosts without a country code identification are assumed to be located in the United States. It is used as an indication of how well a population has advanced to the level of adapting and using advanced communication channels (Internet) to serve its priorities. The data series
<sup>_Rn_is the aggregate land rent paid to all</sup> land in region _n_ . Given the log-normal distribution of land size, it follows that land rent in region _n_ also has a log-normal distribution, as follows:

## **V.C.2 Consumption Across Sectors**

Preferences for agricultural versus manufacturing goods are non-homothetic.<sup>49</sup> For all agents except middlemen, we represent this non-homotheticity using PIGL preferences (Eckert and Peters, 2023), in which an agent’s indirect utility function is given by:<sup>50</sup>

> 47We relax the perfect mobility assumption in Appendix H by introducing frictions to cross-sector mobility.

> 48This assumption is consistent with the empirical distribution of landholdings observed in Sri Lanka’s household survey data (see Appendix C.C).

> 49This assumption is strongly supported by Sri Lanka’s household survey data, in which the expenditure share of agricultural goods decreases with household income (see Appendix Figure A10).

> 50Similarly to Eckert and Peters (2023), we choose PIGL over other classes of non-homothetic preferences (e.g. Stone-Geary) due to its convenient aggregation properties.
varying resolutions. Our dataset comprises over 12 million households in four African countries (Malawi, Mozambique, Burkina Faso, and Madagascar) and, uniquely, contains precisely georeferenced measurements within two Malawian cities as well as repeated measurements of the same locations over time – two features lacking in prior studies.

We use these data to make four contributions relative to earlier work. First, we directly test a new type of deep learning model – specifically, vision transformers – against earlier deep learning architectures based on convolutional neural networks (CNNs) that are common in the literature, as well as against simpler models that use geospatial features and a tabular machine learning approach (XGBoost) for prediction. Specifically, we design a conditioning module that enables our transformer model to handle multi-modal inputs, integrating both satellite imagery and geospatial features simultaneously (see “Methods”). We test models that use satellite imagery from Landsat (30m/pixel), PlanetScope (3m/pixel), and/or SkySat (0.5m/pixel) sensors. We then compare these more sophisticated methods and inputs with simpler methods that rely Page 1 of 11
range of past experience and consistent with seemingly reasonable expectations for the near future in the absence of major global crises.

After reviewing what we know about recent growth, distributional change and absolute poverty reduction in the developing world, the paper discusses the proposed targets for absolute poverty. The discussion then turns to a broader concept of poverty, tailored more closely to the evolving ideas about social inclusion in the developing world. The final section concludes.

# **Recent trends in growth, redistribution and poverty reduction**

Roughly speaking, the measure of poverty obtained for a given distribution of consumption (or income) depends on the mean of that distribution (relative to the poverty line) and the extent of “inequality” in the distribution.<sup>4</sup> Consider the mean first. Using nationally representative surveys, the mean will naturally depend in part on national income. As is well known, the developing world as a whole has been maintaining a growth rate for GDP of around 6% over most of the last decade, though dipping substantially (and temporarily) in 2008-09, due to the global financial crisis. This is a full 2% points higher than the average growth rate of about 4% from the 1960s through to the mid-1990s.

The World Bank’s current growth projections assume that a full-blown Euro crisis will be avoided and the annual growth rate of GDP for the developing world will still be 6% in the coming few years (having fallen slightly this year) (World Bank, 2012b). Given current population projections, a 6% growth rate in the GDP over the coming decade would represent a 4.9% rate for GDP per capita.<sup>5</sup> While the recent growth has not been even across all regions, the three regions that account for the bulk of absolute poverty—East Asia, South Asia and SubSaharan Africa (SSA)—have also seen strong growth in recent years, namely around 8% in East Asia,
the closest formal firm) cannot be solely determined by the owner. It depends also on other factors such as the formal firm’s decision and the availability of specific locations. Finally, a large share of informal businesses in the sample operates from their own house, which is indicative of their limited ability to re-locate. To check the robustness of the results, additional analyses using different measures of distance and the subsample where informal firms operate in fixed premises are conducted. The additional results support the validity of the approach.<sup>11</sup>

# **c. Empirical Methodology**

The estimation of the spillover effects of ICT adoption from formal to informal firms uses a logit model. The specification of the estimation is the following: A A A 0 1 2 , (1) ii ii ii ii Where A A A denotes whether informal business AAnn = ββ + ββ DDAADDAADDnnD _i_ adopts a specific ICT technology (1 = yes, + ββ XX + εε and 0 otherwise), such as computers, tablets, cell phones, mobile money, and website. ii AAnn denotes the distance of informal business _i_ to the closest formal firm, which was captured either ii DDAADDAADDnnD by the standard WBES or the WBMES (see above for details). is a vector of controls that captures the characteristics of informal business _i_ that can affect the digital adoption decisions. Such characteristics include the number of workers in the last month, ii XX the number of years in operation, and a sector dummy as well as the owner's age, education, and prior experience in the same type of business. In an extension of the model, labor productivity is included.<sup>12</sup> Lastly, city dummies and clusters to control for city-specific effects of the three cities included in the surveys are incorporated.

In an extension, an interaction term is added to study how the distance effect varies by observable characteristics of informal businesses, which is formulated as follows: A A A