rafmacalaba/datause-encoder-data huggingface dataset:
de Gorter, H., and Just D.R. (2009). “The Economics of a Biofuel Mandate” _American Journal of Agricultural Economics._ 91(3), p. 738–750.

de Miranda, S., H., G., (2010). “Brazilian Biofuels Policies” A presentation presented at the IATRC Annual Meeting, Berkeley California, December 12- 14, 2010.

Elobeid, A., and S. Tokgoz. (2008). Removing Distortions in the U.S. Ethanol Market: What does it imply for the United States and Brazil? American Journal of Agricultural Economics November 90 (4): 918–932.

Drabik, Dusan. (2011). “The Theory of Biofuel Policy and Food Grain Prices.” Charles H. Dyson School of Applied Economics and Management Working Paper # 2011-20, Cornell - - University, 12 December. http://dyson.cornell.edu/research/researchpdf/wp/2011/Cornell <u>Dyson wp1120.pdf</u> Gopal, A. R., and Kammen, D. M. (2009) “Molasses for ethanol: The economic and environmental impacts of adding a new pathway to the lifecycle greenhouse gas analysis of sugarcane ethanol”, _Environmental Research Letters_ , 4, 1 -5.

Havránek, T., Havránková, Z., and Janda, K. (2012). “Demand for Gasoline is More PriceInelastic than Commonly Thought.” _Energy Economics_ , 34(1): 201–207.

Jank, Marcos. (2012). “The Ethanol Industry in Brazil.” Presentation made to Invited Panel <u>Brazil Focus Session, at the 28th IAAE Triennial Conference, The Global Bio-Economy, Foz do</u> Iguaçu, Brazil, 18- 24 August 2012.

Khanna, M., H. Onal, C.L. Crago, and K. Mino (2013) “Can India Meet Biofuel Policy Targets? Implications for Food and Fuel Prices,” _American Journal of Agricultural Economics_ 95(2): 296302.

Kliauga, Erika M., Harry de Gorter and David R. Just. (2011). “Measuring the Subsidy Component of Biofuel Tax Credits and Exemptions.” Chapter 5, pages 49-64 in Andrew Schmitz, Norbert Wilson, Charles Moss, and David Zilberman (editors) The Economics of <u>Alternative Energy Sources and Globalization. United Arab Emirates: Bentham Science</u> Publishers.

Lapan, H. and G. Moschini (2012). “Second-best Biofuel Policies and the Welfare Effects of Quantity Mandates and Subsidies,” _Journal of Environmental Economics and Management_ , 63, p. 224-241.

Menezes, Tatiane A., Carlos R. Azzoni and Fernando G. Silveira. (2008): Demand elasticities for food products in Brazil: a two-stage budgeting system, _Applied Economics_ , 40:19, 2557-2572.

Rajcaniova, M., Drabik, D., and Ciaian, P. (2011). “How Policies Affect International Biofuel Price Linkages.” Working Paper. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1945022 35
which is neither a large producer or consumer of the products that usually travel on such vessels.<sup>5</sup> The fleet composition effects we observe in PR are not directly relevant for products that are typically containerized, but we further hypothesize that JA protection gives domestically-owned suppliers of container shipping greater scope to link freight charges to the physical weight of the products they transport. One likely mechanism is that air shipping is the only viable alternative transport mode on this route, and the cost of air shipping is highly dependent on physical weight.

We motivate our empirical framework with an adapted version of Krugman and Venables (1996). Among final products, the model’s predictions are those of a conventional gravity model of trade: PR buyers substitute away from U.S. mainland sources in products exposed to JA trade costs, buying instead from other sources. Among products that are upstream in production chains, the model allows a larger set of behavioral responses to trade costs. Conventional substitution responses are possible, but trade in upstream products may also be reduced through the “production location effects” proposed by Hillberry and Hummels (2002). High maritime trade costs with a key supplier mean that industries that would otherwise import sea-shipped upstream products for processing in PR do not locate there at all. The consequence may be reduced levels of _total_ import demand among sea-shipped products, rather than mere substitution away from U.S. mainland sources.

We use a pooled product-level gravity model to study PR’s relative import demand for product characteristics that affect their products’ mode of transport and transport charges.<sup>6</sup> The characteristics we study are the products’ a) vessel share

> 5JA-compliant tanker and bulk vessels are active in U.S. waters, but appear to operate primarily on high volume back-and-forth routes. A November 2023 review of U.S.-flagged vessels built after 2014 on vesselfinder.com identifies several oil/chemical tankers working in the Gulf of Mexico, and others linking the U.S. west coast either to Alaska or to Hawaii and then sometimes Guam. One large U.S.-flagged bulk carrier of recent vintage is active on the Great Lakes.

> 6We focus on imports because most of the vessel freight between the U.S. and PR travels in

3
This analysis is not without limitations. The list of data sets under consideration is not exhaustive and might have missed data sets due to questionnaire unavailability or unavailable translations. A similar review is needed for data sets focused on children and should be extended to high income countries.

Overall, the questions on disability found in surveys/census in the past decade are insufficient to ensure the global and systematic monitoring of progress called for by the 2030 Agenda for Sustainable Development to increase the availability of disaggregated data by disability (target 17.18). Although most LMICs have ratified the CRPD, there is much work to be done to fulfill Article 31 of the CRPD that States Parties undertake to collect appropriate information <mark>, including statistical and research data</mark> ” (United Nations 2006).

There is, however, progress compared to the situation up to the 1990s or early 2000s, when there was no internationally comparable disability data at all, let alone any internationally tested and comparable instruments. There are now some data that can be analyzed to inform disability policies at both national and international levels, notably with the surveys and censuses with functional difficulty questions listed in Tables 1 and 2. More research is needed to analyze such data sets.

At the same time, data collection needs on disability remain staggering: the results above show that there is still more work to do in fulfilling the data revolution that is needed to monitor the implementation of the 2030 Agenda for Sustainable Development as well as the CRPD.

National statistics agencies should use internationally tested functional difficulty questions in their population surveys and censuses (United Nations 2015). International agencies and donors have an important role to play in encouraging the collection of internationally comparable disability data. Functional difficulty questions should be included as part of core questionnaires, not as optional and changeable modules as currently in the DHS (Casebolt 2020). Two widely available data sources in LMICs, the global FINDEX and the LSMS of the World Bank, do not generally have questions on disability and if they do, they sometimes do not apply the best evidence for disability measurement (e.g. the 2012 LSMS for Haiti (ECVMA)). The systematic adoption of the WGSS in the core questionnaires of international surveys such as DHS, the global FINDEX, and LSMS would go some way in producing the data that are needed to assess whether people with disabilities are left behind or if they have proper opportunities and human rights.

5
**Table 1.** Diagnostic accuracy by country

||(1)<br>Diarrhea|(2)<br>Pneumonia|(3)<br>Malaria|(4)<br>Post-partum<br>hemorrhage|(5)<br>Neonatal<br>asphyxia|(6)<br>Tuberculosis|(7)<br>Diabetes|(8)<br>All|
|---|---|---|---|---|---|---|---|---|
|Kenya|0.76<br>(.74-.77)<br>[4505]|0.78<br>(.77-.80)<br>[4491]|NA|0.88<br>(.87-.89)<br>[4481]|0.86<br>(.85-.87)<br>[4476]|0.96<br>(.95-.96)<br>[4488]|0.58<br>(.56-.59)<br>[4489]|0.80<br>(.80-81)<br>[26,930]|
|Madagascar|0.18|0.38|0.96|0.55|0.52|0.84|0.12|0.51|
||(.12-23)|(.31-.45)|(.94-.97)|(.47-.62)|(.45-.60)|(.78-.90)|(.08-.16)|(.48-53)|
||<br>[642]|<br>[642]|<br>[642]|<br>[642]|<br>[642]|<br>[642]|<br>[642]|<br>[4,494]|
|Mozambique|0.73|0.66|0.96|0.76|0.60|0.86|0.34|0.70|
||(.69-76)|(.62-.70)|(.95-.98)|(.72-79)|(.56-.64)|(.83-.89)|(.30-.38)|(.69-.72)|
||<br>[725]|<br>[725]|<br>[725]|<br>[724]|<br>[724]|<br>[725]|<br>[725]|<br>[5,073]|
|Niger|0.92|0.47|0.97|0.62|0.58|0.74|0.11|0.63|
||(.90-95)|(.41-.54)|(.95-.98)|(.65-.68)|(.52-.65)|(.68-.80)|(.07-.14)|(.61-.65)|
||<br>[519]|<br>[519]|<br>[519]|<br>[601]|<br>[601]|<br>[519]|<br>[519]|<br>[3,797]|
|Nigeria|0.26<br>(.24-27)|0.42<br>(.41-.44)|0.88<br>(.87-.89)|0.52<br>(.51-.54)|0.36<br>(.34-.37)|0.58<br>(.56-.60)|0.30<br>(.28-.32)|0.47<br>(.47-.48)|
||<br>[4711]|<br>[4719]|<br>[4669]|<br>[4505]|<br>[4386]|<br>[4676]|<br>[4628]|<br>[32,294]|
|Senegal|0.63<br>(.49-.77)|0.56<br>(.39-.73)|0.04<br>(.00-.08)|NA|NA|0.73<br>(.61-.85)|NA|0.49<br>(.41-.57)|
||<br>[152]|<br>[152]|<br>[152]|||<br>[152]||<br>[608]|
|Sierra Leone|0.95<br>(.93-.97)|0.57<br>(.53-.61)|0.99<br>(.99-1.0)|0.94<br>(.92-.95)|0.86<br>(.84-.90)|0.88<br>(.86-.91)|0.25<br>(.22-.28)|0.78<br>(.77-.79)|
||<br>[826]|<br>[824]|<br>[824]|<br>[824]|<br>[824]|<br>[824]|<br>[824]|<br>[5,770]|
|Tanzania|0.87|0.75|0.97|0.89|0.81|0.88|0.46|0.80|
||(.82-.93)|(.69-.80)|(.95-.99)|(.84-.94)|(.76-.86)|(.84-.92)|(.39-.52)|(.78-.82)|
||<br>[542]|<br>[542]|<br>[542]|<br>[542]|<br>[542]|<br>[542]|<br>[542]|<br>[3,794]|
|Togo|0.18|0.61|0.97|0.58|0.11|0.85|0.53|0.55|
||(.09-.26)|(.49-.73)|(.95-.99)|(.45-.70)|(.04-.18)|(.76-.93)|(.41-.66)|(.50-.60)|
||<br>[302]|<br>[302]|<br>[302]|<br>[298]|<br>[298]|<br>[302]|<br>[302]|<br>[2,106]|
|Uganda|<br>0.39|<br>0.47|<br>0.94|<br>0.65|<br>0.58|<br>0.78|<br>0.49|<br>0.61|
||(34-44)|(43-52)|(92-96)|(60-69)|(53-63)|(74-82)|(44-53)|(60-63)|
||..<br>[709]|..<br>[709]|..<br>[708]|..<br>[709]|..<br>[707]|..<br>[709]|..<br>[709]|..<br>[4,960]|
|All|<br>0.59|<br>0.57|<br>0.85|<br>0.71|<br>0.59|<br>0.81|<br>0.35|<br>0.64|
||(.56-.61)|(.54-.59)|(.82-.89)|(.69-.73)|(.57-.61)|(.79-.83)|(.33-.37)|(.62-.65)|
||<br>[13,633]|<br>[13,625]|<br>[9,083]|<br>[13,326]|<br>[13,198]|<br>[13,579]|<br>[13,380]|<br>[89,824]|

Notes: Mean diagnostic accuracy: i.e., the percentage of health workers who correctly diagnose the condition, by country and condition, with all individual country means calculated using country-specific sampling weights, and the (unweighted) mean across conditions for each country in column 8. 95% confidence intervals in parenthesis and 24
demographic dynamics, etc.) leading to changes in the censored headcount ratio will not necessarily be the same, providing us with a source of exogenous variation.

Our IV regression results, displayed in Table 3, consistently indicate that Poverty Centrality is a significant and negative predictor of the long-run changes in the headcount ratio. Moreover, the F- statistics for the significance of the instrument is always above 400, suggesting the validity of our instrument (see Appendix 3 for first stage results).

Thus, through both of our robustness checks, our findings remain consistent, reinforcing the importance of considering the Poverty Space in analyses of multidimensional poverty dynamics.

**Table 3. Change in headcount ratio models results using IV approach.**

||Change i<br>|_Depe_<br>n censored h<br>|_ndent varia_<br>eadcount ra<br>|_ble:_<br>tio per year<br>|(<br>ΔHci(t)<br>Δt<br>~~)~~<br>|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Initial poverty centrality (Sc(t))<br>||-0.006<sup>***</sup>|-0.005<sup>***</sup>|-0.005<sup>**</sup>|-0.005<sup>***</sup>|
|||(0.001)|(0.002)|(0.002)|(0.002)|
|Initial censored headcount ratio (Hc(t))|||-0.008<sup>*</sup>||-0.009<sup>**</sup>|
||||(0.005)||(0.004)|
|Initial headcount ratios of all other<br>indicators (∑<br>H<br>i≠c<br>ci<sup>(t)) </sup>||||0.008<sup>*</sup>|-0.001|
|||||(0.005)|(0.001)|
|Constant|-0.002<sup>***</sup>|-0.002<sup>**</sup>|-0.001<sup>*</sup>|-0.006<sup>**</sup>|0.001<sup>*</sup>|
||(0.001)|(0.001)|(0.001)|(0.003)|(0.000)|
|Observations|637|637|637|637|637|
|R<sup>2</sup>|0.371|0.407|0.429|0.429|0.429|
|Adjusted R<sup>2</sup><br>|0.287|0.326|0.350|0.350|0.350|
|Instrument F-statistic<br><br>||569.26|411.90|411.90|411.90|
|Country dummy (αc)<br>|Yes|Yes|Yes|Yes|Yes|
|Indicator dummy (βc)<br>_Notes:_In this case the number of data poi<br>|Yes<br>nts increases<br>|Yes<br>because for<br>|Yes<br>some count<br>|Yes<br>ries the mo<br>|Yes<br>st similar<br>|

_Notes:_ In this case the number of data points increases because for some countries the most similar country has data on fewer than 10 indicators. See Table A7 for first-stage results. Robust Standard errors in parentheses.<sup>*</sup> p<0.1,<sup>**</sup> p<0.05,<sup>***</sup> p<0.01.

24
# Figure A2.3. Global interest rates, confidence, and uncertainty A. Nominal interest rates B. Real interest rates

## B. Real interest rates

<!-- Start of picture text --> Percent Percent<br>15 6<br>3<br>10<br>0<br>5<br>-3<br>0 -6<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>C. Business confidence D. Policy uncertainty<br>Index, 2015-18 = 100 Index, 2015-18 = 100<br>102 160<br>101<br>130<br>100<br>100<br>99<br>70<br>98<br>97 40<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>Sources: Bank for International Settlements; Davis (2016); European Commission; Haver Analytics; International<br>Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-operation and Development; country<br>sources; World Bank.<br><!-- End of picture text --> Note: Variables are weighted by GDP in U.S. dollars. Shaded areas indicate global recessions in 1975, 1982, 1991, and 2009, and global downturns in 1958, 1998, 2001, and 2012. World aggregates are not computed if data are available in less than 4 countries.

68
for more direct tests of whether constraints vary systematically across categories of firms and what feedback, if any, there is between firm performance and perceived constraints. The analysis looks at two broad issues. First, it examines how well the subjective rankings correlate well with the realities they are trying to measure; e.g. are firms that experience more outages more likely to report a lack of access to reliable power a constraint? It uses both more objective data within the survey (e.g. the time and monetary costs of complying with regulations, frequency of power outages, losses from crime etc.) as well as outside data sources. Second, it examines whether subjective rankings vary systematically by respondent characteristics, whether what you say you care about depends on who you are. Attention is focused on two firm characteristics -- firm size and firm performance. We measure firm performance based on employment growth, using dummies for whether firms are expanding or contracting.<sup>3</sup> There are three channels through which firm characteristics could affect how constraining a dimension of the business environment is reported to be: i) conditions experienced may vary with firm size or performance (e.g. smaller firms report more limited access to finance or may be easier targets for officials seeking bribes). Indeed, there are concerns that small firms face additional burdens, particularly in developing countries, that may hold them back (Tybout 2000, Hallward-Driemeier and Aterido (2008), Aterido et al. (2009)); ii) relative priorities across dimensions of the investment climate may vary with firm characteristics (e.g. labor skills may be more important for expanding firms while labor regulations may matter more for contracting firms); and, iii)

> 3 Employment growth is correlated with sales growth, but the former has the benefit of not needing to be deflated and is not subject to changes in markups, so growth reflects real changes.  Growth rates are generally calculated over a 3 year period to smooth out fluctuations.

4
Growth in services has been inadequate to make up for these declines in the primary and secondary sectors. Moreover, across the region economy wide employment losses have far outweighed production losses.<sup>10</sup>

**Figure 3.1. Real GDP level in 2001 relative to 1989** (1989=100)

<!-- Start of picture text --> 0 20 40 60 80 100 120 140<br>Poland<br>Slovenia<br>Hungary<br>Slovakia<br>Czech Rep.<br>Estonia<br>Latvia<br>Lithuania EU Accession<br>Albania<br>Croatia<br>Balkans<br>Romania<br>FYR M acedonia<br>Bulgaria Slavic<br>Serbia and M ontenegro<br>M oldova<br>Belarus Caucasus<br>Russia<br>Ukraine<br>Armenia Central Asia<br>Azerbaijan<br>Georgia<br>Uzbekistan<br>Turkmenistan<br>Kazakhstan<br>Kyrgyzstan<br>Tajikistan<br><!-- End of picture text --> Source: UNICEF MONEE project database

> 10 The World Bank.  2000. _Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia._ Washington, D.C.

12
8 It should be noted that the theory being tested is particularly stringent, since it implies that measuring positive or negative saving _at a point in time_ leads to future welfare being higher or lower than current welfare over some interval of time. In the real non-optimal world a positive exogenous shock (such as an improvement in the terms of trade) in the year immediately following the time when saving turned negative could easily swamp the effect of negative saving, and conversely for positive saving and negative shocks.

# **References**

Asheim, G. and Weitzman, M. (2001). Does NNP growth indicate welfare improvement? _Economics Letters_ 73, pp. 233-239.

Dasgupta, P., and Mäler, K-G (2000). Net national product, wealth, and social well-being. _Environment and Development Economics_ 5, pp. 69-93.

Ferreira, S. and J. Vincent (forthcoming). Genuine Savings: Leading Indicator of Sustainable Development? _Economic Development and Cultural Change_ .

Ferreira, S., K. Hamilton and J. Vincent (2003). Comprehensive Wealth and Future Consumption. (mimeo). Washington: The World Bank.

Hamilton, K. and M. Clemens, 1999. Genuine Saving Rates in Developing Countries. _World Bank Economic Review_ 13:2, 333-56.

Hamilton, K., 2003. Sustaining Economic Welfare: Estimating changes in total and per capita wealth. _Environment, Development and Sustainability_ 5:419-436.

Hamilton, K., and J.M. Hartwick, 2005. Investing Exhaustible Resource Rents and the Path of Consumption. _Canadian J. of Economics_ 38:2, 615-21, May 2005.

Pritchett, L. 1996. Where Has All the Education Gone? Policy Research Working Paper 1581. Washington: The World Bank.

Pritchett, L., 2000. The Tyranny of Concepts: CUDIE (Cumulated, Depreciated, Investment Effort) is _not_ Capital. _J. of Economic Growth_ 5 (December), 361-84.

Sala-i-Martin, X., 1997. I Just Ran Two Million Regressions, _American Economic Review_ 87:2, 178-183.

Weitzman, M. L., 1976. On the Welfare Significance of National Product in a Dynamic economy. _Quarterly Journal of Economics_ 90 (1), pp. 156-162.

World Bank, 2002. _World Development Indicators 2002_ . Washington: The World Bank.
relationship of the teacher monthly earnings differential (relative to comparable workers) and harmonized student test scores—and reveals a positive association between the two. Moreover, we observe a similar positive association in Figure 4 that plots the teacher hourly earnings differentials against student test scores. These associations are statistically significant at conventional levels (Table 11).<sup>32</sup> If we drop Namibia—the country with the highest test scores—from the sample, the association with monthly earnings differentials remains, but not the association with hourly earnings differentials. As Table 11 shows, controlling for GDP per capita makes little difference to the coefficient and no difference to the statistical significance.

When we use the rate of learning poverty as a proxy for student performance, we also find an association (this time negative, consistent with the harmonized test score results) between the teacher earnings differential and the proportion of children living in learning poverty. However, because of incomplete data coverage, only 6 countries are included in the analysis and the results are not statistically significant (Table 11).

In our final examination of the association between student learning and earnings, we use microdata from 10 Francophone African countries which include test scores for students and selfreported monthly salaries of teachers (see Appendix 1 for details).<sup>33</sup> We estimate the model both without and with a set of controls (Table A22 Columns 1-6 and 7-12 respectively). We find strongly statistically significant, positive associations when pooling the data across countries in both specifications. While there is some variation depending on the exact specification used at the country level, on the whole, the microdata are consistent with the notion that better paid teachers are in schools with higher performing students.<sup>34</sup> This may or may not be the result of a causal relationship: we cannot, for example, rule out that the best teachers are paid more and are sent to schools with better students—although our controls do capture teacher education and experience.

> 32 As is visible in Figures 3 and 4, the association is driven by two groups of countries: those with low premia and low test scores and those with higher premia and higher test scores. Within these groups the association is close to zero and not statistically significant, although samples are small.

> 33 Note that this is no longer examining the relative pay of teachers—rather the absolute level. 34 This is the case in Grades 2 and 6 combined, in Grade 2, and in Grade 6 without controls; the coefficient is still positive but no longer statistically significant in Grade 6 with controls. When carrying out the analysis country-bycountry, the positive association holds in Grades 2 and 6 in eight countries (six with controls) and is statistically significantly positive in five countries (four with controls). With controls, there are two countries with statistically significant negative associations (Benin and Burundi).

20
# **Table 4   Cointegration between the domestic and the world prices**

Augmented Dickey-Fuller Test (without trend)<sup>1</sup>

||Reform year||ADF statis|tic|
|---|---|---|---|---|
|||Before|After|With structural break|
|Brazil arabica|1990|-1.74|-2.11|-3.22 **|
|Brazil robusta|1990|-1.60|-2.52|-3.28 **|
|Ethiopia arabica|1992|0.11|-3.59 ***|-3.15 **|
|Kenya arabica|1993|-1.95|-4.35 ***|-4.96 ***|
|Tanzania arabica|1994|-2.85 *|-2.83 *|-3.78 ***|
|Tanzania robusta|1994|-2.51|-1.92|-2.28|
|Colombia arabica|1995|-1.78|-3.43 **|-2.50|
|India arabica|1996|-2.66 *|-4.23 ***|-3.42 **|
|India robusta|1996|-2.59 *|-3.40 **|-3.21 **|
|Mexico arabica|1993|-2.10|-2.58 *|-3.53 ***|
|Uganda robusta|1992|-2.76 *|-2.10|-3.67 ***|
|Madagascar robusta|1988|-1.35|-3.24 **|-3.82 ***|
|Togo robusta|1996|-1.67|-4.27 ***|-2.56|
|Angola robusta|1991|-0.58|-2.45|-1.89|
|Cameroon robusta|1995|-1.23|-3.32 **|-2.20|
|Central African Republic robusta|1991|-0.91|-2.59 *|-2.27|
|Ghana robusta|1992|-4.37 ***|-2.82 *|-5.17 ***|

* Null of unit root rejected at 10% significance ** Null of unit root rejected at 5% significance

***  Null of unit root rejected at 1% significance

1 6 months lag length is used 24