rafmacalaba/datause-encoder-data huggingface dataset:
32

**Table 13. The Estimated Effects of the Uruguay Round on MFN Tariff Barriers**

||Tariff|Rate|Tariff|Change|
|---|---|---|---|---|
|Product Group|Pre-<br>Uruguay|Post-<br>Uruguay|Absolute<br>Reduction|Percentage<br>Reduction|
|ALL INDUSTRIAL PRODUCTS'|6.4|4.0|-2.4|38|
|Industrial Tropical Products|4.2|1.9|-2.3|55|
|Natural Resource Products|3.2|2.0|-1.2|38|
|Manufactures of:<sup>2</sup>|||||
|Leather|6.7|5.4|-1.3|19|
|Rubber|5.3|3.4|-1.9|36|
|Wood|5.0|2.1|-2.9|58|
|Paper|4.8|1.6|-3.2|67|
|<br>Textiles & Clothing|15.2|11.4|-3.8|25|
|Metals|5.4|2.6|-2.8|52|
|Chemicals|6.7|3.0|-3.7|55|
|Minerals|4.7|2.9|-1.8|38|
|Food & Agricultural Products<sup>3</sup>|||||
|Cocoa Products|4.5|2.5|-2.0|44|
|Tobacco|17.3|11.2|-6.1|35|
|Coffee, Tea and Sugar|9.4|6.4|-3.0|32|
|Fruits and Vegetables|8.6|5.6|-3.0|35|
|Oilseeds, Fats and Oils|1.7|1.1|-0.6|35|
|Grains|6.6|4.5|-2.1|32|
|Dairy Products|15.8|11.9|-3.9|25|
|<br>Spices, Flowers and Plants|2.2|1.1|-1.1|50|
|ALL NON-ENERGY ITEMS<sup>4</sup>|6.5|3.9|-2.6|40|

'Defined by GATT to include eleven industrial categories (fish and products; wood, pulp, paper and furniture; textiles and clothing; leather, rubber, footwear, travel goods; metals; chemicals and photographic supplies; transport equipment; non-electric machinery; electric machinery; mineral products and precious metals; manufactured articles n.e.s.), nonagricultural tropical products (plaiting products, rubber and tropical wood, jute and hard fibers), and natural resource based products. The latter include: fish and fish products; forestry and forestry products; and non-ferrous metals and minerals. Tariff information from GATT (1993) Table 14.

2Based on GATT (1993) Table 15 and Appendix Table 5. The tariffs shown above are averages for the semi-manufactures and manufactures components of the GATT processing chains. Agricultural products are defined by GATT to include ten agricultural categories (fruit and vegetables; coffee, tea, cocoa, sugar, etc.; grains; animals and products; oilseeds, fats and oils; cut flowers, plants, vegetable materials; beverages and spirits; dairy products; tobacco; other agricultural products) plus agricultural tropical products (tropical beverages; spices and plants; certain oilseeds and oils; tropical roots, rice and tobacco; tropical fruits and nuts).

'The reported percentage tariff reduction for these products is given in GATT (1993) Table 20. Pre-Uruguay Round tariffs were estimated using the World Bank-UNCTAD SMART Database. These statistics, plus the percentage reductions reported by GATT were used to derive the post-Uruguay Round rate.

4Computed using 1992 OECD country trade weights.
# **Background**

There is a growing consensus that poor quality care is a major constraint in further improving health outcomes in low- and middle-income countries (LMICs).<sup>1–3</sup> Based on the most recent estimates, 8.6 million people per year in LMICs die from conditions that could be treated by health systems, and a majority of these deaths are due to receipt of poor quality care.<sup>4</sup> Measuring quality at various levels of the health system—especially where the system fails the people in need—remains rare in LMICs. Existing indicators of structural quality, such as availability of medicine, equipment, and qualified health attendants, are not sufficient to adequately capture the quality of care being offered.<sup>1</sup> The lack of quality measurement can help to explain both why there is limited research on how to effectively transform low-quality health systems into high-quality ones, and why there are few such successful reforms in LMICs. Actionable and credible data on quality, if properly disseminated, can play an important role in holding country health systems accountable for effective and safe care and can form the basis for quality improvement.<sup>5</sup> Our paper reports on large-scale assessments of clinical knowledge, health provider absenteeism, and means to provide care in Sub-Saharan Africa. We use data collected through clinical vignettes, unannounced visits, and visual inspections of over 20,000 providers in over 8,000 facilities in 10 Sub-Saharan African countries, which together represent over 40 percent of the region’s total population. We focus on conditions that account for a large proportion of maternal and child mortality. (We include the STROBE checklist for cross-sectional, observational data analysis in Appendix Section S1.<sup>6</sup> ) We use these data to measure dimensions we would argue are essential for providing quality care. Clinical vignettes tell us what providers know. Unannounced visits to facilities tell us if providers are available at health facilities. Visual inspections of facilities provide information as to the means that health workers have at their disposal. To put this work in 3
an illustrative example in Table 3, where we compare the growth of consumption (second column) against those of wealth indexes. We use two versions of the wealth indexes, the first is the simple aggregation method (or wealth index 1), and the second the PCA method (or wealth index 2, same as Model 3 in Table 2) after pooling all data for the three survey years. The list of the assets that are used is the same as that used for Model 1 in Table 2.<sup>18</sup> Table 3 shows that the growth rate of mean per capita consumption widely differs from that of the wealth indexes between 2010 and 2012: while the former is around 2 percent, the growth rate of wealth index 1 is higher at 10 percent, and even negative and much higher at more than 100 percent for wealth index 2 (Panel A). The growth rate of mean per capita consumption and the growth rate of the wealth indexes differs as well between 2012 and 2014 (Panel B), but a different result emerges where the former is now larger than that of wealth index 1 but still smaller than that of wealth index 2.

This empirical result qualitatively concurs with that offered by Harttgen, Klasen, and Vollmer (2013), who analyze 160 DHS surveys from 33 African and 34 non-African countries and construct wealth indexes in different ways as well. In particular, Harttgen _et al._ (2013) argue that employing wealth indexes as a proxy for trends in household consumption is subject to several different types of biases. For example, one is due to changing preferences for certain assets (e.g., the increasing ownership of smart phones), another changing relative prices among different assets leading to more demand for one asset at the expense of others (e.g., the dramatically decreasing price of smart phones).<sup>19</sup>

> 18 The reason we pool the data is to make the wealth indexes comparable over time, since applying the PCA method to each year separately as with Table 2 would standardize these indexes to a mean 0 for each year. We use the same assets as in Model 1, Table 2 for easier interpretation with the count index; estimation results (not shown) using additional assets in the other models (Models 2 and 3) provide qualitatively similar results.

> 19 Although providing biased poverty estimates in a static period, wealth indexes may not necessarily provide biased estimates of trends in household consumption (or poverty) over time. If the degree of bias for both wealth indexes and household consumption is similar over time, wealth indexes can provide unbiased estimates. As an (extreme) example, if the bias of the mean of a wealth index as a measure of mean household consumption remains constant at 10 percent

17
- [dataset] SOFTEC (2013). Informe tendencial del mercado inmobiliario Ciudad de México (microdata), url. <u>https://www.softec.com.mx/web/index.php/herramientas/reportetendencial2</u>

- Song, S. (1996). Some tests of alternative accessibility measures: A population density approach. _Land Economics, 72(4)_ , 474-482, doi: 10.2307/3146910

- <mark>Suárez, M. (2007) Mercados de trabajo y 1ocalización residencial en la ZMCM. PhD thesis, Universidad Nacional Autónoma de Mexico</mark>

- <mark>Suárez-Lastra, M., & Delgado-Campos, J. (2007). Estructura y eficiencia urbanas. Accesibilidad a empleos, localización residencial e ingreso en la ZMCM 1990-2000.</mark> _<mark>Economía, sociedad y territorio</mark>_ <mark>, 6(23), url.</mark> <u><mark>http://www.redalyc.org/articulo.oa</mark></u>

- <mark>Suárez, M., & Delgado, J. (2009). Is Mexico City polycentric? A trip attraction capacity approach.</mark> _<mark>Urban Studies</mark>_ <mark>, 46(10), 2187-2211, doi:</mark> <u><mark>https://doi.org/10.1177/0042098009339429</mark></u>

- <mark>Suárez, M., Murata, M., & Delgado Campos, J. (2016). Why do the poor travel less? Urban structure, commuting and economic informality in Mexico City.</mark> _<mark>Urban studies</mark>_ <mark>,</mark> _<mark>53</mark>_ <mark>(12), 2548-2566, doi: https://doi.org/10.1177/0042098015596925</mark>

- <mark>Vizuet, G.I. (2010). Política de vivienda y movilidad residencial en la Ciudad de México.</mark> _<mark>Estudios Demográficos y Urbanos 2(74)</mark>_ <mark>, 277-316, url.</mark> <u><mark>http://www.jstor.org/stable/20787554</mark></u>

<mark>Wallace, N.E., & Meese, R.A. (1997). The construction of residential housing price indices: a comparison of repeat-sales, hedonic-regression, and hybrid approaches.</mark> _<mark>The Journal of Real Estate Finance and Economics</mark>_ <mark>,</mark> _<mark>14</mark>_ <mark>(1-2), 51-73, doi:</mark> <u><mark>https://doi.org/10.1023/A:1007715917198</mark></u>

- <mark>Wilson, A.G. (1967). A statistical theory of spatial distribution models.</mark> _<mark>Transportation research</mark>_ <mark>,</mark> _<mark>1</mark>_ <mark>(3), 253-269, doi: https://doi.org/10.1016/0041-1647(67)90035-4</mark>

- <mark>Yoshida, N., & Deichmann, U. (2009). Measurement of accessibility and its applications.</mark> _<mark>Journal of Infrastructure Development</mark>_ <mark>,</mark> _<mark>1</mark>_ <mark>(1), 1-16, doi: https://doi.org/10.1177/097493060900100102</mark>

**<mark>Appendix 1. Complete results of Table 4 Dependent variable: log of housing prices (2000</mark>** **<u><mark>constant prices).</mark></u>**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
||**One-year observations**|**Repeated observations**|**Panel all obs**|
|Private accessibility 2002|0.0144***|-0.00138|-0.00384|
||(0.00442)|(0.00431)|(0.00328)|
|Private accessibility 2003|0.0241***|0.00913***|0.00777**|
||(0.00879)|(0.00337)|(0.00337)|
|Private accessibility 2004|-0.00748|0.00867***|0.00709**|
||(0.0152)|(0.00315)|(0.00310)|
|Private accessibility 2005|0.0248|0.0124***|0.0108***|
||(0.0321)|(0.00302)|(0.00305)|

32
lines adopted by different countries, and could be presented together with poverty lines as an enhanced welfare measurement package. These vulnerability lines could also serve as lower bounds for the middle class lines, and can be used to compare the welfare dynamics for different countries during similar periods, as well as over different time intervals. Our framework is broad enough to allow more disaggregated investigation of welfare dynamics for subpopulation groups using either a bivariate analysis or multivariate analysis model.

We provide in this paper empirical illustrations using both true panel data from the US and Vietnam and synthetic panel data constructed using cross sections from India. These vulnerability lines are thus rather flexible and are amenable to application in most settings with few data requirements. We focus on a money-metric measure of welfare outcome in this paper but our framework may also be extended to incorporate non-monetary welfare measures. Other promising directions for future research include analyzing mobility concepts other than the ones used in this paper, and expanding this framework to more than two periods.

30
**Figure 10**

Forecasted share of nuclear energy in t+30 by energy project and according to year of publication Note: US scenarios are depicted as large squares

|40<br>45<br>50||
|---|---|
|35||
|15<br>20<br>25<br>30<br>**Nuclear share (%x100)**||
|0<br>5<br>10||
|1|970<br>1975<br>1980<br>1985<br>1990<br>1995<br>2000<br>2005<br>2010|
||Department of the Interior<br>EUOGEP<br>Energy Research and Development Agency<br>European Union / DGTREN<br>Ford Foundation<br>Greenpeace<br>Harvard Business School<br>IEA<br>IIASA<br>IPCC<br>OECD<br>PEW|
||Shell International BV<br>Union of Concerned Scientists<br>WEC<br>WEC and IIASA|

18
# Working days lost/labor force, in percent

<!-- Start of picture text --> .26<br>—— Scenario 1: Carbon tax and debt repayment<br>.24 —— Scenario 2: Carbon tax and reduction in direct taxes<br>22 — Scenario 3: Carbon tax and an increase in hh. transfers<br>—— Baseline<br>.20<br>18<br>16<br>14<br>42<br>-10<br>2010 2015 2020 2025 2030 2035 2040<br>Deaths attributable to air pollution<br>35,000<br>— Scenario 1: Carbon tax and debt repayment<br>30,000 —— Scenario 2: Carbon tax and reduction in direct taxes<br>—— Scenario 3: Carbon tax and an increase in hh. transfer,<br>25,000 :<br>—— Baseline<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>2010 2015 2020 2025 2030 2035 2040<br><!-- End of picture text --> Health expenditure attributed to pollution/Government consumption, in percent <!-- Start of picture text -->
-06<br>-05<br>-04<br>-03<br>———. Scenario 1: Carbon tax and debt repayment<br>——. Scenario 2: Carbon tax and reduction in direct taxes<br>-02<br>— Scenario 3: Carbon tax and an increase in hh. transfers<br>—— Baseline<br>-01<br>2010 2015 2020 2025 2030 2035 2040<br><!-- End of picture text -->
2 demonstrates that concentration indeed has a positive effect on secondary market prices.

The theoretical model assumes that the debt renegotiation process possesses three fundamental characteristics: (i) the country's motivation to repay its debt is its fear of being penalized by its creditors, (ii) penalizing the country is costly for the banks, and (iii) although the amount repaid by tha country is shared prc rata by a<sup>11</sup> banks, he laroe bank. fa-a greater than pro rata cost in penalizing the country due to the damage that the penalties inflict on their extensive business interests with these countries. We show that the most favorable subgame-perfect equilibrium for the banks has the country repaying an amount that is an increasing function of the proportion of the debt owned by the large banks. As modeled, the value of the debt increases as the percentage of the debt owned by the large banks increases. We construct a model of the secondary market to show, however, that this does not necessarily imply that the large banks will end up owning the entire stock of debt. The free-rider problem, evident when it comes to discussions of debt forgiveness and new money contributions, also plays a critical role here. A small bank has an incentive to hold out and thereby obtain the return to ownership of a unit of debt whose value will have increased in accordance with the greater portion of the total debt owned by the large banks. Since each small bank has the same incentive, we show that in equilibrium no debt will be bought by the large banks from the small ones in the secondary market.

We conduct an empirical analysis of secondary market prices to determine how thAse are affected by debt concentration. Our measure of the degree of concentration is calculated as the ratio of exposure of the nine largest US
17

**Figure 10:  Share of government revenues from public enterprises***

<!-- Start of picture text --> % of total revenues<br>0% 20% 40% 60% 80%<br>Algeria 60%<br>Egypt 12%<br>Jordan 14%<br>Lebanon 17%<br>Morocco 4%<br>Syria 24%<br>Tunisia 7%<br>Turkey 6%<br>Czech Rep. 2%<br>Hungary 3%<br>Poland 4%<br>Slovakia 5%<br>Slovenia 3%<br>China 7%<br>Thailand 7%<br><!-- End of picture text -->

* Includes mostly revenues from property and tax payments as a percentage of total revenue.  Data refer to the most recent year available during the period specified. Source:  ANIMA- Euromediterranean Network of Investment Promotion Agencies

# _Asia -- China and India_

30. In China, the state’s economic role declined dramatically from 80 percent of GDP in 1978 to 17 percent in 2003, but state enterprises still remain a dominant economic force, employing half of China’s 750 million workers, controlling 57 percent of its industrial assets, and dominating key industries such as financial services, power, and telecommunications (McKinsey Quarterly October 2004). Only recently has China started restructuring its medium and larger state enterprises, but largely through minority share sales or new share offerings via the capital market which came to be viewed as a new source of funds for state-owned companies given the growing strains in the banking sector. Government ownership still remains dominant in these firms: non-tradable state shares account for about two-thirds of equity in listed state-owned companies (Financial Times, March 29, 2005).

31. Similarly, the Indian government launched a “disinvestment” policy in 1991 when aggregate losses of non-financial public enterprises added to the overall fiscal deficit by about 2 to 3 percent of GDP. In the 1990s disinvestment mainly involved the sale of minority shares to raise revenues. Starting in the late 1990s and early 2000s, a few centrally owned enterprises were fully or majority divested to private investors, but these were small compared to the value of the assets of the public sector. The momentum picked up in 2002 with the sale of telecoms shares to a strategic partner, but the program came to a standstill later that year when the proposed sale of two oil companies was postponed. A recent analysis shows that between 1991 and 2004, only 28 percent of planned privatizations were achieved, with 2001-02 being the
**Table 1:  World trade by end use, 2000**

||Number of SITC<br>Rev.3 lines|Exports in<br>Mill. USD|Total exports<br>(%)|
|---|---|---|---|
|Total|3,053|5,900,952.0|100.0%|
|Intermediate|1,873|3,397,270.5|57.6%|
|Consumption|698|1,096,182.5|18.6%|
|Capital|468|1,082,342.4|18.3%|
|Not classified|14|325,156.7|5.5%|

Source: Authors calculations based on ITCS database.

> Capital intensity, _k j_ , and skilled labor intensity,<sup>_h_</sup> _j_<sup>, of 6-digit NAICS industries in the year 2000 are taken</sup> from the U.S. NBER-CES Manufacturing Industry Database. They are measured as the natural logarithm of the total real capital stock per worker in industry _j_ , and the share of non-production workers in total employment of industry _j_ , respectively. We measure institutional intensity<sup>_q_</sup> _j_<sup>of industry</sup><sup>_j_as the share of</sup> products in the industry that are neither reference priced nor sold on an organized exchange. To construct this variable we use the Rauch (1999)’s classification, which groups goods into goods traded on an organized exchange, reference priced goods and non-reference priced goods.  The assumption is that the production of non-reference priced goods requires a relatively high level of relation-specific investments so that these goods are more subject to the hold-up problem than goods sold on an organized exchage or reference priced goods.  As suggested by Nunn (2007), the fact that a good is traded on an organized exchange indicates that its market is thick, hence there is limited scope for the hold-up problem to emerge. Similarly, the fact that a good is reference priced in a publication may be thought of as an indication that there is a reasonable number of potential buyers and sellers of that good,limiting potential hold-up problems. Following Hummels (2001), we define time-sensitive products according to their probability of being transported by air. We measure timeliness

> <sup>_t_</sup> _j_<sup>of industry</sup><sup>_j_as the share of US imports shipped by air.  Data on US imports and shipping</sup> mode at the HS10 digit level are collected by the U.S. Census Bureau and are taken from the homepage of Peter Schott. We use the HS10 to NAICS 1997 correspondence table from Feenstra et al. (2002) to calculate

> timeliness at the NAICS industry level. Since both institutional intensity<sup>_q_</sup> _j_<sup>and timeliness</sup><sup>_t_</sup> _j_<sup>are based on</sup> product level data, we can use the BEC classification to calculate intensities for intermediate and other types of goods that belong to the same NAICS industry.

> Country endowments of capital _Ki_ and human capital _H i_ are measured by the natural logarithm of capital stock per worker and the logarithm of human capital per worker for the year 1988 taken from Hall and Jones

7
# **1 Introduction**

This paper examines the performance of a method for producing small area estimates of the spatial description of economic welfare. The methodology is described in Elbers, Lanjouw and Lanjouw (2002, 2003), henceforth referred to as ELL (2002). These “poverty maps” offer the promise of generating useful data about poverty and inequality at the local level, information which has potential applications in both the policy and research spheres. In this paper, an unusual data set is used to compare community-level welfare measures estimated using the small area estimation method against measures created from direct observations of household expenditure collected over the entire population within those communities.

Poverty maps have two sets of uses. They can be used as tools for geographical targeting of social spending. In a number of countries they have been used by governments and non-governmental organizations to identify those areas where the poor are concentrated as a first step towards directing resources to the poor. While policymakers in wealthy nations are accustomed to having information about local level conditions and welfare readily at hand, in the typical less developed country, information compiled at the local level is scarce and only available through specialized surveys. In such environments poverty maps are a potentially valuable resource.

On the research front, poverty maps have a variety of applications. With the resurgent interest in economic growth theory, and in particular the focus on inequality’s role, spatial profiles of welfare within a country can be useful. Poverty maps can also be used to investigate the spatial relationship between poverty and a variety of outcomes, including health and crime. The research applications for poverty maps are particularly strong when poverty maps can be produced for multiple years in a single country. In such cases poverty maps can be employed for policy evaluation.

2