rafmacalaba/datause-encoder-data huggingface dataset:
fallen from levels between 12.5 and 15 percent in 1995 to lower than 10 percent during 2015 (see Figure 3).

Figure 3: Applied MFN rates have steadily declined over time <!-- Start of picture text --> 15.0<br> 12.5<br> 10.0<br> 7.5<br> 5.0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>Simple Average Trade Weighed<br>MFN tariff (%)<br><!-- End of picture text -->

# Source: WITS

Note: To avoid sample selection bias, tariffs have been calculated for a balanced sub‐sample of countries and missing data have been interpolated. The sub‐sample includes 27 countries with applied MFN rates in at least 15 years between 1995 and 2015 (see Appendix Table A. 3). The data used in the figure are simple averages and trade weighed of MFN rates for all products.

Of the total value of imports, 42 percent trades free under MFN rates. Another 45 percent is subject to MFN rates below 10 percent, and only one‐tenth to MFN rates above 10 percent. In terms of products, 24 percent of tariff lines are subject to zero MFN rates, 23 percent to MFN rates over 15 percent and one‐quarter to rates between 5 and 10 percent (see Figure 4).

Figure 4:Almost two‐thirds of imports by value are subject to MFN rates of less than 5 percent <!-- Start of picture text --> 42.3%<br>35.9%<br>24.2% 24.2% 23.2%<br>20.9%<br>12.7%<br>5.8%<br>4.3% 3.9%<br>2.5%<br>0.1%<br>MFN = 0 MFN < 5% MFN [5%,10%] MFN (10%,15%] MFN > 15% Missing<br>MFN range<br>Global Imports Tariff Lines<br><!-- End of picture text --> Source: Authors’ calculation using ITC/World Bank database On average, agricultural imports are subject to higher MFN rates than manufacturing and natural resources. Whereas more than half the imports of natural resources and around 42 percent of 7
56

|Real GDP CAGR 2011‐2016 (years|**4.1%|N/A<br>N/A|****‐<br>7.3%|
|---|---|---|---|
|may vary), %|||12.3%|
||*2002‐2010,|**2011‐|***2008‐2010,|
||2016||****2011‐2015|

Source: The World Bank Little Data Book 2016, IMF WEO, CIA World Factbook

# **Annex Table 3: Country Statistics Medium Intensity Conflict Countries (.. indicates data not available)**

|Country‐ Medium Intensity Conflict|Sudan|Central<br>African<br>Republic|Democratic<br>Republic<br>of<br>the Congo|Myanm<br>ar|
|---|---|---|---|---|
|Population (million)|39.4|4.8|74.9|53.4|
|GNI, Atlas $ bn|67.3|1.6|28.7|68.1|
|GNI per capita, PPP, $|3920|600|650|..|
|Life Expectancy at birth in 2010|62|48|57|65|
|Life Expectancy at birth in 2014|63|51|59|66|
|Labor force participation rate in 2010 (%<br>of population ages 15+)|53|79|72|79|
|Labor force participation rate in 2014 (%<br>of population ages 15+)|54|79|72|79|
|Gross capital formation in 2000 (% of<br>GDP)|25|11|14|12|
|Gross capital formation in 2010 (% of<br>GDP)|23|14|16|..|
|Gross capital formation in 2014 (% of<br>GDP)|18|10|21|..|
|Individuals using the internet in 2010 (%<br>of population)|16.7|2|0.7|0.3|
|Individuals using the internet in 2014 (%<br>of population)|24.6|4|3|2.1|
|Merchandise trade in 2000 (% of GDP)|27|30|8|..|
|Merchandise trade in 2010 (% of GDP)|33|22|48|..|
|Merchandise trade in 2014 (% of GDP)|18|20|41|42|
|Electrification % of total population in<br>2013, *in 2012|35%|3%|9%|52%|
|Real GDP CAGR 2001‐2010, %|6.8%|2.5%|4.7%|12.0%|
|Real GDP CAGR 2011‐2016,%|3.4%|‐6.1%|6.8%|7.5%|

Source: The World Bank Little Data Book 2016, IMF WEO, CIA World Factbook

**Annex Table 4: Country Statistics not In Conflict Countries (.. indicates data not available)**
19 OEA bank i's operating expenses-to-total assets ratio MDGR = market deposits growth rate D1 = 1 for private, 0 for public banks A finding of b,>O and b2=O supports the traditional S-C-P hypothesis. On the other hand, if b1=0 and b>0, then the efficient structure is supported. It is, of course, possible that both b, and b2 maybe positive and significant, indicating that both factors are operative simultaneously. The effects of market concentration and market share on profits will be given by partial derivatives equal to the coefficients b, and b2 respectively.

The performance measure, the dependent variable, is bank profits measured as the return on assets (ROA), i.e., net income divided by total assets. Other possible performance measures are return on equity (ROE) and bank stock prices. The use of ROE may not be the best measure because banks can divide capital between debt and equity, making the comparison of equity values across banks difficult. The banks in Turkey are closely-held by various industrial groups, individuals, and the government, and their stocks are not traded. Hence, we are unable to observe and use bank stock prices. ROA is the most widely used bank performance measure and, as suggested by Evanoff and Fortier (1988), dividing net income by total assets, which is ROA, makes it a more comparable measure across banks because "bank assets are a more common denomination." Therefore, ROA is the dependent variable." The independent variables include both firm and market specific variables, and they are similar to those utilized in previous studies. Following previous studies, we proxy market structure, with the share of 5 leading banks in total deposits. Hence, concentration ratio is defined as the sum of shares of the leading 5 "<sup>1</sup> Business Week (April 9, 1984), p. 83, and Business Week (April 8, 1985), p. 106, also suggest that ROA is the single best performance measure for banks, as cited in Rhoades (I987).
|_Dependent variable_|Sequence<br>vs. Control<br>(1)|Coaches vs.<br>Control<br>(2)|Wald Test<br>(3)|
|---|---|---|---|
||_Panel A_<br>|||
|Science score|0.550***|0.647***|0.45|
||(0.125)|(0.137)|[0.502]|
|Observations|1,105|1,110||
||_Panel B_<br>|||
|Science score (Basic skills)|0.276***|0.282***|0.01|
||(0.081)|(0.081)|[0.951]|
|Observations|1,105|1,110||
|Science score (Medium skills)|0.571***|0.534***|0.07|
||(0.130)|(0.130)|[0.791]|
|Observations|1,105|1,110||
|Science score (Higher-order skills)|0.416***|0.638***|2.39|
||(0.124)|(0.139)|[0.122]|
|Observations|1,105|1,110||
||_Panel C_|||
|Percent of correct answers|0.103***|0.110***|0.06|
||(0.023)|(0.026)|[0.800]|
|Observations|1,105|1,110||
|Percent of incorrect answers|-0.057***|-0.050***|0.17|
||(0.014)|(0.015)|[0.680]|
|Observations|1,105|1,110||
|Percent of omitted answers|-0.053**|-0.079***|1.62|
||(0.020)|(0.020)|[0.203]|
|Observations|1,105|1,110||

_Note:_ ** significant at 5 percent; *** significant at 1 percent. Robust standard errors in parentheses clustered at school level. The _p_ - values are in brackets in the third column. Controls: (a) students’ characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his/her home), (b) teacher characteristics (gender, age, years of experience, if she/he has postgraduate certificates), and (c) school characteristics (school size, seventh grade size, seventh repetition rate, FEPBA score in Language, school district, or location). All the regressions exclude the classrooms where the Human Body unit was not taught.

_Source_ : Authors analysis based on experimental data generated for this study.

**Table 7. Results on Science learning according to teacher experience**

|_Independent variable_|(1)<br> (2)|(3)|
|---|---|---|

3
Second, the pattern of results speaks against this interpretation. We find that entry increased disproportionately in industries where women establishment owners were more concentrated before the reforms nationally. It seems to us unlikely that there would have been systematically more under-reporting in industries where women’s ownership rates were very high.

# _Potential Channel: Access to Government Contracts or Business_

A second potential channel is that placing women in political positions led to increased government contracts and related business flowing to women-owned establishments, which prompted the higher entry rates. This could have been due to politicians enacting explicit setasides of government contracts for women, favoritism in how contracts were awarded (or the weakening of a prior bias towards male-owned businesses), and/or the general redistribution of government expenditure towards industries and goods where women-owned businesses were better able to secure and fulfill contracts (without explicit set-asides or changes in how contracts are awarded).

Two questions on the NSSO survey inquire about the extent to which the manufacturing establishment either buys from or sells to the government. The questions are only included on the surveys after 2000, so we are unable to undertake a full quantitative analysis in a pre-post format of their response such as we did in Table 3 on other dimensions. Yet, the data are useful for understanding that it is unlikely that the access-to-the-government hypothesis accounts for the bulk of our observed response.

This is most easily observed with the raw statistics. The data for 2000 and 2005 report very limited government business dealings for most manufacturing establishments—less than 1% of establishments report output being sold to government agencies in any year. Even if substantial under-reporting existed for this channel, this very low share is mismatched to the large entry response observed after the elections.

Likewise, the data in their raw format do not align very well with the strong householdbased nature of the women’s entry. In the 2000 survey, 61% of those establishments that report government business dealings are establishments that are also based in households. While a majority, this share is significantly less than the 94% of women-owned establishments that are 3
# **Figure 12(a): Cambodia: Return to skill by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 0.6<br>0.4<br>0.2 Agriculture/Mining<br>Manufacturing<br>0<br>Trade/Services<br>1997 2003-4 2007<br>-0.2<br>-0.4<br>Return<br><!-- End of picture text -->

**Year**

**Figure 12(b): Cambodia: Proportion of skilled workers by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 35<br>30<br>25<br>20 Agriculture/Mining<br>15 Manufacturing<br>10 Trade/Services<br>5<br>0<br>1997 2003-4 2007<br>Year<br>% skilled<br><!-- End of picture text --> 53
19 21. The relative magnitude of the response of infant mortality to hospital versus non hospital expenditures is of interest. In formulating the IMR equation it was expected that the non hospital expenditures, outlined in the background section above, would have greater effect than hospital expenditures in reducing infant mortality, because, among other programs, non hospital expenditures support important preventive care programs directly affecting maternal and child health. This expectation was substantiated. The infant mortality rate is found to be strongly influenced by non hospital health expenditures (REXNP) as is evidenced by the size and high level of statistical significance of the coefficient on REXNP. Conversely, because only a small percent of births take place in a hospital and because many factors other than the quality of the delivery determine infant mortality it is expected that hospital expenditures would have only a negligible effect on infant mortality. Because of the lack of statistical significance and absence of compelling justification for the inclusion of hospital expenditures, the equations omitting REXHP are used in the policy analysis below.

22. Interaction between infant mortality and fertility is confirmed by the estimated equations, although the size and statistical significance of the interaction coefficients vary across alternative estimates. In particular the size and significance of the coefficent on IMR decreases, while that on TFR increases, as the specification changes from alternative 1 to alternative 2 with the omission of hospital expenditures. The estimated coeficients are also highly dependent on the regression technique. The OLS
* Sjaastad's studies of Argentina and Chile analyzed the entire tradable sector, while the others analyzed agriculture only.

Source: Vald6s (1986) Table 2 presents the estimated incidence parameters of seven country studies, five of which focused specifically on the agriculture sector. These studies found a fairly high level of substitution between home goods and importables, implying a strong negative impact on the relative price of exportables to home goods. For example, Bautista (1987) found a persistent and significant bias in relative price incentives against agricultural exports in the Philippines during the period from 1950 to 1980. The bias was not surprising considering the Philippine's strategy of import-substituting industrialization through the 1950s and 1960s. The surprise was that this bias persisted through the 1970s, when the government was supposedly committed to export promotion. Bautista's study also found that a 1O percent increase in the domestic price of importables was associated with an 8 percent decline in the domestic price of agricultural exports relative to home goods. This was essentially a significant tax on agricultural exportables. In Peru, Valdes (1985) found that raising the uniform tariff of manufactured goods by 10 percent imposed an implicit 5.6 percent tax (with respect to home goods) on the production of importables (e.g. rice), and an implicit 6.7 percent tax on the production of exportables (e.g. cotton and sugar).

# _The Mundlak general equilibrium econometric approach_

The previous studies examined the impact of policy on incentives, but did not examine the impact of policy on supply response. Most studies of agriculture's aggregate supply response have used a partial equilibrium approach, and thus have failed to take into account the effects of relative price changes on intersectoral resource flows over time. More specifically, they have failed to take into account relative price changes associated with economy-wide macroeconomic policies. An important contribution to correcting this inadequacy was pioneered by Mundlak and his colleagues. Employing a general equilibrium approach, Mundlak, Cavallo, and Domench (1989) analyzed the experience of Argentina from 1913 to 1984. Argentina grew faster than many countries (including Brazil, Canada, and the US) until 1930, at which point it began to lag behind. Mundlak et.al. found that Argentine agriculture was heavily taxed post-World War II, directly through export taxes, and indirectly through trade restrictions and protection of nonagricultural goods (note that these findings are consistent with those of Diaz-Alejandro's study of Argentina). Simulations in a general equilibrium framework suggested that Argentina would have achieved a much higher rate of economic growth if it had crafted policies that permitted it to benefit from its comparative advantage in agricultural exports. Such policies would have ensured that incentives reflected true terms of trade, without distortions.

**14**
**Table 6B - Basic results for Development Outcomes. (OLS and Instrumental Variables using Share of Tropical Land)** **<u>Human Opportunity Index controlling for Gini Index</u>**

|Regression||Depende|nt Variable: Instituti|onal Index,Average W|BGI 2009||
|---|---|---|---|---|---|---|
||OLS|OLS(Comparison Sampl|e)<br>OLS|IV|IV|IV|
|Human Opportunity Index*|0.348***||0.378***|0.303|0.536*|0.373|
||(0.06)||(0.08)|(0.21)|(0.22)|(0.24)|
|Gini Index*||0.34|0.32||0.35||
|||(0.23)|(0.20)||(0.23)||
|Americas - LAC plus Canada and United States||||||10.483|
|||||||(6.28)|
|Europe and Central Asia||||||0.768<br>|
|||||||(13.76)|
|Middle East and Africa||||||3.378<br>|
|||||||(4.68)|
|constant|25.313***|19.555|11.044|25.728***|5.237|19.492**|
||(2.57)|(10.26)|(9.05)|(5.15)|(10.37)|(6.44)|
|R-sqr|0.268|0.03|0.283||||
|Number of Observations|82|74|74|70|68|70|
|Wald F-statistics from first stage||||11.776|9.648|6.055|
|Regression|||Dependent Variab|le: Infant Mortality|||
||OLS|OLS(Comparison Sampl|e)<br>OLS|IV|IV|IV|
|Human Opportunity Index*|-0.999***||-1.178***|-1.036**|-1.183***|-0.791*|
||(0.11)||(0.14)|(0.35)|(0.33)|(0.39)|
|Gini Index*||(0.47)|(0.39)||(0.42)||
|||(0.51)|(0.36)||(0.49)||
|Americas - LAC plus Canada and United States||||||-21.276|
|||||||(13.41)|
|Europe and Central Asia||||||-8.194<br>|
|||||||(20.76)|
|Middle East and Africa||||||20.969*<br>|
|||||||(8.94)|
|constant|87.795***|82.214***|108.719***|88.882***|111.057***|77.130***|
||(4.40)|(22.94)|(16.53)|(9.17)|(18.81)|(9.46)|
|R-sqr|0.506|0.012|0.512||||
|Number of Observations|82|74|74|70|68|70|
|Wald F-statistics from first stage||||11.776|9.648|6.055|
|Regression|||Dependent Varia|ble: Malnutrition|||
||OLS|OLS(Comparison Sampl|e)<br>OLS|IV|IV|IV|
|Human Opportunity Index*|-0.275***||-0.278***|-0.365**|-0.453***|-0.424*|
||(0.05)||(0.05)|(0.11)|(0.12)|(0.17)|
|Gini Index*||-0.384*|-0.402**||-0.430*||
|||(0.16)|(0.13)||(0.16)||
|Americas - LAC plus Canada and United States||||||-14.360**|
|||||||(5.21)|
|Europe and Central Asia||||||-8.778<br>(8.82)|
|Middle East and Africa||||||-11.196**|
|||||||(3.53)|
|constant|25.389***|35.754***|43.137***|27.308***|48.249***|38.653***|
||(1.75)|(7.24)|(6.12)|(2.95)|(7.79)|(4.36)|
|R-sqr|0.296|0.082|0.385||||
|Number of Observations|66|65|65|62|61|62|
|Wald F-statistics from first stage||||11.805|11.248|5.528|

* p<0.05, ** p<0.01, *** p<0.001

- *Completing Six Year of Education on Time, Household Survey Data

*Gini Index: Average 1960-2010

26
**Figure 2. Population in Low Elevation Coastal Zones, 1990-2100**

<!-- Start of picture text --> 1,100<br> 1,000<br> 900<br> 800<br> 700<br>World Total<br> 600 Developing Countries Total<br>World Rural<br> 500<br>Developing Countries Rural<br> 400<br> 300<br> 200<br> 100<br>1990 2010 2030 2050 2070 2090<br>Population (millions) in LECZ<br><!-- End of picture text --> The low elevation coastal zone (LECZ) is the contiguous area along the coast that is less than 10 meters above sea level.

Developing countries are low and middle‐income economies with 2013 per capita income of $12,745 or less, as defined by the World Development Indicators, available at <u>http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=world‐development‐ indicators.</u> The estimates of LECZ populations are from Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban‐Rural Population and Land Area Estimates Version 2. Palisades, NY: ‐ NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz <u>urban‐rural‐population‐land‐area‐estimates‐v2. Population and land area estimates are subdivided by elevation</u> zone as derived from Shuttle Radar Topographic Mission (SRTM) elevation data at ~90m (3 arc seconds) resolution.

7
with at least one strict inequality and (b)<sup>�</sup><sup>_S_</sup> _s_ =1<sup>_ω_</sup> _s_<sup>_r_(</sup><sup>_ps−qs_)</sup><sup>_≤_0</sup><sup>_∀r_=3</sup><sup>_, . . . , S−_1,where</sup> **_ω_**<sup>_r_</sup> = ( _ω_ 1<sup>_r, . . . , ω_</sup> _S_<sup>_r_)</sup><sup>_∀r_= 3</sup><sup>_, . . . , S −_1areobtainedfromEquation4.1ofTheorem4.2.</sup>

**Proof.** First consider the case when _k_ = 2. Then from Equation 4.1 of Theorem 4.2, it follows that _p_ 1 _≤ q_ 1 and 2 _p_ 1 + _p_ 2 _≤_ 2 _q_ 1 + _q_ 2, with at least one strict inequality. Therefore, conditional (a) is both necessary and sufficient. Whenever, _k ≥_ 3, part (b) follows from Theorem 4.2 by setting _k_ = _S −_ 1.

In summary, the robustness of poverty comparisons for various classes and subclasses of ordinal measures introduced in Sections 2.1 and 3 can be assessed with a battery of dominance tests based on the theorems and corollaries presented in this section.

# **5. Empirical illustration: Sanitation deprivation in Bangladesh**

We now present an empirical illustration in order to showcase the efficacy of our proposed measurement method. In the current global development context, both the United Nations through the Sustainable Development Goals<sup>13</sup> and the World Bank through their Report of the Commission on Global Poverty (World Bank, 2017) have acknowledged the need for assessing, monitoring, and alleviating poverty in multiple dimensions besides the monetary dimension. In practice, most non-income dimensions are assessed by ordinal variables. In this section, we show how our measurement tools may be applied to analyse inter-temporal sanitation deprivation in Bangladesh.

For our analysis, we use the nationally representative Demographic Health Survey (DHS)

> 13Available at https://sustainabledevelopment.un.org/sdgs.

26
# Table 3 (cont.) The Gains From Agricultural Tariff Liberalization Aggregated Data

|Country|Aggregate<br>Gains|St.Dev.<br>Gains|Consumption<br>Efects|Income<br>Efects|Pro-Poor<br>Bias|Gains<br>Poor|Gains<br>Rich|
|---|---|---|---|---|---|---|---|
|Developing world|2.39|0.85|4.33|-1.94|-0.33|2.16|2.48|
|Liberia|2.39|0.52|3.18|-0.79|-0.87|1.81|2.67|
|Madagascar|1.13|0.52|3.49|-2.36|-1.03|0.67|1.70|
|Malawi|0.74|0.88|2.98|-2.25|-2.16|-0.05|2.10|
|Mali|2.29|1.50|3.62|-1.33|3.58|4.18|0.60|
|Mauritania|3.84|1.00|5.14|-1.29|2.30|4.79|2.49|
|Moldova|0.78|0.35|1.40|-0.62|0.44|1.15|0.70|
|Mongolia|2.42|0.47|2.94|-0.53|1.20|2.90|1.69|
|Mozambique|2.32|1.88|4.32|-2.01|-4.76|0.30|5.05|
|Nepal|2.29|0.58|2.76|-0.47|1.51|2.99|1.48|
|Nicaragua|3.41|0.77|4.84|-1.43|1.04|3.85|2.81|
|Niger|2.75|1.06|5.17|-2.42|-2.48|1.45|3.93|
|Nigeria|2.39|1.79|5.25|-2.85|-3.60|-0.33|3.27|
|Pakistan|1.07|0.93|1.57|-0.50|2.43|2.13|-0.30|
|Papua New Guinea|1.63|0.45|2.99|-1.36|-0.35|1.42|1.76|
|Rwanda|2.24|1.63|5.19|-2.95|3.60|4.39|0.79|
|Sierra Leone|3.34|1.01|6.09|-2.75|-1.98|2.52|4.50|
|South Africa|2.52|0.99|2.57|-0.04|2.67|3.65|0.98|
|Sri Lanka|1.99|1.26|3.77|-1.78|3.50|3.75|0.25|
|Tajikistan|2.73|0.38|2.89|-0.15|-0.21|2.66|2.88|
|Tanzania|2.68|1.11|4.66|-1.99|1.56|3.26|1.70|
|Togo|3.35|1.25|5.28|-1.93|-2.91|1.44|4.35|
|Uganda|2.54|0.83|4.21|-1.67|0.43|3.36|2.93|
|Ukraine|2.49|0.14|2.62|-0.13|-0.31|2.30|2.60|
|Uzbekistan|4.23|0.60|5.22|-0.99|-1.37|3.67|5.04|
|Vietnam|1.80|0.75|4.13|-2.33|-1.76|0.82|2.58|
|Yemen, Rep.|2.10|0.28|3.07|-0.97|0.14|2.14|2.00|
|Zambia|7.44|0.46|8.61|-1.17|0.72|7.84|7.12|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of country’s own agricultural import tariffs.

32