rafmacalaba/datause-encoder-data huggingface dataset:
In Table A, the three models are compared in terms of pseudo R-squared and Area Under the ROC Curve (AUC). These indicators show that Model 3 shows the best performance followed by models 2 and 1 in this order. Regressors related to housing and WASH included in models 2 and 3 seem to make the difference with model 1 and contribute significantly in explaining the probability of being poor. Figure 3 shows graphically the same results using the ROC curves. The AUC is almost the same in models 2 and 3 and the corresponding ROC curves practically overlap (the green line in Figure 3), whereas the ROC curve corresponding to model 1 (the blue line in Figure 3) is clearly below.

# **Figure 3 - Comparing poverty models’ ROC curves**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Leakage<br>Model 1 Model 2<br>Model 3 45 degree line<br>1<br>.8<br>Coverage .6.4<br>.2<br>0<br><!-- End of picture text --> Source: Authors’ estimations based on Jordan UNHCR data.

Considering Model 3 as our best poverty model, we can now test its capacity to predict poverty correctly. The exercise consists in using the parameters estimated by the models to predict the dependent variable of the model (poor/non-poor) as if we did not have information on this variable. Table 2 shows the results using different probabilities thresholds. For any given cut point between 0 and 1, we can evaluate the corresponding coverage rate (that is the probability of correctly classifying the poor) and leakage rate (that is the probability of classifying as poor a non-poor). The table compares coverage and leakage rates 12
66 Table D.5: Resources, Industrialization & Employment, Cross-Section, I2D2 Database

|Dep.Var. =<br>**Empl. Sh. (c. 2005):**|(1)<br>MFG|(2)<br>FIRE|(3)<br>MFGFIRE|(4)<br>NTRI|(5)<br>NTRI2|(6)<br>WAGE|(7)<br>SELF|(8)<br>UNPAID|
|---|---|---|---|---|---|---|---|---|
|**Panel A:**<br>Vars. in 2000|||||||||
|MFGSERV2000|0.12***|-0.04|0.08*|0.09|0.14|0.44**|-0.48***|-0.15|
||[0.035]|[0.039]|[0.043]|[0.127]|[0.144]|[0.180]|[0.136]|[0.118]|
|NRXGDP1960_−_2000|-0.06|0.03|-0.03|0.18|0.03|0.41|-0.30|-0.15|
||[0.056]|[0.045]|[0.076]|[0.127]|[0.209]|[0.294]|[0.221]|[0.121]|
|DEINDU1980_−_2000|-0.26|0.14|-0.12|0.12|1.42**|-0.77|1.08*|0.54|
||[0.167]|[0.105]|[0.204]|[0.364]|[0.597]|[0.715]|[0.557]|[0.326]|
|**NRXGDP**|-0.18***|0.07|-0.11|0.08|-0.11|-0.03|0.18|-0.00|
|**- MFGSERV**|[0.04]|[0.05]|[0.07]|[0.17]|[0.20]|[0.27]|[0.22]|[0.15]|
|**DEINDU**|-0.38**|0.18|-0.20|0.03|1.28**|-1.21|1.56***|0.69*|
|**- MFGSERV**|[0.16]|[0.12]|[0.21]|[0.40]|[0.60]|[0.74]|[0.57]|[0.36]|
|**Panel B:**<br>Vars. in 2010|||||||||
|MFGSERV2010|0.10|-0.03|0.07|0.09|0.26|0.69***|-0.62***|-0.23|
||[0.061]|[0.043]|[0.061]|[0.145]|[0.199]|[0.217]|[0.157]|[0.142]|
|NRXGDP1960_−_2010|-0.13**|0.04|-0.10|0.14|0.16|0.42*|-0.28|-0.13|
||[0.065]|[0.045]|[0.070]|[0.143]|[0.223]|[0.244]|[0.195]|[0.134]|
|DEINDU1980_−_2010|-0.09|0.14|0.05|0.10|1.11**|-0.02|0.47|0.23|
||[0.134]|[0.085]|[0.159]|[0.309]|[0.503]|[0.652]|[0.564]|[0.242]|
|**NRXGDP**|-0.23***|0.07|-0.16***|0.05|-0.09|-0.27|0.34*|0.09|
|**- MFGSERV**|[0.05]|[0.07]|[0.06]|[0.20]|[0.20]|[0.22]|[0.18]|[0.18]|
|**DEINDU**|-0.19|0.17|-0.02|0.02|0.86|-0.71|1.09*|0.45|
|**- MFGSERV**|[0.15]|[0.10]|[0.17]|[0.36]|[0.55]|[0.69]|[0.57]|[0.30]|
|Observations|93|90|90|94|91|94|93|93|
|Controls|Y|Y|Y|Y|Y|Y|Y|Y|

_Notes:_ This table shows the correlation between the employment share of each sector/type of employment in urban areas c. 2005 and measures of natural resource exports, industrialization/FIRE-ization, and deindustrialization, defined with respect to 2000 or 2010. Robust SE. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
academic communities alike to understand the dynamics of gender inequality (World Bank, 2010). These studies have also focused on the analytics and operational parameters that will reduce gender bias and enhance women's economic empowerment. The World Bank Development Report (WDR) 2012 devoted to ―Gender Equality and Development‖ is part of that global effort to fill the analytical gaps in support of gender equality. However, for a number of reasons, including data gaps, efforts to understand the gender inequality dynamics and models developed to that effect have often focused on the post-colonial and postindependence period. Consequently, these models have neglected the deeper historical understanding of processes which have shaped the gender relations and dynamics that emerged in the years following independence in Africa. This myopic approach to research also implicitly assumes that gender relations have essentially followed a uniform path, with men consistently and invariably dominating and overpowering women throughout Africa, irrespective of the historical period.

Drawing on historical, anthropological and statistical records going back to the precolonial and colonial periods, this paper seeks to complicate the history of gender relations and production in Africa. It underscores the crucial roles of women in production and reproduction, and kinship as the mechanism that facilitates the social and economic exploitation of women. But women were not without social and economic opportunities in the pre-colonial period, as the case studies in this paper have demonstrated in terms of agriculture and crafts. The liberating potential in such 19<sup>th</sup> century and colonial era developments like the abolition of the slave trade and slavery, the introduction of cash crops, the institutionalization of customary law in dual legal systems were subverted where women were concerned because of the interface in the patriarchal assumptions of colonial officials, missionaries, and African chiefs and elders. The reproduction of structural inequities in gender during the colonial period was most notable in the sphere of education.

The colonial economy stifled global competition by preventing the colonies from engaging in the production of goods that would compete with manufacturing industries in advanced economies and confined African economies to primary commodities and natural resource exports under the resource extraction model of development. One of the direct consequences of that colonial construct which has received a lot of attention in the development community and academia are the excessive exposure of African economies to 30
# **Figure 1. Prevalence of Smoking in the United States, Age 25 and Above, 1940-2000**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>1950 1964 1970<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Year<br>1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000<br>Percentage of individuals smoking<br><!-- End of picture text --> _Note_ : From smoking histories constructed from the 1978, 1979, 1980, 1983, 1985, 1987, 1988, 1990, 1991, 1992, 1994, 1995, 1997, 1998, 1999 and 2000 National Health Interview Surveys. The information about the dangers of smoking diffused gradually: 1950, consensus in medical journals, 1964, first Surgeon General’s Report, 1970, clear health warning on packages.

**Figure 2. Prevalence of Smoking by Education Category in the United States, Age 25 and above, 1940-2000**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>1950 1964 1970<br>25%<br>20%<br>15%<br>10%<br>Less than High School High School Some College College and above<br>1940 1943 19461949 1952 1955 1958 1961 1964 1967 1970 1973 19761979 1982 1985 1988 1991 1994 1997 2000<br>Percentage of individuals smoking<br><!-- End of picture text --> _Note_ : From smoking histories constructed from the 1978, 1979, 1980, 1983, 1985, 1987, 1988, 1990, 1991, 1992, 1994, 1995, 1997, 1998, 1999 and 2000 National Health Interview Surveys. The information about the dangers of smoking diffused gradually: 1950, consensus in medical journals, 1964, first Surgeon General’s Report, 1970, clear health warning on packages.

27
1989, is allowed to vary across over firms and time between 1980 and 1988. Li (1997) estimated it to be 0.158. In addition, _,u1_ is normalized to be 1, **_p,j_** for material, machine, and chemical industries are estimated to be 0.41, 0.35, and 0.48. These estimates are used to compute _M,1_ . [t is important to note that the _uj_ 's are identified only up to the proportion with respect to _1uI;_ thus, if the markup ratio is 1 for the industry with the smallest markup ratio, the markup ratio for the rest of industries are (1/0.35) * _uj,_ respectively.

# **References**

Arrelano, Manuel, and Stephen Bond. 1991. "Some Tests of Specification for Panel Data: Monte Carlo Eviclence and an Application to Employment Equations," _Review of Economic Studies, 58, 277-297._ Barberis, Nicholas, Maxim Boycko, Andrei Shleifer, and Natalia Tsukanova. 1996. "How does Privatization Work? Evidence from the Russian Shops." _The Journal of Political Econom.y,_ 104(4), 764-90. August.

Boardman, Anthony, and Aidan R. Vining. 1989. "Ownership and Performance in Competitive Environments: A Comparison of the Performance of Private, Mixed, and State-Owned Enterprises." _Journal of Law and Economics._ April, 32, 1-33.

Boycko, Maxim, Andrei Shleifer, and Robert W. Vishney. 1994. "Property Rights, Soft Budget Constraints, and Privatization." Mimeo., Harvard University.

Bresnahan, T. 1989. "Empirical Studies of Industries with Market Power," in _Handbook of Industrial Organization,_ eds. R. Schmalensee and R. Willig. Amsterdam: North Holland.

Byrd, William A. 1991. "Contractual Responsibility System in Chinese State-Owned Industry: a Preliminary Assessment," in _Advances in Chinese Industrial Studies._ ed. N. Campbell, volume 2, page 7-35.

Coase, Ronald. 1991. "The Institutional Structure of Production." The Nobel Prize Lecture, delivered December 9, 1991. in Stockholm, Sweden.

Crocker Keith J. and Scott E. Masten. 1988. "Mitigating Contractual Hazards: Unilateral Options and Contract Length." _Rand Journal of Economics,_ 19(1), 327-343, Autumn.

<!-- Start of picture text --> Pi,Pe In( b ) + I , which implies<br>MCI' Mit<br>___ ____ _______ P ~~~~MC. AP. AMC,<br>Pi, - Pi,-] P In( Pi,/MCi, )I=n( P )-In( MC>, ) t " - it<br>MCi, MCI,-, Pi,-,MC, cl- Pi,, l MCh l- Pi,- lMCil,,<br><!-- End of picture text --> The first term of the last equation is output inflation rate, and the second term is proxied by the inflation rate for intermediate inputs.

31
**Figure 2: Historic and projected growth in world population**

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Rural population (billions) Urban population (billions)<br><!-- End of picture text --> Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2008 Revision and World Urbanization Prospects: The 2009 Revision.

Flooding is one of the major natural disasters which disrupt the prosperity, safety and amenity of the residents of human settlements. The term flood encompasses a flow of water over areas which are habitually dry. It covers a range of types of events, many of which can also include other sources of damage such as wind. Sources of floodwater can arise from the sea (in the forms of storm surge or coastal degradation), from glacial melt, snowmelt or rainfall (which can develop into riverine or flash flooding as the volume of water exceeds the capacity of watercourses), and from ground infiltration. Flooding can also occur as the result of failure of watercourses or man-made water containment systems such as dams, reservoirs and pumping systems.

Excess water in itself is not a problem. Rather, the impacts of flooding are felt when this water interacts with natural and human-made environments in a negative sense, causing damage, death and disruption. The experience of flooding for a rural agriculturalist and an urban slum dweller will be very different: while to the farmer the flood is a natural force to be perhaps harnessed or endured for the long term benefits it may bring, for the urban dweller flooding is at best a nuisance and at worst a disaster which destroys everything she or he owns.

The impact of flooding is driven by a combination of natural and human-made factors. In describing flood impacts and discussing flood risk management solutions within this paper, two models of flooding, which are chosen from the multitude of risk frameworks available, are 3
# APPENDIX

## **A Additional figures**

Figure A1: FDI Concentration Ratio (Alternative definitions) <!-- Start of picture text --> 35 50<br>30 45<br>40<br>25<br>35<br>20<br>30<br>15<br>25<br>2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>70<br>55<br>65<br>50<br>60<br>45<br>55<br>40<br>50<br>35<br>45<br>30<br>2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>High Income Developing<br>Concentration Ratio Concentration Ratio<br>(1 largest investing MNEs) (3 largest investing MNEs)<br>Concentration Ratio Concentration Ratio<br>(5 largest investing MNEs) (10 largest investing MNEs)<br><!-- End of picture text --> Source: Authors’ analysis on fDi Markets.

Note: The analysis relies on greenfield FDI investment project capital expenditure. The concentration ratio of country groups is calculated as the weighted average of each country’s concentration ratio , weighted by the country’s total investment. Countries are considered developing if classified as low income, lower-middle income or upper-middle income in 2003 based on the World Bank’s income classification.

17
Agricultural landholding increases indebtedness in the short run without affecting the long-term indebtedness. This is perhaps because households that are highly dependent upon agriculture may find it difficult to smooth income, given the high variability in agricultural income.

Natural calamities increase the probability of indebtedness among rural households in the short-run without having any significant long-term effects on their overall indebtedness. The more MFIs are serving a community, the higher the probability that a borrowing household is indebted. This is equally valid for both the short- and long-run indebtedness of rural households. The presence of commercial banks in a community is also likely to increase the probability of being indebted, at least in the long run. Therefore, like demand-side variables, the supply-side variables play an important role in determining the overall extent of indebtedness.

So far we have considered the impact of both demand- and supply-side variables on the overall measure of indebtedness without making a distinction among the various levels of indebtedness. However, an analysis of the extent or severity of indebtedness is equally important in order to understand the underlying factors that contribute to such severity. Considering that the indebtedness categories (moderate indebtedness, over-indebtedness, and severe indebtedness) represent gradually worsening situations, we run an ordered logit model to determine if the factors contributing to the overall indebtedness play similar roles in determining the severity among the indebted households.

Table 12 presents the results of this exercise based on the most recent household survey data for 2010/11.<sup>14</sup> Restricting the discussion to the flow-based measure of indebtedness, we find that both demand-side and supply-side variables that affect the overall measure of indebtedness also affect the ordering of indebtedness. For example, the probability of severe

> 14 The results presented here are based on an ordered logit regression applied to 2010/11 data after controlling for initial conditions. Alternately, we tried ordered logit models using panel data of 1998/99 and 2010/11, and the results are not qualitatively different from what are reported in Table 12.

21
With preferential tariffs approaching the zero lower bound, the coverage of PTAs in terms of policy areas has widened over time. Analysis based on the new World Bank data set on PTAs content (see Box 2) shows that agreements signed before 1991 included on average 9 provisions whereas agreements signed between 2005 and 2015 included on average 15 provisions (see Figure 15.a). With respect to the provisions included in active agreements, as expected, all agreements include reductions in tariffs on manufacturing goods. At the same time, more than 50 percent of agreements include deeper provisions such as anti‐dumping and countervailing (CVM) measures, rules on competition, movement of capital and intellectual property rights (TRIPS and IPR). Moreover, TBT, investment disciplines and SPS measures are often included in PTAs (see Figure 15.b).

Figure 15: Evolution of depth over time and most common provisions, 1958‐2015

- a. Evolution of depth over time                                                     b. number of agreements per provision

<!-- Start of picture text --> 30 300<br>25<br>250<br>20<br>200<br>15<br>150<br>10<br>5 100<br>0<br>50<br>0<br>Average number of provisions<br>Average number of legally enforceable provisions<br>Source: Calculations based on World Bank PTA content dataset (2016).<br>Number of agreements<br><!-- End of picture text --> Deep PTAs: The Brazilian experience Brazil is a member of only 1 active Preferential Trade Agreement: The Southern Common Market (Mercosur). Mercosur comprises five‐member countries: Argentina, Brazil, Paraguay, Uruguay, and the República Bolivariana de Venezuela.<sup>7</sup> Bolivia is currently in the final stages of the accession to become the sixth member. The group encompasses 290 million people and has a combined GDP of nearly $3.5 trillion. Brazil is also a member of 5 partial scope agreements (PSA)<sup>8</sup> : Global System of Trade Preferences among Developing Countries (GSTP), Latin American Integration Association (LAIA),<sup>9</sup> Protocol on Trade Negotiations (PTN) and MERCOSUR‐India and Mercosur‐SACU.

> 7 Associate members include Chile, Colombia, Ecuador, Guyana, Peru, and Suriname.

> 8A "Partial Scope" Agreement (PS).  "Partial Scope" which is not defined or referred to in the WTO Agreement, means that the agreement covers only certain products.  Partial scope agreements are notified under paragraph 4(a) of the Enabling Clause (source: https://rtais.wto.org/UserGuide/RTAIS_USER_GUIDE_EN.html).

> 9 Brazil has also signed a series of bilateral Economic Complementation Agreements (ACEs) with other Latin American countries such as Colombia, Peru and Chile.  These agreements fall within the legal framework of the Latin American Integration Association (LAIA) and point to a greater opening compared to partial scope agreements.

21
umn (2) looks at the resources firm owners have to pay for the course, and shows a strong positive association of demand with our wealth index. Demand is lower for poorer firm owners.

Column (3) examines how willingness to pay varies with what firm owners expect to get out of training, and with prior experience and interest. We see that entrepreneurs who expect the training to grow their sales by 10 percent or more have a higher willingness to pay for training. That is, there is some evidence that charging for training selects those entrepreneurs who expect to benefit more from it. The correlation is negative and significant at the 10 percent level with having previously done some other form of business training. Our elicited WTP is positively correlated with the baseline WTP completed in the phone survey, but the coefficient is small and only marginally significantly different from zero — this might be because they did not have enough information about the course to gauge WTP without the demonstration session, or because they were trying to answer strategically, and not truthfully, when asked WTP in the phone survey.

Column (4) examines whether willingness to pay is higher for those with more scope for improvement. We see no significant correlations with either baseline levels of business practices and personal initiative, nor with how firm owners feel they compare on these measures compared to other businesses. The coefficients are all positive, so, if anything, those who are already doing better are more likely to want to improve than those with poor practices to begin with. Column (5) considers how personal characteristics are correlated with WTP. We see younger entrepreneurs have higher WTP, while there is no significant association with gender or education. Entrepreneurs who have a higher risk propensity exhibit a statistically significantly higher WTP (at the 10 percent level), which is in line with the argument that training is seen as a risky investment, and some entrepreneurs might not be willing to pay for the training because of failures in the insurance market.

Column (6) examines how firm size is associated with WTP. We see a positive correlation of WTP with having employees, which is statistically significant at the 10 percent level, and a positive and highly significant association with having above median sales. That is, owners of larger firms are willing to pay more. Column (7) pools all the variables together, and then Column (8) uses LASSO to choose a sparser set of predictors from among all of these variables. Overall, we see that younger, wealthier entrepreneurs, who are more willing to take risks, and who have larger firms report higher levels of willingness to pay for the training. This group is therefore less likely to be excluded from the training by charging a positive price.

19