rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 14%<br>Lower Bound (90% Conf.)<br>12% a Mid Point Estimate<br>Upper Bound (90% Conf.)<br>10%<br>8%<br>6%<br>A%<br>2%<br>0%<br>Liberalization since Matching Korea Matching Brazil<br>1997<br><!-- End of picture text --> <!-- Start of picture text --> Upper Bound | (90% Conf.<br>= Mid Point Estimate<br>Lower Bound | (90% Conf.<br>-0.1 -0.08 -0.06 -0.04 -0.02 0<br><!-- End of picture text -->
income to final income, and the poverty gap by 33%. The effects are stronger for girls and children in rural areas than for boys and those living in urban areas. However, this is only the case once the significant in-kind transfers for education and health are considered. Poverty rates increase between market income and consumable income, which implies that the overall fiscal system up to this point has impoverished both boys and girls. The findings in this study highlight the fact that public services are not only essential in delivering fundamental child rights, but also in reducing poverty among children.

Child-focused fiscal incidence analyses provide essential insights into the distribution of taxes, direct transfers and public spending, and allow for a better understanding of the impact of fiscal policies on poverty and inequality among children. These insights are relevant for a wide range of decision makers, including policy makers in local and national governments, international financing facilities and other multilateral organizations, as well as civil society organizations. Furthermore, indicators on both pro-poor public spending on social services as well as the distributive impacts of fiscal policies are now part of the global indicator framework for the Sustainable Development Goals.

Finally, while this study offers an analysis of fiscal incidence in 2018/19, CEQ4C assessments can be used to simulate the effects of potential policy interventions and offer an important toolkit to assess the effects on poverty and inequality of new policy proposals. Our four fiscal policy simulations that focus on universal education and the PSNP improve child welfare. Closing the education gap in Ethiopia in particular is associated with modest reductions in monetary inequality as well as multidimensional and monetary poverty, with varying gender and location effects. PSNP transfers, if doubled, would have a modest reduction effect on monetary poverty and inequality. PSNP transfers, if redistributed from non-poor to poor children, would have larger poverty and inequality effects. Doubling and redistribution jointly result in the largest welfare improvements for all groups of children.

24
# **IV.1 Factors Motivating Reform**

Several factors, including<sup>the poorperformanceof MPTC,broad bipartisan supportfor</sup> reform, pressure from donors,<sup>andtechnologicalandregulatorychangesthatweretaking place</sup> throughout the region, increased<sup>pressurefor reform.In this sub-sectionof thepaper,wediscuss</sup> each of these factors in<sup>tum.</sup>

**_Poor Performance of_**<sup>**_Malawi Post and Telecommunications Corporation(MPTC)._**As</sup> in most of the other case<sup>studycountries, oneofthemainmotivations forreformwasthe</sup> extremely poor performance<sup>of the incumbentoperator.Telephonepenetrationwas lower(see</sup> Figure 1), coverage was<sup>growing more slowly (see Figure 2), and productivity wasfar worse (see</sup> Figure 7) than the averages<sup>forSub-Saharan Africa.Inaddition,servicequality was poorand</sup> service outages were common.<sup>By1998, when reform wasfinally implemented,many observers</sup> were concemed that Malawi's<sup>poorinfrastructurewasirnpactingforeigninvestment and</sup> economic growth. Foreign<sup>enterprises operatingin Malawi ranked inadequateinfrastructureas a</sup> larger constraint on doing<sup>businessinMalawithan theyranked any of 15other policyareasin</sup> 1997.24 In comparison,<sup>corruption wasranked as a larger problemthan infrastructurein15of 22</sup> Sub-Saharan African countries,<sup>taxes in12of 22 countries,and crime in8 of 22 countries.In an</sup> additional question, firms<sup>wereasked howlongittooktogetapublic telephoneline.The</sup> average estimate for Malawi<sup>wasthe third highestamongthe 22Sub-Saharan African countries</sup> for which data was collected.<sup>25</sup>

MPTC's control over the<sup>telecommunicationssectorand its ability to restrict competition</sup> in several potentially competitive<sup>areaswhereit ownedthe maincompany(i.e.,in cellularand</sup>

> 24 Data is from the 1997<sup>WDR PrivateSector Surveycollected for WorldBank (1997).In this survey,enterprises</sup> were asked to judge on<sup>asix-pointscalehow problematicseveraldifferentpolicyareas werefordoing business.</sup> The questionnaire asked<sup>about15areas,includingregulations,price controls,corruption,inflation,crimeand theft</sup> and terrorism. The Sub-Saharan<sup>AfricansurveycoveredBenin,Cote d'Ivoire,Cameroon,Congo,Ghana,Guinea,</sup> Guinea-Bissau, Kenya,<sup>Madagascar, Mali,Mozambique,Mauritius, Malawi, Nigeria,Senegal,Chad,Togo,</sup> Tanzania, Uganda, South<sup>Africa, Zambia,and Zimbabwe.</sup>

25 The question was "How<sup>longdoes it take to get a public telephoneline connected?"withfirms asked to respond</sup> on a 1-6 scale with I being<sup>less thanImonth,5 being morethanIyear and 6 being'difficultto say'.Responsesto</sup> this question should be treated<sup>withsome caution since a disproportionatenumber of 6's mightmake comparisons</sup> difficult.

23
Gustafsson, Bjorn, Li Shi and Terry Sicular. 2008. Inequality and Public Policy in China. Cambridge University Press, New York, NY, USA Haggblade, Steven, Hazell, Peter B.R. and Reardon, Thomas (Editors). 2007. Transforming the Rural Nonfarm Economy. Baltimore: Johns Hopkins University Press.

Hannum, Emily and Albert Park. 2002. Educating China’s Rural Children in the 21st Century. Harvard China Review, 3(2): 8-14.

Hannum, E, Behrman, J., M. Wang, and J. Liu (2008): Education in the Reform Era, in Brandt and T. Rawski Ed: China’s Great Economic Transformation, Cambridge university Press.

Hannum, Emily and Albert Park. 2007. Education and Reform in China. Routledge. Hannum, E and Y. Xie (1994), “Trends in Educational Gender Inequality in China: 1949-1985”, Research in Social Stratification and Mobility, Vol. 13.

Hertz Tom, Tamara Jayasundera, Patrizio Piraino, Sibel Selcuk, Nicole Smith and Alina Veraschagina. 2007. The Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends. The B.E. Journal of Economic Analysis and Policy (Advances), 7(2), Article 10.

Himanshu & Lanjouw, Peter & Murgai, Rinku & Stern, Nicholas (2013). Non-farm diversification, poverty, economic mobility and income inequality : a case study in village India, Policy Research Working Paper Series 6451, The World Bank.

Hirano, K. and Imbens, G.W. (2001), Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization, Health Services and Outcomes Research Methodology, 2, 259-278.

Hirano, K., G.W. Imbens, and G. Ridder (2003), Efficient Estimation of Average Treatment Effects using the Estimated Propensity Score, Econometrica, 71, 1161-1189.

43
- [22] Deininger, K., H. Selod and A. Burns (2012) _The Land Governance Assessment Framework. Identifying and Monitoring Good Practice in the Land Sector_ , The World Bank: Washington DC, pages 147.

- [23] Demsetz, H. (1967) Toward a Theory of Property Rights, _American Economic Review_ , 57, 347-59.

- [24] DeLong, J. Bradford and A. Shleifer (1993) Princes and Merchants: European City Growth Before the Industrial Revolution, _Journal of Law and Economics_ , 36, 671-702.

- [25] De Soto, H. (2000) _The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else_ . New York: Basic Books.

- [26] Doraszelski, U., & Judd, K. L. (2012). Avoiding the Curse of Dimensionality in Dynamic Stochastic Games. _Quantitative Economics_ , 3(1), 53-93.

- [27] Doraszelski, U. and M. Satterthwaite (2010) Computable Markov-perfect industry dynamics, _RAND Journal of Economics_ , 41, 215-243.

- [28] Durand-Lasserve, A. and H. Selod (2009) The Formalization of Urban Land Tenure in Developing Countries, in Lall S., Freire M., Yuen B., Helluin J.-J. (eds), _Urban Land Markets: Improving Land Management for Successful Urbanization_ , Springer, October 2009, chapter 5, 101-132.

- [29] Durand-Lasserve, A., Durand-Lasserve, M. and H. Selod (2015) _Land Delivery Systems in West-African Cities. The Example of Bamako, Mali_ , Washington, DC: World Bank and Agence Fran ̧caise de D ́eveloppement, 106 pages.

- [30] Duranton, G. (2009) _Are Cities Engines of Growth and Prosperity for Developing Countries_ , in Urbanization and Growth, Spence, Annez and Buckley eds., chapter 3, 67-113, The World Bank.

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34
the end of November and December. The non-response rates were relatively high: about 13 percent of firms failed to report a November inventory level, and 10 percent failed to report a December inventory level. Among the firms reporting data, these data increased the correlation between reported profits and adjusted revenues minus expenses from 0.26 to 0.42 for manufacturers, but has almost no effect for retailers, where the correlation falls slightly from 0.35 to 0.34.

In sum, the mismatch between purchase of inputs and the sales of goods associated with those purchases is a major cause of the differences between reported profits and revenues-expenses. When we use data on the markups over inputs costs, we find very significant improvements in the correlations between these two reported measures. Gathering information on the markup of inputs appears to be an effective way of adjusting for timing mismatches. Direct methods of asking for inputs associated with a given month’s sales do not do as well. The direct methods appear to work better for manufacturers than for retail firms. The markup question is applicable even in cross sectional surveys. After making adjustments for the mixing of household and enterprise accounts, and for timing mismatches, we find a very high correlation between reported and calculated profits. We should note also that raw materials represent the vast majority of variable costs in the enterprises from which our data are gathered. In larger enterprises with more paid employees, timing mismatches for labor and other variable costs will also need to be accounted for. We believe measuring markups has promise in these adjustments as well, though our data do not allow us to confirm this.

# _Do reported profits move with reported sales?_

It appears reasonable that firm owners have a good general idea of the level of their profits, since this forms the basis for household consumption and for any reinvestments in the business. However, in many applications one is also interested in the dynamics of profits. One would therefore like to know whether (a) reported profits respond to interventions; and (b) whether reported profits reflect the seasonality present in sales. Evidence that reported profits respond to interventions is seen in de Mel, McKenzie and Woodruff (2007), who show that a randomized experiment which increased the capital 14
15 Figure 3 depicts the pattern of SHI adoption over time in the countries of our sample. Our SHI status dummy is equal to 1 in about half (218 observations) of the 442 country-year combinations for which we have non-missing values of the indicator.

# **_Health sector outcome variables_**

Our health sector outcome measures include: per capita health spending (total, public and private) and the share of spending going on salaries; population health status; hospital activity rates and capacity utilization; and quality-of-care indicators. Our variable definitions and sources are briefly described below and the descriptive statistics for them—for the full sample and disaggregated by SHI status—are presented in Table 2.<sup>20</sup> We measure per capita health spending as total health care expenditures per capita expressed in constant 2000 dollars adjusted for purchasing power parity (deflated using the United States GDP deflator), to allow comparisons in real values between countries and over time (Gerdtham and Jonsson 1992). The source for these figures is the World Bank’s _World Development Indicators (WB-WDI)_ database. The WB-WDI database is the primary World Bank database for development data, obtained from recognized international sources. It contains an expanded set of the economic, health and other time series indicators published in the Bank’s _World Development Reports_ . Average health spending for the period 1990-2004 was US$403 PPP per capita. The Czech Republic, Hungary and Slovenia are the countries with highest spending levels (each with an average of at least US$857 PPP per capita between 1990-2004 and at least US$1,225 PPP per capita in the last year), whereas Azerbaijan, Kyrgyz Republic, Tajikistan and Uzbekistan have the lowest spending levels in our sample (at most US$132 PPP

> 20 The complete list of definitions and sources for our health sector outcome variables can be found in Table 9 in the Appendix.
# Evo, Pablo, Tony, Diego, and Sonny: General Equilibrium Analysis of the Market for Illegal Drugs<sup>∗</sup>

Rómulo A. Chumacero<sup>†</sup>

> ∗I would like to thank Philip Keefer, Norman Loayza, Felipe Morandé, Klaus Schmidt-Hebbel and the participants of the GDN conference for useful comments and suggestions. Cintia Külzer Sacilotto provided able research assistance. The usual disclaimer applies.

> †Department of Economics. University of Chile. E-mail address: rchumace@econ.uchile.cl
couples on ownership and rights to dwelling and non-dwelling land — around 70 and 75 percent of land parcels in Tanzania and Malawi, respectively, 90 percent in Ethiopia, and 94 percent in Cambodia (Appendix Table A1).<sup>8</sup> On valuation specifically, the UN guidelines recommend that in estimating wealth, each asset should be valued individually at its current market price (the _potential sales value_ or _realization value_ , namely how much the respondent would expect to receive if they sold the asset today). This recommendation had been based on the experience under the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), the Gender Asset Gap Project, and the surveys that were supported by the EDGE project, which found that the potential sales value provides a more consistent measure of asset prices as compared to the construction value or rental value of the asset, as well as other approaches to sales valuation such as the “quick sale” price and “reservation” price.<sup>9</sup> The LSMS+ assets modules followed the UN guidelines and asked respondents that of each asset they owned, _“if you were to sell <THIS ASSET> today at current market prices, how much would you receive for it (in local currency)?”_<sup>_10_</sup> The approach was item-by-item and was asked of respondents for a point estimate valuation for each asset they owned (each land parcel, for example), as opposed to an aggregate estimate for all assets within a specific asset class. For financial accounts, respondents were asked about the current value of each account.

> 8 A parcel is defined as a continuous piece of land which can have more than one plot.

9 The “quick sale” price is what the owner could expect to receive if he/she sold right away, i.e. not necessarily at current market prices, and which could lead to an undervaluation of the asset. The “reservation” price is the price that would induce the respondent to sell the asset, even if he/she was not intending to sell; this might lead to an overvaluation. For more discussion see UN (2019).

10 In Ethiopia, since land is owned by the state, respondents were asked to provide the rental as opposed to sales value. In practice, however, land has been bought and sold through side channels.

10
# **1. Introduction**

In an influential article in 1990, Amartya Sen noted that over 100 million women were missing.<sup>1</sup> Sen emphasized the role of economic and legal protection as critical to saving women and improving their well-being. Domestic violence – also known as intimate partner violence – is the most direct and aggressive method of lowering the status of women in every regard. The costs to society are considerable. Data from nine countries (the United States, Australia, the United Kingdom, Nicaragua, Chile, Uganda, Morocco, Bangladesh, and Vietnam) indicate that the economic cost of intimate partner violence is typically between 1 to 2 percent of GDP. Similarly, the economic cost of domestic violence in the EU25 has been estimated at about €16 billion in 2006 (Duvvury et al. 2013). It is worth noting that these cost estimates are on the conservative side, given the severe underreporting of domestic violence. For instance, Palermo et al. (2013), using data from 24 countries, find that gender-based violence is 14 times higher than the number of incidents estimated from combined formal sources, 25 times higher than estimates from police reports, 67 times higher than estimates from medical facilities, and 33 times higher than estimates from social services sources.

The barrage of statistics above mask the actual nature of what these events entail. Consider the case study of Maria de Penha Fernandes in Women, Business, and the Law 2016: Getting to Equal report (World Bank 2015). Maria was shot by her husband in 1983, and two weeks after she returned from the hospital, he attempted to electrocute her. These two consecutive murder attempts left her paraplegic. When a law on domestic violence was finally approved in Brazil in 2006, it was named the Maria de Penha Law. The emotional and psychological consequences this entails is easily lost in the numbers. A good portion of female

> 1 <u>http://www.nybooks.com/articles/archives/1990/dec/20/more‐than‐100‐million‐women‐are‐missing/</u>

2
like Timor-Leste with little infrastructure and value-added crops is challenging and especially so when there are no incentives to upgrade. Therefore, rural infrastructure investments should also consider market access constraints for smallholder farmers. Investments in ICT connectivity would also be of fundamental importance. There is a need to strengthen the enabling environment to allow Timor-Leste access to international markets and benefit from imported technology.

**6.** _Reduce the gender gap in agricultural productivity_ . Although FHHs account for only 10 percent of agricultural households, it is important to close the 12.75 percent gender gap in productivity. Doing so will help improve overall productivity. Since almost all of the gender gap is attributable to females having fewer resources, it can be closed by improving their access to resources such as mechanized inputs, extension services, and credit.

While there is a common inverse relationship between farm size and agricultural productivity, farmers’ efficiency may not vary by farm size if overall input use is considered. Also, new technologies and institutional arrangements may give rise to significant farm economies of scale, hence any differences in productivity driven by farm size may be diminished with advancement in technology and movements into high value-added crops.

The recommendations outlined above echo those from Myanmar’s lessons. Improvements in Myanmar’s agriculture sector accounted for over 46 percent of the country’s poverty reduction recorded between 2005 and 2015 (World Bank, 2019). Over this period, the poverty rate among agricultural households declined from 60 to 41 percent. The factors that contributed to that process included crop diversification, increased use of chemical inputs (fertilizers, pesticides, and herbicides), improved management of irrigation infrastructure, increased mechanization, enhanced access to markets through improved rural infrastructure, and increased access to credit for smallholder farmers (World Bank, 2019).

For Timor-Leste, agriculture is likely to retain a major role in the livelihoods of the poor, and overall economic growth, in the years to come. The success of the agricultural transformation process depends on policy decisions that should be guided by an understanding of the potential drivers of growth of the agriculture sector. The findings of this study provide evidence that complements existing works on Timor-Leste’s agriculture sector that identifies increasing agricultural productivity as a policy necessary for sustaining economic growth of the country. Investment in the sector can be improved by crowding in private sector financing and by using the limited public funds more strategically. Yet, further study and new data are needed to conduct more detailed analyses on where and how specifically investments in the agriculture sector could be effectively allocated to reap the highest potential impact in increasing rural income, which can lead to poverty reduction. It is expected that the upcoming agriculture census will provide an opportunity to do so.

16
Finally, we explore whether perceptions of safety and mobility could be associated with conflict. While this information was not collected in 2014, in Table 6 we report the results of a crosssectional regression analysis that considers a safety index (higher values indicate not feeling safe) and a mobility index (higher values indicate more mobility). There is a clear and significant pattern – women living in districts with medium intensity conflict report feeling more safe and more mobile compared to low intensity districts. However, this result is reversed for women living in strong intensity districts, who report feeling less safe and less mobile during the day (but not at night). These results indicate that there could be changes to the community and neighborhood due to conflict that could be indirectly associated with women’s role in decisionmaking. It could also explain why the female decision-making index is not significantly affected in strong intensity districts, since safety and mobility are diminished.

_Table 6: Mechanisms: Cross-sectional analysis from YHDS 2021_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Sa|fety|Mob|ility|
||At day|At night|Going out to<br>market at day|Going out to<br>work at day|
|Medium|-0.057**|-1.090***|0.794***|0.343***|
||(0.024)|(0.035)|(0.017)|(0.023)|
|Strong|0.718***|0.513***|-0.071***|0.036**|
||(0.015)|(0.031)|(0.013)|(0.017)|
|PSU dummies|Yes|Yes|Yes|Yes|
|Women controls|Yes|Yes|Yes|Yes|
|Partner controls|No|No|No|No|
|Observations|1,561|1,561|1,553|1,460|
|R-squared|0.258|0.380|0.456|0.308|
|Standard errors clustered at the district<br>level.|||||

These results indicate that the main channel for an increase in women’s participation in decision making, particularly in areas that have experienced conflict at a “medium intensity level”, is driven by significant changes to household composition. Potentially due to domestic or international migration of men, as well as the deaths of many men in the battlefields, more households are headed by women than before the war started. While we see better involvement of women in decision making, it should be noted that many of these households remain more vulnerable. Aggregate household consumption per capita, which is measured using the YHDS, is lower among female headed households. The bottom 20 percent and 40 percent of households by consumption per capita are more likely to be female headed households (World Bank, 21