rafmacalaba/datause-encoder-data huggingface dataset:
6 imports are computed as the difference between the consumption and production statistics available from the Energy Information Administration (EIA) of the U.S. Department of Energy. Although the EIA also has import and export statistics, the data for 2009 are not yet available. Vulnerability is computed for a total of 180 countries, including 131 developing countries.

2. For decomposition analysis of changes in vulnerability, net imports are always based on the difference between consumption and production because both appear in the accounting identity used. Changes in inventories are omitted because the data are available primarily for high-income countries only and the contributions are expected to be small in most cases. For countries for which data from the IEA are available, they are used unless the net imports calculated using the EIA data more closely match the net imports computed using the IEA data in 1 above. For all countries in which the IEA does not collect data, the EIA data are used. Decomposition analysis covers a total of 170 countries.

The results of decomposition analysis necessarily exclude countries missing data for any one of the parameters in the accounting identity. In addition, the analysis excludes those countries for which there are serious discrepancies in the oil statistics, as explained in appendix 1.

Although full results for 1999, 2008, and 2009 are presented in appendix 1, the main text of this paper presents results for ∆ V for only the period between 1999 and 2008. The differences between 1999–2008 and 1999–2009 are driven primarily by the difference in the price of oil. As expected, net oil importers were generally less vulnerable, and net oil exporters saw a smaller fraction of their GDP derived from oil export revenue, in 2009 than in 2008.

# **2.2 Passing through of world fuel price increases**

The pass-through coefficient for each fuel is calculated between January 2009 and January 2012 as where all fuel prices are expressed in current U.S. dollars and the reference world prices are the free-on-board (FOB) prices of the fuel in the relevant international market: Northwest Europe, the Persian Gulf, Singapore, and the U.S. Gulf Coast. Two criteria were used to select the two dates:

- The time gap between the two dates is sufficient to allow governments to adjust prices. This would not be an issue if all countries have deregulated downstream markets and market-clearing prices are prevailing, but prices in many are regulated.

- The price difference between the two periods should be sufficiently large to reduce the noise in the data relative to other factors.
and improve local governance (Xu and Yao, 2009). Tsai (2007) argues that villages with more homogenous family lineage generally enjoy better provision of public goods. In the RCRE panel used in this paper, the relationship between homogeneity of family lineage and incidence of village land reallocation is clearly negative (Figure 4) ― the larger the share of households in the same patrilineal clan in the base year (1995), the less likely it is to have a significant land reallocation during the 1995‐ 2003 period.

This negative correlation between homogeneity of lineage and land reallocations may be explained by two distinct features from which more homogenous villages differ from less homogenous ones. First, mutual trust between villagers built through common family lineage may lead to less conflict, which may facilitate land rental transactions and ease the need for land reallocation. Indirect evidence of less conflict in homogenous villages is shown in Figure 5. Both civil disputes and reported criminal acts decrease with the share of households in the largest lineage group.<sup>21</sup> Moreover, disputes over land are far less likely to be the major cause of conflict among villagers with large family lineage clans, but are frequently the most important source of conflict in villages which lack them (Figure 6, left axis). Further, land rental activities are much less frequent in villages without major family lineage groups (Figure 6, right axis), while villages with large family lineage clans rely less on land reallocation to improve the match between labor and land.

Apart from greater levels of trust and reduced conflict, better monitoring of leaders in villages with dominant lineage groups provide an additional explanation for reduced reallocation activity. Improved monitoring in more homogenous villages reduces the scope for using actual or threatened reallocation as a rent‐seeking device (Xu and Yao, 2009; Tsai 2007). Consistent with the monitoring argument, we find that the village representative assembly tends to play a much more important role in the decision‐

> 21 Xu and Yao (2009) report that both civil disputes and criminal cases are lower in those villages in which the head is elected from the largest family lineage clan.

_Land Tenure Insecurity and Migration, Page 21_
# **2. METHODOLOGY**

In this section, we outline the methodology for our investigation of the factors behind Brazil’s spatial disparities in the standard of living. We being with a brief discussion of the measure used for the standard of living of households and their members and then summarize the Oaxaca-Blinder methodology used to decompose differences in mean welfare within regions (i.e. between metro and urban areas, and between urban and rural areas) and between regions (i.e. across metro, across urban, and across rural areas). The Oaxaca-Blinder decomposition allows us to estimate the relative contributions of differences in household characteristics and in returns in accounting for differences in living standards. Next, we describe the quantile regression decomposition methods used to determine the relative importance of covariate and returns effects at different quantiles of the distributions. Lastly, we describe the data used in our analysis.

# **_Comparing Living Standards Within and Across Regions_**

The measure of standard of living that we use in our analysis is the welfare ratio<sup>2</sup> . The welfare ratio is the nominal consumption expenditure per capita deflated by the appropriate region-specific poverty line. The region-specific poverty lines are assumed to incorporate all the cost of living differences faced by the poor in different regions and areas.<sup>3</sup> As a result, this allows us to make comparisons both within and across regions.

There are both conceptual and pragmatic reasons why consumption expenditures available from household surveys are preferable for the purpose of poverty and inequality analysis to an indicator such as household income. It is argued, for example, that consumption expenditures reflect not only what a household is able to command based on its current income, but also whether a household can access credit markets or household savings at times when current incomes are low or even negative (due perhaps to seasonal variation or a harvest failure) to smooth consumption. In this way, a consumption measure is thought to provide a better picture of a household’s longer run standard of living than a measure of current income. Furthermore, consumption expenditures for the poor are often better captured than household incomes. While poor households are probably purchasing and consuming only a relatively narrow range of goods and services, their total income may derive from multiple different activities with strong seasonal variation and with associated costs that are not always easily assigned.

> 2 The welfare ratio and its theoretical properties is discussed by Blackorby and Donaldson (1987). More practical applications of the welfare ratio in the measurement of poverty can be found in Ravallion (1998) and Deaton and Zaidi (2002).

> 3 Since the poverty lines are constructed based on the cost of basic needs (CBN) approach, which in essence is a Laspeyres price index with fixed weights, the welfare ratio is also analogous to “real  expenditures”.

4
themselves can provide some verification services, government education and training programs can also do this, and government can also play a role through criminal background checks, credit records, and other reputation mechanisms.

# **Conclusion**

Employment in developing countries is often dominated by small and informal firms, and thus faces a shortage of good wage formal-sector jobs. Job training and job search policies by themselves are unlikely to generate a lot of new employment, and there is a need for complementary policies that aim to spur the demand for labor. Nevertheless, there is still a role for well-designed policies to help speed up the process of structural transformation in labor markets, in ways that will improve employer-employee matches and thus increase the productivity and wages of available jobs, while also improving the employment prospects of disadvantaged groups.

However, governments often struggle to implement these job training and job search policies successfully at scale. How might these policies be implemented more effectively? We have discussed some general principles, but much depends on tailoring solutions to meet the needs of specific localities, sectors, and types of jobseekers—which is another reason that centralized national programs often struggle. For job training, programs need to be closely tied to market demand, so that employers want to hire workers who are trained. The returns to training to the average jobseeker in a large-scale government program are typically very modest, and much more work is needed to determine how best to target training programs and how to expand successful pilot programs to larger scale. Job search assistance seems to work best when it helps jobseekers learn not just about a particular job, but rather learn something more fundamental that causes them to update their beliefs about the types of jobs they should be considering (including which sectors and locations to search in), and the wage levels and trajectories in those jobs. Efforts to credibly certify the existing skills of workers can help reduce information frictions when education systems do not provide good signals. Performing these tasks well requires investing in good data systems: otherwise, labor markets in which it is difficult for workers, and firms are failing to create lasting and well-paid jobs, will also be challenging for governments to understand.

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Figure 4.8 Frequency of receiving counseling services in middle schools <!-- Start of picture text --> 14% 15%<br>6%<br>26%<br>39%<br>Always Very often Sometimes Rarely Never<br><!-- End of picture text --> _Source_ : Authors.

In summary, Yunnan’s progress and challenges in upper secondary education typifies that of the less developed provinces in China. Yunnan is one of the least developed provinces in China. The lack of human capital is a major constraint for the province’s economic development, as in many less developed regions in China. Yunnan has gradually narrowed the education gap, except at the post compulsory level. Most of Yunnan’s access gains at the upper secondary level has been disproportionately driven by academic track expansion. Several factors could influence the widening gap between academic and vocational education enrollment, including financial constraint and household perceptions toward the upper secondary education. The results of a stakeholder survey conducted by this study show that pursuit of tertiary education through academic high schools is widely recognized among parents and students while less so for secondary vocational school graduates. In terms of perceived quality of vocational education, the interviewed education administers, students, and parents expressed their concerns about the teacher quality of vocational education. However, the perceptions could be a result of misinformation. There is a lack of career counseling services in middle schools, leaving middle school students unprepared for a well-informed decision about which track to choose in Grade 9. Lacking clear ideas about their career planning, middle school students are highly influenced by the perceptions of their parents and teachers rather than their own career interest.

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_Studies_ . 60(1), pp. 35-52 Glaeser, E. and Gyourko, J. (2005) Urban decline and durable housing. _Journal of Political Economy_ , 113(2), pp. 345-375 Henderson, V. and Lee, Y. (2011) Organization of disaster aid delivery: spending your donations. _NBER Working Paper 17707_

> Hornbeck, R. (2012) The enduring impact of the American Dust Bowl: Short and Long-run adjustments to environmental catastrophe. _American Economic Review_ ,102(4),pp. 1477-1507 Hutchinson, W. and Margo, R. (2006) The impact of the civil war on capital intensity and labor productivity in southern manufacturing. _Explorations in Economic History_ , 43(4), pp. 689-704

Jovanovic, B. and Moffitt, R. (1990) An estimate of a sectoral model of labor mobility. _Journal of Political Economy_ , vol. 98, no. 4, pp. 827-852 Lanjouw, J. and Lanjouw, P. (2001) The rural non-farm sector: issues and evidence from developing countries. _Agricultural Economics_ , 26(1), pp. 1-23 Mookherjee, D. and Ray, D (2002) Is equality stable?. _American Economic Review_ , 92(2), pp. 253-259 Mueller, V. and Quisumbing, A. (2011) How resilient are labor markets to natural disasters? The case of the 1998 Bangladesh Flood. _Journal of Development Studies_ , 47(12), pp. 1954-1971 Nose, M. (2013) Micro responses to disaster-relief aid: design problems for aid efficacy. Mimeo. Washington , DC: World Bank.

Wooldridge, J. (2002) Inverse probability weighted M-estimators for sample selection, attrition, and stratification. _Portuguese Economic Journal_ , 1, pp. 117-139 22
Pagp ii

# **TABLE** OF CONTENTS

||Page|
|---|---|
|Executive<br>Summary|iii|
|Issues for Economic and Sector Work|1|
|Women's Economic Contribution|1|
|Equity, Human Capital Accumulation, and Welfare in the Femily<br>(Consumption in the Family; Bargaining in the Family;<br>Education; Health; Nutrition)|9|
|Productive Efficiency<br>(Limited Access and Own production; Discrimination and<br>Disadvantage in the Labor Market; Technical Change;<br>the Vicious Circle)|37|
|How Economic Adjustment Affects Women<br>(Fiscal Policies; Public Sector Employment;<br>Public Services; External Trade Regime;<br>Exchange Rate Policy; Wages and Prices;<br>Monetary and Credit Policy; Deregulation;<br>Privatization; Adjustment|56|
|Managing Population Growth and the Environment<br>(Future Labor Force; Fertility Determinants;<br>Use of Water, Wood, and Other Natural Resources)|63|
|Bibliography|72|
|Annex 1 Assembling the Evidence: Statistical Indicators on WID|80|
|Annex2 Women's Labor Force Participation|85|
|Annex 3<br>WDR WID Table|94|

This paper was prepared by Barbara Herz, PHRWD; with contributions from Lynn Bennett, Consultant; Meera Chatterjee, Consultant; Paul Collier, Consultanc; Eleanor Folta, Consultant; Nancy Gillespie, Consultant; David Grey, INUWS; Shahidur Khandker, PHRWD; John Kurrien, Consultant; Judith McGuire, PHRHN; Anthony Measham, PHRHN; Augusta Molnar, Consultant; Jennifer Mukolwe, Consultant; Barry Popkin, Consultant; Gotz Schreiber, PHRWD; and T. Paul Schultz, Consultant.

We appreciate comments received from many people and particularly thank Paul Isenman, Callisto Madavo, Hans Wyss, Joan Claffey, Susan Cochrane, Christiaan Grootaert, Ralph Harbison, John Hayward, Bernardo Kugler, Francis Lethem, Josette Murphy, John Peberdy, Loretta Schaeffer, Julian Schweitzer and Gerardo Sicat. Ann Hamilton, Director of PHR, provided guidance and support.
and 1 percent on “printing and related support activities”.

Looking at the heterogeneity of digital goods tariffs across countries in AfCFTA, Figure A3 reveals that the level of tariff protection on digital goods correlates negatively with income per capita across African countries, with economies like South Africa imposing tariffs as low as 0.9 percent, while the Central African Republic and Cameroon apply tariffs as high as 15 percent. We also observe that tariffs on digital goods are relatively high in large countries (as measured by population size), including Ethiopia, Nigeria and Egypt. Therefore, the scope to reduce the relatively high tariffs on digital goods in AfCFTA is especially high in low-income countries in the region, and such reduction would impact a sizable share of Africa’s population.

# **4 The quality and quality-adjusted prices of Africa’s imports of digital goods**

It is well-documented that the unit values of internationally traded goods are strongly influenced by differences in quality (Schott, 2004; Hummels and Klenow, 2005; Hallak, 2006; Hallak and Schott, 2011; Feenstra and Romalis, 2014). To undertake meaningful cross-country comparisons of import prices of digital goods, it is therefore important to adjust them for these quality differences. In this section, we present estimates of the quality and quality-adjusted prices of AfCFTA imports of digital goods. We first briefly outline the main features of the methodology we use to estimate the quality and quality-adjusted prices of Africa’s imports of digital goods. We then present the corresponding results.

## **4.1 Methodology**

We estimate the quality and quality-adjusted import prices of digital goods following the methodology proposed by Feenstra and Romalis (2014). This approach exploits a pervasive supplydriven feature of trade data to decompose unit values of traded goods into quality and qualityadjusted price components, exploiting both differences in the demand and supply for quality. Previous studies relied mainly on the demand side to identify quality together with a simple supply side to control for the extensive margin (Schott, 2004; Hallak, 2006; Khandelwal, 2010; Hallak and Schott, 2011), assigning higher quality to imports with a higher market share (or to trade flows higher net exports), conditional on price. Feenstra and Romalis (2014) add more structure to the supply side of product quality, by modeling explicitly the endogenous quality choice of heterogeneous firms. The model gives rise to a “Washington apples” effect, whereby goods of higher quality are shipped longer distances (Alchian and Allen, 1964; Hummels and 8
household and neighborhood level on children’s nutritional status, controlling for the effects of important determinants of nutritional status identified in the literature. Data from the 2000 Ethiopia Demographic and Health Survey (DHS) are used for the analysis, which estimates the probability of children being stunted and underweight.

The paper is structured as follows. Section 2 describes the Ethiopia DHS dataset used for the analysis. Section 3 focuses on the problem of malnutrition in Ethiopia and provides some statistics on the demographic and geographical characteristics of the surveyed population. Section 4 introduces the theoretical framework adopted for the analysis, which follows Alderman, Hentschel, and Sabates (2002), and the empirical strategy employed. Section 5 discusses the econometric specification of the model and the econometric issues that arise in the estimation of the specified model. Section 6 presents the results of the analysis. Section 7 concludes.

# **2. The Ethiopia Survey**

The 2000 Ethiopia DHS is a comprehensive, nationally representative population and health survey conducted in Ethiopia.<sup>2</sup> The survey interviewed 15,367 women between 15 and 49 years of age to collect information regarding fertility and family planning behavior, child mortality, children’s nutritional status, the utilization of maternal and child health services, and knowledge of HIV/AIDS and STDs. The survey was carried by the Central Statistical Authority (CSA), with technical assistance provided by ORC Macro as a part of its MEASURE DHS+ project. The survey took place between February and May 2000, following the harvesting season of the main Meher crops, maize and sorghum, which account for 90 to 95 percent of total cereal production.

A two-stage sampling procedure was adopted, using the 1994 Population and Housing Census as the sampling frame. In the first stage, 540 enumeration areas (EA)—139 in urban areas and 401 in rural areas—were selected using systematic sampling with probabilities proportional to the population size. A listing of all households in each of the selected EA was produced and 27 households per EA were selected in all regions in the second stage. This

> 2 The information concerning the survey described in this section comes from the _Ethiopia Demographic and Health Survey 2000_ report produced by the Central Statistical Authority and ORC Macro.

4
noted earlier are associated with greater flows of technological inputs in those sectors. In contrast, related-party trade in these industries is unresponsive to the other forms of IPAs. In those cases, the third-party trade effects seem more associated with locally generated capacity expansion in U.S. affiliates. One notable finding is that related-party exports in ICT are reduced in all three agreement types. This outcome suggests that affiliate production in electronics goods becomes less reliant on imported inputs from headquarters locations in the wake of joining such an agreement.

# **5. Concluding Remarks**

Our intention in this paper was to apply a treatment-control approach, used in other empirical trade contexts, to the World Bank’s Database on PTAs and to see if it unearthed systematic evidence that PTAs with enforceable IPRs chapters, and those with more specific provisions, have effects on trade in treated countries versus control countries. We note that the approach may not be the best way to identify such impacts because our treatment definitions may not sufficiently exclude other important impacts on trade arising from the agreements themselves or, indeed, from provisions not directly related to intellectual property protection. Nevertheless, it offers a complementary avenue for thinking about such effects.

Our approach is rigorous in its exclusion of intra-PTA trade and its use of extensive fixed effects. These controls, along with the difficulty of identifying the potential effects of actual IPRs provisions, make it challenging to identify systematic effects. Indeed, while we noted earlier that the aggregate sectoral trade analysis reveals some tentative conclusions, they are only weakly supported in the bilateral analysis. Nonetheless, it does seem that, at least as regards legally enforceable IPAs involving the US, the EU, or EFTA, they have detectable impacts on exports of pharmaceuticals and other goods, depending on the income group. Beyond that, we conclude that, as valuable as the World Bank Database on PTAs clearly is, it is difficult with our approach to use that information to detect trade impacts strictly within the context of intellectual-property provisions. Future refinements in the research may offer a clearer path toward that task.

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