rafmacalaba/datause-encoder-data huggingface dataset:
of ���. Still, we want to generate a wealth index _wij_ which offers the best combination of (the elements of the different) household assets ���. Our problem can then be expressed as follows where we now place the term �<sup>�</sup> ��� on the left-hand side to emphasize that � are the (vector of) weights we place on the ��� in an effort to generate the best possible measure of wealth. Note that household assets are just a component of the household and community characteristics ��� in Equation (1).

There are two common approaches to obtaining these weights: the first is to simply let all of them equal 1 (i.e., using simple aggregation that provides a count of the number of assets a household possesses), and the second is to search for a combination of weights that captures the most variation of the ��� by statistical techniques such as the principal component (PCA) method. Some remarks are, however, in order. The PCA method is data-driven by definition, and offers weights that vary depending on the specific data set that is analyzed. For example, a motorbike would be given a small and perhaps statistically insignificant weight in a setting where it is a commonplace asset (i.e., all households have a motorbike), but would be given a larger weight in another setting where the opposite is true. On the other hand, the simple aggregation method would apply the same equal weights in all the settings. As such, the PCA method appears to be best suited for analysis that is restricted to one setting, while the simple aggregation method may be better for comparison of results across different settings.<sup>14</sup> Wealth indexes are a useful substitute for household consumption in the absence of the latter, since almost all surveys these days collect the relevant information on household assets and housing characteristics. However, note that wealth indexes are less accurate than household

> 14 See, for example, Jolliffe (2002) for a comprehensive treatment of PCA methods.

14
**Table 1. Estimation results for annual average firm growth, 2002-2008**

|Variables|No<br>regulations<br>(1)|Overall inst<br>(2)|.<br>Inst.<br>Regulation<br>(3)|Access to<br>finance<br>(4)|Crime<br>(5)|Corruption<br>(6)|<br>Tax<br>(7)|Labor<br>regulation<br>(8)|
|---|---|---|---|---|---|---|---|---|
|**Firm**<br>|||||||||
|**characteristics**|||||||||
|Initial size|-0.053***|-0.077***|-0.078***|-0.077***|-0.077***|-0.077***|-0.077***|-0.077***|
||(0.001)|(0.001)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Age|-0.046***<br>(0.001)|-0.077***<br>(0.003)|-0.077***<br>(0.003)|-0.076***<br>(0.003)|-0.077***<br>(0.003)|-0.077***<br>(0.003)|-0.077***<br>(0.003)|-0.077***<br>(0.003)|
|Initial size × age|0.005***|0.013***|0.013***|0.013***|0.013***|0.013***|0.013***|0.013***|
||(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|TFP|-|0.015***|0.016***|0.015***|0.015***|0.015***|0.016***|0.015***|
||-|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|**Industry**|||||||||
|**characteristics**|||||||||
|Agriculture|0.004|0.031**|0.021|0.015|0.032**|0.031*|0.025|0.032**|
||(0.003)|(0.016)|(0.016)|(0.016)|(0.016)|(0.016)|(0.016)|(0.016)|
|Mining|<br>0.043***<br>(0.004)|<br>-<br>-|<br>-<br>-|<br>-<br>-|<br>-<br>-|<br>-<br>-|<br>-<br>-|<br>-<br>-|
|Construction|<br>0.038***<br>(0.002)|0.060***<br>(0.012)|0.050***<br>(0.012)|0.049***<br>(0.012)|0.060***<br>(0.012)|0.061***<br>(0.012)|0.054***<br>(0.012)|0.060***<br>(0.012)|
|Manufacturing|<br>0.032***|<br>0.041***|<br>0.026**|<br>0.036***|<br>0.039***|<br>0.040***|<br>0.034***|<br>0.039***|
||(0.001)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|Wholesale|0.006***|0.016|0.005|0.008|0.015|0.014|0.007|0.015|
||(0.001)|(0.012)|(0.012)|(0.012)|(0.012)|(0.011)|(0.012)|(0.012)|
|Transport|<br>0.017***|<br>0.019|<br>0.010|<br>0.015|<br>0.018|<br>0.017|<br>0.016|<br>0.019|
||(0.001)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|Overall inst.|<br>-|<br>-0.011*<br>|<br>-<br>|<br>-|<br>-|<br>-|<br>-|<br>-|
||-|(0.006)|-|-|-|-|-|-|
|Inst. Regulation|-|<br>-|<br>0.018***|-|-|-|-|-|
||-|-|(0.004)|-|-|-|-|-|
|Access to finance|-|-|<br>-|<br>-0.025***|-|-|-|-|
||-|-|-|(0.004)|-|-|-|-|
|Crime|-|-|-|<br>-|-0.003|-|-|-|
||-|-|-|-|(0.004)|-|-|-|
|Corruption|-|-|-|**-**|-|-0.007*|-|-|
||-|-|-|**-**|-|(0.004)|-|-|
|Taxation|-|-|-|**-**|-|<br>-|<br>-0.019***|-|
||-|-|-|**-**|-|-|(0.004)||
|Labor regulation|-|-|-|**-**|-|-|<br>-|<br>0.002<br>|
||-|-|-|**-**|-|-|-|(0004)|
|**Fixed Effects**||||||||.|
|Country|871.87***|136.99***|140.55***|130.21***|138.21***|127.37***|139.61***|137.50***|
|R<sup>2</sup>|0.097|0.138|0.139|0.139|0.138|0.138|0.138|0.138|
|Observations|180,986|34,068|34,093|34,071|34,099|34,099|34,099|34,099|

_Notes_ : Constant not reported. Robust standard errors in parenthesis. *, ** and *** denote significance at 10 percent. 5 percent and 1 percent levels, respectively.

17
||The<br>reconstruction<br>duration in years (N)|For a given event, it can be estimated using<br>sophisticated models, or by experts for a set of “typical<br>disasters”, based on the size of the construction sector,<br>the size of the disaster(s) ( ), and the ability of the<br>government,<br>firms<br>and<br>households<br>to<br>finance<br>reconstruction (including through insurance programs,<br>foreign aid, and fiscal space), and the access to out-of-<br>the-region resources (e.g., construction equipment) to<br>facilitate reconstruction.|Fiscal space and sovereign risk<br>financing and insurance<br>Firm and household savings,<br>insurance and access to credit<br>Economic openness and agreements<br>with neighbouring regions/countries<br>to share reconstruction resources|
|---|---|---|---|
||The ripple-effects that<br>amplify<br>(or<br>diminish)<br>instantaneous<br>production losses ( )|This can be expert-based, as no models are available to<br>do a realistic assessment, based on the existence of idle<br>resources, the connection with other regions (to replace<br>missing inputs in supply-chains), and the existence of<br>contingency plans in firms and government.|Infrastructure design standards<br>Incentives for supply-chain risk<br>management<br>Firm insurance schemes|
|Micro-economic<br>resilience ( )|The level of income in<br>the country (represented<br>with )|This parameter can be estimated based on GDP per<br>capita, and represent a global distributional weight (the<br>affected country compared with other countries).<br>Assuming that $1 adds 1 unit of utility in the richest<br>country, then $1 adds unit of utility in other<br>countries(with > .|Policies<br>that<br>promote<br>economic<br>growth|
||The<br>inequality<br>level<br>(represented with )|This parameter can be estimated based on inequality<br>within the affected country, and represent the<br>distributional weight attributed to the poor. Assuming<br>that $1 adds unit of utility for the non-poor, then<br>$1 adds +unit of utilityfor thepoor.|Policies that reduce inequality|
||The “poverty bias” of<br>disasters, i.e. the relative<br>exposure of the poor<br>( ), compared with the<br>share of assets owned by<br>the poor ( ).|This parameter can be estimated using the set of typical<br>disasters. Using hazard maps, and asset and population<br>maps that distinguish poor and non-poor (e.g., using<br>poverty maps), then one can estimate the exposure of<br>the poor and the non-poor, and compare the result with<br>the average value of the assets of the poor and the non-<br>poor.|Land-use plans and urbanization<br>plans that prevent the development<br>of low-income housing in risky areas<br>Building norms, reinforced<br>infrastructure. Support for<br>households to improve their housing<br>conditions (especially in slums).<br>Earlywarningsystem and evacuation|

35
Economic Parametcrs of Dcforcstaoion 48 <!-- Start of picture text --> Logging Tax Case<br>limber Pric Path<br>PdcE<br>Bm .in. ...<br>,nme<br>Land Use<br>L.Md Clas<br>U3 Agnculture I* Managed Forest M Unmanaged Forest<br>Bkwk in. dopid du b.aun condI u.s for confrisoL<br><!-- End of picture text --> Figure 7
workers as compared to males, while industry sector affiliation was the most important variable in the determination of wages for male workers.

In the other pioneering study from this time, Birdsall and Fox investigated male-female earnings among school- teachers. Using the 1% sample of the 1970 census they showed that the mean income of female teachers is less that one half of that of male teachers. Using the male earnings regression as a base, 81% of the differential was attributed to individual characteristics and 10% to discrimination. Nearly 8% of the differential was due to location since males tend to be concentrated more in higher paying geographical areas.

In a further study Birdsall and Behrman used the same data source and estimated wage equations for male and females in urban Brazil. On average men earned about twice that which women earned. The decomposition results indicated that elimination of discrimination might raise the (ln) earnings of females by 22% in the formal sector, 72% in the informal sector, 44% in the domestic sector and 31% overall.

A World Bank sponsored research program on women’s employment and pay in Latin America provides two additional case studies on gender earnings differentials in Brazil.<sup>7</sup> In the first study Stelcner et. al. used a 3% sample of the 1980 Brazilian census containing 3.5 million individuals.<sup>8</sup> They extracted a sub-sample of 200,000 individuals and using the Oaxaca decomposition their research offers the following insights. The national wage gap measured by comparing husband and wives was 29%. The 4
Table 1. Proportion of diarrheal associated deaths to all infant deaths and to post-neo infant deaths (1965-1988)

|YEAR|PROPORTION OF DIARRHEA|L ASSOCIATED DEATHS|
|---|---|---|
||TO ALL DEATHS 0-11 MONTHS|TO DEATH 1-11 MONTHS|
|1965|52.79|62.85|
|1966|55.12|65.05|
|1967|_55.65_|66.15|
|1968|58.21|67.30|
|1969|61.25|71.35|
|1970|48.91|56.90|
|1971|40.51|47.18|
|1972|45.82|52.24|
|1973|49.22|56.88|
|1974|52.44|59.89|
|1975|48.37|55.41|
|1976|52.98|60.66|
|1977|50.81|57.37|
|1978|51.88|59.36|
|1979|52.49|59.38|
|1980|51.56|60.17|
|1981|51.24|58.79|
|1982|47.06|55.93|
|1983|45.35|52.14|
|1984|41.81|48.51|
|1985|36.92|44.24|
|1986|33.95|42.75|
|1987|32.53|40.86|
|1988|28.77|36.66|

Source: Annual Vital Statistics Reports published by Csntral Agency for Public Mobilization and Statistics, Egypt.

4
**Figure 4.** Guinea: Correlation between sectoral productivity and change in employment share during the period 2006-15

<!-- Start of picture text --> β = 0.413; t = 2.17<br><!-- End of picture text --> <!-- Start of picture text --> β = 0.413; t = 2.17<br>min<br>pu<br>con tsc fire<br>man<br>cspspswrt<br>ag<br>-6 -4 -2 0 2<br>Change in employment share<br>Fitted values<br><!-- End of picture text --> <!-- Start of picture text --> Fitted values<br><!-- End of picture text --> Source: Authors’ calculations based on data from the INS.

_Note:_ Size of circle represents employment share in 2006; agr= agriculture; min=mining and quarrying; man=manufacturing; pu=public utilities; con= construction; wrt=wholesale and retail trade, hotels and restaurants; tsc=transport and telecommunications; fire=finance, insurance and real estate; csps=community, social and personal services; ps= government services. β = coeff. of independent variable in regression. ln(p/P) = α + βΔEmp. Share.

**Table 2.** Guinea: trends of labor productivity <u>growth and its components</u>

||**2006-15**|**2006-10**|**2011-15**|
|---|---|---|---|
|Labor productivity growth rate|-0.58|-1.02|0.27|
|Within component|-1.57|-1.53|-0.04|
|Structural change component|0.99|0.51|0.31|

Source: Authors’ calculations based on data from the INS. Note: The same as in Figure 2.

# _4.1.2 More detailed analysis_

As outlined above, the period 2006-15 was marked by several shocks, including a major political transition that followed the death in December 2008 of the second President of Guinea, General Lansana Conté. The political transition period was ended in 2010, with the first ever democratic 15
Policy Research Working Paper 7561

# **Abstract**

School dropout is a growing concern in Central America, and in Latin America as a whole, because of its consequences for economic productivity, the inclusiveness of growth, social cohesion, and increasing youth risks. This paper utilizes more than two decades of household survey data to construct a systematic overview of school dropout at the primary and secondary levels in Central America, including recent trends, causes and consequences, and evidence on what works to reduce dropout. Within each country, poverty, rurality, and indigenous group membership are the strongest correlates of dropout, reflecting several underlying factors that affect the marginal benefits and costs of staying in school. Global and regional evidence increasingly points to common policy priorities for reducing dropout across Central America, including addressing remaining gaps in access at the pre-primary and secondary levels, improving the quality of education at all levels, and strengthening the coverage, targeting, and coherence of existing programs aimed at improving education outcomes. However, additional rigorous evaluations, including cost data, are needed to identify the most effective specific approaches in each country.

This paper is a product of the Education Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at madelman@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
treatment countries as those which joined an IP-related PTA only after they complied with TRIPS.<sup>10</sup> An obvious challenge to this identification strategy arises if the causality between trade and IPRs works in two directions. On the one hand, membership in IPAs might increase members’ trade. On the other hand, member nations may form such agreements because they already undertake a relatively high level of trade in IPRs-sensitive goods. While this is a potential concern, the threat of an endogenous relationship between such trade and the formation of IPRs-related PTAs is limited by a critical factor in how such agreements arise. The basic objective of PTAs is to liberalize within-agreement trade through cuts in tariffs and other trade barriers. Where strong IPRs chapters are included it is typically at the insistence of a single negotiating party. This is especially the case where IP-related PTAs involve both technologically advanced countries that have a strong comparative advantage in creating IP-intensive goods and developing or emerging countries that produce relatively little intellectual property. This situation characterizes the bulk of the IP-related PTAs in our samples in which one partner is the United States, the EU, or EFTA. Moreover, these developed partners typically bring greater bargaining power to the negotiating table. Thus, it is likely that low-income and middle-income countries that join PTAs with higher-income countries primarily agree to significantly stronger IPRs rules to obtain greater and more secure export access to major foreign markets. Put differently, for such countries IPRs are secondary concessions that they would not ordinarily select as a matter of endogenous policy. While this factor does not ensure that the IPRs effects we estimate are exogenous to countries’ trade, it is reasonable to expect that, at least for lowincome and middle-income countries, the policy is effectively randomly assigned.

This argument is subject to one significant qualification, however. In PTAs between developing countries and major developed economies, such as the United States or the EU, the former may not, in fact, receive considerable gains in market access in the latter. This is because the developed countries typically already had low MFN tariffs and few quotas on imports of manufactured goods. In this context, developing-country partners may have anticipated potential gains in inward technology flows, through increases in inward FDI and offshoring. Evidence

> 10 This restriction implies that we do not include the pre-1995 EU/EEA agreements. However, because the World Bank database classifies each of the post-1995 EU enlargements (in 2004, 2007, and 2013) as separate PTAs, these are included in relevant treatment groups.

13
Clark, D., Schaur, G. and V. Kozlova (2013). “Supply Chain Uncertainty as a Trade Barrier,” University of Tennessee mimeo.

Cunningham, S. and M. Shah (2014). “Decriminalizing Indoor Prostitution: Implications for Sexual Violence and Public Health” _NBER Working Paper_ # 20281.

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Dube, Arindrajit and Ben Zipperer (2015) “Pooling Multiple Case Studies using Synthetic Controls: An Application to Minimum Wage Policies,” _IZA Discussion paper_ 8944.

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Heckman, J., Ichimura, H. and P. Todd (1997). “Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme,” _Review of Economic Studies_ 64: 605-654.

Heilmann, K. (2016). “Does Political Conflict Hurt Trade? Evidence from Consumer Boycotts,” _Journal of International Economics_ 99: 179-191 _._ Hirano, K. Imbens, G., and G. Ridder (2003). “Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,” _Econometrica_ 71: 1161-1189.

Hodges Jr., J.L. and E.L. Lehmann (1963). “Estimates of Location Based on Rank Tests,” _Annals of Mathematical Statistics_ 34: 598-611.

Hummels, D., and G. Schaur (2013). “Time as a Trade Barrier,” _American Economic Review_ 103: 2935-2959.

OECD and WTO (2015), _Aid for Trade at a Glance 2015: Reducing Trade Costs for Inclusive, Sustainable Growth_ , WTO, Geneva/OECD Publishing, Paris.

Rosenbaum, P. and D. Rubin (1983). “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” _Biometrika_ 70: 41-55.

Volpe Martincus, C. and J. Carballo (2008). “Is Export Promotion Effective in Developing Countries? Firm-level Evidence on the Intensive and Extensive Margins of Export Growth,” _Journal of International Economics_ 76: 89-106.

Volpe Martincus, C., Carballo, J., and A. Graziano (2015). “Customs,” _Journal of International Economics_ 96: 119-137.

46
# I. THINKING ABOUT POVERTY AND INDUSTRIALIZATION

Does industriali' -ion increase or decrease poverty? There has been much debate among '.ird World analysts on this question of late. Debate on the same question attracted even more attention as the First Industrial Revolution unfolded in Britain in the early 19th century, and it hasn't diminished much since. Pessimists say it increased poverty while optimists say it didn't. An outside observer might well be puzzled that there is a debate at all. After all, what's the counterfactual? Are the pessimists really arguing that tbings would have been better for the poor without an industrial revolution? Can any pessimist really believe that slower growth is better for the poor than faster growth? While the answers might seem obvious at first blush, it turns out that the pessimists deserve our careful attention. What we want to show in this essay is that even if the answers are "no" in both cases, just how much poverty is eradicated by industrialization depends very much on the form that industialization takes. The better we understand that historical moral, the better will we be equipped to understand how contemporary economic growth in the Third World can aid or impede its progress with poverty eradication.

There are two questions that this essay confronts. First, what happened to the share in poverty, and to their living standards, during 19th century industrial revolutions? Second, why did poverty statistics behave the way they did? The first question is easier, and we start tbere.

A recent book by Cynthia Taft Norris and Irma Adelman (1988) makes it clear to us that qualitative evidence simply will not do. They assert with what seems to us unsupported confidence that poverty was worse in those countries where industrialization and growth was more rapid -- as in Britain and Belgium -- than in countries where industrialization and growth was slower

-1-
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda All income and gender groups would receive significantly higher benefits under the current generous system if no reform is introduced. With no minimum pension, no ceilings on pensionable wages and with individual’s last wage used as the wage base in pension calculations, relative pension levels linearly increase with individual preretirement wage in the no-reform scenario – the current situation. This type of a system favors higher income workers, especially those with steeper individual earning profile.

The introduction of any of the two reform options results in a considerable reduction of expected benefits across all income groups and for both genders compared to the noreform option. At the same time, the extent to which individuals are affected by reforms varies substantially depending on individual’s pre-retirement income level. In addition, since both the pure DC and the hybrid system reform options establish a close link between individual’s benefit and lifetime earnings, the reduction in expected benefits is stronger for individuals with higher pre-retirement income.<sup>41</sup> This is shown in Figure 47 where in both reform options, the higher the individual’s last wage, the higher the difference in relative pension levels relative to the no-reform scenario.

**Figure 47: Post reform Relative Pension Levels as a share of No-Reform Relative Pension Levels**

<!-- Start of picture text --> Pure DC-5 %<br>Pure DC-3 %<br>Hybrid-5 %<br>Hybrid-3 %<br><!-- End of picture text --> <!-- Start of picture text --> men<br><!-- End of picture text --> <!-- Start of picture text --> women<br><!-- End of picture text --> <!-- Start of picture text --> men<br>steep career<br><!-- End of picture text -->

**Source:** WB calculations

Another useful indicator in the analysis of the impact of reforms on individuals is pension wealth which is calculated as the present value at retirement of future pension benefits. While replacement rates and relative pension levels measure the value of benefits per time unit, pension wealth quantifies what an individual would expect to receive from the pension system over his lifetime at retirement. The usefulness of pension wealth is

> 41 Higher pre-retirement income is determined by longer working life, higher wage levels, higher wage growth or any combination of these factors.

- 103 -