rafmacalaba/datause-encoder-data huggingface dataset:
In order to select the study population, a listing survey was conducted in Cotonou in MarchApril 2014. This survey was designed in order to obtain a representative sample of all businesses operating in Cotonou including Dantokpa market. All businesses with fixed location, excepting international and nationwide companies, were targeted.

Sampling protocols for inside and outside the market were different:

- _Sampling framework for Danktopa market:_ we used a precise map of the market made by SOGEMA ( _Société de Gestion des Marchés Autonomes)_ , the public company managing markets in Benin. This map allowed to divide geographically the market in small areas. We have then randomly selected areas in the markets in which 50% of the businesses (with fixed location) where sampled for the survey.<sup>9</sup>

- _Sampling framework for other neighborhoods of Cotonou:_ We were able to obtain detailed maps of each of the 144 neighborhoods in Cotonou. Those maps allowed identifying the easy identification of _ilots_ (blocks), the official administrative unit within a neighborhood. We used this administrative unit as a reference for the listing survey sampling. We then used information given by the tax administration (and confirmed by the survey company) in order to characterize neighborhoods as high or low firm density areas. We randomly sampled 38% of _ilots_ in high density neighborhoods and 10% of the _ilots_ in low density neighborhoods. In each _ilots_ 68% of businesses where sampled for the survey in average.

Overall, 19,246 businesses were listed by surveyors. The listing survey allowed us to estimate the total number of businesses operating in Cotonou (with a fixed location, excluding nationwide businesses and liberal professions) to approximately 68,500, including around 5,000 in Dantokpa market.<sup>10</sup> Among those 19,246 businesses, 9,938 businesses were randomly selected to be surveyed. 7,945 (80%) businesses were successfully surveyed, 1,000 (10%) businesses refused to be surveyed, and 995 (10%) businesses were dropped because the business owner was not available or not reached after 4 attempts. Figure 1 details the listing survey results inside and outside the market.

> 9 Few areas were excluded from the sampling frame because they almost exclusively included businesses selling illegal products (i.e. taint oil, medicine, and voodoo products) or by large formal businesses.

> 10 As previously indicated, some sections of Dantokpa market were not included in the listing survey. Therefore, the total number of firms in Dantokpa is probably much higher.

12
welfare is increasing in achievements, but decreasing in deprivations and deprivation-statuses. Second, as we show in Appendix 7.3, deprivationLIMs subsume both achievement-LIMs and union-LIMs. That is, for any achievement-LIM (z, w<sup>A</sup> ), there exists a deprivation-LIM (z<sup>′</sup> , w<sup>D</sup> , k<sup>D</sup> ) that defines the same identification contour and therefore always identifies the same individuals as poor. In our terminology, we say in that case that (z, w<sup>A</sup> ) is mimicked by (z<sup>′</sup> , w<sup>D</sup> , k<sup>D</sup> ). In contrast, no deprivation-LIM has the same identification contour as an intersection-LIM. These relationships are illustrated by the Venn diagram in Figure 6.

<!-- Start of picture text --> deprivation-LIMs<br>status-LIMs<br>achievement-LIMs<br>union-LIMs intersection-LIMs<br><!-- End of picture text -->

**Figure 6:** Venn diagram for different types of LIMs.

In the next subsections, we use our welfarist theory in order to compare LIMs.

# **3.2 Improving LIMs requires little information**

In this subsection, we show by means of an example that very limited information on the shape of indifference curve U may be sufficient in order to improve on some LIMs. This may require no more knowledge than a bundle on the indifference curve U and the marginal rate of substitution at that bundle, or some extreme achievement thresholds ej.

Consider an intersection-LIM whose deprivation bundle z is on the indifference curve U , as illustrated in Figure 7.a. If the practitioner knows the marginal rate of substitution at z, then she can reduce exclusion errors using an achievement-LIM whose identification contour passes through z and whose slope is equal to the marginal rate of substitution at z. These two LIMs do not make inclusion errors, but the achievement-LIM makes fewer exclusion errors. As shown in Figure 7.a, the intersection-LIM makes exclusion errors on the two triangles Intex and Int<sup>′</sup> ex<sup>, which the achievement-LIM</sup> does not make. Hence, the achievement-LIM identifies the poor better than the intersection-LIM.

If the practitioner also has some information on extreme achievement thresholds ej, she can further reduce exclusion errors by constructing a deprivation-LIM that takes advantage of this information. As illustrated in Figure 7.b, the identification contour of this deprivation-LIM mimics that of the achievement-LIM between these asymptotes.<sup>28</sup> Again, the

> 28 Here is how to construct a deprivation-LIM (z′, wD, kD) from the two bundles x and x<sup>′</sup> with x1 > x<sup>′</sup> 1<sup>andx2<x′</sup> 2<sup>thatdefineitsidentificationcontour,i.e.,bundlesx</sup> and x<sup>′</sup> are at the intersection of its three segments. Take the deprivation bundle to be

15
agents to reduce the weight attached to the inflation target in forming expectations (thereby imparting greater persistence to inflation), because default is associated with a perceived increase in the risk of monetization and thus higher future inflation. In a sense, therefore, inflation expectations depend essentially on the fiscal stance–in line with the empirical results of Celasun, Gelos and Prati (2004), based on survey data for Turkey. Although they do not provide a formal characterization of their argument, they note (p. 494) that “...A credible fiscal consolidation is probably the key to reducing inflation, because inflation expectations will decline only if the public perceives that the need to monetize fiscal deficits or inflate away the debt stock has come to an end.”<sup>41</sup> The expected nominal depreciation rate, EDEPR, which affects portfolio decisions and the pricing rules of commercial banks, is defined as a weighted average of its past value and expected changes in the real exchange rate, measured as the difference between expected domestic inflation (given in (130)) and foreign inflation, FINFL, with a one-period lag: EDEPR = χEDEPR−1 + (1 − χ)(EINFL − FINFL−1), (132) where 0 < χ < 1. Thus, when domestic inflation is expected to exceed foreign inflation, that is, when the real exchange rate is expected to appreciate, agents will also expect the nominal exchange rate to depreciate, to prevent a loss in competitiveness.<sup>42</sup> Note that in the model exogenous changes in the probability of default lead in general to an inverse correlation between credibility and the government bond rate. An increase in PDEF , for instance, raises directly the bond 41They also found that inflation expectations appear to be forward-looking, rather than backward-looking. However, this result is not consistent with those obtained by Agénor and Bayraktar (2002), who found that forward- and backward-looking components have similar weights in expectations.

> 42Alternatively, it could be assumed that expectations are rational (or, more precisely, model consistent), so that the expected depreciation rate is equal to the one-period ahead “actual” rate, as derived from the model itself. This, however, is a lot more involved from a computational standpoint. Other options, as suggested to us by Peter Montiel, would be to make the expected future exchange rate a function of the current spot rate (with some elasticity parameter linking the two, and with perhaps a “shift” term to capture exogenous changes in exchange rate expectations), or to make the expected future exchange rate proportional to the model’s steady-state solution for the exchange rate, with the factor of proportionality representing the perceived rate at which the exchange rate converges to its steady-state value.

52
**Figure 5: Factory, wholesale and retail prices (left panel). Implicit wholesale and retail margins (right panel)**

_Source: Foreign Agriculture Service (FAS), reporting Kenya Sugar Board (KSB) data, author calculations. Implicit margins have been smoothed using a four-month moving average (current month and three previous)._

**Entry to the industry that occurred at the end of 2011 may have created some price pressure, but given the scale of the entrants, this is unlikely to explain a significant portion of the price reduction post-crisis.** The entry of additional millers in December 2011, while timed almost perfectly with the decline in prices, is unlikely to have played a large role in bringing sugar prices down. The two collectively accounted for a tiny fraction of the market<sup>44</sup> and would have been unable to significantly undercut the big players.  On the contrary, entry could have been a response to trade protection that allows an opportunity for supernormal profits in the sector.  It is much more plausible that the recovery of domestic production (through the channel of recovering cane deliveries) coupled with an influx of imports eased supply sufficiently for prices to drop.

**There have been a few other sharp spikes in Kenyan sugar prices over the past decade that may be linked with similar combinations of production and import shocks as described in 2011.** Figure 6 shows prices and domestic sugar production together, with vertical lines indicating the price spikes. Clearly each price spike is associated with domestic production declining, but there are also many cases where production declines and there is no associated price spike. What we have seen in our analysis of the 2011 crisis (and the brief comparison of 2010 and 2009) suggests that the periods when the price increased are likely those where there is a reduction in imported sugar, paired with the observed drop in local production. Barriers to trade thus exacerbate local production shocks, creating high prices.

> 44 The two collectively accounted to 5 percent of production in June-July 2012 according to the KSB.  It is likely that this was even lower as they started production in December 2011.

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# Understanding the Dynamics of Labor Income Inequality in Latin America

Carlos Rodríguez-Castelán,<sup>*</sup> Luis F. López-Calva,<sup>†</sup> Nora Lustig,<sup>‡</sup> and Daniel Valderrama<sup>§</sup>

**Keywords:** Inequality, Labor Incomes, Education Premium, Experience Premium, Latin America **JEL codes:** D63, E24, J21, J31, O54

> The authors would like to thank Oscar Barriga Cabanillas, Sarah Knob, Julián Messina, Joana Silva, Robert Zimmermann, and the participants at both the author workshop and the decision meeting of the Regional Study on Wage Inequality organized by the Office of the Chief Economist, Latin America and Caribbean Region, World Bank. The findings, interpretations, and conclusions in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank Group, its Executive Directors, or the countries they represent.

> * Senior Economist, Poverty and Equity Global Practice, World Bank. E-mail: crodriguezc@worldbank.org.

> † Lead Economist, Poverty and Equity Global Practice, World Bank. E-mail: lflopezcalva@worldbank.org.

> ‡ Samuel Z. Stone Professor of Latin American Economics and Director of the Commitment to Equity Institute at Tulane University. Professor Lustig is also nonresident senior fellow at the Center for Global Development and the Inter-American Dialogue. E-mail nlustig@tulane.edu.

> § Research Analyst, Poverty and Equity Global Practice, World Bank. E-mail: dvalderramagonza@worldbank.org.
One of the main weaknesses of the current approach regarding road investments is the fact that it is built on the strong assumption among policy makers that the existence of a road in good condition in rural areas will enable service providers to come and serve rural areas. In most cases, transport services economics is completely neglected, assuming that road condition is the main determinant of transport costs.

A notable exception was Hine and Rutter (2000), who, based on surveys in almost 100 villages in Ghana and Malawi at the end of the 1990s, found that “vehicle accessibility alone does not guarantee [...] transport service access”. Moreover, Lall (2009), which, in the case of Malawi, demonstrated, based on trucking surveys and computation of vehicle operating costs in SSA, that _both_ infrastructure quality and market structure of the trucking industry are important contributors to differences in transport costs. Lall (2009) points out costs due to poor feeder roads are exacerbated by low volumes of trade between rural locations and market centers. With empty backhauls and journeys covering small distances, only a few transport service providers enter the market, charging high prices to cover fixed costs and maximize markups.

Raballand et al. (2010) find in field surveys in Uganda, Cameroon and Burkina Faso that load consolidation at the local level decreases the need for a road accessible by truck to every farm and it decreases investment needs and increases value-added for farmers. From a costbenefit analysis, load consolidation (or agglomeration) is probably the most effective since it mainly reduces road public investment to the secondary network and enables decrease of transport costs due to increased predictability of volumes and strengthened competition between operators.

In the case of this paper, through a randomized evaluation, a passenger bus line was introduced between a rural cluster of villages and a town that serves as a regional trading hub. The study was implemented in rural Malawi, more than two hours by car from the nearest urban location. While most residents of the villages had been to the regional market town (96 percent) and most had been a passenger on a bus prior to the project (81 percent), no regular motorized passenger transportation existed along the road between the market town and the project villages 4
**1997 Tax Revenue Assignments Federation**

# **TABLE 4**

||Federatio|n Revenue Dis<br>(Percent)|tribution||
|---|---|---|---|---|
|Revenues|State|Federation|Cantons|Total (%)|
|Customs Duties|7.4|92.6|0.0|100.0|
|Excise & Special Import Taxes|0.0|100.0|0.0|100.0|
|Wage & Income Taxes|0.0|50.0|50.0|100.0|
|Profits Tax|0.0|50.0|50.0|100.0|
|Sales Tax|0.0|100.0|100.0|100.0|
|Other Taxes|0.0|0.0|100.0|100.0|
|Total Revenues|_1.3_|40.0|58.7|100.0|

The revenue assignments were proposed to create vertical balance in the system, but several important cautions should be noted. First, the assignments will allow horizontal imbalances at the canton and municipal levels. Estimates suggest several cantons would have significant deficits, arising both from the expenditure and revenue sides. Revenues are constrained in some areas because of very weak economies and expenditure needs are larger in some places than others. Second, the expenditure assignments used here are similar to those in place for 1996, except most spending for health, education, and police services are passed to the cantons. Changes in the expenditure assignments will necessitate changes in revenue assignments. Third, very minimal grants from the Federation to the cantons are allowed for in the financing scheme. Own source revenues should be the major financing source because of accountability, concerns about cross subsidies, differences in service demands, and other reasons. Still, a greater role for grants exists, and development of a transfer system would require additional tax assignment to the Federation. Finally, the assignments fail to recognize differences in revenue buoyancy, and will have to be altered over time if expenditures grow at different rates than revenues. Sub-national governments often are assigned taxes with low buoyancy. However, about 60 percent of the revenues assigned here are from the relatively buoyant sales and wage taxes.

Most taxes, including the corporate and personal income, excise, customs, and value added taxes would be best administered at the Entity level, and in several cases at the State level. A limited number of revenue sources, such as the property tax and user fees, could be administered at the sub-Entity level. Taxes are administered at the Entity level in the Republika Srpska, and legislation has been passed for administration at the Federation level as well. Advantages in terms of lower operational costs, coordination of administration, and uniform practices all argue strongly in favor of the Federation. The key is to design an administrative and revenue distribution system that safeguards the cantons and municipalities so that they will be willing to accept Federation administration.

_23_
# **Table 9 (continued)**

**The impact of the business environment on firm performance, by firm size**

|Dependent Var.|Lgrow|ln(LP)||Lgrow|ln(LP)||Lgrow|ln(LP)|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)||(3)|(4)||(5)|(6)|
|_Firm size (Base group: mi_|_cro firm, L <_|_21)_|||||||
|Small-mid firm (21-50)|1.011|0.255***|Small-mid firm (21-50)|1.703|0.403***|Small-mid firm (21-50)|1.431|0.464***|
||(0.973)|(0.093)||(1.404)|(0.122)||(1.471)|(0.130)|
|Middle firm (51-200)|3.929***|0.154|Middle firm (51-200)|3.600**|0.358***|Middle firm (51-200)|3.198**|0.388***|
||(1.158)|(0.104)||(1.404)|(0.106)||(1.353)|(0.118)|
|Big firm (201+)|7.522***|0.595***|Big firm (201+)|6.076***|0.633***|Big firm (201+)|6.116***|0.728***|
||(1.874)|(0.136)||(2.287)|(0.179)||(2.287)|(0.182)|
|Country FE|Yes|Yes|<br>Country FE|||Country FE|||
|Country ctrl|||Country ctrl|Yes|Yes|Country ctrl|Yes|Yes|
|Year, Industry FE|Yes|Yes|Year, Industry FE|Yes|Yes|Year, Industry FE|Yes|Yes|
|Firm ctrl|Yes|Yes|Firm ctrl|Yes|Yes|Firm ctrl|Yes|Yes|
|Observations|49398|44358|Observations|55515|50720|Observations|55242|50670|
|Adjusted_R_<sup>2</sup>|0.081|0.381|Adjusted_R_<sup>2</sup>|0.053|0.346|Adjusted_R_<sup>2</sup>|0.058|0.348|

_Note:_ Standard errors are clustered at the country level. All business environment indicators are standardized with their sample mean and standard deviation. Other control variables include the two competition dummies (having one competitor, having two or more competitors) and their interactions with three firm size dummies (small, medium, and large, with the default being micro firms), biggest owner share, age 6–10, age 11<sup>+</sup> , exporter, foreign firm, foreign share, and city population category dummies.

Firm size: micro = no more than 20 employees; small = 21–50 employees; medium = 51–200 employees; large = 201+ employees.

BE = business environment; DB = Doing Business; ES = Enterprise Surveys; FE = fixed effects; GC = Global Competitiveness Index; HLO = Harmonized Learning Outcome; Lgrow = one-year employment growth rate in percentage points (growth); ln(LP)=ln(sales/number of employees) (labor productivity); WB = World Bank.

* p < 0.1, ** p < 0.05, *** p < 0.01.

37
monitoring and evaluation system that develops the range of skills to be acquired in this life-stage. The US _Knowledge is Power Program_ (KIPP) does just that with disadvantaged youth, setting expectations, requiring behaviors grounded in good socio-economic skills, and working closely with each child to ensure success (Angrist et al 2010). Similar programs exist in developing countries, as well (Heckman and Kautz 2012; Alfonso et al 2012). Finally, once the child reaches working age, two types of programs exist. The first are programs to ease the school-to-work transition, such as augmented apprenticeship programs, which combine socio-emotional skills development, technical training, and job experience; these have shown to increase employment and wages for youth (especially women) in several Latin American countries (Ibarraran and Rosas 2009). The second is continuing in educational institutions to ease the transition, such as technical institutions that are closely linked to the productive sector and complement technical training with pedagogical methods conducive to developing higher-order cognitive and socio-emotional skills. Public efforts to formalize and incentive in-firm training may include a skills certification system that is independent of firms but widely recognized economy-wide,<sup>53</sup> providing incentives to firms to train their workers such as tax breaks as provided by Colombian law (Law 789 of 2002), and providing services and supports to facilitate worker transition out of firms where the worker has exhausted learning opportunities and into firms where new learning can occur, such as job service centers and unemployment insurance.<sup>54</sup>

# **VI. Conclusions**

The review confirms that there is a mismatch between the education sector’s perception of skills demand and that of the productive sector. While the education sector focuses on technical training and believes that it well prepares students for the labor market (Mourshed, Farrell and Barton 2012, IFC 2010), skills demand surveys from around the world show different results. The skills most demanded by employers – socio-emotional and higher-order cognitive – are often outside of school curriculum or teaching methods.

There is remarkable consistency across the world of the skills demanded by employers. Whether a large diversified economy or a small specialized economy, manufacturing or service sectors, developed country or developing, exporters or local market, traditional or modern firms, employers point to the

> 53 While skills certification systems are common in developing countries, there is not, to date, rigorous evaluation evidence that they, indeed, facilitate worker movement across jobs.

> 54 See Banerji et al (2010), Section 3, for a brief discussion on social protection programs to protect against income loss while workers transition to utility improving jobs.

28
# **Table 8:  Serial Correlation of Pair-wise Differences in Country Rankings**

||**Diffe**<br>**(Correlatio**|**rence in R**<br>**n of 1996**|**anks**<br>**with 2005)**||||
|---|---|---|---|---|---|---|
||**dri-eiu**|**dri-prs**|**eiu-prs**|**average R**|**Average**ρ|**Implied (**λ**1+**λ**2)**|
|Voice and Accountability|||0.32|0.32|0.51|0.21|
|Political Stability|0.15|0.42|0.52|0.36|0.40|0.04|
|Government Effectiveness|0.21|0.23|0.09|0.18|0.35|0.18|
|Regulatory Quality|0.28|0.10|-0.06|0.11|0.33|0.23|
|Rule of Law|0.35|0.58|0.31|0.41|0.54|0.16|
|Control of Corruption|0.19|0.34|0.10|0.21|0.38|0.18|
|Average|0.24|0.33|0.21|0.27|0.42|0.17|

43
# **Cost-Effectiveness Measurement in Development:**

# **Accounting for Local Costs and Noisy Impacts**

David K. Evans World Bank Anna Popova World Bank

## JEL Codes: D04, D61, H43, I25

## Keywords: Cost-Effectiveness Analysis; Impact Evaluation; Education

Acknowledgments: The authors are very grateful for comments and guidance from Conner Brannen, Steven Glazerman, Rachel Glennerster, Kristen Himelein, Henry Levin, Patrick McEwan, and Benjamin Olken as well as for feedback from participants in a session at the Society for Research on Educational Effectiveness (SREE) Spring 2014 conference. The authors are especially grateful to Rachel Glennerster and the Abdul Latif Jameel Poverty Action Lab for sharing their cost-effectiveness data and analysis; and to Marie-Hélène Cloutier for providing additional data. The authors can be contacted at David Evans (devans2@worldbank.org) and Anna Popova (apopova@worldbank.org).