rafmacalaba/datause-encoder-data huggingface dataset:
From the 7,945 businesses surveyed, a population of 3,600 businesses was then selected to participate to the study based on the following goals:

- Drop businesses already formal

- Drop businesses that will probably not cooperate in the future or which will be probably difficult to find (i.e. businesses that refused to provide information on profits or turnover during baseline survey)

- Trim the database from (a) businesses very close to formalization who would have formalized anyway and (b) businesses very far from formalization which would not be interested by the program

- Remove businesses that ever got a loan from a commercial bank that will most probably not been interested by the program (less than 3% of informal businesses)

- Reduce the standard deviation of the main outcomes (profit and turnover)<sup>11</sup>

- Include a sufficient number of businesses in Dantokpa market.<sup>12</sup>

Table 2 shows descriptive statistics for businesses selected in the sample and businesses not selected.

The study sample is composed by relatively small businesses with 1.2 employees on average. The great majority of businesses (86%) were created over a year before being surveyed, 55% of businesses were involved in trade activities, 26% worked in services, and 16% were craftsmen. 63% of businesses sampled for the study were owned by women. This reflects the high share of female owners in Dantokpa market. Around 30% of businesses owners never went to school, and less than 20% of the businesses were keeping some type of accounting. As described previously, the majority of businesses paid some form of tax (55%), but the great majority complained about not knowing in advance the amount of taxes owed to the tax authority (74%).

In comparison to similar studies in other contexts, this study population is composed of businesses with slightly smaller sizes. In the study in Malawi (Campos et al, 2015), businesses

> 11 Outside Dantokpa market we excluded businesses with sales or profit lower than XOF 12,000 (USD 55), profit greater than XOF 150,000 (USD 685) or sales greater than XOF 400,000 (USD 1,828). In Dantokpa market, we excluded businesses with sales or profit lower than XOF 10,000 (USD 46), profit greater than XOF 200,000 (USD 914) or sales greater than XOF 500,000 (USD 2,285).

> 12 We choose to take 22% of total study population from Dantokpa market to have the same share of businesses from the market than in the 2008 firm census (INSEA, 2008).

13
- C. Generation and transmission procurement index for Pakistan and comparators, 2015

||**India**|**Pakistan**|**International**<br>**benchmark**|
|---|---|---|---|
|**Procurement of Generation**|**95%**|**100%**|**85%**|
|There is a framework forprocurement|||82%|
|Country allows International competetive bidding or public<br>auctions forprocurement|||94%|
|Types ofprocurement methods allowed||||
|_Unsolicited bids_|||29%|
|_Direct negotiation_|||47%|
|_International competitive tendering_|||88%|
|_Public auctions_|||41%|
|_Stand-alone capacity market_|||0%|
|Auction design score|0.86|NAP|80%|
|_Country usespublic auctionsforprocurement_|||41%|
|Clear and comprehensive established rules||_NAP_|100%|
|Crediblepenalties for violatingthe rules||_NAP_|86%|
|Guarantees andpenalties to ensure timelycompletion||_NAP_|86%|
|Standard,non-negotiable contracts||_NAP_|86%|
|Stapled financingterms or risk mitigation instruments||_NAP_|86%|
|No concerns regardingthe transparencyand fairness of the auction||_NAP_|14%|
|Efforts to inform and attract bidders to the auction||_NAP_|100%|
|**Transmission Procurement**|**83%**|**100%**|**64%**|
|There is a framework for procurement of new transmission<br>lines|||59%|
|Methods used toprocure new transmission-|||69%|
|_Competitive tender_|||65%|
|_Direct negotiation_|||24%|
|_Allprojects are awarded to the incumbent transmission company_|||47%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 69
- 33 -

2.39 While the replacement cost method used in Table 2.5 is a useful tool, there are several problems with the way the calculations were made. These are discussed in the next paragraphs. 13/ 2.40 First, in the table only the option of supplying fuelwood from a government-managed plantation is considered in estimating the stumpage value. But in most developing countries, government-grown plantation fuelwood supplies only a small fraction (probably less than 5 percent) of the total demand. Other sources of supply and other variable costs should have been considered in estimating the stumpage value. Alternatively, if there were no other suitable sources of supply, the costs of damage to the environment from not replacing the fuelwood (externalities) should have been factored in.

2.41 Second, in this table a flat rate fee is used, ignoring the effect of distance from the market. Stumpage fees should be on a sliding scale to take into account all the variable costs, especially the transport costs.

2.42 Third, there are cheaper ways of raising trees. In the Malawi report, the cost of raising trees in government plantations was compared with the cost of growing trees in small farmer woodlots. It was estimated that the costs of the government planting would be three times greater than for the woodlots (the respective discounted values for a 1 ha area at 12 percent discount rate were 894 Kwacha and 258 Kwacha). If the woodlot option had been chosen as the way to determine stum3age fees, the derived stumpage value might have been about 6 Kwacha per m (instead of 15.3 Kwacha) provided the yields of wood from the two options were about the same.

2.43 Fourth, the discount rate chosen can have a significant effect on the derived stumpage value. In Table 2.5, a discount rate of 12 percent was used. Later, after reconsideration, this rate was changed to 8 percent. If 8 percent had been used in the table, the derived stum3age fee to cover replacement cost would have been 10.7 Kwacha per solid m or nearly 5 Kwacha less per m (30%) than the estimated value using the 12 percent discount rate.

2.44 Finally, if a stumpage value is based only on the replacement cost of wood in government plantations, this implies that the government has a monopoly, that it can dictate the price of wood resources, and that there are no freely available substitutes on the market. Even if this is true, it will only be true in the short run. Eventually, other producers will enter the market and will produce substitute goods. However, 13/ Current World Bank work in Malawi has recognized the above drawbacks and has modified specific targets for stumpage fees to a general one of reaching replacement costs by 1996 using a number of options for growing treee. Also the monitoring and revenue collection system is being improved to ensure that more money is collected by Government.
interpret the difference in these numbers – 356,000 people – as the potential impact of the slowdown in economic growth on undernutrition.

Typically researchers have adopted two primary approaches in the analysis of the impact of economic growth on health and nutrition outcomes. One approach is to estimate the income elasticity of demand for calories using household survey data. The primary idea behind these studies is the fact that calorie consumption is an important input in the health production function. Some examples of this approach are Behrman, Foster and Rosenzweig (1997) and Bouis and Haddad (1992). The second approach is to estimate cross-country regressions of some measure of nutrition on per capita income. Our approach is derived from this second approach: we use calorie consumption as our preferred measure of nutritional status and estimate the impact of growth on undernourishment using a parametric relationship between calorie consumption and per-capita income in a cross country setting.

There are some clear limitations to this methodology. First, the cross-country regression nature of the estimation of the calorie-income relationship necessitates the use of per capita GDP which is often not the best measure of individual welfare within each country and given the concavity of the calorie-income curve, may lead to an overestimation of the incidence of undernourishment (Gabbert and Weikard 2005). Second the price elasticity of calorie demand which we use is not country-specific but an approximation based on the literature review discussed earlier. Third, in the case of the food price shock, we have to make simplifying assumptions about the extent to which global price changes were passed onto local consumers, and as such show two scenarios with a full pass-through and a partial pass-through of global prices. These shortcomings notwithstanding these methods remain useful as a way of obtaining an approximate measure of the impact of these shocks on undernourishment.

# **3.0 Results**

We present the results of the two economic shocks discussed earlier (i) estimates of the impact of higher food prices in 2008 on undernourishment levels and (ii) estimates of the impact of a slowdown in growth due to the global economic crisis in 2009 on undernourishment levels.
|**Table**X|II**HazardEs**|**timatesforD **|**urationofU **|**nemployment**|**Spell**||
|---|---|---|---|---|---|---|
|**Variables**|**Baseline**|I|II|III|IV|V|
|**Receivesunemployment**<br>**insurance**(UI)||(0.011)|-----------||0.529<br>(0.009)||
|**Durationof**Ul**(months)**<br>__________________|________~<br>~|~0.002)|0.852||||
|**Durationof**Ul**wlhigh**<br>**replacementratio**||||0.838<br>(0.007)|||
|**Durationof**Ul**w/low**<br>**replacementratio**||||0.862<br>(0.004)|||
|Ul**w/high RR(=1)**||||||0.532<br>(0.010)|
|**U.**w/lowRR(=1)<br>|_ _ _|||__||0.770'<br>~~~~~|
|**ReceivedUlinthepast**|||||1.735<br>(0.0270|2.033<br>(0.034)|
|Female(=1)||||0.958|<br>0.928|0.926|
||(0.017)|(0.017#)|(0.018)|(0.018)|(0.017)|(0.017)|
|**Married(=I)**|1.097"|l.109'|I..114"|1.113-|1.111-|1.104-|
||(0.022)|(0.023)|(0.023)|(0.023)|(0.022)|(0.022)|
|**26-35**|1.040|1.007|0.978|0.977|1.067|1.074|
||(0.024)|(0.024)|(0.023)|(0.023)|(0.025)|_(0.025)_|
|**36-45**|1.017|1.003|1.025|1.019|1.069|1 .057|
||(0.027)|(0.027)|(0.028)|(0.028)|(0.028)|(0.028)|
|**46-55**|0.978|0.983|1.078|68|1.053|1.022|
||(0.030)|(0.032)|(0.036)|(0.036)|(0.033)|(0.032)|
|56-65|0.874"|0.875|0.948|0.940|0.942|0.918|
||(0.048)|(0.049)|(0.055)|(0.055)|-(0.051)|(0.051)|
|**Primary**|1.816|1.893|1.989|1.989|1.772|1.743|
||(0.202)|(0.213)|(0.232)|(0.232)|(0.196)|(0.193)|
|**Apprenticeship**|3.994|4.491|4.975|4.987|3.700|3.544|
||_(0.442)|(0.504)|(0.579)|(0.581)|(0.408)|(0.391)|
|**Secondary**|3.413|4.483|5.397|5.412|3.976|3.828|
||_(0.479|(0.545|(0.630)|(0.632|(0.440|(0423|
|College(+)|6.762|7.481|7.925|7.947"|6.099|5.916|
||(0.780)|(0.876)|(0.962)|(0.965)|(0.700|679)|
|**Nopreviousexp.**|1.129|_1.257_||1.352|1.090|1.046|
||(0.042)|(0.048)|(0.054)|(0.054)|(0.039)|(0.037)|
|**Occupation**|YES|YES|YES|YES|YES|YES|
|**Sector**|YES|YES|YES|YES|YES|YES|
|**Region**|YES|YES|YES|YES|YES|YES|
|<br>Year|YES|YES|YES|YES|YES|YES|
|**Quarter**|YES|YES|YES|YES|YES|YES|
|**Numberifindividuals**|18,141|18,141|18,141|18,141|18,141|18,141|
|**Numberofspells**|30,741|30,741|30,741|30,741|30,741|30,741|
|**Numberofpseudo-spells**|30,741|30,741|30,741|30,741|39,711|_46,577_|
|<br>**Prob> Chi**|0.000|0.000|0.000|0.000|0.000|0.000|

**Source:** Unemployment Registry, 1990-2000.

Coefficients represent hazard ratios (Note: A hazard ration greater (smaller) than I indicates that such variable decreases (increases) unemployment duration).

Robust standard errors in parentheses (errors clustered at the individual level).

Significant at the 5 percent level. Significant at the 10 percent level. Baseline comparison: Illiterate single male, 16-25 years old, with previous employment experience, but with no recorded employment prior to current unemployment spell.

Only exit to employment in the private sector was considered as a successful exit. Qualitatively and quantitatively equivalent results are obtained when exit to both employment in the private sector and employment in a public employment program are considered instead.

53
Columns 7 and 8 show an increase in the average labor productivity and TFP in the districts 0-10 km from the GQ network. These increases are primarily driven by the incumbent establishments of the districts. We do not separately quantify the labor productivity and TFP changes of new entrants similar to Columns 4-6, as much of the impact of new entrants comes from the extensive margin and these plantlevel traits are not defined in these cases. The labor productivity result is also evident in a comparison of Columns 2 and 3, with the difference being that Column 7’s measure is calculated at the plant level. We return to the interpretation of these productivity results after viewing the dynamic specifications.

For comparison, the third row of Panel A provides the interaction for the districts that are 10-50 km from the GQ network. None of the effects that we measure for the 0-10 km districts are observed at this spatial band, which provides a first assurance that these effects can be linked to the GQ upgrades rather than other features like regional growth differences.

Panel B extends the spatial horizons studies in Panel A to include two additional distance bands for districts 50-125 km and 125-200 km from the GQ network. These two bands have 48 and 51 districts, respectively. In this extended framework, we measure effects relative to the 97 districts that are more than 200 km from the GQ network in our sample. Three observations can be made. First, the results for districts 0-10 km are very similar when using the new baseline. Second, the null results generally found for districts 10-50 km from the GQ network mostly extend to districts 50-200 km from the GQ network. Even from a simple association perspective, the manufacturing growth in the period surrounding the GQ upgrades is localized in districts along the GQ network.

As a final and more speculative point, the negative point estimates in Columns 4-6 have a pattern that might suggest a “hollowing-out” of new entry towards districts more proximate to the GQ system after the upgrades. This pattern is similar to Chandra and Thompson’s (2000) finding that U.S. counties that were next to counties through which U.S. highways were constructed were adversely affected. Chandra and Thompson (2000) described their results within a theoretical model of spatial competition whereby regional highway investments aid the nationally-oriented manufacturing industry and lead to the reallocation of economic activity in more regionally-oriented industries. The point estimates suggest a similar force might be occurring within Indian manufacturing as well, but the lack of statistical precision prevents strong conclusions in this regard.

14
Policy Research Working Paper 7268

# **Abstract**

It is often argued that engaging in indoor residual spraying in areas with high coverage of mosquito bed nets may discourage net ownership and use. This is just a case of a public program having perverse incentives. This paper analyzes new data from a randomized control trial conducted in Eritrea, which surprisingly shows the opposite: indoor residual spraying encouraged net acquisition and use. The evidence points to the role of imperfect information. The introduction of indoor residual spraying may have made the problem of malaria more salient, leading to a change in beliefs about its importance and to an increase in private health investments.

This paper is a product of the Impact Evaluation Team, Development Research 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 p.carneiro@ucl.ac.uk.

_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
# **ACKNOWLEDGEMENTS**

We have benefited greatly from discussions with Joseph Shapiro. We are also grateful to participants at the Comparative and International Education Society’s 51st Annual Conference, Baltimore, MD, 2007. All views expressed in this paper are those of the authors and should not be attributed to the World Bank Group.

# **REFERENCES**

Arends-Kuenning, M. and S. Duryea (2006). The effects of parental presence, parents’ education, and household headship on children’s schooling and work in Latin America, _Journal of Family and Economic Issues_ **27** , pp. 263--286.

- Bando, R., L.F. Lopez-Calva and H.A. Patrinos (2005). Child labor, school attendance, and indigenous households: evidence from Mexico, World Bank Policy Research Working Paper Series 3487.

- Basu, K. and P. Van (1998). The Economics of child labor, _American Economic Review_ **88** , pp. 412--427.

- de Janvry A., F. Finan, E. Sadoulet, and R. Vakis (2006). Can conditional cash transfers serve as safety nets in keeping children at school and from working when exposed to shocks? _Journal of Development Economics_ **79** , pp. 349--373.

- Das, J., Q. Do, and B. Özler (2005). Reassessing conditional cash transfer programs, _World Bank Research Observer_ **20** , pp. 57--80

- Duryea, S. and M. Arends-Kuenning (2003). School attendance, child labor, and local labor markets in urban Brazil, _World Development_ **31** , pp. 1165--1178.

- Edmonds, E. (2008). Child labor, in _Handbook of Development Economics Volume 4_ (Eds.) T. P. Schultz and J. Strauss, Elsevier Science, Amsterdam, North-Holland, forthcoming.

- Grootaert, C. and H.A. Patrinos (1999). _The Policy Analysis of Child Labor_ . New York: St. Martin’s Press.

- Guatemala National Statistics Institute and the International Labor Organization (2003). _Estudio cualitativo sobre el trabajo infantil en Guatemala_ .

- Gunnarsson, V., P. Orazem and M. Sanchez (2006). Child labor and school achievement in Latin America, _World Bank Economic Review_ **20** , pp. 31--54.

- Hall, G. and H.A. Patrinos (2006). _Indigenous Peoples, Poverty and Human Development in Latin America_ . London, Palgrave Macmillan.

- Heckt, M. (1999). Mayan education in Guatemala: A pedagogical model and its political context. _International Review of Education_ **45** , pp. 321--337.

- Katz, E. (1995). Gender and trade within the household: Observations from rural Guatemala, _World Development_ **23** , pp. 327--342.

8
Table 3. Standard deviation of commodity prices from city to city at selected dates in state stores.

|Product|December 1991|February 4, 1992<br>rubles p|May 12, 1992<br>er unit|August 25, 1992|
|---|---|---|---|---|
|Beef|5.41|13.33|_10.56_|19.11|
|Sausage 1|6.85|14.53|20.87|25.92|
|Sausage 2|19.92|22.69|31.34|43.07|
|Butter|6.08|39.90|32.12|27.06|
|Vegetable oil|3.63|8.10|23.46|21.09|
|Milk|0.49|1.29|3.59|3.26|
|Sour cream|3.71|10.70|16.70|15.88|
|Cheese|7.59|41.S7|28.93|27.08|
|Eggs|2.60|2.97|5.31|5.92|
|Sugar|1.22|3.71|26.18|18.72|
|Bread 1|0.22|0.34|2.57|3.46|
|Bread 2|0.46|0.45|2.42|4.76|
|Millet groats|-|1.66|3.03|4.97|
|Vermicelli|0.93|_5.95_|4.96|7.47|
|Potatoes|1.05|1.52|2.81|7.48|
|Cabbage|1.05|1.79|5.28|6.76|
|Onions|2.38|3.57|4.12|14.78|
|Apples|4.80|_10.50_|22.70|22.23|
|Cigarettes|1.86|2.78|3.71|7.70|

14
# _Solvency versus Liquidity_

The seriousness of insolvency can be gauged from two vantage points. First, what are the market signals? If bond spreads indicate a high probability of default and keep rising even as official bailout discussions continue (the case with Greece 2010, Russia 1998 and Argentina 2001), then this should be taken as a red flag. Second, what are the country‘s fiscal and growth prospects as conveyed by an assessment of its fundamentals by economists at the IFIs? How likely is it to generate the needed primary surpluses to stabilize and even lower debt? If the chances are dim, accompanying official loans at the risk-free rate with an equal upfront haircut for all investors will then enable more credible fiscal targets and lead to lower long-bond spreads, minimizing reputation costs.

However, it should be admitted that it is not always easy to judge whether the country is dealing with a multiple equilibria (liquidity and confidence) or a fundamental problem (insolvency). Brazil in the summer of 2002 just before Luiz Inacio Lula da Silva, the candidate of the Brazilian Workers‘ Party, was elected president is a case in point. Bond spreads reached 2000 basis points that July as presidential election polls ―...indicated that Lula would win the presidential election....can [investors] be certain that a Brazil run by a president with a past record of sympathizing with default will not take the easy way out?‖ In the article from which this quote is taken, Williamson (2002) argued that fundamentals were sound: primary fiscal surpluses had been raised substantially and budget constraints hardened for the state governments. Besides, the real had been floated in 1999.<sup>40</sup> In his classic on multiple equilibria, Obstfeld (1994) recognizes: ―Ultimately,....crises based on limited foreign reserves [liquidity] must also be based on overall fiscal weakness:

[otherwise]...it would be .....feasible to borrow sufficient reserves to.....fend off any attack [on the fixed exchange rate]‖ (material in square brackets added). Brazil was different in that it had

> 40 Williamson (2002) was in part responding to an estimate by Morris Goldstein that there was a 70 percent chance that Brazil would be forced to restructure its debt by the end of 2003.  See Goldstein (2003).

24