rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 19. Percentage of the population with service connections who do not pay for service<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Poorest quintile 2nd quintile 3rd quintile 4th quintile quintile<br>Source : Banerjee and others 2008.<br><!-- End of picture text --> Another possible measure of affordability is whether the _full economic cost_ of a _subsistence level_ of consumption falls above a _normative affordability threshold_ . Economic cost is defined as the tariff that would fully cover both operating and capital costs and is country specific; reaching an average level of US$0.18/kW for SSA as a whole. Subsistence consumption is defined as 50 kWh per month, which is enough to cover very minimal usage for lighting (roughly one light bulb for two hours per day). The affordability threshold is typically defined as spending on subsistence power needs of between 3 and 5 percent of the total household budget. These values are normative, and are informed by power spending patterns by low income households that have been observed across a wide range of household surveys (recall figure 18 above).

By looking at the distribution of household budgets, one can calculate the percentage of households for which subsistence consumption priced at full economic cost would absorb more than 5 percent of their budgets and thus prove unaffordable. For example, looking across the distribution of household budgets for all of SSA, monthly bills of $2 would be affordable for almost the entire population, whereas monthly bills of $10 would only remain affordable for the entire population of _middle-income_ African countries.

Based on existing average effective tariffs, the bill for subsistence consumption levels looks very affordable _for those that are already connected to the grid_ (Figure 20a). With a 3 percent affordability threshold, the subsistence consumption of 50kWh/month priced at the current average effective tariff is affordable in 60 percent of the sample countries. If the affordability threshold is further raised to 5 percent, the subsistence consumption is affordable in over 90 percent of the sample countries. However, the picture looks very different for those that are _not currently connected to the grid_ . In these cases, the subsistence consumption level priced at the current average effective tariff would only be affordable in about 25 percent of the countries in the sample (Figure 20b). In conclusion, a significant 29
Girls’ enrollment in public schools Unadjusted difference between treatment and comparison districts <!-- Start of picture text --> ice)<br>ft 7<br>24<br>ran] TM<br>hae me a<br>On gee<br>S Oe eT<br>= on]-_ a——— SS SS aS a a ——— a<br>~~ eee a<br>20 pietereserereresereeteteretereieeiere<br>c ae ee STE TTT a 8 wn ee eee<br>D © TTT Tome nnm nee eee<br>Oo + ee<br>= _ueeeweee<br>Lu<br>N<br>2003 2004 2005 2006 2007 2008<br>Year<br>--------- Lower bound see Comparison District<br>oe Treaiment District <-------- Upper Bound<br><!-- End of picture text --> Source: Author's calculations using EMIS School Cansus Data
**Figure 8: Access to a set of (three) basic opportunities**

<u>(children of age 6-11 years in 20 Sub Saharan African countries)</u> <!-- Start of picture text --> 100<br>HOI 6 to 11 years Coverage 6 to 11 years<br>80<br>60<br>40<br>20<br>0<br>%<br>Niger Liberia Ethiopia Congo RD Madagascar Namibia Mozambique Rwanda Sierra Leone Mali Nigeria Zambia Kenya Senegal Tanzania Cameroon Zimbabwe Ghana Uganda Malawi<br><!-- End of picture text --> _Notes:_ 1) A child has access to the set of opportunities if s/he is attending school, and has access to safe water (piped, well or rainwater) <u>and</u> adequate sanitation (pit or flush toilet) in the household; (2) Coverage refers to the percent of children with access to all three services; HOI refers to Human Opportunity Index; and the gap between coverage and ” HOI indicates the “penalty <u>due to inequality of opportunity.</u> _Source_ : Demographic & Health Surveys for different years ( _circa_ 2008), from World Bank (2013, forthcoming).

**Figure** **<u>9: Opportunities in school attendance and access to electricity:Sub-Saharan Africa and Latin America</u>**

<!-- Start of picture text --> 100 School Attendance (10-14 years) - Circa 2008<br>80<br>60<br>40<br>20<br>0<br>100 Access to Electricity (0-16 years) - Circa 2008<br>80<br>60<br>40<br>20<br>0<br>Latin America and Caribbean Sub-Saharan Africa<br>Human Opportunity Index (%) Niger Mali Senegal Liberia Nigeria Sierra Leone Ethiopia Mozambique Madagascar Dem. Rep. Congo Cameroon Tanzania Ghana Zambia Malawi Uganda Zimbabwe Namibia Rwanda Kenya South Africa Guatemala Honduras Nicaragua Ecuador El Salvador Panama Paraguay Mexico Colombia Venezuela, R. B. de Uruguay Jamaica Peru Costa Rica Dominican Republic Argentina Brazil Chile<br>Human Opportunity Index (%) Liberia Uganda Malawi Niger Tanzania Rwanda Mozambique Sierra Leone Madagascar Kenya Ethiopia Mali Zambia Zimbabwe Namibia Cameroon Nigeria Senegal Ghana South Africa Nicaragua Honduras Panama Peru Guatemala El Salvador Jamaica Ecuador Paraguay Brazil Uruguay Mexico Costa Rica Chile Argentina Colombia<br>Dem. Rep. Congo Dominican Republic Venezuela, R. B. de<br><!-- End of picture text --> _Source_ : Demographic & Health Surveys for different years ( _circa_ 2008), from World Bank (2013, forthcoming).

17
Table 6: Effects of forecast errors and forecast growth on worker compensation, high vs low earners, according to managerial skill (managers defined as top 5% earners)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:|||log mont|hly wage|||
|High vs. low earners|high|low|high|low|high|low|
|Definition|5%|95%|15%|85%|25%|75%|
|A. Firms with high-skilled managers|||||||
|Weighted forecast error|0.0191|-0.0006|0.0129|-0.0011|0.0049|-0.0027|
||(0.0128)|(0.0051)|(0.0107)|(0.0047)|(0.0098)|(0.0047)|
|Weighted forecast growth|0.0084|-0.0016|0.0028|-0.0022|-0.0018|-0.0041|
||(0.0076)|(0.0032)|(0.0061)|(0.0032)|(0.0054)|(0.0035)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|9299|9299|9299|9299|9299|9299|
|N (firms)<br>|3682|3682|3682|3682|3682|3682|
|Adj. R<sup>2</sup>|0.0213|0.0374|0.0225|0.0407|0.0259|0.0415|
|RSS|72|123|466|113|377|106|
|B. Firms with low-skilled managers|||||||
|Weighted forecast error|0.0154*|0.0105***|0.0121*|0.0095**|0.0118*|0.0086**|
||(0.0080)|(0.0037)|(0.0073)|(0.0038)|(0.0069)|(0.0037)|
|Weighted forecast growth|0.0062|0.0037|0.0050|0.0038|0.0051|0.0039|
||(0.0051)|(0.0026)|(0.0046)|(0.0027)|(0.0043)|(0.0027)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|11585|11585|11585|11585|11585|11585|
|N (firms)<br>|5063|5063|5063|5063|5063|5063|
|Adj. R<sup>2</sup>|0.0200|0.0250|0.0219|0.0262|0.0262|0.0277|
|RSS|845|165|619|154|511|145|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
**Figure 3: Total Payments from National Government to Provinces (Blue) and Performance Payments from Meeting Tracer Targets (Red)**

<!-- Start of picture text --> 10,000,000<br>9,000,000<br>8,000,000<br>7,000,000<br>6,000,000<br>5,000,000<br>4,000,000<br>3,000,000<br>2,000,000<br>1,000,000<br>0<br>2005-22005-32006-12006-22006-32007-12007-22007-32008-12008-2<br><!-- End of picture text --> _Source:_ Informes de Gestión: Plan Nacer (2005-2008).

**Figure 4: Share of Tracer Performance Targets Achieved**

<!-- Start of picture text --> 0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2005-2 2005-3 2006-1 2006-2 2006-3 2007-1 2007-2 2007-3 2008-1 2008-2<br><!-- End of picture text --> _Source: :_ Informes de Gestión: Plan Nacer (2005-2008).

12
<!-- Start of picture text --> Extension services (last year) Loan (any HH member)<br>40% 35% 25% 22% 22%<br>20%<br>27% 16%<br>30%<br>13%<br>19% 15%<br>20%<br>13% 13% 10%<br>10% 4% 5% 4% 5%<br>0% 0%<br>Inorganic Pesticide Improved Inorganic Pesticide Improved seeds<br>fertilizer seeds fertilizer<br>No credit Credit<br>No Yes<br>3. Reasons for not using agricultural inputs<br>2015 27 27 17 28<br>2008 53 16 12 18<br>0% 50% 100%<br>No knowledge Too expensive Not available<br>Not useful Other<br><!-- End of picture text --> Source: 1‐2: Staff calculations using UNPS 2005/06‐2013/14, 3: NSDS 2015.

We estimate linear probability models to shed some light on the importance of the factors mentioned above on use of the following inputs: inorganic fertilizer, pesticide and improved seeds. This method allows us to consider the relationship between each of these factors— access to markets, rainfall, agricultural output prices, access to information and credit—at once and controlling for other factors. Household fixed effects control for time invariant unobservables at the household level that could impact adoption, which greatly reduces the threat of omitted variable bias. However, the latter is not sufficient to interpret our regressors effects as causal, as time variant unobservables factors could impact both adoption and the covariates. Examples of these are the household’s social capital or farming ability. However, several of our main regressors of interest are largely outside the control of a small hold farming household: access to markets, weather conditions, and to a lesser extent, extension services and crop prices.

We model the decision to use inputs as: where y�,� is an indicator variable capturing household i’s decision to use a given input (i.e. inorganic fertilizer, pesticide and hybrid seeds) at time t, X�,� is a vector of plot controls (area planted at time _t_ , and 26
<!-- Start of picture text --> AWind (onshore) @SolarPV @ Wind (offshore)<br>_. 200<br>= 175<br>= 150 f<br>=8 125100 A 8<br>o<br>Saif A & 4 a<br>ro A<br>5 50<br>= 25<br>2<br>co oO<br>a 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Auction year<br><!-- End of picture text -->
<!-- Start of picture text --> TH index - Landslides:<br>’<br>.<br>SHEDtow<br>BHiG<br>Na data<br><!-- End of picture text --> <!-- Start of picture text --> % of vulnerable hh notown the dwelling<br>by state<br>HM Wh predicted promaciity = 0.50<br>(Learrchelchies,<br>k<br>= -<br>mmB21.52.65 y<br>Gy 15.21 ia,<br>Oj.054.4<br><!-- End of picture text -->
Table 2: Employment and independent activities

||(1)<br>Employment<br>(Has an<br>activity of<br>any type)|(2)<br>Self<br>employed<br>(at least<br>1 activity)|(3)<br>Wage<br>employed<br>(at least<br>1 activity)|(4)<br># Independent<br>Activities|(5)<br>Total<br>hours<br>worked<br>(last 7<br>days)|(6)<br>Hours<br>worked in<br>self<br>employment|(7)<br>Hours<br>worked in<br>wage<br>employment|
|---|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates||||||||
|Treatment (ITT)|0.01|0.02**|-0.03**|0.24**|0.78|1.52|-1.10**|
||(0.01)|(0.01)|(0.01)|(0.10)|(1.50)|(1.17)|(0.51)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|95%|91.6%|10.4%|3.13|40.08|10.59|3.08|
|Observations|2,620|2,620|2,620|2,620|2,620|2,620|2,620|
|Panel B. Treatment Arm Estimates||||||||
|VSLA (T1) (ITT)|0.01|0.02|-0.01|0.21*|2.35|2.01|-0.44|
||(0.01)|(0.01)|(0.02)|(0.11)|(1.88)|(1.42)|(0.61)|
|Cash Grant with repayment (T2) (ITT)|0.02|0.02*|-0.04**|0.30***|0.09|1.12|-1.50***|
||(0.01)|(0.01)|(0.02)|(0.11)|(1.68)|(1.36)|(0.54)|
|Cash Grant (T3) (ITT)|0.01|0.03***|-0.04**|0.16|-0.89|1.40|-1.54**|
||(0.01)|(0.01)|(0.02)|(0.18)|(1.82)|(1.54)|(0.67)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|95%|91.6%|10.4%|3.13|40.08|10.59|3.08|
|p-value T1=T2|0.65|0.69|0.10|0.41|0.20|0.51|0.03|
|p-value T2=T3|0.84|0.40|0.93|0.41|0.56|0.85|0.95|
|p-value T1=T3|0.80|0.22|0.14|0.77|0.09|0.70|0.07|
|p-value T1=T2=T3|0.90|0.44|0.19|0.60|0.22|0.80|0.06|
|Observations|2,620|2,620|2,620|2,620|2,620|2,620|2,620|

Robust standard errors clustered at locality level. Hours worked are winsorized at the 99th percentile. Hours (columns 5-7) are measured for the last 7 days for the three main activities of the individual. The number of independent activities per individual is based on all activities operating in the last 12 months. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
14 total exports increased from 68 percent in 1984-89 to 73 percent in 990 and to 77 percent in 1991. The main engine of CEE-5 export growth was Germany (see Table S)--excluding Germany, the EC markets for Central European products stagnated in 1991. The share of Germany in CEE-S exports to the EC-10 substantially increased for all countries except Hungary, and the share of Germany in total OECD imports from the CEE-S increased between 1989 and 1990 by almost 50 percent--from 34 percent to 49 percent.

**Table** 5: The **Role** of **Gow** n the Export **Grewth** of CEE-5 in 1990 **nd** 1991

**A. Share of** Gerenwy **in OED aid** EC-10 **Ixqorts** from the CEE-5 **(in** percent)

||Bulgar|ia|Czec|hosto|vakia<br>|Hunaar|v|
|---|---|---|---|---|---|---|---|
|1989|1990|1991|1989|1990|1991<br>1989|1990|1991|
|OECD<br>23<br>EC-10<br>32|27<br>35|33<br>42|34<br>43|37<br>51|49<br>33<br>63<br>55|37<br>55|40<br>58|
||Polan|d|R|nia||||
|1989|1990|1991|1989|1990|1991<br>1989|1990|1991|
|OECD<br>32<br>EC-1O<br>46|38<br>50|52<br>58|14<br>20|28<br>37|36<br>33<br>42<br>47|37<br>52|45<br>59|
|**B.**<br>Ainnul Increases<br>in Va|lue of|lcowts fro|m CEE<br>u|ntries<br>|<br>**(mitt.**USS)|||

||Bulg|aria|Czefhg|sloakfe|<br>Hu|naarv|
|---|---|---|---|---|---|---|
||**1990**|**1991**|**1990**|1991|**1990**|1991|
|Germany<br>EC-9(Germany<br>excluded)<br>OtherOECD|72<br>76<br>19|76<br>-10<br>.9|355<br>196<br>110|1388<br>240<br>58|598<br>306<br>249|553<br>197<br>190|
|Memorandum:<br>Share of the increase in<br>OECDimports absorbed by Germany<br>(in ercent)|43|134|54|82|52|59|
|p|Po|ts|R|omania||CEE-3|
||1990|1991|1990|1991|1990|1991|
|Germany<br>EC-9(Germany<br>excluded)<br>Other OECD|1320<br>928<br>308|1147<br>8<br>-4|-127<br>-658<br>-312|40<br>-186<br>-240|2273<br>1431<br>667|3088<br>445<br>203|
|Hemorandu: Share of the increase in<br>OECDpgorts absorbed byGermany<br>(in percent)|52|103|--|--|52|83|

Source: See Table 1 Although Germany has been traditionally the largest OECD trading partAer of Central Europe, its significance increased for each of the CEE-5 economies in 1990 and 1991. The share of Germany in OECD imports from Romania surged from 14 percent in 1989 to 52 percent in 1991, mainly because exports to Germany fell less than to other partners in 1990 and they actually increased in 1991. For Bulgaria, the increase in sales to German firms more than offset the contraction of other OECD markets in 1991. German
32 <!-- Start of picture text --> 5 __ _ _ _ _ _ _ _ _ _ _ _ _ _ _3 00<br>+ 250.<br>+ ~~~~~200.<br>C~~~~~~<br>1150<br>+ ~~~ ~~~~~50 +<br>1 +<br>0 50 100 150 200 250 300 0 50 100 150 200 260 300<br>Pro-crisis Volatiliy Pe-crisis Volatiity<br>Figure 19 Figure 20<br><!-- End of picture text --> ii) Similarly, figures 21 and 22 below show that also the second implication holds: the mean and the volatility of the risk premium have a positive relation.

<!-- Start of picture text --> 300 o 350<br>250<br>300<br>#200 24<br>200<br>~100 +<br>+~~~~~<br>50. 4 L4 150<br>isPsi Men+ssMa<br>0 o 500 1000 1500 2000 10 500 _ _ _ 1000 _ _ _ _ 1500 _ _ _ _ _ 2000 _ _ _ _ 2500<br>Pre-crisis Mean Cdlas Mean<br>Figure 21 Figure 22<br><!-- End of picture text --> _Figure 22_ iii) Finally, we try the following experiment to look for evidence supporting the third implication. For both periods - pre-crisis and crisis - we rank the countries according to the _probability of default_ (1- **i** ) - proxied by the pre-crisis mean of the risk premium - and then we plot the volatility on this rank. For the pre-crisis period the ranking is (from the most likely to the less likely to default): Nigeria, Venezuela, Bulgaria, Ecuador, Argentina, Brazil, Poland, Philippines and Mexico. For the crisis period, we keep the same ranking but we move Mexico from the last to the first position. We report the plots for Par and Discount bonds in figure 23-26.
<!-- Start of picture text --> 30,000 N :<br>\ :<br>\:<br>\ :<br>25,000 ~. NL :<br>é TT |<br>oQ 2 oTM. ~,<br>¥ a<br>fo} : mTM!~,<br>: | —<br>~ 15,000 are<br>RS) : 7S<br>a:<br>:—:-—:» Demand<br>—— Supply<br>5,000<br>5 10 20 30 33.24 40 50<br>Number of workers, (M)<br><!-- End of picture text -->
# Anthropometrics

Standardized weight-for-age, weight-for-height, and height-for-age were calculated using the WHO child growth STATA package. The resulting z-scores compares each child to a standardized global population, accounting for gender and age. A z-score of 0 implies the child falls directly on the global mean of all children of the same gender and age or height, whereas a z-score of 1 or -1 implies the child is one standard deviation from the mean. Standard deviations are used as benchmarks; children below two standard deviations of weight-for-age are considered wasted, and below three standard deviations is severely wasted. Children below two standard deviations of height-for-age are considered stunted, whereas children below three standard deviations are considered severely stunted. Prevalence of wasting and stunting is compared against the expected prevalence for each of these categories based on a standard normal distribution.

# Elevated blood pressure

Elevated blood pressure is defined as a systolic blood pressure greater than or equal to 140 mmHg, or diastolic blood pressure higher than 90 mmHg, based on the average of three readings.

45
# **Table A4: Vulnerability**

|||201|4|||201|5|||201|6|||201|7||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|1|2|3|4|1|2|3|4|1|2|3|4|
|||||||Place o|f resid|ence|||||||||
|Minsk|1.4|0.2|0.6|0.1|0.3|0.4|0.8|0.1|0.6|2.4|1.2|1.7|0.6|1.8|0.6|1.9|
|Large cities|16.1|12.2|12.9|11.4|10.5|16.2|15.9|18.3|12.1|18.6|25.7|30.2|20.1|33.8|28.2|16.3|
|Small cities|16.0|26.8|20.0|25.7|18.0|22.5|25.2|23.8|34.4|31.5|27.9|40.2|33.3|28.7|28.5|29.7|
|Rural area|28.2|29.2|29.4|28.8|43.1|36.7|39.5|39.2|42.4|37.6|42.1|41.1|42.2|42.8|42.6|40.2|
||||||||Age||||||||||
|Child|40.2|43.5|37.0|42.5|40.2|45.9|50.4|51.7|48.4|50.1|63.8|74.3|54.6|69.7|62.2|50.6|
|working age|15.3|16.1|16.7|15.8|19.6|19.4|20.6|21.2|23.0|24.5|25.4|28.8|26.8|30.7|28.8|23.9|
|Retiree|0.8|0.5|0.4|0.1|0.2|0.3|0.2|0.2|0.2|0.2|0.1|0.3|0.2|0.1|0.1|0.1|
||||E|mploy|ment s|tatus f|or work|ing age|popul|ation|||||||
|Employed|5.8|7.9|8.1|7.6|9.3|10.3|10.7|12.4|13.8|16.6|15.9|19.1|19.0|22.0|21.3|16.0|
|Partly employed|34.7|39.6|33.2|27.6|39.7|43.6|23.4|43.2|50.8|37.4|28.2|51.0|46.1|47.2|42.2|46.9|
|Nonemployed|37.2|35.6|36.8|39.1|43.6|40.0|46.5|41.4|42.6|41.3|51.1|50.4|39.6|47.3|41.1|40.3|
|||||Educa|tion le|vel for|workin|g age p|opulati|on|||||||
|high education<br>secondary|2.7|1.2|1.7|0.9|2.2|2.5|3.2|1.6|1.8|1.3|1.6|2.4|1.6|2.0|4.3|1.7|
|specialized|12.7|12.2|11.5|11.6|16.8|18.9|19.4|18.4|23.5|21.4|21.1|25.6|31.6|33.5|36.6|24.6|
|Other|25.4|28.6|30.5|29.0|33.9|32.8|34.0|37.9|37.8|43.7|45.7|50.2|41.6|49.8|40.7|39.1|
||||||Ty|pe of h|ouseho|ld (HH)|||||||||
|HH without children|1.3|1.2|2.0|1.7|3.2|2.2|2.5|1.8|2.8|1.9|3.5|3.4|1.1|3.2|1.8|1.6|
|Retiree HH|1.0|0.9|1.0|0.7|0.8|0.9|1.1|0.8|0.7|0.6|0.8|0.9|1.1|0.8|0.9|0.7|
|HH with children|31.4|33.6|30.8|32.2|33.8|36.3|39.4|41.2|40.5|43.9|48.8|56.2|47.4|55.4|52.1|41.7|
|1 child|23.4|24.4|22.9|23.8|25.7|28.7|31.8|34.0|32.4|36.6|41.4|49.4|41.3|48.6|46.9|35.9|
|2 children|28.7|32.3|26.7|30.1|36.5|37.9|41.7|43.7|44.1|44.9|53.3|60.7|48.9|58.4|52.5|40.1|
|3 and more|||||||||||||||||
|children|97.7|88.4|86.4|80.8|86.1|86.3|88.3|86.5|88.9|96.3|88.3|90.6|89.1|96.0|89.8|95.8|
|singleparent|60.2|62.2|64.9|60.6|60.9|64.4|68.0|64.7|67.1|66.5|68.1|72.7|67.0|65.0|67.8|64.3|

Source: Authors’ estimates based on HSS data.

31
un-winsorized variables, although no differences are statistically significant for either the winsorized or un-winsorized data.

Our baseline measure of profits is calculated as September 2007 sales minus September 2007 costs (unlike sales, we only collected costs for one month in the surveys).<sup>10</sup> We calculate two separate measures of enterprise productivity. The first is the residual from a regression of log sales on log employees and log business assets. The second is return on assets (ROA), defined as profits (calculated as sales minus costs) divided by business assets.

Similarly to sales, the variances of profits, productivity, and ROA are large. For this reason, we include the averages of the 1% winsorized variables in Table 2. We winsorized the top and bottom 1% of outliers for profits, productivity, and ROA (unlike sales, profits, productivity, and ROA are not bounded below by zero and have negative values, which is why we also winsorized the bottom 1%). After winsorizing, average baseline profits are the same in the treatment and control group (about US$10,000). Overall, we find no statistically significant differences in business performance variables at baseline.

Columns 4, 5, and 6 of Table 2 examine whether treatment group enterprises that took-up the program were significantly different at baseline from treatment group enterprises that did not take-up the program. We find that enterprises that took-up the program are more likely to be in manufacturing, have a larger number of employees, are more likely to have male decision makers, and are older. In addition, Panel C of Table 2 shows that enterprises that took-up the program were more productive at baseline than enterprises that did not take-up the program.

We conducted the follow-up survey between March and June 2009 (i.e., one to four months after the intervention ended, which is 12-16 months after the intervention began), re-interviewing 378 enterprises or 88% of the 432 enterprises interviewed at baseline, to measure the impact of the consulting services on business outcomes. Out of the 54 enterprises that could not be re-interviewed, eleven enterprises were confirmed closed<sup>11</sup> , 31 declined to participate in the interview<sup>12</sup> and seven enterprises could not be tracked down despite repeated contact attempts. The remaining five

> 10 de Mel et al. (2009) suggests asking business owners what their profits are in one simple question as an alternative to calculating profits based on responses to specific components. We tried this approach but had a very high non-response rate to this question.

> 11 We verified with the former principal decision maker and/or neighbors that these enterprises had indeed closed. The percentage of closed enterprises was lower in the treatment group (1.4%) than in the control group (3.3%). However, the difference is not statistically significant.

> 12 The percentage of enterprises that refused the interview was slightly higher in the control group (8.7%) than in the treatment group (5.6%), but the difference is not statistically significant.

10
**Figure 2: Job Loss and Firm Exit Rates, Relative Share of SMEs (less than 100 employees)**

<!-- Start of picture text --> strict_exit weak_exit<br>UGA<br>MNGGTMYEM GTMKSVYEM<br>MKDNPL NPL<br>KSVLAONIC LAO<br>LTU LTUMEX<br>MEX MNGBGRNPLNIC<br>TZAMNEHND MNEMNETZA<br>RWACZEMDAUZBKGZGEOVENBLRURYPOLPRYBGRCOLROULVABOLBIHARMSLV ZMBMNGPRYBGRRWAKAZGTMCZEMDANICROUCZEKGLTUTZAKENMDABLRBOLUGALVAURYVENMKDUGHAPRYMKDURYSVKCOLALBARMROUCOLUZBVENHRVKSVLVALAO Z BIHSLVBOLBHND<br>ZMBECU GHA ECUBIHECUMEXUKRPOLCOD<br>KAZKENUKRAZEPANSVK KGZKAZARMZMBCHLHUNPANPOLSRBSLPANAZESHUNGEO V SVKN YEM<br>PERSRB CHLHUNCOD SRBPER KENUKRCODAZE<br>GHA SVN<br>SVN HND BLR ARG RWAGEOUGA<br>ARG CHL PER<br>.4 .5 .6 .7 .8 .9 1 .4 .5 .6 .7 .8 .9 1<br>Share of Exiting Firms (less than 100 emp.) Share of Exiting Firms (less than 100 emp.)<br>1 1<br>.75 .75<br>.5 .5<br>.25 .25<br>0 0<br><!-- End of picture text --> 27