rafmacalaba/datause-encoder-data huggingface dataset:
Table 9 Olley and Pakes Regressions for Grouped Data with Contemporaneous Subsidized and non-Subsidized Spillover Variables (constructed based on income tax) and Tariff Controls: non-SOEs vs. SOEs (All firms, foreign-invested, and domestic firms with zero foreign investment

||All|firms|Foreign-inv|ested firms|Domestic fir<br>inves|ms (0 foreign<br>tment)|
|---|---|---|---|---|---|---|
||non-SOEs|SOEs|non-SOEs|SOEs|non-SOEs|SOEs|
|Forein share (b HK-Taiwan-Macau)|-0.00381|0.0363|0.00368|-0.0766|||
|g y|(0.00302)|(0.0543)|(0.00484)|(0.0675)|||
|Fi h (b th ti)|0.00522|0.0986**|0.0137***|0.0807|||
|oregn sare y oer counres|(0.00313)|(0.0388)|(0.00498)|(0.0690)|||
|State share|-0.00174|-0.0254***|0.00420|0.0180|-0.00206|-0.0257***|
||(0.00290)|(0.00420)|(0.00767)|(0.0307)|(0.00331)|(0.00421)|
|Hor_subsidized|0.117|0.103|0.0512|-0.595|0.163|0.103|
||(0.107)|(0.133)|(0.109)|(0.511)|(0.111)|(0.134)|
|Bw_subsidized|2.189***|2.833***|2.065***|3.203***|2.286***|2.822***|
||(0.644)|(0.611)|(0.475)|(0.986)|(0.799)|(0.628)|
|Fw_subsidized|0.165|0.203|0.238***|0.526**|0.0817|0.213|
||(0.107)|(0.151)|(0.0859)|(0.259)|(0.141)|(0.158)|
|Hor_non_subsidized|0.189*|-0.0729|0.179*|-0.429|0.188|-0.0702|
||(0.104)|(0.117)|(0.103)|(0.355)|(0.114)|(0.119)|
|Bw_non_subsidized|-0.934|-1.173**|-0.649|2.287*|-1.051|-1.175**|
||(0.623)|(0.464)|(0.474)|(1.239)|(0.749)|(0.471)|
|Fw_non_subsidized|0.184**|0.222**|0.261***|0.403**|0.132|0.221**|
||(0.0885)|(0.105)|(0.0683)|(0.163)|(0.122)|(0.110)|
|lnTariff|-0.0346**|-0.0458**|-0.0262*|-0.0712**|-0.0366**|-0.0451**|
||(0.0144)|(0.0184)|(0.0155)|(0.0329)|(0.0163)|(0.0185)|
|lnTariff_backward|-0.0170|0.00734|-0.00343|0.0123|-0.0249|0.00716|
||(0.0179)|(0.0305)|(0.0175)|(0.0727)|(0.0200)|(0.0306)|
|lnTariff_forward|-0.00981|0.00613|-0.0211*|0.0695***|-0.00610|0.00561|
||(0.00782)|(0.00679)|(0.0110)|(0.0233)|(0.00716)|(0.00680)|
|Constant|1.728***|2.499***|1.920***|1.431***|1.673***|2.504***|

49
downloaded from the ESA’s AWS repository managed by MEEO (Meteorological Environmental Earth Observation).<sup>7,8</sup> We filter the bias-corrected XCH4 data for local concentration anomalies, or differences between observed and background CH4 at each point. We calculate background CH4 using the methodology of Hakkarainen et al. (2019), which isolates local anomalies from the three background components described above. We compute the daily median XCH4 for each 10degree latitude band and linearly interpolate the result to each S5P observation with 1-degree resolution. Following Hakkarainen, we use the median as the representative value because it is not skewed by extreme observations. We subtract this background value to compute the local anomaly for each observation. Then we compute monthly mean values of concentration anomalies for each 25-km grid cell in our database.

Figures 1 and 2 display mean annual CH4 concentration anomalies for the terrestrial globe in 2020 and 2021. Country outline maps are superposed to aid interpretation. Although there are some evident year-to-year differences, several common patterns emerge. The first is the effect of pixel quality on coverage density. In both years, exclusion of lower-quality pixels leads to coverage gaps in western China, parts of Siberia, Indonesia, Papua New Guinea, northwestern South America and western Canada. The second common pattern is the widespread incidence of large positive anomalies in continental south, southeast and east Asia; the Persian Gulf region and the North African littoral; South Sudan and southern Nigeria; southern and western Brazil (with an extension into Bolivia); south-central and north-central United States; and numerous regions in the Arctic. In counterpoint, widespread negative anomalies are evident in the western United States and Mexico; southwest China; and a region that includes Mongolia and areas to its north and northeast in China and the Russian Federation.

> 7 https://www.meeo.it/

> 8 - The data are available in NetCDF format at the ESA’s AWS repository, https://meeo <u>s5p.s3.amazonaws.com/OFFL/L2__CH4, with filenames catalogued at</u> https://scihub.copernicus.eu/catalogueview/S5P/

4
# **References**

Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobatón (1999a). “Aggregating Governance Indicators.” World Bank Policy Research Working Paper No. 2195, Washington, D.C.

- ——— (1999b). “Governance Matters.” World Bank Policy Research Working Paper No. 2196, Washington, D.C.

- ——— (2002). “Governance Matters II – Updated Indicators for 2000/01.” World Bank Policy Research Working Paper No. 2772, Washington, D.C.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2004). “Governance Matters III: Governance Indicators for 1996, 1998, 2000, and 2002”. World Bank Economic Review. 18:253-287.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2005). “Governance Matters IV: Governance Indicators for 1996-2004. World Bank Policy Research Working Paper No. 3630. Washington, D.C.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2006a). “Measuring Governance Using Perceptions Data", in Susan Rose-Ackerman, ed. Handbook of Economic Corruption. Edward Elgar.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2006b). “Governance Matters V: Aggregate and Individual Governance Indicators for 1996-2005”. World Bank Policy Research Working Paper No. 4012. Washington, D.C.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2007a). “The Worldwide Governance Indicators Project: Answering the Critics". World Bank Policy Research Working Paper No. 4149. Washington, D.C.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2007b). “Growth and Governance: A Reply/Rejoinder". Journal of Politics. 69(2):555-562.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2007c). “Governance Matters VI: Aggregate and Individual Governance Indicators for 1996-2006”. World Bank Policy Research Working Paper No. 4280. Washington, D.C.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2008). “Governance Matters VII: Aggregate and Individual Governance Indicators for 1996-2007”. World Bank Policy Research Working Paper No. 4654. Washington, D.C.

- Kaufmann, Daniel and Aart Kraay (2008).  "Governance Indicators:  Where Are We and Where Should We Be Going?"  World Bank Research Observer.  Spring 2008.

26
24

# **Table 2:  Correlates of village access to community radio**

( _Commune fixed effects, robust p-values in parentheses_ )

||Number of Community<br>|Share of Community<br>|
|---|---|---|
||Radios|Radios|
|Village population (1,000s)|-.014<br>(0.78)|.002<br>(0.77)|
|Distance of village to nearest urban center|-0.00609<br>(0.14)|-0.000312<br>(0.65)|
|Distance of village to nearest bus or train|-0.00410|-0.000156|
|stop|(0.30)|(0.75)|
|Number of functional private schools|-0.227<br>(0.28)|-0.0133<br>(0.72)|
|Fraction of surveyed households with|-0.383|-0.0825|
|income < 30,000 CFA Francs|(0.23)|(0.20)|
|Fraction of surveyed households with a|0.346|0.109|
|mobile phone|(0.47)|(0.33)|
|Most common language in village and|0.301|0.0249|
|commune is the same|(0.24)|(0.48)|
|Probability that main language in any two|-0.558|0.0608|
|households in the village is the same|(0.27)|(0.35)|
|Mean number of houses in village with brick|0.298|0.159|
|walls|(0.46)|(0.11)|
|Mean number of houses in village with more|-0.355|-0.0584|
|than 5 rooms|(0.52)|(0.54)|
|Fraction of surveyed households with a|1.118|-0.274|
|television|(0.41)|(0.17)|
|Observations|200|200|
|_R_<sup>_2_</sup>|0.78|0.69|

In villages with a larger share of community radios, brick walls are almost significantly more common. Again, the magnitude of the effect is small: a one standard deviation in the fraction of households with brick walls (19 percent) is associated with only a three percent greater share of community radios. These variables are also not jointly significant in this specification. Furthermore,
Creditworthiness is also an issue for many suppliers, given that almost two-thirds of suppliers are not investment grade (meaning their credit rating is below investment grade or they do not have a rating).

In Table 1, Panel C, we present summary statistics of contract characteristics. We have a wide distribution of contract amounts (contract size) varying from about US$ 400 dollars to over US$ 4 billion dollars, with a median of about US$ 3.5 million dollars.<sup>8</sup> Contracts in our sample are generally very long in duration – the average net days is 59.2 and the median is 60 net days. About 75% of contracts in our sample have net days longer than 30 days, which is longer than the ̳typical‘ contract of 30 days previously shown in the literature (Ng, Smith and Smith 1999), possibly because our buyers are relatively large. For example, 20% of contracts have net days of exactly 30 days, 28% have net days of exactly 60 days, and 17% have net days of exactly 75 days.

About 60% of contracts in our sample are denominated in U.S. dollars, followed by almost 40% in euros; this is in line with the distribution of contracts among buyers in Europe and North America (41% and 59%, respectively, as shown in Table 1, Panel A).

In our sample, 13% of contracts (or 3,707 in total) offer early payment discounts.<sup>9</sup> We also examine the discount terms, including the discount and discount days (the number of days within which the buyer has to pay to obtain the discount). Almost two-thirds of discount days are 30 days or less, while 27% are between 30 and 60 days, and 9% are more than 60 days. Some terms seem very common; 20% of discount days are 10 days, 20% are 30 days, and 16% are 60 days. The most common spreads of net days less discount days are 1 day (34% of contracts with discounts), 30 days (29% of contracts), and 20 days (16% of contracts), with the majority (or 63%) of contracts having a spread of net days less discount days equal to or less than 20 days. The mean spread of net days less discount days equals 17 and the mean ratio of discount to net days is 63%.

> 8 The largest contract in our dataset is for a large, diversified U.S. retailer.

> 9 This is a comparable figure to that obtained using SSBF survey data on U.S. firms, indicating that 20% of firms that use trade credit are offered an early payment discount from their suppliers.

9
weekly hours, number of household members and years of education, also the difference between the survey collection results in 2008 and 2007 is applied (example: the independent variable of income is = Income 2008 - Income 2007 and so on with the other variables mentioned).

As a robustness check, this study also estimates a regression including the households that moved from _Canaã_ (households that got the title, sold the property and moved right away). The goal is to check if the land title still has a positive effect even considering those that are not living in the original community.

Given all the conditions mentioned above, the basic econometric structure is the following: _- Yi = α+δ(Land title)+β(Hours worked weekly adult)+β(Income)+β(Households number)+ β(Years of education)+α′Xi+ei_ Furthermore, the main hypothesis to be tested is the following: _H_ 0 _=δ<0 H_ 1 _=δ≥0_

# **7. Results**

The summary of basic statistical results is presented in Table 1 (Sample Means). Consistent with the study’s basic findings, one main aspect demands special attention. The average weekly hours of child labor has decreased in the program households and increased in the non-program households. Additionally, for land title owners, weekly hours worked of adults increase more. This could provide a potential signal that child labor is being substituted by adult work.

Table 1: Sample means - with all households that have children

|||**Pre-Program**<br>**(N = 251)**|||**Post-Program**<br>**(N= 240)**||
|---|---|---|---|---|---|---|
||**Ia**<br>**(program)**|**Ib**<br>**(non-program)**|**IC**<br>**|tΔ|**|**IIa**<br>**(program)**|**IIb**<br>**(non-program)**|**IIC**<br>**|tΔ|**|
|Mean age|42.0|45.0|-3.0|42.8|45.9|-3.1|
|Time in residency<br>(# months)|146.2|158.4|-12.1|157.8|175.0|-17.1|
|Household number<br>(# members)|3.8|4.0|-0.2|3.9|4.1|-0.2|
|Number of rooms|3.3|3.7|-0.7|3.3|3.7|-0.4|
|Income (number of MW)|2.0|3.0|-1.0|2.0|3.0|-1.0|
|Years of Education|9.0|5.0|4.0|9.0|5.0|4.0|
|Hours Worked Weekly|9.8|9.2|0.5|19.5|10.0|9.5|
|Child Labor Hours<br>Weekly|3.5|9.1|-5.6|0.5|11.9|-11.4|

Source: Author's Estimates 15
41 TABLE 10 AVERAGE CAPITAL-OUTPUT RATIO<sup>AND PRICE-COST</sup> MARGIN IN THE PRIVATE AND<sup>PUBLIC SECTORS 1976-85</sup>

|OBS|YEAR|AKORPR|APCMPR|AKORPU|APCMPU|
|---|---|---|---|---|---|
|i<br>|1976<br>|0.446486<br>|0.178182<br>|1.39313<br>|0.212863<br>0110886|
|2<br>|1977<br>|0.486885<br>|0.211588<br>|1.64282<br>|.<br>0112628|
|3<br>|1978<br>|0.487306<br>|0.235953<br>|2.40332<br>139351|.<br>0077184|
|4<br>|1979<br>|0.593754<br>|0.186188<br>|.<br>|.<br>|
|5<br>|1980<br>|0.742177<br>|0.203650<br>|1.75668<br>|0.011172<br>|
|6<br>|1981<br>|0.569563<br>|0.208442<br>|1.62152<br>|0.128247<br>|
|7<br>|1982<br>|0.505812<br>|0.227981<br>|1.77228<br>|0.184682<br>111|
|8<br>|1983<br>|0.540500<br>|0.200874<br>|1.53982<br>1|0.985<br>0123604|
|9<br>|1984<br>|0.542396<br>|0.207466<br>|.46398<br>089707|.<br>0155903|
|10|1985|0.514882|0.189682|.|.|

TABLE 11 AVERAGE CAPITAL-OUTPUT RATIO. CAPITAL-LABOUR RATIO AND PRICE-COST MARGINS OF PRIVATE<sup>ANO PUBLIC FIRMS</sup> IN EXPORTABLE INDUSTRIES

|0BS|YEAR|AKORPR|APCMPR|AKORPU|APCMPU|AKLPR|AKLPU<br>|
|---|---|---|---|---|---|---|---|
|i<br>2<br>|1976<br>1977<br>|0.580456<br>0.669309<br>|0.155202<br>0.197787<br>0240367|0.683867<br>0.639049<br>0866950|0.13809<br>0.12059<br>023156|1274.50<br>1415.71<br>125678|1126.28<br>1047.68<br>119152|
|3<br>|1978<br>|0.585734<br>|.<br>|.<br>|.<br>014006|.<br>137501|.<br>120982|
|4<br>|19,9<br>|0.694064<br>|0. 175666<br>0191397|0.932526<br>0963214|.<br>-020298|.<br>145719|.<br>1378.59|
|5<br>|¶980<br>|0.952137<br>|.<br>|.<br>0746329|.<br>001174|.<br>140868|143877|
|6<br>|1981<br>|0.649498<br>|0.192316<br>0201122|.<br>0957154|.<br>031630|.<br>139406|.<br>175464|
|7<br>|1982<br>|0.539446<br>|.<br>|.<br>|.<br>019944|.<br>140304|.<br>148512|
|8<br>|1983<br>|0.535004<br>|0.172590<br>1|0.713704<br>0755367|.<br>010753|.<br>139549|.<br>168832|
|9<br>|1984<br>|0.498329<br>|0.97079<br>|.<br>0578339|.<br>021349|.<br>134759|.<br>173017|
|10|19Lo|0.469841|0.178169|.|.|.|.|

AKORPR-AVERAGE CAPITAL-OUTPUT RATIO IN THE PRIVATE SECTOR AKORPU-AVERAGE CAPITAL-OUTPUT RATIO<sup>IN THE PUBLIC SECTOR</sup> APCMPR-AVERAGE PRICE-COST MARGIN IN THE<sup>PRIVATE SECTOR</sup> APCMPU-AVERAGE PRICE-COST MARGIN IN THE PUBLIC<sup>SECTOR</sup> AKLPR.AVERAGE CAPITAL-LABOR<sup>RATIO IN THE PRIVATE SECTOR</sup> AKLPU'AVERAGE CAPITAL-LABOR<sup>RATIO IN THE PUBLIC SECTOR</sup>
**<u>Table 16: Estimated log cell consumption per capita differences, evaluated at mean attributes for reference woman at different ages</u>**

||Age gr|oups:|15-40|41-50|51-60|61 +|
|---|---|---|---|---|---|---|
||**_Urban:_**|Remarried widow|11.774|11.874|12.133|12.302|
|||Widow|0.549**|0.536***|0.405**|0.347+|
|||Once married|0.783***|0.578***|0.429***|0.343|
|**_1_**||Divorcee|0.527*|0.492**|0.349|0.338|
|**_el_**||Remarried divorcee|0.405+|0.315|0.105|-0.364|
|**_an_**|**_Rural_**:|Remarried widow|11.416|11.516|11.775|11.944|
|**_P_**||Widow|0.280|0.266+|0.135|0.077|
|||Once married|0.559***|0.354***|0.205+|0.119|
|||Divorcee|0.524***|0.489**|0.345+|0.334|
|||Remarried divorcee|0.541***|0.450**|0.240|-0.229|
||**_Urban_**:|Widow|12.443|12.632|12.681|12.638|
|||Remarried widow|-0.766***|-0.544*|-0.409|-0.532**|
|||Once married|-0.029|0.091|0.011|-0.077|
|**_2_**||Divorcee|-0.163|-0.027|-0.117|-0.246|
|**_el_**||Remarried divorcee|-0.283|-0.388|-0.523*|-0.820**|
|**_an_**|**_Rural_**:|Widow|11.816|12.005|12.054|12.011|
|**_P_**||Remarried widow|-0.497**|-0.275|-0.140|-0.262|
|||Once married|0.017|0.137|0.057|-0.031|
|||Divorcee|0.103|0.239|0.149|0.020|
|||Remarried divorcee|0.122|0.016|-0.118|-0.415|
||**_Urban:_**|Once married|12.451|12.430|12.478|12.556|
|||Widow|−0.098|0.062|0.011|0.080|
|||Remarried widow|-0.806***|-0.489***|-0.376***|-0.259|
|**_3_**||Divorcee|-0.361+|-0.250|-0.095|-0.087|
|**_el_**||Remarried divorcee|-0.400**|-0.273|-0.311*|-0.474**|
|**_an_**|**_Rural:_**|Once married|11.869|11.848|11.896|11.974|
|**_P_**||Widow|−0.144|0.017|−0.035|0.035|
|||Remarried widow|-0.582***|-0.265+|-0.152|-0.035|
|||Divorcee|-0.140|-0.030|0.125|0.133|
|||Remarried divorcee|-0.041|0.086|0.048|-0.115|

Note: Women 15 and older.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12. Significance tests refer to differences relative to the reference marital status estimated consumption. Consumptions are predicted using own parameters and mean attributes of the reference marital status group for the indicated age range. Source: Authors’ estimations using PSF1.

46
participated in a business training program. While we know nothing about the content of this previous training, it may make our average effect of zero less surprising. However, it makes the heterogeneity results – both the positive and the negative impacts, much more salient. It also implies that our results are best applied to contexts in which there has been a history of business training.

The second point on external validity is that our follow-up survey was conducted two years after the start of the intervention. While this interval is not shorter than in many other similar studies, it may not be a sufficiently long period for all the potential benefits of training to materialize (a point noted in McKenzie and Woodruff (2013)). As the implementer extended the treatment to the control group shortly after the follow-up survey, we cannot collect additional data on the impact of the treatment. This unfortunately prevents us from estimating the long-term impact of the intervention.

Taken together, our results indicate that, first, training needs to be targeted to the ‘right’ entrepreneurs. As other studies have confirmed, not all people are able to become successful entrepreneurs. To be part of the MKUBWA programs entrepreneurs needed to be committed to their business which was defined as having been in business full-time for at least a year. The estimates here indicate that this may have been the relevant targeting variable, but the threshold was set too low. Second, training alone does not guarantee results, especially in the short run, and needs to build on other skills acquired through experience. Third, training may be effective to change business practices, but this per se does not generate higher profits – either because longer investments are necessary or because other factors are important to produce positive impacts on business outcomes. More research is needed to understand this relationship, between improved practices and business outcomes.

Finally, the way training is provided and its ability to respond to specific needs of the entrepreneurs is also important. In our case, the “enhanced” package – more flexible and tailored to the needs of individual entrepreneurs – produced better results. This deserves special consideration especially when the targeted group is made of women, who have specific constraints that need to be addressed to create the conditions for the training to be effective.

17
Table 4: Probit regressions for land reformn participation

|Mean of variables|Regre<br>(1)|ssions<br>(2)|
|---|---|---|
|Number of rooms<br>3.90|<br>-0.003|_-0.005*_|
||(1.47)|(2.06)|
|No access to running water<br>9.0%|0.647**|0.662**|
||(21.89)|(21.17)|
|No toilet<br>62.2%|0.034*<br>|0.055**<br>|
||(2.06)|(3.26)|
|Roads passable most of the time<br>33.0%|-0.052**|-0.056**|
||(3.33)|(3.63)|
|Roads passable all of the time<br>40.0%|-0.291**|-0.282**|
||(14.94)|(14.52)|
|Head's education<br>2.96|0.003|0.001|
||(1.16)|(0.33)|
|Head's age<br>50.29|0.008*|0.007*|
||(2.24)|(2.30)|
|Head's age squared<br>2783.10|0,000+*|0.000**|
||(2.82)|(2.91)|
|Female head<br>32.7%|0.006|0.035*|
||(0.40)|(2.22)|
|Number of persons younger than 14<br>2.02|-0.028**|-0.026**|
||(6.74)|(6.21)|
|No. of persons age 14-60<br>3.35|0.007+|0.002|
||(1.72)|(0.52)|
|No. of persons age more than 60<br>0.43|0.085**|0.074**|
||(6.28)|(5.42)|
|Free State<br>7.8%|0.443**|0.420**|
||(12.05)|(10.59)|
|Gauteng<br>1.2%|0.815**|0.828**|
||(11.19)|(10.61)|
|KwaZulu<br>19.2%|-0.079**|-0.091**|
||(3.58)|(3.73)|
|Mpumalanga<br>13.8%|0.188**<br>|0.169**<br>|
||(6.58)|(5.32)|
|Northern Cape<br>2.9%|0.813**|0.828**|
||(14.76)|(14.13)|
|Northern Province<br>18.2%|-0.019<br>|-0.003<br>|
||(0.76)|(0.13)|
|Northwestern Province<br>14.8%|-0.028|0.013|
||(0.96)|(0.40)|
|Western Cape<br>3.5%|0.790**|0.788**|
||(17.73)|(16.07)|
|Household has electricity<br>28.9%||0.147**|
|||(7.73)|
|Household owns cattle<br>17.5%||0.145**|
|||(6.14)|
|Household owns sheep or goatt<br>16.4%||0.005|
|||(0.22)|
|Houeshold owns poultry<br>28.3%||0.054**|
|||(2.64)|
|Household owns pigs<br>8.5%||0.015|
|||(0.54)|
|Pseudo R'|0.4645|0.4914|
|Log-likelihood|-1285.73|-1220.33|
|No. of observations<br>4256|4256|4255|
|Absolute value of robust z-statistics in parentheses|||

* significant at 5%; ** significant at 1%; + significant at 10% Source: Land reform M&E survey plus SALDRU sample for non-whites in rural areas only.
other hand, some interesting innovations have also being added, such as the implementation of a community support component.

These differences in design and implementation seem to have translated into differences in impacts. Impact can be examined through different lenses; perhaps the most simple and comprehensive way to begin with is to analyze beneficiary families’ accomplishment of the minimum welfare conditions defined by the interventions. For this purpose, we present in Table 4 the achievements under _Red Unidos_ and _Chile Solidario_ , using data on the percentages of households with goals achieved at baseline and five years later for _Red Unidos_ (which initially was the time period thought for the family support component), and two years later for _Chile Solidario_ (at the end of the _Programa Puente_ ). For comparability purposes and to have a longer span of time to see changes, we only consider the early cohort of households when both interventions started (i.e. the 2003 cohort for _Chile Solidario_ , and the 2009 cohort for _Red Unidos_ ). Thus, the following indicators track performance at the beginning of implementation, and may miss improvements in performance that could have happened during the implementation through learning and as a result of changes. Also, the indicators do not allow for understanding _when_ improvements happened. For _Red Unidos_ in particular, changes could have happened at the beginning or at the end of the five year period (or gradually) – but our data are not able to capture it. Nevertheless, they provide an interesting insight on how the initial design we have reviewed may have influenced outcomes. Please note that the indicators in Table 4 only refer to the subgroup of the beneficiary population for which the indicators have relevance.

Monitoring indicators suggest that in both interventions, beneficiaries’ social indicators progressed substantially (Table 4). After two years of treatment, most indicators of _Chile Solidario_ were met by more than 90 percent of its beneficiaries, from a baseline of 78 percent, even for dimensions that started from a relatively low baseline. The indicator that started with the lowest baseline (families with income above the extreme poverty line) shows a dramatic improvement, from 33 percent to 86 percent. Progress was also observed in _Red Unidos_ , which, on average, as measured by the indicators, covered worse off beneficiaries: the initial cohort of beneficiaries of _Red Unidos_ achieved an average of 49 percent of the goals at baseline. Five years later, the same cohort had an average of 70 percent of goals achieved.

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