rafmacalaba/datause-encoder-data huggingface dataset:
|Observations|9,978|4,626|5,352|3,982|3,131|2,865|
|---|---|---|---|---|---|---|
|Adjusted R square|0.304|0.300|0.342|0.301|0.280|0.352|
|Entertainment|0.519***|0.394***|1.020***|0.417**|0.618***|0.036|
||(0.139)|(0.141)|(0.310)|(0.194)|(0.181)|(0.300)|
|Observations|9,955|4,616|5,339|3,967|3,126|2,862|
|Adjusted R square|0.284|0.288|0.299|0.271|0.281|0.319|
|Automobiles|0.311***|0.204*|1.234***|0.210|0.423*|0.846***|
||(0.118)|(0.106)|(0.273)|(0.135)|(0.219)|(0.295)|
|Observations|9,850|4,565|5,285|3,912|3,088|2,850|
|Adjusted R square|0.241|0.250|0.238|0.240|0.239|0.252|
|Other vehicles|0.085|0.062|0.728**|0.007|0.288|-0.061|
||(0.146)|(0.139)|(0.364)|(0.132)|(0.395)|(0.561)|
|Observations|9,940|4,614|5,326|3,960|3,119|2,861|
|Adjusted R square|0.397|0.403|0.395|0.402|0.399|0.392|
|Durable goods|0.187|0.052|0.853**|0.176|0.121|0.181|
||(0.118)|(0.110)|(0.389)|(0.121)|(0.188)|(0.929)|
|Observations|10,141|4,718|5,423|4,027|3,180|2,934|
|Adjusted R square|0.435|0.432|0.443|0.423|0.455|0.429|
|Medical|0.136|0.097|0.442**|0.040|0.291**|0.515|
||(0.089)|(0.101)|(0.195)|(0.134)|(0.142)|(0.388)|
|Observations|10,045|4,679|5,366|3,978|3,154|2,913|
|Adjusted R square|0.376|0.369|0.389|0.383|0.383|0.359|
|Health and fitness|0.183*|0.055|0.345*|0.118|0.179|0.383|
||(0.098)|(0.093)|(0.195)|(0.116)|(0.163)|(0.307)|
|Observations|10,144|4,722|5,422|4,040|3,179|2,925|
|Adjusted R square|0.309|0.293|0.372|0.304|0.330|0.300|
|Education|-0.142|-0.131|0.174|-0.231***|0.035|-0.089|
||(0.114)|(0.126)|(0.274)|(0.086)|(0.196)|(0.358)|
|Observations|10,081|4,695|5,386|4,007|3,165|2,909|
|Adjusted R square|0.221|0.231|0.220|0.226|0.217|0.236|
|Home repair|-0.124|-0.156*|0.033|-0.114|-0.263|-0.213|
||(0.087)|(0.091)|(0.342)|(0.081)|(0.162)|(0.660)|
|Observations|9,975|4,633|5,342|3,975|3,134|2,866|
|Adjusted R square|0.452|0.434|0.471|0.483|0.455|0.407|
|Gifts|-0.062|-0.054|0.227|-0.115|0.053|0.011|
||(0.100)|(0.098)|(0.275)|(0.134)|(0.133)|(0.608)|
|Observations|8,279|3,870|4,409|3,214|2,623|2,442|
|Adjusted R square|0.111|0.108|0.118|0.078|0.122|0.165|

Note: Alternative extended model, with additional variables of control, including the lagged household per capita income, legged log of household expenditure per capita of the consumption category in question, household average years of schooling household gender ratio, average age, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 40
Table 5.7: Gini Indices for Haiti and its Regions, 2001

|Region|Metropolitan|Urban|Rural|Total Haiti|
|---|---|---|---|---|
|Artibonite|NA|78.8|64.4|69.8|
|Center|NA|51.5|57.0|55.6|
|Grand-Anse|NA|59.2|58.5|58.8|
|North|NA|66.3|55.5|66.1|
|Northeast|NA|65.1|72.4|69.7|
|Northwest|NA|53.9|55.5|55.8|
|West|61.4|57.7|54.8|63.7|
|South|NA|56.1|55.7|56.9|
|Southeast|NA|58.5|51.2|53.8|
|Haiti|61.4|66.7|58.9|66.2|

_Source:_ Calculations based on HLCS 2001.

<!-- Start of picture text --> Figure 5.4: Gini Indices for Haiti and its Regions, 2001<br>90<br>80 Metropolitan<br>Urban<br>70 Rural<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Source: Calculations based on HLCS 2001.<br>Artibonite CenterGrand-Anse North Northeast Northwest West South Southeast Haiti<br>Indices<br><!-- End of picture text --> Changes in inequality are typically very slow, except during periods of radical social and institutional change. Where inequality has fallen it has usually happened in association with a substantial expansion and equalization of educational attainment, as in Korea and Malaysia in the 1970s and 1980s. Haiti’s expansion in education (a reduction in educational inequalities) has thus far been too small to have a significant effect on skills composition.

28
<!-- Start of picture text --> 19. In the last ten years,<br>difficulties in dealing with - 0.5<br>government officials have<br>g 0.4<br>increased Os<br>remained O<br>about the same C1<br>decreased<br>don'tknow DX 0.2 -<br>g 0.1<br>0.0<br>t -0.1<br>World LDC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text --> The results shows that in all regions (with exception of transition economies) businessmen consider their relations with government officials less difficult than 10 years ago. The highest decrease in those difficulties was noted in Asia and in developed countries. The situation was the worse in the CIS.

<!-- Start of picture text --> In 60 percent of the surveyed 70 -<br>countries the majority of<br>entrepreneurs thought that it 60-<br>is now easier to deal with<br>their governments than it 50 l<br>was 10 (5 years for<br>transitional economies) years 40<br>ago. The percentage of 3<br>countries where businessmen 30<br>think that the difficulties<br>decreased is highest in Asia 20-<br>(100 percent) followed by 1 i<br>developed countries (73 10<br>percent), and MNA (two out 2 IlJ A<br>of tree countries surveyed). o<br>In CEE only two out of World LDC DC SSEA MNA CEE LAC SSA CIS<br>eleven surveyed countries | numiber ofcountries where difficulties decreased orrernaied the same number of surveyed countries<br>found that difficulties in<br>dealing with their<br>government decreased over the last 5 years.<br><!-- End of picture text -->

35
of-province family migration in both stipend and non-stipend districts is rather uncommon, particularly in rural areas. Instead, most migration is in the form of temporary out-migration of a member of the household — often of working men. It is very unlikely that this migration was affected by the stipend program. Finally, some simple econometric models give no evidence that the FSSP affect either type of migration after controlling for most of the covariates used in the analysis.

A final compositional issue could arise if the conditional stipend motivates households located in non-stipend districts but adjacent to stipend districts to send their girls to schools there. This would increase the demand for education in stipend districts but reduce it in non-stipend districts, leading to no changes in net enrollment across the province. In order to check the possibility of crossover effects between contiguous stipend and non-stipend districts, econometric models of educational outcomes were estimated on a subsample of schools and households located in treatment and comparison districts that do not have common borders.<sup>39</sup> The direction, magnitude, and significance of the findings using these samples are for the most part consistent with the results obtained with the full sample, with the exception of some models that employ school level data for which the findings are qualitatively similar but not statistically significant.

A natural issue for a program like the FSSP focused exclusively to public schools is families could have moved their daughters who were previously enrolled in private schools to public schools in order to become eligible for the stipend. As a result, the observed increase in female enrollment in the public system may be offset (partially or completely) by a fall in enrollment in private schools, hence reducing the overall effectiveness of the program. Nevertheless, the main set of results of this paper uses household-level data and defines progression and completion irrespective of the type of school in which the girl is enrolled. Therefore, the DD and RDD models using these data provide estimates of the net impacts of the FSSP that already account for the possible crowding out effect induced by the program.<sup>40</sup>

> 39 The sample size of these models is reduced in some cases by more than 50 percent.

> 40 At baseline, there were no statistically significant differences between stipend and non stipend districts in the proportion of girls switching from public to private vs. private to public at the middle school level

28
- SO2 Sulfur dioxide (SO2) is a colorless gas with a sharp odor Health: Hospital admissions and emergency that is readily soluble in water. It is predominantly room visits linked to asthma and other derived from the combustion of fossil fuels such as coal. respiratory conditions. <u>Climate: Acid rain</u>

- BC Black carbon (BC) is a major component of PM2.5 and Health: Short- and long-term exposure to BC is it is sometimes referred to as soot. Its main sources are associated with cardiovascular health effects from incomplete combustion of fossil fuels, biofuels, and premature mortality. <u>and biomass.</u>

- CO2 Carbon dioxide (CO2) is the primary source of manClimate: Global warming made GHG emitted mainly from the combustion of <u>fossil fuels.</u>

Source: (WHO, 2021) The compilation of normalized average emission factors for brick kilns in South Asia and Nepal is summarized in Tables A2.7 – A2.9.

**Table A2.7** : Normalized average production-based emission factors for brick kilns (g/kg of fired brick)

|||Particu|late Matt|er||Flue gas|pollutant|||
|---|---|---|---|---|---|---|---|---|---|
|Kiln type|Data source|SPM|PM10|PM2.5|BC|SO2|NOx|CO|CO2|
|CK|2|1.60|1.60|0.97|0.290|3.60|0.07|-|-|
||4|-|-|0.64|0.001|1.05|-|6.50|-|
|DDK|1|1.56|-|0.97|0.290|n.d.|-|5.78|282|
||3|-|-|0.49|0.190|-|-|13.20|-|
||6|-|-|-|0.192|<0.001|-|13.20|271|
||8|1.56|-|-|-|<0.001|-|5.01|526|
|FCBTK|1|0.86|-|0.18|0.130|0.66|-|2.25|115|
||2|2.20|0.36|0.21|0.170|3.60|0.07|-|-|
||3|-|-|0.18|0.157|-|-|2.03|-|
||4<sup>a</sup>|-|-|0.30|0.100|2.80|-|1.20|-|
||5|0.86|-|0.18|0.130|0.66|-|2.25|115|
||6|-|-|-|0.151|0.65|-|2.23|124|
||7|1.18|-|-|0.130|-|-|2.00|131|
||8|0.89|-|-|-|-|-|3.63|179|
||9|-|-|0.20|0.003|1.2|-|-|96|
|ZZK ND|1|0.26|-|0.13|0.040|0.32|-|1.47|103|
||2|0.31|0.20|0.12|0.035|0.88|0.06|-|-|
||3|-|-|0.13|0.020|-|-|0.60|-|
||4<sup>b</sup>|-|-|0.18|0.030|1.70|-|1.30|-|
||5|0.26|-|0.13|0.040|0.32|-|1.47|103|
||6|-|-|-|0.010|0.13|-|0.96|103|
||7|0.22|-|-|0.010|-|-|0.29|105|
||8|022||||006||035|119|
|||.|-|-|-|.|-|.||
|ZZK FD|3|-|-|0.05|0.011|-|-|1.23|-|
||4<sup>a</sup>|-|-|0.24|0.030|1.70|-|2.20|-|
||7|0.24|-|-|0.020|-|-|1.62|98|
||8|0.24|-|-|-|0.24|-|2.04|96|
||9|-|-|010|010|090|-|-|82|
|||||.|.|.||||
|VSBK|1|0.11|-|0.09|0.002|0.54|-|1.84|70|
||2|0.31|0.18|0.11|0.002|0.54|-||-|
||3|-|-|0.07|0.002||-|1.95|-|
||5|0.11|-|0.09|0.002|0.54|-|1.84|70|
||6|-|-|-|0.002|0.59|-|2.19|69|
||7|0.15|-|-|0.001||-|1.84|71|

50
14 Figure 1: Indexes of real international prices of rice, wheat, maize and groundnut oil, 197276 (1972 = 100) and 2006-10 (2006 = 100)

# (a) Rice

<!-- Start of picture text --> 400<br>350<br>1972‐1976<br>300 2006‐2010<br>250<br>200<br>150<br>100<br><!-- End of picture text -->

# (b) Wheat

<!-- Start of picture text --> 400<br>350<br>1972‐1976<br>300<br>2006‐2010<br>250<br>200<br>150<br>100<br><!-- End of picture text -->
# PPR Working Paner Series

||iwh|Auhor<br>|Dfor|Contact<br>paper|
|---|---|---|---|---|
|WPS223|Overvalued and Undervalued<br>Exchange Rates in an Equilibrium<br>Optimizing Model|Jose Saul Lizondo|August 1989|R. Luz<br>61588|
|WPS224|The Economics of the Government<br>Budget Constraint|Stanley Fischer|May 1989|S. Fischer<br>33774|
|WPS225|Targeting Assistance to the Poor:<br>Using Household Survey Data|Paul Glewwe<br>Oussama Kanaan|June 1989|B. Rosa<br>33751|
|WPS226|Inflation and the Costs of<br>Stabilization: Cout,try Experiences,<br>Conceptual Issues, and Policy<br>Lessons|Andres Solimano<br>|July 1989|E. Khine<br>61763|
|WPS227|Institutional Reforms in Sector<br>Adjustment Operations|Samuel Paul|July 1989|E. Madrona<br>61712|
|WPS228|Recent Economic Performance of<br>Developing Countries|Robert Lynn<br>F. Desmond McCarthy|July 1989|M. Divino<br>33739|
|WPS229|The Effect of Demographic Change<br>on Saving for Life-Cycle Motives<br>in Developing Countries|s Steven B. Webb<br>Heidi S. Zia|July 1989|E. Khine<br>61765|
|WPS230|Unemployment, Migration, and<br>Wages in Turkey, 1962-85|Bent Hansen|July 1989|J. Timmins<br>39248|
|WPS231|The World Bank Revised Minimum<br>Standard Model: Concepts and<br>Issues|Doug Addison|May 1989|J. Onwuemene-<br>Kocha<br>61750|
|WPS232|Women and Food Security in Kenya|Nadine R. Horenstein|June 1989|M. Villar<br>33752|
|WPS233|Public Enterprise Reform in<br>Adjustment Lending|John Nellis|August 1989|R. Malcolm<br>61708|
|WPS234|A Consistency Framework<br>Macroeconomic Analysis|William Easterly|June 1989|R. Luz<br>61760|
|WPS235|Borrowing, Resource Transfers,<br>and External Shocks to Developing<br>Countries: Historical and<br>Counterfactual|Steven B. Webb<br>Heidi S. Zia|July 1989|E. Khine<br>61765|
|WPS236|Education and Earnings in Peru's<br>Informal Nonfarm Family Enterprises|Peter Moock<br>Philip Musgrove<br>Morton Stelcner|July 1989|M. Fisher<br>34819|
**Table** la: **Poverty, Inequality and Unemployment in South Africa 1995**

|Indicator|Share Pct|Number of people|
|---|---|---|
|Poverty Rate Total|49.9|19 700,000|
|Poverty rate in non-urban areas|70.9|13 700,000|
|Poverty rate in urban areas|28.5|6,000,000|
|Poverty share of non-urban areas|71.6||
|African poverty rate|60.7|18 300,000|
|White poverty rate|1|44,000|
|Unemployment Rate|29.3|4 250,000|
|Income share of poorest 40% of households|11||
|National Gini coefficient|0.52||

**Table lb: Provincial Distribution of Poverty 1995**

|Province|Population|% Hhds living in<br>poverty|% Ind. Living in<br>poverty|Poverty gap<br>R million|Poverty Gap as a<br>% of GGP|% of Population<br>Non-Urban|
|---|---|---|---|---|---|---|
|Eastern Cape|6,302,526|<br>40.4|50.0|3303|11.4|63.4|
|Free State|6,633,504|<br>56.8|64.0|3716|15.7|31.4|
|Gauteng|7,348,423|<br>29.7|41.0|917|0.6|3.0|
|KwaZulu-Natal|8,417,021|<br>36.1|47.1|1159|_2.0_|56.9|
|Mpumulanga|2,800,712|<br>33.8|45.1|968|3.1|60.9|
|North West|3,354,825|<br>15.4|21.1|1551|7.3|65.1|
|Northern Cape|840,321|<br>38.2|48.0|257|3.2|29.9|
|Northern Province|4,929,368|<br>61.9|69.3|2948|21.4|89.0|
|Western Cape|3,956,874|<br>14.1|17.9|529|1.0|11.1|
|South Africa|40,583,574|<br>35.2|45.7|15348|4.0|46.3|

**Table 2: Expenditures on the three key RDP Programs R Million**

|Year|Pensions|Housing<br>Land Reform|
|---|---|---|
|95/96|5,417|821<br>25|
|96/97|6,049|1,453<br>104|
|97/98|6,717|2,635<br>197|
|98/99|7,382|3,019<br>407|
|99/00|6,247|2,971<br>388|
|00/01|6,564|2,905<br>499|
|01/02|6,833|3,417<br>517|
|Total|45,209|17,222<br>2,137|

Source: Derived from National Expenditure Survey and MTEF
|**Table 3. Effect of earmarked**|**relations on inter**<br>(1)|**est rate spreads**<br>(2)|**of working**<br>(3)|**capital loans (c**<br>(4)|**ross-section co**<br>(5)|**mparison)**<br>(6)|
|---|---|---|---|---|---|---|
|Earmarkfbt* Riskft|0.703***<br>(0.078)|0.677***<br>(0.174)|0.401**<br>(0.193)|0.597***<br>(0.078)|0.494***<br>(0.122)|0.202<br>(0.275)|
|Earmarkfbt|0.380**|0.300|0.143|0.472**|0.412**|0.255|
||(0.149)|(0.235)|(0.156)|(0.207)|(0.198)|(0.184)|
|Earmarkfbt* Riskft*MPft||||0.401***<br>(0.114)|0.681***<br>(0.198)|0.737**<br>(0.280)|
|Riskft* MPft||||-0.129|-0.545***|-0.185|
|||||(0.143)|(0.114)|(0.161)|
|Earmarkfbt* MPft||||-0.349<br>(0.257)|-0.439***<br>(0.103)|-0.434**<br>(0.187)|
|Observations|259,709|179,786|182,955|259,709|179,786|182,955|
|R-squared|0.716|0.802|0.837|0.716|0.802|0.837|
|Mean interest rate spread|28.10|28.04|28.81|28.10|28.04|28.81|
|Firm*Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Location*Sector*Month FE|No|Yes|No|No|Yes|No|
|Firm*Year FE|No|No|Yes|No|No|Yes|
|Notes: The table shows estimates of OLS<br>credit relationship with a bank. The depe<br>that equals one for all consecutive period<br>equals one for firms with credit risk prior<br>average rating of loans issued to firms on<br>market power in period_t_of banks operati<br>include the length of the relationship durat<br>indicating if the loan was collateralized).<br>effects for the type of loan (whether the l<br>level.|regressions on our loan<br>ndent variable correspo<br>s after a firm receives i<br>to the start of their ea<br>e year prior to the star<br>ng in firm_f_'s zip code (<br>ion of each firm-bank p<br>All regressions includ<br>oan has a fixed rate or a|-level data for the s<br>nds to the interest<br>ts first earmarked l<br>rmarked relationshi<br>t of their earmarke<br>measured by the H<br>air and loan charac<br>e fixed effects for t<br>rate indexed by B|ample of firms th<br>rate spread of wo<br>oan from a bank<br>p above the med<br>d credit relations<br>erfindahl Index)<br>teristics at the ti<br>he ten loan ratin<br>razil's CDI). Sta|at at any time in o<br>rking capital loan<br>, and zero otherwi<br>ian and zero other<br>hip._MPft_is an ind<br>is above the media<br>me of issuance (ma<br>g categories establ<br>ndard errors are do|ur sample period be<br>s._Earmarkfbt_is an i<br>se._Riskft _is an indic<br>wise. Credit risk is<br>icator variable that<br>n and zero otherwis<br>turity, volume and a<br>ished by regulators,<br>ubled clustered at t|gin an earmarked<br>ndicator variable<br>ator variable that<br>measured by the<br>equals one if the<br>e. Other controls<br>dummy variable<br>as well as fixed<br>he bank and time|

21
given the small output recovery, led to a 10/11% improvement in productivity. Variation in employment changes over branches increased in 1993 and there has continued to be large intrabranch variation. This can probably be interpreted as a measure of reallocation shocks, as structural shocks come finally to dominate aggregate effects.

Since 1993 with increasing title transformation, more recent job losses have been concentrated not only in the remaining state sector but also in privatized firms, though the bulk of layoffs in the latter appear to precede privatization. The latter outcome in part arises because privatization agreements normally include employment; hence making it difficult to fire after title change.

Despite the fact that the labor demand estimation reported in Table 2 has quite conventional properties, including wages negatively signed and significant; revenues and/or sales positively signed and significant 4, it is nevertheless clear that there are a major bloc of firms -- both state and private -- that continue to hoard labor and use this as a major bargaining chip with govermment and the banking system.

This is confinned for 1992 where a crude estimate of the total number of employees retained in chronic loss making units is of the order of 170,000 5. These loss makers -- principally the 41 huge loss makers -- likely face continuing and major employment restructuring but at present continue to attract 'soft' financing partly because -- in employment terms - they are seen as too large to fail.

Nevertheless, it is important to note that major loss-makers have already initiated large scale employment adjustments. One measure of this is that those firms now classed by the governent as strategic, reduced employment by nearly 50% between 1989 and 1993, as indicated in Table 3. This suggests two things. First, labor shedding has already occurred; restructuring now implies going beyond this. Second, the reluctance to enforce closure can partly be linked to monopsony in local labor markets. Excluding MAV (Railways), it looks as if roughly half of current employment is in firms that are in spatial terms equivalent to

> 4 Whether using CSO 3 digit data (as in Commander, Kollo, Ugaz and Vilagi 1994) or the Forecast Survey, as in Table 2.

**S** These numbers and the discussion that follows are based on balance sheet information.

7
# **Table 4.  The Impact of Conflict-Related Shocks by IPC Classification**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|ln (FCS)|ln (rCSI)|Indicator<br>for Water<br>Source<br>Being<br>Insufficient|Indicator<br>for Using a<br>Primitive<br>Energy<br>Source|Indicator<br>for No<br>Municipal<br>Garbage<br>Collection|Indicator for<br>Experiencing<br>3 or More<br>Deprivations|
|Post Blockade Indicator|-0.031|0.176**|0.048|0.009|0.023|0.065**|
||[0.021]|[0.072]|[0.028]|[0.014]|[0.024]|[0.027]|
|Interaction Between IPC Status and the Post<br>Announcement Indicator|||||||
|____________________<br>Post Announcement Indicator*IPC 4 Indicator|-0.014|-0.009|0.033|0.064**|0.062|0.078*|
||[0.043]|[0.111]|[0.037]|[0.029]|[0.058]|[0.045]|
|Observations|2,422|2,422|2,422|2,422|2,422|2,422|

Note: This table estimates how households were impacted by the complete air and sea blockade that began during November 2017. The post blockade indicator is an indicator equaling one if the household was interviewed after November 6, 2017, which was the day the blockade began. All specifications include governorate fixed effects. Standard errors are clustered by governorate and reported in parentheses. The data used in these specifications is the household-level data from the November 2017 mobile Vulnerability and Analysis Mapping Survey. * denotes significance at the 1 percent level; ** denotes significance at the 5 percent <u>level; and *** denotes significance at the 1 percent level.</u> 24
# **7. Conclusions and recommendations**

The comparison of _Chile Solidario_ and _Red Unidos_ , two interventions with similar objectives, reveals differences in impacts that can be traced both to differences in design and implementation and to how the overall institutional and social environment has interacted with the intervention. We conclude by reviewing some of these factors.

**Social intermediation services are effective tools for reaching the extreme and chronic poor.** Evidence consistently shows that the extreme and chronic poor exclude themselves from social life – even from programs aimed at supporting them. There is therefore a need to go beyond the classical “passive” social assistance approach, where coverage is often dictated by budgetary considerations and poverty status, and the assumption that the poor will successfully seek out and enroll in social programs, and to move towards “active” approaches that seek out the poor and assist them to identify their goals and efficiently use the social protection system to achieve those goals. In that view, social intermediation services are effective tools for reaching the chronic poor and ensure they benefit from social assistance appropriate to each family’s specific needs. The analysis of _Chile Solidario_ shows that, in two years, such an intervention was able to substantially improve the well-being of its beneficiaries along all dimensions. After two years of treatment, most indicators were met by more than 90 percent of its beneficiaries, even for dimensions that started from a relatively low baseline. Moreover, impact evaluations from _Chile Solidario_ show that impacts can go beyond access. The implementation of the family support service in addition to cash transfers can help some beneficiaries to gain enough skills and confidence to permanently escape poverty on their own. The _Red Unidos_ experience also shows that even simple outreach and planning for welfare enhancing goals increases information and affects less tangible dimensions underlying the poor’s ability to integrate into the social protection system, namely behavioral factors.

## **Social intermediation services have to be tailored to local capacity and conditions.**

Social intermediation services do not need to be as elaborate as _Chile Solidario_ or _Red Unidos_ to lead to positive effects. Proactively identifying, seeking and registering the extreme poor into social programs can be done – at least to some extent – without the use of sophisticated family support components that have the more ambitious objective of helping families to overcome behavioral barriers. Achieving such an objective requires a 39