rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> In low- and middle-income counmies, the domestic<br>financial sector is now as liberalized as it was in high-<br>income counties in the early 1990s.<br>1.0<br>08 Domestic financial sector<br>= 03<br>x as<br>5 0.6 a ~<br>a 0 4 * . ai’<br>O17 »! “Tees »<br>pee ee<br>0.0<br>an)Pec=: Seewy =FRRt —)EE~<br>= = = = = = = = = el a<br>Year<br>Tiberalization oftelecoms and electricity started in the<br>1990s for most income groups.<br>1.0<br>Telecoms and electricity<br>09<br>= 08<br>307<br>=<br>: 0.6<br>204 ’<br>z 03 —<br>S02 4<br>wer<br>0.0 iS See pms rt Fo ia<br>ry r= x ~ ny 4 = > I-- = ry<br>&= &= &= &= &= F= F= FRE= = = SBra<br>Year<br><!-- End of picture text -->
**Table 1: Sample statistics by farmers’ gender in Uganda**

||Male-h<br>house|eaded<br>holds<br>|Female<br>hous|-headed<br>eholds<br>|<br>hous|All<br>eholds<br>|
|---|---|---|---|---|---|---|
||mean|std. dev.|mean|std. dev.|mean|std. dev.|
|Maize yield (tons/ha)|1.22|0.79|1.14|0.73|1.20|0.77|
|Maize area (combined all seasons in ha)|4.27|3.32|3.29|2.74|3.99|3.20|
|Share of agricultural output sold, by value|0.26|0.29|0.21|0.26|0.25|0.28|
|Extension officer visited|0.24|0.43|0.19|0.40|0.23|0.42|
|Fertilizer use (kg/ha)|0.38|2.40|0.16|1.07|0.32|2.12|
|Manure use (kg/ha)|28.70|204.28|6.11|34.89|22.31|174.27|
|Family labor (days/ha)|65.17|66.35|82.08|136.89|69.95|92.22|
|Hired labor (days/ha)|4.88|9.48|5.05|9.35|4.93|9.44|
|Wealth Index (Principal Component)|-0.33|1.21|-0.58|1.23|-0.40|1.22|
|Wage or business income ($US)|0.82|2.73|0.19|0.63|0.64|2.35|
|Share of maize area in pure stands|0.54|0.33|0.56|0.33|0.55|0.33|
|Maize area/ plots managed|1.02|0.87|0.83|0.65|0.96|0.82|
|Number of crops managed|5.01|1.88|4.88|1.76|4.97|1.85|
|Age of household head|45.99|14.45|51.76|14.49|47.62|14.69|
|<br>Family members, ages 14-60 per ha|1.35|2.09|1.14|1.63|1.29|1.98|
|Population density (people/sq. meter)|317.28|206.92|336.67|206.68|322.81|206.97|
|Difference from average rainfall (mm)|87.30|108.13|89.81|114.36|88.01|109.89|
|Late start for season 1 rains (weeks)|0.67|3.81|0.44|3.82|0.61|3.82|
|Late start for season 2 rains (weeks)|1.03|1.94|1.05|1.88|1.04|1.92|
|Market participation (share of households)|||||||
|Output sold|0.67|0.47|0.62|0.49|0.66|0.47|
|Fertilizer used|0.06|0.24|0.04|0.20|0.06|0.23|
|Improved seeds used|0.30|0.46|0.22|0.41|0.28|0.45|
|Manure used|0.14|0.35|0.09|0.29|0.13|0.33|
|Labor hired|0.58|0.49|0.53|0.50|0.56|0.50|

Source: LSMS-ISA Uganda, 2009-2010.

16
# **4 Results**

We first look at the overall impact of the policies put in place to limit mobility on daily crashes (Table 1). During the initial curfew period, there is a significant overall decrease of 1.04 crashes with a fatality or injury per day (Column 1). This is over a mean of 3.27 crashes per day, which represents a 32% decrease. This effect includes the impact of the curfew but also other policies that limited mobility such as the closing of schools, bars and restaurants, and the work from home mandate for government workers and others.

**Table 1:** Impact of Mobility Reducing Policies on Daily Crashes

|||All Hours||Only Curfew Hours|
|---|---|---|---|---|
||OLS<br>(1)|Poisson<br>(2)|NegBi<br>(3)|OLS<br>(4)|
|Curfew Period (Post)|1.888|0.566*|0.565*|0.947*|
||(1.340)|(0.335)|(0.336)|(0.519)|
|_I_2020 (2020)|1.397***<br>(0.484)|0.442***<br>(0.142)|0.445***<br>(0.142)|0.585***<br>(0.194)|
|Curfew Period (Post) x 2020|-1.041**<br>(0.480)|-0.340**<br>(0.141)|-0.348**<br>(0.141)|-1.166***<br>(0.221)|
|Constant|-149.844***<br>(52.882)|-46.290***<br>(15.033)|-46.784***<br>(15.076)|-31.433<br>(25.185)|
|Observations|295|295|295|295|
|R<sup>2</sup>|0.172|.|.|0.198|

_Notes_ . Robust standard errors are reported in parentheses. *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include fixed effects for week and day of the week, and controls for weather (temperature, precipitation and two measures of wind).

Given the count nature of our outcome of interest, we also estimate both Poisson and negative binomial models of crashes (Columns 2 and 3). The results similarly show large and significant reductions in crashes of around 34%, which are comparable to the magnitude from the OLS estimate. We reject the null of no over-dispersion of the dependent variable (p=.057); therefore, the model is more correctly estimated using the negative binomial compared to the Poisson. Similar to Green et al. (2016), we compare the mean squared residuals for both the negative binomial and the linear specification to determine whether to continue with the negative binomial. We find the results are very similar, but slightly better for the OLS specification; therefore, we continue with the OLS specification for the rest of the paper.

We look specifically at the curfew hours interval, from 20:00 until 5:00. Crashes decrease by 1.17 crashes per day during the curfew (Table 1 Column 4), which is around a 100% reduction in crashes. This reduction aligns with the raw data in Figure 2, which shows weekly crashes going almost down to zero during the curfew period. With almost no cars on the road (Figure 1a), this large reduction in crashes is to be expected.

Overall, COVID policies led to a decrease in crashes, particularly during curfew hours; however, curfews can cause spillovers—affecting the number of crashes during non-curfew hours. To examine spillovers, we estimate the impacts 10
30 policy is complex and not easily amenable<sup>to broad, internationally comparable</sup> macroeconomic indicators.

The recent endogenous growth literature<sup>has created a new class of</sup> models in which fiscal policy can have long-run<sup>steady-state growth effects.</sup> The ideas behind these models, however, h<sup>'e been around for a very long time.</sup> Governments can accelerate growth by providing<sup>essential public goods, and</sup> well-designed taxes and subsidies can close<sup>the gap between private and social</sup> costs. On the other hand, government funds<sup>may be spent on activities for</sup> which there is not a clear role for the government.<sup>Thus, broad measures of</sup> government expenditures may not appropriately<sup>measure the provision of growth-</sup> inducing social services. Furthermore, even<sup>if one obtains more disaggregated</sup> data on government expenditures, funds may<sup>be spent effectively or</sup> ineffectively, so that using simple expenditure<sup>data without accounting for</sup> government efficiency may be very misleading.<sup>Similarly, it is very difficult</sup> to construct meaningful measures of something<sup>as complex as a country's "tax</sup> system" while appropriately considering international<sup>differences in the</sup> structure of taxes and the size of the tax<sup>base.</sup> Even putting aside the differential growth<sup>effects of different types of</sup> government expenditures and the differential<sup>growth effects of different types</sup> of taxes, there may be complex tradeoffs<sup>between the beneficial effects of</sup> government services and the deleterious effects<sup>of distortionary taxes. In</sup> Barro (1990) and Easterly (1990), growth increases<sup>with taxation and</sup> expenditures at low levels and then decrease<sup>as the distortionary effects of</sup> taxation exceed the beneficial effects of public<sup>goods. Government</sup> expenditures and growth are positively correlated<sup>when government expenditures</sup> are below the optimum amount, negatively<sup>related when they are above, and</sup>
<!-- Start of picture text --> 2000 0.70%<br>1500 0.60%<br>0.50%<br>ca 1000 a<br>a) U)<br>2 0.40% 5<br>Cc 500 -<br>2 030%<br>= es o Oo<br>0.20% TM<br>“500 0.10%<br>-1000 0.00%<br>2013 2014 2015 2016<br>mam QFD from collection losses mami QFD from T&D losses<br>mmm QFD from underpricing —@®—Total QFD as% of GDP<br><!-- End of picture text -->
variance is connected to 12 basis points increase in household saving.

Next, we go back to the concern of livestock ownership being associated with income needs and not necessarily with precautionary motive. Like before, we exclude all households that report some direct livestock activity as one of their three main sources of income and focus on the smaller sample of households. Table 9 shows the results of the exercise with the smaller sample and we find that our results are robust to the exclusion. In fact, the point estimates that we obtain for the smaller sample are higher compared to what we find for the benchmark sample. A 1 percent increase in the variance of income is associated with a 2.8 – 3.8 percent increase in the household wealth accumulated in the form of gold, silver, and livestock compared to a 2.3 – 3.4 percent increase seen in the baseline sample. We also detect an increase in the point estimates for the broader measure of wealth. Table 10 further shows that the previous results on the jewelry and the livestock accumulation separately are also preserved under this specification.

As the last robustness check for the results on precautionary wealth, we exclude households who reside in Helmand and Kandahar which are the two most conflict-prone provinces in the country. Our motivation behind this exercise is to check if the results are driven by the extreme nature of the conflict seen in the two provinces or if the behavior is prevalent in other provinces also. Table 11 reports the outcome after we drop Helmand and Kandahar. The wealth measures still respond to changes in income uncertainty. The point estimates also survive a major correction and remain close to the benchmark case. A 1 percent increase in the variance of income is associated with a 2.4–3.1 percent increase in the accumulation of the narrow measure and a 0.4 – 0.5 percent higher accumulation of the broader measure of wealth. We also find that further decomposing the narrow measure to its constituents yields similar results as those obtained earlier as seen in table 12.

# **5 Conclusion**

Using data from a comprehensive household level survey in Afghanistan, we examine the association between income uncertainty and wealth accumulation. Our analysis yields many expected results. First, we find that the stock of household wealth increases systematically with increasing income uncertainty after controlling for other factors including the level of household income. The elasticity of jewelry and livestock accumulation is particularly high. Second, we also find that households with higher uncertainty also spend less at similar levels of income. Third, the precautionary behavior is most active for the poorest households and decreases as income rises. This last finding suggests that poor households have much lower access to borrowing channels, either formal or informal, which makes self-insurance the dominant instrument of coping with 17
25

- Jensen, J., T. Rutherford and D. Tarr. “The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization.” _Review of Development Economics_ 10(2006) (forthcoming).

- Kee, H.L., A. Nicita and M. Olarreaga. “Estimating Trade Restrictiveness Indices.” Policy Research Working Paper 3840, World Bank, Washington DC, February 2006.

- Krueger, A.O., M. Schiff and A. Valdes. “Agricultural Incentives in Developing Countries: Measuring the Effect of Sectoral and Economy-wide Policies.” _World Bank Economic Review_ 2(1988): 255-72.

- Lerner, A. P. “The Symmetry Between Export and Import Taxes.” _Economica_ 3(1936): 306-13.

- Lin, Y.F., R. Tao, M. Liu and Q. Zhang. “Rural Direct Taxation and Government Regulation in China: Economic Analysis and Policy Implications.” Working Paper, China Center for Economic Research, Beijing University, August 2002.

- Lindert, P. “Historical Patterns of Agricultural Protection.” In _Agriculture and the State_ , edited by P. Timmer, Ithaca: Cornell University Press, 1991.

- Lopez, R. and G.I. Galinato. “Why Governments Should Stop Subsidies to Private Goods: Evidence from Rural Latin America.” _Journal of Public Economics_ 91 (forthcoming 2007).

- Martin, W. and K. Anderson. “The Doha Agenda Negotiations on Agriculture.” _American Journal of Agricultural Economics_ 88 (2006): December.

- Melitz, M.J.  “The Impact of Trade on Intra-Industry Reallocations and Aggregate industry productivity.” _Econometrica_ 71(6): 1695-1725, November.
# **Table 8a: Predicted change in the probability of selecting each animal from uniform** **<u>climate scenarios for large farms</u>**

||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|
|Baseline probability|24.5%|33.4%|15.1%|20.8%|6.1%|
|Increase temp 2.5°C|-4.6%|4.5%|-1.5%|3.4%|-1.8%|
|Increase temp 5°C|-10.0%|8.7%|-3.8%|8.7%|-3.5%|
|Decrease rain 15%|-1.2%|1.2%|0.1%|0.5%|-0.6%|
|Increase rain 15%|0.6%|-1.0%|0.3%|-0.6%|0.7%|

# **Table 8b: Predicted change in net income per animal from uniform climate scenarios for large farms** **<u>(US$)</u>**

||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|
|<br>Baseline income|146.01|120.18|6.04|12.08|1.68|
|Increase temp 2.5°C|-15.30|-3.75|-1.54|-5.19|-0.43|
|Increase temp 5°C|-28.11|-21.10|-2.48|-8.29|-0.87|
|Decrease rain 15%|12.19|-1.31|0.39|1.41|-0.14|
|Increase rain 15%|-4.60|9.01|-0.25|-0.59|0.21|

**Table 8c: Predicted change in number of each animal per farm from uniform climate** **<u>scenarios for large farms (animals/household)</u>**

||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|
|<br>Baseline number|77.58|35.82|23.81|49.40|1907.25|
|Increase temp 2.5°C|-3.64|1.26|6.85|-1.27|-178.14|
|Increase temp 5°C|-13.91|1.31|15.96|-4.91|-369.30|
|Decrease rain 15%|4.86|0.36|0.76|5.75|47.37|
|Increase rain 15%|-2.20|-0.54|-1.20|-2.69|-10.11|

**Table 8d. Predicted change in expected income from uniform climate scenarios for large farms** **<u>(US$)</u>**

||**Mean**<br>**(US$/farm)**|**Total**<br>**(billions US$)**|**% change**|**Bootstrap**<br>**lower 95 %**|**Bootstrap**<br>**upper 95%**|
|---|---|---|---|---|---|
|Expected income|7826|60||||
|Increase temp 2.5°C|-2033|-16|-26.0%|-2504|-1617|
|Increase temp 5°C|-5206|-40|-66.5%|-6843|-4664|
|Decrease rain 15%|160|1|2.0%|121|199|
|Increase rain 15%|-152|-1|-1.9%|-189|-77|

32
<!-- Start of picture text --> 40<br>Chart 5c: Mathematics: Coefficients component of the test score<br>35 gap between students in large vs. small communities - 2003 vs.<br>2007<br>30<br>L<br>o 25<br>c<br>a 20<br>t Math-2007<br>i 15<br>Math-2003<br>o<br>10<br>n<br>5<br>G<br>a 0<br>p 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>Percentile<br>70<br>Chart 6a: Science: Test score gap at percentiles between students<br>in large vs. small communities - 2003 vs. 2007<br>60<br>L<br>50<br>o<br>c 40<br>a<br>t<br>30 Science-2007<br>i<br>Science-2003<br>o 20<br>n<br>10<br>G<br>a 0<br>p 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>Percentile<br><!-- End of picture text --> 20
For one macro-region, O’Higgins, we did not have even one meteorological station, so we assumed that precipitation there was the average between the region immediately north of it (Metropolitan Region) and the region immediately south of it (Maule).

# **Table 3: Rules used to infer precipitation in municipalities for which precipitation data was not available in Chile**

|**Macro-region**|**Available**<br>**meteorological**<br>**stations**|**Average annual**<br>**precipitation**<br>**for station**<br>**(mm)**|**Average annual**<br>**precipitation for**<br>**macro-region (mm)**|
|---|---|---|---|
|1. Tarapacá|- Arica<br>- Iquique|0.9<br>2.1|1.5|
|2. Antofagasta|- Antofagasta|3.5|3.5|
|3. Atacama|- Caldera|29.3|32.3|
||- Copiapo|11.4||
||- Vallenar|56.1||
|4. Coquimbo|- La Serena|119.0|119.0|
|5. Valparaíso|- Quintero<br>- Valparaíso|329.0<br>372.5|350.8|
|0. Metropolitan Region|- Santiago|312.5|325.4|
||- Pudahual|338.2||
|6. O’Higgins|||523.6|
|7. Maule|- Curicó|721.7|721.7|
|8. Bío Bío|- Concepción|1276.4|1276.4|
|9. Araucanía|- Temuco|1152.7|1152.7|
|10. Los Lagos|- Valdivia<br>- Osorno|2445.3<br>1436.9|1800.0|
||- Puerto Montt<br>- Quellón|1972.8<br>1344.8||
|11. Aisén|- Aisén|2826.1|2826.1 (West)|
||- Coihaique|596.1|596.1 (East)|
|12. Magallanes<br>_ce:_Authors’ estimation based o|- PuntaArenas<br>n data from www.worldcl|397.4<br>imate.org<br> .|397.4|

_Source:_ Authors’ estimation based on data from <u>www.worldclimate.org</u> . _Note:_ Island stations excluded due to dramatically different precipitation patterns than in mainland Chile.

In order to assess the climate change trends in the different parts of Chile, we obtained monthly temperature and precipitation data from 1948 to 2008 from the Monthly Climatic Data for the World (MCDW) publication of the US National Climatic Data Center.<sup>2</sup> Section 4 below contains a more detailed description and analysis of this data.

> 2 This data is available for free at <u>http://www7.ncdc.noaa.gov/IPS/mcdw/mcdw.html</u> (although in a very inconvenient format).

6
Proposition 1. Cross-region transfer impact multipliers in the rigid-price/wage NK model and NC models are given by:

|(a) _M_<sup>_NK_</sup><br>_Tr_ <sup>=</sup><br>_α_<br>1_−α_<br>�<br>��<br>�<br>_×_1_×_ <sup>1</sup><sup>_−β_</sup><br>1_−βρ_<br>�<br>��<br>�<br>+|_α_<br>1_−αω_<br>�<br>��<br>�|_× ω ×_[1_−_<sup>1</sup><sup>_−β_</sup><br>1_−βρ_<sup>]</sup><br>�<br>��<br>�|
|---|---|---|
|_Local GE_(_perm_)<br><br>_MPC_(_perm_)|_Local GE_(_temp_)|_MPC_(_temp_)|
|(b)_M_<sup>_NC_</sup><br>_Tr_ <sup>=</sup><br>_−_<br>1<br>1 +_φ_<br>�<br>��<br>�<br>_×_1_×_ <sup>1</sup><sup>_−β_</sup><br>1_−βρ_<br>�<br>��<br>�<br>+|0||
|_Local GE_(_perm_)<br><br>_MPC_(_perm_)|||

Proposition 1 decomposes the cross-region transfer multipliers in each model into two terms relating to the permanent and temporary components of the transfer. The permanent component of a $1 cross-region transfer payment is given by the annuity value of the transfer $(1 _− β_ ) _/_ (1 _− βρ_ ) . The temporary _−_ component is the excess of the initial $1 payment over its annuity value: $(1 (1 _− β_ ) _/_ (1 _− βρ_ )) . As the transfer shock becomes perfectly persistent ( _ρ →_ 1) , the permanent component equals unity and the temporary component is zero (and vice versa when _ρ_ = 0 for one-o payments). The cross-region transfer multipliers depend on the fraction of each component that is spent, as well as the local GE eects, which I discuss in turn.

The permanent component is spent by all households (Ricardian and handto-mouth) according to the permanent income hypothesis, yielding a MPC of 1 _×_ (1 _− β_ ) _/_ (1 _− βρ_ ) . This is the same in simple NK and NC models. The temporary component is spent only by hand-to-mouth households. These households receive a fraction _ω_ of the temporary component of the transfer $(1 _−_ (1 _− β_ ) _/_ (1 _− βρ_ )) , yielding a MPC of 1 _× ω_ [1 _−_ (1 _− β_ ) _/_ (1 _− βρ_ )] . The micro literature on the MPC (e.g. JPS) focuses on the consumption response to one-o transfers ( _ρ_ = 0) and so only identies _MPC_ ( _temp_ ) _≈ ω_ , which is only one part of the overall transfer multiplier. In NC models, _ω_ = 0 , yielding _MPC_ ( _temp_ ) _≈_ 0 , and a cross-region transfer multiplier that is trivially close to zero for one-o shocks.

Local GE eects amplify or dampen the MPC in the expressions in Proposition 1. In NK models, output is demand-determined in the short run, so local demand and output initially increase by the fraction _α_ that is spent on locally produced goods ( _α_ is consumption home bias for an atomistic region, in the numerator in the expression for local GE eects). This increase is then 32
# **Table 4b: Communes that rehabilitated more pre-project than RTP1 allocation: Double difference estimates of project** **<u>impact on rural road kilometer increments (total km)</u>**

|||**Total samp**|**le**||**Treatment sup**|**port**||**Common sup**|**port**|
|---|---|---|---|---|---|---|---|---|---|
||**DD**|**PS kernel**<br>**matched DD**|**PS weighted**<br>**DD**|**DD**|**PS kernel**<br>**matched DD**|<br>**PS weighted**<br>**DD**|**DD**|**PS kernel**<br>**matched DD**|**PS weighted**<br>**DD**|
|**Total km**|3.26|4.07|5.14|3.02|4.04|5.13|2.17|2.73|4.28|
||(1.78)*|(2.23)*|(2.09)**|(1.78)*|(2.60)**|(2.09)**|(1.63)|(2.43)|(1.96)**|
|**_Of which:_**||||||||||
|<br>**Built**|2.45|2.75|2.91|2.44|2.74|2.91|2.33|2.54|2.80|
||(1.41)*|(1.24)**|(1.31)**|(1.41)*|(1.29)**|(1.31)**|(1.47)|(1.28)**|(1.35)**|
|**Rehabilitated**|0.80|1.32|2.23|0.58|1.30|2.22|-0.16|0.20|1.48|
||(1.60)|(1.77)|(2.10)|(1.54)|(2.13)|(2.10)|(1.49)|(1.93)|(2.07)|
|**Allocated by RTP1**<br>(Project database)||<br>3.30|||<br>3.19|||<br>3.19||
|<br>**p-value for test**:<br>rehab=allocated|0.13|0.27|0.61|0.09|0.36|0.61|0.03|0.13|0.42|
|total=allocated|0.98|0.73|0.38|0.87|0.38|0.77|0.48|0.58|0.82|

Note: (i) Standard errors clustered at the district level are given in parentheses; PS kernel matched standard errors are obtained by bootstrapping (100 repetitions). (ii) ** and * indicate significance levels of 5 and 10%; (iii) The total sample has 141 observations (96 comparison and 45 project communes); treatment support has 138 observations (93 comparison and 45 project communes); common support has 134 observations ( 93 comparison and 41 project communes). (iv) The sample of project communes for this table are those that rehabilitated more km pre-project than were allocated by RTP1.

27
|House has improved floor|8.2|3.5|4.7|
|---|---|---|---|
||(2.0)|(3.0)|(4.1)|
|House has radio|39.8|42.3|-2.5|
||(2.4)|(4.6)|(5.2)|
|House has television|5.0|2.7|2.4|
||(2.2)|(2.4)|(4.6)|
|**Household composition and ethnicity indicators**||||
|Two+ children under 5 yrs|13.1|15.5|-2.4|
||(1.3)|(6.1)|(3.8)|
|Religion is Hindu|86.3|97.2|-10.9|
||(3.5)|(2.5)|(7.0)|
|Ethnic group||||
|Brahmin|14.2|11.0|3.2|
||(1.8)|(2.6)|(3.8)|
|Chhetri|20.1|43.1|-23.0<sup>**</sup>|
||(4.4)|(13.3)|(10.6)|
|Occupational|17.8|15.6|2.2|
||(4.1)|(5.6)|(8.5)|
|All other|47.9|30.3|17.6|
||(7.9)|(20.8)|(18.2)|

Notes: Weighted to national level with weights provided by the Nepal DHS in 1996. Standard errors in parentheses. The notation<sup>***</sup> is p<0.01,<sup>**</sup> is p<0.05,<sup>*</sup> is p<0.10.

Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002), and Pradhan _et al._ (1997).

37
Rodríguez-García R y Kusek J (2007) Planning and managing for HIV/AIDS results. A handbook. Global AIDS Monitoring and Evaluation Team - GAMET, World Bank Global HIV/AIDS Program. Washington. The World Bank Rodríguez-García R. et al. (2009) Effective partnerships achieve results, En breve November 2000; Number 148, The World Bank Rodríguez-García R, Gaillard ME, Aran-Matero D (2010) AIDS Resource Efficiency in Latin America. World Bank.

Rodríguez-García R. et al. (2011) Analyzing Community Responses to HIV and AIDS. Operational Framework and Typology, Policy Research Working Paper 5532, The World Bank World Health Organization (2000). _World Health Report 2000. Health systems: improving performance._ Geneva, World Health Organization

# **2. Relación de personas entrevistadas**

Agudelo, Eliana. Ciudad Salud (IPS) Alquichire, Carlos. SaludCoop (IPS) Álvarez, Carlos. Sanitas (IPS) Ariza, Francisco. Ministerio de Educación Nacional (MEN) Ávila, Sandra. Comité de Prevención y Control del VIH/SIDA de las Fuerzas Armadas y Policía Nacional – COPRECOS Barrera, Alexander. Ciudad Salud (IPS) Belalcazar, Elba. UNFPA Bonilla, Edgard. Programa de Fármaco Vigilancia. INVIMA Campillo, Gustavo. Fundación Rasa Castaño, Martha. Secretaría de Salud, Departamento Valle del Cauca Castiblanco, Daniel. Programa de Fármaco Vigilancia. INVIMA Correa, Diana. Ecoopsos (EPS) Durán, Patricia. Programa de Salud y Seguridad Social de la Defensoría del Pueblo 61