rafmacalaba/datause-encoder-data huggingface dataset:
Table 2. Descriptive Statistics, Mexico and Chile

|||**Mex**|**ico**|||**Chile**||
|---|---|---|---|---|---|---|---|
||All ages|12 to 14|15 to 17|18 to 24|All ages|15 to 17|18 to 24|
|**Behaviors andOutcomes**||||||||
|% inactive|20.3|8.9|17.7|29.9|23.4|6.7|33.9|
|% droppingout of school before completinghigh school|29.9|10.0|33.0|42.3|14.2|8.4|17.8|
|% havingsex|52.1|-|13.3|56.0|59.1|26.9|79.4|
|% of the sexuallyactive using protection|50.6|-|54.0|52.5|38.1|40.11|38.7|
|% reportingat least 1 child|19.4|-|2.5|28.9|18.6|3.9|27.9|
|% everparticipatingin at least 1 activity (organization,club)|27.3|30.7|26.0|23.4|78.8|81.2|77.3|
|% reportingtheycanjustify gettingdrunk|3.4|3.1|3.7|3.6|-|-|-|
|% reportingtheycanjustifydruguse|1.1|1.1|1.0|0.8|-|-|-|
|**Risk and Protective Factors**||||||||
|% female|53.5|50.7|52.9|55.9|53.0|50.5|54.6|
|Average age|17.2|13.0|16.0|20.8|18.9|16.0|20.8|
|% withparents who have aprimarydegree or less|56.1|56.3|58.0|55.0|38.1|38.8|37.7|
|%rural|24.7|28.1|26.0|21.6|13.2|13.8|12.9|
|% indigenous|-|-|-|-|10.8|11.5|10.4|
|% whose parents hit them when they do something which<br>bothers theparents|6.6|8.1|4.7|6.6|-|-|-|
|% whose parents insult them when they do something which<br>bothers theparents|2.6|2.1|2.7|2.8|-|-|-|
|% whose parents accuse them in front of others when they do<br>somethingwhich bothers theparents|1.2|1.2|1.2|1.3|-|-|-|
|% whose parents punish them when they do something which<br>bothers theparents|22.2|26.2|22.7|19.0|-|-|-|
|%reporting physical orpsychological abuse in home|-|-|-|-|6.3|6.2|6.4|
|%reporting problems arisingfromsubstance abusein home|-|-|-|-|8.5|7.0|9.5|
|% reportingsocial exclusion|53.2|66.8|53.9|43.1|17.7|10.1|22.5|
|% livingwith bothparents|70.3|83.1|77.2|57.2|-|-|-|
|% livingwith neitherparent|16.0|3.8|7.9|29.3|-|-|-|

23
Despite this consensus, there are good reasons why donor PSM projects may remain overambitious and untailored to their contexts. As detailed in the World Bank’s “Approach to Public Sector Management for 2011–2020” (World Bank 2012b), project managers may have incentives to set overly ambitious reform goals to sell their projects to the client and to meet expectations within the Bank. TLLs may find it less risky to adopt well-tested reform designs than to experiment with tailored approaches—while client governments may ask for “cutting-edge” approaches because they are politically attractive.

It is not obvious whether these dynamics imply that PSM projects should perform better or worse in countries with higher initial administrative capacity. If TTLs were able to tailor PSM projects perfectly to initial administrative capacity, then they should set more modest objectives in low-capacity countries and more ambitious objectives in high-capacity countries. In this case, initial administrative capability would not decide project performance. If TTLs, however, tend to set overly ambitious project targets, this might make projects perform worse in low-capacity countries, where the level of ambition risks being particularly out of tune with existing capacity. But one could also argue that decreasing marginal returns on reform and reform satiation in high-capacity countries might reduce the opportunities for “satisfactory” project implementation.

# **3.5 Country Context: Economic and Human Development**

Economic and human development may positively influence the success of PSM reform projects through multiple channels. For example, countries with higher income levels may have a better-qualified and more-specialized workforce—and a better-financed public sector—and thus higher capacity for successful PSM reform. A better-educated public, meanwhile, may dispose of more means to hold government to account, even in authoritarian settings.

# **3.6 Reform Content**

PSM reform projects have diverse aims and varying levels of difficulty (research question 1). For example, de jure reforms may be easier to achieve than de facto change. Introducing MTEFs has little in common with internal audit reform. Efforts to downsize the public administration will involve different challenges than pay reforms aimed at attracting and retaining qualified employees. While this calls for a granular distinction between different PSM reform content areas, because of data limitations this paper delineates only four broad groups of PSM reform: civil service, financial management, decentralization, and tax administration.

How does the performance of Bank projects in these four content areas compare? The debate on this question has been shaped by an IEG report on the World Bank’s PSM lending portfolio, entitled “Public Sector Reform. What Works and Why” (World Bank 2008). The report ranks project performance across 14
**Table 4:** Effects of Turnovers on Nepotism

||**Village head survey**|**Bureau**|**crat survey**|
|---|---|---|---|
||Employs relative|Parent was leader|Parent served ingovt|
||(1)|(2)|(3)|
|New village head|-0.385<sup>_∗∗∗_</sup>|-0.066|-0.168<sup>_∗∗_</sup>|
||(0.155)|(0.043)|(0.069)|
||[0.178]|[0.051]|[0.082]|
|Observations|442|1067|1067|
|Control mean|0.36|0.054|0.27|
|Robust p-value|0.008|0.109|0.0340|
|<br>Bandwidth size (%)|12.7|17.5|22.5|
|Effective obs.|149|466|550|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are village heads in column 1 and bureaucrats in columns 2-3. The dependent variable is: in column 1, a dummy equal to 1 if relatives of the village head are employed in the village government; in column 2, a dummy equal to 1 if the bureaucrat reports having a parent who served as village head; in column 3, a dummy equal to 1 if the bureaucrat reports having a parent who served in the village government. See Section 4 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Conventional standard errors in parentheses and robust bias-corrected standard errors in brackets. In columns 2–3, standard errors are additionally clustered by village. Significance levels are based on the robust bias-corrected standard errors (clustered by village in columns 2–3).

36
# **Figure 2: Impact of maternity leave on Female workers over the current range of maternity leave**

<!-- Start of picture text --> 8.00<br>7.13<br>7.00<br>6.00<br>5.00<br>4.00<br>3.00<br>2.00 1.41<br>0.83<br>1.00 0.59 0.46 0.38 0.32 0.28 0.24 0.22 0.20 0.18 0.17 0.15<br>0.00<br>Increase in maternity leave days<br>% point increase in Female workers<br><!-- End of picture text --> Source: Author’s own calcualtions using ES data. Note: The values shows are based on the estimated coefficient value of Maternity leave (logs) variable in the final baseline specification (column 5, table 1).

49
||**(1)**<br>**Price dispersion (%)**|**(2)**<br>**Price dispersion (%)**|
|---|---|---|
|Tanzania's harvest|0.104<sup>***</sup>|0.105<sup>***</sup>|
||(0.03)|(0.03)|
|Mozambique's harvest|−0.386<sup>***</sup>|−0.384<sup>***</sup>|
||(0.12)|(0.12)|
|Zambia's harvest|−0.030|−0.031|
||(0.07)|(0.07)|
|Observations|46,402|46,402|

_Note:_ The regression results are based on the specification in equation 9. Standard errors are clustered by region pairs. Both regressions include country pair-by-year fixed effects. Controls include harvest quantity in neighboring countries and distance between market pairs. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

**Figure 8: Comparison of price responsiveness to harvest ‘with’ and ‘without’ export bans**

<!-- Start of picture text --> 0.30<br>0.20<br>0.10<br>0.07<br>0.00<br>-0.09<br>-0.10 -0.16 -0.15 -0.16<br>-0.18<br>-0.20 -0.25 -0.24<br>-0.30<br>-0.40<br>-0.50<br>Harvest Fallow Planting Lean Harvest Fallow Planting Lean<br>(Apr - Jun) (Jul - Sep) (Oct - Dec) (Jan - Mar) (Apr - Jun) (Jul - Sep) (Oct - Dec) (Jan - Mar)<br>No export ban Export ban<br>Note : Figure presents the coefficients on the interaction of quarterly season dummies with the indicator for non-Malawi<br>market pair, harvest levels and dummies for periods with and without an export ban in Malawi from estimating the<br>regression specification in equation 10. The bars show the point estimates, and the error bars show the 95% confidence<br>intervals based on standard errors clustered by region pairs.<br>without export bans<br>% change in price dispersion following a 1<br>kg/capita increase in annual harvest, with and<br><!-- End of picture text -->

# _5.3 Welfare implications of the price effects of export bans_

We examine the welfare implications of the export bans by estimating the NBR for households, that is, the value of net gain/loss (as a percentage of total household expenditure) due to changes in maize price 23
Herrmann and Mihaljeck, 2010; Cetorelli and Golberg, 2011), while others have used syndicated loan market data to show how cross-border lending was impacted by the crisis (e.g., De Haas and van Horen 2012, 2013 and Giannetti and Laeven, 2012).

A second, and much more closely related, strand of the literature focuses on how the crisis affected lending by foreign bank affiliates in emerging markets. For example, using a bank-level panel data set for banks in emerging Europe, De Haas et al. (2012) show that foreign bank affiliates reduced their lending earlier and faster than domestic banks in the region. Fungácová et al. (2013) find a similar result for Russia. Using also bank-level data, Cull and Martinez Peria (2013) compare the behavior of foreign vis-a-vis domestic banks in eight Eastern European and six Latin American countries. The authors find that while foreign banks clearly reduced their lending more than domestic banks in Eastern Europe, the differences were much less pronounced and robust in the case of Latin America.

Our paper is most closely related to Claessens and van Horen (2013). Using a database including 3,615 banks (of which 1,198 are foreign) operating in 118 countries, the authors compare lending growth by foreign banks relative to domestic banks (without distinguishing between private and government-owned banks) during 2005-2009. They find that foreign banks reduced loan growth more than domestic banks during 2009. However, they provide some evidence of heterogeneity across foreign banks. In particular, they find that foreign banks that funded their operations from local deposits reduced their lending less during the global crisis. Though Claessens and van Horen (2013) provide substantial evidence regarding the behavior of foreign banks during the recent global crisis, they ignore important questions such as: Are the findings that foreign banks contract lending more than domestic banks during the crisis driven by the behavior of government-owned banks or do these results survive when comparing foreign 4
**Table 2. Characteristics of the poor, vulnerable, middle class, and upper class in Mexico, 2012**

||**< $4**|**$4 – 10**|**$10 – 50**|**> $50**||
|---|---|---|---|---|---|
||**(poor)**|**(vulnerable)**|**(middle)**|**(upper)**|**Total**|
|**Monthly per capita income, at PPP**|$73.5|$203.5|$570.1|$2,681.5|$356.9|
|**Geography**||||||
|Urban|51.5%|<br>77.6%|91.3%|94.9%|76.8%|
|Rural|48.5%|<br>22.4%|8.7%|5.1%|23.2%|
||_100%_|<br>_100%_|_100%_|_100%_|_100%_|
|**Demographics**||||||
|Age of the household head|49.1|<br>48.3|48.7|48.7|48.7|
|Household size|5.2|<br>5.0|4.1|3.2|4.7|
|Incidence ofphysical or mental disabilities|7.3%|<br>6.0%|5.1%|2.9%|5.9%|
|Indigenous|15.1%|<br>4.5%|2.1%|0.8%|5.9%|
|_Age groups _||||||
|0 - 5years|13.7%|<br>11.4%|7.7%|5.0%|10.5%|
|6 - 11years|15.8%|<br>12.4%|8.0%|4.8%|11.5%|
|12 - 14years|7.1%|<br>6.3%|4.5%|2.3%|5.8%|
|15 - 17years|5.8%|<br>6.4%|5.1%|3.5%|5.8%|
|18 - 25years|11.2%|<br>14.4%|16.2%|12.9%|14.3%|
|25years or more|46.4%|<br>49.0%|58.5%|71.4%|52.2%|
||_100%_|<br>_100%_|_100%_|_100%_|_100%_|
|65years or more|8.1%|<br>7.1%|7.2%|8.1%|7.4%|
|**Education of the household head**||||||
|Incompleteprimaryor less|50.0%|<br>35.1%|20.7%|9.6%|32.8%|
|Completeprimaryor incomplete secondary|23.2%|<br>22.6%|16.0%|7.0%|20.1%|
|Complete secondaryor more|26.8%|<br>42.3%|63.3%|83.4%|47.1%|
||_100%_|<br>_100%_|_100%_|_100%_|_100%_|
|_School assistance by age groups _||||||
|6 - 11years|97.7%|<br>99.1%|99.7%|99.4%|98.8%|
|12 - 14years|89.1%|<br>94.0%|96.3%|97.4%|93.3%|
|15 - 17years|61.9%|<br>70.0%|78.8%|89.6%|71.1%|
|18 - 23years|23.0%|<br>30.0%|44.0%|64.7%|34.8%|
|6 - 23years|73.1%|<br>71.6%|72.6%|81.1%|72.4%|
|**Quality of dwelling and access to basic services**||||||
|Dirth floor|9.0%|<br>3.1%|0.8%|0.2%|3.6%|
|Fragile walls|3.4%|<br>1.6%|0.6%|0.4%|1.6%|
|Fragile ceilings|4.1%|<br>2.1%|0.6%|0.0%|2.0%|
|Overcrowded|19.7%|<br>10.6%|2.6%|0.2%|9.7%|
|No runningwater|18.8%|<br>8.2%|3.4%|1.2%|8.8%|
|No sewage|22.3%|<br>8.2%|2.1%|0.3%|9.1%|
|No electrical energy|1.7%|<br>0.6%|0.1%|0.0%|0.7%|

12
Grals and Zhong Sstrgic nrdependnce in the Emt-Wast Ou Tmde _-( _T-S--_ i;a (I1) _aP0_<sup>_8ar_</sup> The transiter equalizes the marginal revenue and the marginal cost of raising its transit fee. When the supply price is fixed, a higher transit fee is passed through to a higher import price, which depresses the demand for gas. Ihis first order condition can be rearranged into _-T-S T(dOG_ **_p0 v ai'o_** _T_ 1' -r G p0lJ (12) _a_ apR This clearly states tiatthe mark-up ratio of the transiter is determined as the inverse of the product of the price elasticity of gas demand and the elasticity of the gas price with respec **t** o the transit fee. The transiter's optimal reaction function is thus implicitly defined by: The left hand is the transiter's ratio of marginal profit and marginal cost, an index of the transiter's negotiation power. The right hand side is the market condition in terms of relative prices, expenditure shares and the adjustments with respect to changes in the supply price can be thus derived as Hence, whether the transiter follows the supplier's price movement depends crucially on (a) the market reactions reflected from the elasticity of substitution, (b) the transiter's negotiation power reflected from the marginal profit-marginal cost ratio; and (c) the transiter's current profitabflity reflected from the pric-cost ratio. If thi transiter foresees that a big decline in gas demand may occur due to a price rise, It may be willing to reduce its transit fee to rotain the quantity at the level which maximizes its transit profit.

# C. The Supplier's Decision

Knowing how both the importer and the transiter adjust their strategies, the gas supplier as the Stackelberg leader detennines a supply price, PR, to maximize its profit. That is _Max II - (P`x)G - Fx (15)_ p where C is the unit production cost and F, is the fixed supply cost. The first order condition is <!-- Start of picture text --> ap<br>alpx aT ap)<br><!-- End of picture text --> where the reaction of T with respect to change of P1 is determined through the transiter's best reaction fiuntion (10). This first order condition can be rewritten as <!-- Start of picture text --> aG dPG dP T<br>PR-C. Pc;<br>P5 aP0 aP1 AT aPx<br>p. P,<br><!-- End of picture text --> Hence, the profit margin of the gas supplier is determined through both direct and indirect effects of price changes. Raising the supply price will not only push up direcdy the final sales price in the importing country, but also provoke the transiter tooptinally adjust its transit fee setting. The decision rule is iii : I
<!-- Start of picture text --> .<br>g Bangladesh: IPV experience<br>c<br>ia)<br>5<br>a<br>x<br>vo<br>2<br>= Urban<br>ie}<br>§<br>8 °<br>5 Higher education<br>tr<br>5<br>4a<br>i<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing ipy experience on urban, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state<br><!-- End of picture text --> <!-- Start of picture text --> India: IPV experience<br>an<br>c o Oo<br>t<br>a<br>Fy<br>am<br>&<br>He<br>ie]<br>S Urban<br>Bog an<br>ra Higher education<br>o<br>§<br>pa<br>tiea<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing ipv experience on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> ~<br>8 Nepal: IPV experience<br>x<br>OL<br>><br>Qa<br>9°<br>5 Urban<br>2<br>a<br>8 - Higher education<br>=";<br>5<br>4<br>(a)<br>us<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing ipv experience on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> on Pakistan: IPV experience<br>a<br>x<br>ve<br>><br>2<br>5<br>5 Urban<br>3<br>a Higher education<br>Q<br>=.<br>5<br>fa<br>i]<br>ii(a<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing ipy experience on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
Table 2: Summary statistics and balance across experiment arms (percent)

|Variable|(1)<br>Information<br>Mean/SE|(2)<br>Reward<br>Mean/SE|(3)<br>Penalty<br>Mean/SE|(1)-(2)|T-test<br>Diference<br>(1)-(3)|(2)-(3)|
|---|---|---|---|---|---|---|
|**Outcomes**<sup>_†_</sup>|||||||
|Across cases|52.90<br>(0.69)|57.76<br>(0.72)|56.64<br>(0.71)|-4.86***|-3.73***|1.12|
|Across responses|56.20<br>(0.40)|58.72<br>(0.44)|58.37<br>(0.44)|-2.52***|-2.17***|0.35|
|Total payout (Naira)<br>**Covariates**|1,750.00<br>(0.00)|1,841.90<br>(14.70)|1,818.63<br>(15.18)|-91.90***|-68.63***|23.28|
|Male|27.52<br>(2.11)|22.10<br>(1.94)|20.70<br>(1.89)|5.42*|6.82**|1.40|
|Age _≥_median|50.11<br>(2.37)|54.49<br>(2.33)|53.81<br>(2.33)|-4.37|-3.70|0.67|
|Doctor or nurse|7.38<br>(1.24)|10.94<br>(1.46)|10.24<br>(1.42)|-3.56*|-2.86|0.70|
|Years since qualifed _≥_median|49.89<br>(2.37)|49.89<br>(2.34)|52.07<br>(2.33)|-0.00|-2.18|-2.18|
|Knowledge _≥_median<br>**NSHIP pilot status**|52.57<br>(2.36)|52.08<br>(2.34)|51.20<br>(2.34)|0.49|1.37|0.88|
|PFP|44.74<br>(2.35)|42.67<br>(2.32)|42.70<br>(2.31)|2.07|2.04|-0.03|
|DFF|42.06<br>(2.34)|45.08<br>(2.33)|42.92<br>(2.31)|-3.02|-0.86|2.16|
|Control|13.20<br>(1.60)|12.25<br>(1.54)|14.38<br>(1.64)|0.95|-1.18|-2.13|
|N|447|457|459||||

_Notes_ : †Across cases weighs each case equally; across responses weighs each response equally. The median for knowledge is 51.52 percent. The value displayed for t-tests are the differences in the means across the groups. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

42
Table 1: Health <u>projects</u> embeddings evolution over time

|Period|N|Mean X|Mean Y|Mean Distance|
|---|---|---|---|---|
|1990_−_99|195|7.84|9.64|5.01|
|2000_−_09|275|7.75|7.65|5.55|
|2010_−_19|308|7.65|9.89|2.79|
|2020_−_21|203|9.44|11.76|1.19|

_Note:_ Embeddings are projections into abstract high-dimensional vector space expressing inter-relationships, so lack physical units. This table reports the reduction of health projects embedding in two dimensions (X and Y), centered on the 1990 _−_ 1999 mean.

sector and decade _Psd_ and _|Psd|_ = _Nsd_ , we calculate:

and similarly construct _Ecd_<sup>_∗_foreachcountry</sup><sup>_c_.Wethendefinethe</sup><sup>_sector_</sup> _distance_ , denoted _Tp_<sup>_s_for a given project</sup><sup>_p_, as</sup><sup>_∥Ep−E_</sup> _sd_<sup>_∗∥_.Similarly, the</sup><sup>_country_</sup> _distance_ , denoted _Tp_<sup>_c_,isdefinedas</sup><sup>_∥Ep−E_</sup> _cd_<sup>_∗∥_.</sup> The sector distance can be interpreted as the degree to which the project document is, in the deep texture of its language, adjusting sectoral knowledge to local country realities, and the country distance a similar measure of how sectoral peculiarities are being brought to bear on local problems.

Further work is needed to determine what design aspects, as reflected in the text of the project documents, are most clearly associated with development outcomes. In this paper, we restrict ourselves to embeddings generated from the project development objectives (PDO), results framework indicators and implementation completion report (ICR). But even these appear to be capturing the degree of specificity to country circumstances. To illustrate, here is the PDO of a health project with very low (sector distance more than 1 standard deviation smaller) contextualization: The revised PDOs are to: (i) improve coverage, utilization and quality of health care services in the territory of the Recipient, and (ii) strengthen the Government’s stewardship functions in the health sector.

By contrast, more contextualized PDOs are more specific with respect to population groups, periods and outcomes. Here are another two examples with high (more than one standard deviation above average distance to sector mean) contextualization: The Project’s development objective is to ensure access to improved and sustained water and sanitation services in rural communities in [redacted country name]. This would be accomplished through the implementation of the new Rural Water Supply and Sanitation 9
providers.<sup>14</sup> Details of the sampling and the visits are in Appendix A.

Data from this ‘representative sample’ allow us to compare care provided across typical public and private clinics in rural MP (all our estimates are re-weighted by the inverse of the sampling probabilities to provide population representative averages). However, this comparison would reflect a combination of varying composition of providers (including their knowledge or professionalism), as well as the effect of practicing in the private sector.

To isolate the role of private sector practice, we identified the universe of public MBBS doctors posted to PHCs and CHCs from all five study districts, even if these clinics were not located in the village-based sampling scheme. We then identified the private practices of these doctors (we found a private practice for 61 percent). We sampled and successfully administered SP visits to 118 public MBBS doctors. Our ‘dual sample’ consists of the 88 doctors in this MBBS sample who also have a private practice, and for 69 of these, SPs presented cases in both their public and private practices. The ‘dual sample’ enables a comparison of the quality of care provided by the same doctor on the same case across his public and private practices. We note that our completion rates were higher in the private (93 percent) compared to public practices (75 percent). The lower public sector completion rates resulted from long-term absences and leave among sampled public sector providers, leading to non-completion despite multiple attempts. If (a) there is heterogeneity in the public-private difference across doctors, and (b) doctors who are more absent also provide lower quality care in their public sector practice, our public-private differences may be underestimated. Appendix A and Tables A.1 and A.2 provide further details on the sampling and construction of the representative and the dual sample of providers.

Table 2 (columns 1-3) provides summary statistics for the representative sample of providers. The providers are mostly middle-aged men and just under 60 percent have completed 12 or more years of education (Table 2, Panel A). Providers practices have been open for 13-15 years, and private and public providers self-report an average of 16 and 28 patients per day, respectively. Most practices (82 percent of private and 100 percent of public) dispense medicines in the clinic itself and are equipped with the infrastructure and medical devices required for routine examinations, such as stethoscopes and blood pressure cuffs. In the representative sample, public providers are more likely to have an MBBS degrees (26 percent vs. 8 percent) and private providers charge an average of Rs.51 per interaction. Consistent with nominally priced public care, our SPs paid Rs.3.7 on average in public clinics.

Column 4 presents summary statistics on the universe of public MBBS doctors, while

> 14In one case, a sampled village was near a market with over a hundred different healthcare providers. In this one case, we sampled over 20 private providers. See Appendix A for details on sampling.

11