rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix 1 Table 6: Summary statistics on selected teacher practices observed during classroom observations in the nationally representative sample of 400 schools**

||All (N=|400)|Rural (N|=271)|Urban (|N=129)|
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD|
|**Use of materials**|||||||
|Textbook was used by teacher|0.87|(0.33)|0.88|(0.32)|0.85|(0.36)|
|Teacher writes on the blackboard|0.99|(0.10)|0.99|(0.10)|0.99|(0.09)|
|Pupils write on the blackboard|0.40|(0.49)|0.41|(0.49)|0.38|(0.49)|
|Teacher uses local information to make learning relevant|0.46|(0.50)|0.45|(0.50)|0.47|(0.50)|
|**Teacher demeanor**|||||||
|Teacher mostly standing (as opposed to sitting)|0.95|(0.22)|0.97|(0.18)|0.91|(0.28)|
|Teacher visits children individually|0.60|(0.49)|0.59|(0.49)|0.62|(0.49)|
|Teacher calls pupil by name while teaching|0.80|(0.40)|0.81|(0.39)|0.78|(0.41)|
|Teacher smiling, laughing, or joking with pupils|0.56|(0.50)|0.54|(0.50)|0.59|(0.49)|
|Teacher hit pupil (1=no)|0.94|(0.24)|0.93|(0.25)|0.95|(0.23)|
|**Pedagogical practices**|||||||
|Teacher ask questions that required learners to recall information|0.73|(0.44)|0.73|(0.45)|0.74|(0.44)|
|Teacher ask learners to to demonstrate their understanding|0.93|(0.25)|0.94|(0.24)|0.92|(0.27)|
|Teacher ask questions that required learners to apply information|0.82|(0.38)|0.84|(0.37)|0.79|(0.41)|
|Teacher ask questions requiring learners to use their creativity|0.75|(0.43)|0.73|(0.44)|0.79|(0.41)|
|Teacher gives feedback and/or encouragement to students (scale, 1=|0.71|(0.39)|0.70|(0.40)|0.74|(0.38)|
|Teacher gives feedback that was correcting a mistake (scale, 1=yes)|0.74|(0.39)|0.75|(0.37)|0.72|(0.42)|
|Teachers gives feedback that was scolding a mistake (scale, 1=no)|0.73|(0.40)|0.74|(0.39)|0.71|(0.42)|
|Teacher introduces the lesson at the start of class|0.90|(0.31)|0.88|(0.33)|0.93|(0.26)|
|Teacher summarize the lesson at the end of class|0.45|(0.50)|0.46|(0.50)|0.42|(0.50)|
|Teacher assigns homework|0.43|(0.50)|0.48|(0.50)|0.33|(0.47)|
|Teacher reviews homeworks|0.24|(0.43)|0.27|(0.44)|0.18|(0.38)|
|Teacher uses local langague for instructions|0.11|(0.31)|0.08|(0.27)|0.16|(0.37)|

Source: SDI 2016 classroom observations. Notes: All answers are binary 1=yes 0=no, unless otherwise indicated as scale variables (where 3 categories were rescaled to be between 0 and 1) A1-8
# **Appendix A1: Class Membership Parameter Estimates from Latent Class Analysis**

Table A1.1 shows results for the class membership parameters. In estimating the probability that an individual was in each class, we used gender, age, education, and the size of the enterprise in which the individual was working at the time of the survey. Class 3 is the omitted class, and all class membership parameters are normalized to zero for this class.

**Table A1.1: Latent Class Membership Results**

||**Class 1**|**Class 2**|
|---|---|---|
|Female|-0.36|-0.078|
||(0.63)|(0.18)|
|Age|0.01|0.002|
||(0.01)|(0.006)|
|SomePrimary|0.24<br>(0.49)|0.0005<br>(0.225)|
|Some Secondary|0.26<br>(0.63)|-0.2<br>(0.256)|
|SomeHighSchool|1.07**<br>(0.43)|-0.28<br>(0.28)|
|HighSchool|0.31|-0.84***|
||(0.47)|(0.29)|
|Bachelors|0.85**|-0.72***|
||(0.42)|(0.27)|
|Missing education|0.81<br>(1.45)|-0.59<br>(1.31)|
|6-10 employees|0.84**|0.47|
||(0.4)|(0.31)|
|11+employees|0.62**<br>(0.25)|0.2<br>(0.17)|
|Missing enterprise size|1.84<br>(1.8)|0.26<br>(1.73)|
|Constant|-1.66*|0.44|
||(0.97)|(0.38)|

_Source_ : Authors’ analysis based on survey data. _Note_ : Class 3 is the omitted class. Omitted education level is none; omitted enterprise size is 1-5 employees. Sampling weights are not applied. *, ** and *** represent statistical significance at the 10%, 5% and 1% levels, respectively.

50
Table 3: Marriage penalty by women’s education

|Dependent variable|||Working|||
|---|---|---|---|---|---|
|Sample|SAR|BGD|IND|MDV|NPL|
||(1)|(2)|(3)|(4)|(5)|
|_P_|_anel A: No _|_child sample_||||
|Post-marriage|-0.129***|-0.158***|-0.170***|-0.032|-0.058***|
||(0.009)|(0.022)|(0.010)|(0.050)|(0.021)|
|Post-marriage _×_ Higher education|0.059***|0.065**|0.064***|-0.033|0.067*|
||(0.015)|(0.030)|(0.018)|(0.085)|(0.039)|
|No. of obs.|8922|1712|4847|440|1916|
|R-squared|0.263|0.154|0.17|0.174|0.131|
|Birth Cohort FE|Yes|Yes|Yes|Yes|Yes|
|Country _×_ Year FE|Yes|Yes|Yes|Yes|Yes|
|_Pa_|_nel B: Ignor_|_e child sampl_|_e_|||
|Post-marriage|-0.167***<br>(0.007)|-0.165***<br>(0.018)|-0.198***<br>(0.008)|-0.228***<br>(0.047)|-0.112***<br>(0.018)|
|Post-marriage _×_ Higher education|0.061***|0.078***|0.040***|0.126*|0.082**|
||(0.012)|(0.022)|(0.014)|(0.075)|(0.034)|
|No. of obs.|11065|2163|5887|578|2434|
|R-squared|0.275|0.151|0.198|0.187|0.099|
|Birth Cohort FE|Yes|Yes|Yes|Yes|Yes|
|Country _×_ Year FE|Yes|Yes|Yes|Yes|Yes|

Note: Standard errors in parentheses clustered at the cohort level. All estimates weighted by cohort size. SAR - South Asia Region, BGD - Bangladesh, IND - India, MDV - Maldives, NPL - Nepal. No child sample refers matching post-marriage observations in the sample without children. Ignore child sample refers matching postmarriage observations in the full sample. Cohort FE refer to birth cohort. Women’s education is an indicator variable for any post-secondary education. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

25
# Table <u>S2: Descriptive statistics of WUG level variables by institution</u>

||----- Fu<br>|ll sample analys<br>|is -----<br>|----- Su<br>|b-sample analys<br>|is -----<br>|
|---|---|---|---|---|---|---|
||All|Manager<br>appointment|No change|All (sub-<br>sample)|Manager<br>appointment|No change|
|Volumetric priceof reservoir water|0.0611|0.0625|0.0606|0.0522|0.0625|0.0425|
||(0.0379)|(0.0302)|(0.0402)|(1.342)|(0.0302)|(0.0444)|
|Volumetric priceof reservoir water<br>suared|0.00514|0.00476|0.00526|0.00419|0.00476|0.00366|
|q|(0.00545)|(0.00443)|(0.00577)|(0.858)|(0.00443)|(0.00537)|
|No. of parcel|112.9|115.5|112.1|114.9|115.5|114.3|
||(50.23)|(51.19)|(50.41)|(2.261)|(51.19)|(52.01)|
|Gini of parcel size|0.255|0.260|0.254|0.244|0.260|0.229|
||(0.136)|(0.135)|(0.137)|(1.894)|(0.135)|(0.124)|
|Simplicity of water flow direction|0.129|0.150|0.122|0.131|0.150|0.114|
||(0.0653)|(0.0711)|(0.0626)|(2.293)|(0.0711)|(0.0335)|
|Earth canal length|0.999|1.062|0.980|1.382|1.062|1.683|
||(1.627)|(1.924)|(1.543)|(0.685)|(1.924)|(2.113)|
|No. of ponds per member (before reform)|0.707|0.647|0.726|0.594|0.647|0.545|
||(0.543)|(0.391)|(0.585)|(1.642)|(0.391)|(0.337)|
|No. of pumps per member (before reform)|0.849|0.778|0.871|0.776|0.778|0.774|
||(0.494)|(0.346)|(0.533)|(2.115)|(0.346)|(0.396)|
|Observations|67|16|51|33|16|17|

_Note_ : Standard deviations are in parentheses _Source:_ Authors’ analysis based on data sources discussed in the text.

34
27 The second best option is to concentrate on completion rates, which tell the observer at least how many students fLnish the cycle they have begun. One needs to make here the relatively strong assumption that students have learned the curriculum of their cycle and oeen adequately tested on it. Furthermore, data on completion rates are relatively scant, particularly for the purposes of cross-country analysis. (See for instance data on primary school completion Table Education 3.) More easily available but carrying less relevant information are enrollment rates, both gross and net. Enrollment figures are generally used as the main indicator of the health of an education system, in their ratio form, i.e., as a percentage of the relevant school age population. Gross enrollment rates (GERs) for a given education level give the percentage of total number of children of all ages enrolled at that level over the total number of children in the specific age groups. Net enrollment rates (NERs) give the percentage of children of the typical age group for a given school level as a percentage of the number that age in the population (e.g., 6-11 years olds enrolled in primary school as percent of the 6-11 year old population). Because of repeaters, children who stay in school beyond the set age or who enter school before the regular age, gross enrollment ratios often go beyond 100 per cent, making aggregate analysis less accurate. Net enrollment ratios are adjusted for repeaters and remain within the 100 per cent limit.<sup>3</sup> Tables Education 1 and 2 present net and gross enrollment rates. Enrollment figures include students in both public and private schools. The data are derived from other tables in the Bank's recent policy 2Net enrollment ratios over l00X are the result of inconsistencies in the enrollment data or errors due to the interpolation of the population data.
**<u>Table 1. Equity principles proposed in the climate change literature</u>**

|_Principles→_<br>_Papers↓_|_Equal per_<br>_capita_<br>_emissions_|_Historic_<br>_responsibility_|_Ability_<br>_to pay_|_Preserving_<br>_future_<br>_development_<br>_opportunities_|_Adjustment_<br>_costs_|
|---|---|---|---|---|---|
|Agarwal and Narain<br>(1991), Saran (2009),<br>Ghosh (2010)|Y|Y||||
|Antholis (2009)|N|||||
|Baeret. al. (2007)||Y|Y|||
|Bhagwati(2009)||Y||||
|Birdsall and<br>Subramanian(2009)||||Y||
|Bosetti and Frankel<br>(2009)||Y|Y|||
|Brazil(1997)||Y||||
|Cao (2008)||Y|Y|||
|Chakravarti et. al.<br>(2009)|||Y|||
|Cooper(2008)||Y|Y|||
|Frankel(2007)|Y|Y?|Y|Y||
|GTZ (2004)|Y|Y|Y|||
|Joshi and Patel<br>(2009)||||Y||
|Jacoby et. al. (2007)|Y||Y|Y||
|Kanitkar et. al.<br>(2010)|Y|Y||||
|Meyer(2000)|Y|||||
|Mulleret. al(2007)||Y||||
|Panet. al. (2008)||Y||||
|Panagariya (2009)||Y||||
|Parikh and Parikh<br>(2009)|Y|Y||||
|Posner and Sunnstein<br>(2009); Posner and<br>Weisbach(2010)|N?||Y?|||
|Spence (2009)|||Y|||
|Stern (2009a)<br>Stern(2009b)||Y|Y||Y?|
|UNDP(2008)|||||Y|
|WBGU (2009)|Y|||||
|Winkleret. al(2006)||Y|Y||Y|

_Y implies support; N implies opposition; no entry implies neither support nor opposition;? suggests qualification ._

# _1. Equal per capita emissions_

The first fairness-based criterion is that regardless of past actions and future opportunities, every citizen of the planet today has an equal right to the atmosphere as a reservoir for absorbing GHG emissions. This criterion is rooted in the idea that all humans are—from the perspective of 6
consumers would benefit from subsidy removal if they are compensated through a direct cash transfer when the removal of the subsidy does not affect non-energy production costs. But, if energy subsidy removal increases the non-energy costs of other production sectors, the benefits get diluted depending upon the share of energy in total consumer expenditure, energy intensity and the existing rate of energy subsidy. While the Breton and Mirzapour (2016) study has its own limitations (e.g., being a partial equilibrium analysis, it does not adequately capture interactions between economic agents and between production sectors and it does not account for international trade), the approach is more appropriate than the input-output and econometric approaches used in the other studies discussed above.

Computable general equilibrium (CGE) models are more credible than the other approaches discussed above (econometric, input-out, partial equilibrium) when it comes to analyzing the economic impacts of energy subsidy removal or reform. Therefore, recent studies of the economic consequences of energy subsidy removal/reform increasingly employ CGE models (see e.g., Gelan, 2018; Gharibnavaz and Waschik, 2015; Elshennawy (2014); Glomm and Jung, 2014; Solaymani and Kari, 2014).

Using an intertemporal general equilibrium model of Egypt with 10 types of households and 21 industrial sectors, Elshennawy (2014) finds that energy subsidy removal reduces welfare no matter whether the subsidy is reduced instantaneously or gradually. However, the findings of this analysis are not convincing, as the paper does not mention how the savings from subsidy removal are utilized. If the savings are not used to compensate consumers through a transfer or tax cut or invested in production sectors which have a comparative advantage, it is possible that subsidy removal could cause welfare loss. A CGE modeling study for Kuwait (Gelan, 2018) confirms this. It finds that a 25% reduction of subsidies to oil, gas and electricity causes a 0.28% reduction of GDP if the saved subsidy is not recycled to the economy (i.e., government uses it for public consumption and/or savings). On the other hand, subsidy removal increases GDP by 1.01% if the saved subsidy is recycled to consumers as a cash transfer. Gharibnavaz and Waschik (2015) also report similar findings for the Islamic Republic of Iran. Employing a CGE model of the Islamic Republic of Iran with households differentiated by income (into 20 groups) and 21 production sectors, it finds that energy and food subsidy reforms that increase real government revenues by 30% would cause welfare of all households together to increase by 45% if households receive lump-sum transfers from the saved subsidies. More interesting is that low-income 7
importance of these other factors suggests that tax policy is a poor instrument to compensate for various negative factors in the investment climate of a country. Many countries from South America to Sub-Saharan Africa have offered investment incentives for businesses to locate in underdeveloped, more costly, and otherwise unattractive regions with little success in generating (sustainable) investment flows to those areas. This experience strongly suggests that the fiscal investment incentives popular in developing countries have not been effective in making up for fundamental weaknesses in the investment climate.

# **III. New evidence from the mid-80s: A look for Details**

The relative little importance of tax policy does not mean that it does not exert any impact on FDI. Looking at foreign direct investment figures, it is certainly not a coincidence that FDI in tax haven countries in the Caribbean and South Pacific grew more than fivefold between 1985 and 1994, to over $200 billion. Ireland's tax policy has been generally recognized as a key factor in its success to attract international investors over the past two decades. In fact, the simple position described in the preceding section was not completely accurate. It is not true that tax policy and incentives fail to attract investors; they do affect the decisions of _some_ investors _some_ of the time.

Even in the 1 970s, there were researchers who started to look into more detailed FDI data and came out with conclusions that made the result of previous studies more vulnerable to criticism. Forsyth in his 1972 study provides support for the view that inducements and incentives may often not play a key role in influencing the decision to undertake a particular foreign investment. However, once other factors have provoked the decisions to set up production facilities in a broad area, then the more precise location decision may be strongly affected by such factors.

> 6 It has to be noted that in principle the locational decision of the firm may be affected by the marginal and average rates in two opposite directions. When the average level is high in one country, it is possible that the overall profit level may be less than in another country even though its marginal rate is lower.

8
|**Table 5: Was there differential attrition by formalization**<br>Dependent Variable: Interviewed in Endline Survey|**action?**<br>(1)|(2)|
|---|---|---|
|Probably formalized in intervention window|0.0115|0.152***|
||(0.0557)|(0.0553)|
|Formalized*Assigned to Free Cost Treatment|-0.0928<br>(0.108)||
|Formalized*Assigned to Communication Treatment|-0.0512<br>(0.120)||
|Formalized*Assigned to Get Inspector||-0.0489<br>(0.0831)|
|Formalized*Assigned to Indirect Inspector||0.0172<br>(0.0914)|
|Sample Size|1,341|2,547|
|Notes: Robust standard errors in parentheses, clustered<br>*, **, and *** indicate significance at the 10, 5 and 1 per<br>Regressions also control for randomization strata, and fo<br>assignment and are for the sample chosen to attempt to<br>Sampling weights used in column (2).|at block leve<br>cent levels re<br>r treatment<br>survey at fol|l.<br>spectively.<br>group<br>low-up|

37
Table 8: Coffee price shocks and child malnutrition

||W|AZ|W|HZ|H|AZ|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||Panel|A: House|hold fxed|efects||
|Land share of cofee*Log price in utero|0.492|0.352|1.150*|1.179*|-0.565|-0.863|
||(0.808)|(0.867)|(0.591)|(0.649)|(1.077)|(1.047)|
|Land share of cofee*Log price post birth||-0.514<br>(0.935)||0.059<br>(0.975)||-1.033<br>(1.205)|
|_N_|3153|3153|3153|3153|3153|3153|
|_R_<sup>2</sup>|0.629|0.629|0.582|0.582|0.555|0.555|
|||Panel B|: Village/|Kebele fxe|d efects||
|Land share of cofee*Log price in utero|0.972*|1.091*|1.084*|1.331**|0.485|0.375|
||(0.506)|(0.604)|(0.563)|(0.623)|(0.652)|(0.719)|
|Land share of cofee*Log price post birth||0.365||0.755||-0.336|
|||(0.711)||(0.657)||(0.727)|
|_N_|3994|3994|3994|3994|3994|3994|
|_R_<sup>2</sup>|0.230|0.230|0.205|0.205|0.204|0.204|

Notes: Standard errors are clustered at zone level. This table is based on children in three rounds (2011, 2013, and 2015) of ESS survey and anthropometric measures of children under 5 years of age, after removing biologically implausible extreme values (WAZ, WHZ and HAZ values of _>_ 5 or _< −_ 5). All regressions include, whether mother is educated, gender and age of the child, month of birth and year of birth fixed effects, and log village rainfall. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _Log price in utero_ is log of the average coffee price when the child was in utero. _Log price post birth_ is log of the average coffee price over the period since the child birth to the survey month. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 29
|P147213|2015|2017|5/31/2017|5 Active|Dominican Republic|
|---|---|---|---|---|---|
|P147213|2015|2018|12/7/2017|6 Active|Dominican Republic|
|P147213|2015|2018|6/24/2018|7 Active|Dominican Republic|
|P147213|2015|2019|12/18/2018|8 Active|Dominican Republic|
|P147213|2015|2019|6/25/2019|9 Active|Dominican Republic|
|P147213|2015|2020|12/23/2019|10 Active|Dominican Republic|
|P147213|2015|2020|6/25/2020|11 Active|Dominican Republic|
|P147235|2014|2014|6/3/2014|1 Closed|West Bank and Gaza|
|P147235|2014|2015|1/16/2015|2 Closed|West Bank and Gaza|
|P147235|2014|2016|7/28/2015|3 Closed|West Bank and Gaza|
|P147235|2014|2016|2/6/2016|4 Closed|West Bank and Gaza|
|P147235|2014|2017|8/30/2016|5 Closed|West Bank and Gaza|
|P147235|2014|2017|6/8/2017|6 Closed|West Bank and Gaza|
|P147235|2014|2020|5/17/2020|7 Closed|West Bank and Gaza|
|P147241|2014|2017|12/5/2016|1 Closed|Ghana|
|P147241|2014|2017|6/28/2017|2 Closed|Ghana|
|P147241|2014|2018|2/21/2018|3 Closed|Ghana|
|P147241|2014|2019|8/30/2018|4 Closed|Ghana|
|P147244|2014|2014|4/7/2014|1 Closed|Mexico|
|P147244|2014|2015|1/26/2015|2 Closed|Mexico|
|P147244|2014|2020|7/26/2019|3 Closed|Mexico|
|P147250|2014|2015|9/27/2014|1 Active|Guyana|
|P147250|2014|2015|4/17/2015|2 Active|Guyana|
|P147250|2014|2016|10/30/2015|3 Active|Guyana|
|P147250|2014|2016|5/12/2016|4 Active|Guyana|
|P147250|2014|2017|11/15/2016|5 Active|Guyana|
|P147250|2014|2017|5/26/2017|6 Active|Guyana|
|P147250|2014|2018|12/25/2017|7 Active|Guyana|
|P147250|2014|2018|6/19/2018|8 Active|Guyana|
|P147250|2014|2019|12/28/2018|9 Active|Guyana|
|P147250|2014|2019|5/2/2019|10 Active|Guyana|
|P147250|2014|2020|11/7/2019|11 Active|Guyana|
|P147250|2014|2020|5/13/2020|12 Active|Guyana|
|P147257|2014|2015|7/1/2014|1 Closed|Morocco|
|P147257|2014|2015|1/2/2015|2 Closed|Morocco|
|P147258|2016|2017|4/12/2017|1 Closed|Uganda|
|P147258|2016|2018|11/29/2017|2 Closed|Uganda|
|P147258|2016|2019|12/5/2018|3 Closed|Uganda|
|P147272|2016|2016|3/31/2016|1 Active|Brazil|
|P147272|2016|2017|11/29/2016|2 Active|Brazil|
|P147272|2016|2017|6/24/2017|3 Active|Brazil|
|P147272|2016|2018|1/29/2018|4 Active|Brazil|
|P147272|2016|2019|11/7/2018|5 Active|Brazil|
|P147272|2016|2019|4/13/2019|6 Active|Brazil|
|P147272|2016|2020|11/14/2019|7 Active|Brazil|
|P147272|2016|2020|5/14/2020|8 Active|Brazil|
|P147277|2016|2016|5/13/2016|1 Active|Dominican Republic|
<!-- Start of picture text --> 0.38 -0.47-<br>0 2O0<br>0,0<br>a,<br>- 0.03- 0 0.00j_<br>.t2 35stance from local market in hours (log scaleT .stance frbm local market in hours (log scale)<br>Non-farm wage employment Non-farm self-employment<br>0Fg0.23 - 0.84 -<br>00<br>0 ~~~~~~~~~~~~~~~0<br>a,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I<br>U, U<br>-0.03 0.00<br>.12 2 ~ 12sae<br>cpistance frbm local mlarket in hours (logm lcal mrketsnchous (lo<br>Agricultural wage employment Agricultural self-employment<br>Figure 2. Employment and distance from local market<br><!-- End of picture text -->
**Table A.12: Correlates of price charged**

(private interactions, excludes cases where all medicines are unidentifiable)

||**(1)**<br>|**(2)**<br>|**(3)**<br>**Fees i**<br>|**(4)**<br>**n Rs.**<br>|**(5)**<br>|**(6)**<br>|
|---|---|---|---|---|---|---|
||**Representa**|**tive sample**|**Dual pract**|**ice sample**|**Pooled**|**sample**|
||Binary|Multiple|Binary|Multiple|Binary|Multiple|
||regressions|regression|regressions|regression|regressions|regression|
|Time spent with SP (minutes)|1.720***|0.618|2.625***|2.279***|1.484***|0.709*|
||(0.476)|(0.477)|(0.587)|(0.692)|(0.377)|(0.401)|
|Percentage of checklist items|0.397***|0.339***|0.364***|0.055|0.386***|0.291***|
||(0.089)|(0.096)|(0.100)|(0.129)|(0.071)|(0.084)|
|Correct diagnosis (unconditional)|-4.269|-3.647*|7.504|5.494|2.690|2.685|
||(3.978)|(1.993)|(9.350)|(9.046)|(4.658)|(4.148)|
|Correct treatment|6.199***|-1.564|7.744*|4.475|7.306***|0.602|
||(1.757)|(2.919)|(4.145)|(4.967)|(1.934)|(2.404)|
|Palliative treatment|7.711***|2.198|10.435**|7.757|7.796***|3.542**|
||(1.810)|(1.722)|(4.242)|(4.873)|(1.743)|(1.726)|
|Unnecessary treatment|15.794***|3.147|14.973***|5.137|15.655***|4.888*|
||(2.842)|(2.963)|(5.032)|(6.240)|(2.451)|(2.746)|
|Dispensed medicines|19.525***|16.400***|16.118***|12.371*|16.511***|15.688***|
||(2.993)|(2.726)|(6.070)|(7.019)|(2.319)|(2.830)|
|Prescribed medicines|-2.931|-4.331|7.540|-2.854|0.071|-4.133|
||(3.600)|(3.639)|(5.997)|(6.734)|(2.918)|(3.202)|
|Number of medicines|5.540***|1.630|5.863***|3.016|5.283***|1.111|
||(0.842)|(1.394)|(1.783)|(2.987)|(0.787)|(1.348)|
|Referred/Asked to see child|-20.348***|-10.054***|-9.882**|-4.867|-17.533***|-11.860***|
||(4.999)|(3.683)|(4.763)|(4.888)|(3.911)|(3.021)|
|Has MBBS|23.517***|27.905***|||14.155***|23.516***|
||(6.150)|(7.830)|||(4.369)|(3.923)|
|Has some qualification|4.305|6.067***|||2.127|6.952***|
||(3.768)|(2.282)|||(3.376)|(2.370)|
|Patient load during visit|1.017|0.867**|-0.073|-0.285|0.512|0.276|
||(0.888)|(0.404)|(0.807)|(0.810)|(0.748)|(0.581)|
|Age of provider|-0.186|-0.111|0.267|0.248|-0.119|-0.018|
||(0.155)|(0.100)|(0.239)|(0.218)|(0.126)|(0.089)|
|Gender of provider (1=Male)|-8.238**|-5.876|-1.284|-3.760|-7.475**|-3.810|
||(3.518)|(4.543)|(4.882)|(5.580)|(2.961)|(3.919)|
|Constant||9.745||-11.295||2.234|
|||(7.179)||(11.810)||(6.345)|
|R2||0.446||0.444||0.398|
|Number of observations||495||154||649|
|Mean price charged||27.638||32.740||28.849|
|SD||26.557||28.592||27.118|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. Observations are standardized provider-patient interactions. Interpretation of coefficents in "Binary regressions" needs caution. Each coefficient represents a separate regression of prices on the row variable and case and district fixed effects. Multiple regressions include case and district fixed effects.

72