rafmacalaba/datause-encoder-data huggingface dataset:
|||**Log [Self**|**-reported Pro**|**ductivity (X**|**OF/ha)]**|||||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|Household head has Secondary|<br>0.048*|<br>0.001|<br>-0.001|<br>0.006|<br>0.007|<br>-0.004|<br>-0.021**|<br>-0.021*|<br>-0.040**|
|education|(0.028)|(0.017)|(0.015)|(0.013)|(0.011)|(0.010)|(0.011)|(0.011)|(0.016)|
|Household head has Tertiary education|0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000*|-0.000|-0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Household head disability|0.018|-0.010|-0.010|-0.006|-0.001|-0.010|-0.002|-0.003|-0.006|
||(0.021)|(0.014)|(0.015)|(0.012)|(0.013)|(0.012)|(0.011)|(0.012)|(0.014)|
|Dependency ratio|0.145|0.087|0.029|-0.049|-0.064|-0.079|-0.047|-0.004|-0.080|
||(0.095)|(0.077)|(0.060)|(0.059)|(0.055)|(0.052)|(0.056)|(0.060)|(0.066)|
|Household Male Labor Use (Number)|0.083|0.028|-0.022|-0.041|-0.086***|-0.053|-0.033|-0.037|-0.050|
||(0.052)|(0.041)|(0.033)|(0.034)|(0.030)|(0.034)|(0.030)|(0.035)|(0.048)|
|Household Female Labor Use|-0.076|-0.081|-0.128|-0.043|-0.013|0.067|0.049|-0.118|-0.017|
|(Number)|(0.131)|(0.110)|(0.108)|(0.107)|(0.090)|(0.090)|(0.093)|(0.099)|(0.114)|
|Household Child Labor Use (Number)|0.013|0.011|0.003|0.016|0.015|0.007|-0.005|0.004|0.015|
||(0.017)|(0.017)|(0.014)|(0.013)|(0.014)|(0.015)|(0.011)|(0.014)|(0.013)|
|Hired Labor Use (Number)|-0.003|0.013|-0.003|-0.036|-0.026|-0.028|-0.010|0.042**|0.020|
||(0.022)|(0.021)|(0.023)|(0.023)|(0.019)|(0.021)|(0.022)|(0.017)|(0.020)|
|Number of Fields Managed by the|0.008|-0.132|0.083|-0.009|-0.210|-0.232|0.025|0.070|-0.054|
|<br>household|(0.367)|(0.245)|(0.226)|(0.197)|(0.208)|(0.225)|(0.196)|(0.217)|(0.230)|
|Total number of crops produced|0.174|0.255|-0.094|-0.202|0.192|0.312|0.059|0.075|0.075|
||(0.403)|(0.284)|(0.226)|(0.206)|(0.199)|(0.222)|(0.193)|(0.224)|(0.254)|
|Household used any purchased seed|<br>-0.025|<br>-0.002|<br>0.050**|<br>0.035*|<br>0.028|<br>0.014|<br>0.008|<br>0.010|<br>-0.027|
||(0.032)|(0.029)|(0.024)|(0.021)|(0.017)|(0.016)|(0.014)|(0.016)|(0.024)|
|Plots that Use Chemical Fertilizer (%|<br>-0.022|<br>-0.016|<br>-0.010|<br>-0.001|<br>-0.008|<br>0.023|<br>0.032**|<br>0.027*|<br>0.025|
|<br>of total)|(0.015)|(0.012)|(0.011)|(0.012)|(0.011)|(0.015)|(0.014)|(0.016)|(0.021)|
|<br>Plots that Use Pesticide (% of total)|<br>0.006|<br>0.010|<br>-0.014|<br>-0.011|<br>0.013|<br>-0.006|<br>-0.019|<br>-0.017|<br>-0.017|
||(0.020)|(0.023)|(0.019)|(0.018)|(0.017)|(0.017)|(0.017)|(0.022)|(0.027)|
|Export crops dummy (1 if Household|<br>0.068|<br>-0.056|<br>-0.038|<br>-0.036|<br>-0.027|<br>-0.053|<br>-0.044|<br>-0.010|<br>0.128|
|<br>produced any export crop)|(0.122)|(0.096)|(0.083)|(0.071)|(0.058)|(0.058)|(0.055)|(0.061)|(0.087)|
|Constant|-0.497|-0.183|0.995**|0.588|-0.325|0.078|0.348|0.178|1.661***|
||(0.613)|(0.502)|(0.463)|(0.424)|(0.414)|(0.401)|(0.428)|(0.489)|(0.605)|
|Observations|5428|5428|5428|5428|5428|5428|5428|5428|5428|

_Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively._ 16
Equation 1, the first stage, is estimated by regressing a dummy that takes the value of one when municipality _m_ in year _t_ receives Fonden, _fmt_ , on an indicator variable that takes the value of one when an observation falls above the threshold, _zmt_ . That is, _zmt_ = 1( _rmt ≥ cmt_ ) where _rmt_ is the amount of rainfall on the day requested and _cmt_ is the percentile 90 threshold. The function _g_ ( _rmt − cmt_ ) represents the relationship between the outcome and the forcing variable, that is, millimeters of rainfall to the percentile 90 threshold. We will consider various ways of modeling this relationship. First we will model _g_ ( _·_ ) as a flexible polynomial function on either side of the threshold and use the full sample to estimate it. Second, we will assume that _g_ ( _·_ ) is linear and use a sample that falls within an optimal bandwidth to estimate it. To determine the bandwidth we will use the methods proposed by Imbens and Kalyanaraman (2012) and Calonico et al. (2014b).

Equation 2, the reduced form, is derived by regressing our measure of local economic activity, _ymt_ , on _zmt_ . The estimation procedure is analogous to that of equation 1. Next, we derive the impact of Fonden on local economic activity, _π_ 1, by rescaling the reduced form coefficient of _zmt_ by its first stage coefficient.<sup>3</sup> Following Henderson et al. (2012) we include year fixed effects in all of our specifications in order to ensure the comparability of satellite imagery over time.

# **4 Data**

For our analysis, we use data from several sources. We proxy changes in municipal level economic activity by using imagery from the United States Air Force Defense Meteorological Satellite Program (DMSP). Specifically, we use imagery gathered by three satellites: F15, F16 and F18. These satellites are in a 101 min near-polar orbit. This orbit implies that the satellites are capable of observing every location on earth at roughly the same local time each day. In the case of Mexico the satellite overpass occurs between 7 and 8 pm local time. These satellites use Operational Linescan System (OLS) sensors to measure the intensity of earth based lights. The National Oceanic and Atmospheric Administration (NOAA) has developed a methodology to generate image composites that filter transient light observed in the raw images. Natural sources of transient lights include, for example, the bright half of the lunar cycle, auroral activity, and forest fires, see Elvidge et al. (1997) for details on the filtering process. The resulting stable cloud-free night light composites measure, by and large, man-made lights. These measures of night lights have been shown to be good proxies for economic activity at the country level

> 3In order to derive standard errors for _π_ ˆ1 we will instead estimate the coefficient using 2SLS.

6
Table 17: Impact of 3G/4G access on poverty status ($3.20 poverty line) by group (fixed effects models)

||Female|Headed|Consu|mption|Re|gion|
|---|---|---|---|---|---|---|
||No|Yes|Low|High|South|North|
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.013|-0.034|-0.031**|-0.000|-0.044**|-0.002|
||(0.016)|(0.033)|(0.014)|(0.024)|(0.021)|(0.018)|
|Medium or strong 3G/4G|<br>-0.027|<br>-0.010|<br>-0.065***|<br>-0.024|<br>-0.035|<br>-0.001|
|for one year|(0.017)|(0.032)|(0.016)|(0.026)|(0.023)|(0.019)|
|<br>Medium or strong 3G/4G|<br>-0.026|<br>0.029|<br>-0.090***|<br>-0.069***|<br>-0.003|<br>0.000|
|for two years or more|(0.017)|(0.031)|(0.017)|(0.024)|(0.023)|(0.019)|
|Medium or strong 3G/4G|-0.023|0.000|-0.109***|-0.083***|0.001|0.016|
|for three years or more|(0.016)|(0.033)|(0.020)|(0.023)|(0.023)|(0.021)|
|# of observations|10,772|2,959|7,246|6,297|6,747|6,985|
||Age House|hold Head|Localit|yType|Edu|cation|
|||50 or|||Primary|Less than|
||Under 50|older|Rural|Urban|<br>or more|primary|
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.004|-0.030|-0.011|-0.028|-0.020|-0.022|
||(0.020)|(0.019)|(0.016)|(0.030)|(0.018)|(0.031)|
|Medium or strong 3G/4G|-0.004|-0.035|-0.022|-0.029|-0.029|-0.036|
|<br>for one year|(0.021)|(0.022)|(0.020)|(0.027)|(0.018)|(0.033)|
|Medium or strong 3G/4G|-0.010|-0.016|-0.036*|-0.007|-0.015|-0.020|
|for two years or more|(0.019)|(0.021)|(0.020)|(0.027)|(0.018)|(0.032)|
|Medium or strong 3G/4G|-0.005|-0.030|-0.076***|0.039|-0.008|-0.089***|
|for three years or more|(0.019)|(0.023)|(0.019)|(0.027)|(0.018)|(0.030)|
|# of observations|6,738|6,574|9,442|4,290|8,645|2,076|
||Liter|acy|Children|under 3|||
||Illiterate|Literate|No|Yes|||
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.022|-0.003|-0.019|-0.015|||
||(0.024)|(0.019)|(0.020)|(0.018)|||
|Medium or strong 3G/4G|-0.026|-0.025|-0.035*|-0.014|||
|<br>for one year|(0.026)|(0.019)|(0.020)|(0.019)|||
|Medium or strong 3G/4G|-0.036|-0.013|-0.026|-0.009|||
|<br>for two years or more|(0.025)|(0.019)|(0.020)|(0.019)|||
|<br>Medium or strong 3G/4G|<br>-0.083***|<br>-0.013|<br>-0.039*|<br>-0.006|||
|<br>for three years or more|(0.027)|(0.019)|(0.021)|(0.019)|||
|# of observations|4,976|8,587|6,423|7,309|||

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. *** p<0.01, ** p<0.05, * p<0.1.

26
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.2 Infrastructure endowments by income group, Sub-Saharan Africa vs. other world regions**

||L<br>|ow-incom<br>|e<br>|Co<br>Lower<br>|untry gro<br>-middle-i<br>|up<br>ncome<br>|Upper<br>|-middle-i<br>|ncome<br>|
|---|---|---|---|---|---|---|---|---|---|
||Africa|Other|Ratio|Africa|Other|Ratio|Africa|Other|Ratio|
|Sector and measure|(A)|(B)|(C=B/<br>A)|(D)|(E)|(F=E/<br>D)|(G)|(H)|(I=H/<br>G)|
|_Transport_||||||||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|31|134|4.3|94|141|1.5|238|781|3.3|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|290|728|2.5|1,176|1,919|1.6|11,086|7,415|0.7|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|137|211|1.5|215|343|1.6|293|1,171|4.0|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|1,535|2,194|1.4|4,233|10,624|2.5|14,179|13,375|0.9|
|_Information and communication technology_||||||||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|10|78|7.7|106|131|1.2|120|274|2.3|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|55|86|1.6|201|298|1.5|422|554|1.3|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|2.0|3.2|1.6|5.1|8.0|1.6|10.3|26.2|2.5|
|_Energy_||||||||||
|Electrical generating capacity (MW per 1<br>million people, 2003)|37|326|8.8|256|434|1.7|246|861|3.5|
|Access to electricity<br>(% of households with access, 2004)|16|41|2.6|35|80|2.3|28|95|3.4|
|_Water and sanitation_||||||||||
|Water<br>(% of households with access, 2002)|60|72|1.2|75|86|1.2|90|93|1.0|
|Sanitation<br>(% of households with access, 2002)|34|51|1.5|48|74|1.5|39|90|2.3|

_Sources_ : As for table 2.1.

Nevertheless, the African average masks significant variations within the region (table 2.3). To compare subregional endowments, we used four country groupings. They are SADC (all the SADC countries except Tanzania); the East African Community (EAC) of Kenya, Tanzania, and Uganda; the Economic Community of West African States (all the ECOWAS countries); and Central Africa, a default category comprised of Burundi, Cameroon, the Central African Republic, Chad, Comoros, Congo, Rep., Equatorial Guinea, Eritrea, Ethiopia, Gabon, Madagascar, Mauritania, Mozambique, Rwanda, Sao Tome and Principe, Somalia and Sudan.

Comparing infrastructure endowments across these subregions reveals that the SADC countries have a substantial advantage over the others. That advantage is most pronounced in the case of paved roads, ICT, and power, where SADC is ahead of the other regions by several multiples. Generating capacity (per capita) in SADC, for example, is more than five times that reported in other parts of Africa, although, strikingly, household access to electricity is relatively similar. At the other end of the spectrum, EAC has the lowest infrastructure endowment on most measures. Western and central Africa are similar in their results. The least divergence in stocks 7
|r missed payment. This dummy variable<br> that month is nil, and 0 otherwise. The<br>if the loan was disbursed after November<br>unt the client received for that particular<br>`us loans` is the number of loans that the<br>h of the observation and the month when<br>mong clients who received both individual<br>nt of which was disbursed after November<br>th each point estimate.<br>(4)<br>0.049<br>(0.049)<br>0.000<br>(0.000)<br>0.030<br>(0.041)<br>0.004<br>(0.004)<br>0.269**<br>(0.112)<br>Yes<br>Yes<br>0.174<br>1584<br>0.140|
|---|
|a dummy fo<br>orrower for <br>e equal to 1<br>o the amo<br>`of Previo`<br>en the mont<br>ity loans a<br> most rece<br>eses benea<br>(3)<br>0.018<br>(0.025)<br>-0.000<br>(0.000)<br>0.065**<br>(0.029)<br>-0.025<br>(0.015)<br>-0.001<br>(0.003)<br>0.129***<br>(0.032)<br>No<br>No<br>0.016<br>1584<br>0.140|
|variable is <br>d by the b<br>my variabl<br>`unt` refers t<br>e. `Number `<br>ence betwe<br>idual liabil<br>y loans, the<br> in parenth<br>(2)<br>-0.003<br>(0.023)<br>-0.000<br>(0.000)<br>0.074***<br>(0.027)<br>0.112***<br>(0.025)<br>No<br>No<br>0.013<br>1584<br>0.140|
|dependent <br>ount repai<br>`nt`is a dum<br> `Loan Amo`<br> the sampl<br>s the difer<br>ons of indiv<br>ual liabilit<br>l, are given<br>(1)<br>0.002<br>(0.025)<br>0.139***<br>(0.020)<br>No<br>No<br>0.000<br>1584<br>0.140<br>01|
|This table reports results from a falsifcation test, where the <br>takes on the value of 1 for a particular month if the total am<br>regressions correspond with those in Table 3. `False Treatme`<br>2006, 0 otherwise. Observations are at the loan-month level. <br>loan. `Behrampura` is a dummy for one of the MFI branches in<br>client received before the current loan. `Loan Age in Months`i<br>the loan was disbursed, plus 1. The sample contains observati<br>and a group liability loan, and who obtained at least 2 individ<br>2006. Robust standard errors, clustered at the individual leve<br>False Treatment<br>Loan Amount<br>Behrampura<br>Number of Previous Loans<br>Loan Age in Months<br>Constant<br>Control for Calendar Month<br>Individual FEs<br>R-squared<br>N<br>Mean of Dep Var<br>* _p <_0_._10, ** _p <_0_._05, *** _p <_0_._|

25
||Nea|rest Neighbor Matching|Estimates||Coarsened Exact Matching|Estimates|
|---|---|---|---|---|---|---|
||(1)<br>ODA as a Share<br>of GNI <5%<br>IEG Outcome<br>Rating [binary]|(2)<br>ODA as a Share of<br>GNI >=5% &< 20%<br>IEG Outcome<br>Rating [binary]|(3)<br>ODA as a Share of<br>GNI >=20% IEG<br>Outcome Rating<br>[binary]|(4)<br>ODA as a Share<br>of GNI <5%<br>IEG Outcome<br>Rating [binary]|(5)<br>ODA as a Share of<br>GNI >=5% and < 20%<br>IEG Outcome Rating<br>[binary]|(6)<br>ODA as a Share of GNI<br>>=20% IEG Outcome<br>Rating [binary]|
|Average<br>Treatment<br>Effect on the Treated|-0.00160|-0.146*|-0.0149|0.032|-0.199***|-0.006|
||(0.0616)|(0.0743)|(0.105)|(0.061)|(0.072)|(0.095)|
|Constant||||0.663***|0.740***|0.792***|
|||||(0.021)|(0.031)|(0.054)|
|Observations|1,207|615|153|552|254|87|
|Percent Exact Country<br>|93.68%|97.03%|81.73%|100%|100%|100%|
|Matches<br>Standard errors in parent<br>*** p<0.01, ** p<0.05,|heses<br>* p<0.1||||||

_Note:_ ODA = official development assistance; GNI = gross national income; IEG = International Evaluation Group.

54
||O|LS|||Fuzzy R|egression D|iscontinu|ity Design|||
|---|---|---|---|---|---|---|---|---|---|---|
||||Li|near|Qua|dratic|Cov|ariates|Sub|sample|
|**Expenditure Shares**|||||||||||
|Nutrition|0.017|(0.0024)|0.1|(0.024)|0.14|(0.069)|0.1|(0.024)|0.18|(0.069)|
|Education|-0.0013|(0.00044)|0.0093|(0.003)|0.021|(0.0076)|0.0087|(0.0029)|0.0057|(0.009)|
|School Materials|0.0014|(0.00016)|0.0071|(0.0018)|0.011|(0.0059)|0.0072|(0.0018)|0.0059|(0.0064)|
|Pharmaceuticals|-0.00091|(0.00088)|-0.028|(0.0069)|-0.024|(0.018)|-0.026|(0.0068)|-0.023|(0.021)|
|Health Care|-0.0012|(0.001)|-0.026|(0.0082)|-0.03|(0.022)|-0.027|(0.0081)|-0.039|(0.025)|
|**Perceived Constraints**|||||||||||
|Pharmaceuticals|0.14|(0.012)|0.36|(0.13)|-0.083|(0.32)|0.4|(0.12)|-0.11|(0.34)|
|Health Services|0.018|(0.0032)|0.065|(0.044)|0.083|(0.13)|0.064|(0.043)|-0.081|(0.13)|
|Socio-economic vars.|No||No||No||Yes||No||
|Regional dummies|549||549||549||549||549||

Table 10: Summary of results: The impact of Bolsa Fam ́ılia on private investments in health and education Note: Standard errors in parentheses. Clustering applied to all models at the level of sampling strata (550 strata), socio-economic controls include: nr. of hh members, nr. of women, female hh head, black hh head, Asian hh head, Pardo hh head, indigenous hh head, hh head illiterate, Universal religion, Catholic religion, Batista religion,Evangelical religion, Assembly of God religion, Lutheran, religion, nb. of whites, nb. of blacks in hh, nb. of Asians in hh, nb. of pardo in hh, nb. of indigenous in hh, Health insurance, child or teen pregnant (10-17), young adult pregnant (18-25), credit card, bank account. Detailed results are reported in tables 26, 30 and 32 for the first stage and Tables 27 , 28, 29, 31 and 33 for the second stage results.
and complex environments and heterogeneous and complex decision makers. In: T. Kugler, J. C. Smith, T. Connolly, Y.-J. Son (Eds)., _Decision Modeling and Behavior in Uncertain and Complex Environments_ (pp. 57-76). Berlin: Springer.

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Rohrmann, B. and Renn, O. (2000). Risk Perception Research: An Introduction, in Renn, O. and Rohrmann, B. (eds.), _Cross-Cultural Risk Perception: A Survey of Empirical Studies_ , Kluwer Academic Publ., Dordrecht and Boston, pp. 11-54. Robinson, T., & Chatraw, J. (2006). _Saving God's Green Earth: Rediscovering the Church's Responsibility to Environmental Stewardship._ Norcross, GA: Ampelon Publishing.

Schneider, W., & Chein, J.M. (2003). Controlled and automatic processing: Behavior, theory, and biological mechanisms, _Cognitive Science, 27,_ 525–559.

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44
Figure 2. Distribution of people according to GDI per capita of the country where they live (year 2000) <!-- Start of picture text --> China<br>India<br>Russia, Brazil<br>USA<br>Mexico W.Europe, Japan<br>0 10000 20000 30000<br>gdp per capita in ppp<br>40<br>30<br>20<br>Percent<br>10<br>0<br><!-- End of picture text --> Note: horizontal axis: GDP per capita in 1995 international prices; vertical axis: share of world population. Source: Milanovic (2005, p. 94).

24
cooperation and development; and second in the World Bank’s influential 2004 World “ _”_ Development Report (WDR) Making Services Work for Poor People _._ The 2004 WDR proposed that in addition to traditional development strategies that relied on political (elections) and managerial mechanisms to hold government service-delivery agencies accountable – what it called “the long route of accountability” -- there was also a “short route” that could generate accountability for service delivery by giving client groups much greater control over information, resources, and choice. Several of the WDR’s examples came from the CDD literature, which in turn then began to draw on the WDR model to provide a theoretical foundation to justify its continued expansion.

As even its authors agreed at the time, the 2004 report rested on very little empirical evidence. Since the time of its publication, however, the number of case examples and the corpus of rigorous evaluations have grown considerably. Not surprisingly, not all of the model has worked out as neatly as originally expressed, nor does all of the evidence point in a unilinear direction. Nevertheless, many of its original hypotheses about the role that information, choice, and programs can play in improving local-level service delivery have proven to be useful and also are able to be cast in operational terms.

Our goal in this paper is to examine what the evidence shows us about the utility of CDD programs for helping governments improve the lives and futures of the poor. We also address recent critiques of the CDD approach. As both proponents and managers of national community development programs, we are firm believers that CDD offers governments a useful new tool for improving the lives of the poor. However, CDD is not a fix-all tool that can be applied indiscriminately to all contexts, nor does it in any way replace the need for the kinds of structural transformations in developing countries that will create new industrial and service jobs, provide technical services that lie well beyond the communities’ purview, and promote other large-scale improvements to human welfare.

CDD is a useful tool in a people-centric development strategy. The challenge is to avoid putting the cart before the horse: the key insight from the CDD experience is that poor people’s agency can drive development much more than it currently does, not that CDD should replace sectoral or transformational programs. But in contexts where more traditional approaches have not been able to reach the poor, having a new approach that developing country governments can use to engage communities that are poor and often hard to reach, and in ways that are popular, sustainable, and effective, is already a valuable contribution.

3
51 Table 4: Estimated impacts of hospital payment methods on health sector outcomes

||Preferred<br>||Prefe<br>|rred mod<br>|el estimat<br>|es<br>||
|---|---|---|---|---|---|---|---|
|Dependent variable|model|FFS|p-value|Impact|PBP|p-value|Impact|
|Inpatient admissions|DID|0.35*|0.093|2.1%|0.04|0.827|0.3%|
|Hosp discharges - circulatory|DID|156.01**|0.009|8.0%|84.64|0.494|4.4%|
|Hosp discharges - cerebrovas|RT|16.61|0.244|4.8%|-12.79|0.302|-3.7%|
|Hosp discharges - respiratory|DID|169.51**|0.004|8.2%|59.15|0.301|2.9%|
|Hosp discharges - digestive|RT|111.2**|0.026|6.8%|17.11|0.608|1.0%|
|Hosp discharges - musculo|DID|50.07*|0.079|6.3%|-21.52|0.320|-2.7%|
|Length of stay|DID|-0.31|0.347|-2.5%|-0.44**|0.033|-3.5%|
|Hospital beds|DT|0.09|0.749|1.1%|-0.12|0.575|-1.5%|
|Bed occupancyrate|RT|0.50|0.819|0.7%|-3.79**|0.044|-5.2%|
|Health expenditures - Total|DID|81.39**|0.007|19.9%|46.15*|0.057|11.3%|
|Health expenditures - Public|DID|51.99*|0.051|17.1%|21.61|0.450|7.1%|
|Health expenditures - Private|DID|29.39**|0.034|27.8%|24.54*|0.064|23.2%|
|SDR ischemic heart disease|RT|-2.89|0.679|-0.9%|-12.66*|0.099|-4.1%|
|SDR diabetes|RT|-0.51|0.708|-3.4%|-1.12|0.215|-7.6%|
|SDR cerebrovascular diseases|DID|-1.95|0.582|-1.1%|-8.14**|0.035|-4.6%|
|SDR female breast cancer|DID|0.35|0.567|1.6%|-0.45|0.390|-2.1%|
|SDR appendicitis|DID|0.00|0.974|-0.8%|-0.05|0.170|-17.7%|
|SDR hernia & intestinal|DID|0.03|0.823|1.5%|0.11|0.202|4.9%|
|SDR adverse effects|DID|0.01|0.857|5.8%|-0.04|0.570|-19.5%|

_Notes:_ Results refer to the coefficients of the fee-for-service (FFS) and patient-based (PBP) payment methods dummies estimated in the preferred model for each outcome—difference-in-differences (DID), random trend (RT) or differential trend (DT) model. P-values from two-sided _t_ -tests with cluster-robust standard errors. FFS and PBP percentage impacts implied by the corresponding coefficients are calculated over the mean outcome variable in the corresponding estimating sub-sample. The symbols * and ** denote statistical significance at the 10% and 5% levels, respectively.