WPS7348


Policy Research Working Paper                           7348




   Long-Run Effects of Temporary Incentives
        on Medical Care Productivity
                                   Pablo Celhay
                                   Paul Gertler
                                 Paula Giovagnoli
                                Christel Vermeersch




Health Nutrition and Population Global Practice Group
June 2015
Policy Research Working Paper 7348


  Abstract
 The adoption of new clinical practice patterns by medical                          group while the incentives were being paid, and this effect
 care providers is often challenging, even when the patterns                        persisted at least 15 months and likely 24 months or more
 are believed to be efficacious and profitable. This paper uses                     after the incentives ended. These results are consistent with
 a randomized field experiment to examine the effects of                            a model where the incentives enable providers to address the
 temporary financial incentives paid to medical care clinics                        fixed costs of overcoming organizational inertia in innova-
 for the initiation of prenatal care in the first trimester of                      tion, and suggest that temporary incentives may be effective
 pregnancy. The rate of early initiation of prenatal care was                       at motivating improvements in long-run provider perfor-
 34 percent higher in the treatment group than in the control                       mance at a substantially lower cost than permanent incentives.




  This paper is a product of the Health Nutrition and Population Global Practice Group. It is part of a larger effort by the
  World Bank to provide open access to its research and make a contribution to development policy discussions around the
  world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be
  contacted at cvermeersch@worldbank.org.




         The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
         issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
         names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
         of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
         its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.


                                                       Produced by the Research Support Team
                                      	
    Long‐Run	Effects	of	Temporary	Incentives	on	Medical	Care	Productivity	
	              	                                 	
                                          Pablo	Celhay	
                                           Paul	Gertler	
                                        Paula	Giovagnoli	
                                       Christel	Vermeersch	
                                                 	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
JEL	Classification:	I12,	I13,	I15,	I18	
	
Keywords:	 Keywords:	 Pay‐for‐performance,	 results‐based	 financing,	 provider	 performance,	 birth	
outcomes,	impact	evaluation,	maternal	and	child	health,	organizational	inertia,	temporary	incentives	
	
Author	 Affiliation:	Pablo	Celhay	(pcelhay@uchicago.edu)	is	a	Ph.D.	candidate	at	the	University	of	
Chicago.	Paul	Gertler	(Gertler@haas.berkeley.edu)	is	the	Li	Ka	Shing	Professor	at	the	University	of	
California,	Berkeley.	Paula	Giovagnoli	(pgiovagnoli@worldbank.org)	is	an	Economist	with	the	World	
Bank.	Christel	Vermeersch	(cvermeersch@worldbank.org)	is	a	Senior	Economist	at	the	World	Bank.	
	
	                               	
Acknowledgements:	The	experiment	described	in	this	paper	was	developed	under	the	leadership	
of	 Martin	 Sabignoso,	 National	 Coordinator	 of	 Plan	 Nacer	 and	 Humberto	 Silva,	 National	 Head	 of	
Strategic	Planning	of	Plan	Nacer,	Ministry	of	Health,	Argentina.	Together	with	the	national	team,	Luis	
Lopez	 Torres	 and	 Bettina	 Petrella	 from	 the	 Misiones	 Office	 of	 Plan	 Nacer	 oversaw	 the	
implementation	 of	 the	 pilot,	 facilitated	 access	 to	 provincial	 data,	 supported	 the	 authors	 in	
interpreting	datasets	and	the	provincial	legal	framework	and	in	carrying	out	the	in‐depth	interviews.	
Fernando	 Bazán	 Torres,	 Ramiro	 Flores	 Cruz,	 Santiago	 Garriga,	 Alfredo	 Palacios,	 Rafael	 Ramirez,	
Silvestre	Rios	Centeno,	Gabriela	Moreno,	and	Adam	Ross	provided	excellent	assistance	and	project	
management	support.	Alvaro	S.	Ocariz,	Javier	Minsky	and	the	staff	of	the	Information	Technology	unit	
at	 Central	 Implementation	 Unit	 (UEC)	 at	 the	 Ministry	 of	 Health	 provided	 valuable	 support	 in	
identifying	sources	of	data.	The	authors	acknowledge	the	contributions	of	Sebastian	Martinez,	Luis	
Perez	Campoy,	Vanina	Camporeale	and	Daniela	Romero	in	the	initial	design	of	the	pilot.	The	authors	
also	 thank	 Ned	 Augenblick,	 Dan	 Black,	 Nick	 Bloom,	 Megan	 Busse,	 Stefano	 DellaVigna,	 Damien	 de	
Walque,	 Emanuela	 Galasso,	 Jeff	 Grogger,	 Petra	 Vergeer,	 as	 well	 as	 participants	 in	 seminars	 at	 UC	
Berkeley,	 Northwestern	 University	 and	 Chicago	 University	 for	 helpful	 comments.	 The	 authors	
gratefully	acknowledge	financial	support	from	the	Health	Results	Innovation	Trust	Fund	(HRITF)	and	
the	Strategic	Impact	Evaluation	Fund	(SIEF)	of	the	World	Bank.	The	authors	declare	that	they	have	
no	financial	or	material	interests	in	the	results	of	this	paper.	




                                                      2	
	
1 INTRODUCTION		
Successful	organizations	are	able	to	efficiently	and	reliably	produce	high	quality	products	through	
the	use	of	reproducible	and	stable	routines.1	Routines	shape	the	production	process	by	defining	each	
person’s	role	and	their	patterns	of	action,	and	by	coordinating	the	tasks	performed	by	the	different	
team	 members.2	 They	 can	 be	 thought	 of	 as	 organizational	 habits	 that	 reduce	 the	 complexity	 of	
decision‐making,	facilitate	coordination	across	team	members,	and	speed	production.	However,	once	
established,	 routines	 are	 costly	 to	 change.	 The	 cost	 of	 adjustment	 includes	 the	 time	 and	 money	
needed	to	retool	routines,	an	adjustment	period	in	which	production	is	less	reliable	while	the	new	
routines	are	being	learned,	and	possibly	psychological	resistance	to	change.	As	a	result,	organizations	
tend	to	be	resistant	to	adopting	structural	changes	that	are	thought	to	be	productive	and	profitable	
(Hannan	and	Freeman	1984;	Carroll	and	Hannan	2000).	While	organizational	routines	are	necessary	
for	efficient	and	reliable	production,	they	can	result	in	organizational	inertia	to	innovation.	

               Nowhere	are	organizational	routines	more	important	than	in	the	production	of	medical	care	
services	(Hoff	2014).	Medical	care	entails	coordinating	a	large,	complex	set	of	tasks	such	as	deciding	
what	 information	 to	 collect	 from	 the	 patient,	 assessing	 social	 and	 medical	 risks,	 deciding	 what	
diagnostic	 tests	 to	 prescribe,	 interpreting	 symptoms	 and	 test	 results,	 and	 prescribing	 and	
implementing	 treatments.3	 	 Typically,	 a	 team	 coordinated	 by	 a	 physician	 implements	 these	 tasks.	
Nurses	often	take	medical	and	social	histories,	conduct	preliminary	physical	exams,	and	administer	
injections.	Laboratory	technicians	analyze	blood	and	urine.	Pharmacists	dispense	drugs	and	monitor	
negative	 drug	 interactions.	 Physical	 and	 occupational	 therapists	 provide	 rehabilitation	 services.	
Community	 health‐workers	 provide	 outreach,	 promotion	 and	 preventive	 services,	 and	 follow‐up	
care	 to	 patients.	 Clinics	 establish	 practice	 routines	 that	 are	 consistent	 with	 their	 training	 and	
experience	to	standardize	and	coordinate	care.		

               There	 is	 substantial	 evidence	 of	 organizational	 inertia	 in	 medical	 care	 as	 indicated	 by	 the	
remarkably	low	level	of	compliance	with	Clinical	Practice	Guidelines	(CPGs)	worldwide	(Figure	1).	

																																																													
      1	Organizational	routine	has	been	studied	extensively	since	popularized	by	Nelson	and	Winter	(1982).	In	a	

review	 of	 the	 literature	 Becker	 (2004)	 defines	 routines	 as	 “recurrent	 interaction	 patterns”	 within	 an	
organization,	or	as	“established	rules,	or	standard	operating	procedures”.	
   2	 Often	 relationships	 between	 team	 members	 and	 management	 are	 enforced	 by	 informal	 relational	

contracts	(Gibbons	and	Henderson	2012	and	2013).	
   3	 Complex	 production	 technologies	 with	 sophisticated	 routines	 such	 as	 medical	 care	 require	 strong	

management	to	 be	 efficient	 and	 productive.	 	 Bloom	 et	al.	 (2014)	 provide	 evidence	 that	 better	management	
increases	public	hospital	productivity.		


                                                                	
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CPGs	define	medical	care	production	possibility	frontiers	in	that	they	prescribe	the	clinical	content	
of	care	that	maximizes	the	likelihood	of	successful	health	outcomes	based	on	medical	science,	clinical	
trials,	and	practitioner	consensus.	Local	CPGs	are	regularly	updated	and	serve	as	the	basis	of	training	
in	medical	schools	and	practitioner	refresher	courses.	While	the	lack	of	compliance	with	CPGs	may	
in	part	reflect	a	lack	of	knowledge,	evidence	shows	that	practitioners	often	provide	a	standard	of	care	
well	below	their	level	of	knowledge	of	CPGs.4	In	a	systematic	review	of	the	literature	on	reasons	for	
non‐compliance	 of	 CPGs,	 Cabana	 et	 al.	 (1999)	 report	 that	 resistance	 to	 changing	 existing	 practice	
patterns	is	one	of	the	most	important	barriers	to	CPG	adherence.	For	example,	Grol	and	Grimshaw	
(2003)	surveyed	nurses	and	doctors	in	the	UK	about	the	adoption	of	new	hand	hygiene	guidelines.	
Forty‐nine	percent	responded	that	resistance	to	changing	old	routines	was	an	obstacle	to	complying	
with	new	guidelines.5		

               Changing	deep‐rooted	habits	is	hard	and	even	small	costs	of	adjustment	may	inhibit	changes	
in	 favor	 of	 maintaining	 the	 status	 quo,	 (DellaVigna	 2009;	 Thaler	 and	 Sunstein	 2009).6	 In	 these	
circumstances,	temporary	incentives	may	speed	adoption	by	helping	to	compensate	providers	for	
the	initial	fixed	costs	of	changing	their	practice	pattern	routines.	This	amounts	to	paying	providers	a	
time‐limited	per	unit	incentive	for	the	provision	of	a	component	of	the	CPGs	for	a	specific	condition.7		

               The	use	of	temporary	incentives	to	overcome	organizational	inertia	in	firms	is	similar	in	spirit	
to	 the	 use	 of	 temporary	 incentives	 to	 change	 individual	 and	 consumer	 behavior.	 Firms	 often	 use	
temporary	price	discounts,	such	as	sales	and	coupons,	to	market	their	products	(Blattberg	and	Neslin	
1990;	Kirmani	and	Rao	2000;	and	Dupas	2014).	Discounts	encourage	individuals	to	purchase	goods	
that	they	are	not	in	the	habit	of	buying	which	in	turn	allow	them	to	update	their	beliefs	about	the	
product’s	benefits.		Similarly,	temporary	incentives	have	been	used	to	try	to	help	individuals	develop	
better	 health	 habits	 such	 as	 exercise	 and	 quitting	 smoking.8	 Recently,	 temporary	 incentives	 have	

																																																													
      4	 See	 Das	 and	 Hammer	 (2005);	 Das	 and	 Gertler	 (2007);	 Das,	 Hammer	 and	 Leonard	 (2008);	 Barber	 and	

Gertler	(2009);	Leonard	and	Masatu	(2010);	Gertler	and	Vermeersch	(2012);	and	Monahan,	M.	et	al.	(2015).	
   5	For	more	evidence	of	organizational	inertia	serving	as	a	barrier	to	CPG	compliance	see	Grol	(1990);	Hudak,	

O’Donnell	and	Mazyrka	(1995);	Main,	Cohen	and	DiClemente	(1995);	and	Pathman	et	al.	(1996).	
   6	We	use	a	different	definition	of	habits	than	the	behavioral	economics	literature	where	habits	are	based	on	

the	addiction	of	models	of	Becker	and	Murphy	(1988).	Instead,	we	rely	on	the	notions	of	fast	and	slow	thinking	
discussed	in	Kahneman	(2012)	where	tasks	performed	based	on	fast	thinking	become	habits.	
   7	Paying	an	upfront	lump	sum	amount	is	another	option.	However,	it	may	be	harder	to	ensure	and	verify	the	

actual	 change	 in	 practice	 patterns.	 By	 paying	 based	 on	 actual	 performance	 the	 incentives	 also	 include	 a	
commitment	device	for	compliance.	
   8	See	for	example	Volpp	et	al.	(2008);	Volpp	et	al.	(2009);	Charness	and	Gneezy	(2009);	John	et	al.	(2011);	

Royer	et	al.	(2012);	Cawley	and	Price	(2013);	and	Acland	and	Levy	(2015).	


                                                                	
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been	used	to	stimulate	long‐term	savings	in	the	form	of	initially	high	interest	rates	and	price‐linked	
savings	or	lotteries	(Gertler	et	al.	2015,	and	Schaner	2015).		To	our	knowledge,	our	study	is	the	first	
to	 use	 a	 field	 experiment	 to	 examine	 the	 effects	 of	 temporary	 incentives	 on	 long‐run	 firm	
performance.	

           We	test	the	effects	of	temporary	incentives	paid	to	clinics	for	early	initiation	of	prenatal	care	
using	a	field	experiment	conducted	with	Plan	Nacer,	an	Argentine	government	program	that	provides	
health	 insurance	 to	 otherwise	 uninsured	 pregnant	 women	 and	 children.9	 Prenatal	 care	 by	 skilled	
health	professionals	beginning	in	the	first	trimester	of	pregnancy	is	essential	for	good	maternal	and	
newborn	 health	 outcomes,	 and	 is	 part	 of	 standard	 medical	 training	 throughout	 the	 world	 (WHO	
2006).	 Through	 early	 initiation	 of	 care,	 providers	 are	 able	 to	 detect	 and	 correct	 important	 health	
conditions	such	as	infections	or	anemia	before	they	jeopardize	maternal	or	newborn	outcomes	as	
well	as	advise	mothers	on	proper	prenatal	nutrition	and	prevention	activities	(Schwarcz	et	al.	2001;	
Carroli	et	al.	2001a	and	2001b;	Campbell	and	Graham	2006).	Despite	these	recommendations	and	
the	 scientific	 evidence,	 take‐up	 of	 early	 initiation	 of	 prenatal	 care	 remains	 low	 worldwide	 (WHO	
2014).			

           The	field	experiment	randomized	temporary	financial	incentives	to	health	care	clinics	in	which	
treatment	clinics	were	paid	a	200%	premium	for	early	initiation	of	prenatal	care,	i.e.	before	week	13.		
We	find	that	the	rate	of	early	initiation	of	prenatal	care	was	34%	higher	in	the	treatment	group	than	
in	 the	 control	 group	 (0.42	 versus	 0.31)	 while	 the	 incentives	 were	 being	 paid,	 and	 that	 the	 higher	
levels	of	 early	initiation	 of	prenatal	care	in	the	treatment	group	persisted	at	least	 15	 months	 and	
likely	 more	 than	 24	 months	 after	 the	 incentives	 ended.	 We	 document	 that	 clinics	 changed	 their	
routines	 by	 developing	 strategies	 to	 identify	 likely	 pregnant	 women	 and	 expanding	 the	 role	 of	
community	health	workers	to	find	pregnant	women	and	encourage	them	to	start	care	early,	and	that	
these	changes	in	routines	also	persisted	at	least	15	months	after	the	incentives	ended.	Despite	the	
large	 effect	 of	 the	 incentives	 on	 early	 initiation	 of	 care,	 we	 find	 no	 evidence	 of	 an	 effect	 on	 birth	
outcomes.		

           Our	results	may	explain	the	mechanism	behind	recent	evidence	that	permanent	performance	
incentives	 do	 indeed	 improve	 both	 quality	 and	 quantity	 of	 care.10	 The	 standard	 neoclassical	

																																																													
      9	In	2013,	Plan	Nacer	was	expanded	to	other	populations	and	renamed	Programa	Sumar.	

      10	See	for	example	Basinga	et	al.	(2011);	Flores	et	al.	(2013);	Bonfrer	et	al.	(2013);	De	Walque	et	al.	(2015);	

Gertler	and	Vermeersch	(2013);	Gertler	et	al.	(2014);	and	Huillery	and	Seban	(2014).	Miller	and	Babiarz	(2013)	
provide	a	review.		


                                                                	
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explanation	is	that	providers	are	reallocating	their	effort	across	services	in	response	to	the	increased	
profit	 opportunities.11	 However,	 previous	 studies	 have	 been	 unable	 to	 distinguish	 between	 this	
mechanism	 and	organizational	inertia.	One	way	 to	distinguish	between	the	two	mechanisms	is	to	
observe	what	happens	when	incentives	are	removed.	While	the	incentives	are	in	play	both	models	
predict	 a	 positive	 response.	 However,	 once	 the	 incentives	 are	 removed,	 practice	 patterns	 should	
revert	to	prior	levels	in	the	standard	models	but	continue	at	the	higher	levels	under	organizational	
inertia.		

           Understanding	 the	 mechanism	 by	 which	 financial	 incentives	 work	 is	 not	 only	 scientifically	
interesting,	but	also	policy	relevant.	If	temporary	financial	incentives	are	able	to	induce	providers	to	
adopt	permanent	changes	to	their	clinical	practice	patterns,	then	temporary	incentives	can	achieve	
a	boost	in	performance	at	a	substantially	cheaper	cost	than	permanent	incentives.	Our	results	suggest	
that	 the	 mechanism	 behind	 positive	 provider	 responses	 to	 price	 increases	 is	 more	 related	 to	
adjustment	 costs	than	to	responding	to	higher	 profit	margins.	In	this	case,	long‐term	increases	 in	
productivity	can	be	achieved	more	cheaply	than	through	a	permanent	increase	in	fees.			


2 CONCEPTUAL	FRAMEWORK	
We	develop	a	stylized	model	of	clinical	practice	patterns	where	clinics	incur	a	fixed	cost	to	change	
clinical	practice	routines.	We	assume	that	patients	are	identical,	that	clinics	provide	the	same	services	
to	all	patients,	and	that	demand	is	exogenously	determined.			

               Objective	Function:	Clinics	have	a	pay‐off	function	       ∝      ,	where	 	is	profits,	H	is	health	
of	the	representative	patient,	 N	is	the	number	of	patients,	and	∝	∈ 0,1 	is	the	provider’s	intrinsic	
value	of	a	unit	of	patient	health.	12	As	∝	rises	the	clinic	is	willing	to	sacrifice	more	income	for	patient	
health.	When	∝	takes	on	value	0,	the	clinic	is	purely	extrinsically	motivated,	and	when	∝	is	1	the	clinic	
is	purely	intrinsically	motivated.	While	we	allow	for	both	extrinsic	and	intrinsic	motivation	in	the	
model,	all	of	the	results	follow	even	with	pure	extrinsic	motivation.	Allowing	for	intrinsic	motivation	
does	 not	 change	 the	 direction	 of	 the	 predictions	 just	 the	 magnitude.	 Moreover,	 pure	 intrinsic	




																																																													
      11	See	Baker	et	al.	(1988);	Holmstrom	and	Milgrom	(1991);	Gibbons	(1997);	and	Lazear	(2000).	

     12	There	is	evidence	to	support	intrinsic	motivation	as	at	least	partially	motivating	medical	care	providers.	

See	for	example	Leonard	and	Masatu	(2010);	Kolstad	(2013);	and	Clemenes	and	Gotlieb	(2014).		


                                                                	
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motivation	 by	 itself	 does	 not	 predict	 that	 temporary	 incentives	 would	 have	 long	 terms	 effects	 on	
productivity.	13	

               Health	Production	Function:	Treatment	technology,	as	defined	by	CPGs,	involves	two	services,	
    and	 	 where	                         1	 if	 the	 clinic	 provides	 the	 service	 and	 0	 if	 not.	 	 If	 the	 clinic	 provides	 both	
services,	then	it	is	operating	at	the	production	possibilities	frontier.	The	health	production	function	
for	the	representative	patient	is	                                                     ,	where	 	is	a	mean	zero	random	shock.		

               Clinical	 Practice	 Routine:	 Consider	a	clinic	whose	current	clinical	practice	pattern	routine	is	
to	provide	 	to	all	patients.	In	this	case,	 	is	the	clinic’s	existing	clinical	practice	pattern	routine,	and	
    	is	an	additional	service	that	the	clinic	could	choose	to	add	to	its	practice	routine.	If	the	clinic	wants	
to	integrate	the	provision	of	                                  	into	its	practice	pattern	routine	then	it	must	incur	an	upfront	fixed	
cost	 F.	The	fixed	cost	includes	the	cost	of	retooling	to	be	able	to	provide	 ,	the	cost	of	less	reliable	
service	provision	while	the	new	routine	is	being	learned,	and	the	cost	of	overcoming	psychological	
resistance	to	change.	

	              Profits:	Clinics	are	paid	 	for	 	and	the	marginal	cost	of	providing	 	to	a	patient	is	 .	Clinic	
profits	can	then	be	expressed	as:		

                                                                ∑                                         										,	   	   	       (1)	

where	 	is	the	clinic’s	discount	rate.	

               Adoption:		The	clinic	adopts	                            	if	

                                                                    1            0     0	     .	    	      	             	   	       (2)	

Substitution	 of	(1)	 and	(2)	into	the	pay‐off	function	 and	rearranging	terms	allows	us	to	write	the	
condition	in	(3)	as:	

                                                     ∑                                      	 .		   	      	             	   	       (3)	


																																																													
      13	Without	some	sort	of	fixed	costs	of	adjustment,	both	intrinsically	and	extrinsically	motivated	providers	

would	 still	 operate	 at	 the	 efficient	 frontier.	 Moreover,	 the	 intrinsic	 motivation	 literature	 suggests	 that	
incentives	 can	 negatively	 impact	 performance.	 The	 psychology	 literature	 in	 particular	 has	 long	 argued	 that	
performance‐contingent	incentives	can	be	demotivating	for	intrinsically	motivated	workers.	For	example	see	
Deci	(1971);	Pittman	and	Heller	(1987);	Deci	et	al.	(1999);	Deci	(2001);	Eccles	and	Wigfiel	(2002);	Deci	and	
Ryan	 (2010).	 Benabou	 and	 Tirole	 (2003)	 embed	 these	 ideas	 in	 principle‐agent	 models	 that	 they	 use	 to	
demonstrate	 the	 mechanisms	 through	 which	 financial	 incentives	 can	 “crowd‐out”	 intrinsic	 motivation	 and	
thereby	negatively	affect	performance.	Recent	laboratory	experimental	evidence	on	performance‐contingent	
contracts	confirms	that	incentives	in	the	presence	of	intrinsic	motivation	can	result	in	worse	performance.	For	
example	see	Fehr	and	Falk	(1999);	Fehr	and	Schmidt	(2000);	Gneezy	and	Ruitichini	(2000a	and	2000b);	and	
Ariely	et	al.	(2009).		

                                                                                  	
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Clinics	are	more	likely	to	adopt	 	if	the	profit	margin	from	 	is	higher,	they	are	more	intrinsically	
motivated,	the	effect	of	                          	on	patient	health	is	higher,	they	have	higher	patient	volumes,	and	they	
have	lower	discount	rates.	

               Organizational	 inertia:	 Inertia	 is	 defined	 as	 when	 the	 present	 value	 of	 the	 fixed	 costs	 of	
changing	 organizational	 routine	 prevents	 the	 clinic	 from	 adopting	 a	 valuable	 improvement	 to	
production.	 The	 conditions	 are	                                            0	 and	 ∑                          ,	 i.e.	   	 is	
valuable	but	not	adopted	because	of	the	fixed	cost	of	adjusting	organizational	routine	to	be	able	to	
provide	 .	Clinics	who	are	more	intrinsically	motivated	(i.e.	higher	 )	are	less	likely	to	be	frozen	by	
organizational	inertia	and	maybe	even	willing	to	lose	money	in	order	to	adopt	 ,	especially	if	 	is	
very	productive	(i.e.	higher	 ).	

               Temporary	 Incentives:	Organizational	inertia	can	be	overcome	with	a	temporary	increase	in	
    ,	the	price	of	 .14	Consider	an	increase	to	the	price	paid	in	period	1	that	disappears	in	subsequent	
periods.	Without	loss	of	generality	we	can	simplify	the	model	to	2	periods	with	 	as	the	discount	rate.		
In	this	case,	the	increase	of	 	in	 	in	period	1	necessary	to	induce	the	provider	to	adopt	 	is:	

                                                           ≧    1                    .			                             							(4)	

The	temporary	incentive,	 ,	at	minimum	covers	the	remainder	of	the	fixed	cost	of	adjustment	that	is	
not	 paid	 for	 the	 discounted	 present	 value	 of	 the	 future	 stream	 of	 surplus	 generated	 from	 the	
provision	of	 .	The	incentive	goes	down	with	scale	 ,	the	profit	margin	                               ,	the	extent	to	which	
clinics	are	extrinsically	motivated	times	the	marginal	product	of	 	in	the	health	production	function	
           ,	and	the	discount	rate.	

	              Cross‐Price	 Effects:	 One	 concern	 voiced	 in	 the	 literature	 is	 that	 price	 increases	 for	 some	
services	might	lead	to	a	reallocation	of	effort	from	other	services	that	remain	unchanged	leading	to	
negative	cross‐price	effects.	The	implicit	underlying	model	in	these	papers	is	an	individual	physician	
allocating	 time	 between	 activities	 with	 a	 time	 budget	 constraint.	 In	 our	 model	 of	 a	 medical	 care	
organization	 that	 can	 hire	 more	 staff,	 cross‐price	 effects	 are	 generated	 based	 on	 the	 nature	 of	
economies	 of	 scope	 in	 either	 the	 health	 care	 production	 function	 or	 cost	 function.	 If	 both	 the	
production	and	cost	functions	are	additively	separable,	then	there	are	no	cross‐price	effects.	If	the	



																																																													
     14	The	alternative	is	a	lump	sum	payment	that	is	vulnerable	to	the	possibility	of	noncompliance	and	maybe	

difficult	to	verify.		However,	a	temporary	increase	in	 	requires	the	clinic	to	change	routines	and	actually	adopt	
   	in	order	to	get	paid.	In	this	sense	the	temporary	price	increase	also	includes	a	commitment	device	and	hence	
is	ex	ante	preferable.	

                                                                         	
                                                                         6	
	
functions	are	not	separable,	then	it	is	possible	to	have	either	negative	or	positive	cross‐price	effects	
depending	the	nature	of	substitutability	in	the	production	and	cost	functions.		


3 EXPERIMENTAL	DESIGN	
The	field	experiment	was	conducted	by	Plan	Nacer,	a	public	insurance	program	that	began	in	2005	
to	improve	access	to	quality	health	care	for	otherwise	uninsured	pregnant	women	and	children	less	
than	6	years	old	(Musgrove	2010;	Gertler	et	al.	2014).	Like	Medicaid	in	the	U.S.	and	Seguro	Popular	
in	 Mexico,	 the	 national	 Plan	 Nacer	 program	 transfers	 funds	 to	 local	 governments,	 in	 this	 case	
Provinces,	who	are	then	responsible	for	enrolling	beneficiaries,	organizing	the	provision	of	services,	
and	paying	medical	care	providers.	An	innovative	feature	of	the	Argentine	program	is	that	it	uses	
financial	 incentives	 to	 ensure	 that	 beneficiaries	 receive	 high‐quality	 care.	 Financing	 from	 the	
National	level	to	Provinces	is	based	for	60%	on	program	enrollment	and	for	40%	on	performance.	
Provinces	then	use	those	funds	to	pay	public	health	care	facilities	on	a	fee‐for‐service	basis	for	health	
care	 provided	 to	 program	 beneficiaries.	 The	 national	 government	 determines	 the	 content	 of	 the	
benefits	package,	which	is	uniform	across	provinces,	while	provincial	governments	set	the	price	they	
will	pay	to	providers	for	each	service	in	that	package.	Health	facilities	are	free	to	choose	how	to	use	
realized	revenues	within	relatively	broad	guidelines.	Some,	though	not	all,	provinces	allow	health	
facilities	to	pay	bonuses	to	personnel.	

        Plan	Nacer	scaled	up	by	first	recruiting	and	training	clinics	in	the	operations	of	its	program,	
including	fee	structure,	billing,	and	other	rules.	The	program	regularly	retrains	the	clinics	to	keep	
them	up	to	date	on	any	changes	and	reinforce	areas	that	are	perceived	to	be	weak.	After	clinics	are	
enrolled,	 clinic	 community	 outreach	 staff	 identify	 eligible	 women	 and	 children	 in	 the	 clinics’	
catchment	areas	in	order	to	enroll	them	into	the	program.	Clinic	outreach	staff	also	regularly	contact	
beneficiaries	to	encourage	them	to	take	advantage	of	program	benefits.		

        The	 field	 experiment	 was	 conducted	 with	 primary	 health	 care	 clinics	 in	 the	 Province	 of	
Misiones,	one	of	the	poorest	in	the	country	and	with	high	rates	of	maternal	and	child	mortality.	In	
Misiones,	the	clinic	is	allowed	to	use	up	to	50%	of	revenue	from	Plan	Nacer	fees	to	pay	bonuses	to	
facility	personnel	at	the	discretion	of	the	facility	director.	The	rollout	of	Plan	Nacer	in	Misiones	was	
completed	 in	 2008	 long	 before	 the	 pilot	 study.	 As	 such,	 both	 providers	 and	 beneficiaries	 were	
knowledgeable	of	the	operation	of	Plan	Nacer	before	the	experiment	began.		

        The	experimental	intervention	was	designed	to	encourage	early	initiation	of	prenatal	care	for	
Plan	Nacer	beneficiaries,	thereby	aligning	the	incentives	in	Plan	Nacer	with	official	Argentine	clinical	

                                                     	
                                                     7	
	
practice	 guidelines,	 medical	 school	 training,	 and	 international	 scientific	 evidence.	 Before	 the	
experiment,	 only	 one‐third	 of	 Plan	 Nacer	 beneficiaries	 were	 initiating	 care	 in	 the	 first	 trimester	
(National	 Ministry	 of	 Health,	 2009	 and	 2010).	 The	 experiment	 randomized	 temporary	 financial	
incentives	to	primary	health	care	clinics	in	which	treatment	clinics	were	paid	a	200%	premium	for	
early	initiation	of	prenatal	care,	i.e.	before	week	13.		

               Table	 1	 presents	 the	 payment	 schedule	 for	 the	 periods	 before,	 during	 and	 after	 the	
intervention.	Prior	to	the	intervention	period,	the	province	paid	facilities	$40	ARS	for	each	prenatal	
visit	regardless	of	when	it	occurred	or	whether	it	was	the	first	or	a	subsequent	visit.15	During	the	
intervention	period	the	fee	was	increased	to	$120	ARS	for	1st	visits	that	occurred	before	week	13	but	
remained	at	$40	ARS	for	subsequent	visits.	After	that,	the	intervention	period	fees	reverted	to	the	
original	payment	of	$40	ARS	for	all	visits.	The	modification	amounted	to	a	3‐fold	increase	in	the	fee	
for	1st	visits	before	week	13.	The	modified	fee	structure	was	implemented	for	8	months	‐	from	May	
2010	 to	 December	 2010.	 Facilities	 selected	 to	 receive	 the	 modified	 fee	 structure	 were	 invited	 to	
participate	and	notified	of	the	time‐limited	implementation	on	April	14,	2010.	Facility	directors	were	
required	to	sign	a	formal	modification	of	their	existing	contract	with	Plan	Nacer	 in	order	to	receive	
the	modified	fee	structure.		

               The	 study	 design	 included	 37	 clinics	 out	 of	 262	 primary	 care	 facilities	 of	 the	 province,	 of	
which	 18	 were	 randomly	 assigned	 to	 the	 treatment	 group	 and	 were	 offered	 the	 modified	 fee	
schedule.	 The	 other	 19	 formed	 the	 control	 group.	 Table	 2	 shows	 that	 compliance	 with	 treatment	
assignment	was	not	perfect:	out	of	18	facilities	assigned	to	the	treatment	group,	14	were	actually	
treated	as	three	refused	to	sign	the	agreement	and	a	fourth	closed	before	the	intervention	started.	In	
addition,	 one	 of	 the	 facilities	 originally	 assigned	 to	 the	 control	 group	 was	 mistakenly	 offered	 the	
treatment	and	agreed	to	the	modified	fee	structure.	In	the	end,	there	were	36	facilities	in	the	study	
excluding	the	one	that	closed.	


4 DATA	
The	 Province	 of	 Misiones	 maintains	 a	 well‐developed	 and	 long‐established	 automated	 medical	
record	information	system	managed	by	the	provincial	authorities.	Personnel	at	public	primary	health	
clinics	 and	 hospitals	 digitize	 a	 record	 of	 each	 service	 provided	 to	 each	 patient.	 The	 data	 are	 of	
unusually	 high	 quality	 in	 that	 key	 outcomes	 such	 as	 dates	 of	 visits,	 services	 delivered,	 and	 birth	
weight	are	recorded	at	the	time	of	care	by	the	provider;	therefore	we	do	not	need	to	rely	on	maternal	
																																																													
      15	The	exchange	rate	for	$1	ARS	was	around	$0.25	USD	between	2009	through	2011.		


                                                                	
                                                                8	
	
recall	of	these	variables	collected	in	surveys	long	after	the	visit.	The	data	used	in	the	analysis	are	
extracted	from	these	clinic	records	and	contain	information	on	the	universe	of	patients	for	the	36	
clinics	in	the	study.	The	records	also	include	the	individual’s	national	identity	number,	which	is	used	
to	link	the	individual	clinic	medical	records	from	primary	health	facilities	with	the	registry	of	health	
insurance	coverage,	the	registry	of	Plan	Nacer	beneficiaries,	and	hospital	medical	records.	In	all,	97%	
of	the	primary	clinic	medical	records	were	merged	with	the	data	on	insurance	status	and	program	
beneficiary	 status.	 In	 addition,	 75%	 of	 these	 were	 successfully	 merged	 with	 medical	 records	 data	
from	hospitals.	Therefore	our	analysis	is	able	to	 evaluate	the	impact	of	the	intervention	for	those	
women	who	initiated	their	prenatal	care	in	one	of	the	primary	care	clinics	of	the	sample.	

      4.1        ANALYSIS	SAMPLE	

        Figure	2	depicts	the	timeline	of	the	study	and	the	availability	of	data	divided	into	4	different	
sub‐periods:	 (i)	 a	 16‐months	 pre‐intervention	 period	 from	 January	 2009	 to	 April	 2010,	 (ii)	 an	 8‐
month	intervention	period	from	May	2010	to	December	2010,	(iii)	a	15‐month	“post‐intervention	
period	I”	from	January	2011	to	March	2012	and	(iv)	a	9‐month	“post‐intervention	period	II”	from	
April	2012	to	December	2012.		

        Prenatal	 care	 data	 was	 consistently	 collected	 for	 the	 first	 3	 periods	 from	 January	 2009	
through	March	2012.	Starting	in	April	2012,	however,	Misiones	adopted	a	new	information	system	
and	 as	 a	 result	 data	 from	 post‐intervention	 period	 II	 cannot	 easily	 be	 compared	 to	 data	 from	 the	
earlier	periods.	In	particular,	the	new	system	changed	the	codes	used	to	classify	the	reason	for	visits	
in	order	to	facilitate	billing.	If	in	the	first	visit	the	attending	physician	requested	an	ultrasound	to	
confirm	a	pregnancy,	this	first	visit	was	labeled	as	a	“care	visit”	while	the	subsequent	(second)	visit,	
was	labeled	as	the	first	prenatal	visit,	if	indeed	the	ultrasound	confirmed	the	pregnancy.	On	average,	
this	would	led	to	a	reduction	in	the	share	of	women	who	had	a	visit	labeled	as	“first	prenatal	visit”	
before	week	13	 and	 an	increase	in	the	weeks	pregnant	at	the	time	 of	this	visit.	If	the	new	coding	
system	 affected	 the	 treatment	 and	 control	 groups	 in	 the	 same	 way,	 the	 differences	 between	 the	
treatment	and	control	groups	would	still	capture	the	impact	of	the	incentives,	albeit	possibly	with	
some	 measurement	 error.	 Therefore,	 we	 analyze	 the	 data	 from	 post‐intervention	 period	 II	
separately,	and	interpret	the	results	with	caution.		




                                                       	
                                                       9	
	
        The	analysis	sample	includes	pregnant	women	who	were	beneficiaries	of	Plan	Nacer	at	the	
time	of	their	first	prenatal	visit.	16	While	information	on	prenatal	care	utilization	is	available	for	the	
full	sample	period,	information	related	to	birth	outcomes	is	only	available	for	women	who	gave	birth	
in	a	public	hospital	through	2011,	i.e.	women	who	became	pregnant	before	May	2011.	

       4.2       MEASUREMENT	OF	WEEKS	PREGNANT	AT	1ST	PRENATAL	VISIT	

       We	construct	the	number	of	weeks	of	pregnancy	at	the	time	of	the	first	prenatal	visit	as	the	
difference	between	the	date	of	the	first	visit	and	the	last	menstrual	date	(LMD).	The	LMD	is	routinely	
collected	 at	 the	 time	 of	 the	 visit	 to	 calculate	 the	 estimated	 date	 of	 delivery	 (EDD)	 and	 both	 are	
routinely	recorded	in	the	patient’s	medical	record	at	the	clinic.17	

        One	potential	problem	is	that	medical	personnel	in	treatment	facilities	might	misreport	the	
date	of	late	first	visit	as	occurring	before	week	13	so	that	they	could	bill	to	the	program.	We	think	
this	is	unlikely	for	the	following	reasons.	First,	the	week	of	visit	is	constructed	from	the	date	of	the	
first	prenatal	visit	and	the	LMD,	both	of	which	along	with	the	EDD	are	recorded	in	real	time	in	the	
medical	record.	In	order	to	falsely	report	that	a	first	visit	occurred	in	the	first	12	weeks,	the	provider	
would	have	to	alter	the	date	of	the	first	visit	relative	to	either	the	LMD	or	the	EDD	in	the	medical	
record.	This	would	require	some	effort	if	done	in	real	time	and	would	be	noticeable	by	auditors	if	
altered	ex	post.	Second,	Plan	Nacer	uses	external	auditors	to	verify	the	accuracy	of	clinic	billing.	The	
auditors	compare	the	detailed	clinical	records	to	the	billing	requests	to	find	inconsistencies	and	the	
latter	can	lead	to	substantial	financial	penalties	for	the	provinces.	Finally,	clinical	records	are	legal	
documents	in	Argentina	and	practitioners	could	lose	their	medical	license	if	caught	systematically	
misreporting	for	financial	gain.		

        To	corroborate	our	belief	that	false	reporting	in	the	clinic	records	is	unlikely,	we	empirically	
test	whether	there	is	any	evidence	of	systematic	misreporting	using	data	from	an	alternative	source.	
Specifically,	we	use	gestational	age	at	birth	measured	by	physical	examination	obtained	from	hospital	
records	to	construct	a	second	estimate	of	the	LMD	and	weeks	pregnant	at	the	time	of	the	first	prenatal	
visit.	The	hospital	personnel	that	attend	the	birth	do	not	have	any	incentive	to	misreport	hospital	
records.	We	then	compare	the	estimated	week	of	first	visit	based	on	gestational	age	at	birth	to	the	



																																																													
      16	We	excluded	non‐beneficiaries	because	most	of	them	have	private	health	insurance	and	as	such	are	likely	

to	receive	some	of	care	and	deliver	at	private	facilities.	Since	we	do	not	have	data	from	private	facilities,	the	
outcomes	of	most	of	these	observations	are	censored.	
    17	For	10%	of	the	sample	LDM	was	not	recorded.	For	those	cases,	we	use	the	EDD	to	recover	the	LMD.		



                                                       	
                                                       10	
	
week	of	first	visit	reported	by	the	health	facilities.	The	results	do	not	show	any	evidence	of	systematic	
misreporting	due	to	incentives.	Appendix	A	provides	a	detailed	discussion	of	the	analysis	and	results.	

            4.3               DESCRIPTIVE	STATISTICS	AND	BASELINE	BALANCE	

               Table	3	reports	the	descriptive	statistics	for	the	key	outcomes	of	interest	and	demographic	
characteristics	 at	 baseline,	 i.e.	 in	 the	 16‐month	 pre‐intervention	 period	 (Jan	 2009	 –	 April	 2010).	
Outcomes	 are	 balanced	 at	 baseline	 in	 that	 there	 are	 no	 statistically	 significant	 differences	 in	 the	
means	 of	 variables	 between	 the	 treatment	 and	 control	 groups.	 On	 average	 women	 had	 their	 first	
prenatal	visit	about	17.5	weeks	into	their	pregnancy	with	about	one‐third	of	women	having	that	visit	
before	week	13.	Women	completed	about	4.7	prenatal	visits	over	the	course	of	their	pregnancy	and	
more	than	80%	of	them	received	a	tetanus	vaccine.	Newborns	weighed	approximately	3,300	grams	
on	average,	while	about	6%	of	them	were	born	with	low	birth	weight	(i.e.	less	than	2,500	grams),	and	
slightly	more	than	9%	of	births	were	born	prematurely.	


5 IDENTIFICATION	AND	ESTIMATION	
We	 estimate	 both	 the	 intent‐to‐treat	 (ITT)	 and	 local	 average	 treatment	 (LATE)	 effects	 of	 the	
incentives	 on	 outcomes.	 The	 ITT	 is	 the	 effect	 of	 assigning	 a	 clinic	 to	 treatment	 on	 outcomes,	
regardless	of	compliance.	It	compares	the	mean	outcome	of	the	group	assigned	to	treatment	to	the	
mean	outcome	of	the	group	assigned	to	control	and	is	estimated	by	regressing	the	outcome	against	
an	indicator	of	whether	the	clinic	was	assigned	to	treatment.	The	LATE	is	the	effect	of	a	clinic	actually	
receiving	 the	 incentives	 and	 is	 estimated	 regressing	 the	 outcome	 against	 whether	 the	 clinic	 was	
actually	 treated,	 using	 the	 clinic’s	 randomized	 assignment	 status	 as	 an	 instrumental	 variable	 for	
actual	treatment	(Imbens	and	Angrist	1994).	In	both	cases,	the	treatment	effect	is	identified	off	the	
variation	 induced	 by	 the	 randomized	 assignment	 status.	 In	 the	 discussion	 of	 results	 in	 the	 next	
section,	we	report	the	LATE	estimates.18		

               Our	sample	is	clustered	within	36	health	clinics	since	the	random	assignment	of	treatment	
occurred	at	the	clinic	level.	As	such,	there	may	be	intra‐cluster	correlation	that	must	be	considered	
for	statistical	inference.	Standard	methods	of	correcting	standard	errors	rely	on	large	sample	theory	
both	in	the	number	of	observations	and	in	the	number	of	clusters.	Given	the	small	number	of	clusters	
in	our	sample,	we	instead	use	statistical	inference	methods	that	are	robust	to	randomized	assignment	
of	treatment	among	a	small	number	of	clusters.	Specifically,	we	use	the	Wild	bootstrap	method	to	

																																																													
      18	The	ITT	results	are	almost	identical	to	the	LATE	estimates,	which	is	expected	given	the	relatively	high	

compliance	rates	to	the	original	assignment.	The	ITT	results	are	presented	in	Appendix	C.	

                                                                	
                                                                11	
	
generate	 p‐values	 for	 hypothesis	 testing	 in	 ITT	 models	 (Cameron	 et	 al.	 2008)	 and	 an	 analogous	
method	 for	 hypothesis	 testing	 in	 the	 LATE	 models	 (Gelbach	 et	 al.	 2009).	 	 Our	 Wild	 bootstrap	
procedure	 assigns	 symmetric	 weights	 and	 equal	 probability	 after	 re‐sampling	 residuals,	 and	 uses	
999	replications	(Davidson	and	Flachaire	2008).	


6 TIMING	OF	FIRST	PRENATAL	VISIT	
In	 this	 section	 we	 report	 the	 results	 of	 analyses	 of	 the	 effects	 of	 the	 temporary	 incentives	 on	 the	
timing	of	the	first	prenatal	visit	and	mechanisms	by	which	clinics	achieved	those	results.	

       6.1       DENSITIES		

        Figure	3	compares	the	densities	of	weeks	pregnant	at	the	time	of	the	first	prenatal	visits	for	
the	clinics	assigned	to	the	treatment	and	control	groups.	Panel	A	shows	that	there	is	no	difference	
between	the	densities	of	the	treatment	and	control	groups	in	the	pre‐intervention	period.	Panel	B	
shows	 that	 the	 treatment	 group	 density	 is	 to	 the	 left	 of	 the	 control	 group	 density	 during	 the	
intervention	period.	Finally,	Panel	C	and	D	show	that	the	treatment	group	density	is	placed	to	the	left	
of	the	control	group	density	during	post‐intervention	periods	I	and	II.		Kolmogorov‐Smirnov	tests	for	
equality	of	the	distributions	cannot	be	rejected	for	the	pre‐intervention	analysis,	but	are	rejected	for	
the	 intervention	 and	 both	 post‐intervention	 periods	 with	 p‐values	 of	 0.031,	 0.004,	 and	 0.009	
respectively.	These	results	imply	that	the	temporary	incentives	led	to	earlier	initiation	of	care	in	the	
treatment	 group	 compared	 to	 the	 control	 group	 in	 the	 intervention	 period	 and	 that	 these	 higher	
levels	of	care	persisted	for	at	least	15	months	and	likely	for	24	months	and	more	after	the	higher	fees	
were	removed.	

       6.2       SHORT‐RUN	EFFECTS	

        Table	4	reports	the	estimates	of	the	effects	of	the	temporary	fees	on	the	early	initiation	of	
care.	Panel	A	reports	the	results	for	weeks	pregnant	at	the	time	of	the	first	prenatal	visit	and	Panel	B	
reports	the	results	for	whether	the	first	visit	occurred	before	week	13.	The	first	column	reports	the	
results	for	the	intervention	period	and	the	second	and	third	columns	report	the	results	for	the	post‐
intervention	periods.	During	the	intervention	period,	on	average	women	in	the	treatment	group	had	
their	1st	visit	about	1.5	weeks	earlier	in	their	pregnancy	than	women	in	the	control	group.	The	share	
of	women	in	the	treatment	group	who	had	their	 1st	visit	before	week	 13	is	11	percentage	points	
higher	than	the	control	group;	approximately	35%	higher	than	the	control	group.	Both	estimates	are	
significantly	different	from	zero	at	conventional	p‐values.	


                                                        	
                                                        12	
	
            6.3               LONG‐RUN	EFFECTS	

               Our	 model	 of	 behavioral	 inertia	 provided	 clear	 predictions	 about	 provider	 behavior	 once	
temporary	 incentives	 disappear:	 i.e.	 if	 the	 fee	 increase	 is	 enough	 to	 overcome	 the	 fixed	 costs	 of	
adapting	a	new	practice,	clinics	should	maintain	higher	levels	of	prenatal	care	after	incentives	are	
removed.	 Column	 2	 of	 Table	 4	 reports	 estimated	 impact	 of	 the	 temporary	 fee	 increase	 on	 early	
initiation	of	care	in	the	15‐month	period	after	the	fees	were	removed.		On	average,	women	in	the	
treatment	group	started	their	care	1.6	weeks	earlier	than	those	in	the	control	group.	The	difference	
between	the	treatment	and	control	groups	in	the	share	of	women	who	had	their	1st	visit	before	week	
13	was	8	percentage	points.	Both	estimates	are	statistically	different	from	zero	at	conventional	levels.	
Further,	we	cannot	reject	the	null	hypothesis	that	the	impact	is	different	in	the	intervention	and	post‐
intervention	periods.	These	results	are	consistent	with	the	hypothesis	that	temporary	incentives	help	
overcome	behavioral	inertia	and	motivate	long‐run	changes	in	performance.	

               While	there	is	no	significant	different	between	the	effect	during	the	intervention	and	the	post‐
intervention	periods,	one	concern	may	be	that	the	effect	of	treatment	slowly	trended	towards	zero	
after	the	incentives	ended.	To	test	this	hypothesis,	we	plot	the	mean	number	of	weeks	pregnant	at	
the	 time	 of	 first	 prenatal	 visit	 for	 treatment	 and	 control	 groups,	 before,	 during	 and	 after	 the	
intervention	(Figure	4).19	We	split	the	pre‐intervention	period	into	two	sub‐periods	of	6‐months	each	
and	the	post‐intervention	period	into	3	sub‐periods:	the	first	two	are	6	months	and	the	third	is	3	
months.	The	treatment	effect	is	the	difference	between	the	two	lines.	While	the	treatment	and	control	
groups	have	similar	trends	before	the	intervention,	the	treatment	group	appears	to	receive	earlier	
care	during	the	intervention,	and	the	change	persists	after	the	end	of	the	intervention.	Notice	that	
there	is	little	if	any	fall	off	over	the	post‐intervention	period.	Rather	the	treatment	effects	remain	
fairly	constant	over	the	15	‐month	post‐intervention	period	I.	Figure	5	depicts	the	same	relationship	
for	the	share	of	women	who	receive	care	before	week	13	of	pregnancy.20	Again,	the	effects	of	the	
intervention	appear	to	continue	at	a	steady	rate	after	it	is	discontinued.		

            6.4               LONGER‐RUN	EFFECTS	
            The	period	of	analysis	in	our	main	results	is	restricted	to	January	2009	to	March	2012.	Recall	
that	 starting	 in	 April	 2012,	 the	 visit	 coding	 system	 changed.	 Hence	 starting	 in	 April	 2012	 what	 is	
reported	as	first	visits	in	the	data	is	actually	a	mix	of	first	and	second	visits.	As	a	result	the	average	of	
weeks	pregnant	at	first	visit	increases	and	the	share	of	pregnant	women	whose	first	visit	was	before	
																																																													
      19	 As	 discussed	 above,	 the	 information	 from	 post‐intervention	 period	 II	 (April‐December	 2012)	 uses	 a	

different	metric	and	is	therefore	not	included	in	this	figure.		
    20	Ibidem.	


                                                                	
                                                                13	
	
week	13	falls	relative	to	previous	periods.	Column	3	in	Table	4	shows	the	results	for	this	last	period.	
The	mean	average	of	weeks	pregnant	at	the	time	of	the	first	visit	for	the	control	group	is	substantially	
higher	for	this	period	than	for	previous	periods	and	the	mean	share	that	had	their	first	visit	before	
week	13	is	substantially	lower,	suggesting	that	there	is	measurement	error	in	our	main	outcome	in	
this	period.	However,	this	difference	in	coding	should	have	a	similar	effect	in	treatment	and	control	
clinics	 given	 the	 randomized	 assignment	 of	 the	 treatment.	 Therefore	 the	 difference	 between	
treatment	and	control	clinics	should	cancel	out	the	measurement	error	and	provide	us	with	unbiased	
estimates	of	the	impact.		

      The	 results	 in	 Table	 4	 show	 a	 statistically	 significant	 reduction	 in	 the	 number	 of	 weeks	
pregnant	at	the	time	of	the	first	visit	and	a	statistically	significant	increase	in	the	share	of	pregnant	
women	who	had	their	first	visit	before	week	13.	These	results	suggest	that	improved	productivity	
from	the	temporary	fee	increase	persisted	at	least	24	months	after	the	fees	were	removed.			

      6.5        ROBUSTNESS	

        We	implement	three	robustness	checks.	First,	the	main	sample	may	include	pregnancies	that	
start	in	one	period	and	end	in	another,	which	could	cloud	the	effect	of	the	incentives	on	timing	of	the	
first	visit.	For	example,	a	woman	who	is	6	months	pregnant	and	has	not	had	a	prenatal	visit	when	the	
intervention	 starts	 and	 subsequently	 receives	 her	 first	 prenatal	 checkup	 during	 the	 intervention,	
would	 be	 counted	 as	 a	 third	 trimester	 first	 visit	 during	 the	 intervention	 period,	 even	 though	 the	
intervention	 cannot	 affect	 whether	 she	 receives	 prenatal	 care	 before	 week	 13.	 Hence,	 in	 this	
robustness	test	we	re‐estimate	the	models	on	a	restricted	sample	where	women	are	no	more	than	
one	month	pregnant	in	the	first	month	of	the	period	and	no	less	than	3‐months	pregnant	in	the	last	
month	of	the	period.	The	results,	reported	in	Panels	B	of	Appendix	Tables	B1	and	B2,	are	very	close	
in	magnitude	and	statistical	significance	to	the	main	results	in	Table	4.		

        Second,	even	though	there	were	no	statistical	differences	in	baseline	means,	it	is	possible	that	
randomization	 was	 not	 able	 to	 fully	 balance	 the	 treatment	 and	 control	 groups	 on	 unobservable	
characteristics	given	the	small	number	of	clinics.		In	order	to	test	for	this	possibility,	we	estimate	the	
models	using	difference‐in‐differences	with	clinic	and	month	fixed	effects.	The	results,	reported	in	
Panels	C	of	Appendix	Table	B1	and	B2,	are	very	close	in	magnitude	and	statistical	significance	to	the	
main	results	in	Table	4.		

        Finally,	in	studies	involving	a	small	sample	of	clusters	there	is	a	concern	that	a	few	outliers	
may	drive	the	average	effect	found	in	the	previous	sections.	We	explore	this	possibility	by	estimating	
clinic‐specific	treatment	effects	whereby	we	compare	each	treated	clinic	individually	to	the	control	

                                                      	
                                                      14	
	
clinics	as	a	group.	Appendix	Figures	B1	and	B2	plot	these	individual	clinic	treatment	effects	for	the	
outcomes	of	weeks	pregnant	at	the	time	of	the	first	prenatal	visit	(B1)	and	for	the	probability	of	that	
the	first	visit	occurred	before	week	13	(B2),	respectively.	The	results	are	sorted	along	the	x‐axis	from	
the	 lowest	 to	 the	 highest	 estimated	 effect,	 while	 the	 dashed	 blue	 line	 is	 the	 intent‐to‐treat	 effect	
calculated	by	pooling	the	intervention	and	the	first	post	intervention	period.		The	solid	black	line	
represents	a	zero	treatment	effect.	The	vertical	lines	are	95%	confidence	intervals	constructed	using	
standard	errors	obtained	from	the	Wild	bootstrap	procedure.		The	figures	show	that	the	hypothesis	
of	no	treatment	effect	is	rejected	for	11	out	of	17	clinics	in	Figure	B1	and	12	out	of	17	clinics	in	Figure	
B2.	In	addition,	the	treatment	effects	have	the	expected	sign	in	15	out	17	clinics	in	Figure	B1	and	14	
out	of	clinics	in	Figure	B2.	This	provides	evidence	that	our	results	are	not	driven	by	a	few	large‐effect	
clinics.		

            6.6               MECHANISMS	

               In	order	to	better	understand	how	clinics	were	able	to	achieve	such	large	increases	in	the	
share	 of	 women	 who	 initiated	 prenatal	 care	 before	 week	 13,	 we	 conducted	 a	 series	 of	 in‐depth	
interviews	with	professionals	in	a	sub‐sample	of	5	treatment	clinics	and	3	comparison	clinics.21	We	
find	that	treatment	clinics	adopted	new	practices	and	changed	routines	in	order	to	increase	early	
initiation	 of	 prenatal	 care.	 After	 the	 initial	 invitation	 to	 participate	 in	 the	 pilot,	 all	 5	 interviewed	
treatment	clinics	organized	a	team	meeting	with	the	staff	in	order	to	discuss	strategies	to	respond	to	
the	 new	 incentive	 scheme.	 Various	 treatment	 clinics	 adopted	 different	 strategies,	 but	 all	 of	 them	
involved	expanding	the	scope	of	work	of	community	health	workers	to	identify	and	encourage	newly	
pregnant	Plan	Nacer	to	initiate	their	prenatal	care	early.	In	some	clinics,	the	director	supported	the	
change	 in	 strategies	 by	 changing	 the	 way	 the	 financial	 incentives	 were	 distributed	 between	 staff	
members.22	In	particular,	some	of	them	started	allocating	the	incentives	conditional	on	the	number	



																																																													
      21	 The	 clinics	 interviewed	 are	 located	 in	 Posadas,	 the	 capital	 of	 Misiones	 Province.	 Each	 interview	 took	

approximately	45	minutes.	The	interviews	were	carried	out	in	May	2015.	
    22	Up	to	2013,	any	health	facility	participating	in	 Plan	 Nacer	in	Misiones	was	able	to	use	up	to	10%	of	their	

Plan	Nacer	funds	to	pay	incentives	to	personnel.	If	the	facility	achieved	a	set	of	health	targets	measuring	using	
performance	 indicators	 (tracers)	 set	 by	 the	 province,	 that	 facility	 was	 able	 to	 use	 up	 to	 50%	 of	 funds	 for	
monetary	incentives	to	health	professionals.	The	bonuses	could	be	assigned	to	any	person	working	at	the	health	
facility,	including	the	health	workers,	administrative	personnel,	volunteers,	and	even	to	personnel	affiliated	
with	other	programs	as	long	as	they	were	not	absent	for	more	than	10	working	days	in	a	month,	they	did	not	
participate	 in	 a	 strike	 organized	 by	 the	 union,	 and	 they	 were	 not	 subjected	 to	 a	 disciplinary	 sanction	
(suspension	without	 pay	 or	 dismissal).	 In	 all	cases,	the	 final	 decision	 regarding	assignment	of	incentives	to	
personnel	was	the	prerogative	of	the	clinic	director.	


                                                                	
                                                                15	
	
of	pregnant	women	that	each	team	member	brought	to	the	clinic	in	a	month.		This	allocation	further	
incentivized	health	workers	to	test	new	practices.	

               The	 in‐depth	 interviews	 uncovered	 several	 innovative	 strategies	 that	 treatment	 clinics	
developed	to	identify	pregnancies	early.	For	instance,	health	workers	started	to	follow	up	women	
who	 used	 birth	 control	 pills.23	 Specifically	 community	 health	 workers	 prioritized	 home	 visits	 to	
women	who	had	not	picked	up	their	pills.	Second,	health	workers	started	targeting	women	at	high	
risk	 of	 not	 coming	 in	 for	 an	 early	 checkup.	 According	 to	 the	 interviewed	 doctors,	 mothers	 who	
already	 have	 children	 are	 less	 likely	 to	 initiate	 their	 prenatal	 visits	 early	 in	 a	 new	 pregnancy.	
However,	 many	 of	 these	 women	 are	 also	 eligible	 for	 weekly	 free	 milk	 distribution	 for	 their	 older	
children.	Health	workers	met	these	mothers	at	the	time	of	the	milk	distribution,	enquired	about	their	
last	 menstruation	 date,	 and	 offered	 an	 instant‐read	 pregnancy	 test	 to	 those	 women	 whose	
menstruation	was	overdue.	Third,	health	workers	identified	difficulties	in	providing	early	prenatal	
care	 to	 adolescents,	 as	 they	 might	 be	 unwilling	 to	 reveal	 a	 pregnancy,	 especially	 to	 their	 parents.	
Community	health	workers	therefore	decided	to	change	the	timing	of	home	visits,	so	as	to	increase	
the	chance	of	finding	adolescents	by	themselves.	In	one	of	the	interviewed	clinics,	the	work	flow	was	
modified	so	as	to	ensure	predictable	availability	of	a	gynecologist	on	certain	days	of	the	week.	This	
in	turn	provided	an	 easy	way	for	community	health	workers	and	 administrative	staff	to	schedule	
patient	appointments.	Other	clinics	introduced	new	ways	of	keeping	track	of	“at	risk”	patients,	such	
as	a	notebook	that	kept	track	of	any	visits	to	the	homes	of	women	that	were	at	risk,	or	a	map	that	
identified	 catchment	 areas	 of	 community	 health	 workers	 with	 corresponding	 (potential)	
pregnancies.	

               We	 are	 able	 to	 substantiate	 the	 claims	 of	 increased	 outreach	 using	 clinic	 administrative	
records	 on	 the	 number	 of	 community	 outreach	 activities	 that	 resulted	 in	 actual	 maternal‐child	
service	at	the	clinic.24	Figure	6	displays	the	average	and	median	number	of	outreach	activities	that	
resulted	 in	 actual	 maternal‐child	 services	 for	 the	 pre‐intervention,	 intervention,	 and	 post‐
intervention	I	periods.25	The	results	show	that	there	is	little	difference	in	outreach	activities	between	
treatment	and	control	clinics	in	the	pre‐intervention	period.	In	the	intervention	period	the	treatment	

																																																													
      23	Birth	control	pills	are	dispensed	free	of	charge	by	each	health	facility’s	pharmacy	unit,	though	women	

cannot	collect	more	than	a	monthly	supply	at	any	one	time.	The	pharmacy	unit	keeps	records	of	all	birth	control	
pill	collections.	
    24	 Plan	 Nacer	 finances	 clinic	 outreach	 activities	 on	 a	 fee‐for‐service	 basis	 and	 employs	 an	 external	

independent	auditor	to	audit	clinic	activity	reports.	Treatment	and	comparison	clinics	were	paid	the	same	fee	
for	these	activities	before,	during	and	after	the	experiment.	
    25	The	medians	are	better	measures	of	central	tendency	as	the	densities	of	both	activities	are	asymmetric	

heavily	skewed	to	the	right.	

                                                                	
                                                                16	
	
group	evidenced	substantially	more	activities	than	the	control	group,	and	this	difference	is	sustained	
through	the	post‐intervention	period.		

               We	use	the	data	to	estimate	the	differences	in	log	number	of	activities	between	the	treatment	
and	control	groups.	The	results	show	no	differences	in	activities	in	the	pre‐intervention	period	and	
positive	 and	 statistically	 significant	 higher	 levels	 of	 activities	 in	 the	 treatment	 clinics	 in	 the	
intervention	and	post‐intervention	I	periods	(Table	5).	Again,	we	cannot	reject	that	the	hypothesis	
that	 the	 effects	 are	 different	 in	 the	 intervention	 and	 post‐intervention	 periods	 implying	 that	 the	
increase	in	successful	outreach	activities	persisted	after	the	temporary	incentives	were	removed.		

            6.7               PSYCHOLOGICAL	BARRIERS	

               In	 the	 previous	 subsection	 we	 documented	 tangible	 costs	 of	 adjustment	 to	 increase	 early	
initiation	 of	 prenatal	 care.	 An	 additional	 potential	 cost	 of	 adjustment	 is	 psychological	 barriers	 to	
change.	One	way	to	overcome	psychological	resistance	is	to	make	the	guideline	or	task	more	salient	
in	the	minds	of	the	clinic	staff.	26	The	issue	is	not	one	of	lack	of	knowledge	or	information	as	initiating	
care	 in	 the	 first	 trimester	 has	 been	 in	 CPGs	 since	 the	 1970s	 and	 has	 been	 a	 long‐standing	 part	 of	
standard	medical	education.		Rather	the	issue	is	the	importance	or	priority	that	staff	place	on	the	
task.	

               The	temporary	incentives	might	have	increased	the	importance	of	early	initiation	of	care	in	
the	staff’s	minds,	thereby	making	it	a	higher	priority	for	action.	The	higher	the	priority	of	a	task,	the	
less	likely	psychological	barriers	would	stand	in	the	way	of	adoption.	Kahneman	(2012,	pp	8)	states	
that	“…frequently	 mentioned	topics	populate	the	 mind…”	more	than	 others	and	“…people	tend	to	
assess	the	relative	importance	of	issues	by	the	ease	with	which	they	are	retrieved	from	memory”.	As	
such,	salience	“…is	enhanced	by	mere	mention	of	an	event”	(Kahneman	2012,	pp	331).	If	incomplete	
or	non‐adoption	of	a	task	is	a	matter	of	salience	then	the	observed	treatment	effects	may	be	explained	
by	the	fact	that	temporary	incentives	help	to	overcome	this	type	of	psychological	barrier	to	change.		

               While	we	do	not	have	information	on	the	salience	of	early	initiation	of	care	during	or	shortly	
after	the	experiment,	we	explore	whether	the	temporary	fee	increase	made	early	initiation	of	care	
more	important	in	the	minds	of	the	clinic	staff	after	the	end	of	the	experiment,	using	an	online	survey	
administered	to	the	chief	medical	officer	of	each	clinic	about	the	absolute	and	relative	importance	of	

																																																													
    26	 Taylor	 and	 Thompson	 (1982)	 define	 salience	 as,	 “…the	 phenomenon	 that	 when	 one's	 attention	 is	

differentially	directed	to	one	portion	of	the	environment	rather	than	to	others,	the	information	contained	in	
that	portion	will	receive	disproportionate	weighting	in	subsequent	judgments”.	See	Bordalo	et	al.	(2012,	2013)	
for	a	more	recent	discussion	of	salience	and	choice	theory.	See	De	Mel	et	al.	(2013),	and	Karlan	et	al.	(2015)	for	
empirical	analysis	of	salience	effects	through	informational	reminders.	

                                                                	
                                                                17	
	
seven	 different	 prenatal	 care	 procedures	 including	 initiating	 prenatal	 care	 prior	 to	 week	 13	 of	
pregnancy	(see	Appendix	D).	

        Figures	8	and	9	compare	the	absolute	score	and	relative	ranking	of	the	procedures	in	terms	
of	importance	for	prenatal	care.	The	absolute	scores	ranges	 from	 0	to	 5,	 with	5	being	the	highest	
while	the	relative	ranking	sorts	the	seven	practices	from	1	to	7,	with	1	being	the	highest	ranking.	Our	
outcomes	 of	 interest	 are	 the	 absolute	 score	 and	 relative	 ranking	 assigned	 to	 early	 initiation	 of	
prenatal	care.	Figure	8	shows	that	the	absolute	score	assigned	to	early	prenatal	care	is	on	average	
4.8	in	the	treatment	group	and	4.7	in	the	control	group.	Figure	9	shows	that	on	average	the	relative	
ranking	for	this	practice	is	also	similar	between	the	two	groups,	2.0	for	the	treatment	group	and	1.9	
for	 the	 control	 group.	 Moreover,	 these	 differences	 are	 not	 statistically	 significant	 at	 conventional	
levels	(see	Appendix	D).	These	results	suggest	that	the	early	initiation	of	prenatal	care	is	of	similar	
high	absolute	and	relative	importance	and	that	temporary	fees	did	not	have	a	lasting	effect	on	either	
the	absolute	nor	relative	importance.		

      6.8        ALTERNATIVE	EXPLANATIONS	

      One	 alternative	 explanation	 for	 the	 short‐term	 treatment	 effects	 is	 that	 the	 incentives	 are	
causing	treatment	clinics	to	try	to	attract	pregnant	women	who	otherwise	would	have	used	other	
clinics.	This	is	unlikely	to	be	true	as	beneficiary	women	are	assigned	to	specific	clinics	when	enrolled	
in	Plan	Nacer.	Moreover,	the	number	of	patients	per	month	and	the	share	that	initiate	care	before	
week	13	are	the	same	in	the	pre‐	and	post‐intervention	periods	for	control	clinics,	and	the	average	
monthly	 number	 of	 patients	 is	 also	 the	 same	 in	 the	 pre‐	 and	 post‐intervention	 periods	 for	 the	
treatment	clinics.		

      An	alternative	explanation	for	long‐run	results	is	that	after	the	temporary	incentives	ended,	
women	who	were	pregnant	during	the	intervention	periods	passed	the	message	of	the	importance	
of	 early	 initiation	 of	 care	 onto	 other	 beneficiary	 women	 who	 became	 pregnant	 during	 the	 post‐
intervention	period.	Hence,	the	persistence	of	the	effect	of	the	incentives	after	the	incentives	might	
be	 caused	 by	 an	 informational	 spillover.	 However,	 the	 higher	 amount	 of	 the	 community	 outreach	
activities	 in	 treatment	 clinics,	 the	 mechanism	 used	 to	 generate	 higher	 early	 initiation	 of	 care,	
continued	into	the	post‐experimental	period	at	the	same	level	as	in	the	intervention	period.	Hence,	if	
there	 were	 information	 spillovers	 in	 the	 post‐intervention	 period,	 then	 one	 would	 expect	 to	 see	
higher	treatment	effects	in	the	post‐intervention	period	than	in	the	intervention	period.		

      Finally,	one	might	argue	that	the	clinics	continued	the	new	routines	after	the	temporary	fees	
were	eliminated	because	they	faced	a	large	fixed	cost	of	reverting	to	the	old	routines	and	not	because	

                                                      	
                                                      18	
	
the	new	routines	added	net	value.	However,	in	this	case,	we	think	that	the	fixed	costs	of	reversing	the	
routines	were	small,	because	the	community	health	workers	could	simply	have	returned	to	their	old	
patterns	of	activities.		


7 CROSS‐PRICE	EFFECTS	(SPILLOVER)	
               While	the	modified	fee	schedule	was	designed	to	affect	the	timing	of	the	first	prenatal	visit,	
we	might	expect	providers	to	reduce	effort	supplied	to	other	services,	resulting	in	a	lower	provision	
of	 such	 services	 to	 patients.	 We	 test	 for	 this	 by	 estimating	 the	 effect	 of	 the	 incentives	 on	 the	
probability	of	pregnant	women	having	a	valid	tetanus	vaccine,	and	the	number	of	prenatal	visits.	The	
results	presented	in	Table	6	report	no	evidence	of	cross‐price	effects,	positive	or	negative,	in	either	
the	intervention	period	or	in	post‐intervention	period	I.	In	fact,	the	levels	of	these	services	appear	to	
be	constant	over	time.	While	the	concern	about	crowding‐out	is	typically	for	a	context	of	individual	
providers	facing	time	and	effort	constraints,	our	results	are	consistent	with	a	firm	setting	where	there	
are	no	overall	effort	or	time	constraints.	


8 BIRTH	OUTCOMES	
            Next	 we	 address	 the	 question	 of	 whether	 the	 effect	 of	 the	 incentives	 for	 early	 initiation	 of	
prenatal	 care	 translated	 into	 improved	 birth	 outcomes	 as	 measured	 by	 birth	 weight,	 low	 birth	
weight,	and	premature	birth.	As	shown	in	Figure	7	and	reported	in	Table	7	we	find	no	effect	of	the	
incentives	on	birth	outcomes	in	either	the	intervention	period	or	in	the	post‐intervention	period.		

            There	are	a	number	of	possible	reasons	for	this.	First,	the	sample	could	be	too	small	to	be	able	
to	detect	a	statistically	significant	effect	on	outcomes.	However,	the	point	estimates	are	very	small,	
half	of	them	are	negative	and	they	are	of	similar	magnitude	to	differences	between	treatment	and	
control	groups	in	the	pre‐intervention	period.		Second,	given	that	the	results	on	birth	outcomes	are	
obtained	from	an	analysis	of	a	subsample	of	beneficiaries	for	whom	we	were	able	to	merge	prenatal	
care	records	with	hospital	medical	records,	it	is	possible	that	the	results	in	Table	4	do	not	hold	for	
this	subsample.	We	therefore	replicate	the	prenatal	care	analysis	using	only	the	subsample	of	women	
for	whom	hospital	medical	records	are	available.	Overall,	we	obtain	similar	results	to	those	obtained	
with	the	full	sample.27	Third,	despite	the	medical	literature	and	CPG	recommendation,	it	is	possible	
that	 early	 initiation	 of	 care	 matters	 only	 a	 small	 amount	 for	 the	 general	 population	 of	 pregnant	


																																																													
      27	Results	of	this	analysis	are	available	upon	request.	


                                                                	
                                                                19	
	
women,	even	if	early	initiation	of	care	matters	a	great	deal	for	high‐risk	patients.	High	risk	patients	
include,	 among	 others,	 smokers,	 substance	 abusers,	 those	 with	 poor	 medical	 and	 pregnancy	
histories,	and	those	who	start	prenatal	care	very	late	in	their	third	trimester	or	only	when	a	problem	
occurs.		It	may	be	that	the	increase	in	early	initiation	of	care	comes	from	primarily	low‐risk	mothers	
who	are	less	likely	to	benefit	from	early	initiation	of	care.	One	would	think	that	it	would	be	easier	to	
persuade	 low‐risk	 mothers	 to	 come	 a	 littler	 earlier	 than	 to	 convince	 high‐risk	 mothers	 who	 are	
reluctant	to	come	for	any	care	at	all.		

        In	fact,	this	is	consistent	with	the	small	reduction	in	the	average	weeks	pregnant	at	the	time	
of	the	first	prenatal	visit.		On	average,	women	in	the	treatment	group	initiated	prenatal	care	about	
1.5	 weeks	 earlier	 than	 women	 in	 the	 control	 group.	 Prenatal	 care	 may	 affect	 birth	 outcomes	 by	
diagnosing	 and	 treating	 illness	such	 as	 hypertension	 and	 gestational	 diabetes	 as	 well	 as	 trying	 to	
change	maternal	behavior	through	promoting	activities	such	as	good	nutrition,	not	smoking	and	not	
consuming	alcohol.	If	the	intervention	had	induced	high‐risk	women	who	otherwise	would	have	had	
1st	visit	much	later	in	the	pregnancy,	then	the	incentives	may	have	had	a	measurable	impact	on	birth	
outcomes.	Hence,	while	the	incentives	were	effective	in	increasing	early	initiation	of	care,	they	did	
not	manage	to	sufficiently	affect	the	group	most	likely	to	benefit.	The	solution	might	be	to	condition	
incentives	on	attending	high‐risk	women,	but	risk	is	difficult	and	expensive	to	identify	and	verify	and	
therefore	may	not	be	contractible.			


9 DISCUSSION	
We	examine	the	effects	of	temporary	financial	incentives	for	medical	care	providers	to	increase	early	
initiation	of	prenatal	care	for	pregnant	women	using	a	randomized	controlled	trial	in	Argentina.	The	
intervention	randomly	allocates	a	three‐fold	increase	in	the	fee	paid	to	health	facilities	for	each	initial	
prenatal	visit	that	occurs	before	week	13	of	pregnancy.	This	premium	was	implemented	for	a	period	
of	8	months	and	then	ended.	Using	data	on	health	services	and	birth	outcomes	from	medical	records,	
we	investigate	both	the	short‐term	effects	of	the	incentive	and	whether	the	effects	persist	once	the	
direct	monetary	compensation	disappears.		

        Our	 results	 suggest	 that	 the	 temporary	 incentives	 motivated	 long‐run	 changes	 in	
performance.	We	find	that	the	incentives	led	to	pregnant	women	being	35%	more	likely	to	initiate	
prenatal	care	before	week	13	and	that	the	higher	levels	of	early	initiation	of	care	persisted	for	at	least	
15	months	and	likely	more	than	24	months	after	the	incentives	ended.	These	results	are	consistent	
with	 a	 model	 of	 providers	 who	 face	 a	 fixed	 cost	 to	 changing	 their	 clinical	 practice	 routines,	 i.e.	


                                                       	
                                                       20	
	
organizational	 inertia.	 Temporary	 incentives	 induced	 providers	 to	 adopt	 changes	 to	 their	 clinical	
practice	patterns	by	helping	them	to	overcome	inertia.	Once	they	adopt	changes	to	practice	patterns	
that	they	believe	are	beneficial	to	patients,	the	changes	persist	even	after	the	monetary	incentives	
disappear.	These	results	are	consistent	with	the	findings	from	in‐depth	interviews	that	evidenced	
that	treatment	clinics	adopted	innovative	practices	and	changed	routines	in	order	to	increase	early	
initiation	of	prenatal	care.	

        Our	study	adds	to	the	growing	body	of	evidence	that	incentives	are	effective	in	improving	
provider	performance.		Our	results	also	have	a	number	of	important	policy	implications.	First,	our	
results	 suggest	 that	 temporary	 incentives	 may	 be	 effective	 in	 motivating	 long‐term	 provider	
performance	 at	 a	 substantially	 lower	 cost	 than	 permanent	 incentives.	 Second,	 while	 we	 find	 that	
incentives	are	able	to	motivate	changes	in	clinical	practice	patterns,	we	did	not	find	improvements	
in	health	outcomes.	The	monetary	incentives	that	were	implemented	were	not	able	to	sufficiently	
reach	those	women	for	whom	early	initiation	of	prenatal	care	would	have	the	largest	health	impact.	
Therefore,	incentives	may	be	made	more	effective	by	defining	ex‐ante	the	population	most	likely	to	
benefit,	and	tailoring	incentives	towards	this	population.	However,	tailoring	incentives	to	high	risk	
populations	 or	 those	 most	 likely	 to	 benefit	 from	 the	 services	 may	 not	 be	 contractible	 as	 these	
characteristics	 are	 typically	 not	 observable.	 This	 is	 maybe	 a	 major	 limitation	 of	 using	 incentive	
contracts	to	improve	health	outcomes.			

	                                	




                                                     	
                                                     21	
	
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       goals,”	World	Health	Organization,	Geneva.	
	                                   	




                                                          	
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                                          FIGURES	AND	TABLES	
                    Figure	1:	Provider	Compliance	with	Clinical	Practice	Guidelines	




                                                                                                               	
Source:	Authors’	elaboration	based	on	(‐)	Schuster	et	al.	(1998);	(+)	Grol	(2001);	(++)	Campbell	et	al.	(2007);	
(*)	Das	and	Gertler	(2007);	and	(#)	Gertler	and	Vermeersch	(2012).		
	

	                                 	




                                                      	
                                                      27	
	
    Figure	2:	Timeline	and	Data	Availability	




                                                	

	   	




                      	
                      28	
	
                       Figure	3:	Densities	of	Weeks	Pregnant	at	1st	Prenatal	Visit	




		
Notes:	 Densities	 estimated	 using	 an	 Epanechnikov	 kernel	 with	 optimal	 bandwidth.	 P‐vales	 of	 Kolmogorov‐
Smirnov	tests	of	equality	of	distributions	between	groups	reported	below	figure.	The	two	vertical	lines	indicate	
weeks	13	and	20	of	pregnancy.	Source:	Authors’	own	elaboration	based	on	data	from	the	provincial	medical	
record	information	system.		
	

	                                 	




                                                       	
                                                       29	
	
                    Figure	4:	Mean	Number	of	Weeks	Pregnant	at	1st	Prenatal	Visit	




Notes:	The	first	two	points	(circles)	are	means	for	6‐month	periods	prior	to	the	intervention	period.	The	third	
point	(Diamond)	corresponds	to	the	intervention	period.	The	fourth	and	fifth	points	(triangles)	correspond	to	
6‐months	periods	after	the	intervention	period,	while	the	last	point	(triangle)	is	for	a	3‐month	period.		
	
	
	
	                                 	




                                                      	
                                                      30	
	
        Figure	5:	Proportion	of	Mothers	with	1st	Prenatal	Visit	before	Week	13	of	Pregnancy		




                                                       	
Notes:	The	first	two	points	(circles)	are	means	for	6‐month	periods	prior	to	the	intervention	period.	The	third	
point	(Diamond)	corresponds	to	the	intervention	period.	The	fourth	and	fifth	points	(triangles)	correspond	to	
6‐months	periods	after	the	intervention	period,	while	the	last	point	(triangle)	is	for	a	3‐month	period.		
	
	
	                                 	




                                                      	
                                                      31	
	
                             Figure	6:	Number	of	Clinic	Outreach	Activities	




                                                                                                              	
Notes:	The	height	of	the	bars	report	the	mean	and	median	number	of	outreach	activities	that	resulted	in	actual	
maternal‐child	service	at	the	clinic,	per	trimester	for	the	pre‐intervention	period	(January	2009‐April	2010),	
the	intervention	period	(May‐December	2010),	and	post‐intervention	period	I	(January	2011‐March	2012)	




                                                     	
                                                     32	
	
                                      Figure	7:	Birth	Weight	Densities	

	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
Notes:	 Densities	 estimated	 using	 an	 Epanechnikov	 kernel	 with	 optimal	 bandwidth.	 P‐vales	 of	 Kolmogorov‐
Smirnov	 tests	 of	 equality	 of	 distributions	 between	 groups	 reported	 below	 figure.	 Source:	 Authors’	 own	
elaboration	based	on	medical	record	information	system.		
	

                                                       	
                                                       33	
	
                   Figure	8:	Absolute	Score	of	Importance	of	Prenatal	Care	Services	




                                                                                                     	
Notes:	This	graph	reports	the	average	of	the	absolute	score	that	measures	the	importance	given	by	clinics	to	
seven	different	prenatal	care	procedures	including	initiating	prenatal	care	prior	to	week	13	of	pregnancy.	The	
data	were	collected	using	a	short	online	survey	conducted	in	the	clinics	that	participated	in	the	experiment.	
(see	Appendix	D)	The	absolute	scores	range	from	1	to	5,	with	5	being	the	highest	score	in	terms	of	importance.	
The	respond	was	coded	zero	if	the	respondent	reported	that	this	procedure	is	inappropriate	for	a	pregnant	
woman.		
                                                       	

	                                	




                                                     	
                                                     34	
	
                    Figure	9:	Relative	Ranking	of	Importance	of	Prenatal	Care	Services	




                                                                                                                	
Notes:	This	graph	reports	the	average	of	the	relative	ranking	that	measures	the	degree	of	priority	given	by	
clinics	 to	 seven	 different	 prenatal	 care	 procedures	 including	 initiating	 prenatal	 care	 prior	 to	 week	 13	 of	
pregnancy.	The	data	were	collected	using	a	short	online	survey	conducted	in	the	clinics	that	participated	in	the	
experiment.	(see	Appendix	D)	The	relative	scores	aimed	to	rank	the	seven	practices	from	1	to	7,	with	1	being	
the	highest	ranking.		In	practice	however,	the	survey	instrument	allowed	the	respondent	to	repeat	numbers.	
	                                    	




                                                           	
                                                           35	
	
	
                                                                  	

                                         Table	1:	Payments	for	1st	Prenatal	Visit	

             Time	Period	       	                     Dates	                   	    Payment	for	1st	Prenatal	Visit	

                                                                                   Before	Week	        At	week	13	of	
        	                       	        Begin	                  End	          	      13	of	       	   pregnancy	or	
                                                                                    pregnancy	             after	

            Pre‐Intervention	 	       January	2009	        April	2010	         	    $	40	ARS	      	     $	40	ARS	

                Intervention	 	        May	2010	         December	2010	        	    $	120	ARS	     	     $	40	ARS	

            Post	Intervention	 	      January	2011	      December	2012	        	    $	40	ARS	      	     $	40	ARS	

    Source:	National	Ministry	of	Health,	Argentina	(2010b)	
    	
                                                                  	
                                                                  	
	

                                    Table	2:	Clinic	Assignment	and	Compliance	Status	

                                                	         Actually	Treated	          	
                                   Assigned	to	
                                                                                         Total		
                                    Treatment	 	                                     	
                                                          Yes	           No	

                                             Yes	 	        14	            4	         	    18	

                                             No	 	         1	            18	         	    19	

                                         Total		 	         15	           22	         	    37	

                       									Source:	Authors’	elaboration.	
	                                        	




                                                                 	
                                                                 36	
	
                                     Table	3:	Baseline	Descriptive	Statistics	

                                                      Assigned	
    	                                                                        	Assigned	          p‐Value	for	test	of	
                                                	    Treatment	        	                    	
    	                                                                      Control	Group	        equality	of	means	
                                                       Group	
                                                                                            	                Wild	
                                                    Mean	                  Mean	                 Large	
    	                                           	                N	    	              N	                     Boot‐
                                                    (s.d.)	                (s.d.)	          	   sample	
                                                                                                           Strapped	

         Weeks	Pregnant	at	1st	Prenatal	Visit	 	     17.5	      743	   	    17.6	    497	   	    0.89	        0.84	
                                             	 	    (7.48)	       	    	   (7.74)	     	    	      	            	

        1st	Visit	before	Week	13	of	Pregnancy		 	    0.35	      743	   	    0.33	    497	   	    0.57	        0.56	
                                             	 	    (0.48)	       	    	   (0.47)	     	    	      	            	

        Tetanus	Vaccine	During	Prenatal	Visit	 	     0.80	      743	   	    0.84	    497	   	    0.34	        0.41	
                                             	 	    (0.40)	       	    	   (0.37)	     	    	      	            	

                   Number	of	Prenatal	Visits	 	      4.68	      743	   	    4.28	    497	   	    0.39	        0.45	
                                            	 	     (2.94)	       	    	   (2.77)	     	    	      	            	

                        Birth	Weight	(grams)	 	     3,328	      552	   	   3,291	    379	   	    0.36	        0.37	
                                            	 	     (519)	        	    	   (558)	      	    	      	            	

           Low	Birth	Weight	(<	2500	grams)		 	       0.06	      552	   	    0.06	    379	   	    0.96	        0.98	
                                          	 	       (0.23)	       	    	   (0.23)	     	    	      	            	

    Premature	(gestational	age	<	37	weeks)	 	        0.09	      319	   	    0.10	    249	   	    0.83	        0.82	
                                          	 	       (0.29)	       	    	    0.30	      	    	      	            	

                                Maternal	Age	 	     25.36	      354	   	   25.75	    270	   	    0.47	        0.48	
                                            	 	     (6.49)	      	     	   6.10	      	     	      	            	

            Number	of	Previous	Pregnancies	 	        2.31	      354	   	    2.10	    273	   	    0.29	        0.32	
                                          	 	       (2.39)	       	    	   (2.10)	     	    	      	            	

                             First	Pregnancy	 	      0.25	      354	   	    0.26	    273	   	    0.70	        0.77	
                                            	 	     (0.43)	       	    	   (0.44)	     	    	                    

Notes:	This	table	presents	means	and	standard	deviations	in	parentheses	for	the	treatment	and	control	groups	
during	the	16‐month	pre‐intervention	period	from	January	2009	through	April	2010.	P‐values	for	tests	equality	
of	 treatment	 and	 control	 groups	 means	 are	 presented	 in	 the	 last	 2	 columns.	 We	 present	 both	 the	 p‐value	
computed	for	large	samples	and	a	Wild	bootstrapped	p‐value	that	is	robust	in	samples	with	small	numbers	of	
clusters	(Cameron	et	al.	2008).	Our	Wild	bootstrap	procedure	assigns	symmetric	weights	and	equal	probability	
after	re‐sampling	residuals	(Davidson	and	Flachaire	2008)	and	uses	999	replications.	
	                                     	




                                                          	
                                                          37	
	
              Table	4:	Effects	on	Temporary	Incentives	on	Timing	of	1st	Prenatal	Visit	

                 	                   	            (1)	                        (2)	                         (3)	
                                     	                                Post‐Intervention											 Post‐Intervention	
                 	                       Intervention	Period		             Period	I	                   Period	II	
                                                                  (Jan	2011	–	March	2012)	 (April	–	Dec	2012)	
    A.	Weeks	Pregnant	at	1st	Prenatal	Visit	

                   Treatment	 	                ‐1.47**	                     ‐1.63**	                     ‐2.47**	
                              	 	               (0.71)	                      (0.75)	                      (1.02)	

          Large	Sample	p‐value	 	                0.04	                        0.03	                       0.02	
     Wild	Bootstrapped	p‐value	 	                0.08	                        0.03	                       0.03	
           Control	Group	Mean	 	                17.80	                       17.90	                      20.10	
                     Sample	Size	 	              769	                        1,296	                       710	
    B.	First	Prenatal	Visit	Before	Week	13	of	Pregnancy	

                   Treatment	 	                 0.11**	                      0.08**	                     0.08**	
                              	 	               (0.04)	                      (0.04)	                     (0.04)	
          Large	Sample	p‐value	 	                0.01	                        0.02	                       0.04	
     Wild	Bootstrapped	p‐value	 	                0.03	                        0.05	                       0.06	
           Control	Group	Mean	 	                 0.31	                        0.34	                       0.27	
                     Sample	Size	 	              769	                        1,296	                       710	

Notes:	This	table	reports	LATE	estimates	of	the	treatment	effect	of	the	modified	fee	schedule	on	indicators	of	
the	 timing	 of	 the	 1st	 prenatal	 visit.	 The	 differences	 are	 estimated	 from	 2SLS	 regressions	 of	 the	 dependent	
variable	on	actual	treatment	status	instrumented	with	clinic	treatment	assignment	type.	The	 p‐values	are	for	
2‐sided	hypothesis	tests	of	the	null	that	the	difference	is	equal	to	zero.	We	present	both	the	p‐value	computed	
for	large	samples	and	a	Wild	bootstrapped	 p‐value	that	is	robust	in	samples	with	small	numbers	of	clusters	
(Cameron	et	al.	2008).	Our	Wild	bootstrap	procedure	assigns	symmetric	weights	and	equal	probability	after	
re‐sampling	residuals	(Davidson	and	Flachaire	2008)	and	uses	999	replications.	Column	(1)	reports	the	results	
for	the	sample	observed	in	an	8‐month	intervention	period	(May	2010	–	December	2010).	Column	(2)	reports	
the	results	for	the	sample	observed	in	the	15‐month	period	following	the	end	of	the	intervention	(January	2011	
–	March	2012).	Column	(3)	reports	the	results	for	the	9‐month	period	after	the	change	in	the	coding	of	the	first	
prenatal	visit	(April	2012	–	December	2012).	Standard	errors	are	in	parentheses.	*	p	<	0.10,	**	p	<	0.05,	***	p	<	
0.01.	
    	                                        	




                                                           	
                                                           38	
	
                           Table	5:	Impact	on	Log	Number	of	Outreach	Activities		

                       	                        	               (1)	                             (2)	
                                                	                                    Post‐Intervention	Period	I	
                       	                                Intervention	Period		
                                                                                     (Jan	2011	–	March	2012)	

                                  Treatment	 	                 0.47**	                          0.56**	
                                           	 	                 (0.23)	                          (0.22)	

                      Large	Sample	p‐value	 	                   0.04	                            0.01	
                 Wild	Bootstrapped	p‐value	 	                   0.04	                            0.02	
                 Log	(Control	Group	Mean)	 	                    1.93	                            1.93	
                               Sample	Size	 	                   324	                             324	

Notes:	This	table	reports	LATE	estimates	of	the	treatment	effect	of	the	modified	fee	schedule.	The	dependent	
variable	is	the	log	of	the	number	of	clinic	outreach	activities	that	resulted	in	actual	maternal‐child	service	at	
the	clinic	per	trimester.	The	p‐values	are	for	2‐sided	hypothesis	tests	of	the	null	that	the	difference	is	equal	to	
zero.	We	present	both	the	p‐value	computed	for	large	samples	and	a	Wild	bootstrapped	p‐value	that	is	robust	
in	 samples	 with	 small	 numbers	 of	 clusters	 (Cameron	 et	 al.	 2008).	 Our	 Wild	 bootstrap	 assigns	 symmetric	
weights	 and	 equal	 probability	 after	 re‐sampling	 residuals	 (Davidson	 and	 Flachaire	 2008)	 and	 uses	 999	
replications.	These	are	only	computed	for	the	coefficients	of	treatment	interacted	with	each	period.	Standard	
errors	are	in	parentheses.	*	p	<	0.10,	**	p	<	0.05,	***	p	<	0.01.	
	

	                                  	




                                                        	
                                                        39	
	
                                  Table	6:	Cross‐Price	Effects	(Spillover)	

                       	                       	                 (1)	                             (2)	
                                               	                                      Post‐Intervention	Period	I		
                       	                                Intervention	Period		
                                                                                           (Jan	–	Dec	2011)	
    A.	Tetanus	Vaccine	                        	                   	                                	
                                            	
                                 Treatment	                     0.02	                             ‐0.02	
                                          	 	                  (0.08)	                           (0.05)	

                     Large	Sample	p‐value	 	                    0.76	                             0.62	
                Wild	Bootstrapped	p‐value	 	                    0.75	                             0.67	
                       Control	Group	Mean	 	                    0.79	                             0.84	
                               Sample	Size	 	                    769	                            1,053	
    A.	Number	of	visits	                       	                   	                                	
                                            	
                                 Treatment	                     0.39	                             0.51	
                                          	 	                  (0.33)	                           (0.58)	

                     Large	Sample	p‐value	 	                    0.24	                             0.38	
                Wild	Bootstrapped	p‐value	 	                    0.27	                             0.41	
                       Control	Group	Mean	 	                    4.05	                             4.40	
                               Sample	Size	 	                    769	                            1,053	

Notes:	This	table	reports	LATE	estimates	of	the	treatment	effect	of	the	modified	fee	schedule	on	indicators	of	
other	 services.	 The	 differences	 are	 estimated	 from	 2SLS	 regressions	 of	 the	 dependent	 variable	 on	 actual	
treatment	status	instrumented	with	clinic	treatment	assignment	type.	The	p‐values	are	for	2‐sided	hypothesis	
tests	of	the	null	that	the	difference	is	equal	to	zero.	We	present	both	the	 p‐value	computed	for	large	samples	
and	a	Wild	bootstrapped	p‐value	that	is	robust	in	samples	with	small	numbers	of	clusters	(Cameron	et	al.	2008).	
Our	Wild	bootstrap	procedure	assigns	symmetric	weights	and	equal	probability	after	re‐sampling	residuals	
(Davidson	 and	 Flachaire	 2008)	 and	 uses	 999	 replications.	 Column	 (1)	 reports	 the	 results	 for	 the	 sample	
observed	in	an	8‐month	intervention	period	(May	2010	–	December	2010).	Column	(3)	reports	the	results	for	
the	sample	observed	in	the	12‐month	period	following	the	end	of	the	intervention	(January	2011	–	December	
2011).	Standard	errors	are	in	parentheses.	*	p	<	0.10,	**	p	<	0.05,	***	p	<	0.01.	
	                                   	




                                                         	
                                                         40	
	
                              Table	7:	Impact	of	Incentives	on	Birth	Outcomes	

                       	                        	                 (1)	                              (2)	
                                                	                                      Post‐Intervention	Period	I	
                       	                                 Intervention	Period		
                                                                                            (Jan	–	Dec	2011)	
    A.	Birth	Weight	                            	                   	                                 	
                                             	
                                  Treatment	                     ‐37.34	                          25.109	
                                           	 	                  (48.61)	                          (40.67)	

                     Large	Sample	p‐value	 	                     0.44	                             0.54	
                Wild	Bootstrapped	p‐value	 	                     0.49	                             0.51	
                       Control	Group	Mean	 	                     3,304	                            3,279	
                               Sample	Size	 	                     555	                              802	
    B.	Low	Birth	Weight	                        	                   	                                 	
                                             	
                                  Treatment	                     0.01	                             ‐0.01	
                                           	 	                  (0.02)	                           (0.02)	

                     Large	Sample	p‐value	 	                     0.63	                             0.60	
                Wild	Bootstrapped	p‐value	 	                     0.61	                             0.56	
                       Control	Group	Mean	 	                     0.05	                             0.06	
                               Sample	Size	 	                     555	                              802	
    B.	Premature	                               	                   	                                 	
                                             	
                                  Treatment	                     0.03	                             ‐0.04	
                                           	 	                  (0.03)	                           (0.02)	

                     Large	Sample	p‐value	 	                     0.31	                             0.08	
                Wild	Bootstrapped	p‐value	 	                     0.28	                             0.12	
                       Control	Group	Mean	 	                     0.09	                             0.12	
                               Sample	Size	 	                     414	                              708	

Notes:	This	table	reports	LATE	estimates	of	the	treatment	effect	of	the	modified	fee	schedule	for	on	indicators	
of	 birth	 outcomes.	 The	 observations	 include	 woman	 for	 whom	 we	 are	 able	 to	 obtain	 information	 on	 birth	
outcomes	provided	in	public	hospital	birth	records.	The	differences	are	estimated	from	2SLS	regressions	of	the	
dependent	 variable	 on	 actual	 treatment	 status	 instrumented	 with	 clinic	 treatment	 assignment	 type.	 The	 p‐
values	are	for	2‐sided	hypothesis	tests	of	the	null	that	the	difference	is	equal	to	zero.	We	present	both	the	 p‐
value	 computed	 for	 large	 samples	 and	 a	 Wild	 bootstrapped	 p‐value	 that	 is	 robust	 in	 samples	 with	 small	
numbers	of	clusters	(Cameron	et	al.	2008).	Our	Wild	bootstrap	procedure	assigns	symmetric	weights	and	equal	
probability	after	re‐sampling	residuals	(Davidson	and	Flachaire	2008)	and	uses	999	replications.	Column	(1)	
reports	the	results	for	the	sample	observed	in	an	8‐month	intervention	period	(May	2010	–	December	2010).	
Column	 (2)	 reports	 the	 results	 for	 the	 sample	 observed	 in	 the	 12‐month	 period	 following	 the	 end	 of	 the	
intervention	(January	2011	–	December	2011).	Standard	errors	are	in	parentheses.	*	p	<	0.10,	**	p	<	0.05,	***	p	
<	0.01.	
	
	                                   	

                                                          	
                                                          41	
	
APPENDIX	A:	TEST	OF	MISREPORTING	WEEKS	PREGNANT	AT	1ST	PRENATAL	VISIT	

One	concern	is	that	the	financial	incentives	may	cause	clinics	to	misreport	the	week	of	pregnancy	at	
the	first	visit.	In	this	appendix	we	report	the	results	of	test	for	this	behavior.	Recall	that	in	our	main	
analysis	we	construct	the	week	of	pregnancy	at	the	first	visit	using	the	date	of	the	first	visit	and	the	
last	 menstrual	 date	 (LMD)	 as	 reported	 by	 the	 women.	 If	 the	 latter	 is	 not	 available	 we	 use	 the	
estimated	date	of	birth	(EDD)	as	recorded	by	the	physician	in	the	first	visit.	The	EDD	is	calculated	off	
the	LMD	as	reported	by	the	women	during	her	first	visit.	While	clinic	medical	records	should	contain	
both	dates,	about	10%	of	records	are	missing	the	LMD.		

        One	possible	way	of	misreporting	the	week	of	pregnancy	at	the	first	visit	is	to	change	the	LMD	
and	the	EDD	in	the	patient’s	clinical	medical	record.	For	instance,	if	a	woman	is	in	her	21st	week	of	
pregnancy	at	the	first	visit,	the	physician	could	add	7	days	to	the	LMD	and	EDD	so	that	the	visit	falls	
into	the	20th	week	of	pregnancy.	Both	would	have	to	be	changed	in	order	to	deceive	the	auditors.		
        To	 test	 for	 this	 possibility	 we	 use	 gestational	 age	 at	 birth	 (GAB)	 in	 weeks	 measured	 by	
physical	examination	at	the	time	of	birth,	registered	in	the	hospital	medical	record.	We	then	compare	
the	weeks	elapsed	from	the	first	prenatal	visit	to	the	delivery	date	based	on	GAB	to	weeks	elapsed	
from	first	visit	to	the	delivery	date	based	on	EDD.	While	EDD	is	collected	by	the	clinic	who	has	an	
incentive	 to	 misreport,	 the	 GAB	 is	 collected	 by	 the	 hospital	 at	 time	 of	 delivery	 where	 there	 is	 no	
incentive	to	misreport.	
        Figure	A1	plots	the	number	of	weeks	to	delivery	from	the	time	of	the	1st	visit	based	on	GAB	
(y‐axis)	to	the	one	based	on	EDD	(x‐axis).	If	there	is	no	difference	between	the	two	measures,	then	
all	of	the	dates	should	fall	on	the	45‐degree	blue	line.	There	should	be	some	differences	as	EDD	is	an	
estimate	that	assumes	no	prematurity	at	birth,	and	there	could	be	data	entry	in	GAB	and	EDD	and	
recall	errors	in	EDD.	Figure	A1	shows	that	almost	all	of	the	data	embrace	the	blue	45‐degree	line	and	
most	of	the	observations	off	the	line	are	situated	above	it,	consistent	with	prematurity	explaining	the	
differences.	
        If	the	clinic	changes	the	EDD	in	order	to	capture	higher	payments,	we	would	expect	greater	
differences,	 for	 the	 treatment	 group,	 between	 GAB	 and	 EDD	 below	 the	 12‐week	 thresholds	 than	
above	it	during	the	intervention	period	when	the	incentives	are	in	force,	but	no	differences	in	the	
pre‐intervention	 period.	 In	 order	 to	 test	 this,	 we	 estimate	 the	 following	 difference	 in	 difference	
regression:	
        	
                                                           13                   13           															(A1)	


                                                        	
                                                        42	
	
                                                                                                                  	
where	             	is	weeks	of	pregnant	at	the	first	visit	based	on	EDD	for	individual	i	getting	care	in	clinic	

j,	         is	the	number	of	weeks	at	the	first	visit	based	on	GAB	for	individual	 i	getting	care	in	clinic	j,	

      	is	a	clinic	fixed	effect,	            13 	is	an	indicator	of	whether	the	clinic	reported	the	first	visit	to	
be	in	the	first	12	weeks	based	on	EDD,	 	is	an	indicator	of	whether	the	clinic	was	actually	treated,	
and	       is	an	error	term.		
            In	the	absence	of	misreporting	and	no	prematurity	there	should	be	no	difference	between	the	
two	measures	and	 	would	have	a	coefficient	of	1.	However,	because	premature	births	occur	before	
EDD,	we	expect	 	to	be	close	to	but	less	than	one.	Then	we	can	interpret	the	other	coefficients	as	the	
effect	on	             	 	          accounting	for	average	weeks	of	prematurity.	So	the	dependent	variable	is	
the	error	in	EDD	in	forecasting	actual	delivery	date.	Equation	(A1)	takes	on	a	difference	in	difference	
interpretation	in	the	sense	the	we	are	differencing	the	change	in	the	forecast	error	between	the	pre‐
intervention	and	intervention	periods	for	the	group	of	pregnant	women	for	which	a	clinic	reports	as	
having	their	first	visit	before	13	weeks	and	the	group	of	pregnant	women	for	which	a	clinic	reports	
having	the	first	visit	in	week	13	or	later.	If	there	is	no	difference	in	the	error	for	the	treatment	group	
in	the	post	period	then	 ,	the	interaction	between	treatment	and	reported	having	the	first	period	
before	week	13,	will	be	zero.	We	find	no	evidence	of	misclassification	by	treated	clinics	(See	Table	
A1).	




                                                           	
                                                           43	
	
	
                                                         Figure	A1		

                         Comparison	of	Weeks	Pregnant	at	1st	Prenatal	Visit																																																																														
               Based	on	Gestational	Age	at	Birth	and	Based	on	Date	of	Last	Menstruation	




                                                                	
    Source:	Authors’	own	elaboration	based	on	data	from	the	provincial	medical	record	information	system.	

	                                     	




                                                              44	
	
       Table	A1:	Test	for	Misreporting	Weeks	Pregnant	at	1st	Prenatal	Visit	

    Dependent	Variable:		
    Weeks	Pregnant	at	1st	Prenatal	Visit,	by	Gestational	Age	at	Birth	

                             Weeks	Pregnant	by	EDD	                 0.90***	
                                                  	                 (0.02)	

                      1(Weeks	Pregnant	by	EDD<13)	                    ‐0.13	
                                                 	                   (0.31)	

     1(Weeks	Pregnant	by	EDD<13	)	x	1(Treated=1)	                     ‐0.03	
                                                	                    (0.44)	

                                             Constant	              1.33***	
                                                     	              (0.39)	

                                         Observations	               1730	
                                          Adjusted	R2	                   0.82	

    The	dependent	variable	is	weeks	pregnant	at	the	first	prenatal	visit	constructed	
    using	gestational	age	at	birth.	The	independent	variable	is	weeks	pregnant	at	the	
    first	visit	constructed	by	using	the	last	day	of	menstruation	or	estimated	delivery	
    date	(EDD).	The	interaction	term	interacts	a	dichotomous	indicator	for	whether	
    the	visit	was	before	week	13	and	a	dichotomous	indicator	for	whether	the	clinic	
    was	actually	treated.	The	regression	controls	for	clinic	fixed	effects	by	adding	a	
    binary	indicator	for	each	clinic	in	the	sample.	Standard	errors	are	in	parentheses.	
    *	p	<	0.10,	**	p	<	0.05,	***	p	<	0.01.	
	

	

	

	

	

	

	

	

	

	

	




                                            45	
	
APPENDIX	B:	ROBUSTNESS	TEST	RESULTS		

        Figure	B1:	Individual	Clinic	Treatment	Effects	for	Weeks	Pregnant	at	1st	Prenatal	Visit	

                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
                                                                                                         	
Notes:	This	figure	plots	individual	clinic	treatment	effects	for	the	outcome	of	weeks	pregnant	at	first	prenatal	
visit.	We	run	OLS	regression	of	the	outcome	comparing	each	clinic	assigned	to	the	treatment	group	to	all	clinics	
assigned	to	the	control	group	pooling	the	intervention	period	and	the	post‐intervention	period	I	(	hence	May	
2010‐March	 2012).	 One	 treatment	 clinic	 is	 not	 included	 because	 of	 its	 insufficient	 sample	 size.	 This	 clinic	
corresponds	to	one	of	the	two	that	did	not	take	up	treatment.	The	triangle	symbol	refers	to	the	clinic	that	was	
assigned	to	treatment	but	did	not	take	up	the	treatment.	The	x‐axis	is	sorted	from	the	lowest	to	the	highest	
clinic‐specific	impact.	The	dashed	blue	line	is	the	intent‐to‐treat	effect	calculated	by	pooling	the	intervention	
and	 the	 first	 post	 intervention	 period.	 The	 vertical	 lines	 are	 95%	 confidence	 intervals	 constructed	 using	
standard	errors	obtained	from	the	Wild	bootstrap	procedure.			
	
	
	
	
	
	
	
	                                    	



                                                           46	
	
    Figure	B2:	Individual	Clinic	Treatment	Effects	for	1st	Prenatal	Visit	before	Week	13	of	Pregnancy	

	




                                                                                                         	
	Notes:	This	figure	plots	individual	clinic	treatment	effects	for	the	outcome	of	first	prenatal	visit	before	week	
13.	We	run	OLS	regression	of	the	outcome	comparing	each	clinic	assigned	to	the	treatment	group	to	all	clinics	
assigned	to	the	control	group	pooling	the	intervention	period	and	post	intervention	period	I	(hence	May	2010‐
March	2012).	One	treatment	clinic	is	not	included	because	of	its	insufficient	sample	size.	This	clinic	corresponds	
to	one	of	the	two	that	did	not	take	up	treatment.	The	triangle	symbol	refers	to	the	clinic	that	was	assigned	to	
treatment	but	did	not	take	up	the	treatment.	The	x‐axis	is	sorted	from	the	lowest	to	the	highest	clinic‐specific	
impact.	The	dashed	blue	line	is	the	intent‐to‐treat	effect	calculated	by	pooling	the	intervention	and	the	first	
post	intervention	period.	The	vertical	lines	are	95%	confidence	intervals	constructed	using	standard	errors	
obtained	from	the	Wild	bootstrap	procedure.		
	                                  	




                                                       47	
	
                Table	B1:	Robustness	Tests	for	Weeks	Pregnant	at	1st	Prenatal	Visit	

                 	                 	            (1)	                        (2)	                       (3)	
                                   	                                Post‐Intervention											 Post‐Intervention	
                 	                     Intervention	Period		             Period	I	                   Period	II	
                                                                (Jan	2011	–	March	2012)	 (April	–	Dec	2012)	
    A.	Results	from	Table	4	

                   Treatment	 	               ‐1.47**	                    ‐1.63**	                   ‐2.47**	
                              	 	              (0.71)	                     (0.75)	                    (1.02)	

          Large	Sample	p‐value	 	              0.04	                       0.03	                       0.02	
     Wild	Bootstrapped	p‐value	 	              0.08	                       0.03	                       0.03	
           Control	Group	Mean	 	               17.80	                     17.90	                      20.10	
                     Sample	Size	 	            769	                       1,296	                       710	
    B.		Estimates	Using	Restricted	Sample	

                      Treatment	 	            ‐1.47*	                    ‐2.01***	                    ‐2.01*	
                               	 	            (0.77)	                     (0.70)	                     (1.11)	

          Large	Sample	p‐value	 	              0.06	                       0.00	                       0.07	
     Wild	Bootstrapped	p‐value	 	              0.09	                       0.02	                       0.12	
           Control	Group	Mean	 	               17.96	                     18.32	                      17.01	
                     Sample	Size	 	            760	                       1,326	                       425	
    C.		Difference‐in‐Differences	Estimates	

                      Treatment	 	            ‐1.35**	                   ‐1.74***	                    ‐2.35*	
                               	 	             (0.64)	                    (0.63)	                     (1.31)	

          Large	Sample	p‐value	 	              0.036	                     0.005	                      0.072	
     Wild	Bootstrapped	p‐value	 	              0.060	                     0.014	                      0.144	
           Control	Group	Mean	 	               17.80	                     17.90	                      20.10	
                     Sample	Size	 	            4,015	                     4,015	                      4,015	

Notes:	This	table	reports	LATE	estimates	of	the	treatment	effect	of	the	modified	fee	schedule	on	weeks	pregnant	
at	1st	prenatal	visit.	The	p‐values	are	for	2‐sided	hypothesis	tests	of	the	null	that	the	difference	is	equal	to	zero.	
We	present	both	the	 p‐value	computed	for	large	samples	and	a	Wild	bootstrapped	 p‐value	that	is	robust	in	
samples	 with	 small	 numbers	 of	 clusters	 (Cameron	 et	 al.	 2008).	 Our	 Wild	 bootstrap	 procedure	 assigns	
symmetric	weights	and	equal	probability	after	re‐sampling	residuals	(Davidson	and	Flachaire	2008)	and	uses	
999	replications.	Column	(1)	reports	the	results	for	the	sample	observed	in	an	8‐month	intervention	period	
(May	2010	–	December	2010).	Column	(2)	reports	the	results	for	the	sample	observed	in	the	15‐month	period	
following	the	end	of	the	intervention	(January	2011	–	March	2012).	Column	(3)	reports	the	results	for	the	9‐
month	period	after	the	change	in	the	coding	of	the	first	prenatal	visit	(April	2012	–	December	2012).	Standard	
errors	are	in	parentheses.	*	p	<	0.10,	**	p	<	0.05,	***	p	<	0.01.	
	
	



                                                         48	
	
	
                     Table	B2:	Robustness	Tests	for	1st	Prenatal	Visit	before	Week	13	

                 	                 	            (1)	                        (2)	                       (3)	
                                   	                                Post‐Intervention											 Post‐Intervention	
                 	                     Intervention	Period		             Period	I	                   Period	II	
                                                                (Jan	2011	–	March	2012)	 (April	–	Dec	2012)	
    A.	Results	from	Table	4	

                   Treatment	 	               0.11**	                     0.08**	                     0.08**	
                              	 	             (0.04)	                     (0.04)	                     (0.04)	

          Large	Sample	p‐value	 	              0.01	                       0.02	                       0.04	
     Wild	Bootstrapped	p‐value	 	              0.03	                       0.05	                       0.06	
           Control	Group	Mean	 	               0.31	                       0.34	                       0.27	
                      Sample	Size	 	           769	                       1,296	                       710	
    B.		Estimates	Using	Restricted	Sample	

                       Treatment	 	           0.09**	                     0.10**	                     0.10*	
                                	 	           (0.04)	                     (0.04)	                     (0.06)	

          Large	Sample	p‐value	 	              0.03	                       0.01	                       0.08	
     Wild	Bootstrapped	p‐value	 	              0.08	                       0.02	                       0.11	
           Control	Group	Mean	 	               0.31	                       0.33	                       0.36	
                      Sample	Size	 	           760	                       1,326	                       425	
    C.		Difference‐in‐Differences	Estimates	

                       Treatment	 	           0.09*	                       0.07	                       0.07	
                                	 	           (0.05)	                     (0.05)	                     (0.06)	

          Large	Sample	p‐value	 	              0.08	                       0.11	                       0.23	
     Wild	Bootstrapped	p‐value	 	              0.13	                       0.17	                       0.24	
           Control	Group	Mean	 	               0.31	                       0.34	                       0.27	
                      Sample	Size	 	           4,015	                     4,015	                      4,015	

Notes:	This	table	reports	LATE	estimates	of	the	treatment	effect	of	the	modified	fee	schedule	an	indicator	of	
whether	the	1st	prenatal	visit	occurred	before	week	13	of	pregnancy.	The	p‐values	are	for	2‐sided	hypothesis	
tests	of	the	null	that	the	difference	is	equal	to	zero.	We	present	both	the	 p‐value	computed	for	large	samples	
and	a	Wild	bootstrapped	p‐value	that	is	robust	in	samples	with	small	numbers	of	clusters	(Cameron	et	al.	2008).	
Our	Wild	bootstrap	procedure	assigns	symmetric	weights	and	equal	probability	after	re‐sampling	residuals	
(Davidson	 and	 Flachaire	 2008)	 and	 uses	 999	 replications.	 Column	 (1)	 reports	 the	 results	 for	 the	 sample	
observed	in	an	8‐month	intervention	period	(May	2010	–	December	2010).	Column	(2)	reports	the	results	for	
the	 sample	 observed	 in	 the	 15‐month	 period	 following	 the	 end	 of	 the	 intervention	 (January	 2011	 –	 March	
2012).	Column	(3)	reports	the	results	for	the	9‐month	period	after	the	change	in	coding	of	the	first	prenatal	
visit	(April	2012	–	December	2012).	Standard	errors	in	parentheses.	*	p	<	0.10,	**	p	<	0.05,	***	p	<	0.01.	




                                                         49	
	
APPENDIX	C:	ITT	RESULTS		

                   Table	C1:	ITT	Estimates	of	the	Effect	of	Temporary	Incentives	on																																																	
                                           Timing	of	1st	Prenatal	Visit	

                    	                  	         (1)	                     (2)	                       (3)	
                                       	                          Post‐Intervention									
                                                                                             Post‐Intervention	
                                             Intervention	             Period	I	
                    	                                                                            Period	II	
                                                Period		          (Jan	2011	–	March	
                                                                                             (April	–	Dec	2012)	
                                                                        2012)	
     A.	Weeks	Pregnant	at	1st	Prenatal	Visit	

                      Treatment	 	             ‐1.39**	                 ‐1.59**	                  ‐2.47**	
                                 	 	            (0.67)	                  (0.73)	                   (1.02)	
             Large	Sample	p‐value	 	             0.04	                   0.03	                      0.02	
       Wild	Bootstrapped	p‐value	 	              0.09	                   0.03	                      0.03	
              Control	Group	Mean	 	             17.80	                   17.90	                    20.10	
                        Sample	Size	 	           769	                    1,296	                     710	
     B.	First	Prenatal	Visit	Before	Week	13	of	Pregnancy	

                         Treatment	 	          0.10***	                 0.08**	                    0.08**	
                                  	 	          (0.04)	                  (0.04)	                    (0.04)	
             Large	Sample	p‐value	 	             0.01	                   0.02	                      0.04	
       Wild	Bootstrapped	p‐value	 	              0.03	                   0.05	                      0.08	
              Control	Group	Mean	 	              0.31	                   0.34	                      0.27	
                        Sample	Size	 	           769	                    1,296	                     710	
Notes:	This	table	reports	ITT	estimates	of	the	treatment	effect	of	the	modified	fee	schedule	on	indicators	of	the	
timing	of	the	1st	prenatal	visit.	The	LATE	estimates	are	reported	in	Table	4.	The	differences	are	estimated	from	
OLS	 regressions	 of	 the	 dependent	 variable	 on	 an	 indicator	 for	 clinic	 treatment	 random	 assignment.	 The	 p‐
values	are	for	2‐sided	hypothesis	tests	of	the	null	that	the	difference	is	equal	to	zero.	We	present	both	the	 p‐
value	 computed	 for	 large	 samples	 and	 a	 Wild	 bootstrapped	 p‐value	 that	 is	 robust	 in	 samples	 with	 small	
numbers	of	clusters	(Cameron	et	al.	2008).	Our	Wild	bootstrap	procedure	assigns	symmetric	weights	and	equal	
probability	after	re‐sampling	residuals	(Davidson	and	Flachaire	2008)	and	uses	999	replications.	Column	(1)	
reports	the	results	for	the	sample	observed	in	an	8‐month	intervention	period	(May	2010	–	December	2010).	
Column	 (2)	 reports	 the	 results	 for	 the	 sample	 observed	 in	 the	 15‐month	 period	 following	 the	 end	 of	 the	
intervention	(January	2011	–	March	2012).	Column	(3)	reports	the	results	for	the	9‐month	period	after	the	
change	 in	 the	 coding	 of	 the	 first	 prenatal	 visit	 (April	 2012	 –	 December	 2012).	 Standard	 errors	 are	 in	
parentheses.	*	p	<	0.10,	**	p	<	0.05,	***	p	<	0.01.	
    	                                     	




                                                          50	
	
                              Table	C2:	ITT	of	Cross‐Price	Effects	(Spillover)	

                        	                    	                  (1)	                              (2)	
                                             	                                        Post‐Intervention	Period		
                        	                             Intervention	Period		
                                                                                          (Jan	–	Dec	2011)	
     A.	Tetanus	Vaccine	                     	                    	                                	

                               Treatment	 	                   0.02	                              ‐0.02	
                                        	 	                  (0.07)	                            (0.05)	
                  Large	Sample	p‐value	 	                       0.76	                            0.62	
             Wild	Bootstrapped	p‐value	 	                       0.80	                            0.59	
                    Control	Group	Mean	 	                       0.79	                            0.84	
                            Sample	Size	 	                      769	                            1,053	
     A.	Number	of	visits	                    	                    	                                	

                               Treatment	 	                   0.37	                              0.50	
                                        	 	                  (0.32)	                            (0.57)	
                  Large	Sample	p‐value	 	                       0.24	                            0.38	
             Wild	Bootstrapped	p‐value	 	                       0.27	                            0.40	
                    Control	Group	Mean	 	                       4.05	                            4.40	
                            Sample	Size	 	                      769	                            1,053	

Notes:	This	table	reports	ITT	estimates	of	the	treatment	effect	of	the	modified	fee	schedule	on	indicators	of	
other	services.	The	LATE	estimates	are	reported	in	Table	5.	The	differences	are	estimated	from	OLS	regressions	
of	the	dependent	variable	on	an	indicator	for	clinic	treatment	random	assignment.	The	p‐values	are	for	2‐sided	
hypothesis	tests	of	the	null	that	the	difference	is	equal	to	zero.	We	present	both	the	p‐value	computed	for	large	
samples	and	a	Wild	bootstrapped	 p‐value	that	is	robust	in	samples	with	small	numbers	of	clusters	(Cameron	
et	al.	2008).	Our	Wild	bootstrap	procedure	assigns	symmetric	weights	and	equal	probability	after	re‐sampling	
residuals	 (Davidson	 and	 Flachaire	 2008)	 and	 uses	 999	 replications.	 Column	 (1)	 reports	 the	 results	 for	 the	
sample	 observed	 in	an	 8‐month	 intervention	period	 (May	 2010	 –	December	 2010).	 Column	 (3)	 reports	 the	
results	for	the	sample	observed	in	the	12‐month	period	following	the	end	of	the	intervention	(January	2011	–	
December	2011).	Standard	errors	are	in	parentheses.	*	p	<	0.10,	**	p	<	0.05,	***	p	<	0.01.	
	                                    	




                                                          51	
	
                           Table	C3:	ITT	Effects	of	Incentives	on	Birth	Outcomes	

                       	                    	                 (1)	                            (2)	
                                            	                                     Post‐Intervention	Period	
                       	                            Intervention	Period		
                                                                                      (Jan	–	Dec	2011)	
     A.	Birth	Weight	                       	                   	                               	

                             Treatment	 	                  ‐34.88	                          24.48	
                                      	 	                 (45.38)	                         (39.63)	
                 Large	Sample	p‐value	 	                      0.44	                          0.54	
            Wild	Bootstrapped	p‐value	 	                      0.46	                          0.57	
                   Control	Group	Mean	 	                  3304.82	                         3279.13	
                           Sample	Size	 	                     555	                           802	
     B.	Low	Birth	Weight	                   	                   	                               	

                             Treatment	 	                   0.01	                            ‐0.01	
                                      	 	                  (0.02)	                          (0.01)	
                 Large	Sample	p‐value	 	                      0.63	                          0.60	
            Wild	Bootstrapped	p‐value	 	                      0.61	                          0.63	
                   Control	Group	Mean	 	                      0.05	                          0.06	
                           Sample	Size	 	                     555	                           802	
     B.	Premature	                          	                   	                               	

                             Treatment	 	                   0.03	                           ‐0.04*	
                                      	 	                  (0.03)	                          (0.02)	
                 Large	Sample	p‐value	 	                      0.31	                          0.08	
            Wild	Bootstrapped	p‐value	 	                      0.32	                          0.09	
                   Control	Group	Mean	 	                      0.09	                          0.12	
                           Sample	Size	 	                     414	                           708	

Notes:	This	table	reports	ITT	estimates	of	the	treatment	effect	of	the	modified	fee	schedule	for	on	indicators	of	
birth	outcomes.	The	LATE	estimates	are	reported	in	Table	6.	The	observations	include	woman	for	whom	we	
are	able	to	obtain	information	on	birth	outcomes	provided	in	public	hospital	birth	records.	The	differences	are	
estimated	 from	 OLS	 regressions	 of	 the	 dependent	 variable	 on	 an	 indicator	 for	 clinic	 treatment	 random	
assignment.	The	 p‐values	are	for	2‐sided	hypothesis	tests	of	the	null	that	the	difference	is	equal	to	zero.	We	
present	both	the	p‐value	computed	for	large	samples	and	a	Wild	bootstrapped	p‐value	that	is	robust	in	samples	
with	 small	 numbers	 of	 clusters	 (Cameron	 et	 al.	 2008).	 Our	 Wild	 bootstrap	 procedure	 assigns	 symmetric	
weights	 and	 equal	 probability	 after	 re‐sampling	 residuals	 (Davidson	 and	 Flachaire	 2008)	 and	 uses	 999	
replications.	Column	(1)	reports	the	results	for	the	sample	observed	in	an	8‐month	intervention	period	(May	
2010	 –	 December	 2010).	 Column	 (2)	 reports	 the	 results	 for	 the	 sample	 observed	 in	 the	 12‐month	 period	
following	the	end	of	the	intervention	(January	2011	–	December	2011).	Standard	errors	are	in	parentheses.	*	p	
<	0.10,	**	p	<	0.05,	***	p	<	0.01.	
	
	


                                                        52	
	
APPENDIX	D:	ONLINE	SURVEY	OF	CLINICS	
	
        In	collaboration	with	the	Provincial	Management	Unit	of	the	program	(UGPS),	in	May	2015	
we	conducted	a	short	online	survey	(using	Survey	Monkey®)	in	those	clinics	that	participated	in	the	
pilot.	The	survey	aims	to	measure	the	absolute	and	relative	importance	of	seven	different	prenatal	
care	procedures	including	initiating	prenatal	care	prior	to	week	13	of	pregnancy.	The	absolute	scores	
range	from	1	to	5,	with	5	being	the	highest	score	in	terms	of	importance,	and	an	additional	option	of	
zero	 indicating	 that	 the	 procedure	 is	 not	 appropriate	 for	 a	 pregnant	 woman.	 Hence,	 the	 absolute	
score	ranges	from	0	to	5	points.	The	relative	ranking	aimed	to	sort	the	seven	practices	from	1	to	7,	
with	1	being	the	highest	ranking.		In	practice	however,	the	survey	instrument	allowed	the	respondent	
to	repeat	numbers.	
        The	survey	was	sent	out	to	by	email	to	clinics	directors	(or	the	next	person	in	rank).	We	were	
unable	 to	 obtain	 current	 email	 addresses	 for	 8	 out	 of	 the	 36	 clinics.	 Another	 4	 clinics	 confirmed	
having	received	the	email	but	refused	to	answer	it.	Out	of	the	24	clinics	that	did	respond	to	the	survey,	
21	 fully	 completed	 it	 while	 3	 only	 partially	 completed	 it.	 Out	 of	 the	 21	 clinics	 with	 complete	
responses,	13	belong	to	the	treatment	group	and	8	to	the	control	group.	Appendix	Table	D1	shows	
that	there	are	no	significant	differences	in	baseline	characteristics	between	clinics	that	responded	to	
the	survey	and	clinics	that	did	not	respond.	In	addition,	we	account	for	survey	non‐response	using	
Inverse	 Probability	 Weighting	 based	 on	 the	 logistic	 regression	 reported	 in	 Table	 D2	 (Wooldridge	
2007).	We	report	results	for	both	IPW	and	non‐IPW	regressions.	
        Figures	8	and	9	do	not	suggest	any	difference	in	the	absolute	score	and	relative	ranking	of	the	
procedures	 between	 treatment	 and	 control	 clinics.	 To	 test	 for	 the	 significance	 of	 the	 differences	
between	the	two	groups,	we	run	an	OLS	regression	of	the	absolute	score	and	the	relative	ranking	
against	a	binary	indicator	for	treatment.	To	account	for	the	small	sample	size	we	also	compute	the	p‐
value	 for	 the	 differences	 in	 means	 permuting	 our	 data	 and	 using	 a	 random	 sample	 of	 10,000	
permutations.		The	results	are	shown	in	Tables	D3	and	D4.		

	
	                                 	




                                                       53	
	
Online	Survey	Questionnaire	
	
         We	ask	for	your	collaboration	in	completing	a	brief	survey	about	prenatal	care	services	
provided	at	your	health	facility.		
	
         Important:	When	answering	the	survey,	please	think	of	a	hypothetical	case	of	a	woman	with	
the	following	characteristics:	
     25	years	old	
     Living	in	the	same	neighborhood	where	your	health	facility	is	located	
     Without	any	apparent	sign	of	disease	
     6	weeks	pregnant	
     Had	a	previous	low‐risk	pregnancy		
       	
1. Please	assign	a	score	between	1	to	5	to	each	of	the	following	services	that	could	be	
   delivered	to	the	pregnant	woman	presented	in	the	hypothetical	case.	

       1	corresponds	to	a	service	to	which	you	assign	the	lowest	importance		
       5	corresponds	to	a	service	to	which	you	assign	the	highest	importance	

                                                                                                       Not	
                                                                                                       appropriate	
	                                                   1	             2	       3	       4	       5	       for	a	
                                                                                                       pregnant	
                                                                                                       woman	
    Prenatal	ultrasound	                                 	
                                                                        	        	        	        	          	




    Thorax	X‐Ray	                                        	              	        	        	        	          	




    First	prenatal	visit	before	week	13	of	
    pregnancy		                                          	              	        	        	        	          	




    Bio‐psycho‐social	pregnancy	counseling	visit	        	              	        	        	        	          	




    Combined	Diphtheria/Tetanus	vaccine	                 	              	        	        	        	          	




    Blood	test	with	serology	                            	              	        	        	        	          	




    Blood	test	without	serology	                         	              	        	        	        	          	




                                                             54	
	
Please	rank	in	order	of	priority	(from	1	to	7)	the	following	7	health	services	that	could	be	
delivered	to	the	pregnant	woman	of	the	hypothetical	case.		
	
       1	corresponds	to	the	service	you	would	prioritize	the	most		
       7	corresponds	to	the	service	you	would	prioritize	the	least	
       	
Prenatal	ultrasound	
                                                               




Thorax	X‐Ray	
                                                               




First	prenatal	visit	before	week	13	of	pregnancy		
                                                               




Bio‐psycho‐social	pregnancy	counseling	visit	
                                                               




Combined	Diphtheria/Tetanus	vaccine	
                                                               




Blood	test	with	serology	
                                                               




Blood	test	without	serology	
                                                               




 
	

	                                	




                                                     55	
	
                 Table	D1:	Baseline	Characteristics	of	Clinics,	by	Online	Survey	Response	Status	
	
                                                                         Non‐
    		                                                                                 Respondent	 P‐value	         Obs.	
                                                                      respondent	
                 Number	of	Pregnant	Women	Attended	per	Year	             48.60	            64.90	         0.33	      36	
                           Weeks	Pregnant	at	1st	Prenatal	Visit	         17.44	            16.77	         0.15	      36	
                          1st	Visit	before	Week	13	of	Pregnancy	          0.34	            0.38	          0.27	      36	
         %	of	Pregnant	Women	who	are	Plan	Nacer	Beneficiaries	            0.61	            0.64	          0.59	      36	
                          Tetanus	Vaccine	During	Prenatal	Visit	          0.74	            0.81	          0.22	      36	
                                     Number	of	Prenatal	Visits	           4.26	            4.42	          0.72	      36	
                                          Birth	Weight	(Grams)	          3,283	            3,320	         0.33	      36	
                                       Gestational	Age	(Weeks)	          38.65	            38.47	         0.57	      31	
                             Low	Birth	Weight	(<	2500	Grams)	             0.06	            0.07	          0.73	      31	
                       Premature	(Gestational	Age	<	37	Weeks)	            0.10	            0.13	          0.60	      31	
	Notes:	 This	 table	 reports	 the	 means	 of	 baseline	 characteristics	 for	 clinics	 that	 responded	 to	 the	 May	 2015	
online	 survey	 and	 for	 clinics	 that	 did	 not	 respond.	 The	 characteristics	 are	 taken	 from	 the	 medical	 records	
information	system	(2009).	The	p‐values	for	the	tests	of	differences	in	means	are	computed	using	permutation	
tests	that	are	robust	for	small	sample	sizes.		
	

	
                                                             	
                                                             	
	                                     	




                                                            56	
	
                         Table	D2:	Probability	of	Responding	to	the	Online	Survey,		
                                  Logit	Coefficients	and	Marginal	Effects	
	
           	                                                                      Coeff.	        Marg.	Eff.	

           Treatment	Group	                                                       1.498	           0.274	
           	                                                                     (1.111)	         (0.180)	

           Birth	Weight	(grams)	                                                  0.100	           0.018	
           	                                                                     (1.076)	         (0.196)	

           Weeks	Pregnant	at	1st	Prenatal	Visit	                                  ‐0.594	          ‐0.109	
           	                                                                     (0.648)	         (0.121)	

           1st	Visit	before	Week	13	of	Pregnancy	                                 ‐3.590	          ‐0.657	
           	                                                                     (9.026)	         (1.670)	

           %	of	Pregnant	Women	who	are	Plan	Nacer	Beneficiaries	                  1.620	           0.296	
           	                                                                     (4.359)	         (0.774)	

           Tetanus	Vaccine	During	Prenatal	Visit	                                 3.350	           0.613	
           	                                                                     (3.817)	         (0.646)	

           Number	of	Prenatal	Visits	                                             ‐0.099	          ‐0.018	
           	                                                                     (0.559)	         (0.101)	

           Constant	                                                             7.644	               	
           	                                                                    (18.248)	             	

           Observations	                                                           36	               36	
	
Notes:	 This	 table	 reports	 the	 coefficients	 and	 marginal	 effects	 from	 a	 logit	 regression	 that	 estimates	 the	
probability	that	a	clinic	responded	to	the	May	2015	online	survey.	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	
	

                                                           57	
	
           Table	D3:	Differences	in	Absolute	Score	and	Relative	Ranking	of	Early	Prenatal	Care	
	
                                                     Absolute	Score		              Relative	Ranking		
                                                    (1)												   (2)		     (3)		          (4)		
      		
                                                   	OLS	            OLS‐IPW	    OLS	         OLS‐IPW	
           Difference	(Treatment	–	Control)	       0.20	           0.13	        0.10	         0.14	
                           	                      (0.22)	         (0.92)	      (0.21)	       (0.89)	

                Large	Sample	p‐value	              0.38	           0.89	       0.65	          0.88	
                 Permutation	p‐value	              0.35	           1.00	       0.46	          0.99	
                    Observations	                   20	             20	         20	            20	
                 Control	group	mean	               4.57	           1.88	       4.66	          1.88	
	
	
Notes:	Column	(1)	shows	the	differences	between	treatment	and	control	clinics	in	the	absolute	score	assigned	
to	the	practice	of	early	prenatal	care	without	any	adjustment	of	sample	loss.	Column	(2)	adjusts	for	sample	loss	
by	Inverse	Probability	Weighting.	Column	(3)	shows	the	differences	between	treatment	and	control	clinics	in	
the	relative	ranking	assigned	to	early	prenatal	care	among	seven	different	practices.	Column	(4)	is	the	same	as	
Column	(3)	but	adjusts	for	sample	loss	by	Inverse	Probability	Weighting.	(Wooldridge	2007)	The	coefficients	
are	obtained	from	an	OLS	regression	of	each	outcome	against	a	treatment	binary	indicator.	The	third	row	shows	
the	P‐value	obtained	from	permuting	the	data	using	a	random	sample	of	10,000	permutations.	Standard	errors	
are	in	parentheses.		We	lose	one	observation	in	each	case	because	of	missing	data	in	each	specific	question.	
	
	
	




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