rafmacalaba/datause-encoder-data huggingface dataset:
the Federal District. Through the school identifiers (CCT), we assign each individual in the resulting sample to one of the three high school tracks, thereby allowing us to measure how expected returns vary across those.

Fourth, we rely upon data on economic activity taking place in private establishments with a fixed location in urban areas from the 2008 Mexico’s Economic Census, which classifies activities with considerable detail - up to 6 digits of the North American Industrial Classification System (NAICS). Average figures on firm sales, value added, number of workers and labor remunerations for the municipality of Ecatepec provide us with a detailed description of the key economic sectors in the context under study, and allow linking the main drivers of the local labor demand with the different curricular specializations provided by the vocational programs of the COMIPEMS.

# **3 Empirical Strategy and Results**

## **3.1 A (Fuzzy) Regression Discontinuity Design**

The eligibility for the _Oportunidades_ transfer solely depends on whether the household poverty score exceeds or not a fixed cutoff, that is time invariant during the period under study and unknown by potential beneficiaries. Hence, the likelihood of receiving the program benefits can be interpreted as the result of a local randomization in a neighborhood of the eligibility cutoff [Lee, 2008].

Formally, let _X_ denotes the household eligibility score, _c_ the cutoff value of eligibility, and _B_ a program treatment indicator. The local average treatment effect (LATE) of _Oportunidades_ transfers on track choice ( _Y_ ) is identified by where the numerator in equation 1 defines the Intention to Treat (ITT) effect of _Oportunidades_ transfers on track choices. When the denominator in equation 1 is exactly one (perfect compliance), the design is said to be sharp. If it is less than one, the design is said to be fuzzy. In this paper, we 2. If you obtain a university degree, what monthly income do you expect to have on average five years from now?

11
Our paper tackles this gap in the context of malaria, where misallocation is a major problem. In our study setting in urban Mali, around 60 percent of malaria-negative patients at public health clinics received an antimalarial prescription.<sup>2</sup> Market forces do not appear to – reward better malaria care neither price nor patient satisfaction are positively correlated with better malaria treatment outcomes. Moreover, even though rapid diagnostic tests (RDTs) for malaria are accurate, easy to administer, and available for free in the public sector, adoption and adherence are poor: in our sample, over one-third of patients prescribed an antimalarial were not tested, and around two-thirds of RDT-tested malaria negative patients were prescribed an antimalarial.

Our analysis uses data from a randomized controlled trial we conducted with 58 public health clinics in the greater Bamako region. We collaborated with Mali’s National Malaria Control Program to design two cross-randomized interventions that provided information about RDTs to health workers and patients, respectively. Half of the clinics were randomly selected for a training intervention with providers, delivered as an extension to a basic training on malaria testing and policy that all clinics received. The extended training was designed to increase the use of RDTs as a high-quality and low-cost diagnostic technology and to improve adherence to test results. It emphasized the low error rate of rapid detection tests in both laboratory and field settings and the dangers of overusing antimalarials. The patient-side intervention was randomized at the clinic _×_ day level and was delivered as a video, shown on a tablet, on the importance of malaria testing and how to read an RDT result, recorded in the local language. The objective was to align patients’ understanding of the use RDTs with recommended medical practice and to empower them to resist unnecessary treatment.

We collected detailed data from health workers and patients to connect quality of care with beliefs, knowledge, and patient satisfaction. We elicited provider beliefs about the reliability of malaria RDTs before and after the training intervention. Using a detailed patient survey at the clinic, we captured pre-consultation beliefs about malaria status as well as malaria testing and treatment outcomes. We also conducted a post-consultation survey visit at patients’ homes. At this visit we administered a malaria RDT to observe their true malaria status and measured their ex-ante beliefs about the outcome of this test. Finally, we recorded patient satisfaction with the provider’s testing and treatment decisions.

We find that under the status quo, providers incorrectly believed that RDTs had limited ability to detect mild malaria cases and relied too strongly on more time- and skill-intensive (and therefore less accurate in practice) microscopy-based tests, sometimes carrying out

> 2Other studies in Sub-Saharan Africa find similarly high rates of overprescription (see for example Reyburn et al., 2004; Hamer et al., 2007; Bisoffi et al., 2009; Ansah et al., 2010).

2
impact evaluation studies do incorporate one or more of these duration effects. The most commonly addressed source of duration effects is cohort affiliation. This is not surprising since many interventions such as education and nutrition programs are allocated on the basis of age, in terms of both timing of entry into and exit from the program. On the other hand, implementation lags are recognized but often not explicitly addressed.

What can be done to capture timing and the variation in length of program exposure? First, _improve the quality of program data_ . Such data could come from administrative records on the design and implementation details of a program, combined with survey data on program take-up by beneficiaries. Program data on the timing of implementation are likely to be available from program management units, but these data may not be available at the desired level of disaggregation, which might be the district, community, providers, or individual depending on where the variation in timing is thought to be the greatest. Compiling such data on large programs that decentralize to numerous local offices could be costly. There is obviously a difference in the primary concern of the high-level program manager and of the evaluator. The program manager’s concern is the disbursement of project funds and the procurement of major expenditure items, whereas the evaluator’s concern would be to ascertain when the funds and inputs reach treatment areas or beneficiaries.

Second, _choose the timing of the evaluation keeping in mind the time path of program impacts_ . Figure 1 illustrates that program impact, however measured, can change over time for various reasons discussed in the paper, so there are risks of not finding significant impact when a program is evaluated too early or too late. The learning process by program operators or by beneficiaries could produce a curve showing increasing impact over time, while a pioneering effect could show a very early steep rise in program impact that is not sustainable. Figure 1 thus 28
|She is unable to keep|0.48|0.36|0.41||0.28|0.07**|0.08**|
|---|---|---|---|---|---|---|---|
|up with all household<br>tasks<br>without help|(0.02)|(0.02)|(0.03)||(0.03)|||
|She is well|0.98<br>|0.98|0.99||0.96|-0.01|0.03**|
|educated|(0.01)|(0.01)|(0.01)||(0.01)|||
|**A woman's husband'**|**s employmen**|**t status**||||||
|Her husband is|0.96|0.94|0.94||0.93|0.01|0.01|
|not working and|(0.01)|(0.01)|(0.01)||(0.02)|||
|has no income||||||||
|Her husband was|0.91|0.81|0.92||0.79|-0.01|0.02|
|previously|(0.01)|(0.01)|(0.02)||(0.02)|||
|unemployed [e.g.,<br>no income] but<br>has now found a<br>job||||||||
|Her husband is a|0.93|0.91|0.90||0.81|0.03|0.10***|
|migrant worker|(0.01)|(0.01)|(0.02)||(0.02)|||
|and is not sending||||||||
|her money||||||||
|**A woman'sjobs' char**|**acteristics**|||||||
|She returns home|0.88|0.75|0.85||0.67|0.03|0.09***|
|from work after 5<br>pm|(0.01)|(0.02)|(0.02)||(0.03)|||
|She works in a|0.93|0.93|0.86||0.86|0.06***|0.07***|
|governmentjob|(0.01)|(0.01)|(0.02)||(0.02)|||
|Her workplace is|0.80|0.63|0.74||0.52|0.06**|0.11***|
|not close to her|(0.01)|(0.02)|(0.03)||(0.03)|||
|home||||||||
|The job she is in|0.98|0.96|0.98||0.97|-0.01|-0.00|
|pays verywell|(0.01)|(0.01)|(0.01)||(0.01)|||
|She has to|0.67|0.46|0.63||0.38|0.07**|0.08**|
|occasionally travel|(0.02)|(0.02)|(0.03)||(0.03)|||
|for work outside||||||||
|the village/city||||||||
|Her job is|0.70|0.50|0.65||0.43|-0.01|0.07**|
|<br>physically|(0.02)|(0.02)|(0.03)||(0.03)|||
|demanding||||||||
|She works in a|0.51|0.29|0.48||0.24|0.03|0.05|
|hotel/restaurant|(0.02)|(0.02)|(0.03)||(0.03)|||
|Observations|85|0||3|00|1|,150|

55
Table 14: Main Results - By Gap Between Expected and Actual Baseline Score (Degree of _Overestimation_ )

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Time-use<br>Index|Efort<br>Index|Self-discipline<br>Index|Confdence<br>Index|Aspiration<br>Index|Math<br>Z-score|English<br>Z-score|
|||Panel A:|Overestimating|Baseline Perform|ance (Gap|_≥_points)||
|Goal-Setting|0.096**|0.116**|0.103**|0.018|-0.008|0.092|0.010|
||(0.046)|(0.052)|(0.046)|(0.035)|(0.040)|(0.069)|(0.067)|
|Goal-Setting + Recognition|0.074|0.087|0.079*|0.022|-0.023|0.041|0.024|
||(0.047)|(0.057)|(0.044)|(0.043)|(0.045)|(0.068)|(0.064)|
||(0.055)|(0.067)|(0.052)|(0.050)|(0.052)|(0.096)|(0.067)|
|Observations|9,908|9,254|10,186|10,114|9,421|10,480|10,480|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|
||Pa|nel B: Not|Overestimating|Baseline Perfor|mance (Gap|_<_ 10 point|s)|
|Goal-Setting|0.133***|0.074|0.061|-0.003|0.065|0.033|0.156**|
||(0.047)|(0.051)|(0.044)|(0.039)|(0.043)|(0.100)|(0.074)|
|Goal-Setting + Recognition|0.145**|0.014|0.068|-0.053|-0.021|0.040|0.127*|
||(0.058)|(0.057)|(0.045)|(0.045)|(0.051)|(0.094)|(0.071)|
|Observations|8,621|8,111|8,830|8,805|8,276|9,085|9,085|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|

_Notes_ : This table reports the heterogeneity (by level of overestimation of baseline performance) of the impact of interventions on main outcomes of interest: Time-use Index, Effort Index, Self-Discipline Index, Confidence Index, Aspiration Index, Math test score, and English test score. Construction of these indices is discussed in Section 2. Panel A has the sample of only male students while Panel B reports the results for the sample of female students. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.
progress in reducing tariffs on imported good, there are still great margins for improvement in incorporating in legislation the principle of non-discrimination against foreign parties and in eliminating residual regulatory barriers to trade and investment.

# **3.1 Tariffs**

**First generation reforms rapidly succeeded in eliminating all quantitative trade restrictions.** The EU Eastern Enlargement project led to extending preferential status for Croatia’s participation in a European trade area.  As a result Croatia’s tariff levels (measured here as MFN tariffs on industrial goods) are much lower in 2007 than those of comparator middle income countries and in line with the best practice of the pre-accession OECD CEE (Figure 8).  In particular, Hungary and Poland entered the EU with substantially higher tariff levels than Croatia’s in 2006.  In addition, Croatia’s tariff levels have been lowered to those of the EU common market as of its entry into the EU as of January 1, 2007.

**Figure 8: Tariffs**

<!-- Start of picture text --> 6.0<br>5.0<br>5.0<br>4.3<br>4.0<br>4.0<br>3.0<br>3.0<br>2.0<br>2.0<br>1.4<br>1.0 1.0 1.0 1.0 1.0<br>1.0<br>0.0<br>Slovak EU15 Czech Croatia Albania OECD Bulgaria Hungary Poland M ICs Romania<br>Republic Rep. 2007 2007<br><!-- End of picture text --> _Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia and Albania, 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see Annex I._

# **3.2 Barriers to foreign direct investment**

**Barriers to foreign direct investment include foreign ownership barriers, discriminatory procedures against foreign firms, and other barriers to trade and investment** , all of which are presented in detail below.

**Croatia has relatively low foreign ownership barriers.** These take the form of statutory or other legal limits to the proportion of shares that can be acquired by foreign investor or of special voting rights that can be exercised in case of acquisition of equity by foreign investors.  Such restriction may apply in general or be limited to specific sectors that are considered ‘strategic’ such as air transport, telecommunications, and electricity generation. Croatia fares relatively well in this respect since regulatory barriers remain only in specific sectors, among which the PMR capture the 50% foreign ownership ceiling in the airlines sector, that fairly common across EU countries (Figure 9).

10
**Table 3: Height-for-age Z-score (Nepal)**

|||**Model**<br>|**1- Without C**<br>|**ontrols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|Un<br>|conditionalqu<br>|antile regressi<br>|on<br>|
|||10th|25th|50th|75th|
|Per capita kcal|0.101***|0.114***|0.136***|0.111***|0.028|
||(0.029)|(0.038)|(0.033)|(0.035)|(0.041)|
|1-SSR|0.199***|0.183***|0.174***|0.240***|0.148***|
||(0.033)|(0.043)|(0.037)|(0.039)|(0.046)|
|1 - SSEXR|0.202***|0.195***|0.168***|0.232***|0.161***|
||(0.033)|(0.042)|(0.037)|(0.038)|(0.044)|
|FCS|0.256***|0.218***|0.250***|0.297***|0.250***|
||(0.032)|(0.042)|(0.036)|(0.037)|(0.044)|
|HDDS|0.230***|0.196***|0.228***|0.255***|0.204***|
||(0.032)|(0.043)|(0.037)|(0.037)|(0.043)|
|Per capita consumption|0.279***|0.212***|0.225***|0.301***|0.273***|
||(0.043)|(0.047)|(0.043)|(0.052)|(0.051)|
|Food share|-0.267***|-0.233***|-0.223***|-0.310***|-0.249***|
||(0.035)|(0.039)|(0.036)|(0.040)|(0.050)|

|||**Model**<br>|**1- Without C**<br>|**ontrols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|Un<br>|conditionalqu<br>|antile regressio<br>|n<br>|
|||10th|25th|50th|75th|
|Per capita kcal|0.085***|0.099**|0.114***|0.094***|0.031|
||(0.028)|(0.039)|(0.033)|(0.036)|(0.040)|
|1-SSR|0.012|0.043|0.004|0.032|-0.069|
||(0.036)|(0.052)|(0.044)|(0.044)|(0.050)|
|1 - SSEXR|0.038|0.073|0.015|0.042|-0.025|
||(0.033)|(0.047)|(0.042)|(0.041)|(0.045)|
|FCS|0.094***|0.128***|0.122***|0.118***|0.074|
||(0.033)|(0.047)|(0.041)|(0.041)|(0.048)|
|HDDS|0.083**|0.106**|0.100**|0.080**|0.05|
||(0.034)|(0.047)|(0.040)|(0.041)|(0.047)|
|Per capita consumption|0.077**|0.068|0.061|0.073*|0.052|
||(0.032)|(0.042)|(0.036)|(0.040)|(0.055)|
|Food share|-0.065|-0.076|-0.041|-0.076|-0.033|
||(0.040)|(0.054)|(0.047)|(0.049)|(0.059)|

OLS estimates reveal per capita consumption, with a beta coefficient of 0.279, to be the most important, followed by food share, food consumption score, and dietary diversity score, with per capita caloric availability being the least important. At the lower tail of the height-for-age distribution, food share and food consumption score show higher association with height-for-age, followed by per capita 20
# 8 Conclusion

In this paper, we address two key issues that bedevil the implementation of conditional cash transfer schemes: targeting to maximize additionality, and setting payments to minimize social cost. We analyze Mexico’s national level payments for ecosystem services program, but the methods used are broadly applicable to many conditional cash transfer settings. We first show how simple predictions of deforestation risk can be linked to existing targeting efforts to identify land with likely higher benefits. We then combine this information with estimates of willingness to accept derived from a referendum question, using a well-established contingent valuation method to improve the cost-effectiveness of CCT targeting.

We present a simple strategy for adjusting the current payment scheme to more closely align with applicants’ willingness to accept levels. We associate willingness to accept with current program payment rate-setting practices and show that the current program could be significantly expanded without sacrificing environmental and social impact. This application of the referendum approach is lower in cost and less complicated than implementing an auction to establish appropriate payment levels. Furthermore, it is significantly more likely to be practical and politically feasible where future CCTs will be scaled-up.

While implementing periodic surveys to recalibrate targeting and payment levels is not costless or without challenge, we believe that the likelihood of strategic bias in surveys used for this purpose can be minimized through sampling subsets of program applicants on an occasional basis. This is feasible in our case because (1) governments do no recalibrate prices on a yearly basis, thus allowing significant time to pass between questioning and price calibration; (2) the program is large enough to allow representative sampling without repeats. In addition, if re-sampled, repeated surveying of applicants may actually provide an opportunity to extract useful information. A mixture of new and previously treated sample members would allow researchers to assemble information to improve the cost-effectiveness of CCTs (via the new sample members) while also measuring possible strategic bias (by comparing to the older sample members). Therefore, it may be both low cost and informative to integrate this type of referendum question every few years for a random subset of program beneficiaries.

The analysis in this paper also suggests scope to expand the number of properties enrolled by 42
17 A second way of examining economic growth, not pursued in this paper, is to specify a production function which would allow a decomposition of the growth rate of output per worker into factor deepening (the growth of physical capital and education stock per worker) and TFP growth: _y_ **_=zr*k_** + **_a,*h 'W_** where k and h are the growth rates of physical capital per worker and human capital per worker respectively. Then in order to estimate the total impact of labor force growth on output growth one needs to decompose the effect between the effect of more rapid I on k,h, and TFP. In a companion paper (Pritchett, 1994). we use recently created data on the physical capital stock (Nehrlu and Dhareshewar, 1993) and years of schooling (Nebru, Dubey, Swanson, 1994, Barro and Lee 1993) to calculate this decomposition. The result of which is that more rapid population growth is not associated with slower growth of capital per worker, is not associated with slower growth of the schooling of the labor force, and is mildly associated with slower TFP growth7. This paper will focus on the non-producton fimction estimates.

Availability of data us to work with a sample of 78 countries of which 22 are OECD and 56 are developing (in this case developing is defmed as non-OECD) countries<sup>18</sup> .

17 The real difficulty with jmplemeuting this approach tumrns out to be the estmation of the impact of factor accumulation on growth as the augmented Solow model (a la Mankiw, Romer, Weil, 1992) imposes theoretical restrictions on the coefficients (e.g., that they be equal to **factor** shares **in** GDP) that the data have difficulty supporting. Pritchett (1994) is agnostic about TFP measurement, using coefficients both from esdmation and from theory.

**13** Except for Turkey, which is included as a developing country.
patterns of compliance in allocating scarce enforcement resources, leading to a correlation between past and present import refusals, even after controlling for other factors. We refer to this as the “own reputation” effect. In addition, the structure of the US import refusals system is suggestive of two other effects that might be in operation. One is a “sector reputation” effect, by which we mean the possibility that import refusals for a particular product are associated with past import refusals affecting closely related products. The second is a “neighbor reputation” effect, namely the possibility that import refusals affecting a given product from one country might be more likely if neighboring exporters of the same product have a history of non-compliance. In the remainder of this section, we outline the data and model we will use to test for the existence of all three effects.

# **3.1 US Import Refusals Data**

This paper uses a new dataset of US import refusals for the period 1998-2008. It extends the data used in Jouanjean (2011), and covers HS chapters 3 (fish and crustaceans), 7 (vegetables), 8 (fruits), 9 (coffee, tea, mate, and spices), 16 (preparations of meat, fish, and crustaceans), and 20 (preparations of vegetables, fruits, and nuts). The reason for focusing on these sectors is that they correspond to FDA Industry Codes 16 (fishery and seafood products), 20-22 (fruit and fruit products), and 24-25 (vegetables and vegetable products). Refusals in these sectors accounted for over 50% of all FDA import violations over the 1998-2004 period (Buzby et al., 2008). We are therefore confident that by focusing on these three sectors, we are capturing an important part of overall import refusal activity in the US. This subsection describes the US import refusals regime in more detail, focusing on the way in which the data used here were collected.

Since 1998, the FDA has implemented an automatic system governing the admission process for FDAregulated shipments of foreign-origin products presented for entry into the US. The system, known as the “Operational and Administrative System for Import Support” or OASIS, is designed to simplify 10
6 TABLE 2.1: ETHIOPIA NATIONAL ENERGY BALANCE (1982) **Final** Energy Consumption ('000 towe

|Sector|Biomass<br>Euels|Electricity|Petroleum|Total|Share<br> <br>_(%)_|
|---|---|---|---|---|---|
|Industry|30|35|102|167|2.1|
|Transport|||349|349|4.4|
|Agriculture||1|31|32|0.4|
|Households|7,402|16|18|7,436|92.8|
|Comrnerce/Govt.|-||1|-a|_L4_|
|Total<br>Share|7,432<br>92.7|63<br>0.8|521<br>6.5|8,016<br>100.0|100.0|

SOURCE: Ethiopia: Issues and Options in the Energy Sector. Joint UNDP/World Bank ESMAP; Washington, DC, July 1984 (Report No. 4741-ET) The advantages of using the energy balance are that: (a) it identifies the relative importance of the household sub-sector in the ove:all energy consumption profile; (b) it pinpoints the key fuels in the sub-sector; and (c) it is available for most countries. The limitations of using energy balances for detailed analysis regarding residential fuel use are:

- the data are almost always based on information provided by suppliers; thus, they are more reliable for commercial fuels where transactions are recorded and less so for traditional energy sources (either collected or marketed) where no regular records are kept;

- because of this supply orientation, the aggregate figures do not reflect actual demand;

-information is presented only at the global level; no disaggregation is possible to pinpoint issues and problems that might affect specific re.gions, income classes or types of consumers; and

- consumption of biomass, often the most important household fuel, is usually a very rough estimate based on a set of untested assumptions.

Thus, energy balances are an insufficient, if not entirely inappropriate, means for focusing on the real issues and options in household energy.

# National Budget Surveys

These are large, statistically representative surveys which seek to develop data and trends on how families spend and save. They are usually conducted every 3 - 10 years, may invole several rounds with repeat visits in each round and focus on cash expenditure. These efforts have several advantages: (a) they are high-visibility efforts which receive substantial resources; (b) they are generally well-planned and statistically valid; and (c) they are nationwide. However, the energy analyst seeking to use results from these surveys faces several constraints:

- fuel consumption is rarely measured; it is usually based on iz-ollection and is thus unreliable;