rafmacalaba/datause-encoder-data huggingface dataset:
of OLS. Given that OLS regressions are subject to important empirical limitations in the case of binary outcome variables (Stock and Watson 2015) we are not worried about the insignificance in Column 6.

Table 2: The 2012 reform effect on employment rates

||Logit|Logit|Logit|Logit|Probit|OLS|
|---|---|---|---|---|---|---|
|Mom(six-year-old) _×_ Post2012|0.0263|0.0386|0.0401|0.0403|0.0405|0.00191|
||(0.0143)|(0.00788)|(0.00886)|(0.00905)|(0.0106)|(0.0182)|
|Mean (Dep. Var)|.612|.612|.612|.612|.612|.612|
|St. Dv. (Dep. Var.)|.487|.487|.487|.487|.487|.487|
|Demographic controls|No|Yes|Yes|Yes|Yes|Yes|
|Year fxed efects|No|No|Yes|Yes|Yes|Yes|
|State fxed efects|No|No|Yes|Yes|Yes|Yes|
|Region by year unemployment rate|No|No|No|Yes|Yes|Yes|
|N|26,114|26,114|26,114|26,114|26,114|26,114|

Notes: The sample consists of mothers of six-year-old children (the treatment group) and three- to fouryear-old children (the control group). Standard errors are clustered and in parentheses. Source: EU-LSF (2010-2019).

Across each set of controls, the DD estimate is stable between 2.6 and 4.1 percentage points (or 4.3 percent and 6.6 percent from an average of 61.2 percent). Results are significant at the one-percent significance level. These findings imply that the reform led to a considerable increase in the maternal labor supply of mothers of six-year-old children and that substituting maternal childcare with childcare provided by the government is an effective way to increase the maternal labor supply.

The size of the effect is similar to the impact of the EITC on maternal employment in the US (Bastian 2020) or the impact of the universal childcare reforms in Germany (Geyer, Haan, and Wrohlich 2015). The impact is lower than the one found for universal, full-time public childcare for 3-year-olds in Spain (Nollenberger and Rodr ́ıguez-Planas 2015) or for greater grandparent availability (Bratti, Frattini, and Scervini 2018), which could be due to preparatory classes only taking place in the morning.

Results are robust to restricting the age range of mothers (Online Appendix Table B1 and B2), abstracting from survey weights (Online Appendix Table B3), using women without 12
�.<sup>11</sup> The distances between each pair of housing projects � to employment subcenter � are calculated using the urban road maps obtained from INEGI and hence reflect the road traveled by individuals rather than the straight-line (Euclidean) distance. Employment in each subcenter was calculated using the Economic Censuses of 1999, 2004 and 2009, disaggregated to the AGEB (census tract) level.

Figure 1 shows the identified subcenters and the location of the projects included in the data set. Most of the subcenters are relatively stable across the three years analyzed. Subcenters are located mostly in the Western and Southern parts of the city with some also located in the North. The East side of the city is less developed in terms of employment and is also the area where most of the lower-income families live. Ten subcenters were identified in 1999, 13 in 2004 and 11 in 2009. Most of the subcenters are consistent through the years, identifying important economic sites such as Santa Fe (the financial district on the Western side), the Olympic Village on the South side, Pantitlán and the wholesale market in the Southeast, and some important employment centers in the North such as Tlanepantla, Cuautitlán and Ecatepec.

> 11 Summary of statistics of accessibility measures are available in the appendix.

12
- 27 -

Table 9 cont'd.

|Country|Year||Earnings|(local curr.)||Ra|te of Retun (%|)|
|---|---|---|---|---|---|---|---|---|
|||No Ed.|Primary|Secondary/<br>Gen-Voc|Univ.|Primary|Secondary/<br>Gen-Voc|Univ|
|Paraguay|1990|145,371|177,895|282,777|599,067|10.801'|11.82|13.77|
|Peru|1990|6,593|7,431|9,014<br>9,013"<br>9,036|22,778|9.90|4.14<br>4.04p<br>6.362'|13.28|
|Uruguay|1989|68,495|132,583|171,160<br>173,317WV<br>168,486'|263,405<br>|30.55|8.98<br>8.151'<br>10.15k|14.05|
|Venezuela|1989|3,778|5,983|8,013<br>7,672W<br>9,994'|13,802|17.55|9.66<br>8.9421<br>13.10(|8.12|

Source: Annex 4.

Notes: ' The rate of retum is with respect to less-than-primary education. Earnings for individuals of secondary general education.

**E** amings for individuals of secondary vocational education.

> SJ Rate of return to secondary generl education with respect to pinmy education.

> J' Rate of return to secondary vocational education with respec **t** o primary education.
uncertain and Nordhaus (2007) points out that this dynamic uncertainty works against permit systems. With economic growth and changes in technologies, base-year emissions of the type often used in permit systems, become increasingly irrelevant. Making adjustments to allocations implies welfare transfers and this reopens equity issues, although the same can be said about changes to tax rates or transfers.

In principle, carbon tax systems effectively fix the price of emissions while permit systems fix emission levels and uncertainty. Consequently, permit systems result in price variability while tax system results in emission variability. In turn, this has implications for two other types of uncertainty: uncertainty about the relationship between emissions and climate change and uncertainty about the relationship between tax rates and emission levels. If only the first type of uncertainty was present, a preference for one instrument over the other could be made based on the relative nonlinearities in costs and benefits, with costs that are nonlinear relative to benefits favoring price systems (Weitzman, 1974).

Because both types of uncertainty are present, the choice may be less clear. Pearce (1991) and subsequent authors have argued that a fixed price for carbon has an uncertain effect on the environment, the safest approach is to directly target the proximate cause of carbon accumulation, emissions, rather than rely on relationships between price and emissions that are imprecisely understood. This is especially important because damages from climate change are potentially large and irreversible. At the same time, Nordhaus (2007) points out that carbon taxes generate stable incentives for investment and for the development of new technologies, which are key to long-term success.

Evidence from the commodity storage literature, suggests another less-discussed difference between carbon taxes and permit systems that has to do with the pricing of uncertainty. In tradable permit systems, forward and futures markets evolve in order to determine a price at which inventories of unused permits are carried forward. These markets can be utilized to provide an implicit market-driven evaluation of evolving risks profiles, information that is not provided under carbon-taxes. Moreover, while uncertainty creates incentives to delay irreversible investments that reduce emissions, uncertainty also drives up the shadow price of permits held in inventory, thereby reducing current emission levels and partly reducing the negative effects of uncertainty on investment.<sup>16</sup>

> 16 See Larson (2007) in the context of commodity markets and Considine and Larson (2006) in the context of the US sulfur dioxide permit markets.

10
restrictions on wages, especially because we rely on the district ...xed e¤ects in the estimation.<sup>19</sup> Since data on malaria prevalence from pre-eradication era (before 1945) are not available at the DSD level, we devise an alternative way to exploit DSD-level variations in LDO restrictions due to di¤erences in historical malaria prevalence.

# 4.2. Identifying Instruments for Land Restrictions: Interaction of District Level Malaria with DSD Level Geographic Characteristics

There were signi...cant variations in the historical malaria prevalence across di¤erent DSDs within the same district, and it is only natural that the district level average by itself would be of little help in understanding the variations across DSDs. For example, in Ja¤na district, Ja¤na city was almost malaria free while south Ja¤na su¤ered from severe malaria in early 1930s (Newman, 1965, p. 35). Our approach to constructing instruments that represent historical DSD level malaria incidence is to ...nd DSD characteristics that can essentially be used as “weights" to recover the variations in malaria prevalence across di¤erent DSDs within a district from the district average malaria data. For this purpose, we use proportion of land above 1000 feet elevation, and a measure of inland water in a DSD. The higher elevation of a DSD makes it less susceptible to malaria as it is di¢ cult for anopheles mosquitos, the carrier of malaria parasite, to survive and breed in high altitude. For example, in Badulla district of Sri Lanka, historically the mountainous region was e¤ectively malaria free, but the low lying area was infested with highly endemic malaria (Newman, 1965, P. 35). The spleen rates reported in Rustomjee (1944) show the e¤ect of altitude clearly; the average spleen rate for 1938-41 was 2.5 percent for areas above 3000 feet and the corresponding ...gure for the areas below 1500 feet was 43.7 percent. The water body, especially stagnant water, on the other hand is very suitable breeding ground for anopheles mosquitos. We interact district level malaria estimate (Gabaldon’s “endemicity index” based on the enlarged spleen rates from Table 4, page 34 in Newman (1965)) with the elevation and inland water to create instruments for the proportion of LDO land in a DSD.<sup>20</sup> Since the e¤ect of higher elevation

> 19 Note that even though the partial correlation between the district level malaria and LDO incidence seems strong, it is not enough for identi...cation. Because, to achieve identi...cations, what is important is the power of malaria variations across districts in explaining DSD level LDO incidence after controlling for all other regressors including the district ...xed e¤ects.

> 20 Gabaldon’s endemicity index is the lowest spleen rate recorded in the previous ...ve years, divided by 5. Gabaldon (1949) suggests that an endemic index of over 10 indicates highly severe malaria, and an index of less than 3 very low endemicity.

17
Chemicals and Rs. 314,000 and Rs. 204,000 for printing and publishing. This suggests that these industry sectors are not very efficient in transforming inputs into higher value outputs.

Next, we use the Ellison-Glaeser (1997) index of concentration to see if industrial activity within sectors is clustered across locations. Their concentration index can be defined as: where r is the extent to which an industry is geographically concentrated, s; is the region i's share of the study industry, xi is the regional share of the total employment, and H is the Herfindahl

> industry plant size distribution index, _H_ = _,V z2 ._ The EG index is explicitly derived from the micro foundations of a firm's location choice. It takes on a value close to zero when the distribution of plant location is completely random (as opposed to a uniform distribution). Therefore, a non zero value implies agglomeration or clustering above and beyond what we would observe if the firm's location decisions are random (in general, an industry is highly concentrated if r 2

> 0.05, moderately concentrated if r is between 0.02 and 0.05, and not concentrated if r <0.02). The index is designed to allow comparisons across industries, countries, and over time. Therefore, in

> principle it is possible to compare the concentration of industries in the U.S. and Mexico or that of high-tech and low-tech industries.

> The EG concentration index in Table 2 is computed at the state level, using data from the

> sampling frame of the ASI. Therefore, the employment summaries in Table 2 reflect the universe of all employment in registered establishments. We find evidence of high spatial concentration

> for the Leather and Metals sectors, and moderate concentration in Food Products, Textiles, Me-

> chanical Machinery and Computing and Electronics. Firms in the Paper Products and Chemicals sectors do not exhibit patterns of spatial concentration.

23
# **IV.2.  Results**

The Tobit estimation on panel data is defined as follows.

_VRSTEit_<sup>=</sup> _f_ ( _WAGEit_ , _GOVEXPit_ , _PUBTOTit_ , _GDPPCit_ , _URBAN it_ , _AIDSit_ , _GINI it_ , _EXTAIDit_ , _INSTit_ , _CONS_ ) where _VRSTEit_ = Variable returns to scale DEA efficiency score for single output and multiple output cases.

- _WAGEit_ = Wages and salaries (% of total public expenditure)

- _GOVEXPit_ = Total government expenditure (% of GDP)

- _PUBTOTit_ = Share of expenditures publicly financed (public/total)

- _GDPPCit_ = GDP per capita in constant 1995 US dollars

- _URBAN it_ = Urban population (% of total)

- _AIDSit_ = Dummy variable for HIV/AIDS

- _GINI it_ = Gini Coefficient

- _EXTAID  = it_ External aid (% of fiscal revenue)

- _INSTit_ = Institutional indicators including ICRG country risk, World

- Governance Research Indicators (Corruption Control), or a dummy for state failures from the State Failure Task Force database.

- _CONS_ = Constant

Tables 13 and 14 report the results for the single-input single-output case and the multiple-input multiple-output case, respectively. The more interesting findings are:

- a. We find that countries with larger expenditure levels also register the more inefficient scores.  This result is robust to changes in the output indicator selected, to considering health or education, and to adopting either the single –output or multiple output frameworks. The trade-off between size of expenditure and efficiency is quite robust.

- b. Countries in which the wage bill represents a higher fraction of total expenditure tend to be more inefficient.  This result does not hold for health in the multiple output framework. This difference could be due partly to the relatively decreasing number of health care professionals in the world, especially in the poorer countries (Liese, et.al., 2003).  Further investigation would be required to examine why this is not the case in education.

- c. Countries in which public financing is a larger share of total expenditure on the service also register lower efficiency scores.  This is probably due to differential productivity rates in the provision of services. Further research would be needed to explain why this is the case in health services.    Recent case studies of water

28
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# **1 Introduction**

Paying for performance (PFP) is a common contracting approach in settings with principal-agent problems (Prendergast, 1999; Lazear, 2000; Duflo et al., 2012; Rothstein, 2015; DellaVigna and Pope, 2018). Such contracts typically provide agents with a checklist of selected outputs and an incentive assigned to each output, and therefore engage agents through two economic channels. First, the checklist explicitly communicates what outputs the principal values and prioritizes. Agents may respond to this information alone, for example, because it reduces their uncertainty about how to allocate their effort or because they are intrinsically motivated (Arrow, 1963). Second, agents may respond to the financial incentives for the specified outputs. They may also respond differently depending on whether the incentives are framed as rewards or penalties (Kahneman and Tversky, 1984; Kahneman et al., 1991; Hossain and List, 2012).

In this paper, we examine the direct and indirect impacts of adding incentives to information on health workers’ adherence to clinical guidelines. In an incentivized framed field experiment embedded in a survey of 691 primary care clinics in Nigeria, we randomized 1,363 maternity care workers to three study arms: information, rewards, and penalties. We asked workers to review records, so-called partographs, of five fictitious patients receiving labor and delivery care and to identify clinically necessary—or indicated—actions for each patient. All participants received a participation fee and a checklist of seven common clinical actions related to maternity care of which five were associated with varying prices. Participants in the rewards arm could receive the associated price for correctly identifying the need for the incentivized actions. The penalty contract is isomorphic to the reward contract: we deducted the same amounts for participants who did not recommend the same five actions when they would have been appropriate. We measure performance as the share of indicated actions participants recommend, using the universe of possible clinical actions, which includes actions that were not listed but may be indicated. We assess impacts on overall performance as well as separately for three types of actions: (1) actions that are listed and incentivized in the rewards and penalties arms, (2) those listed but unincentivized, and (3) those neither listed nor incentivized.

Our analysis leverages several key features of the experiment design. First, we randomly listed 2
is to increase the signal-to-noise ratio that these indicators get from the data they use. As we discuss below, this can be done by improving on their ad-hoc weights or by increasing the information they extract from the variables they use.

Consider then the case where the goal is to identify which region has low well-being (Objective 2). Both synthetic indicators and objective indicators could be relevant for cross-region comparisons. Synthetic indicators allow accounting for some non-monetary variables for which the joint distribution is missing. Objective indicators allow accounting for the joint-distribution for the subset of variables for which this information is available. As we discuss below, hybrid indicators that are part-synthetic and part-objective would allow harnessing both types of data. These hybrid indicators have the potential to simultaneously account for consumption, health and longevity.

# **3 Opportunities for improvement**

We present alternative opportunities that we believe could help further improve global well-being measurement practices. We start by explaining the criteria used to select these opportunities.

## **3.1 Criteria for selecting opportunities**

Improving the “signal-to-noise ratio” of mainstream global well-being indicators can be done in many different ways. A very large literature explores different routes, notably through the use of new data sources such as mobile phone, remote sensing or biometric data (Steele et al., 2017; Chi et al., 2022; Blanchflower and Bryson, 2022), aggregation methods to account for heterogeneous preferences (Fleurbaey and Maniquet, 2011; Piacquadio, 2017; Decancq et al., 2019; Boarini et al., 2022) or yet alternative preference elicitation methods (Benjamin et al., 2014b; Decancq and Nys, 2021). In this review, we only consider a small subset of opportunities, which we selected based on a few criteria. In a nutshell, these criteria relate to (1) the communicability of the indicator, (2) the simplicity of its construction method and (3) the potential for short term application, i.e., data availability and maturity.<sup>20</sup> We shortly detail these criteria, which admittedly all include elements of subjectivity.

First, we focus on indicators whose value can easily be communicated to policy makers and the public. This excludes indicators whose units are not intuitive to the lay person. We focus mostly on poverty measures because we believe they are the most successful distribution-sensitive well-being indicators.<sup>21</sup> Arguably, everyone can make an intuitive indicators and these two poverty measures.

> 20The first two criteria are echoed in the expert report on poverty measurement (Atkinson, 2016).

> 21The two main social well-being indicators discussed beyond academic circles are mean income and poverty head-count ratios (Kraay et al., 2023). The former does not account for inequalities across

15
revenue per capita results in 0.37 R$ more per capita education spending—close to education spending’s average 30% share of total expenditure. Column (10) shows that education spending as a fraction of revenue is increasing in revenue; a standard deviation revenue increase leads to 2.7 percentage points more revenue going to education.

OLS estimates are sufficiently different from IV estimates to suggest bias. Both suggest that higher revenue is associated with higher pre-primary enrollment and higher education spending, but the OLS estimates are smaller in magnitude than the IV estimates. This is consistent with the previously-described channels of downward bias. If revenue is higher precisely where there are fewer child care needs or a mayor with a high discount rate, then OLS would mute the effects of revenue on pre-primary enrollment and education spending due to its correlation with these unobservables. Under OLS, revenue has a near-zero effect on both teacher and infrastructure quality (columns 4 and 6), which is statistically insignificant in the case of infrastructure quality.

# **6.3 The roles of income and inequality**

According to the predictions, median income and inequality moderate the effects of a reforminduced revenue increase. One expects higher median income and higher inequality to result in: (i) a smaller increase in public pre-primary enrollment, (ii) a smaller decrease in public pre-primary quality, (iii) a smaller increase in public education expenditure, and (iv) a larger increase in public expenditure on goods without private sector substitutes, like infrastructure. In this section, I test these predictions and find empirical support for each.

Table 8 shows the results for public pre-primary enrollment. Per capita revenue has a positive effect on enrollment, but this effect is statistically significantly smaller (at the 0.01 level) in richer and more unequal municipalities. By column (5), if two otherwise identical municipalities have Gini coefficients at the 25th and the 75th percentiles (0.37 and 0.41), a revenue increase that would lead a median inequality municipality to enroll 100 new pre-primary students would lead the more unequal municipality to enroll only 87, and the more equal municipality to enroll 113. Also by column (5), if two otherwise identical municipalities have median per capita incomes at the 25th and 75th percentiles (1150 and 3020 in constant 2005 R$/year), a 24