rafmacalaba/datause-encoder-data huggingface dataset:
# **Dependent variable**

In the Enterprise Surveys, firms were asked whether any fixed assets such as machinery, vehicles, equipment, land, or buildings were purchased (new or used) in fiscal year 2012. The amount spent on purchases of fixed assets was also collected. Thus, there are two dependent variables. The first is a binary variable that takes on a value of 1 if the firm purchased any fixed assets, and 0 if it did not. The second is the amount of expenditure on the fixed assets. For the three economies, 42 percent of the firms purchased fixed assets, with Tanzania having the highest (54 percent) followed by Kenya (46 percent) and finally, Uganda (28 percent). Kenyan firms spent the most on fixed assets with a mean of USD 1,205,834, followed by Tanzania (37,200 USD), and Uganda (22,422 USD). Summary statistics are presented in table 3.

# **Main explanatory variables**

The main explanatory variable is a binary variable that takes on a value of 1 if a firm has used mobile money for any transaction, and 0 if it has not. For the three East African economies in the sample, 54 percent of the firms have used mobile money for transactions. Across countries, the percent of firms using mobile money ranges from 50 to 60 percent, with 50 percent in Kenya, 57 percent in Uganda, and 60 percent in Tanzania.

We do consider additional measures of mobile money use in order to capture whether the purpose for which mobile money is used matters. In the sample considered for analysis, 9 percent of firms use mobile money to pay employees, 24 percent to pay suppliers, 23 percent to pay utility bills, and 36 percent to receive payments from customers. There is heterogeneity across the three countries. In Uganda, 28 percent of firms use mobile money to pay suppliers, which is higher than Kenya (20 percent) and Tanzania (25 percent). For paying utility bills using mobile money, Tanzania has the highest percentage of firms (41 percent) followed by Uganda (21 percent) and Kenya (16 percent). There is little variability in both the percent of firms using mobile money to receive payments from customers and pay employees across countries, although far more firms use mobile money to receive payments from customers than to pay employees. Between 35 and 38 percent of firms receive payment from costumers via mobile money (i.e. 38 percent of firms in Uganda, 36 percent in Tanzania, and 35 percent in Kenya) and about 10 percent of firms in Kenya 8
Table 10: Caregiver handwashing knowledge and availability of handwashing material

|VARIABLES|Knows when to<br>wash hands<br>(index)<br>(1)|Knows best<br>method to<br>wash hands<br>(2)|Washed hands<br>with soap in<br>last 24 hours<br>(3)|HH soap<br>expenditure in<br>last month (TZS)<br>(4)|HH has a<br>handwashing<br>device<br>(5)|HH has a<br>handwashing<br>station with soap<br>(6)|HH has a fixed<br>handwashing<br>device<br>(7)|Handwashing<br>device within<br>6m of toilet<br>(8)|
|---|---|---|---|---|---|---|---|---|
|HWWS (β1)|0.045**|0.021|0.003|144.695|-0.015|-0.013|0.017**|0.012|
||(0.018)|(0.019)|(0.021)|(116.570)|(0.039)|(0.017)|(0.007)|(0.017)|
|TSSM ( β2)|0.007|0.053**|0.018|269.014**|0.025|-0.030*|-0.001|0.024|
||(0.017)|(0.022)|(0.020)|(120.058)|(0.040)|(0.018)|(0.006)|(0.018)|
|HWWS + TSSM (β3)|0.040**|0.057***|0.020|212.111*|-0.004|-0.018|0.028***|0.063***|
||(0.017)|(0.021)|(0.021)|(122.888)|(0.039)|(0.016)|(0.008)|(0.020)|
|p-values β1= β2|0.031|0.089|0.462|0.294|0.285|0.317|0.028|0.509|
|for F-test β1= β3|0.776|0.054|0.440|0.565|0.776|0.734|0.259|0.013|
|β2=β3|0.049|0.844|0.953|0.650|0.439|0.440|0.001|0.075|
|_Observations_|3,614|3,614|3,599|3,453|3,419|3,295|3,419|2,800|
|_R-squared_|0.072|0.051|0.059|0.050|0.352|0.149|0.040|0.105|
|_Control Mean_|0.302|0.822|0.823|4131.177|0.484|0.080|0.012|0.037|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects.

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A further employment-related issue concerns the overall welfare impact of any regulation-induced job loss. As Masur and Posner (2011) note, the standard RIA assumption is that in a full employment world there are few transitional impacts associated with any individual job loss, as displaced workers can readily find comparable work elsewhere. Yet, as they note, especially in times of high unemployment, there are likely to be some lost wages, as well as the potential for depreciation in workers’ human capital and increased family instability. If properly monetized, such impacts could be substantial. Importantly, if the results were actually included in a formal BCA, it could influence the desired degree of stringency of a rule. Although the quantification and monetization of these effects is limited, further analysis may reveal this to be a nontrivial effect.

# **IV. The Mexican Regulatory System**

Mexico is a particularly interesting case to examine regulatory issues in the context of green growth in a developing nation. A recent World Bank study reviewed the Worldwide Governance Indicators for 212 countries and territories, measuring six dimensions of governance, 1996-2007 (Kaufman et al., 2008): Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Efficiency, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on 35 data sources provided by 32 different organizations, reflecting the views of experts, as well as citizen and firm survey respondents worldwide. Despite its relatively low scores in the areas of Political Stability and Absence of Violence/Terrorism, Rule of Law, and Control of Corruption, Mexico scores reasonably well in the area of Regulatory Quality, ranking above the median among all 212 countries for this category (OECD). Mexico also shows modest improvements in Regulatory Quality for the three most recent years in the study. Interestingly, Mexico’s scores for Regulatory Quality are significantly higher than for other large, industrial countries in Latin America (with the exception of Chile, which scores higher than all other countries in the region). This section describes various aspects of regulation in Mexico, with particular emphasis on green growth issues. Unsurprisingly, the focus is on _ex ante_ analysis. There are, however, a limited number of evaluations of existing regulations that were recently published in a government report that provide some useful information about regulatory performance.

Starting in the year 2000, Mexico introduced various legal, institutional and policy changes to its regulatory system, including the creation of the Federal Regulatory Improvement Commission ( _Comisión Federal de Mejora Regulatoria_ , COFEMER), a commission charged with the role of ensuring regulatory quality. As an independent body of the Ministry of the Economy, COFEMER promotes reforms across all agencies of 19
psychic costs of moving. Drawing on the data from the ENOE, a rotating panel survey conducted in Mexico, we show that migration to the United States is more likely for initially married than for unmarried individuals among the individuals who co-resided with their parents prior to migration.<sup>2</sup> The final section of the paper then attempts to draw out the implications of this changing nature of the family for migration decisions, research, and policy. A first implication is that migration decisions may be dynamically and locationally inconsistent, since the family unit that is being considered in the decision at one location and point in time is different from what the same individual would consider if they move elsewhere. Second, it is difficult to visualize the spouse and children you are giving up by not moving somewhere different, and this inability to picture counterfactuals and the opportunity cost of not moving can result in a bias towards the status quo of not migrating. Third, to the extent that they anticipate the possibility of these changes, non-migrant family members might be reluctant to finance the migration of an unmarried individual. Fourth, extreme care needs to be taken when attempting to ask what is the impact of migration on the migrants’ family, since in many cases this might be an ill-defined question, as the counterfactual of not migrating could also involve an entirely different family. We give examples of which treatment effects can and cannot be recovered as a result, and discuss what types of questions in the migration literature are more and less likely to be affected by counterfactual families.

Our paper contributes to two main literatures. The first is a methodological literature which focuses on how to identify migration impacts given selection into migration. The main focus of this literature has been on self-selection on the basis of individual and fixed family characteristics (e.g., Borjas, 1987; Gibson et al., 2013). We show how the endogeneity with family structure affects migration decisions and impacts even when the main factors influencing individual self-selection are held constant. Second, we build on a small but growing strand of literature at the crossroads between migration and family economics. This includes three papers on international migration out of Mexico, with Bertoli and Murard (2020) and Bertoli et al. (2023), which document the extent of changes in co-residence choices after the occurrence of a migration episode, and Bratti et al. (2020), who analyze withinsibship selection into migration; and three papers focusing on internal migration in Indonesia,

> 2The Mexican Family Life Survey would allow tracing the evolution of the family composition after the occurrence of an international migration episode; however, the limited sample size implies that the number of migration episodes is much lower than those that we can observe in the ENOE.

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between a strong federal structure and lower per capita spending at the municipal level. However, spending per capita at the municipal level tends to go up with per capita income, with all countries with per capita income above 20,000 euros spending 1000 euros or more per capita at the local level. This one structural variable seems to predict to some extent local spending, even in widely differing systems. Overall, this table illustrates that there are many different ways to structure public finances to deliver good services at the local level

# **Table 2.1 Sub-National Government Spending Aggregates--Selected Countries of the European Union 2005 and India**

|**Sub National Govern**<br>**Country**|**ment Spending--**<br>**Municipal**<br>**Spending Euro**<br>**Per Capita**|**Selected Countri**<br>**Municipal % of**<br>**Subnational**|**es of the Europea**<br> <br>**Municipal**<br>**Spending as %**<br>**of GDP**|**n Union and Indi**<br>**Total**<br>**Government**<br>**Spending as a**<br>**Share of GDP**|**a**<br>**Municipal as %**<br>**of Total**<br>**Government**<br>**Spending**|**GDP Per**<br>**Capita**<br>**Euro 2005**|**GDP per**<br>**Capita US $**<br>**PPP 2008**|**% Urban**|
|---|---|---|---|---|---|---|---|---|
|Malta|75|100%|0.6%|45.0%|1.3%|11,745|24,700|95%|
|Bulgaria|180|100%|5.9%|39.5%|14.9%|2,835|12,394|70%|
|Slovakia|360|100%|6.8%|38.0%|17.9%|7,075|22,081|56%|
|Poland|540|92%|12.0%|43.3%|27.7%|6,405|17,625|62%|
|Hungary|810|75%|9.7%|49.9%|19.4%|8,815|19,329|66%|
|Spain|1,000|24%|5.0%|45.0%|11.1%|20,933|31,955|77%|
|Ireland|2,620|100%|6.7%|34.4%|19.5%|38,844|44,195|60%|
|France|1,220|81%|8.8%|53.7%|16.4%|27,348|34,045|77%|
|Finland|5,960|96%|19.0%|50.5%|37.7%|29,904|35,426|61%|
|Denmark|9,210|72%|23.6%|53.1%|44.5%|38,433|37,304|86%|
|Memo Items:|||||||||
|Memo Item: India Rs.<br>Avge 1999-2004|678|5%|0.8%|33.8%|2.3%||2,972|29%|
|Rs. Converted to|||||||||
|Euros<br>Sources: Dexia:2008 a|13<br>nd Mohanty et alia:|2007, World Deve|lopment Indicators.||||||

Comparing all these countries to India shows it to be an outlier in most respects, even when taking into account the low level of income in India. The country in the EU whose finances appear to most resemble India’s is Malta, a tiny country with a population of only 400,000, a mere neighborhood in one of India’s major cities. Bulgaria’s per capita income is six times that of India, but spending at the municipal level is 18 times that of a sample of 35 of the larger municipal corporations in India. India’s municipal spending as a share of sub-national spending, at only about 5%, is a small fraction of even the most federalized country in this group, Spain. This is particularly noteworthy given India’s size and complex city system.

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OZIER - REPLICATION REDUX – p. 30 of 41 but what can others take from this, in their efforts toward transparency and reproducibility? And where, again, does this leave deworming?

# 4.1. How should replication work?

Citing several examples including this one, Clemens (2017) wrote about the broader category into which this situation falls, regarding what separates a replication from a robustness check: _“Confusion in the meaning of replication harms research... anyone can find ‘plausible’_ _ways to change someone else’s regression so that coefficient estimates change... A well‐_ _known form of this problem is that it is a simple matter to change any result into a null_ _result by running modified versions of the same test that are underpowered by construction.” (p. 334)_ Indeed, in this case, changing the definition of treatment, splitting the sample, and applying uneven weights does reduce the power of the study by construction.<sup>26</sup> What possible benefit is there in approaching a study in this way? Two recent examples provide some insight. Galiani, Gertler, and Romero (2018) point out (via a survey of editors) that academic journals are part of this: while all editors surveyed indicated interest in publishing a replication study that overturned the results of the original, only a quarter were interested in replications that upheld the original result.<sup>27</sup> Replication teams, sometimes perceived as setting out simply to overturn others’ work, have even been called “replication bullies” for this (Meyer and Chabris, 2014). This may seem pernicious, but journals are intended to print new findings; a successful re‐running of someone’s code just isn’t very much new information

> 26 To “underpower,” or to “reduce power,” in this context, means making estimates less precise, and thus less likely to be statistically different from zero even when an effect is present.

> 27 Prior to Galiani, Gertler, and Romero (2018), Glass (1976) quoted Lewis M. Branscomb as writing, “when professional advancement and peer recognition are so heavily oriented toward original discovery, and research funding is largely restricted to original ... research, it is hard to motivate a scientist to write scholarly reviews.” Dewald, Thursby, and Anderson (1986) also paraphrased Thomas S. Kuhn in a similar vein: “Thomas Kuhn (1970) emphasized that replication – however valuable in the search for knowledge – does not fit within the ‘puzzle‐ solving’ paradigm which defines the reward structure in scientific research.”
previously were fatal become non‐fatal, and the survivors of these conditions are recorded as living with any subsequent disability. This makes it challenging to use this particular measure as an indicator of health that leads to improvements in aggregate productivity.

- The second is that data on YLD are extremely heavily imputed and extrapolated to compensate for the scarcity of the primary sources measuring the relevant non‐fatal health conditions. While such extrapolation is unavoidable if some estimate is required (no matter how imprecise)<sup>20</sup> , it makes it difficult to use this measure in a policy advocacy index since the links from the imputed health indicators to primary country data sources recognizeable by policymakers in the country are complex.  In fact, in many cases, imputed estimates of specific health conditions have no directly‐measured counterparts in the country itself.<sup>21</sup> In this case, the policy advocacy role of the HCI can be undermined by relying on heavily‐imputed data, since it risks “papering over” data gaps, reducing the incentives to fill these gaps with new data collection work.

- The third is that using imputed data makes it difficult to track and interpret changes over time in the index.  To oversimplify, if health conditions are imputed using a measure of living standards such as GDP per capita, then changes in GDP per capita (i.e. growth) will lead to changes in imputed health indicators that may have little to do with actual health outcomes or policy interventions to improve them.

While these limitations of more comprehensive but imputed indicators of non‐fatal health outcomes are real, the limitations of the data on ASR and stunting used in the HCI are real as well. Measurement of ASR requires data on death rates by age. While these are readily available in countries with strong vital registries, in roughly the poorest quarter of countries such data are missing or incomplete. In these countries, the UN Population Division instead estimates death rates by age by linking together the limited available age‐specific mortality data with “model life tables” capturing the typical pattern of distribution of deaths by age. As noted above, data on stunting are increasingly available, but primarily

> 20 Commendably the GBD estimates of YLD are accompanied by confidence bands, which are very wide given the underlying data limitations.  For example, for most countries the confidence bands for adult YLD encompass nearly the entire range of point estimates of YLD, which transparently indicates the substantial uncertainty associated with these estimates.

> 21 These gaps are illustrated using metadata reporting the dates of the primary sources underlying the GBD estimates of the top 10 global causes of YLD.  Taking 2017 as a reference point, calculate the fraction of countries for which no data source was available for each cause in the previous 10 years (so that the data necessarily were imputed/extrapolated beyond 10 years).  This fraction is greater than 75 percent for developing countries, for 8 out of the top 10 global causes of YLD (anemia and diabetes are the only two exceptions).

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giving the relationship in Figure 1, whereby poorer states have no higher participation in MGNREGS, despite the greater demand for work on the scheme.

To better understand this strong rationing effect of poverty, in Dutta et al. (2012) we study more closely the performance of the state of India with the highest poverty rate, Bihar. Drawing on various (qualitative and quantitative) data sources, including our own specialpurpose surveys, we argue that low administrative capacity for implementing the scheme and weak empowerment of poor people are at work in Bihar. Furthermore, we argue that changing one alone will not assure that MGNREGS will reach its potential in India‘s poorest areas. Effective action on both fronts will be necessary,

# **3. Is rationing undermining the self-targeting mechanism?**

By insisting that participants do physically demanding manual work at a low wage rate, workfare schemes such as MGNREGS aim to be self-targeted, in that non-poor people will not want to participate. The substantial rationing that we have demonstrated above raises the question of how well this self-targeting mechanism works in practice. The fact that there is rationing does not mean that targeting will not be pro-poor. For one thing, the manual work requirement at a low wage rate will still discourage non-poor people from wanting to participate. For another, the local authorities doing the rationing may well favor the poor. The local officials who are deciding who gets work could either enhance or diminish the scheme‘s targeting performance. There has been no comprehensive national assessment of targeting performance. The quantitative studies that have been done so far have been based on selected samples and the tests used have often been problematic.<sup>12</sup> What does the evidence from the NSS survey for 2009/10 suggest?

> 12 Jha et al. (2009) report evidence that households with larger landholdings were more likely to participate in the scheme in Andhra Pradesh (AP), though they find evidence of better targeting in Rajasthan. They conclude that the scheme is being ―captured‖ by the non poor in AP. Note, however, that their regressions control for other variables that may well capture poverty, including occupation and whether the household has a BPL card. The full regressions are not presented in their paper, but it may well be that having a BPL card (say) is already capturing the pro-poor targeting of MGNREGS, but that the BPL card puts too high a weight on landlessness from the point of view of explaining participation. Then the amount of land may appear to have the wrong sign, even though the scheme is targeting the poor. By contrast, the results of Liu and Deininger (2010) suggest quite pro-poor targeting of MGNREGS in AP. Shariff (2009) reports participation regressions for MGNREGS in selected backward districts of northern states (including some districts in Bihar). Some of the regression coefficients also suggest perverse targeting. Shariff is careful in interpreting the results though the same inferential concerns hold as for the study by

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