rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Kernel density estimate<br>*<br>+<br>=<br>a<br>TM<br>24 6 8 10 12<br>Log (employment)<br>——— Uttar Pradesh<br>——— Uttarakhand<br>termel = epanechnikey, bandwidth = 0.1861, sample: locations within 100 kms on either side of<br><!-- End of picture text --> <!-- Start of picture text --> Kernel density estimate<br>=<br>o<br>=<br>at<br>mM<br>0 2 4 6 8<br>Log (total firms)<br>——— Uttar Pradesh<br>——— Uttarakhand<br>= epanechnikev, bandwidth = 0.1533, sample: locations within 100 kms on either side of borde<br><!-- End of picture text --> termel = epanechnikey, bandwidth = 0.1861, sample: locations within 100 kms on either side of bordesernel = epanechnikev, bandwidth = 0.1533, sample: locations within 100 kms on either side of borde
broadly hold at country-level.

# **3.5 Summary of theoretical prediction**

We developed a simple theoretical framework, built around a student-level education production function, to explore why management practices might matter in schools. Using this framework, we showed that people management practices may be contributing to higher student test scores through two channels: teacher selection and teacher incentives. The predictions that these channels imply for intermediate school outcomes—fewer teacher shortages, higher teacher motivation, and higher teacher effort in schools with strong people management than in schools with weak people management—are all well-supported in our PISA data for Latin America.

We also showed that operations management practices may be contributing to higher student test scores via two channels: teacher selection and household incentives. The empirical support for the predictions that these channels imply for intermediate school outcomes—fewer teacher shortages, higher teacher motivation, higher teacher effort, and higher household effort in schools with strong operations management than in schools with weak operations management—is also strong.

While this does not represent definitive causal evidence, this combination of theory and descriptive empirical analysis offers an insight into why management appears to matter in schools and so we cautiously move on to policy. For example, what type of school management practices might be changed to drive improved student learning? Our analysis suggests that people management is a good place to start. While it may not be feasible (on political or budgetary grounds) for governments to introduce performance pay in public schools, it may be possible to conduct assessments to judge teacher effectiveness, and for these appraisals to lead to changes in public recognition, and to opportunities for professional development, likelihood of career advancement, and greater responsibilities and leadership. Such practices also reward and develop good performers and create a good employee proposition and could improve both teacher selection and incentives.

Beyond the difference in people management across private and public sectors, a striking feature of the PISA data is that there is substantial variation in the strength of operations management practices within the public sector. Some public schools are adopting management practices that 25
32 differences are statistically significant (p-values < 0.01) and indicate stronger correlation with the square-root measure.<sup>17</sup>

# **4. Conclusion**

Eradicating extreme poverty figures prominently is international development dialogue and is ultimately the goal of many economic development organizations and institutions. The World Bank’s monitoring and reporting on progress in ending extreme poverty is widely accepted and directly informs the status of target 1.1 of the United Nations Sustainable Development Goal, to eradicate extreme poverty for all people everywhere. Measuring progress in ending poverty is a difficult measurement challenge that rests upon many assumptions. The analysis in this paper examines how our understanding of who is poor and where they live is affected by changing one of these assumptions. The methodology used by the World Bank divides total household consumption or income (depending on the welfare metric used for measuring poverty in a particular country) and divides this by the number of people in the household. This method allocates household resources on a per-capita basis and assumes that there are no household economies of scale. This is to say it assumes that a household of six people needs three times as many resources as a household of two people to attain the same standard of living. Decades ago, when food budget shares were much higher and prior to the World Bank’s focus on measuring global poverty (previously rich countries were not part of the poverty estimates), this assumption may have been tenable. But in a world that has grown richer and is experiencing declining budget shares for food, increasing for shelter; and further in consideration of the expanded objective of

> 17 One test suggests greater correlation with the per-capita measure (Table 11, column (7) for the asset index) and 6 differences are not statistically significant.

24
manner (Ban and Rao, 2008b).

First, we find that the presence of a female president has no discernible impact on the likelihood that female citizens participate within the _gram sabha_ (Table 4, Models 1 and 2). Tat is, the mere presence of a female president does not seem to encourage more women to atend or speak within the _gram sabha_ , suggesting that the “role model” effects of such incumbents may not be sufficient to affect deliberative participation in the short term. Tough these results might seem surprising given the optimism around female quotas, they are quite consistent with evidence from Bengal in Chatopadhyay and Duflo (2004) and South India in Ban and Rao (2008a), which find no effect of female reservation on the political behavior of ordinary women.

By contrast, we find that the presence of a female president does have a meaningful and significant impact on the ability of women to be heard and responded to. Focusing only on citizen speakers, Table 14 regresses our measures of agenda-seting power on indicators for gender of the speaker and the gender of the village council president. As expected, women speakers are at a considerable disadvantage relative to male speakers (roughly 14 percentage points less likely to drive the next issue discussed), but this disadvantage is essentially reversed under female presidents. For clarity, we visualize the interactions in Figure 5, which plots the coefficient estimates for the effect of being a female speaker under male and female presidents respectively. While under male presidents, women are significantly less likely than men to set the agenda, under female presidents, differences between the genders are not only smaller in magnitude, but statistically insignificant.

To beter understand whether and how female presidents themselves might be elevating the voices of other women, we also look at whether women citizens are more likely to generate a relevant response from state officials. Table 13 presents the results for both politician responsiveness (Models 1 - 3) and administrator responsiveness (Models 4 - 6). Te data are striking: overall, women are 18 percentage points less likely than men to receive a relevant response from elected officials – a meaningful decline given that men receive topical responses 70 percent of the time. Importantly, however, the presence of a _female_ president can ameliorate the neglect that female 34
**<u>Table 1: Descriptive statistics, panel sample 1997/98-2004</u>**

||_whole_<br>_sample_||Baseline199<br>_Seecal_<br>_commu_|7-98<br>_ine_<br>_nities_<br>|_non-See_<br>_commu_|_caline_<br>_nities_<br>|
|---|---|---|---|---|---|---|
||mean|_std.err._|mean|_std.err._|mean|_std.err_.|
|Height for age z-score|-1.882|_0.040_|-1.942|_0.068_|-1.831|_0.048_|
|Weight for age z-score|-1.723|_0.025_|-1.789|_0.047_|-1.667|_0.028_|
|Underweight (-2SD)|0.427|_0.009_|0.452|_0.014_|0.405|_0.013_|
|Underweight (-3SD)|0.105|_0.005_|0.126|_0.011_|0.088|_0.004_|
|Stunting (-2SD)|0.475|_0.011_|0.490|_0.018_|0.463|_0.014_|
|Stunting (-3SD)|0.193|_0.007_|0.207|_0.013_|0.180|_0.009_|
|No. obs.|18,177||6,76|1|11,4|16|
||||Follow-up2<br>|004<br>|||
|Height for age z-score|-1.974|_0.028_|-2.017|_0.039_|-1.943|_0.039_|
|Weight for age z-score|-1.584|_0.020_|-1.599|_0.031_|-1.574|_0.027_|
|Underweight (-2SD)|0.350|_0.008_|0.357|_0.013_|0.344|_0.011_|
|Underweight (-3SD)|0.074|_0.004_|0.076|_0.006_|0.072|_0.005_|
|Stunting (-2SD)|0.491|_0.009_|0.506|_0.014_|0.479|_0.012_|
|Stunting (-3SD)|0.188|_0.006_|0.192|_0.010_|0.184|_0.009_|
|No. obs.|12,367||4,48|0|7,88|7|

Note: Anthropometrics measures calculated using sampling weights. Individual sample using all 420 panel clusters/communities interviewed in both survey years.

22
# **Abstract**

This paper tests the hypothesis that education improves health and increases people’s life expectancy. Smoking histories—reconstructed from retrospective data in the National Health Interview Surveys in the United States—show that after 1950, when information about the dangers associated with tobacco consumption started to diffuse, the prevalence of smoking declined earlier and most dramatically for college graduates. More educated individuals are also more likely to quit smoking: incidence analysis of smoking cessation shows a strong education effect. The instrumental variable approach, which relies on the fact that during the Vietnam War college attendance provided a strategy to avoid the draft, indicates that education does affect decisions about whether to smoke or stop smoking _._ ii
# FIGURE 16.

# POSTNEONAIAL MORTALITY TRENDS BY MOTHER'S EDUCATION IN COTE D'IVOIRE, 1970-86 (LSMS 1985/1986)

<!-- Start of picture text --> No schooling<br>|0/<br>Pi<br>Primary education -<br>and above<br>%\ /<br>0.<br>u70 a97u Am m<br>Year (1)<br><!-- End of picture text -->

Note: (1) DMR 5 - YEAR MOVING AVERAGE.
people in “initial and continuing training courses” that carry a moderate in-class training component (less than 500 hours) and train in skills relevant to a particular occupation. The structure of _Pronatec_ is such that several federal ministries administer and contribute to course and trainee selection.

– The focus of this paper is _Pronatec-MDIC_ the segment of _Pronatec_ administered by Brazil’s Ministry of Industry, Foreign Trade and Services (hereafter MDIC). The unique feature of _Pronatec-MDIC_ is its “demand-driven” nature: MDIC receives explicit requests for specific skills training courses from individual businesses. This program began with only a limited number of training courses in 2013, greatly expanded in 2014, and drastically reduced its scale in 2015 due to federal budget constraints. In 2014, more than 2,000 firms applied for more than 16,000 skills training courses to be given to current or prospective employees across a wide range of industries and occupations. Ultimately more than 300,000 prospective trainees were registered for _Pronatec-MDIC_ -requested courses in 2014 or 2015, with approximately 40 percent of these registrants completing their training course. Below, we discuss the characteristics of the data sources used to analyze effects of the program.

# **3.1 Program administrative records**

Figure 1 summarizes the process from application to course provision in the _Pronatec-MDIC_ program. MDIC receives course requests through a standardized process in which firms indicate the skills, occupation, or course they are looking to have taught, and the number of “seats” (individuals trained) they would like to be provisioned in the given municipality for which they are making the request. These requests have no pecuniary cost for the requesting firms. The requests go through an initial screening by MDIC staff in terms of their viability and appropriateness.<sup>2</sup> Approximately one half of firms’ requests were denied at this stage.

> 2In correspondence with MDIC staff involved in administering _Pronatec-MDIC_ , we were informed that the review process was made to ensure a reasonable volume of seats requested relative to the firm’s scale and

6
network, Mjt. The coefficient on this term will be of interest for identifying the impact of migration at different levels of the wealth distribution within villages.

Finally, the regressors included in ∆Xit and ∆Zjt might not be strictly exogenous. For example, income shocks that affect household consumption decisions may also have an impact on household composition, land characteristics or village policy. Below, we first estimate models that exclude ∆Xit and ∆Zjt, then successively add village and household regressors, treating them as exogenous and then as pre-determined but not strictly exogenous. For models in which regressors are treated as pre-determined, we use a standard panel data approach to control for possible endogeneity bias. Specifically, we instrument first-differenced predetermined variables with their t − 2 lagged levels [Xit−2, Zjt−2] in specifications which include these regressors. Xit−2 and Zjt−2 will be valid instruments as long as they are correlated with ∆Xit and ∆Zjt, but uncorrelated with any timevarying household unobservables included in the differenced error term, ∆�it.<sup>22</sup>

# 4.3 Understanding the Years-Since-IDs Instrument

ID distribution was the responsibility of county level offices of the Ministry of Civil Affairs, and these are distinctly separate from the Ministries of Agriculture and Finance which set policies affecting land, credit, taxation and poverty alleviation. Therefore it is plausible that ID distribution is not systematically related to unobservable policy decisions that have a direct effect on household consumption. Still, a function of the years since IDs were issued is not an ideal strategy for identifying village out-migration. Ideally, a policy would exist that was randomly implemented, affecting the ability to migrate from some counties but not others. As the differential timing of ID card distribution was not necessarily random, we must be concerned that counties with specific characteristics or that followed specific policies were singled out to receive ID cards earlier than other counties, or that features of counties receiving IDs earlier are systematically correlated with other policies affecting consumption growth. These counties, one might argue, were “allowed” to build up migrant networks faster than others.

To evaluate the plausibility of using years-since-ID-distribution as an instrument, we first categorize villages as receiving cards prior to 1988, in 1988, or after 1988, and look for significant differences in observable average village characteristics measured in 1988 (Table 2). In the third row of each characteristic, we report the p-value of t-tests of the equality of the mean within each

> 22 Wooldridge (2002) provides a helpful introduction to standard panel data approaches to control for endogeneity bias of regressors that are predetermined but not strictly exogenous.

14
- Broda, Christian and David E. Weinstein (2006), “Globalization and the Gains from Variety,” _Quarterly Journal of Economics_ , Vol. 121 (2), 541-585.

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- Costinot, Arnaud and Andres Rodriguez-Clare (2014), “Trade Theory with Numbers: Quantifying the Consequences of Globalization,” in _Handbook of International Economics,_ Elhanan Helpman, Kenneth Rogoff and Gita Gopinath (eds.), Vol. 4, 197-262, Amsterdam: Elsevier.

- Costinot, Arnaud and Andres Rodriguez-Clare (2013), “Online Appendix to Trade Theory with Numbers: Quantifying the Consequences of Globalization,” March. Available at: https://economics.mit.edu/files/9215

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41
# **Appendix Table 5. Nutrition/health metrics for women of reproductive age**

|**Nutrition/health outcome**<br>**variables**|Data source|Data availability|Notes|
|---|---|---|---|
|**Anaemia**pregnant women (15-<br>49 years) and non-pregnant<br>women (15-49 years)|UNICEF Global Database<br>(based on WHO GHO)|All countries,<br>Latest year: 2019||
|**Underweight**in WRA (20-49<br>years)|UNICEF Global Database<br>(based on NCD-RisC)|All countries, Latest<br>year: 2016|Percentage of women 20-49 years of age with a<br>BMI <18.5 kg/m2<br>Data also available for men from NCD-RisC.|
|**Overweight and obesity**in<br>WRA|UNICEF Global Database<br>(based on NCD-RisC)|All countries, Latest<br>year: 2016|Percentage of women 20–49 years of age with a<br>BMI ≥ 25 kg/m<sup>2</sup><br>Data also available for men from NCD-RisC.|
|**Low-birth weight**prevalence<br>and prevalence of newborns<br>without birthweight data|UNICEF Global Database|N=148 (54 no LBW<br>estimate)<br>Latest year:**2015**<br>(will be updated later<br>this year)||
|**Stillbirth**rate and number of<br>stillbirths|UNICEF Global Database|Latest year: 2021||
|**Preterm birth**|Modeled estimates by<br>Chawanpaiboon et al. 2019<sup>5</sup>|Latest year: 2014||

44
2.46 -

-

cedis/kg). If this Is taken Into account then the domestic price should be bounded below by tnis critical level.

If, as seems to be likely over the near future, the world price of cocoa Is such that 140 codis/kg represents more than 55 percent of the export price, then this lower bound will Imply that the Bank's recommendatlon Is too low, and the price should be accordingly adjusted. But what If 55 percent of the world price Is above this critical level? And Is there a case for raising the prico above the critical level even if the export price Is low. Finally, how should the producer price respond to varlatlons In the expected world price, for example, as forecast by the World Bank's Commodity Divislon or by other consultants such as Bateman? Here the Ghanaian survey data throws useful light on the standard problem facing the choice of any tax. The level of taxes on any particular commodity or Income source Involves a balance between the need for revenue, the distortlonary costs Introduced by the tax, and the distributlonal consequences of the tax. A tax on cocoa must be compared with alternative ways of ralsing the same revenue -- In particular, we need to ask whether it would be better, on balance, to Increase cocoa taxes and reduce other taxes, or, conversely, to reduce cocoa taxes and Increase other taxes. For present purposes, the alternative source of tax revenue Is considered to be a general increase In taxes on non-food consumer goods produced In the modern sector or kIported. It may well be the case that particular goods are even better choices as bases for Increased taxes, and If so, that will further strengthen the case for switching away from cocoa taxes to these alternatives.

If the Goverrunont Is concerned with the distributlon of income, and believes that taxes should fall more heavily on richer members of society, then much will depend on how egalitarian the Government Is. In the technical paper this Is captured by a 'coefficient of Inequality averslon', which may take the values 0.5, 1.0, or 2.0. To take the central value of 1.0, this kupiles that the social cost of a tax of S1 on a person whose annual Income Is $5,000 Is considered to be twice that of a person with twice his Income --
_αj βj γj_ **Table 7: Antitrust, Misallocation, and Productivity.** The term _TFPRjk ≡ PjkYjk/Ljk_<sup>_K_</sup> _jk_<sup>_M_</sup> _jk_<sup>isthetotalfactorrevenue</sup> productivity of establishment _k_ in the treated industry of case _j_ , where _Pjk_ is the price of sales, _Yjk_ is the quantity of sales, _Ljk_ is the number of employees, _Kjk_ is the value of fixed assets, and _Mjk_ is the value of intermediate inputs. The term _TFPQj ≡_ � _k_ �( _PjkYjk_ ) _σ−σ_ 1 _/Lαjkj_<sup>_K_</sup> _jkβj_<sup>_M_</sup> _jkγj_ � _σ−_ 1 is industry total factor quantity productivity in the absence of distortions as in Hsieh and Klenow (2009), which is derived assuming a CES demand function and within industry substitution parameter _σ_ . This corresponds to the productivity in a benchmark economy where there are no differences in markups or factor market constraints across firms. Production function parameters _αj_ , _βj_ , and _γj_ are estimated separately for treatment and control industries using a control function approach in *** ** * the Busso, Fentanes T ́ellez and Levy Algazi (2019) panel for each case _j_ . Standard errors in parentheses. p _<_ 0.01, p _<_ 0.05, p _<_ 0.1.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Change in|market share|unweighted|correlation of|variance of|_σ_ = 3|_σ_ = 5|
||weighted average|average|market share and||||
||ln(TFPR_jk_)|ln(TFPR_jk_)|ln(TFPR_jk_)|ln(TFPR_jk_)|ln(TFPQ_j_)|ln(TFPQ_j_)|
|Financial sanction (=1)|0.009*|0.011*|-0.013|0.034|0.076***|0.108*|
||(0.005)|(0.006)|(0.056)|(0.115)|(0.030)|(0.061)|
|Constant|0.000|-0.002|0.133***|0.187***|-0.052***|-0.040|
||(0.003)|(0.004)|(0.036)|(0.072)|(0.019)|(0.038)|
|_γ_<sup>2</sup>|0.000153|0.000605|0.0510|0.265|0.0112|0.0425|
|Number of cases|90|90|90|90|90|90|
|Meta analysis framework|RE|RE|RE|RE|RE|RE|
|Outcome change estimator|SDID|SDID|SDID|SDID|SDID|SDID|