rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3: Main results for women’s career and income, decision-making power, and expenditures**

## **Panel A: Effects on women’s promotions and income (Pooled waves)**

||(1)|(2)|(3)|(4)<br>IV|
|---|---|---|---|---|
||Supervisor<br>(Dummy)|Personal<br>income<br>(Taka)|Household<br>income<br>(Taka)|Personal<br>income<br>(Taka)|
|selected|0_._24***|688_._96|83_._99||
||(0_._06)|(487_._32)|(1350_._47)||
||[0_._00]|[0_._26]|[0_._85]||
|supervisor||||1695_._02|
|||||(2079_._10)|
|||||[0_._46]|
|N|363|363|363|363|
|Nominated mean|0_._09|9883_._31|23758_._90|9883_._31|
|Nominated SD|0_._29|3203_._56|7963_._02|3203_._56|

**Panel B: Effects on women’s decision-making power**

||Decisio|n-making (In|dex)|
|---|---|---|---|
||Pooled waves|Wave 1|Wave 2|
|selected|0_._11|0_._42*|_−_0_._12|
||(0_._15)|(0_._24)|(0_._11)|
||[0_._46]|[0_._17]|[0_._37]|
|N|363|181|182|
|Nominated mean|_−_0_._16|_−_0_._48|0_._22|
|Nominated SD|1_._13|1_._38|0_._58|

**Panel C: Effects on assignable goods expenditures and remittances (Pooled waves)**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcomes|as % in house|hold income)||
|selected|2_._71**|1_._22**|0_._32|_−_0_._37|3_._48**|
||(1_._07)|(0_._62)|(0_._57)|(0_._72)|(1_._40)|
||[0_._05]|[0_._12]|[0_._50]|[0_._50]|[0_._05]|
|N|363|146|363|161|362|
|Nominated mean|5_._32|1_._65|3_._92|2_._69|6_._00|
|Nominated SD|4_._48|2_._41|3_._99|3_._65|8_._63|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses, sharpened q-values in squared brackets. Controls chosen using pdslasso (ivlasso for Panel A, Column 4) from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. In Panel A, Column 4 Supervisor is being instrumented by selected, for which the first stage is shown in Panel A, Column 1. The sample in Panel C, Column 2 (Column 4) is limited to those respondent with a daughter (son), or a female (male) child in the household.

41
repetition, we use Mexico’s School Census (also called Statistics 911), an annual listing of background and outcome data for Mexican primary schools. The dropout rate in any given school year t (spanning calendar years t and t+1) is defined as 1- (number of students enrolled at end of school year t divided by the number who enrolled at any time in school year t). The failure rate in school year t is defined as 1-(number of students who passed grade in school year t divided by the number who were enrolled at end of school year t). Lastly, the repetition rate in school year t is defined as 1-(number of students who were repeating their grade at beginning of school year t+1 divided by total enrollment at beginning of school year t+1). Mexico’s school census also includes some information on the schools themselves: the number of classes, the ratio of teachers to students, the school type (indigenous or non-indigenous), and the number of rooms in the school. We include each of these variables, as a school’s size and teaching burden may influence its likelihood of requesting PEC funds.

To measure locality background data, we use a version of Mexico’s 2000 Census of Population and Housing prepared by the Mexico’s National Population Council (CONAPO). CONAPO used the complete Census data to construct a poverty index for each Mexican locality. PEC advertises to schools based on the CONAPO marginality index of a school’s locality. That index is a function of eight variables from Mexico’s 2000 census. Rather than use the index itself, we use the richer information contained in these eight locality-level variables. Mexico conducted the census only in 2000, giving data on these outcomes only for the baseline of PEC.

Lastly, we include participation data from Mexico’s National Council for Educational Development (CONAFE) and Oportunidades (formerly PROGRESA), two social programs run by Mexico’s federal government. CONAFE targets mainly rural areas while Oportunidades has expanded from a rural focus to offer scholarships to poor urban students 4 Thus the control groups for both definitions of treatment are identical.

8
- 20 -

Despite its scientific elegance, the model came under heavy criticism in the early 1970s, particularly from a Rand Corporation analysis of a collection of federal policies and social programs. This study sought to find out why some federal social change projects were more successful than others. The authors concluded, with reference to evaluations, that most "... ask the wrong questions and use the wrong measures. [The] study suggests that the problem with contemporary evaluation is deeply rooted -- that it stems from a fundamental lack of fit between the evaluation paradigm and the way that local projects operate and change takes place." (McLaughlin, 1980: p. 42).

The principal criticism was that the model failed to take into account the factors of principal importance in determining project effectiveness at local levels. The major findings regarding successful projects were that:

- Implementatior dominated the outcome of planned change efforts;

- Local factors shaped implementation more than the adoption of a particular technology, the availability of information, or the funding level;

- Mutual adaptation between the project and the participants characterized implementation, with both changing over time.

These in turn depended on important local factors. Local administrative support; staff commitment, training and capability; site selection; and the broad-based participation of key stakeholders in the project were all valid predictors of project success.

But these factors were rarely addressed in evaluations based on the prevailing paradigm. The reasons lay in the nature of the paradigm itself: ... most project evaluations use an "impact" or "production function" model that correlates initial project inputs, such as level of funding and technology, to project outputs such as student achievement gains. This evaluation approach assumes that project treatment has a primary and direct effect on project outcomes. Local institutional factors and other variables that are important in the implementation process typically are omitted. Evaluators have lightly referred to these local factors as components of the "black box." The contents of the black box, it turns out, matter more to project outcomes than do the other factors that evaluators attempt to calibrate and assess. Insofar as unidentified causal variables have major and direct effects on project outcomes, an evaluation that fails to measure these variables can produce a specious finding of "positive program effects" or of "no significant difference." (McLaughlin, 1980: p. 42)
# **Fig 4 Tajikistan, 2009 (attending school, ages 7-17)**

<!-- Start of picture text --> Fig 4a Fig 4b<br><!-- End of picture text --> <!-- Start of picture text --> Unitary expenditure on education Unitary expenditure on education<br>by quintiles of consumption (2009) by quintiles of probability (2009)<br>39 38 35 35 38 30 35 36 37 45<br>-22 -26 -29 -40 -22 -25 -37 -31<br>-128 -122<br>-166 -168<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>50 50<br>0 0<br>US$ -50 US$ -50<br>-100 -100<br>-150 -150<br><!-- End of picture text --> Source: Author’s estimates from Tajikistan Living Standards Survey (2007), World Bank (2012), Abras, Cuesta and Tiwari (2012)

**Fig 5 Paraguay, 2009 (attending public school, ages 5-17)**

<!-- Start of picture text --> Fig 5a Fig 5b<br><!-- End of picture text --> <!-- Start of picture text --> Unitary public expenditure on education Unitary public expenditure on education<br>by quintiles of incomes (2009) by quintiles of probability (2009)<br>222 229 227 236<br>234 214 245 231 233<br>167 149 130 117 219<br>60 165 172 140<br>95<br>-56 -80 -98 -119 -48 -73 -91 34<br>-173 -138<br>-185<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>300<br>200<br>200<br>100<br>100<br>US$ 0 US$<br>0<br>-100<br>-100<br>-200 -200<br><!-- End of picture text --> _Source_ : Authors’ estimates from INEC’s EPH 2009, Ministerio de Educación de Paraguay (2009, 2010), Ministerio de Finanzas de Paraguay (2013), Cuesta and Suarez-Becerra (2013) 18
# pootnoteo

1. Much of our summary of air pollution problems in developing countries is taken from a paper written by Alan J. Krupnick entitled "Urban Air Pollution in Developing Countries: Problems and Policies." It was prepared for a UNU/WIDER Conference on "The Environment and Emerging Development Issues," Helsinki, September 3-7, 1990.

2. Indoor pollution poses less of a social problem, invoking the need for a government policy, because the cost of such pollution is borne mostly by the polluter himself. Rather than being an externality, the problem of indoor pollution may be one of lack of information or mis-information, as well as an inadequate market structure which makes alternative fuels unavailable to rural households.

3. Paradoxically, recent steps by the state-run oil company PEMEX to reduce lead in gasoline may have had the effect of raising ozone levels in Mexico City. In the absence of catalytic converters on most Mexican cars, the unleaded gasoline introduced by PEMEX in 1986 caused ozone levels to shoot up. The relatively modest ozone standard of 0.11 ppm is exceeded on more than 300 days each year (Nuccio, Ornelas, and Restrepo, 1990).

4. For a discussion of benefit-cost techniques for evaluating public infrastructure projects in developing countries, the interested reader is directed to Dixon and Hufschmidt (1986). In one chapter, they evaluate the benefits and costs of additional water treatment facilities in a suburban region of Beijing.

5. This was first noticed in medieval Europe where common pasturelands were typically overgrazed and less productive than private pasturelands. Much of a country's air and water resources are common property and subject to this "tragedy of the commons".

6. In fact to the extent that the refining of gasoline involves external costs of air pollution, the price paid at the pump is less than the social cost of producing the gasoline.

7. This assumes that the private supply price of gasoline remains constant at one dollar when the Pigouvian tax is imposed. More generally, the private supply price of gasoline may fall when a tax is imposed.

8. Payments to consumers would be undesirable under circumstances where such payments may attract "nuisance" claims--for example, households moving into areas of high pollution, at a cost to themselves, in order to receive the compensating payments.

69
we find that only the CCT intervention has a positive and significant impact, and we reject equality of the CCT and UCT coefficients. However, using the school-based enrollment measure (column 4), we find positive and significant impacts for both the CCT and UCT interventions and no statistical difference between the coefficients.

In Table 4 (columns 2 and 5), we present results using the Equation 2 difference-indifference strategy. As previously discussed, results show no impact of the conditional or unconditional transfers at round 2 for school year 2008-2009, the program’s first year, because the transfers were delivered too late in that school year. However, the results show significant impacts of transfers at round 3 for school year 2009-2010, when the transfers were delivered on time. More precisely, columns 2 and 5 show significant positive impacts in the CCT villages for children ages 7 to 15, using both enrollment measures, while there is a positive but not significant coefficient for the UCT villages. At round 3, the CCT and the UCT coefficients are significantly different from each other using the self-reported but not the school roster measure. These results are confirmed in columns 3 and 6 when we use the Equation 3 difference-indifference specification using only the baseline and last follow-up surveys (rounds 1 and 3).<sup>23</sup> Overall, when looking at all school-age children, across the two measures of enrollment and the three alternative specifications, Table 4 allows us to conclude that the cash transfer intervention had no impact on school enrollment in the first year (2008-2009), but CCTs had a positive impact on enrollment in the second year. The impact of the UCT intervention on enrollment for all children is less clear and often not statistically significant. The remainder of our analysis extends our discussion of Figure 1 to investigate how the impacts of the conditional and unconditional cash transfers vary with the type of child.

> 23 The point estimates in columns 1 and 4 are larger than in the difference-in-difference specifications in the other columns suggesting that controlling for residual baseline variation is important.

18
<!-- Start of picture text --> Burundi<br>1.2<br>1<br>0.8<br>g<br>B06<br>es<br>0.4<br>0.2<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Ethiopia<br>oe<br>os<br>os<br>g<br>5 os<br>—<br>a<br>aL<br>os — x0 — 20<br><!-- End of picture text --> <!-- Start of picture text --> Kenya<br>2.5<br>2<br>g LS<br>a<br>Zz<br>2<br>0.5<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Lesotho<br>7<br>6<br>5<br>84<br>o<br>a=]<br>Bs<br>2<br>1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text -->
sample of households with two respondents, which allows us to abstract from characteristics and traits that do not vary within the household. The results suggest that the very young (18-24) group in Eastern Europe have a higher assessment of the household’s economic welfare, and that the elderly (65+ group) in the CIS appear to have a lower assessment, suggesting a frame of reference bias. Women in the CIS subsample also appear to report a lower household welfare level when answering the ladder question. Jointly age and sex appear to be significant in all but the Western European subsample.

**Table 4: Differences in within-household assessments of household economic welfare**

||**Full Sample**|**Western**<br>**Europe **|**New EU**|**Europe, non-**<br>**EU**|**CIS**|
|---|---|---|---|---|---|
|Age 18-24|0.093***|0.052|0.128**|0.092*|0.047|
||(0.029)|(0.153)|(0.058)|(0.052)|(0.049)|
|25-34|0.034|0.024|0.060|0.045|-0.014|
||(0.029)|(0.118)|(0.055)|(0.050)|(0.048)|
|35-44 – Baseline||||||
|45-54|-0.027|-0.013|-0.038|-0.051|-0.016|
||(0.027)|(0.111)|(0.054)|(0.048)|(0.047)|
|55-64|-0.024|-0.013|-0.031|0.030|-0.074|
||(0.032)|(0.125)|(0.065)|(0.056)|(0.053)|
|65+|-0.068**|0.081|-0.015|-0.055|-0.129**|
||(0.034)|(0.141)|(0.074)|(0.058)|(0.055)|
|Male – Baseline||||||
|Female|-0.018|-0.052|-0.013|0.039*|-0.066***|
||(0.011)|(0.040)|(0.019)|(0.022)|(0.021)|
|Constant|4.619***|5.128***|4.772***|4.692***|4.406***|
||(0.020)|(0.077)|(0.039)|(0.035)|(0.034)|
|F|5.911|0.438|2.061|3.519|3.373|
|Prob>F|0.000|0.854|0.054|0.002|0.003|
|N|27522.000|1680.000|8118.000|7712.000|9032.000|

Notes: Fixed effects regressions. Robust standard errors, clustered by household in parentheses. Significance: * 0.10 ** 0.05 *** 0.01

# **5. Conclusion**

Our results do not reveal substantial biases in accounts of life satisfaction due to scaling. In particular, the relationship between important determinants of life satisfaction and reported life satisfaction at the individual level is robust to alternative formulations and scales of the life satisfaction question. Furthermore, the country-level relationships between subjective well-being and objective measures of welfare are broadly consistent across the two subjective well-being scales (with the exception of Central Asia), although the ranking of countries is not invariant to the scale of the life satisfaction measure.

18
14 The headcount index of poverty in Bangladesh responds to distributionallv neutral growth with an elasticity of about unity, using the BBS poverty line for 1981/82. For example, an annual growth rate in mean consumption per capita of two percent, and at the same rate across the whole distribution, would reduce the proportion of the population who are poor by about two percent per year. This elasticity is lower than those recently estimated for some other countries; Datt and Ravallion (1989) estimate that the elasticity of the headcount index of poverty to distributionally neutral growth is -2.2 in India (based on 1983 data), and Ravallion and Huppi (1989) obtain an elasticity of -2.1 using Indonesian data for 1984. The elasticities to distributionally neutral growth are higher (in absolute value) for the alternative measures, and highest for the preferred (distributionally sensitive) measure; this was also found in the studies for other countries mentioned above.

The growth elasticity can be sensitive to the choice of poverty line.<sup>10</sup> The elasticity based on the lower poverty line in Table 3 is close to the value of about two obtained for other countries in the above studies. The lower poverty line generates headcount indices which are a good deal closer to the figures for India and Indonesia, and so it appears that much of the difference between these countries in growth elasticities is accountable to the fact that the proportion of poor (as judged by the usual local poverty lines) is higher in Bangladesh.

The headcount index of poverty is found to be quite unresponsive to changes in the Gini index of inequality, and rural poverty is actually found to increase slightly with decreases in the Gini for the implicit BBS poverty line; the latter finding reflects the fact that the rural poverty line is
the data at the 95th percentile. In a very small number of cases, errors were so large that it seemed infeasible for even the most uninformed bureaucrat to have made such an error, and it looked likely to be a data entry problem. Either way, the interpretation of the remaining data is based on the errors of 95% of the claims of public officials in our study.

Our core transformation is to create z-scores of each of the variables by subtracting the mean and dividing by the standard deviation of all the errors related to a particular indicator. This scales the claims by an empirical measure of a ‘normalized’ measure of error for that indicator. The interpretation of the z-score is a unitless measure of how many standard deviations of the full distributions of error away from the benchmark data an official’s assessment is. Thus, it is a measure of whether an individual official made a ‘large’ error relative to the empirical distribution of errors we observe for that indicator. Comparisons across indicators therefore take into account the empirical distribution of errors for each indicator. They compare the scale of an official’s error relative to the errors made by all other officials in our data set, indicator by indicator.

Defining z-scores in this way assumes that the tails of the raw errors are roughly symmetric for each indicator, such that an error 10% below and above the truth are equally likely. However, the distribution of errors of a number of the indicators we study are right-skewed, such that we may be underestimating the severity of an error in the sense that few people would make it. To test the robustness of our core measure to this criticism, we define a ‘skewness-adjusted’ z-score, where we test for skewness of the distribution of a raw indicator and replace skewed indicators with their log. We then take the z-score of these transformed variables in a similar way to the above measure.

Finally, we move away from distributional concerns by taking the ordinal rank of absolute errors within an indicator and divide this by the maximum rank, thereby creating a ‘rank-proportion’ score. This can be interpreted as the percentile of error made by an official relative to other officials in our data set. The Data Appendix provides further details on how the error variables we use in the analysis were created.

# **2.5 Measuring the Incentive Environment**

To understand the incentive environments in which public officials in the organizations we are studying make decisions on information absorption, we also collected data on management practices. Following recent efforts to collect data on the management practices of public administrations (Rasul and Rogger, 2018; Rasul et al., 2018) we used a public sector version of the World Management Survey (WMS; Bloom and Van Reenen, 2007; Bloom et al., 2012) to elicit measures of the management practices under which public officials operate.

of the underlying benchmark data, and then created the various measures outlined below. Taking this approach does not qualitatively change our core results.

12
# **Predicting School Dropout with Administrative Data: New Evidence from Guatemala and Honduras**

Melissa Adelman, Francisco Haimovich, Andres Ham, and Emmanuel Vazquez* Keywords: Dropout, Prediction, Early Warning, Guatemala, Honduras JEL Classification: I20, I21, C51, C53

*The authors thank the Ministry of Education of Guatemala ( _Ministerio de Educación_ – MINEDUC) and the Secretary of Education of Honduras ( _Secretaría de Educación_ – SEDUC) for providing access to the anonymized administrative data on which this study is based.
> <sup>33</sup>

Banerjee and Somanathan (2007) have matched electoral constituencies to the administrative districts for which the Census of India provides data on availability of public infrastructure. They have further identified some key public infrastructure that proliferated across all national electoral districts between 1971 and 1991, when a strong national party committed itself to universal coverage. Using their dataset, we control for five commune infrastructure variables, the proportion of villages in an electoral constituency that had a middle school, tap water, electricity, post and telegraph facilities, and paved roads, as measured by the 1991 Census. We also include population density as measured by the Census, as yet another district characteristic that might contribute to omitted variable bias. These results are reported in the final three columns of Table 6.

In the third and fourth specifications reported in Table 6, the size and significance of the coefficient on _PartyStronghold_ remain unchanged. In the third specification with state fixed effects, the size of the coefficients falls slightly, suggesting that there is some correlation between a district being a party stronghold and its available public infrastructure from past public investments by state and national governments.

# **Other unobserved constituency characteristics**

Two other unobserved constituency characteristics that might be important are opportunities for MP rent-seeking; and the extent to which constituency voters are informed about MP actions. The scope for these to bias our results is limited, though, by controls for state fixed effects and the inclusion of MPLADS spending prior to 1999. In addition, the most plausible arguments suggest that these omitted characteristics would exert no bias or a bias in favor of rejecting our hypothesis that party strongholds exhibit lower MPLADS spending. With regard to rent-seeking, the most plausible argument is that, insulated from competitive pressures by party dominance and only weakly supervised by party leaders,