rafmacalaba/datause-encoder-data huggingface dataset:
observable characteristics<sup>7</sup> . In order to solve the fundamental missing data problem, all we have to do is to find for each participant, one or more nonparticipants with the same values of observables. This is where matching comes in. In general, matching estimators of program effect impute the missing potential outcomes using only the outcomes of the matched individuals from the comparison group.

For matching to be feasible, there must be individuals in the comparison group with the same values of the covariates as the participant of interest. This requires an _overlap_ in the distribution of observables between the treated and the comparison groups. The overlap assumption is usually stated as: A weaker version of the overlap assumption requires only the following (Imbens 2004).

This implies the possible existence of a nonparticipant analogue for each participant. This is all that is required for the estimation of the mean impact on the treated (Smith and Todd 2005a). When this condition is not met, then it would be impossible to find matches for a fraction of program participants.

To fully appreciate the point of the overlap assumption, consider situations where, for some values of **x** , we have either _p_ ( _x_ ) = 0 or _p_ ( _x_ ) = 1. Individuals with such covariates are such that either they never receive treatment or they always receive it. If they always receive treatment, then they have no counterparts in the comparison group. On the other hand, if they never receive treatment, then they have no counterparts in the treated group. Thus, it would be impossible to use matching methods on such cases (Heckman, Ichimura and Todd 1998). In these circumstances, it is recommended to restrict matching and hence the estimation of the treatment effect on the region of common support. This implies using only nonparticipants whose propensity scores overlap with those of the participants.

> 7 Hence the name “selection on observables” for this orthogonality assumption which implies that unobservables play no role in determining participation (Dehejia and Wahba 2002).

7
_Table 3_

# _Impacts of Foreign Ownership on Wages_

|||Full Sample<sup>*1</sup><br>||Subset of Wo<br>|rkers in the For<br>|mal Sector<sup>*2</sup><br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(1)|(2)|(3)|
|Foreign dummy|.40***|.50***|.38***|.25***|.32***|.26***|
||(.034)|(.037)|(.035)|(.037)|(.038)|(.035)|
|State dummy|.25***|.31***|.28***|.042|.13***|.15***|
||(.21)|(.025)|(.023)|(.028)|(.031)|(.028)|
|Private dummy|.11***|.14***|.071***||||
||(.022)|(.023)|(.021)||||
|Female|-.15***|-.10***|-.11***|-.061***|.0091|-.0012|
||(.016)|(.015)|(.015)|(.021)|(.021)|(.020)|
|Married|.020|.018|.052***|.0068|.015|.069***|
||(.019)|(.019)|(.018)|(.029)|(.028)|(.026)|
|Minority|-.11***|-.11***|-.061**|-.094**|-.054|.012|
||(.029)|(.029)|(.030)|(.045)|(.045)|(.048)|
|Education|.048***|.049***|.053***|.075***|.079***|.078***|
||(.002)|(.0025)|(.0024)|(.0032)|(.0036)|(.0034)|
|Potential experience|.032***|.031***|.027***|.041***|.041***|.032***|
||(.0030)|(.0027)|(.0025)|(.0044)|(.0042)|(.0039)|
|Experience<sup>2</sup>|-.0006|-.0006***|-.0005***|-.0007***|-.0007***|-.0005***|
||(.0006)|(.00006)|(.00006)|(.0001)|(.0001)|(.000095)|
|Urban|||.090***|||.12***|
||||(.016)|||(.021)|
|Industry dummies||X|X||X|X|
|Regional dummies|||X|||X|
|Number of obs.<br>|6623|6623|6623|3514|3514|3514|
|R<sup>2</sup>|.24|.28|.35|.20|.25|.34|

Notes:

*1The informal wage sector is the omitted ownership category.

*2The private domestic sector is the omitted ownership category.

The dependent variable is the log of hourly wage.

*, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level, respectively. The robust standard errors are between parentheses.

30
A second route to evaluation is offered through prioritization. In cases in which the number of eligible applicants in a municipality exceeds the number of available slots in a given year, applicants are ordered by a number of vulnerability criteria, each of which adds or subtracts a given amount to a numeric score. Applicants with the highest such score are then enrolled first, giving rise to a municipalityspecific threshold value below which enrollment in the program is postponed. The score ranges from a minimum of 7 to a theoretical maximum of 22. The only component that adds negative values is age for individuals 64 years old or younger. In theory, this mechanism allows identification to be based on a comparison between closely similar households living in different municipalities with different thresholds. Closer examination, however, reveals that the prioritization score is only binding in a few cases and almost only for the relatively young (that is, applicants in their 50s or early 60s), given that age is the single most important element in the score. Table 6 shows that, of the 1,105 municipalities on which we have 2013 prioritization data, only 78 did not apply prioritization at all. However, almost − − all the other municipalities had low threshold values; the lion’s share was between 4 and 7 (a score that can only correspond to a person in his 50s), and only 18 municipalities had a positive threshold value. The bottom line is that the prioritization threshold does not yield sufficient variance to act as a feasible identification strategy.

**Table 6: Distribution of the Prioritization Threshold across Municipalities**

|_Threshold value_|_Municipalities, number_|
|---|---|
|Did not apply prioritization|78|
|−7|258|
|−6|267|
|−5|186|
|−4|133|
|−3|86|
|−2|42|
|−1|27|
|0|9|
|1|13|
|2|1|
|3|1|
|4|1|
|5|1|
|6|1|

16
but does not appear to be transiting into the world of formal paid work. Our use of the term “home status” reflects that individuals in this group may be engaged in unpaid work in the household or in home-based enterprise. The jobless rate is the proportion of the population that is either unemployed or idle. It avoids arbitrary distinctions between being out of the labor force and being unemployed.

# **III. The Interrelationship of Alternative Measures of Youth Time Use**

To what extent do these indicators reveal consistent inferences about youth time allocation? Do they reveal unique information or are some essentially measuring the same thing? Are the indicators consistent across countries, or do they mean different things in different settings?

The data used in this study come mostly (although not exclusively) from the World Bank’s Microdata Development Data Platform (DDP). This platform is a compendium of Household Surveys that comprises most of the countries in the world. From this DDP data base we selected those countries with adequate and reasonable data. Adequacy was defined, in general, as being nationally representative. Reasonable data was defined as having good quality data, appropriately documented, and also that has some minimum set of desired variables. Since these surveys were conducted by the National Statistical Offices of each country, it was necessary to perform some minimum standardization to make the variables reasonable comparable from one country to another, and also from one period to another. A description of the countries, years and name of the surveys can be found in Appendix Table IV.

## **Specific Example**

Table 1 is an example of these different indicators in a sample of selected countries. Of the nine countries, the strongest labor markets reflected by a low UR (Vietnam and Uganda) also are the strongest according to high ER and low JR. However, Pakistan has the third lowest unemployment rate but the highest jobless rate. Because relatively few Pakistani youth who are not employed are in the labor force, the Pakistan youth UR is artificially low. This reflects a very high home status rate in Pakistan, particularly for women, and so those without jobs are not counted among the unemployed because they are not counted as actively seeking work. Bulgaria also has a relatively high jobless rate, but has a high unemployment rate as well because, in contrast to Pakistan, a high proportion of the jobless are counted as actively seeking employment.

In evaluating the pattern of labor market indicators in Table 1, it seems that the unemployment rate provides a very noisy measure of the strength of the youth labor market in developing countries. The employment rate provides a better measure of the economy’s ability to generate jobs and the jobless rate is a better measure of how the outof-school population is faring in the labor market. Among the strongest youth markets according to ER and JR criteria (Vietnam, Poland, Bolivia and Uganda) the UR varies from 0.05 to 0.19. Of the weakest (Bulgaria, Egypt, El Salvador, Pakistan, and Niger) 5
Policy Research Working Paper 8148

# **Abstract**

Improving women’s agency, namely their ability to define goals and act on them, is crucial for advancing gender equality and the empowerment of women. Yet, existing frameworks for women’s agency measurement—both disorganized and partial—provide a fragmented understanding of the constraints women face in exercising their agency, restricting the design of quality interventions and evaluation of their impact. This paper proposes a multidisciplinary framework containing the three critical dimensions of agency: goal-setting, perceived control and ability (“sense of agency”), and acting on goals. For each dimension, the paper (i) reviews existing measurement approaches and what is known about their relative quality; (ii) presents new empirical evidence from Sub-Saharan Africa: validating vignettes as a measurement tool for goal-setting, examining gender and regional discrepancies in response to sense-of-agency measures, and investigating what information spousal disagreement over decision-making roles can provide about the intra-household process of acting on goals; and (iii) highlights priorities for future research to improve the measurement of women’s agency.

This paper is a product of the Gender Innovation Lab, Office of the Chief Economist, Africa Region and the the Living Standards Measurement Study, Development Data Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at adonald@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 19: Pooled regression estimates of the effect of ICT use on wages: Manufacturing

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Dependent variable: ln(Wage of whi**|**te-collar wo**|**rkers)**||||
|Share of labor that uses computer|0.0021***|0.0018***|0.0006***|0.0027***|0.0014***|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Share of labor that uses Internet|0.0017***|0.0014***|0.0005***|0.0024***|0.0013***|
||(0.0002)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|ln(stock of computer equipment/worker)|0.0792***|0.0488***|0.0438***|0.0380***|0.0360***|
||(0.0037)|(0.0040)|(0.0035)|(0.0036)|(0.0033)|
|**Dependent variable: ln(Wage of blue**|**-collar wor**|**kers)**||||
|Share of labor that uses computer|0.0011***|0.0008***|0.0000|0.0015***|0.0008***|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Share of labor that uses Internet|0.0007***|0.0004***|-0.0001|0.0013***|0.0007***|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|ln(stock of computer equipment/worker)|0.0832***|0.0518***|0.0471***|0.0425***|0.0399***|
||(0.0030)|(0.0032)|(0.0029)|(0.0029)|(0.0028)|
|Controls||||||
|ln(capital/worker)|No|Yes|Yes|Yes|Yes|
|Age dummies|No|No|Yes|No|Yes|
|Size dummies|No|No|Yes|No|Yes|
|Sectoral efects|No|No|No|Yes|Yes|
|Observations|22,876|22,876|22,876|22,876|22,876|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

48
and thus more or less likely to be also later acquired by foreign investors.<sup>5</sup> The impact of these spillovers and externalities is presumably substantial, given that the measured effects of financial liberalization often differ depending on the aggregation level of the used data. For example, foreign investors seem to have strong impact on the productivity measured both at the firm level (Varela 2018) and at the country level (Bonfiglioli 2008; Bekaert, Harvey and Lundblad 2011; Chari, Henry and Sasson 2012), while at the industry level there might be no productivity effects at all (Levchenko, Ranci`ere, and Thoenig 2009).<sup>6</sup> The effects of financial liberalization on investment and economic activity also seem to be stronger when measured at a more disaggregated level (Henry 2007; Bekaert et al 2011). It is thus important to distinguish between the impact of financial liberalization on firms attracting foreign investors and the overall net effect on the recipient economy (Mitton 2006, p. 646).

In our paper, we sidestep the issue of economic externalities and examine the full structural impact of financial liberalization with the help of product-level data covering countries’ complete export portfolios. Furthermore, the Heckscher-Ohlin framework of international trade allows us to look beyond the usual focus on the misallocation of physical capital and consider also other factors of production like labor, land, and human capital.

Second, we contribute to the literature examining the disciplining impact of foreign investors. Foreign investors might act more forcefully as external monitors because they lack business relations with local firms and thus do not feel loyal to the local management (e.g., Gillan and Starks 2003; Ferreira and Matos 2008; Aggarwal et al. 2011; Bena et al. 2017; Luong et al. 2017). This disciplining impact can also extend beyond the firms where foreign investors ultimately acquire equity stakes. For instance, foreign-owned firms can generate substantial spillovers in terms of improved corporate governance to

> 5 This so-called “feedback effect” has been so far analyzed mostly in the domestic context of US firms and investors (see, e.g., Bradley et al. 2010; Edmans, Goldstein, and Jiang 2012), but it could also affect an empirical analysis focusing on the structural impact of foreign investors.

> 6 These contradictory results could be reconciled if the increase in the aggregate productivity was driven mostly by reallocation of resources from less productive to more productive sectors (Bonfiglioli 2008, p. 353). However, recent evidence suggests that the increases in aggregate productivity following financial liberalization in India and Hungary have been driven by increasing productivity within firms rather than such reallocation effects (Bollard, Klenow and Sharma 2013; Varela 2018).

5
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# _sh_delhi.pdf._

- Goodman, Robert. 2001. Psychometric properties of the strengths and difficulties questionnaire. _Journal of the American Academy of Child and Adolescent Psychiatry_ 40 (11): 1337-1345.

- Haddad, Lawrence James, Endang Achadi, Mohamed Ag Bendech, Arti Ahuja, Komal Bhatia, Zulfiqar Bhutta, Monika Blössner, Elaine Borghi, Esi Colecraft, and Mercedes de Onis. 2014. Global Nutrition Report 2014: Actions and accountability to accelerate the world s progress on nutrition

- Handa, Sudhanshu, Luisa Natali, David Seidenfeld, and Gelson Tembo. 2016. The impact of Zambia’s unconditional child grant on schooling and work: results from a large-scale social experiment. _Journal of Development Effectiveness_ 8 (3): 346-367.

- Haushofer, Johannes and Jeremy Shapiro. 2016. The Short-Term Impact Of Unconditional Cash Transfers To The Poor: Experimental Evidence From Kenya. _Quarterly Journal of Economics_ 131 (4): 1973-2042.

- Heath, Rachel and A. Mushfiq Mobarak. 2015. Manufacturing growth and the lives of Bangladeshi women. _Journal of Development Economics_ 115:1-15.

- Heckman, James J and Chase O Corbin. 2016. Capabilities and Skills. _Journal of Human Development and Capabilities_ 17 (3): 342-359.

- Heckman, James J and Stefano Mosso. 2014. The economics of human development and social mobility. _IZA DP No. 800._

- Heckman, James J and Tim Kautz. 2013. Fostering and measuring skills: Interventions that improve character and cognition. _NBER Working Paper 19656._ National Bureau of Economic Research.

- Hoynes, Hilary, Diane Whitmore Schanzenbach, and Douglas Almond. 2016. Long-Run Impacts of Childhood Access to the Safety Net. _Am Econ Rev_ 106 (4): 903-934.

- Jensen, Robert. 2012. Do labor market opportunities affect young women's work and family decisions? Experimental evidence from India. _Q J Econ_ 127 (2): 753-792.

- LaFave, Daniel and Duncan Thomas. 2016. Height and Cognition at Work: Labor Market Productivity in a Low Income Setting. _NBER Working Paper 22290._ National Bureau of Economic Research.

- Lee, David S. 2009. Training, wages, and sample selection: Estimating sharp bounds on treatment effects. _The Review of Economic Studies_ 76 (3): 1071-1102.

- Levine, Ruth, Cynthia Lloyd, Margaret Greene, and Caren Grown. 2008. Girls Count: A global investment and action agenda. Reprint, 2009. Washington, D.C.: Center for Global Development.

- Lloyd, Cynthia B and Juliet Young. 2009. New lessons: the power of educating adolescent girls: a Girls Count report on adolescent girls. The Population Council, Inc. http://www.popcouncil.org/uploads/pdfs/2009PGY_NewLessons.pdf.

- Manley, James, Seth Gitter, and Vanya Slavchevska. 2013. How effective are cash transfers at improving nutritional status? _World Development_ 48:133-155.

- Milligan, Kevin and Mark Stabile. 2009. Do child tax benefits affect the wellbeing of children?

40
the omitted effect. Certainly, the decision to attend private school may be correlated with the child’s ability to learn. At the same time, the assumption is weaker than OLS estimation of lagged value-added model since the model (M8) allows for the omitted fixed effect to be correlated with lagged achievement.

# 2.1.3 Levels GMM: Conditional mean stationarity

In some instances, it may be reasonable to assume that, while learning heterogeneity exists, it does not affect achievement gains. A talented child may be so far ahead that imperfect persistence cancels the benefit of faster learning. That is, individual heterogeneity may be uncorrelated with gains, _yit_<sup>_∗−y_</sup> _it_<sup>_∗_</sup> _−_ 1<sup>, but not necessarily with</sup><sup>_learning, y_</sup> _it_<sup>_∗−βy_</sup> _it_<sup>_∗_</sup> _−_ 1<sup>.This situation</sup> arises when the initial conditions have reached a convergent level with respect to the fixed effect such that where _t_ = 1 is the first observed period and not the first period in the learning life-cycle. Blundell and Bond (1998) discuss this type of conditional mean stationarity restriction in considerable depth. As they point out, the key assumption is that initial deviations, _di_ , are uncorrelated with the level of _ηi/_ (1 _− β_ ). It does not imply that the achievement path, _{yi_<sup>_∗_</sup> 1<sup>_, y_</sup> _i_<sup>_∗_</sup> 2<sup>_, . . . , y_</sup> _iT_<sup>_∗}_,is</sup> stationary; inputs, including time dummies, continue to spur achievement and can be nonstationary. The assumption only requires that, conditional on the full set of controls and common time dummies, the individual effect does not influence achievement gains.

While this assumption seems too strong in the context of education, we discuss it because the dynamic panel literature has documented large downward biases of other estimators when the instruments are weak (e.g. Blundell and Bond, 1998). This occurs when persistence is perfect ( _β_ = 1) since the lagged value-added model then exhibits a unit root and lagged tests scores become weak instruments in the differenced model. The conditional mean stationarity assumption provides an additional _T −_ 2 non-redundant moment conditions that can augment the system GMM estimators. While a fully efficient approach uses these additional moments along with typical moments in the differenced equation, the conditional mean stationarity assumption ensures strong instruments in the levels equation to identify _β_ . Thus, if we prefer simplicity over efficiency, we can estimate the model using levels GMM or 2SLS and avoid the need to use a system estimator. In this simpler approach, we instrument the undifferenced value-added model (5) using lagged changes in achievement, _△yi_<sup>_∗t−_1</sup> , and either changes in inputs, _△_ x<sup>_t_</sup> _i_<sup>, or inputs directly, x</sup><sup>_t_</sup> _i_<sup>, depending on whether inputs are constantly correlated (M9)</sup> or are uncorrelated with the individual effect (M10).

9