rafmacalaba/datause-encoder-data huggingface dataset:
# Potential Selection Bias

The issue of potential selection bias among births to vaccinated and unvaccinated mothers has been less of a concern in the studies on the impact of tetanus immunization. One reason is that several of the major studies have been based upon controlled field experiments whose design effectively eliminated the possibility of such selection bias. The field trial by Newell, et al. in Colombia (1966), and the 1974 cholera vaccine trial in Matlab, on which the studies by Black, et al. (1980) and Koenig, et al. (1991b) were based, were both double-blind, controlled field trials. A second reason why selection bias may be of less concern with regard to neonatal immunization studies has been the absence of evidence suggesting that unimmunized mothers differ from immunized mothers in behavior (other than immunization) which predisposes the children of the former to higher risks of neonatal tetanus. A third and related factor has been the absence of evidence suggesting that children at risk of death from tetanus represent an especially vulnerable group who are also at much higher risk of death from other neonatal causes.

Nevertheless, in the studies considered which were not based upon controlled experiments, the possibility of potential selection bias cannot be ruled out. For example, in the study in Jew Guinea, a primary reason for non-immunization (86 cases) was that mothers came too late in pregnancy to be immunized. The possibility that such women constitute a highly selective group whose children are predisposed toward higher risks of tetanus cannot be ruled out, although the authors view this as unlikely.

Selection bias may have played a more important role in some of tthe Matlab findings reported by Rahman, et al. (1982). Although maternal tetanus coverage rates subsequently reached very high levels (when offered to all reproductive-aged women), at the time of this study, when mada available only during pregnancy, or.ly one-third of eligible women had accepted immunization. It could plausibly be argued that these women may represent a highly selective group who would face lower mortality risks-- and unimmunized women conversely higher mortality risks-even in the absence of this program. (The dependent variable of 4-14 day mortality leaves open the possibility for selection bias with respect to both tetanus as well non-tetanus deaths, since this measure reflects both). This would help to explain the much larger reduction in neonatal mortality rates among births to mothers who received two injections during the 1978-79 program compared to the other studies from Matlab (49 percent lower). This might also partially account for the absence of a significant effect among the small one dose group, which may consist of a higher proportion of high risk mothers.

29
**The Haves and the Have Nots: Civic Technologies and the Pathways to Government**

# **Responsiveness**

Jonathan Mellon<sup>12</sup> * Tiago C. Peixoto<sup>2</sup> Fredrik M. Sjoberg<sup>2</sup> 1 West Point Department of Systems Engineering 606 Thayer Rd, West Point, NY 10996 USA 2 World Bank, 1818 H Street, NW Washington, DC 20433 USA

* Corresponding author: jonamellon@gmail.com

Classification (JEL): P16, D72, D73, H41, and H7.

Keywords: digital government, civic technology, digital divide, political participation, government responsiveness.
Wagstaff (2000) calculates the CI of child mortality for 9 developing countries using a similar approach to that applied here.<sup>12</sup> He finds that the index ranges from -0.322 in Brazil to -0.016 in Vietnam with a median value of -.132 in Nepal. In all but two of the countries he studies he finds the CI to be negative and highly significantly different from zero. The order of magnitude of the CI of disability among youth is somewhat lower than that of mortality. The median CI of child mortality across the nine developing countries in Wagstaff (2000) was -.132, while it is –0.02 for disability among youth 6 to 17 in the 8 surveys reported in Table 4. In the two surveys where the CI for disability is negative and significantly different from zero it is -0.084 (Indonesia) and –0.110 (Romania) suggesting that in these two countries the order of magnitude is similar to that for child mortality.

# **4. Disability and schooling**

We turn now to the relationship between disability and schooling among the school-age population (defined for the purpose of this analysis as ages 6 to 17). Table 5 shows the percent of youth that are currently in school disaggregated between those who are generally of primary (6 to 11) and secondary (12 to 17) school age. Youth with a disability are almost always substantially less likely to be in school than those without. The deficit among children 6 to 11 years old ranges from a shortfall of 15 percentage points in Mozambique to 59 percentage points in Indonesia. In the latter country, whereas 89 percent of children 6 to 11 without a disability are in school, only 29 percent of those with a disability are in school. Among older children and youth the gap covers a similar range (from 15 percentage points in Cambodia to 58 percentage points in Indonesia), with the exception of Burundi where the gap is zero. On average the gaps are larger among the older group: the median is a 26 percentage point shortfall among 6 to 11 year olds, and a 31 percentage point shortfall among 12 to 17 year olds.

**<u>Table 5. Percent reported to be in school</u>**

||_With_<br>_disability_|_Ages 6-11_<br>_Without_<br>_disability_|_Difference_|_With_<br>_disability_|_Ages 12-17_<sup>_+_</sup><br>_Without_<br>_disability_|_Difference_|
|---|---|---|---|---|---|---|
|Burundi 2000|14.6|37.2|-22.6|48.0|47.8|0.2|
|Cambodia 1999<sup>#</sup>|18.1|58.2|-40.1|30.6|68.0|-37.4|
|Cambodia 2000|37.8|66.8|-29.0|46.5|61.7|-15.2|
|Indonesia 2003<sup>#</sup>|29.2|88.5|-59.3|18.3|75.9|-57.6|
|Jamaica 1998|70.5|99.4|-28.9|50.2|85.9|-35.7|
|Mongolia 2000|41.0|58.0|-17.0|47.1|72.6|-25.5|
|Mozambique 1996<sup>#</sup>|34.2|49.2|-15.0|29.3|48.4|-19.1|
|Romania1995<sup>#</sup>|57.7|79.2|-21.5|35.7|83.7|-48.0|

_Note_ :<sup>+</sup> Maximum age is 14 in Burundi.<sup>#</sup> Survey includes vision, hearing, speech, and physical disabilities.

12 The countries included in the Wagstaff (2000) study are Brazil, Cote d’Ivoire, Ghana, Nepal, Nicaragua, Pakistan, Philippines, South Africa, Vietnam.

9
**Figure 2: GDP per Capita vs.  Regulation Indices**

<!-- Start of picture text --> Overall Regulation Product Market Regulation<br>Correlation: -0.81*** Correlation: -0.87***<br>USA CHE DNKNLDISL FINSWENORAUT JPNBEL FRA NLDUSADNKFINSWECHEBELISLNORAUTJPN FRA<br>GBR CANAUSIRL ESPISRITA GBRCANAUSIRL ESPITA ISR<br>KOR PRTGRC PRT GRCKOR<br>ARG ARG<br>URY URY<br>MYSCHLJAMBWATURZAFTHATTOZMBLKAKENCOLSLVPERPNGGTMIDNGHACRIBGDDOMPHLPAKINDPANHNDMEXJORTUNMARZWEBOLSENNICPRYCIVEGYBRAECUGMBTGOVENIRN HTISYR COG CHLTURMYSJAMZAFBWATHAZMBCOLLKAPERTTOSLVKENPANMEXCRIIDNTUNJORGTMPAKGHAINDMARPHLEGYBRABGDPNGZWENICSENHNDDOMCIVBOLPRYVENIRNGMBECUTGOSYR COG HTI<br>NGA NERMDG BFA SLE NGA NERMDGBFA SLE<br>MWI MWI<br>.2 .3 .4 .5 .6 .7 0 .2 .4 .6 .8<br>Overall Regulation Index Product Market Regulation Index<br>Labor Regulation Fiscal Burden<br>Correlation: -0.12 Correlation: 0.49***<br>USA AUTJPNCHEDNK FRANOR NLDBELSWE FIN CHE ISLUSA NORFIN SWEAUTJPNDNKNLDFRA BEL<br>GBR CANISRAUS IRL ITAESP IRL GBR AUSESP CAN ISR ITA<br>KOR GRC PRT KOR GRCPRT<br>ARG ARG<br>URY URY<br>PNGMYSZWEKENBWATTOJAMGHAZAF SYRLKAZMBBGDIRNINDDOMMARCHLSENCIV TURECUIDNPAKCOLTUNHNDTHAPHLEGYCRIGTMNIC BOLTGOSLVJORHTIPRYCOGPERVENPANMEXBRA HTI PRYSLVDOMBOLGTMECUCHLBGDHNDGMBJAMTHAPHLPERTGOCRIIDNMYSVEN COLBWANICPNGMEXLKAZMBPANTTOJORPAKKENBRAGHACIVSENIND COGTURMARIRNZAFTUNZWEEGY SYR<br>NGA NER BFA MDG SLE MDGNGASLENER BFA<br>MWI MWI<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Labor Regulation Index Fiscal Burden Index<br>12 12<br>10 10<br>8 8<br>Log of GDP per capita Log of GDP per capita<br>6 6<br>4 4<br>12 12<br>10 10<br>8 8<br>Log of GDP per capita Log of GDP per capita<br>6 6<br>4 4<br><!-- End of picture text --> _Note: *, **, and *** denote significance at the 10%, 5%, and 1% level respectively._ 34
Table 6 Effect of Club Membership: Instrumental Variable Estimation

||Model 1<br>(A)|Model 2<br>(A+B)|Model 3<br>(A+C)|Model 4<br>(A+B+C)|OLS<br>(A+B+C)|
|---|---|---|---|---|---|
|A) First Stage|_−_0_._539<sup>_∗∗∗_</sup>|_−_0_._551<sup>_∗∗∗_</sup>|_−_0_._539<sup>_∗∗∗_</sup>|_−_0_._549<sup>_∗∗∗_</sup>||
||(0_._138)|(0_._132)|(0_._138)|(0_._132)||
||0_._15|0_._23|0_._18|0_._23||
|B) Second Stage||||||
|Output per acre|1_._222<sup>_∗∗∗_</sup>|0_._716<sup>_∗_</sup>|1_._228<sup>_∗∗∗_</sup>|0_._739<sup>_∗∗_</sup>|0_._188<sup>_∗∗∗_</sup>|
||(0_._407)|(0_._383)|(0_._392)|(0_._369)|(0_._065)|
||0_._22|0_._35|0_._27|0_._39|0_._42|
|Sales per acre|1_._918<sup>_∗∗_</sup>|1_._468<sup>_∗∗_</sup>|1_._292<sup>_∗∗∗_</sup>|0_._890<sup>_∗∗_</sup>|0_._177<sup>_∗∗_</sup>|
||(0_._796)|(0_._734)|(0_._493)|(0_._452)|(0_._077)|
||0_._01|0_._20|0_._21|0_._35|0_._40|
|Tobacco land share|_−_0_._106|_−_0_._259|_−_0_._078|_−_0_._242|0_._054|
||(0_._321)|(0_._298)|(0_._325)|(0_._299)|(0_._046)|
||0_._11|0_._17|0_._17|0_._23|0_._25|
|Tobacco unit value|0_._054|_−_0_._194|0_._063|_−_0_._188|_−_0_._011|
||(0_._491)|(0_._494)|(0_._484)|(0_._448)|(0_._055)|
||0_._03|0_._05|0_._03|0_._06|0_._07|

Notes: Robust standard errors in parenthesis. ***, **, and * denote statistically significance at 1, 5, and 10 percent, respectively. The R-squared of the regression is reported below the standard errors. Control set A: unmarried-female, female, unmarried, household size, proportion of members age 8-12, age 12-18, 18-45, 46+, educational categories Control set B: area allocated to tobacco, months away from households, weak health, visits from agricultural field assistants, percentage of farmers in the community that grows, tobacco experience: whether farmer grew tobacco in any of the last 5 seasons, average month when tobacco is sold, dummy for own baling. Control set C: ganyu (temporary labor), manure, fertilizer 23:21:0+4S (kg/acre), fertilizer DAP (kg/acre), fertilizer CAN (kg/acre), fertilizer UREA (kg/acre), fertilizer COMPOUND D (kg/acre).

26
# **Policy Research Working Paper Series**

||**1tile**|**Author**|**Date**|**Coritact**<br>**for paper**|
|---|---|---|---|---|
|W PS1_?23_|fiasterrl[FUropo,eSFxporiorirp with<br>Banking Reform. Is There a Rrle for<br>Banks in the Tran(sition<br>9|Alfrodo Iho.rrer|Decrornhwb 1q9g|**N**Jce.s<br>33688|
|WF'S1236|The IMPacd of Two T'ier Producer<br>arid Cornsumrner<br>Food Pricirig in Iida|M.murice Schiff|December 1993|S Fallori<br>38009|
|WPS1 237|Banfk Pprforfimnrice andrdthre liipact<br>of Finanrcial nestructuring in a<br>Macroecononaric faramework: A New<br>Application|YaVuz Boray<br>Hrctor Sierra|December 1993|C. LIm<br>310864|
|WPS1**238**|Kenya: Strurtural Adjustmentinthe<br>1980s|Gurushri Swamy|January 1994|V Sa!danha<br>35742|
|WPS12'9|Principles of Requlitcry PolQ(.R<br>Designl|David E. M Sappington|January 1994|WDF-<br>31 393|
|VWfSt_240_|Financing<br>the Sto'rM MroC<br>n"rcr<br>Crisis<br>'ri R<br>'s,99?**9'**|**Wj!K3ni**Faster:y<br>f I<br>fau'o Vr: 01 da Cm**nia**|Ja'ruary 1994|fBMartin<br>3902?f|
|WPS i 241|Regjiation,<br>rrsimtions,ani<br>Cornrnlmriment in t1heBrtftsh<br>TelpeomrruniciAtirii-sSector|**PabaoI Sp,**e<br>Voqeis|Ja:)uary 1994|B. Moore<br>15r2n<br>35261|
|WPS1,74?|Flracial<br>Pocio;s in Sooiiist<br>C.^rrfrnies in Tr rn-s: 1!on|EP.-ris Pleskov,c.|January 1994|M. Jandu<br>33103|
|w'r3.'S1|243<br>Are Institutiona! Investors an<br>Important Source of Porfioro iriestm<br>inErrergirqMr'ke:W9|Pr:r**Ch.'J"lari**<br>eFt|January 1994|R Vo<br>31047|
|VWPS1<br>2,44|)tf fic)1 i es o f T'r-,<trr<br>r q**PK**<br>Based Cap;ital<br>wrPrer<br>to<br>Developing Countries|Fdw?Frst<br>J|JKre<br>13nuary1994|P. Sirtim Aboagye<br>38526|
|WPS1245|The Adaing-Up Problemn: Strategies<br>for Primary Commodity Exports<br>ir Sub Saharan Africa|Takarnasa Akiyama<br>Donard F Larson|January 1994|A._Kim_<br>33715|
|WPS1246|Determinants of Cross-Country<br>Income Inequality: An Augmeritud"<br>Kuznets' Hypothesis|Branko M'anovic|January 1994|R. Martin<br>39065|
|WPS1247|Compiex Transactions Under<br>Uncertainty. Brazil'sMachirieToo! Ind|Andrew Stone<br>ustry|January 1994|P. Sintim-Aboagy<br>37644|
|**WPS1248**|**Do**Domestic Firms Benefr from<br>Foreign Direct Investment<br>7 Evidence<br>from Panel Data|Brian Artken<br>Ann Harrison|February 1994|D. Ballantyne<br>37947|
# **2.1 Principal Components Analysis of the Fixed Factors Model**<sup>3</sup>

In Stata, estimation of the Principal components model proceeds as in a fixed factor model. Let **Y** be the mean-removed data matrix and scaled by 1 _/√N_ so that the matrix **S** = **Y**<sup>_′_</sup> **Y** is the data covariance matrix. Consider the (non-random factor) model for **Y** : where **A** is the factor loadings matrix and **F** is the matrix containing the factor scores. Using least squares to fit the (fixed data) model implies estimating **F** and **A** (for a given _k_ , see Section 4 on determining _k_ ) in order to minimize the sum of the squares of the residual matrix: The singular value decomposition (SVD) theorem indicates that a solution with the largest _k_ singular values _γ_ 1 _, γ_ 2 _, . . ., γk_ is given as: where **u** _j_ is a ( _p ×_ 1) vector, and **v** _j_ is a ( _N ×_ 1) vector, and _γj_ is the _j_ th eigenvalue of the data covariance **S** . Define the matrices: **V** _k_ = [ **v** 1 _,_ **v** 2 _, . . .,_ **v** _k_ ] _,_ **U** _k_ = [ **u** 1 _,_ **u** 2 _, . . . ,_ **u** _k_ ] and **Γ** _k_ = diag[ _γ_ 1 _, γ_ 2 _, . . . , γk_ ]. Their dimensionalities are: **V** _k_ : ( _N × k_ ) _,_ **U** _k_ : ( _p × k_ ) _, γk_ : ( _k × k_ ). Then the solution is: Note that there is not a unique solution for **F**<sup>ˆ</sup> and **A**<sup>ˆ</sup> individually. Our solution will be in the direction of “simple” structures for **A**<sup>ˆ</sup> .

> 3The principal components (PC) method is applicable to both, fixed and random factors models (Reyment and Joreskog, 1993). We focus on PC as applied to fixed factors since Stata estimates PC for the fixed factors model. We indicate how to estimate the PC model for random factors in fn. 4

7
|Table 6.3: School Attendance of 10–14-year-olds and Income Quintile<br>Dispersed Rural Areas of Argentina,2003 (percent)|
|---|
|5 (richest)<br>91.1|
|4<br>91.6|
|3<br>90.4|
|2<br>92.5|
|1(poorest)<br>86.3|
|Source: Own calculation based on RHS 2003.|

The incidence of education is fairly equal across income quintiles. As Table 6.3 shows, the trend is only slightly increasing for successively higher income quintiles, indicating a slightly regressive nature of benefit incidence in primary education. The first quintile receives 86 percent of primary school services while the fourth and fifth quintiles receive more than 91 percent each. Compared to other countries in the region this seems surprisingly equal. The big policy question is whether the lower participation of the lowest quintile is supply or demand driven. If the problem is related to lack of demand a program such as _Bolsa Escola_ in Brazil may increase school attendance of the poor. Most regions do have schools, but the students’ travel time to reach the schools may be significant, particularly in Northwest and Northeast Argentina.

Education appears to reduce the risk of falling into poverty in Argentina (World Bank 2003). Large gaps between the poor and nonpoor exist in school attendance. Since the number of those under age 14 is not increasing (see Section 2), Argentina has been presented with an excellent opportunity to increase access of the poor to primary school and above (there are lower rates of participation by the first quintile than by other quintiles) and to improve the quality of education. Policies to improve access of the poor to secondary and higher education linked with improved quality of education and increased focus on technical skills should be one of the key pillars of the government’s rural poverty reduction strategy.

# **Basic Infrastructure Services**

Basic infrastructure services contribute to higher well-being and productivity. Some services such as potable water and sanitation contribute directly to overall well-being and health status. Others such as electricity and telephones help households use their homes productively for income generation. Research reveals that access to basic services is highly correlated with a lower probability of being poor. Inequities in access to such services abound in rural Argentina, both between the poor and nonpoor and by geographical area. Key gaps for the rural poor exist in energy and potable water.

Access to public infrastructure services is poor for many services in rural Argentina and the rural and urban services gap is large. Argentina’s rural population has little access to safe water; only 30 percent have access compared to 85 percent in urban areas. Rural 33
**Table 3:** Regression results of errors in yields against ML selected correlates of measurement errors in yields

||[logSR_−_<br>|logCC]<br>|
|---|---|---|
||OLS|IV|
|Household labor|0.191<sup>_∗∗∗_</sup><br>|0.173<sup>_∗∗∗_</sup><br>|
||(0.040)|(0.043)|
|Plot area|_−_0.259<sup>_∗∗∗_</sup><br>|_−_0.286<sup>_∗∗∗_</sup><br>|
||(0.092)|(0.096)|
|Seeds|0.148<sup>_∗_</sup>|0.148<sup>_∗_</sup>|
||(0.087)|(0.082)|
|Unit SR harvest|_−_0.121<sup>_∗∗∗_</sup>|_−_0.122<sup>_∗∗∗_</sup>|
||(0.038)|(0.038)|
|*Respondent v1 : female|1.433<sup>_∗∗∗_</sup>|1.410<sup>_∗∗∗_</sup>|
||(0.424)|(0.431)|
|*Respondent v1 : manager|0.415|0.433|
||(0.356)|(0.372)|
|*Respondent v2 : age|0.012<sup>_∗∗∗_</sup>|0.012<sup>_∗∗∗_</sup>|
||(0.004)|(0.004)|
|*Respondent v2 : female|0.0003|0.0004|
||(0.016)|(0.016)|
|*Respondent v2 : manager|_−_0.096|_−_0.095|
||(0.525)|(0.534)|
|Dammage crop-cut|0.169|0.159|
||(0.192)|(0.199)|
|Respondent literate x # plots of Hh|_−_0.063<sup>_∗∗∗_</sup>|_−_0.063<sup>_∗∗∗_</sup>|
||(0.024)|(0.024)|
|Rain fall - July|0.005|0.005|
||(0.004)|(0.004)|
|Rain fall - December|0.001|0.003|
||(0.029)|(0.029)|
|Number of cotton plots|_−_0.121<sup>_∗∗∗_</sup><br>(0.046)|_−_0.132<sup>_∗∗∗_</sup><br>(0.046)|
|Pre-harvest loss|_−_0.008<sup>_∗∗∗_</sup>|_−_0.008<sup>_∗∗∗_</sup>|
||(0.002)|(0.002)|
|*Respondent v1 : age|0.012|0.011|
||(0.015)|(0.015)|
|Enumerator Fixed efect?|Yes|Yes|
|Observations<br>|577|577|
|R<sup>2</sup>|0.301|0.302|
|Adjusted R<sup>2</sup>|0.256|0.256|

_Notes:_ Labor, plot area and quantity of seeds are in log form. In the second column, household labor is instrumentalized with household size. * indicates that the variable is interacted with enumerator fixed effect. v1 indicates postplanting visit and v2 indicates post-harvest visit. Enumerator fixed effect included and errors clustered by enumerator. Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 11
**Table 3: Discrete choice models (Probit) for satisfaction with education services**

||Full sampl|e|Poor distri|cts|Rich distr|icts|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(1)|(2)|(1)|(2)|
|**Dimensions of facility qu**|**ality**||||||
|Index for infrastructure|0.027|0.030|0.086***|0.082***|-0.007|-0.005|
|Index for teacher quality|0.029|0.033|-0.037|-0.028|0.066**|0.066**|
|Index: student<br>performance|-0.018|-0.021|-0.032|-0.031|-0.026|-0.024|
|Index for student|0.008|0.006|0.086**|0.073*|0.001|-0.001|
|coverage|||||||
|<br>**Institutional and governa**|**nce environ**|**ment**|||||
|Index for BOS|0.049**|0.051**|-0.003|0.007|0.085***|0.089***|
|Provider responsiveness|0.007|0.079|0.146|0.206*|-0.125|-0.015|
|Index for participation|0.109*|0.102|-0.081|-0.086|0.211**|0.231***|
|**Components of expectatio**<br>|**n**||||||
|Age of the household<br>head|-0.001||-0.001||-0.003||
|Gender: female|0.025||0.050||0.024||
|Level of education:<br>Primary|0.029||-0.072||0.197||
|Junior High|-0.108||-0.404**||0.191||
|Higher education|-0.152||-0.316*||0.040||
|Religion: Catholic|-0.068||0.178||-0.233||
|Christian|0.068||0.116||0.019||
|Other|-0.116||0.285||-0.139||
|Log per capita monthly<br>exp.|-0.014||-0.049||0.004||
|Elite/Association with<br>elites|-0.142**||-0.241**||-0.052||
|Index for sources and<br>info|0.036||0.151***||-0.020||
|Index for expectation||-0.050***||-0.036||-0.059**|
|**Location dummies**|||||||
|Urban area|-0.124|-0.114|-0.109|-0.035|-0.114|-0.108|
|Region: Kalimantan|-0.230***|-0.260***|-0.536***|-0.540***|-0.015|-0.034|
|<br>NTT|-0.068|-0.112|-0.238|-0.198|-0.234|-0.289|
|Sulawesi|-0.221**|-0.241***|-0.480***|-0.522***|0.036|0.034|
|Sumatra|-0.349***|-0.369***|-1.122***|-1.160***|-0.147|-0.135|
|Constant|0.464|0.198**|1.276*|0.485***|-0.068|-0.054|
|# Observations<br>Note: *** 1% level of signif|2557<br>icance, ** 5%|2557<br>level of sign|1094<br>ificance, * 10|1094<br>% level of sig|1463<br>nificance|1463|

39
|**TABLE III. Effect of m**<br>**_Depe_**|**aternal mental**<br>**_ndent variable_**<br>|**health on**<br>**_: Standardi_**<br>|**children voca**<br>**_zed PPVT_**<br>|**bulary at age**<br>|**5**<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||OLS|with PDS-s|elected|IV with P|DS-selected|variables and|
|_Panel A. OLS and IV second-stage estimations_|variables|and full re|gressor set||full regresso|r set|
||MHI-1|MHI-2|MHI-3|MHI-1|MHI-2|MHI-3|
|Maternal Mental Health (-)|-0.0314|-0.0157|-0.0173|-1.025*|-0.499*|-0.536**|
||(0.0513)|(0.0254)|(0.0275)|(0.527)|(0.255)|(0.274)|
|Observations|1,095|1,095|1,095|1,095|1,095|1,095|
|Mother controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|
|_Panel B. First-stage estimation_||||MHI-1|MHI-2|MHI-3|
|Shock: Crop or livestock loss||||0.573***|0.616***|0.300***|
|||||(0.165)|(0.179)|(0.088)|
|Observations||||1,095|1,095|1,095|
|Mother controls||||Yes|Yes|Yes|
|Child controls||||Yes|Yes|Yes|
|Household controls||||Yes|Yes|Yes|
|Weak identification F-Stats (Full IV set)<br>Table III presents the effects of maternal mental health on|children vocabula|ry at the age o|f 5 years. Depen|11.53<br>dent variable w|11.69<br>as measured u|11.75<br>sing the|

Table III presents the effects of maternal mental health on children vocabulary at the age of 5 years. Dependent variable was measured using the standardized value of the PPVT test. Mental health indexes are standardized values of the SRQ-20 items using three different estimation approaches. Columns (1)-(3) present estimated coefficients using OLS and columns (4)-(6) show coefficients using IV. Both approaches were implemented using the option PDS-selected variables and full regressor available in the LASSO command. Selected instrument was suffering crop or livestock loss during pregnancy or within the first year of index child. Selected controls are mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for age Z-score. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. * and ** indicate statistical significance at 10% and 5%, respectively. Robust standard errors in parentheses

# **<mark>TABLE IV. Effect of maternal mental health on children vocabulary at age 8</mark>**

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