rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table A.1. Using Market Access with travel times from other years:  1988, and 2004, Lasso</u>**

|**Table A.1, Panel A**: 1988|(1)<br>ln(Landgini, 2012)|(2)<br>ln(Landless, 2012)|(3)<br>ln(Tech use, 2012)|(4)<br>ln(Land Sale, 2012)|
|---|---|---|---|---|
|**ln(MA1988)**|**0.260*****|**0.732*****|**0.372*****|**0.495**|
||**(0.07)**|**(0.19)**|**(0.12)**|**(2.62)**|
|ln(Slope)|-0.033|-0.060|-0.065|-0.591|
||(0.03)|(0.10)|(0.05)|(1.13)|
|Elevation|-0.251|-0.148|0.177|3.611|
||(0.16)|(0.45)|(0.27)|(5.49)|
|Crop Suitability|-0.003**|-0.007*|-0.001|0.004|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.052|0.155|0.017|-1.143|
||(0.06)|(0.17)|(0.10)|(2.14)|
|ln(Temperature)|-0.552|-0.363|0.459|2.242|
||(0.42)|(1.13)|(0.75)|(15.62)|
|ln(Rain CV)|0.161|0.497|0.457*|10.056*|
||(0.14)|(0.43)|(0.26)|(5.73)|
|ln(Temp. seasonality)|-0.022|-0.060|-0.087|-4.483|
||(0.08)|(0.22)|(0.12)|(3.27)|
|Pop. density, 1961|0.016**|0.017|-0.009|-0.076|
||(0.01)|(0.02)|(0.01)|(0.33)|
|Constant|-1.725|-12.714**|-10.57***|-13.192|
||(2.33)|(6.29)|(3.88)|(89.47)|
|First Stage|||||
|ln(dist. to GQ) x Elevation|-0.258**|-0.310***|-0.310***|-0.310***|
||(0.11)|(0.11)|(0.11)|(0.11)|
|ln(km of railroad) x Crop|0.002***|0.002***|0.002***|0.002***|
|Suitability|(0.00)|(0.00)|(0.00)|(0.00)|
|Weak Identification|7.10|8.59|8.59|8.59|
||0.0011|0.0003|0.0003|0.0003|
|Observations|200|212|212|212|
|R-squared|0.210|0.322|0.511|0.146|
- 39 -

impaired reaction time.' Prospective suadies in Boston," Cincinnati? Cleveland," and Port Pirie (Australia)" have examined the relationship between alternative measures of intelligence and concenrations of lead in blood in children. Taken together, these studies indicate an association between general measures of intelligence and either pre- or post-natal blood lead concentrations. No dhesold level has been identified.

Needleainn and Gatsonis<sup>5</sup> conducted a meta-analysis using several recent studies that relate lead exposure with neurodevelopmental effects in children. From their review of 24 studies, six can be used to provide regression coefficients that relate a I pg!dl change in blood lead to subsequent IQ decrements 8691 Reanalysis of the studies was conducted by the Centers for Disease Control (CDC)9

-which computed the estimated change in IQ for every unit change in blood lead. Each regression coefficient was weighted by the inverse of the variance around each coefficient. Thus, they obtained a result that indicated each I pgIdl change in blood lead results in a 0.25 point change in IQ. Wmneke et al.93 combined results from eight countries using a cotmnon study protocol to examine the effects of blood lead on IQ. The strongest effects were observed between blood lead and visual-motor inegration. Intelligence scores were also related to lead levels, but the effects were less consistent across the groups. However, the aggregate impact using all 1700 children suggested that mean IQ dropped by 0.12 for every pg/dl increase in blood lead. This estimate supports the CDC estimate for the U.S., which is incorporated into our estimates. Therefore- the association between blood lead and IQ is assumed to be:

IQ decrement = 0.25 IQ points * change in blood lead (pug/dl) Again, it is useful to relate the health effect to ambient concentrations; this requires the establishment of a relation between air lead and blood lead. These studies typically compare ambient concentrations and blood lead levels for individuals residing near some lead source with a "control group". The studies attempt to control for age, sex, and other potential confounders and effect modifiers. Although these studies linking blood lead in children to air lead suffer from problems with me
_Annex 1 Table 1: Varying trimming threshold on anthropometry data for 6-36-month olds_

|Height-for-<br>Age Z score|Stunting|Severe<br>stunting|Weight-for-<br>Age Z score|<sup>Underweight</sup>|
|---|---|---|---|---|
|_Trimming>-3 and<3 SD_|||||
|Program village<br>-0.262*|0.089|0.008|-0.169|0.066*|
|(0.139)|(0.058)|(0.008)|(0.145)|(0.040)|
|Control Observations<br>131|131|131|145|145|
|Treated Observations<br>142|142|142|151|151|
|Control Mean<br>-1.271|0.341|0.001|-0.821|0.160|
|_Trimming>-4 and<4 SD_|||||
|Program village<br>-0.335**|0.113*|0.045|-0.152|0.065|
|(0.159)|(0.060)|(0.029)|(0.159)|(0.043)|
|Control Observations<br>140|140|140|153|153|
|Treated Observations<br>156|156|156|172|172|
|Control Mean<br>-1.371|0.382|0.166|-0.947|0.206|
|_Trimming>-6 and<6 SD_|||||
|Program village<br>-0.533***|0.129**|0.067*|-0.102|0.057|
|(0.188)|(0.059)|(0.037)|(0.166)|(0.043)|
|Control Observations<br>147|147|147|156|156|
|Treated Observations<br>164|164|164|175|175|
|Control Mean<br>-1.452|0.402|0.106|-0.986|0.217|
|_Trimming>-7 and<7 SD_|||||
|Program village<br>-0.396*|0.129**|0.067*|-0.134|0.057|
|(0.204)|(0.059)|(0.037)|(0.175)|(0.043)|
|Control Observations<br>149|149|149|156|156|
|Treated Observations<br>164|164|164|176|176|
|Control Mean<br>-1.457|0.402|0.106|-1.003|0.216|

*** p<0.01, ** p<0.05, * p<0.1. All specifications include linear controls for child age (in months). Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 130 villages Source: 2011 _Pantawid_ impact evaluation survey

65
2011; Birn and Solórzano, 1999). Health improvements were concentrated among young children aged 0–5, with no detectable health improvements for elderly individuals similarly required to visit health clinics. Further, health improvements took time to materialize; we observe no improvements after 1.5 years, but 0.76 fewer sick days per month for 0–5 year olds after 2.5 years.<sup>2</sup> This suggests the importance of looking beyond very short-term impacts. Reductions in sick days were largest in villages with more baseline health workers per capita, consistent with improvements being sensitive to capacity constraints.

Overall, this evidence suggests a variety of mechanisms through which cash transfers may help to lift the burden of disease in Sub-Saharan Africa. We further show that these results are robust to adjustments for multiple hypothesis testing, estimation of linear as well as non-linear models, and both intent-to-treat and treatment on the treated estimates.

The remainder of the paper is organized as follows: Section 2 provides background information on health and the health care system in Tanzania, as well as the health conditions of Tanzania’s pilot CCT program. Section 3 describes the evaluation design, data, and outcomes of interest. Section 4 presents our empirical specification, the groups over which we examine heterogeneous treatment effects, balance tables showing the outcome of our randomization, and analysis of attrition. Section 5 characterizes our main empirical results and several robustness checks. Section 6 considers how our main impacts vary across different types of villages and households, and what this implies for the mechanisms likely driving treatment effects. Section 7 concludes.

# **2 Background**

## **2.1 Health care and health in rural Tanzania**

Tanzania is, in many respects, close to the Africa regional average in terms of health statistics. In 2012, 17.3 percent of the population contracted malaria versus 18.6 percent in Africa as a

> 2When we refer to illness in the last month, we are in all cases referring to the last four weeks.

5
or after 2006q2. Altogether the period spans from 2005q3 to 2007q1, and the number of times that each individual worker changed firm type (from formal to informal or vice versa) or size (from any one of the four firm sizes considered to any of the other three) is recorded.

**Table 7: Mobility of individual workers across firm size and type, 2005q3 – 2007q1**

_<u>(shares)</u>_

||**_Firm type _**|**_Firm size_**|
|---|---|---|
|**_Education_**|**_Change*_**|**_Change*_**|
|_Incomplete_<br>_Primary_|15.26|31.86|
|_Complete_<br>_Primary_|16.41|33.93|
|_Incomplete_<br>_Junior High_|20.09|36.84|
|_Complete_<br>_Junior High_|19.33|38.98|
|_Incomplete_<br>_Senior High_|21.02|44.83|
|_Complete_<br>_Senior High_|20.3|45.99|
|_University_|21.15|47.67|

_Source:_ Authors’ computations based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note:_ *Change refers to at least one movement over one year. _Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and completed Junior High education but did not move on to Senior High. _Incomplete Senior High_ refers to persons who attended Senior High but did not complete it. _Complete Senior High_ refers to persons who attended and completed Senior High but did not move on to University. _University_ refers to persons who attended and obtained a University Degree.

Mobility is very high even for such a brief period of time, and is in fact slightly increasing with educational levels. During one year, anywhere between 15 to 21 per cent of workers change firm type. Mobility across firm sizes is even higher, and again is increasing with educational level. This level of mobility, consistent with analysis presented elsewhere for Mexico (e.g., Levy 2008, Maloney 2004), allows us to have more robust estimates of the changes in the size and type distribution of firms on workers’ earnings obtained from the counterfactual exercise. The 22
differences between biomass and “clean” fuels. As Table 8 shows, mean PM10 in cleanfuel households is 133 ug/m<sup>3</sup> – little higher than ambient pollution in the urban area where most clean fuels are used. For biomass-using households, on the other hand, the average concentration is 242 ug/m<sup>3</sup> . Among biomass fuels, we fine a statistically significant difference only for jute (about 40 ug/m<sup>3</sup> lower). However, as we noted in Section 7, very few households in our sample use jute as cooking fuel.

For biomass fuels, a cautionary note is introduced by large differences in median and mean exposures recorded by our 24-hour monitors. Very large pollution “spikes” for short periods (e.g., 10,000 ug/m<sup>3</sup> or higher) under some conditions can have large effects on estimated mean concentrations. Median concentrations are often much lower. This difference highlights the importance of better information about the time-structure of the relationship between pollution and respiratory disease. To illustrate the problem, for two houses with identical mean concentrations, is it better to experience two daily PM10 spikes of 5,000 ug/m<sup>3</sup> , with very low levels for the rest of the day, or constant exposure at 150ug/m<sup>3</sup> over the 24-hour cycle? Limited evidence from time series studies has not yet provided a robust answer.<sup>5</sup> Given the intensity of indoor pollution spikes under some conditions, further research on this issue seems warranted.

(4) How important is fuel choice for pollution when we account for other household characteristics? Although fuel choice certainly affects indoor air pollution, our results suggest that its role is secondary to the role of ventilation factors for Bangladeshi

> 5   The available evidence is for outdoor particulates in industrial economies, and may have limited relevance for indoor air pollution from biomass fuels.  Studies of short exposures to outdoor particulate concentrations suggest some impact on heart rate variability and the rate of heart attacks.  However, a recent study in Palm Springs, California suggests that the short-period effect disappears when 24-hour average exposure is controlled for.  Similarly, average exposures seem to dominate day-to-day variations in daily time series studies.  Our thanks to Dr. Bart Ostro, Chief, California Office of Environmental Health Hazard Assessment, for his insights.

38
increased. Figure 2 summarizes the trends in the sex ratios of under-5 mortality in the two countries. In China the female disadvantage in child survival increased sharply until 2000, and appears to have improved somewhat thereafter. In India the female disadvantage in child survival has been lower, but gender differentials appear to be worsening.

In interpreting these data, we need to bear in mind the problems of collecting accurate data on child mortality in these settings. Over and above these difficulties, sex differentials in child mortality tend to be underestimated, because if a girl dies early in life parents are less likely to report it than for a boy. Girls who died very soon after birth (for example in cases of infanticide) are especially likely to be under-reported.

# **_Trends in the sex ratios of surviving children in East and South Asia_**

We therefore focus our analysis on the sex ratios of surviving children, which captures the end result after the effects of both prenatal and postnatal sex selection have manifested themselves. Another advantage of analyzing child sex ratios is that comparable data are available from censuses in all three countries. Unfortunately, India has only decadal censuses, unlike South Korea and China, which also have mid-decadal censuses or sample censuses.

Figure 3 shows the child sex ratios for all three settings. South Korea and China show a sharp increase till the mid-1990s, followed in South Korea by a steady decrease and in China by a continued rise.<sup>10</sup> India has the least masculine child sex ratios of all these settings, but they began to rise significantly from the 1980s.

It needs to be noted that the data on China’s child sex ratios are subject to several sources of fluctuation. Firstly, sex ratios at birth fluctuate by animal year in East Asia, as noted above. A study in South Korea showed that parents even shift the reported birth year of a child if they were born close to a more auspicious birth year for children or for children of a particular gender (Lee and Paik 2006). This effect seems to have diminished in recent years in both South Korea and China (Figure 1). Secondly, there can be variation in data quality between years, and between provinces, depending on national and local factors. Thirdly and perhaps most importantly, the mid-decadal data derive from one percent sample censuses, which can differ in quality and robustness of estimates from the full censuses which are conducted decadally. The combined impact of these factors reduces our ability to derive precise conclusions from the trends we observe, especially when analyzing quinquennial trends at sub-national level.

# **3. China and India: A Closer Look at Trends in the Child Sex Ratios**

# **_National trends_**

We turn to a closer examination of the trends in China and India, large populous countries which account for the bulk of the total “missing girls” in Asia. Given the lack of mid4
# **AII.5. Price puzzles in EMEs and other small open economies**

The prevalence of the price puzzle is widespread across many EMEs and small open advanced economies.

Previous studies on Brazil have shown that while monetary policy has the anticipated impact on economic activity, its effect on inflation is less conclusive, with many studies indicating the presence of a price puzzle to a certain degree (Costa Filho 2017). For example, Minella (2003) reports that a contractionary monetary policy shock does not induce a fall in the inflation rate in the short run. Similarly, Cysne (2004; 2005) find evidence of a small and temporary price puzzle lasting for one to three quarters. Luporini (2008) also find evidence of the price puzzle, suggesting that the inclusion of a commodity price index in the model did not solve the price puzzle for Brazil. On the other hand, Céspedes et al. (2008) argue that there is no evidence of the price puzzle because they observe that a contractionary monetary policy shock indeed reduces the price level, although it takes a long time to be effective and statistical significance is less pronounced. Carvalho and Rossi Júnior (2009) also argue against the existence of a price puzzle, but their confidence intervals for the price response to a monetary shock encompass zero.

For Chile, the presence of the price puzzle has been documented by Pedersen (2017) examining the period from 2002 to 2016. While the empirical results are not statistically significant over the full sample period, the study reveals that inflation increases in response to an unexpected monetary policy shock in the precrisis period. However, after the crisis, inflation decreases following a contractionary monetary policy shock.

Chowdhury et al. (2006) examine three economies (Chile, Czechia, and India) and demonstrate that inflation rises for several months before falling in response to monetary tightening, suggesting the price puzzle. Héricourt (2005) argues that despite numerous proposed solutions, the price puzzle remains unresolved in the case of Czechia. The inclusion of a broad commodity price index has been insufficient to resolve the puzzle. Additionally, Giordani’s (2004) proposition of simultaneous inclusion of output and output gap in the VAR to mimic IS and Philips curves has not resulted in any change in price behavior.

Javid and Munir (2010) attribute the currency depreciations as a source of price puzzle, citing cases where exchange rate depreciation follows monetary tightening. Creel and Levasseur (2005) rationalize this exchange rate puzzle in terms of market expectations regarding the sustainability of sovereign debt and the probability of default, particularly with high levels of government debt and deficits. Beckers (2020) finds evidence of the price puzzle in Australia data, even when controlling for the systemic response of the cash rate to the Reserve Bank’s own inflation forecasts. This is attributed to an additional but omitted systematic response of the cash rate to credit market shocks, leading to a positive correlation between cash rate changes and future inflation.

47
economic outcomes for the two types of schooling, does not necessarily imply a clean bill of health for vocational schools. At issue is not the comparative economic outcomes of vocational and academic schools as such, but rather these outcomes in relation to costs.

While reliable information on the costs of secondary vocational and academic schooling in Israel is lacking, a preliminary attempt at measuring comparative costs is made in Table 5. Based upon official estimates of national expenditure on academic secondary schools and on vocational and agricultural schools, respectively, the table shows the proportional differential of national expenditure per pupil in vocational over academic schools (Column 5). Only for the first two budget years of available data (1969/70 and 1970/71) is this proportion larger than unity indicating higher per student vocational school costs; henceforth it is generally negative. This indicates that vocational education (on average, though not necessarily in particular subjects, such as electronics) is the - less expensive for recent years, by over 10 per-cent and for the most recent year (1984/85) by about a quarter. In 1982/83, the year corresponding to the Census, vocational schools were 8 percent cheaper.

These result may occasion some surprise. Available international evidence shows a cost advantage to academic schools. In Israel, too, the view is widely held that such factors as higher equipment costs and smaller class size in vocation_1 schools (23.4 pupils per class compared with 31.9 in general schools, in 1986/87) place such schools at a cost (per pupil) disadvantage of up to a third, compared with general schools. Against this,

- 22 -
As argued earlier, feminization of madrasas is a recent phenomenon and from policy point of view, a clear understanding of the association between growth in female secondary enrollment and madrasas is extremely important. In this context, a potentially important explanation could be the rise of NGO schools such as BRAC’s non-formal primary schools which increased the number of girls completing primary education in the community. This in turn may create an upward pressure on local madrasas to open up to admitting female students. To test for the presence of such externalities, we report regression estimates of equation (2) at the madrasa level. Results are reported in Table 3. Each regression treats “the number of BRAC schools in the union” as an endogenous variable which is instrumented using data on “the number of BRAC branches” and “the number of BRAC female members”. Clearly, presence of BRAC schools in the union has a positive effect on female enrollment growth in madrasas. We also estimated equation (2) using data on secondary schools. However, BRAC schools had no impact on female enrollment growth in secondary schools (see Appendix Table 4). This suggests that the externality effect of BRAC schools is only specific to madrasas. Lastly, the effect of BRAC school remains even when we use an alternative measure where we replace “the number of BRAC schools” by “the number of female students enrolled in BRAC schools” (see Appendix Table 5). Once again, BRAC variable is treated as an endogenous regressor. We find that the number of female students enrolled in BRAC schools positively affects enrollment growth in madrasas.

# **5. Conclusion**

Nongovernmental organizations (NGOs) play an increasingly important role in the delivery of public goods in developing countries. Although this role is well acknowledged in the development literature, relatively less known is the potential externalities that arise from independent NGO interventions in the form of impact on the formal, conventional providers of public services such as state and state-aided private bodies.

We test for the presence of such externalities for Bangladesh where different types of service providers are present in the education sector. Alongside for-profit schools, there exist BRAC schools and madrasas that arguably charge relatively minor fees and serve economically marginalized children and isolated communities. Given that BRAC schools operate exclusively in the primary sector as opposed to madrasas (that have a greater presence in the secondary sector), any effect of BRAC schools on secondary madrasas would be taken as an evidence of a spill-over effect on the latter.

Externality effect of NGO intervention is studied in the context of the recent feminization of the madrasa sector where a previously predominantly all-boys religious education system opened up to girls. First, we find that Bangladeshi regions that had more registered madrasas saw a higher growth in enrollment of girls in secondary grades (holding number of secular secondary schools constant and initial enrollment) during 1999-2003. Regions that had more “converted” madrasas also experienced higher 13
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**_JRgre_** _3 Projected populion growth rates, 1990-2000_

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