rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 3: Incidence of excess growth among the poorest 40 percent on health MDG indicators**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth<br>Full immunization Measles ANC4+ SBA<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth<br>.12 .2 .12 .25<br>.1 .1<br>.15 .2<br>.08 .08<br>.06 .1 .06 .15<br>.04.02 .05 .04.02 .1<br>0 0 0 .05<br>-.02-.04 -.05 -.02-.04 0<br>Excess growth among poorest 40% p.a. -.06 Excess growth among poorest 40% p.a. -.1 Excess growth among poorest 40% p.a. -.06 Excess growth among poorest 40% p.a. -.05<br>.15 .08 .3 .1<br>.1 .06 .25 .05<br>.04<br>.05 .02 .2 0<br>0 0 .15<br>-.05<br>-.05 -.02 .1<br>-.04 -.1<br>-.1 -.06 .05<br>-.15 -.08 0 -.15<br>Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a. -.1 Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a.<br>-.2 -.05 -.2<br>.1 .1 .15 .5<br>.05 .08 .4<br>0 .06.04 .1 .3.2<br>-.05 .02 .05 .1<br>-.1 0-.02 0 0<br>-.15-.2 -.04-.06 -.05 -.1-.2<br>Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a.<br>-.25 -.08 -.1 -.3<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The annual growth is as a fraction, so e.g. 0.02 means 2% annual growth.

28
the least intensive rankings. The shape of the distribution of pollutant intensities is also of interest. Almost perfectly exponential, it provides some hope that problems associated with toxic releases can be ameliorated by measures targeted at only a few sectors. However, it should be borne in mind that this index does not rank total sectoral releases, so that it is quite possible for a highly pollution intensive sector to have little impact on the total level of releases and transfers. Nor does the index incorporate any abatement cost considerations.® Table 4.1: Four Digit ISIC Codes and Descriptionsin Descending Order of Linear Acute Human Toxic intensity Index (Risk Weighted Pounds/1987 US S Million Output Value)

|Four Digit ISIC Description|rsrce<br>Code|Linear Acute<br>Human Toxic<br>Intensity||
|---|---|---|---|
|JIpusTRraLcuewrcansexceprrenrrrzer<br><br><br>||<br><br>||<br>ste<br><br>|||<br>|
|<br><br>[cammeRresANDuenrumeemursurns<br>|<br>|sas<br>|<br>|<br>soa||||
|<br><br>SYNTHETIC RESINS, PLASTICS MATERIALS, & MANMADE FIBRES<br> <br><br>|<br>a<br>|<br><br>|<br><br><br>|
|Jpuasticspropuers,wc.<br>sc<br><br><br>||||
|<br>wee<br>s<br><br> <br><br>||||
|<br><br> <br><br><br>||<br><br><br><br>||
|<br><br>pour,PAPER&papensonnpagricues<br>||set|ez|||

®See Hartman, Raymond; Wheeler, David and Singh, Manjula, "The Cost of Air Pollution Abatement," Policy Research Department Working Paper, The World Bank, Washington, D.C. 1994, for information on abatement cost by industry sector.

23
**Table 13 Regression Results for Manufacturing Industries**

|VARIABLES|I|II|III|IV|V|VI|VII|VIII|IX|
|---|---|---|---|---|---|---|---|---|---|
|NetJobCr|-0.323|-0.407|-0.309|-0.359|-0.312|-0.319|-0.313|-0.328||
||(0.174)*|(0.182)**|(0.174)*|(0.169)**|(0.178)*|(0.174)*|(0.173)*|(0.172)*||
|Log(labor)|0.140||0.136|0.135|0.143|0.140|0.141|0.135|0.144|
||(0.008)***||(0.008)***|(0.008)***|(0.009)***|(0.008)***|(0.008)***|(0.008)***|(0.008)***|
|Age|0.001|0.005|0.001|0.001|0.001|0.001|0.001|0.001|0.001|
||(0.001)|(0.001)***|(0.001)*|(0.001)*|(0.001)|(0.001)|(0.000)|(0.001)**|(0.001)|
|Foreign|0.308|0.424|0.310|0.304|0.300|0.305|0.307|0.299|0.302|
||(0.033)***|(0.029)***|(0.034)***|(0.033)***|(0.033)***|(0.034)***|(0.034)***|(0.034)***|(0.035)***|
|Growth||0.177<br>(0.058)***||||||||
|ExternalFin|||0.001|||||0.001||
||||(0.000)***|||||(0.000)***||
|Innovate||||0.124||||0.112||
|||||(0.020)***||||(0.020)***||
|NonProdWorke|r||||0.080|||0.059||
||||||(0.046)*|||(0.045)||
|Capital City||||||0.011||-0.008||
|||||||(0.030)||(0.029)||
|>1Million||||||-0.000||-0.005||
|||||||(0.043)||(0.043)||
|250K-1Million||||||-0.033||-0.035||
|||||||(0.025)||(0.025)||
|50K-250K||||||-0.043||-0.043||
|||||||(0.027)||(0.028)||
|Multi-plant|||||||-0.002|-0.010||
||||||||(0.029)|(0.028)||
|US*NetJobCr|||||||||-0.015|
||||||||||(0.008)*|
|Observations|3804|3763|3804|3800|3759|3804|3795|3747|3676|
|R2(p)|0.280|0.183|0.284|0.290|0.281|0.281|0.280|0.295|0.281|
|Robust standard<br>and country fixe<br>level, (III) contr<br>of non-producti<br>plant enterprise,<br>performs analys|errors cluster<br>d effects. (I)<br>ols for use of<br>on workers to<br>(VIII) includ<br>is relative to|ed by country<br>main result for<br>external financ<br>all workers,(V<br>es all controls.<br>US industries.|and industry a<br>manufacturing<br>e for investme<br>I) controls for<br>Coefficients f<br>*** p<0.01, **|re in parenthes<br>industries, (I<br>nt, (IV) contr<br>population of<br>or the probit r<br>p<0.05, * p<|es. In the regr<br>I) uses employ<br>ols for past inn<br>the city that fi<br>egressions sho<br>0.1.|essions, I con<br>ment growth<br>ovative exper<br>rm is located,<br>w the margina|trol for 2-digi<br>as a control in<br>ience of the f<br>(VII) controls<br>l effects at th|t industry, su<br>stead of emp<br>irm, (V) cont<br>for being pa<br>eir mean valu|rvey year,<br>loyment<br>rols for ratio<br>rt of a multi-<br>es, (IV)|

30
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

# 8 References

Brocklehurst, C., A. Pandurangi, and L. Ramanathan. 2002. “Water Tariffs and Subsidies in South Asia: Tariff Structures in Six South Asian Cities: Do They Target Subsidies Well?” Water and Sanitation Program Paper 2. World Bank, Washington DC.

Cardone, R., and C. Fonseca. 2003. “Financing and Cost Recovery.” Thematic Overview Paper 7. IRC International Water and Sanitation Center, Delft, the Netherlands.

- Foster, V., and T. Yepes. 2006. “Is Cost Recovery a Feasible Objective for Water and Electricity? The Latin American Experience.” Policy Research Working Paper 3943. World Bank, Washington DC.

- Kingdom, W., M. Van Ginneken, and C. Brocklehurst. 2004. “Full Cost Recovery in Urban Water Supply Systems: Implications for Affordability and Subsidy Design.” Unpublished paper, World Bank, Washington DC.

- Komives, K., V. Foster, J. Halpern, and Q. Wodon, with support from R. Abdullah (2005), _Water, Electricity, and the Poor: Who Benefits from Utility Subsidies?_ World Bank, Washington, DC.

- Komives, K., and L. S. Prokopy. 2000. “Cost Recovery in the BPD Water and Sanitation Cluster Focus Projects: Results, Attitudes, Lessons and Strategies.” BPD Water and Sanitation Cluster, U.K.

- Olivier, A. 2006. “Water Tariff Increase in Manus: An Evaluation of the Impact on Households.” Working Paper DT/2006/10. DIAL, Paris.

- Raghupati, U., and V. Foster. 2002. “Water Tariffs and Subsidies in South Asia: A Scorecard for India.” Water and Sanitation Program Paper 2. World Bank, Washington DC.

- Stalker and Komives, 2001. “Cost Recovery: Partnership Frameworks for Financially Sustainable Water and Sanitation Projects.” BPD, London.

- Uganda Ministry of Water and Environment. 2006. “The Review of Tariffs of Piped Water Supplies (Small Towns, Rural Growth Centers, and Gravity Schemes).” Warner Consultants Ltd, Kampala.

- Water Utility Partnership for Capacity Building in Africa. 2002. “Final Project Summary Report: Service Providers Performance Indicators and Benchmarking Network Project.” Abidjan.

- Whittington, D. 1992. “Possible Adverse Effects of Increasing Block Tariffs in Developing Countries.” _Economic Development and Cultural Change_ 41(1): 75–87.

- Whittington, D., and J. Boland. 2000. “Water Tariff Design in Developing Countries: Disadvantages of Increasing Block Tariffs and Advantages of Uniform Block Tariff with Rebate Designs.” In Ariel Dinar (ed.), _The Political Economy of Water Pricing Reforms_ , (215–235). Washington, D.C.: Oxford University Press.

26
(demand pressure) will be translated in to a three times higher increase in marginal costs in Macedonia relative to Slovakia. In other words, the flexibility of the potential output to adapt to the increased demand is much lower in Macedonia. The elasticity of CPI inflation to changes in the nominal effective exchange rate, λ 2 , is estimated to be 0.024 and 0.037 for Macedonia and the Slovak Republic, respectively. The about 65 percent lower exchange rate pass-through in Macedonia could imply either higher monopolistic power of the importers, or a higher share of services in final sales of imports in Macedonia. Further, the second-round effect of CPI inflation on wages as a result of an exchange rate increase could be higher in the Slovak Republic, which would further enforce the exchange rate pass-through. Overall, the AS curve fits the data of the Slovak Republic better than those of Macedonia with the respective adjusted R-squares being 0.86 and 0.78. Based on the relative estimates of the standard deviation of AS shocks σ _AS_ , Macedonia appears to be subject to somewhat higher supply shocks.

When estimating the MP reaction function for NBRM we have included a dummy variable to account for the two spikes in the NBRM interest rate series in 2001Q2 and 2002Q4.<sup>10</sup> The estimate of the β coefficient is 3.76 for Macedonia which satisfies the stability conditions (the model has a stable solution), see also Benigno et al. (2007). The estimates of the MP rule for Slovakia imply that the SNB smoothes the interest rate – where<sup>ρ</sup> _i_<sup>is estimated to be 0.64 – increases interest rate in response to increasing</sup> expected inflation, with α = .102 , and depreciation of the Slovak koruna, β = .040 . Overall, the MP equation fits better the data of the Slovak Republic, and only with the use of a dummy variable for the two spikes in the NBRM interest rate series the adjusted R squares for Macedonia and the Slovak Republic turned out to be 0.70 and 0.46, respectively. Nevertheless, the MP discretionary shock within the context of the presented MP rules is significantly higher in Macedonia, with the standard deviation of 3.55, than in the Slovak Republic; estimated standard deviation of 1.10.

> 10 The spike in 2001Q2 was due to the domestic conflict and the Central bank sterilization of the surge in government defense related expenditures. The situation stabilized by early 2002, but in 2002Q4 the Central Bank had to intervene again because the government went on a spending spree just before the September 2002 elections. Once the government changed, the new authorities immediately entered into a stand-by agreement with the IMF and tightened both fiscal and monetary policy considerably.

12
# Table 5

# Average Impact of Anti-Dumping on Income Mekong Provinces

||Total I|ncome|Per Capit|a Income|Net I|ncome|
|---|---|---|---|---|---|---|
||M4|M6|M4|M6|M4|M8|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A) Quadratic Model|||||||
|Low Exposure|_−_0_._062<sup>_∗∗_</sup>|_−_0_._047<sup>_∗∗_</sup>|_−_0_._064<sup>_∗∗_</sup>|_−_0_._048<sup>_∗∗_</sup>|_−_0_._081<sup>_∗∗∗_</sup>|_−_0_._062<sup>_∗∗∗_</sup>|
|(_s_<sup>_c_ </sup>= 0_._055)|(0_._031)|(0_._022)|(0_._030)|(0_._022)|(0_._030)|(0_._021)|
|Mean Exposure|_−_0_._113<sup>_∗∗_</sup>|_−_0_._091<sup>_∗∗_</sup>|_−_0_._117<sup>_∗∗_</sup>|_−_0_._094<sup>_∗∗_</sup>|_−_0_._147<sup>_∗∗∗_</sup>|_−_0_._120<sup>_∗∗∗_</sup>|
|(_s_<sup>_c_ </sup>= 0_._112)|(0_._054)|(0_._042)|(0_._053)|(0_._041)|(0_._052)|(0_._040)|
|High Exposure|_−_0_._169<sup>_∗∗_</sup>|_−_0_._139<sup>_∗∗_</sup>|_−_0_._176<sup>_∗∗_</sup>|_−_0_._143<sup>_∗∗_</sup>|_−_0_._217<sup>_∗∗∗_</sup>|_−_0_._183<sup>_∗∗∗_</sup>|
|(_s_<sup>_c_ </sup>= 0_._200)|(0_._078)|(0_._062)|(0_._077)|(0_._061)|(0_._074)|(0_._059)|
|Observations|561|788|561|788|561|788|
|R<sup>2 </sup>(within)|0.162|0.194|0.155|0.190|0.158|0.188|
|B) Partially Linear Model|||||||
|Low Exposure|_−_0_._068<sup>_∗∗∗_</sup>|_−_0_._047<sup>_∗∗∗_</sup>|_−_0_._072<sup>_∗∗∗_</sup>|_−_0_._049<sup>_∗∗∗_</sup>|_−_0_._096<sup>_∗∗∗_</sup>|_−_0_._074<sup>_∗∗∗_</sup>|
|(_s_<sup>_c_ </sup>= 0_._055)|(0_._022)|(0_._018)|(0_._023)|(0_._018)|(0_._023)|(0_._020)|
|Mean Exposure|_−_0_._124<sup>_∗∗∗_</sup>|_−_0_._097<sup>_∗∗∗_</sup>|_−_0_._131<sup>_∗∗∗_</sup>|_−_0_._099<sup>_∗∗∗_</sup>|_−_0_._171<sup>_∗∗∗_</sup>|_−_0_._147<sup>_∗∗∗_</sup>|
|(_s_<sup>_c_ </sup>= 0_._112)|(0_._034)|(0_._029)|(0_._035)|(0_._030)|(0_._035)|(0_._033)|
|High Eaxposure|_−_0_._179<sup>_∗∗∗_</sup>|_−_0_._152<sup>_∗∗∗_</sup>|_−_0_._184<sup>_∗∗∗_</sup>|_−_0_._154<sup>_∗∗∗_</sup>|_−_0_._239<sup>_∗∗∗_</sup>|_−_0_._220<sup>_∗∗∗_</sup>|
|(_s_<sup>_c_ </sup>= 0_._200)|(0_._056)|(0_._045)|(0_._057)|(0_._046)|(0_._058)|(0_._051)|
|Observations|561|788|561|788|561|788|

Note: Estimates of a growth equation for total household income (columns 1 and 2), per capita household income (columns 3 and 4), and net income (columns 5 and 6). Panel A) reports results from the quadratic model at three different levels of exposure measured by the pre-shock fish shares: the median (low exposure), the mean (average exposure), and the median share for farmers with shares above the mean (high exposure). Panel B) reports results from a partially linear model. Mekong 4 (M4) and Mekong 6 (M6) refer to two sets of Mekong provinces that specialize in catfish production: M4 includes An Giang, Can Tho, Dong Thap and Vinh Long, and M6 adds Soc Trang and Tien Giang. Robust standard errors within parenthesis: _∗_ , _∗∗_ , _∗∗∗_ , significant at 10%, 5%, and 1% level, respectively.

41
# **1.3 Does Aid Mitigate Inequality?**

Another challenge of this research is to empirically investigate whether aid has mitigated inequality. If the ultimate goal of aid is to reduce or end poverty, aid needs to disproportionally benefit the poor and reduce inequality. If the benefits of aid are not extended to the poor, the gap between the rich and the poor cannot be narrowed.

Analyzing the impact of aid on poverty reduction from both growth and distributional perspectives can be considered another original contribution of this study, as there is little past empirical research on aid that has paid attention to the distributional impact of aid for achieving poverty reduction.

# **1.4 Can Policies, Institutions, and Globalization Be Good for the Poor?**

We look at the effects of aid on the poor in comparison with other factors including policy, institutions, and the facets of globalization. Past literature (Dollar & Kraay, 2001, 2002; Dollar et al., 2013; Milanovic, 2005) seemed to agree that there was no factor of policy, institutions, or globalization that significantly benefitted the poor other than through growth. Our earlier study provides a literature survey and empirical stocktaking of the impacts of (1) globalization (via trade, FDI, and remittances) on growth, (2) inequality on the poor, and (3) inequality on the Gini Index (Hirano & Otsubo, 2012).<sup>2</sup> Our earlier study also found that no factor of globalization has a significant impact on the poor other than through growth. Taking these assertions into account, we reexamine the impact of policies, institutions, and globalization on the poor with the new dataset, while proposing that development aid could be a key factor with significant impact on the poor.

# **2. Methods and Empirical Models**

In order to examine the effectiveness of aid on growth, inequality and poverty reduction, we conducted a series of cross-country analyses using the two-stage least squares (TSLS) estimator. We used the unbalanced panel data of worldwide income, aid, poverty, distribution, policy and institutions.

# **2.1 Leg 1: Aid and Growth (Growth Regressions)**

We conducted the following regressions, employing the Barro-type ad hoc growth equations (Barro, 1991, 1997) for conditional convergence, with elements of exogenous conditions such as the fertility rate and terms of trade ( _X_ ); a set of important factors to consider such as policies, institutions, globalization, and other potential determinants ( _Z_ ); and aid (economic aid, social aid, and aggregate aid) ( _A_ ):

> 2 The results of our earlier study will be compared with the results of this study in section 4.

5
**Figure 14. Use of innovation inputs vs innovation rates across developing East Asian countries (percent of all firms)**

<!-- Start of picture text --> 50.0%<br>45.0%<br>40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>Cambodia Indonesia Lao Malaysia Philippines Thailand Vietnam Total<br>R&D technology license trainning Any innovation (product OR process)<br><!-- End of picture text --> Source: Own elaboration based on ES data Note: tabulations are adjusted by sampling weights

# **Box 2. How R&D activities, training activities and acquisition of technology licenses affect the probability to innovate across developing East Asian countries**

To assess how the overall probability to innovate is influenced by innovation inputs, the logit model applied in Box 1 is estimated again, now with R&D, technology license and training activities as additional (dummy) explanatory variables. Again, parameters are estimated by maximum likelihood using sampling weights and standard errors are clustered by country. Table A3 in the Annex presents – for each type of innovation output - the odds ratio of each innovation input. Firms that perform R&D activities are more likely to innovate when compared with those that do not perform; the probability of introducing product or process innovation among firms that perform R&D activities is 7 times higher when compared with those that do not perform, and this applies to all types of innovation activities except for firms that have introduced an innovation product that is new to the market. Training activities for innovation is another relevant innovation input: firms that provide formal training for the development of new products and processes are almost 3 times more likely to innovate (in product or process) than those firms that do not offer the same type of training. This positive association holds for other types of innovation outputs: organizational and marketing innovation, and also for those firms that have introduced innovative products that are new to the market. Purchasing or licensing technology also increases the probability to innovate, but in this case the positive association applies only to process, organizational or marketing innovation.

The figure below displays the average marginal effect of using each innovation input on the probability to introduce innovation across its different formats. Results show that, on average, firms that perform R&D activities are 32 percent more likely to introduce product or process innovation than firms that do not perform R&D. Offering training for innovative activities increases the probability to innovate products and processes by almost 15 percent when compared to firms that do not offer this type of training. For marketing innovation, the acquisition or licensing of technology increases the probability to innovate by 18.4 percent when compared to firms that do not purchase or license technology.

16
**Table 12: SDI: Student test scores regressed on classroom observation variables (level 1) and teacher test scores**

||All teachers|||Teacher|s with test sc|ore <=0||Teache|rs with test s|core >0|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Share of time teaching|0.9511***|0.332|0.9780***|0.331|2.0179***|0.869*|2.1418***|1.384***|0.4731|0.497**|0.6670*|0.532|
||(0.2988)|(0.273)|(0.2885)|(0.274)|(0.5006)|(0.499)|(0.5465)|(0.488)|(0.3697)|(0.244)|(0.3529)|(0.325)|
|Share of time lost|-0.9479**|-0.293|-0.9438**|-0.204|-1.0031*|0.351|-1.1645**|-0.224|-0.8813|-0.478|-0.7651|-0.511|
||(0.4329)|(0.422)|(0.3975)|(0.402)|(0.5166)|(0.651)|(0.5056)|(0.664)|(0.8938)|(0.526)|(0.7635)|(0.593)|
|Good Teacher Demeanor|-0.2982|-0.287|||0.1310|1.278**|||-0.3142|-0.886**|||
||(0.4045)|(0.366)|||(0.6488)|(0.579)|||(0.6050)|(0.376)|||
|Good Pedagogical Practice|-0.6219|-0.837*|||-1.0111|-1.349**|||0.0417|-0.490|||
||(0.4646)|(0.428)|||(0.6522)|(0.554)|||(0.8876)|(0.487)|||
|Classroom Environment|-0.1665|-0.204|||-0.0051|-0.0585|||-0.4918*|-0.326**|||
||(0.1875)|(0.166)|||(0.2012)|(0.149)|||(0.2754)|(0.158)|||
|Availability of materials|1.0799|1.223*|||0.5545|0.206|||1.7949**|2.279***|||
||(0.7206)|(0.633)|||(1.0664)|(0.723)|||(0.8268)|(0.672)|||
|Use of materials|-0.2564|0.223|||-0.3344|-0.315|||-0.3834|0.426*|||
||(0.3118)|(0.291)|||(0.5303)|(0.497)|||(0.3993)|(0.232)|||
|SDI PC1|||-0.1311***|-0.0923**|||-0.1106|-0.0284|||-0.1242*|-0.120**|
||||(0.0437)|(0.0394)|||(0.0682)|(0.0654)|||(0.0619)|(0.0455)|
|SDI PC2|||0.0184|0.0186|||0.0251|0.0130|||-0.0110|0.0284|
||||(0.0545)|(0.0479)|||(0.0778)|(0.0634)|||(0.0565)|(0.0453)|
|Teacher Test Score|-0.0143|0.0298|0.0012|0.0511|0.2070**|0.257***|0.1926**|0.213***|-0.4860*|-0.310|-0.3753|-0.280|
||(0.1097)|(0.0719)|(0.1045)|(0.0770)|(0.0959)|(0.0667)|(0.0747)|(0.0587)|(0.2639)|(0.225)|(0.3218)|(0.310)|
|Observations|4,619|4,586|4,619|4,586|2,333|2,300|2,333|2,300|2,286|2,286|2,286|2,286|
|R-squared|0.057|0.245|0.049|0.229|0.104|0.303|0.100|0.288|0.083|0.322|0.050|0.270|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Teacher Test|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|F-Test: Observation vars.|0.003|0.029|0.005|0.202|0.001|0.024|0.000|0.074|0.003|0.000|0.155|0.025|
|F-Test: Student vars.||0.058||0.037||0.000||0.000||0.009||0.002|
|F-Test: Household vars.||0.000||0.000||0.000||0.000||0.000||0.000|
|F-Test: Teacher vars.||0.635||0.614||0.122||0.391||0.001||0.077|
|F-Test: School vars.||0.000||0.000||0.029||0.045||0.000||0.000|

Note: Standard errors clustered at the teacher level in parentheses. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

52