rafmacalaba/datause-encoder-data huggingface dataset:
specific trade remedy investigations is 72 per cent during the crisis period of 2008 through 1Q 2009, which is a slight increase from 68 per cent in 2007.

**Table 3. Exporters Targeted by Product-Level use of AD, CVD and CSG, 1Q 2007 - 1Q 2009**

|||**Initiations**|<br>**1Q**||**Measures**|<br>**1Q**|
|---|---|---|---|---|---|---|
|**Exporting (affected) country**|**2007**<br>**Total **|**2008**<br>**Total **|<br>**2009**<br>**Total **|**2007**<br>**Total **|**2008**<br>**Total **|<br>**2009**<br>**Total **|
|Total|156|217|46|104|119|36|
|_Developing country exporters_|106|159|30|71|81|29|
|(percent of total)|(0.68)|(0.73)|(0.65)|(0.68)|(0.68)|(0.81)|
|_Developed country exporters_|50|58|16|33|38|7|
|China|63|86|19|46|49|16|
|(percent of total)|(0.40)|(0.40)|(0.41)|(0.44)|(0.41)|(0.44)|
|South Korea|12|9|2|5|7|2|
|European Union|8|15|6|4|8|1|
|Thailand|7|12|2|5|1|3|
|U.S.|8|10|2|4|6|1|
|Taiwan|6|9|0|7|7|2|
|Malaysia|5|10|0|4|2|3|
|Indonesia|5|11|2|3|4|2|
|India|5|9|0|4|3|2|
|Japan|4|3|0|5|2|0|
|Russia|6|2|0|0|5|0|
|UAE|3|0|0|0|1|1|
|Brazil|2|3|4|2|2|0|
|Turkey|3|3|0|0|3|0|
|Others|19|35|9|15|19|3|

Source: Compiled by the author from the _Global Antidumping Database_

# **2.5 China as the export target**

The use of country-specific trade remedy instruments such as AD, CVD and CSG documented in table 3 also illustrates the intensity with which these using countries are targeting exports from China. Of the country-specific trade remedy initiations and measures imposed, China alone constituted roughly 40 10
|Public Procurement Reform Project II|Zafrul Islam|
|---|---|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Public Procurement Reform Project II|Zafrul Islam|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|Clean Air and Sustainable Environment Project|Harinath Sesha Appalarajugari|
|(APL1)Bono de Desarrollo Humano|David Seth Warren|
|(APL1)Bono de Desarrollo Humano|David Seth Warren|
|(APL1)Bono de Desarrollo Humano|David Seth Warren|
|(APL1)Bono de Desarrollo Humano|David Seth Warren|
|Education System Realignment & StrengtheningProgram(APL #2)|Nino Kutateladze|
|Education System Realignment & StrengtheningProgram(APL #2)|Nino Kutateladze|
|Education System Realignment & StrengtheningProgram(APL #2)|Nino Kutateladze|
αi are household fixed effects, C<sup>`′`</sup> it<sup>is a vector of household characteristics in 2009, 2011 and</sup> 2014, accounting for the change in observable characteristics over time. μ̂<sup>D</sup> gives an estimate of the TOT. In our sample we actually only have a small number of beneficiaries. It might therefore be more appropriate to estimate the intent to treat (ITT). However, given that the eligibility depends on the poverty category of the household which cannot be identified given the discrepancy in the different databases we refrain from doing so. The standard errors are clustered at the village level. By definition, the difference-in-difference strategy controls for group fixed effects as well as common trends between beneficiaries and non-beneficiaries. We disaggregate the analysis by beneficiary type and household headship. Due to the small sample size of beneficiaries in each group we cannot differentiate it further. We complement this part of the analysis with the information from the in-depth interviews conducted with beneficiaries drawing a more nuanced picture on the links between households’ initial endowments, the opportunities and obstacles faced for accessing VUP, for using the cash transfers for investment, and the resulting asset accumulation.

15
impact assessment in Bangladesh has been hindered by the relative scarcity of individual-level multivariate analyses.

In this paper, we attempt a more precise quantification using a household panel database constructed from five Bangladesh Demographic and Health Surveys (2000, 2004, 2007, 2011, 2014). We also revisit the role of maternal beliefs in child nutrition and health first investigated by Choudhury and Ahmed (2011), extending the research to potentially-perverse status beliefs associated with income and higher education.

The remainder of the paper is organized as follows. Section 2 introduces our panel database, with a particular focus on its temporal and spatial components. Sections 3, 4 and 5 develop and estimate models for assessing children’s intake of animal-source foods, the fish share of that intake, and the impact of fish consumption on health status. We summarize and conclude the paper in Section 6.

# **2.  Data and Estimation Strategy**

We construct our panel from georeferenced survey data collected by the Bangladesh Demographic and Health Surveys for 2000, 2004, 2007, 2011 and 2014 (NIPORT 2001, 2005, 2009, 2011, 2016). We include data for 2,263 DHS clusters, 27,827 households and 36,491 individuals, distributed across years as indicated in Table 1.

# **Table 1:  DHS survey statistics by year, 2000-2014**

|**Year**|**DHS**<br>**Clusters**|**Households**|**Individuals**|
|---|---|---|---|
|**2000**|**341**|**4,847**|**6,813**|
|**2004**|**361**|**4,999**|**6,900**|
|**2007**|**361**|**4,626**|**6,150**|
|**2011**|**600**|**6,882**|**8,742**|
|**2014**|**600**|**6,473**|**7,886**|
|**Total**|**2,263**|**27,827**|**36,491**|

4
# **III. The Survey**

# **A.  Traffic Fatalities in Delhi, India**

Over the past three decades, Delhi, India has experienced a nine-fold increase in motor vehicles. This has led not only to vehicular pollution, but also to road accidents. According to the statistics released by the Delhi Police (2002) about 2000 persons are killed each year in traffic crashes in Delhi. This implies a death rate of approximately 14 persons per 100,000, about the same as the United States. In terms of fatalities per vehicle, however, the death rate in Delhi—60 fatalities per 100,000 vehicles—is more than three times the equivalent figure for the US.

In Delhi, as in many developing countries, the majority of traffic fatalities occur among vulnerable road users. In 2001, 47% of fatalities occurred among pedestrians, 12% among bicyclists or cycle rickshaw drivers, and 20% among drivers of motorized two-wheelers. The vehicles at fault were most often trucks and buses: In 2001 they accounted for 70% of accidents in which the vehicle at fault was recorded, while cars were responsible for only 15% of fatal accidents.

The incidence of traffic fatalities by gender is especially relevant for our study. In Delhi, 10 men die in a traffic crash for every woman who dies. To be more precise, the road traffic death rate for adult males is 36 per 100,000, whereas it is only 3.6 per 100,000 for adult women.<sup>4</sup> This likely reflects differences in exposure, i.e., in kilometers traveled, as well as in risk-taking behavior. It also implies that estimates of willingness to pay for improvements in road safety should focus on the willingness to pay of men, as they are likely to be the main beneficiaries of reductions in the risk of fatal accidents.

> respondents what they would pay to travel on a safer road but do not associate this with a reduction in the respondent’s personal risk reduction.

> 4 The death rate for children is approximately 2 per 100,000.

- 6 -
The average upward mobility gap for the 1980s birth cohort varies from 0.8 (in Uzbekistan, with respect to fathers) to 9.1 (in Tunisia, with respect to mothers). When looking at the 1950s birth cohort, the upward mobility gap varies from 0.7 (in Mauritania, with respect to fathers) to 8.3 (in Viet Nam, with respect to mothers). While the upward mobility gap calculated with respect to fathers and the one calculated with respect to mothers are correlated (figure 1), the values with respect to mothers are, in most cases, above those calculated with respect to fathers. This results from the lower average educational attainment among mothers.

High values of the upward mobility gap usually occur when the share of children having more education than their parents is high (the horizontal axis in figure 2), and the increase in years of education among children who are more educated than their parents is large (the vertical axis in <u>figure 2). For the 1980s birth cohort, the median share of upward movers (calculated relative to</u> their mother’s education) is 61 percent (58 percent relative to their fathers’). The median increase in years of education is 5.7 relative to mothers’ education and 5.3 years relative to fathers’.

<u>Figure 3</u> plots the values of upward mobility for every birth cohort, calculated with respect to mothers’ education (a similar pattern is found when mobility is calculated with respect to fathers’ education). For Europe and Central Asia, we plot the values ranging from the 1930s birth cohorts to the 1990s birth cohorts, using all the information available from LiTS. For the remaining regions of the world, for which we use information from GDIM, we plot the values from the cohorts born between the 1950s and the 1980s. In almost every country, upward mobility is lowest for the younger generations. The patterns of upward mobility across the world are heterogeneous. In South Asia, the Middle East and North Africa, Sub-Saharan Africa, and East Asia and the Pacific, the younger generation have witnessed an increase in upward mobility. In Latin America, in highincome countries, and particularly in Europe and Central Asia, younger generations have lower upward mobility than older generations. In Europe and Central Asia, the median upward mobility of the cohort born in the 1990s was about 40 percent less than those born in the 1930s or 1940s.<sup>15</sup> The secular increase in education levels might explain part of this movement in the more educated

> 15 The largest differential in upward mobility between people born in the 1930s and the 1990s is in Kazakhstan, where upward mobility for the youngest generation was almost 85 percent lower than for the oldest generation. In this country, while older generations had established careers and resources from the Soviet era, young people faced a collapsing economy, high unemployment, and dismantled social safety nets. The rapid shift to a market economy, coupled with industrial decline and disrupted education reforms that were adapted to fit local political agendas rather than embracing their intended purposes, left the youth ill-equipped for new economic realities (Silova 2005).

13
# **Table 6**

**Drivers of Gross Capital Inflows: WORLD, 1980‐99 vs. 2000‐17** _Dependent Variable: GROSS Inflows, Total and By Type (% GDP) Sample: 104 countries, 1980‐2017 (annual) Estimation method: Instrumental Variables (IV)_

|Variables|Total<br>1980-99|Total<br>2000-17|FDI<br>1980-99|FDI<br>2000-17|PI<br>1980-99|PI<br>2000-17|OI<br>1980-99|OI<br>2000-17|
|---|---|---|---|---|---|---|---|---|
|_Domestic Factors_|||||||||
|GDP Growth|0.0181|0.0139**|0.00642|0.00705**|0.00645|0.00470*|0.00572|0.00381|
||(0.0128)|(0.00623)|(0.00505)|(0.00318)|(0.00673)|(0.00248)|(0.00794)|(0.00470)|
|GDP per capita|0.963***|0.136**|0.214|0.0405|0.411**|0.0546**|0.342|0.0703|
|_(in logs)_|(0.344)|(0.0665)|(0.136)|(0.0353)|(0.182)|(0.0275)|(0.213)|(0.0502)|
|CPI Inflation|0.00167**|0.000908|1.16e-05|0.000745*|0.000670*|0.000260|0.000949**|6.27e-05|
||(0.000732)|(0.000837)|(0.000289)|(0.000410)|(0.000384)|(0.000321)|(0.000453)|(0.000635)|
|Primary Balance|-0.00330*|0.00402***|0.000484|-0.00256***|-0.000242|0.00124***|-0.00318***|0.00353***|
|_(% of GDP)_|(0.00184)|(0.00122)|(0.000725)|(0.000559)|(0.000969)|(0.000437)|(0.00114)|(0.000928)|
|Exchange rate flexibility|-0.0107***|0.00315|-7.74e-05|-0.00106|-0.00604***|5.09e-05|-0.00457**|0.00479**|
||(0.00341)|(0.00279)|(0.00134)|(0.00141)|(0.00179)|(0.00110)|(0.00211)|(0.00212)|
|Investment Profile|-0.00456|0.0207***|0.000773|0.00101|0.00170|0.00570***|-0.00651**|0.0128***|
||(0.00467)|(0.00386)|(0.00184)|(0.00194)|(0.00246)|(0.00153)|(0.00289)|(0.00294)|
|Trade Openness|0.0404|-0.0247|-0.0150|-0.0260*|0.0216|-0.0203*|0.0344|0.0107|
|_(% of GDP, logs)_|(0.0739)|(0.0311)|(0.0291)|(0.0154)|(0.0389)|(0.0121)|(0.0457)|(0.0237)|
|Financial Openness|0.0234***|0.0347***|0.00129|0.00617|0.00770*|0.0114***|0.0120**|0.0157**|
|_(Chinn‐Ito Index)_|(0.00800)|(0.00813)|(0.00314)|(0.00399)|(0.00419)|(0.00313)|(0.00495)|(0.00616)|
|_External Factors_|||||||||
|Foreign Growth|-0.00252|0.00369*|0.000677|0.000529|-0.00382|0.000442|0.00159|0.00320**|
||(0.00916)|(0.00192)|(0.00362)|(0.000956)|(0.00483)|(0.000747)|(0.00567)|(0.00146)|
|VIX Index|0.0148|3.13e-07|0.0194**|0.0148|-0.00270|0.00791|0.00155|-0.0116|
||(0.0204)|(0.0338)|(0.00801)|(0.0174)|(0.0107)|(0.0134)|(0.0126)|(0.0256)|
|US Economic Policy|0.00207**|-0.000373|0.000380|-0.000276**|0.00115***|-0.000107|0.000597|-0.000128|
|Uncertainty|(0.000810)|(0.000241)|(0.000320)|(0.000121)|(0.000428)|(9.40e-05)|(0.000501)|(0.000183)|
|Commodity Terms of|-0.694|-0.0250|-0.266|0.00431|-0.251|-0.00316|-0.174|-0.0247|
|Trade (in logs)|(0.510)|(0.0238)|(0.200)|(0.0117)|(0.267)|(0.00919)|(0.315)|(0.0180)|
|Oil Price|0.0668|-0.0130|-0.00712|-0.0148|0.00907|0.0356***|0.0522**|-0.0334|
|(in logs)|(0.0416)|(0.0304)|(0.0163)|(0.0151)|(0.0216)|(0.0117)|(0.0257)|(0.0231)|
|Minerals and Metals|0.0174|0.00754|-0.00839|0.0498***|0.0449|-0.0597***|-0.0193|0.0187|
|Prices (in logs)|(0.0792)|(0.0345)|(0.0314)|(0.0171)|(0.0419)|(0.0133)|(0.0490)|(0.0262)|
|Observations|441|1,669|450|1,699|451|1,703|441|1,672|
|No. Countries|67|103|68|104|68|104|67|103|

_Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1_ 24
# Table 3: **Summary Statistics** – Respondent and Loan Characteristics

This table reports summary statistics for all accounts in the sample frame. _Debt Relief_ is the total amount of debt relief granted to a household. _Total Debt_ is the amount of total self reported debt at the time of the survey and prior to the program. _Bank Debt_ includes loans from commercial and cooperative banks, _Informal Debt_ includes loans from friends and family, shopkeepers, traders, and moneylenders. _Investment_ in agricultural inputs includes spending on irrigation, nonorganic fertilizer, pesticides and ploughing, gross and per acre, and is averaged over two pre-program seasons. _Productivity_ is measured as total revenue from all agricultural activities, gross and per acre, averaged over two pre-program seasons. Robust standard errors, in brackets, are clustered by bank and district. +p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01 _Panel A: Treatment_

||N|Mean|Median|StDev|Min|Max|ˆ_τRD_|
|---|---|---|---|---|---|---|---|
|Log debt relief, Rs|2,897|9.96|10.08|1.02|2.4|13.53||
|Treated [_≤_2 ha]|1,716|10.44|10.51|0.82|3.09|13.53|1.383***|
|Control [_>_2 ha]|1,181|9.284|9.386|0.87|2.4|11.94|(0.07)|

_Panel B.1: Debt_

||N|Mean|Median|StDev|Min|Max|ˆ_τRD_|
|---|---|---|---|---|---|---|---|
|Total Debt, Pre-Program||||||||
|Log total debt, Rs|2,825|10.83|10.71|1.04|6.91|15.13||
|_Treated_|1,672|10.73|10.65|0.98|6.91|15.02|0.03|
|_Control_|1,152|10.99|10.82|1.11|6.91|15.13|(0.02)|
|Bank debt, %|2,732|86.71|100|28.35|0.00|100||
|_Treated_|1,600|85.9|100|29.32|0.00|100|-1.303|
|_Control_|1,132|87.86|100|26.9|0.00|100|(1.45)|
|Informal debt, %|2,732|8.44|0.00|22.27|0.00|100||
|_Treated_|1,599|9.09|0.00|23.69|0.00|100|0.325|
|_Control_|1,133|7.52|0.00|20.08|0.00|100|(1.18)|

_Panel B.2: Investment and Productivity_

||N|Mean|Median|StDev|Min|Max|ˆ_τRD_|
|---|---|---|---|---|---|---|---|
|Investment, Pre-Program||||||||
|Log investment, Rs|2,571|9.29|9.33|0.91|5.74|13.82||
|_Treated_|1,505|9.2|9.25|0.88|5.74|13.82|-0.101|
|_Control_|1,066|9.43|9.47|0.94|6.21|12.88|(0.06)|
|Log investment per acre|2,601|8.47|8.7|1.58|00.0|14.23||
|_Treated_|1,521|8.46|8.7|1.61|0.00|14.23|-0.008|
|_Control_|1,080|8.49|8.7|1.54|0.00|12.9|(0.13)|
|Productivity, Pre-Program||||||||
|Log revenue, Rs|2,521|10.74|10.82|1.03|6.62|13.15||
|_Treated_|1,478|10.65|10.78|1.03|6.62|13.12|-0.108|
|_Control_|1,043|10.86|10.92|1.01|7.09|13.15|(0.06)|
|Log revenue per acre|2,488|9.44|9.54|0.99|5.25|16.03||
|_Treated_|1,455|9.45|9.45|1.02|5.25|16.03|-0.077|
|_Control_|1,033|9.44|9.44|0.95|5.41|14.1|(0.07)|

34
39

**Table 5**

# **Current account reversals and remittances**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||ED4<br>|ED4|ED4<br><sup>th</sup>|BP5<br>|BP5|BP5<br><sup>th</sup>|
||median|mean|75 pct|median|mean|75 pct|
|CA<br>REM|-2.790<br>[0.229]***<br>-0.018|-2.805<br>[0.228]***<br>0.189|-2.799<br>[0.227]***<br>0.080|-1.762<br>[0.213]***<br>0.053|-1.769<br>[0.213]***<br>0.111|-1.772<br>[0.212]***<br>0.070|
||[0.490]|[0.502]|[0.522]|[0.420]|[0.427]|[0.433]|
|RES|-1.053<br>[0.452]**|-0.980<br>[0.404]**|-0.974<br>[0.391]**|-1.363<br>[0.495]***|-1.210<br>[0.450]***|-1.177<br>[0.435]***|
|EXTD|0.162|0.201|0.199|0.151|0.173|0.180|
|SHORTD|[0.084]*<br>-0.031|[0.084]**<br>-0.017|[0.085]**<br>-0.012|[0.066]**<br>-0.029|[0.072]**<br>-0.020|[0.072]**<br>-0.013|
||[0.144]|[0.144]|[0.145]|[0.107]|[0.108]|[0.108]|
|CREGRO|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|
||[0.002]|[0.002]|[0.002]|[0.001]|[0.001]|[0.001]|
|PPPGDP|-0.018|-0.018|-0.018|-0.007|-0.008|-0.009|
||[0.025]|[0.025]|[0.025]|[0.025]|[0.025]|[0.025]|
|OPEN|0.188<br>[0.106]*|0.206<br>[0.106]*|0.203<br>[0.105]*|0.220<br>[0.095]**|0.234<br>[0.095]**|0.234<br>[0.095]**|
|OT|-0.692|-0.733|-0.740|-0.476|-0.490|-0.513|
||[0.346]**|[0.347]**|[0.340]**|[0.302]|[0.304]|[0.294]*|
|CONCD|-0.013|-0.012|-0.012|0.026|0.014|0.023|
||[0.113]|[0.113]|[0.113]|[0.102]|[0.102]|[0.102]|
|RES^2|1.631|1.540|1.478|2.185|2.079|1.987|
||[0.832]*|[0.812]*|[0.795]*|[1.031]**|[1.018]**|[0.996]**|
|EXTD^2|-0.027|-0.038|-0.038|-0.036|-0.044|-0.046|
||[0.024]|[0.021]*|[0.021]*|[0.015]**|[0.016]***|[0.016]***|
|RES*DREM|0.470<br>[0.316]|0.532<br>[0.312]*|0.630<br>[0.315]**|0.694<br>[0.303]**|0.683<br>[0.300]**|0.759<br>[0.303]**|
|EXTD*DRE<br>|-0.038|-0.096|-0.097|-0.041|-0.071|-0.087|
|M|||||||
||[0.049]|[0.050]*|[0.051]*|[0.040]|[0.045]|[0.041]**|
|Observations|1214|1214|1214|1191|1191|1191|
|R-squared|0.30|0.31|0.31|0.22|0.22|0.22|

_Notes_ : Estimation by linear probability model. Robust standard errors in brackets. All regressions contain country fixed effect and year dummies. The dependent variable ED4 takes the value 1 if a reduction of a current account deficit of at least 4 percentage points of GDP occurs at time t, and zero otherwise. The dependent variable BP5 takes the value 1 if a reduction of a current account deficit of at least 5 percentage points of GDP and equal to at least half of the initial current account deficit occurs at time t, and zero otherwise. DREM is a dummy variable that takes the value 1 if in a given year the level of workers’ remittances over GDP of a country is above the median or mean or 75<sup>th</sup> percentile value (as specified at the top of each column) of that year’s (cross-country) distribution. All other explanatory variables are described in Table A1 and lagged one period. * significant at 10%; ** significant at 5%; *** significant at 1%
Table A4: Early Life Exposure to Coffee Mills and Test Scores: Effects by Subjects

|||All Subjects||||ST|EM|||
|---|---|---|---|---|---|---|---|---|---|
|||Aggregate|||Maths|||Science||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|CatchmentArea X Post|0.0492***|0.0522***|0.0487***|0.0414***|0.0376**|0.0352**|0.0273|0.0366**|0.0341**|
||(0.0178)|(0.0171)|(0.0169)|(0.0156)|(0.0170)|(0.0167)|(0.0176)|(0.0170)|(0.0169)|
||[ 0.015 ]|[ 0.013 ]|[ 0.014 ]|[ 0.018 ]|[ 0.042 ]|[ 0.048 ]|[ 0.138 ]|[ 0.045 ]|[ 0.056 ]|
|R-squared|0.3866|0.4260|0.4260|0.3459|0.3764|0.3764|0.3782|0.4190|0.4190|
|Observations|1487359|1487040|1487040|1487359|1487040|1487040|1487359|1487040|1487040|
||||||Non STEM|||||
|||Social|||English||K|inyarwand|a|
|CatchmentArea X Post|0.0330*|0.0321*|0.0294*|0.0486***|0.0546***|0.0488***|0.0594***|0.0639***|0.0631***|
||(0.0189)|(0.0173)|(0.0172)|(0.0172)|(0.0168)|(0.0165)|(0.0184)|(0.0186)|(0.0183)|
||[ 0.098 ]|[ 0.08 ]|[ 0.103 ]|[ 0.014 ]|[ 0.013 ]|[ 0.013 ]|[ 0.013 ]|[ 0.013 ]|[ 0.013 ]|
|R-squared|0.3841|0.4226|0.4226|0.4056|0.4469|0.4469|0.2316|0.2767|0.2767|
|Observations|1487359|1487040|1487040|1487359|1487040|1487040|1487359|1487040|1487040|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|

Notes: The dependent variable is the standardized test scores in the respective subject groups in the Rwanda Primary School National exam. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. CatchmentArea X Post is an indicator variable equal to 1 if a student was born within the vicinity ( _≤_ 5km) of an operational coffee mill and 0 if otherwise. Controls include gender of the student, average annual temperature, and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at the school level in parenthesis. False discovery rate (FDR) adjusted p-values for _Catchment AreaX Post_ are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda.

> _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level

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