rafmacalaba/datause-encoder-data huggingface dataset:
productivity differences. Our approach to constructing an instrument that represents historical sub-district level malaria incidence is to find exogenous sub-district characteristic(s) that can essentially be used as “weights to recover the variations in malaria prevalence across different sub-districts from the district average malaria data. A large literature on malaria in tropical countries identify a few ecological characteristics that can potentially be used to generate the sub-district level historical malaria estimates. Among the candidate ecological variables, rainfall is perhaps the most reliable predictor of spatial malaria variation in the specific context of Sri Lanka (Briet et al., 2003, 2008). We thus use rainfall in a sub-district as the relevant exogenous characteristic to uncover the incidence of historical malaria across sub-districts. The effects of rainfall on the incidence of malaria, however, can be different in different countries.<sup>10</sup> In Sri Lanka, the relationship between malaria and rainfall is negative across geographic space, as higher rainfall washes out the breeding grounds of _Anopheles Culicifacies_ , and _Anopheles Subpictus_ , the main malaria vectors in Sri Lanka (Clemesha, 1934; Rustomjee, 1944; Briet et al., 2008). An interaction of rainfall with historical malaria is used as an instrument in our empirical analysis. As we discuss in the empirical strategy below, all regressions control for rainfall directly to capture any productivity effect of rainfall.

The HIES 2002 also collected information on education, age, gender, ethnicity and religion. The individual and household level explanatory variables are defined from the HIES 2002. HIES 2002 however did not collect information on health status of the household members. We draw information on the chronic illness of household heads from HIES2006 data (Table A.20, p.99 in the final report on HIES 2006/7). The information on anemia prevalence rate among nonpregnant women is drawn from Demographic and Health Survey 2006/7 (Table 6, p.19, DHS report (2009)).<sup>11</sup> The area characteristics including rainfall, slope, area and land quality are drawn from various GIS data sources. Appendix Table A.1 provides summary statistics for all variable included in our analysis.

> 10Many researchers in Asia found that rainfall reduces malaria incidence/prevalence by washing out the breeding grounds of Anopheles mosquito (Wijesundera, 1988.)

> 11Anemia status was determined by haemoglobin level in blood. Anyone with haemoglobin level below 7.0g/dl is classified as severely anemic, and with haemoglobin level between 7.0-10.p g/dl classified as having moderate to mild anemia.

13
that existed at the time, with a total slum population of 5.4 million, or 35% of the overall population in these city corporations (Angeles et al. 2009).<sup>14,15</sup> The 2005 independent census had several interesting findings on the characteristics of slum settlements. We note four of these findings. First, there were large differences in the population sizes, and housing and public environmental conditions, of slum settlements within and across cities. Large slum settlements were a feature of the larger cities. Second, waste management (garbage disposal and collection, and sanitation) tended to be poorer than other public environmental facilities in slum settlements. Third, the vast majority of slum settlements were situated on private lands, and slum residents tended to have secure tenure. In addition, housing construction tended to be of better quality in slum settlements on private lands, which we presume is a result of residents having secure tenure. Fourth, a large share of slum residents were rural migrants, mostly from rural communities near to the city corporation.

The 2006 and 2013 BUHS reports (NIPORT et al. 2008; NIPORT et al. 2015) and case studies using other data find that slum residents generally have poor mean socioeconomic circumstances and outcomes (for example, see Islam, Farukuzzaman, and Islam 2014; Nahar and Rahman 2013; Hossain, Moniruzzaman, and Islam 2010). The findings are consistent with evidence for slum residents globally (for example, see UN-Habitat 2016). These results, however, directly follow from the definition of a slum settlement.

C. _Urban child growth in international and national perspectives_ _Urban Bangladesh versus urban areas in other developing countries_ : Using statistics compiled by WHO, we compare the urban child stunting rate for Bangladesh to those of other developing countries. Statistics for all 40 countries in the comparison are for the period 2005–09. Bangladesh ranks poorly, at no. 34 (Figure 1, Panel A). The country’s rank depends on the urban subpopulation in terms of household wealth quintile (poorest quintile versus richest quintile). Bangladesh’s urban stunting rate ranks worse with respect to the poorest than the richest (no. 34

> 14 A settlement was defined to be slum if it had at least 10 households and met four of the following five conditions: poor housing quality, high population density or overcrowded housing, poor water supply and sanitation facilities, insecure tenure, and a majority of households who are poor.

> 15 While the statistics from the 2005 and 2014 censuses may not reconcile easily, the basic message is identical: slum settlements and their populations are a major feature of urban areas in the country.

10
_Table 4: Hard Aid for Trade Facilitation : Bilateral-5Year Fixed Effects and Year Fixed Effects Included_

||1<br>|2<br>|3<br>|4<br>|5<br>|6<br>|7<br>|
|---|---|---|---|---|---|---|---|
||Baseline<br>– all<br>positive<br>trade|Baseline<br>with per<br>capita<br>GDP|Only<br>countries<br>receiving<br>AfTF /1/|Recipients<br>only;<br>trade with<br>recipients<br>& ROW|Recipients<br>with<br>developing<br>world only|Recipients<br>with<br>developing<br>& bilateral<br>donor; no<br>intra-<br>donor<br>trade|Base-line<br>(2) with<br>lags|
|ln_tariff|-0.200***|-0.201***|-0.233**|-0.218***|-0.214***|-0.202***|-0.319***|
||[0.035]|[0.035]|[0.104]|[0.048]|[0.043]|[0.036]|[0.073]|
|ln_rep_gdp|0.686***|1.599***|-4.770***|0.757|1.394***|1.534***|3.274***|
||[0.045]|[0.322]|[1.292]|[0.482]|[0.466]|[0.327]|[1.008]|
|ln_part_gdp|0.253***|-0.689**|-7.763***|-2.345***|-1.569***|-0.750***|-0.092|
||[0.036]|[0.270]|[1.744]|[0.495]|[0.407]|[0.274]|[0.612]|
|ln_hard_rep|0.004*|0.004|-0.012|0.015***|0.005|0.004|0.017**|
||[0.003]|[0.003]|[0.024]|[0.005]|[0.003]|[0.003]|[0.008]|
|ln_hard_part|0.002|0.002|0.047**|0.001|0|0.001|-0.001|
||[0.002]|[0.002]|[0.023]|[0.006]|[0.003]|[0.002]|[0.004]|
|ln_rep_gdp_cap||-0.909***|5.422***|-0.092|-0.6|-0.843***|-2.915***|
|||[0.320]|[1.277]|[0.473]|[0.452]|[0.324]|[1.012]|
|ln_part_gdp_cap||0.945***|8.092***|2.581***|1.702***|1.004***|0.386|
|||[0.264]|[1.727]|[0.483]|[0.396]|[0.267]|[0.600]|
|L.ln_hard_rep|||||||0.058***<br>[0.009]|
|L2.ln_hard_rep|||||||0.003<br>[0.006]|
|L.ln_hard_part|||||||-0.005<br>[0.004]|
|L2.ln_hard_part|||||||0.006*<br>[0.004]|
|Observations|108304|107806|12424|52181|57861|104637|32592|
|Number of Bilat - 5year F.E.|39309|39125|6576|22181|25237|38386|13461|
|Robust standard errors in brackets||||||||

* significant at 10%; ** significant at 5%; *** significant at 1% /1/ A4TF “Aid for Trade Facilitation’ Includes Bilateral- 5 year fixed effects and year fixed effects

29
Educational attainment remains at dismally low levels in several developing countries. The fraction of children who persist to grade 5 was below 40 percent in Chad and Madagascar, below 50 percent in Angola, Ethiopia, and Rwanda in 2009 (World Bank 2013). In addition, great disparities in schooling by gender and place of residence still exist in many parts of the world. Despite the progress made toward equality in schooling, women continue to lag behind men in many developing countries.<sup>1</sup> For instance, the gender schooling gap in primary schools exceeds 10 percentage points in Yemen, Niger, Cote d'Ivoire, Mali, and Pakistan. Urban-rural divide in schooling is another stylized fact in many parts of the developing world. Orazem and King (2008) report that urban-rural gaps around the world are even larger than the gender gaps. Gender and urban-rural disparities in schooling outcomes in Turkey—the context of this study—are also quite significant. In this paper, we investigate how the extension of compulsory schooling from 5 to 8 years in Turkey in 1997 affects schooling attainment by gender and urban/rural residence and whether this schooling reform narrows or further exacerbates the existing differences across these groups. While this paper does not examine outcomes beyond schooling attainment such as labor market and social outcomes, it lays the groundwork for the analysis of the consequences of increased schooling.<sup>2</sup> Within a period of three years following the 1997 reform, the number of students in grades 1 to 8 increased from about 9 million to 10.5 million, which represents a 15-percent rise compared to the 1-percent fall in the preceding three years. This is due to both the large number of additional years that are made compulsory (3 years) and the high drop-out rate in noncompulsory schooling grades before the policy. Equally interesting are the large spillover 1 Orazem and King (2008) as well as Grant and Behrman (2010) report significant improvement over time in the schooling attainment of women relative to that of men.

2 See, e.g., Kırdar et al. (2010), Aydemir and Kırdar (2013), Güleşçi and Meyersson (2013), Güneş (2013), Dinçer et al. (2014) for the social and labor market consequences of increased schooling in this context.

2
25

**Table S. 4 – continued from previous page**

|Variable<br>Sector|Female 15-49<br>Rural|Urban|Male 15-54<br>Rural|Urban|
|---|---|---|---|---|
||(30.283)|(23.322)|(88.961)|(57.968)|
|88.pr<br>~~n~~co2|-26.098|86.408***|-34.399|-115.926**|
||(20.850)|(15.093)|(89.084)|(58.013)|
|89.pr<br>~~n~~co2|-60.671***|28.643|-78.134|-108.434*|
||(20.595)|(19.289)|(89.551)|(59.149)|
|90.pr<br>~~n~~co2|-20.635|19.209|-63.367|-112.576*|
||(26.392)|(17.745)|(89.924)|(58.769)|
|91.pr<br>~~n~~co2|-44.442**|41.323**|-49.942|-129.327**|
||(21.331)|(16.201)|(89.769)|(58.200)|
|93.pr<br>~~n~~co2|-56.940***|40.803***|-76.659|-137.442**|
||(18.920)|(13.861)|(88.856)|(57.855)|
|95.pr<br>~~n~~co2|-53.757***|38.149***|-86.577|-145.830**|
||(18.598)|(12.773)|(88.818)|(57.801)|
|96.pr<br>~~n~~co2|-14.113|47.322***|-45.125|-131.301**|
||(21.736)|(16.051)|(89.218)|(58.378)|
|98.pr<br>~~n~~co2|-2.751|100.383***|-39.608|-92.754|
||(32.704)|(37.701)|(88.866)|(57.872)|
|100.pr<br>~~n~~co2|-40.732**|54.888***|-72.404|-125.669**|
||(18.715)|(13.497)|(88.876)|(58.096)|
|active|-3.271|-15.972***|12.291***|-7.061|
||(2.355)|(4.860)|(3.402)|(5.951)|
|land|3.641***|1.291***|4.160***|4.341***|
||(0.129)|(0.296)|(0.260)|(0.536)|
|land2|-0.041***|-0.011**|-0.046***|-0.054***|
||(0.002)|(0.004)|(0.003)|(0.007)|
|1.pr<br>~~n~~co2|||-76.017|-86.825|
||||(92.276)|(61.018)|
|Constant|927.127***|673.685***|499.366***|525.518***|
||(22.777)|(26.364)|(92.027)|(66.540)|
|Observations|202,189|113,497|106,724|65,853|
|R-squared|0.135|0.169|0.351|0.274|

_Source_ : Authors’ calculations using National Family Health Survey rounds 2, 3, and 4. _Note_ : Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All regressions use the provided sample weights. Reported National Classification of Occupation <u>(NCO) codes use 1968 classification, with 100 for individuals missing the code (e.g.</u> women not in the labor force and students).
storability of sorghum/millet (World Bank, 2011) and more steady supplies for rice (through greater reliance on import and the potential of multiple crops per year through irrigation).

The cross-country perspective provides one way to “benchmark” food price seasonality (recall that some seasonality in prices is expected given the production cycle and storage (and/or trade) costs). The second approach is to compare domestic price variability with that on world markets. This is a feasible approach for those food crops which enter into international trade, such as maize and rice. In particular, the 2000-2012 conditional wholesale maize and rice series seasonal factors derived from equation (1) for national average price series<sup>21</sup> can be compared with the seasonal factors derived in the same way for the SAFEX white maize cash price and the Bangkok rice price. SAFEX, which is part of the Johannesburg Stock Exchange, is the leading world futures market for white maize and provides the standard benchmark price for white maize both in South Africa and neighboring countries. Thailand is the leading world rice exporter and the Bangkok price is widely taken as indicating the world rice price.<sup>22</sup> Comparison of the seasonal factor of the SAFEX maize price with those observed for wholesale maize in each of the three countries, confirms the substantially greater seasonality in the national market price series. They exhibit a seasonal range of 24% in Tanzania (January to August), 26% in Uganda (June to September), and 48% in Malawi (February to May) against a seasonal range of 13% for SAFEX prices (January to July) (Figure 2)<sup>23</sup> . Malawi and Tanzania exhibit consistent seasonality, which follows that of SAFEX, with respective one (before) and two (after) month lags and lagged respective correlations among seasonal factors that are equal or above 90%. There are clearly two price seasons in Tanzania and Malawi, and seasonality appears to be twice as high (three times higher in Malawi) as what is observed at the SAFEX level. Local factors in Tanzania and Malawi

> 21 Those are obtained by taking the geometric average of all prices for each month observation across market locations in each country.

> 22 Prices are monthly averages in South African rand (maize) and US dollars (rice), converted to local currencies at monthly average exchange rates. Sources: SAFEX (maize) and IMF, _International Financial Statistics_ (rice and exchange rates).

> 23 The conditional seasonal gaps reported in Table 4 are averaged across the markets. The seasonal gaps for each country in Figure 2 here are calculated from the national price series, which explains the slight difference in magnitude.

19
|(2)<br>(3)<br>(4)<br>Public Sector|0.139<br>(0.604)<br>0.038<br>(0.172)<br>0.062<br>(0.274)<br>1.7<br>12.081<br>3.118<br>2311<br>2317<br>2311|gth. All regressions in the table<br>ude the respondent’s gender, age,<br>munity-level variables include the<br>GDP PPP, share of the urban<br>% of GDP), IIAG score, and|
|---|---|---|
|(4)<br>(1)|IV Regression<br>0.062<br>(0.278)<br>0.187*<br>(0.104)<br>48.601<br>4.73<br>21103<br>2319|e F test of instrument stren<br>sehold-level variables incl<br>TV or mobile phone. Com<br>s include total population,<br>al natural-resources rents (|
|(2)<br>(3)<br>Private Sector|0.173*<br>(0.094)<br>0.170*<br>(0.096)<br>54.26<br>65.889<br>21103<br>21148|l. F-stat is the IV frst-stag<br>variables. Individual/hou<br>hold, and ownership of a<br>ls. Country-level variable<br>vice as a share of GDP, tot|
|(1)|Electricity Access in Town<br>0.265*<br>(0.159)<br>Household Connected to Grid<br>Reliable Electricity in Town<br>Household has Reliable Electricity<br>F Stat<br>13.968<br>Observations<br>21152|Notes: Robust standard errors in parentheses clustered at PSU leve<br>control for individual/household-, community-, and country-level<br>education, quality of housing, employment status, size of the house<br>presence of piped-water systems, paved roads, schools, and hospita<br>population, population density, value added of agriculture and ser<br>ethno-linguistic fragmentation score.<br>_∗_Signifcant at 10 percent level<br>_∗∗_Signifcant at 5 percent level<br>_∗∗∗_Signifcant at 1 percent level|

29
Table S.20: Impacts on children’s educational achievement: complementarity <u>(OLS)</u>

|Dependent variable:|||One if|pass|||
|---|---|---|---|---|---|---|
|_X_:|No. of||Schoo|l-level ratio|of||
||students<br>in each|no. of<br>teachers|no. of<br>blackboards|no. of<br>benches|no. of<br>fans|no. of<br>toilets|
||school-|to|to|to|to|to|
||grade|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|
||(a)|(b)|(c)|(d)|(e)|(f)|
|**December in 2013**|||||||
|All products|-0.083|-0.072|0.099|0.018|0.008|0.017|
||(0.067)|(0.124)|(0.105)|(0.107)|(0.032)|(0.053)|
|One product|0.090|-0.002|0.056|0.038|-0.004|-0.023|
||(0.066)|(0.087)|(0.064)|(0.092)|(0.027)|(0.054)|
|All products _× X_|0.002*|3.935|-4.001|0.011|3.444**|1.474|
||(0.001)|(4.898)|(4.548)|(0.211)|(1.524)|(11.093)|
|One product _× X_|-0.002|-0.045|-3.125|-0.094|0.414|5.329|
||(0.002)|(4.323)|(3.745)|(0.192)|(3.675)|(9.225)|
|No. of students|-0.004*|-|-|-|-|-|
|in each school-grade|(0.002)||||||
|R-squared|0.333|0.325|0.325|0.325|0.326|0.325|
|No. of obs.|849|850|850|850|850|850|
||(g)|(h)|(i)|(j)|(k)|(l)|
|**December in 2014**|||||||
|All products|-0.216**|-0.106|-0.015|-0.025|-0.057|-0.026|
||(0.088)|(0.160)|(0.122)|(0.125)|(0.047)|(0.080)|
|One product|-0.137|-0.106|0.120|0.208|-0.020|-0.048|
||(0.110)|(0.160)|(0.120)|(0.170)|(0.041)|(0.058)|
|All products _× X_|0.003**|2.023|-2.258|-0.075|-0.217|-8.498|
||(0.001)|(6.993)|(6.497)|(0.242)|(1.652)|(14.958)|
|One product _× X_|0.002|3.410|-7.558|-0.527|-0.895|6.572|
||(0.002)|(5.996)|(6.081)|(0.369)|(2.894)|(14.453)|
|No. of students|-0.001|-|-|-|-|-|
|in each school-grade|(0.002)||||||
|R-squared|0.151|0.146|0.147|0.148|0.145|0.146|
|No. of obs.|844|845|845|845|845|845|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean of _X_|50.26|0.02|0.01|0.43|0.00|0.00|
|Std. of _X_|20.97|0.00|0.00|0.12|0.01|0.00|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number). (4) The reported mean and standard deviation were estimated at the level of the sample respondents.

65
# Table 15. Interacting linkages with distance

||Dep var|iable: Norm|alized share|of technology|-related job|adverts|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Pioneer|4.723***|4.561***|4.596***|4.556***|3.822***|3.800***|
||(0.412)|(0.402)|(0.402)|(0.402)|(0.921)|(0.921)|
|Pioneer_×_distance|-0.420**|-0.445**|-0.419**|-0.440**|-0.331|-0.363*|
||(0.209)|(0.210)|(0.209)|(0.209)|(0.215)|(0.215)|
|Pioneer_×_distance_×_years since emergence|0.047**|0.049**|0.047**|0.048**|0.035*|0.037*|
||(0.020)|(0.020)|(0.020)|(0.020)|(0.020)|(0.020)|
|**Linkages of pioneer frms**|||||||
|Buyer||2.132***||||1.550**|
|||(0.626)||||(0.702)|
|Supplier|||1.581***|||0.893**|
||||(0.477)|||(0.442)|
|Innovation partner||||1.926***||0.704|
|||||(0.610)||(0.717)|
|Affliate|||||1.023|0.761|
||||||(0.975)|(0.970)|
|**Linkages of pioneer frms in space**|||||||
|Buyer_×_distance||-0.041||||-0.011|
|||(0.094)||||(0.104)|
|Supplier_×_distance|||-0.139**|||-0.130**|
||||(0.064)|||(0.063)|
|Innovation partner_×_distance||||-0.041||0.060|
|||||(0.087)||(0.103)|
|Affliate_×_distance|||||-0.109|-0.104|
||||||(0.130)|(0.130)|
|**Linkages of pioneer frms in space over time**|||||||
|Buyer_×_distance_×_years since emergence||0.001||||0.002|
|||(0.008)||||(0.009)|
|Supplier_×_distance_×_years since emergence|||0.001|||0.001|
||||(0.006)|||(0.006)|
|Innovation partner_×_distance_×_years since emergence||||-0.003||-0.006|
|||||(0.008)||(0.009)|
|Affliate_×_distance_×_years since emergence|||||0.013|0.014|
||||||(0.012)|(0.012)|
|Technology FE|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.041|0.041|0.041|0.041|0.041|0.041|
|<br>Observations|9,834,969|9,834,969|9,834,969|9,834,969|9,834,969|9,834,969|

Notes: Standard errors clustered at the firm level in parentheses. The normalized share is capped at the 99<sup>_th_</sup> percentile of non-zero observations. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Distance refers to the distance to the nearest pioneer city, in logs.

# **4.3.5 Technologies that emerged after the Lightcast data start date**

The analysis above considered technologies that emerged after 2005, such that their linkages could be identified through the Factset data. However, our Lightcast panel data of job postings begins from 2014 only. Therefore there are several technologies for which in the early years of emergence there is no data in Lightcast. Our coefficients on the years since emergence variables may be disproportionately stemming from the impacts during the later years after emergence. To consider whether this is the case, we also run a robustness check keeping only the technologies that emerged from 2014 onwards, for which there is Lightcast data for all the years directly after emergence. This results in a significantly 33
- 49 -

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