rafmacalaba/datause-encoder-data huggingface dataset:
Table A11: Heterogeneity in the Complementary Effect of Internet and Roads on Employment: Age

||(1)<br>|(2)<br>**Worked during**<br>**past year**|(3)<br>|(4)|(5)<br>**Currently**<br>**working**|(6)|(7)|(8)<br>**Hours**<br>**worked**|(9)|
|---|---|---|---|---|---|---|---|---|---|
||**20-34**<br>**groupe age**<br>**Sample**|**35 -44**<br>**groupe age**<br>**Sample**|**45-64**<br>**groupe age**<br>**Sample**<br>**D**|**20-34**<br>**groupe age**<br>**Sample**<br>**HS**|**35 -44**<br>**groupe age**<br>**Sample**|**45-64**<br>**groupe age**<br>**Sample**|**20-34**<br>**groupe age**<br>**Sample**|**35 -44**<br>**groupe age**<br>**Sample**<br>**LSMS**|**45-64**<br>**groupe age**<br>**Sample**|
|Submarine internet|0.0923**|0.0608*|0.0808**|0.0560*|0.0492*|0.0584*|-0.331*|-0.241|-0.607**|
||(0.0283)|(0.0240)|(0.0288)|(0.0252)|(0.0242)|(0.0291)|(0.170)|(0.162)|(0.292)|
|Proximity to road (KM)|0.00799***|0.00796***|0.00638***|0.00794***|0.00810***|0.00706***|0.761**|0.111|0.797***|
||(0.000520)|(0.000521)|(0.000623)|(0.000496)|(0.000537)|(0.000695)|(0.371)|(2.579)|(0.296)|
|Submarine internet * road|0.0201**|0.0107*|0.0141*|0.0128*|0.00880|0.0107|4.862**|1.901|5.125***|
||(0.00646)|(0.00491)|(0.00606)|(0.00565)|(0.00489)|(0.00598)|(2.415)|(4.434)|(1.868)|
|N|451922|201169|103244|420560|186046|89811|242117|118111|104618|
|ymean|0.692|0.813|0.844|0.626|0.753|0.785|1.537|1.155|1.228|
|r2|0.0253|0.0308|0.0484|0.0601|0.0626|0.0686|-0.268|-0.0907|-0.628|
|r~~2~~<br>a|0.0251|0.0306|0.0480|0.0600|0.0623|0.0681|-0.268|-0.0909|-0.628|
|Kleibergen-Paap F-statistic|77.79|76.70|50.13|79.84|78.45|50.85|7.427|7.935|6.247|
|_P > F_|0|0|0|0|0|0|0|0|0|
|Cragg-Donald Wald F-statistic|445.4|283.5|119.0|419.2|264.0|107.6|505.1|233.7|301.1|
|Windmeijer F-statistic|26.02|28.51|15.81|24.45|26.28|14.45|7.42|7.94|6.25|
|Kleibergen-Paap Wald F-statistic|18.19|18.10|11.03|18.70|18.55|11.22|1.422|1.322|4.995|
|Age|✗|✗|✗|✗|✗|✗|✗|✗|✗|
|Gender|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Place of residence|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Education|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Country FE<br>|✓<br>|✓<br>|✓<br>|✓<br>|✓<br>|✓<br>|✓<br>|✓<br>|✓<br>|
|Grid (20*20) connectivity FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|

_Robust standard errors in parentheses clustered at the grid level. ∗p < ._ 10 _∗∗p < ._ 05 _∗∗∗ p <_ 0 _._ 01 _. We use a strategy that combines difference-in-differences and instrumental variables techniques. The causal effect of Internet is estimated using difference-in-differences, while the effects of the road and its interaction with the Internet are estimated using an instrumental variables approach. Our instruments are: the distance to the hypothetical road, the interaction between the distance to the hypothetical road and an indicator for whether an individual (or a firm) is connected to submarine Internet, lightning, and terrain elevation._ 61
# **7. REFERENCES**

- Ahmad, R., & Weiser, E. T. (2006). _Fostering Public Participation in Budget-making: Case Studies from Indonesia, the Marshall Islands, and Pakistan_ .

- Berner, M. (2001). Citizen Participation in Local Government Budgeting. _Popular Government_ , _Spring_ .

- Brück, T., Esenaliev, D., Kroeger, A., Kudebayeva, A., Mirkasimov, B., & Steiner, S. (2014). Household survey data for research on well-being and behavior in Central Asia. _Journal of Comparative Economics_ , _42_ (3), 819–835. doi:10.1016/j.jce.2013.02.003

- Im, T., Lee, H., Cho, W., & Campbell, J. W. (2014). Citizen Preference and Resource Allocation: The Case for Participatory Budgeting in Seoul. _Local Government Studies_ , _40_ (1), 102–120. doi:10.1080/03003930.2013.812963

International Budget Partnership. (2012). _Open Budget Survey 2012_ .

- Irvin, R. A., & Stansbury, J. (2004). Citizen Participation in Decision Making: Is It Worth the Effort? _Public Administration Review_ , _64_ (1), 55–65. doi:10.1111/j.1540-6210.2004.00346.x

- Khagram, S., Fung, A., & de Renzio, P. (2013). _Open Budgets: The Political Economy of Transparency, Participation, and Accountability_ . Brookings Institution Press.

- The World Bank. (2014). _Worldwide Governance Indicators: Country Data Report for Kyrgyz Republic, 1996-2013_ . Washington D.C.

Wampler, B. (2012). Participatory Budgeting: Core Principles and Key Impacts. _Journal of Public Deliberation_ , _8_ (2), 1–13.

22
the father’s education. I use the same instrumental variable in this paper to examine the effect of schooling on maternal health care use.

To summarize, there is a general consensus in the empirical literature that education is an important determinant of the demand for health inputs, including the utilization of health services. It is also accepted that schooling is endogenous and has not been adequately controlled for in the majority of studies (Strauss and Thomas 1995; Lindelow 2004).

# **3. School construction program in Indonesia**

A major school construction program, Sekolah Dasar Inpres Program (SDIP) which was undertaken by the Government of Indonesia (GOI) from 1973/74 onwards forms the basis for the instrumental variable used to measure the effect of education in this study. A brief description of the program follows. The identification strategy based on Duflo (2001) is described in a later section.

GOI undertook a series of large-scale social sector development programs in the 1970s, which were aimed at improving equity across provinces. Oil price increases in the 1970s meant that Indonesia, a major oil exporter in world markets could mobilize oil revenues to finance numerous centrally-administered development programs termed “presidential instructions” (INPRES). SDIP was one of the first INPRES programs and, by far, the largest at the time it was launched in 1973/74 (Duflo 2001). As oil prices rose and real expenditures on regional development doubled between 1973 and 1980, SDIP gained in importance. Between 1973-74 and 1978-79 more than 61,000 primary school buildings – 9
# **3. Data**

An important factor in this analysis is that not all elementary schools that are Single-Shift are certified as CES, neither all the Single-Shift middle schools are certified as CMS. However, only the certified “Carioca” styles have the defined organization as presented above. Moreover, not all treated schools became Single-Shift at the same time. Starting in 2011, a number of new schools became Single-Shift every year after, and a number of schools also left the program. The sample used in the regression estimations is schools that have complete information for all years. That is, we restrict the sample to schools that have information on all variables used so we can have a balanced panel.

Table 3 and Figure 3 show the number of schools and number of students, respectively, in the sample for the estimations using IDEB as the outcome. As shown in Table 3, of the 22 schools that became Single-Shift in 2011, 21 are part of the sample. In 2012, 60 schools with complete information were added to the program, summing to 81 schools in total. In 2013, there were 38 new schools with complete information, however, 4 schools that were Single-Shift left the program (1 elementary and 3 middle schools). In 2014, 24 new schools were added, and 5 schools left the program. Finally, in 2015, five new school became CES.

|||||**Table**|**3: Sam**|**ple **|**Size**|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||**Elem**|**entar**|**y Scho**|**ols**|||**M**|**iddle**|**Schoo**|**ls**||**Total**|
|**Total Sample**|||**53**|**9**|||||**3**|**27**|||**866**|
|**Total treatment**|||91||||||3|9|||130|
|**Control**|||44|8|||||2|88|||736|
||**Sing**|**le-Shift (**<br>**CES)**|**non-**||**CES**||**Singl**|**e-Shift**<br>**CMS)**|**(non-**||**CMS**||**Total**|
|<br>**-**|Total|Entered|Left|Total|Entered|Left|Total|Entered|Left|Total|Entered|Left||
|2011<br>**the**<br>**gle**|3|3|0|0|0|0|8|8|0|10|10|0|21|
|2012<br>**en**<br>**ol**<br>**Sin**<br>**ft**|55|52|0|1|1|0|8|0|0|16|7|0|80|
|2013<br>**wh**<br>**cho**<br>**me**<br>**Shi**|77|23|1|2|1|0|11|6|3|23|7|0|113|
|2014<br>**ear**<br>**s**<br>**ca**<br>|83|10|4|7|5|0|16|5|1|23|0|0|129|
|2015<br>**Y**<br>**be**|79|0|4|12|5|0|16|0|0|23|0|0|130|

Notes: CES refers to Carioca Elementary Schools. CMS refers to Carioca Middle Schools. Ginasos Olimpicos are included in CMS schools. This is the sample with complete information from 2007 to 2015, including the control variables used in the regression models.

Data Source: Rio de Janeiro Municipal Secretariat of Education.

13
the distance of the household to market increases. This is an approximate measure of household remoteness.

We expect the transaction costs associated with distance to market to be absorbed by the small maize producers because they are price takers in both harvest and lean seasons<sup>8</sup> . Village maize buyers (both local and non local) are also price takers that mostly operate locally<sup>9</sup> . They do not transact directly with consumers as they sell the maize they have collected to other traders or processors. They depend on the daily prices reported in regional markets and the central Dar es Salaam market to establish their buying prices<sup>10</sup> . Price determination in these reference markets is based mostly on national supply and demand conditions since maize tends to behave as a nontradable good with the exception of some road or rail-linked transit markets (World Bank, 2000). Hence, the main avenue for a buyer to recover the costs they have incurred is to pass them on to the maize producers. Similarly, the most obvious way for a buyer to increase their trading profit margin is by pushing producer prices down as far as possible given the competitive environment and the rent opportunities.

We also consider whether the effect of distance to market is subject to seasonal variation and whether search costs have a role to play in explaining the variation. We expect search costs to be higher during the harvest season due to the seasonally differentiated pattern of buyer entry. Our previous brief description of the maize marketing system partially explained the reasoning behind this hypothesis. We described how non local maize buyers are more active in the harvest season as they enter the market directly to procure maize in villages, whereas in the lean season they would procure indirectly using marketplaces and buyer networks. Whilst the former is a more involved and time consuming process, the latter is more coordinated. The implication of this seasonally differentiated buying strategy is higher search related transaction costs in the harvest season<sup>11</sup> . Hence, we anticipate that costs will have a stronger negative effect on maize prices in the harvest season as household distance to market increases.

But what explains this pattern of buyer activity? Ideally, we would expect buyers to minimize all of their costs for any transaction, so that if procuring maize indirectly through village-based

> 8 When interviewed, they reported that they have little room for negotiation with buyers and that they tended to readily accept the prices offered to them.

> 9 89 percent of rural traders operate within the confines of their ward or district (World Bank, 2007).

> 10 Traders in regional markets reported that they refer to the wholesale markets in Dar es Salaam to establish trading prices when interviewed.

> 11 The search costs of non local buyers are higher than those of the village-based buyers irrespective of season. Non local buyers travel longer distances to procure maize, have less information about areas in abundance and have less established networks at the village level.

5
34 Table 2. Modcl of Current Contraceptive Use in Tunisia: Estimated Coefficients and t values for Structural **g'id** Reduced Form Equations (Urban and Rural Areas Combined)

||J<br>_____________|-|Structural Fquation<br>|s<br>|Reduced Form<br>|
|---|---|---|---|---|---|
|Variables|Births|**Deatlis**|Fertility<br>Intention+|Contraceptive<br>Use|Contraceptive<br>Use|
|Age15-25|*3.03(18.21)"'|-0.49 (7.78)"..|-0.41 (3.56)..|0.7)(2.12)"|-0.64 (4.94)...|
|Age26-30|-2.07 (15.27)"'|-0.40 (7.21)"|-0.52(5.48)'''|0.88 (3.24)-*|-0.15 (1.27)|
|Age31-35|-1.02 (8.66)**|-0.29 (5.53).'|0.5J (5.10)..|0.67 (2.94)..|0.10 (0.87)|
|Education spouse|-0.04 (2.73)..|-0.01 (1.38)|0.03 (2.67)OO|0.06 (2.99)"'e|0.05 (2.67)"'||
|Education 4-6|-0.08 (0.63)|-0.77 (1.57)|0.01 (0.14)|-0.04 (0.29)|-0.15 (1.05)|
|Education> 7|-0.08 (0.41)|-0.13 (2.36)"'|0.00 (0.01)|0.02 (0.11)|-0.10 (0.47)|
|Rural age 12|0.23 (2.31)"|0.02 (0.69)|-0.10(1.36)|-0.24 (1.82)'|-0.22 (1.63)|
|Small city|0.30 (2.61)"'|0.05 (1.4i)|-0.06 (0.62)|0.17 (1.05)|0.09 (0.60)|
|Urban<br>=0488|(4.31)"'|-0.23(2.86)"*|-0.07 (0.32)|-0.42(0.76)|-0.60(1.06)|
|Remote|0.06 (0.42)|-0.00 (0.01)|0.02 (0.15)|-0.29 (1.66)'|-0.25 (1.55)|
|Doctor<5, age 20|-0.20 (1.78)'|0.02 (0.63)||_|I|
|Clinic2 < 5,age 20|-0.07(0.64)|0.04 (1.27)|||I|
|Clinic3<5,age20|-0.20(1.71)'|0.02 (0.60)|||.<br>I|
|Hopital<br><5, age 20|-0.21 (1.60)|-0.02 (0.41)||||
|Pharmacy**c**5, age 20|-0.65(4.18)".|-0.02 (0.56)||||
|Doc[or<10,age20#|-0.18(0.60)|0.45(1.86)'||||
|Clinic2<10,**age**20 #|-0.73(2.47)"'|-0.33 (4.23)_|||_<br>_|
|Clinic3 < 10,age20 #|-094(4.96)...|-0.1! (2.31)"*||||
|llospital<10, age 20 #|0.78 (2.28)"|0.26 (2.87)...||_||
|Pharmacy**c**10, age 20 #|-1.18 (4.98)*"|-0.10(1.39)||||
|Good water||-0.83(1.88)'|||-0.00 (0.02)|
|**Good**sanitation||-0.42 (0.79)|||0.25 (1.66)'|
|Methods available|||0.10 (3.13)"'|0.28 (5.02)"'|0.24 (5.39)*'|
|Message|||0.06 (1.07)|0.32 (3.32)"'o|0.33 (3.51)"'_|
|OCwnland|||-0.13(1.11)||-0.27 (1.51)|
|Sum assets|||-0.02(1.16)|0.12 (3.26)".|0.06 (1.90)'|
|Ed opportunities < 5 km|||-0.01(0.14)||-0.18(1.64)|
|Ed opportunities<10**km**|||-0.01 (0.09)||0.09 (0.48)|
|Community mortality||**_**|-4.39 (3.13)"||-5.43 (2.09)'|
# **6. References**

<mark>Djoumessi, Y. F., & Mbongo, L. D. B. E. (2022). An analysis of information Communication Technologies for natural disaster management in Africa.</mark> _<mark>International Journal of Disaster Risk Reduction</mark>_ <mark>,</mark> _<mark>68</mark>_ <mark>, 102722.</mark> <mark>Kianersi, S., Jules, R., Zhang, Y., Luetke, M., & Rosenberg, M. (2021). Associations between hurricane exposure, food insecurity, and microfinance; a cross-sectional study in Haiti.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>145</mark>_ <mark>, 105530.</mark> <mark>Kolbe, A. R., Hutson, R. A., Shannon, H., Trzcinski, E., Miles, B., Levitz, N., ... & Muggah, R. (2010). Mortality, crime and access to basic needs before and after the Haiti earthquake: a random survey of Portau-Prince households.</mark> _<mark>Medicine, conflict and survival</mark>_ <mark>,</mark> _<mark>26</mark>_ <mark>(4), 281-297.</mark> <mark>Llorente-Marrón, M., Díaz-Fernández, M., Méndez-Rodríguez, P., & Gonzalez Arias, R. (2020a). Social vulnerability, gender and disasters. The case of Haiti in 2010.</mark> _<mark>Sustainability</mark>_ <mark>,</mark> _<mark>12</mark>_ <mark>(9), 3574.</mark> <mark>Llorente-Marrón, M., Díaz-Fernández, M., Moreno, S. D., & Méndez-Rodríguez, P. (2020b). Socioeconomic consequences of natural disasters on gender relations: The case of Haiti.</mark> _<mark>International Journal of Disaster Risk Reduction</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>, 101693.</mark> <mark>Marcelin, L. H., Cela, T., & Shultz, J. M. (2016). Haiti and the politics of governance and community responses to Hurricane Matthew.</mark> _<mark>Disaster Health</mark>_ <mark>,</mark> _<mark>3</mark>_ <mark>(4), 151-161.</mark> <mark>Ndambuki, D. K., & Al Hitmi, H. K. (2021). Transportation Planning and Resilience in Countries with High Risks of Natural Disasters: The Case of Haiti.</mark> _<mark>Available at SSRN 4008746</mark>_ <mark>.</mark> <mark>Rahill, G. J., Ganapati, N. E., Clérismé, J. C., & Mukherji, A. (2014). Shelter recovery in urban Haiti after the earthquake: the dual role of social capital.</mark> _<mark>Disasters</mark>_ <mark>,</mark> _<mark>38</mark>_ <mark>(s1), S73-S93.</mark> <mark>World Bank. 2019c. Strengthening Disaster Risk Management and Climate Resilience Project. Project Appraisal Document P165870. World Bank, Washington, DC.</mark> 18
20

|Table 7<br>**Fare Comparison of Similar**U.S.**and European Routes**|
|---|
|_Route_<br>_Miles_<br>_Fare_|
|Boston to New York<br>187<br>$153|
|London to Paris<br>211<br>$263|
|Washington to New York<br>216<br>$153|
|Houston to New Orleans<br>302<br>$89|
|Copenhagen to Oslo<br>311<br>$315|
|Dallas to Minneapolis<br>853<br>$435|
|Frankfurt to Madrid<br>887<br>$720|

Source: Airfare Management Unit (1995, 1996) and Consulting Services Group (1995, 1996) <!-- Start of picture text --> Table 8<br>The Effects of Too Much (Protective) Energy Regulation in the<br>I___________ European Community<br>Country Cosf Country Cost<br>Germany 12 France 7<br>Italy 10 Netherlands 7<br>Portugal 10 United States 7<br>Belgium 9 Greece 7<br>Spain 9 Denmark 6<br>Britain 8 Finland 6<br>Luxembourg 8 Norway 5<br>Ireland 7 Sweden 4<br><!-- End of picture text --> a Cost of electricity rounded to the nearest cent per kilowatt-hour.

Source: Electricity Association Services Ltd. (1996)
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Lippman, L., Moore, K. A., Guzman, L., Ryberg, R., McIntosh, H., Ramos, M., & Kuhfeld, M. (2014). Flourishing children: Defining and testing indicators of positive development. Springer.

Nehru, V., E. Swanson, et al. (1995). "A new database on human capital stock in developing and industrial countries: Sources, methodology, and results." Journal of Development Economics 46(2): 379-401.

Mello, Z. R. (2008) “Gender Variation in Developmental Trajectories of Educational and Occupational Expectations and Attainment from Adolescence to Adulthood” _Developmental Psychology_ , 44(4), 1069–1080 Miguel, E. and M. Kremer (2004) “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities”, _Econometrica_ , Vol. 72, No. 1, pp. 159–217 Schoon, I. and S. Parsons (2002) “Teenage Aspirations for Future Careers and Occupational Outcomes”, _Journal of Vocational Behavior_ , 60(2), 262–288 Schoon, I., P. Martin, and A. Ross (2007) “Career Transitions in Times of Social Change: His and Her Story”, _Journal of Vocational Behavior_ , 70(1), 78–96 Schoon, I. and E. Polek (2011) “Teenage Career Aspirations and Adult Career Attainment: The Role of Gender, Social Background and General Cognitive Ability”, _International Journal of Behavioral Development_ , 35(3) 210–217 Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy scale. In J. Weinman, S. Wright & M. Johnston (Eds.), Measures in health psychology: A user's portfolio. Causal and control beliefs (pp. 35-37). Windsor, UK: Nfer-Nelson Wilson, T. D. (2011) _Redirect: The Surprising New Science of Psychological Change_ , Penguin Group, London World Bank (2014) World Development Report 2015: Mind, Society and Behavior. World Bank: Washington, D.C.

Zimmerman, M. (2000). Empowerment theory: Psychological, organizational and community levels of analysis. American Journal of Community Psychology, Vol. 23, No. 5 15
# **Table 2**

# **Changes in Employment of Migrants versus Changes in Employment of Local Residents in China, June 2008, December 2008, and June 2009 (% change from six months earlier)**

|||Jun. 200<br>|8 Decemb<br>|er 2008<br>Jun-09<br>|
|---|---|---|---|---|
|All firms|migrants|4.76|-0.88|5.29|
||local|3.23|-0.07|2.09|
|Nonexporters|migrants|5.44|1.23|5.71|
||local|3.51|0.06|3.35|
|Exporters|migrants|4.22|-2.74|5.01|
||local|2.80|-0.27|-0.01|

_Source_ : CNFS (2009) 28
<!-- Start of picture text --> 24 0.00 Standard deviation in 2030<br>QO 175<br>22 ©) 40.0<br>() 60.0<br>20 () 80.0<br>(_) 100.0<br>18 () 119.0<br>16 Data: Sample covered<br>© 1980_2014<br>14 ROL 0+ 1990_20142000_2014<br>12 4 X 2005_2014<br>fo) O ROC<br>9 ROC Country typology<br>MOGC LOGC Mroc<br>nNc 103 All ROC wallMi nRoc<br>sg 6 4 x HOGC ( @ HOGCLOGC<br>=, Ail MOGC ROC LOGC sm mocc<br>e mae + nroc<br>NROC 0)<br>0 0.00 LOGC<br>> x<br>HOGC LOGC HOGC<br>-4<br>-6<br>-8<br>-10 0.00<br>-10-8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24<br>Median in 2016<br><!-- End of picture text -->
variable has been pursued also by (Schenker, et al., 2010), who feed self-reported health measures into a model to multiply impute clinical values in a different survey.<sup>7</sup> For illustration, Table 5 and Table 6 show the details of the Ordinary Least Squares (OLS) imputation model for Malawi and Ethiopia, respectively. In addition to farmer-reported plot area, we include plot manager, household and other plots attributes as predictors. The model specification differs slightly between the IHS3 and the ESS2 depending of the availability of the variables or the specificity of the data set. For example, the raw data on farmer-reported plot areas could have been expressed in non-standard measurement units in the ESS2, as such we add dummy variables for these units in the imputation model for Ethiopia.

> 7 The literature on the use of MI to address missing information problems is vast and cuts across several disciplines, including but not limited to economics, statistics, sociology, public health, medicine, and epidemiology. The empirical work relying on MI to deal with missingness in income data is noteworthy given the types of household surveys that inform our analyses, and considering the rates of item non-response that are dealt with in the literature on income and that present similarities to the patterns in Table 1 (Schenker, et al., 2006); (Zarnoch, et al., 2010); (Giusti & Little, 2011); (Ahearn, et al., 2011); (Vermaak, 2011).

15