rafmacalaba/data-use-mentions-extended huggingface dataset:
15 There are two village level variables besides social capital. First, the median distance of dwellings in the cluster to a market for crops included as a proxy for the market integration of the village'<sup>5</sup> . In addition, a set of dummy variables for six agro-climatic regions of Tanzania are also included to control partially for the economic and agro-climatic diversity of the country.

_Is asocial capital'capital?_ Column 1 of table 3 presents the results of OLS estimation of the partial correlation between a cluster's average household per person expenditures and social capital, controlling for this set of variables. The coefficient on the social capital index is empirically large and moderately statistically significant (p-level .08). Households in villages with higher levels of social capital have significantly higher expenditures.

However, 'social capital" or associational life may simply be a normal consumption good so that richer households consume more, that is, perhaps associational life is not 'capital' but "consumption" consumed more by households with greater income or leisure'<sup>6</sup> . If richer individuals live together then one would tend to find that richer villages are associated with higher village social capital. In the U.S., for example, the average income of neighborhoods would be associated with higher ownership of luxury cars, but this does not imply if poorer is In the above mentioned World Bank study of rural poverty, distance to market was interacted with an index of road quality and produced a strong income effect. In our case we could not replicate the road quality index with our HRDS sample and so used the simpler, but less appealing, measure of distance alone.

16 This argument is weakened by the fact that the social capital index is only partly a measure of associational _activity,_ as it more reflects the nature of groups of which individuals are members which is at least plausibly less related to income that is the magnitude of activity. To use Putnam's illustration,
for time-invariant differences between different categories of cells. The coefficient on ActiveAid _ikt_ captures whether _Yikt_ is higher when a project is active (relative to periods before the arrival of aid) and _β_ whether it is more so in cells that hosted a historical mission. The coefficient on CompletedAid _ikt_ captures whether _Yikt_ is higher after the completion of a project (relative to periods before the arrival of aid) and _δ_ whether it is more so in cells that hosted a historical mission. The coefficients of interest are _β_ and _δ_ , because it is not a priori clear when the effects of aid should materialize.

It is important to stress that the main goal here is to test whether mission cells cause higher or lower aid effectiveness, and not to test whether aid is effective per se, as this is outside the scope of the article. As such, the identifying assumption is that conditional on EverAid _ik_ = 1, cells with and without missions have parallel trends in _Yikt_ . Identification of _β_ and _δ_ (the coefficients of interest) does not require any assumption about parallel trends between cells that received aid at different points in time or between cells that ever/never received aid. These assumptions would be arguably very demanding, as they amount to saying that the timing of aid implementation is random. In contrast, the identifying assumption used here is much less stringent, and it states that development trends (not levels) between cells with and without missions as of 1903 are the same in the period 1995–2014.

To implement this strategy it is necessary to have a measure of economic development at the cell level observed at several points in time. Proxies of development are constructed from individual-level georeferenced survey data from the DHS. Specifically, this test uses the individual recode of the DHS, which includes women of reproductive age (15–49), as it has the highest country coverage (in some countries the DHS surveys only women). Four questions are selected that cover different dimensions of development and are asked consistently across countries
- **Elvidge, Christopher D., Feng-Chi Hsu, Kimberly E. Baugh, and Tilottama Ghosh** , “National Trends in Satellite Observed Lighting: 1992-2012.,” in Qihao Weng, ed., _Global Urban Monitoring and Assessment Through Earth Observation._ , CRC Press, 2014.

- **Eycken, H Vander and F Vander Vorst** , “Le Blocage de la Croissance en R ́epublique D ́emocratique du Congo,” _Revue Tiers Monde_ , 1967, pp. 411–434.

- **Fairhead, James** , “Paths of Authority: Roads, the State and the Market in Eastern Zaire,” _The European Journal of Development Research_ , 1992, _4_ (2), 17–35.

- **Getmanski, Anne, Guy Grossman, and Austin L. Wright** , “Border walls and smuggling spillovers,” _Quarterly Journal of Political Science_ , 2019, _14_ (3), 329–347.

- **Gonz ́alez, Felipe, Josepa Miquel-Florensa, Mounu Prem, and St ́ephane Straub** , “The Dark Side of Infrastructure: Roads, Repression, and Land in Authoritarian Paraguay,” _SSRN Working Paper_ , 2023.

- **Harari, Mariaflavia and Eliana La Ferrara** , “Conflict, Climate, and Cells: A Disaggregated Analysis,” _The Review of Economics and Statistics_ , October 2018, _100_ (4), 594–608.

- **Hendrix, Cullen S.** , “Head for the Hills? Rough Terrain, State Capacity, and Civil War Onset,” _Civil Wars_ , 2011, _13_ (4), 345–370.

- **Herbst, Jeffrey** , _States and Power in Africa: Comparative Lessons in Authority and Control_ , Vol. 149, Princeton University Press, 2014.

- **Idler, A.** , “The Logic of Illicit Flows in Armed Conflict,” _World Politics_ , 2020, _72_ (3
an inverse spatial relationship between cholera prevalence and proximity to both refuse dumps and classified reservoirs (Anamzui-Ya 2012). In Zimbabwe (Luque Fernandez et al. 2012) identified a spatial pattern in the distribution of cholera cases in an epidemic with Harare, characterized by a lower cholera risk in the suburbs with highest elevation. A parallel study to this one was also conducted in Harare, Zimbabwe taking into account a similar suite of geospatial covariates (Joseph et al. 2023). There have also been other efforts to map cholera risk in Zambia using spatial methods. For example Tambatamba et al analyzed the distribution of cholera cases, the mode of cholera transmission, and the risk factors affecting cholera infection in a peri-urban area of Lusaka by using a Geographic Information System (GIS) and a matched case-control method (Tambatamba et al. 2008). Other authors looked explicitly at precipitation patterns, and the association between drainage network availability and cholera cases with a regression analysis (Sasaki et al. 2009).
28. When grouped by occupation, a similar distribution of HEs is also evident. Using the ILFS data, results suggest that more than 75 percent of HEs are in just two categories of activity: service shopkeepers and craftsmen. Owing to lack of education, the number of HEs providing professional services is low, i.e., hardly 2 percent of the total (Table A1.10). Most workers with high levels of education prefer to work in firms.

## **Box 3.1:  The Success Story of a Small Entrepreneur**

The 25-year old Hafsa Hassan from Matwara used her TSh 150,000 savings to start a business buying and selling cashew nuts. In 2003, she joined the Matwara Small Entrepreneurship Development Association (MSEDA) from where she was able to get a TSh 500,000 loan to expand her business, and avail of various training opportunities, including study tours with SIDO, ILO, and other NGOs. With only 25 kilogram of cashew nuts to sell at start-up, Hafsa is currently trading on more than 250 kilograms of cashew nuts and has a working capital of more than TSh 1.5 million. Commenting on the reasons why her business is doing well, she remarked: “ _Unahitaji kuwa mbunifu na mtafutaji wa misaada kama unataka kufanikiwa kama mjasiriamali mdogo. Nilianza kidogo kidogo, lakini nilihakikisha natafuta taarifa muhimu kuhusu maendelea ya biashara kokote nilikosikia. Nilipoingia MSEDA ikawa ndiyo kama Mungu kanifungulia....nilikutana na wenzangu, tukabadilishana mawazo na uzoefu. Tulipata mafunzo kutoka sehemu mbalimbali. Kwa sasa naweza kusema mimi ni kati ya kinamama ambao ukija miaka michache ijayo utakuta nina kampuni yangu ya kubangua na kuuza korosho_ .” This means, “You need to be creative and look for where you can get assistance if you want to succeed as a small entrepreneur. I started with a small capital, but I was constantly looking for important information and opportunities that could help me succeed. When I joined MSEDA, it was like God opening the door for me. I met
of large output and employment losses in the wake of the financial turbulence of 2008, a number of advanced economies and EMDEs employed wide-ranging expansionary fiscal policy measures during 2008–09 (Kose and Ohnsorge 2019). These coordinated measures were instrumental in supporting global demand at the height of the global financial crisis and in limiting the decline in activity. However, as public debt and financing requirements rose significantly, market pressures and—perhaps more important—political constraints led advanced economies to withdraw fiscal support in 2010.<sup>29</sup> The change in fiscal policy stance led to an unprecedented outcome, with quite different paths for government expenditures in advanced economies from past recoveries, when policy was expansionary for longer, with continued increases in real primary government

> 28 The incidence of sudden stops in capital inflows tipping countries into financial distress was about half of that prior to 2008, and centered in economies where pre-crisis credit booms were funded by large capital inflows and where banks had narrow deposit bases, such as some economies in ECA (Feyen et al. 2014). Data taken from Forbes and Warnock (2012) indicate that more than 80 percent of countries in the sample experienced sudden stops in 2009, while the share was around 46 percent, on average, in 1982 and 1991. In addition, many sudden stops episodes were observed during the global downturns of 1998 and 2001. Eichengreen and Gupta (2016) also document the high incidence of sudden stops during the 2009 global recession.

> 29 In advanced economies, government expenditures started increasing gradually since 2015 as public investment picked up in major economies. A number of countries have implemented tax reforms to stimulate activity over the past decade (IMF 2019; OECD 2019).

17
bilateral trade between India and its neighbors.<sup>3</sup> However, these estimates are based on surveys carried out prior to 1995. It should be noted that it is likely that unilateral trade reforms

> 2 Srinivasan and Canonero. (1994, 1997) and Pigato, Farah, Itakura, Jun, Martin, Murrel and Srinivasan (1997), and recent studes include Panagariya (2001) and Mukherji (2001).

> 3 Taneja (1999) for estimates of informal trade between India and its neighbors. It must be noted these estimates are based on surveys carried out incorporating important smuggling centers and extrapolated to arrive at national figures. Thus, they are prone to higher margins of error.

2
the Jordanian neighbor respondent has secondary education or higher. The outcomes are as defined in table G1. The regressions have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%).

Table (G19) Jordanian Neighbor Primary Outcomes Heterogeneity by: Socioeconomic Status

||(1)<br>Social Attitudes<br>and Perceptions|(2)<br>Economic Attitudes<br>and Perceptions|(3)<br>PolicyPreferences|(4)<br>Altruism to Syrians|
|---|---|---|---|---|
|Treat|-0.382*|0.048|-0.320|0.511|
||(0.208)|(0.265)|(0.215)|(0.328)|
|Treat*Above Median Expenditure|0.086|-0.017|0.832***|-0.401|
||(0.270)|(0.310)|(0.288)|(0.504)|
|Above Median Expenditure|-0.191***|-0.054|-0.266***|0.011|
||(0.074)|(0.074)|(0.067)|(0.114)|
|p-value: T + T*Het|0.12|0.87|0.02|0.71|
|Control Mean|0.50
# School Meals, Educational Achievement and School Competition: Evidence from a Randomized Evaluation

Christel Vermeersch<sup>�</sup> and Michael Kremer<sup>yz</sup> November 1, 2004

## Abstract

This paper examines the e¤ects of subsidized school meals on school participation, educational achievement, and school ...nance in a developing country setting. We use data from a program that was implemented in 25 randomly chosen preschools in a pool of 50. Children’s school participation was 30 percent higher in the treatment group than in the comparison group. The meals program led to higher curriculum test scores, but only in schools where the teacher was relatively experienced prior to the program. The school meals displaced teaching time and led to larger class sizes. Despite improved incentives, teacher absenteeism remained at a high level of 30 percent. Treatment schools raised their fees, and comparison schools close to treatment schools decreased their fees. Some of the price e¤ects are due to a combination of capacity constraints and pupil transfers that would not happen if the school meals were o¤ered in all schools. The intention-to-treat estimator of the e¤ect of the randomized program incorporates those price e¤ects, and therefore it should be considered a lower bound on the e¤ect of generalized school meals. This insight on price e¤ects generalizes to other randomized program evaluations.

> �Nu¢ eld College, University of Oxford; Email: vermeersch@post.harvard.edu

> yDepartment of Economics, Harvard University, The Brookings Institution and NBER; Email: mkremer@fas.harvard.edu

> zThe analysis in this paper is based on the data that were available as of April 15th, 2004. Future versions will incorporate new data as they become available. Results might change accordingly. We would like to thank Francesco Caselli, Gary Chamberlain, Makhtar Diop, Caroline Hoxby, Gary Chamberlain, Edward Miguel, Michael Murray, Mark Rosenzweig and various participants in seminars for helpful comments
Rapid ICT adoption raises important questions about what the ensuing effects might be on firm operations. For instance, Bloom et al. (2009) investigate how ICT affects worker autonomy, distinguishing between reduction of costs in communication and information. According to their findings, worker autonomy shrinks for the former while in the later, decisions are decentralized and worker autonomy increases. Their findings, however, are silent regarding how workers’ autonomy affects productivity.

Several papers have analyzed empirically the link between ICT and productivity. At the sector level, Basu et al. (2003)<sup>5</sup> examine how the adoption of ICT could explain differences in US and UK productivity performance. Their focus is on the impact via the reduction of ICT prices. According to the authors, cheap ICT capital is likely to create major changes only if firms can deploy their other inputs in radically different and productivity-enhancing ways. By comparing UK and US performance, the authors reached the conclusion that unmeasured investments in intangible organization capital associated with the role of ICT can explain the divergent productivity patterns of these two countries after 1995.

Similar arguments are used in papers using plant level data, such as in Bartel et al (2005) and Bartel et al. (2009). These papers suggest that the reduction of ICT costs induces ICT investments, which eventually diminish the cost of setup time and raise production of customized products. As a result, firms require more skilled labor leading to productivity growth. Bartel et al (2005) corroborate these channels using valves manufacturing plant level data from the US; and Bartel et al (2009) show that the effect of increased labor skills requirements complementary to ICT are stronger in the UK than the US, also using plant level data for the same valves manufacturing sector.

Using firm-level data, Bresnahan et al (2002) also suggest that the reduction of ICT prices increases investments in work organization and product and service innovations, which raise the demand for skilled workers enhancing productivity growth. Their research,
# **Annex 2. Ahmedabad and Mumbai: Computation of Income Distributions and Allocation of Households Across Housing Stocks**

## **A. Income Distribution**

Data about income distribution in Ahmedabad and Mumbai is reported in the recent NCAER publication.<sup>19</sup> The income distribution in 2007-8 represented in 2004-5 prices is shown in Table 1.

### **Table 1: Income Distribution: Ahmedabad and Mumbai**

|**Income Distribution 2007**<br>|**-8, Mean Income in**<br>**Ahme**<br>|**2004-5 Prices**<br>**dabad**<br>||**Mu**|**mbai**<br>|
|---|---|---|---|---|---|
|**Class**|**% of Total**<br>**Households**|**Annual Total**<br>**Income of the**<br>**Class INR in**<br>**Million**|**% of Total**<br>**Income**||**Annual Total**<br>**Income of the**<br>**Class INR in**<br>**Million**|
|**Low Income**|**13.6**|**38,906**||**10.8**|**213,372**|
|**Aspirants**|**43.9**|**125,585**||**41.1 **|**811,998**|
|**Middle Class**|**39.
and their relationship with urbanicity. However, some of these data are likely to be out of date for many of the same reasons that a traditional Census exercise is complicated. The rapid and enormous movement of people caused by the conflict is likely to have compounded this problem. Building newer and more up to date population sample frames should be a priority for researchers interested in South Sudan. This could be achieved either by conducting a traditional census, or by leveraging the recently available satellite imagery using and machine‐learning based methods to extract features. Such extract could be used to help define settled areas and their associated population density to create a predictive population surface (Engstrom et al., 2017a; Jean et al., 2016; Pasquale et al., 2017). Based on this, new sample frames can be built to use for future data collection work, which is badly needed in the context of South Sudan.

The model structure was voluntarily kept simple (linear combination) to ease its interpretation given that it was constructed as a proof of concept to show the potential of spatial data for imputing poverty to supplement poverty survey estimates. Furthermore, although where these techniques may have the most value, which is where there might have been a crisis or emergency or where safety is a concern, these techniques are also the most difficult to apply. Indeed, the link between poverty and such variables is much more likely to be structural than transient across much of South Sudan. Indeed, a set of issues that arise in this estimation method is the difficulty of modeling the dynamics of poverty and shocks. Many of the areas where the enumerators could not go were inaccessible because of recent conflict and it is difficult to account for this in a cross‐sectional model as such, given the potentially endogenous nature of conflict and poverty whereby some conflict events are concentrated around wealthier areas. One of the areas for future research might be to leverage the time series that area available for various types of geo‐ spatial data to try
<mark>percentage of regressions in which childcare attendance is significant at the 1% significance level. We repeat the analysis for different gaps in children’s birth month: from 1-3 months, only 1 month, only 2 months, and only 3 months.</mark> <mark>Figure 6 shows the distribution of the p-value of the variable “born earlier” in these regressions. For the birth month gap from 1-3 months, only 3.3% of regressions show a significant effect of “being born earlier” on parental outcomes. For a gap of 1 month, 1.8% of regressions show a significant effect of “being born 1 month earlier” on parental outcomes. No regressions show that the effect of being born earlier is significant at the 1% level. Thus, there is no evidence of the effect of being born earlier (for children of the same age) on parental outcomes.</mark>

# _5.5. Spill-over effects_

<mark>Table 5 presents 2SLS regressions of several household outcomes on childcare attendance. Taking advantage of childcare has a positive effect on per capita income. Childcare attendance increases the labor market participation and wages of women and as a result, increases household income. This income effect carries over to a poverty-reducing effect. A household is defined as poor if its per capita income is below the poverty line. The probability of being poor is reduced by 0.22 when children are placed in childcare. This finding suggests the important role of childcare in Vietnam where there is a low rate of childcare and a low rate of participation by women in the labor market.</mark><sup>17</sup> <mark>It is common in Vietnam for grandparents who live with their children to take care of the grandchildren. In the 2016 VHLSS, in approximately 21% of households, grandparents live with their children. We test whether formal childcare in a center can substitute for informal childcare from grandparents by running a
5 comprehensive definition of government should include all these entities, in practice, central government expenditures are often all that is recorded given the availability and reliability of expenditure data.

While measures of state and local expenditures are often simply unavailable, public enterprises pose problems that go beyond the problem of non-existent data. Since the U.N. System of National Accounts does not call 3 for any unique t:eatment of public enterprises as organs of government, public enterprise information rarely finds its way into sources concerned with quantifyin **g** overnmcnt expenditures. The absence of data on public enterprises is particularly troubling, especially for developing nations, where the growth in state enterprises is believed to be a dominant form of government expansioni in the past 10-20 years.4 While expenditure data on state and local governments and public enterprises may generally be absent from standard sources, other problems confront calculation of central government expenditures as a measure of total government spending. Often omitt3d are off-budget activities including tax expenditures, subsidies, government lending and government loan guarantees. Tax expenditures refer to the use of tax concessiont for specific groups as a substitute for direct expenditures. The attractiveness of these fiscal instruments is their lack of transparency and overall c-ntrolability; however, at the same time they may be costly as they impart distortions to public resource mobilization and allocation. Furthermore, tax expenditures can confound both cross-country and intertemporal comparisons of the size and